PROGRAM
International Conference on Circulating Fluidized Beds and Fluidization Technology - CFB-10
May 1 - 5, 2011
Sunriver Resort Sunriver, Oregon, USA
Conference Chair
Ted M. Knowlton Particulate Solid Research, Inc, USA
Sunday, May 1, 2011
One-Day Seminar on Fluidization 8:00 - 8:30 am 8:30 - 10:15 am 10:15 - 10:30 am 10:30 - 11:15 am 11:15 - 12:00 am
Registration Hydrodynamics (J. Grace) Coffee Break Scaling and Scale-Up (T. Knowlton) Cyclone Design and Operation (T. Knowlton)
12:00 - 1:00 pm
LUNCH
1:00 - 1:45 pm 1:45 - 2:30 pm 2:30 - 2:45 pm 2:45 - 4:15 pm 4:15 - 4:45 pm 4:45 pm
Standpipes/Non-Mechanical Valves (T. Knowlton) Heat Transfer (J. Grace) Coffee Break Reactors and Combustors (J. Werther) Question-and-Answer Period with Instructors Adjourn
Conference Registration 2:00 - 6:00 pm
Activity Program Meeting 5:30 - 6:30 pm
Reception/Dinner 6:30 - 9:30 pm
Special Poster Session/Social Hour 9:30 pm - 11:00 pm
Monday, May 2, 2011 7 - 8:15 am
BREAKFAST
8:15 - 8:30 am
CONFERENCE OPENING PLENARY SESSION Chairman: R. Cocco
8:30 - 9:30 am
P-1: Reflections on Mathematical Models and Simulation of Gas-Particle Flows Sankaran Sundaresan, Princeton University
9:30 - 9:40 am
COFFEE BREAK SESSION 1 Solids Flow and Circulation Co-Chairs: J. Zhu U. Muschelknautz
9:40 - 9:54 am
1-1: Gas Tracer Study in a NonMechanical L-Valve M.M.Yazdanpanah, A. Hoteit, A. Forret Thierry Gauthier IFP Energies Nouvelles Arnaud Delabarre Université Henri Poincaré, France
9:54 - 10:08 am
2-1: Process Decoupling of Plasma Enhanced Synthesis of Chlorinated Polyvinyl Chloride (CPVC) Particles in a Circulating Fluidized Bed W. Lu, T. Cao, Y. Cheng Tsinghua University, China
1-2: Investigation on the Hydrodynamic 2-2 Manufacture of Granular Properties in the External Loop of a Polysilicon from Trichlorosilane in an Circulating Fluidized Bed with a Loop Internally Circulating Fluidized Bed Seal Reactor X. Yao, T. Wang, H. Yang, H. Zhang, Q. Liu J. Lv Tsinghua University, China
10:08 - 10:22 am
SESSION 2 Novel Fluidized Bed Processes Co-Chairs: P. Basu Y. Cheng
C. Wang, T. Wang, Z. Wang Tsinghua University, China
1-3: Hydrodynamics of a Dual Fluidized 2-3 High-Flux Triple Bed Circulating Bed System Which has Internal Mixing Fluidized Bed (TBCFB) Gasifier for Channels Between CFB and BFB Exergy Recuperative IGCC/IGFC Reactor U. Lee, I. Choi, W. Yang, Y. Kim, Y. Choi Korea Institute of Industrial Technology J. Song - SeenTec Co., Ltd. Korea
C. Fushimi, G. Guan, M. Ishizuka Y. Nakamura, A. Tsutsumi The University of Tokyo, Japan Y. Suzuki, National Institute of Industrial Science, Japan E.W.C. Lim, Y. Cheng, C-H. Wang National University of Singapore, Singapore
Monday, May 2, 2011 (continued) 10:22 - 10:36 am
1-4: Particle Flow in L-Valves D. Subbarao University Teknologi PETRONAS, Malaysia
A. Weinert, A. Reichold, P. Bielansky C. Schonberger, B. Schumi Vienna University of Technology Austria
COFFEE BREAK
10:36 - 11:00 am 11:00 - 11:14 am
2-4: Bio-Gasoline from Jatropha Oil: New Applications for the FCC- Process
1-5: A Generalized Flow Diagram for Fluid-Solid Vertical Transport X. Bi University of British Columbia, Canada
2-5 Waste Wood Gasification: Distribution of Nitrogen, Sulphur and Chlorine in a Dual Fluidised Bed Steam Gasifier V. Wilk, C. Aichernig, H. Hofbauer Vienna University of Technology, Austria
11:14 - 11:28 am
1-6: Cold Model Investigations of a 2-6: Removal of Nitrate from Water High Temperature Looping Process in a Using Fluidized Bed Ion Exchange Dual Circulating Fluidized Bed System Column A.R. Bidwe, C. Hawthorne, A. Charitos, M.A.M. Ammar Arab Beddai, V.V. Basava Rao Dominguez, H. Dieter, A. Schuster, G. Osmania University Scheffknecht India University of Stuttgart, Germany
11:28 - 11:42 am
1-7: Hydrodynamics of a Loop Seal Operated in a Circulating Fluidized Bed: Influence of the Operating Parameters on Gas and Solid Flow Patterns
2-7: A Pyrolysis Pilot Unit Integrated to a Circulating Fluidized Bed Boiler Experiences from a Pilot Project
1-8: Effects of Particle Properties on Cluster Characteristics in a 2-D CFB Riser
2-8: Production of Gasoline and Gaseous Olefins by Catalytic Cracking of Pyrolysis Oil
J. Xu and J. Zhu University of Western Ontario, Canada
P. Bielansky, A. Reichhold, A. Weinert Vienna University of Technology Austria
1-9: Flow Field in a Novel Short Residence Time Gas-Solid Separator
2-9: Energetic Optimization of the Lignin Pyrolysis for the Production of Aromatic Hydrocarbons
J. Autio, J. Lehto, Metso Power Oy P. Jokela, UPM J. Alin, Fortum R. Solimene, R. Chirone Istituto di Recerche sulla Combustione - CNR A. Oasmaa, Y. Solantausta, VTT Finland P. Bareschino Universita degli Studi del Sannio P. Salatino Universita degli Studi di Napoli Federico II Italy
11:42 - 11:56 am
11:56 - 12:10 pm
M. Liu, C. Zhou, C. Lu, Z. Wang China University of Petroleum China
M. Franck, B. Lorenz, E-U. Hartge, S. Heinrich, J. Werther Hamburg University of Technology, Germany
Monday, May 2, 2011 (continued) 12:10 - 12:24 pm
1-10: Cold Model Study on 2-10: Studies on Propane Interconnected Fluidized Bed Reactors Dehydrogenation to Propylene in a for Multi-Generation Systems and Gas-Solid-Sold Fluidized Bed Reactor Chemical Looping Processes G.A. Ryabov, O.M. Folomeyev, D.A. Sankin, K.V.Khaneyev, All-Russian Thermal Engineering Institute, Russia
Y. Chu, T. Wu, Y. Li, Z. Nawaz, T. Wang F. Wei Tsinghua University, China
12:30 - 2:00 pm
LUNCH
2:00 - 5:30 pm
FREE TIME SESSION 3 Mathematical Modeling I Co-Chairs: N. Mostoufi F. Johnsson
5:30 - 5:44 pm
5:44 - 5:58 pm
3-1: A Modeling Study of Gas Streaming in a Deep Fluidized Bed of Geldart A Particles
4-1: The Development of a Novel Cu-Mn Oxygen Carrier for the Chemical Looping Gasification of Biomass
S. Karimipour, T. Pugsley University of Saskatchewan Canada
M. Aghabararnejad, J. Chaouki, G.S. Patience Ecole Polytechnique de Montreal Canada
3-2: Effects of Gas Velocity and Solid Hold-Up on the Sub-Grid Behavior of Riser Flows
4-2:CO2 Looping Cycle for CO2 Separation
C.C. Milioli, F. E. Milioli University of São Paulo, Brazil
5:58 - 6:12 pm
SESSION 4 Chemical Looping Co-Chairs: E.-U. Hartge R. Gupta
T. Shimizu, H. T. Takahashi, Narisawa, L. Li, H. Kim Niigata University Japan
3-3: Numerical Simulations of a 4-3: Fluid Dynamic Effects of Ring-Type Circulating Fluidized Bed with a Square Internals in a Dual Circulating Fluidized Cross-Section Bed System T. Li, S. Pannala, C. Guenther National Energy Technology Laboratory S. Pannala Oak Ridge Institute for Science and Education, USA
D.C. Guio Perez, K. Marx, T. Proell H. Hofbauer Vienna University of Technology Austria
Monday, May 2, 2011 (continued) 6:12 - 6:26 pm
3-4: High-Resolution Simulations of 4-4: Design Requirements for Gas-Solids Jet Penetration Into a High- Pressurized Chemical Looping Density Riser Flow Reforming T. Li, C. Guenther National Energy Technology Laboratory, USA
6:26 - 6:40 pm
3-5: Simulation of Particle-Gas Flow in 4-5: The Influence of Carbon Stripper a Cyclone Using URANS Efficiency on CO2 Capture Rate in a Chemical-Looping Combustion A. Karvinen, H. Ahlstedt, Tampere University of Process for Solid Fuels Technology M. Palonen, Metso Power Oy Finland
6:40 - 6:54 pm
K. Marx, T. Proell, H. Hofbauer Vienna Institute of Technology Austria
M. Kramp, A. Thon, E-U. Hartge, S. Heinrich J. Werther Hamburg University of Technology Germany
3-6: Evaluation of a Lagrangian 4-6: Study of Calcination-Carbonation Discrete Phase Modeling Approach for of Calcium Carbonate in Different Application to Industrial Scale Bubbling Fluidizing Mediums for Chemical Fluidized Beds Looping Gasification in Circulating Fluidized Beds S. Cloete, S.T. Johansen, M. Braun, S. Amini, SINTEF Materials and Chemistry, Norway B. Acharya, A. Dutta, P. Basu M. Braun, B. Popoff Dalhousie University Ansys, Germany Canada
6:54 - 7:08 pm
7:08 - 7:22 pm
3-7: Effect of Wall Boundary Conditions 4-7: Understanding Standpipe and Mesh Refinement on Numerical Hydrodynamics Using Electrical Simulation of Pressurized Dense Capacitance Tomography Fluidized Bed for Polymerization C. Qui, R. Joachim Reactor Industrial Tomography Systems, USA P. Fede, O. Simonin, R. Ansart, H. Neau Universite de Toulouse, France I. Ghouila INEOS, France
S.B.R. Karri Particulate Solid Research, Inc., USA
3-8 Fluidized Bed Membrane Reactor for Steam Reforming of Higher Hydrocarbons: Model Sensitivity
4-8: A Practical Model for a Dense-Bed Countercurrent FCC Regenerator
M.A. Rakib, J.R. Grace, C.J, Lim University of British Columbia, Canada
Y. Zhang, C. Lu China University of Petroleum China
7:30 - 9:15 pm
DINNER
9:15 - 11:00 pm
POSTER SESSION and SOCIAL HOUR for Papers Presented in Sessions 1, 2, 3 and 4
Tuesday, May 3, 2011 7 - 8:30 am
Breakfast PLENARY SESSION 2 Chairman: J. Werther
8:30 - 9:30 am
P-2: Electrostatic Phenomena in Fluidized Systems: Present Status of Understanding, and Research Needs Xiaotao Bi, University of British Columbia
9:30 - 9:40 am
COFFEE BREAK SESSION 5 Dynamics of Gas-Solids Flow Co-Chairs: J. Grace B. Formisani
9:40 - 9:54 am
5-1: Design Criteria of Uniflow Cyclones for the Separation of Solid Particles from Gases U. Muschelknautz, P. Pattis, M. Reinalter, M. Kraxner MCI Management Center Innsbruck Austria
9:54 - 10:08 am
SESSION 6 Combustion and Gasification Co-Chairs: W. Nowak A. Luckos
6-1: Experimental Study on the Effects of Gas Permeation Through Flat Membranes on the Hydrodynamics in Fluidized Beds J.F. de Jong, M. van Sint Annaland, J.A.M. Kuipers, Eindhoven University of Technology, The Netherlands
5-2: Erosion in Second Stage Cyclones: 6-2: Experimental Study on Reforming Effects of Cyclone Length and Outlet Activity and Oxygen Transfer of FeGas Velocity Olivine in a Dual Circulating Fluidized Bed System S.B. Reddy Karri, R. Cocco and T.M. Knowlton Particulate Solid Research, Inc., USA S. Koppatz, T. Proell, C. Pfeifer, H. Hofbauer Vienna University of Technology, Austria
10:08 - 10:22 am
5-3: Correlation of the Minimum 6-3: Study of Recarbonation in Spouting Velocity for the Design of Circulating Fluidized Bed Combustion Open-Sided Draft Tube Conical Spouted Beds for the Treatment of Fine I. Hyytiainen, H. Lemmetyinen, Tampere University of Technology Materials M. Olazar, H. Altzibar, G. Lopez, I. Estiati, J. Bilbao, University of the Basque Country Spain
10:22 - 10:36 am
A. Mahlamaki, M. Palonen, M. Varonen, Metso Power Oy Finland
5-4: Hydrodynamics of Conical Spouted 6-4: Coal Ignition Temperature in Beds with High Density Particles Oxygen-Enriched CFB Boiler S. Sari, D. Zaglanmis, M. Koksal, Hacettepe University A. Polat, Middle East Technical University, Turkey
J. Chao, H. Yang, J. Lu, H. Zhang, Q. Liu Y. Wu Tsinghua University China
Tuesday, May 3, 2011 (continued) 10:36 - 11:00 am 11:00 - 11:14 am
COFFEE BREAK 5-5: Experiments and Modeling of Micro-Jet Assisted Fluidization of Nanopowder
6-5: Co-Combustion of Various Biowastes with a High-Sulfur Turkish Lignite in a Circulating Fluidized Bed Combustor with Air Staging
J.R. van Ommen, N. Loojie, Delft University of Technology, The Netherlands A. Atimtay, M. Vario, Middle East Technical D.M. King, A. Weimer, S. Johnson, University University, Turkey of Colorado, USA H. Olgun, U. Kayahan, B. Bay, A. Unlu, R. Pfeffer, University of Arizona, USA TUBITAK-MRC Energy Institute, Turkey B.G.M. van Wachem, Imperial College London, M. C. Celebi, H. Atakul, Istanbul Technical U.K. University, Turkey G. Bardakcioglu, M. Ozcan, GAMA Power Systems Engineering and Contracting, Turkey
11:14 - 11:28 am
11:28 - 11:42 am
5-6: Effect of Gas Bypassing in Deep Beds on Cyclone Dipleg Operation
6-6: Oxy-Combustion of Different Coals in a Circulating Fluidized Bed
A.S. Issangya, S.B. Reddy Karri, T.M. Knowlton, R. Cocco Particulate Solid Research, Inc., USA
M. Kosowska-Golachowska, K. Klos, T. Musial, Czestochowa University of Technology, Poland A. Luckos, Sasol Technology, South Africa
5-7: Fluidization Behavior in a GasSolid Fluidized Bed with Thermally Induced Inter-Particle Forces
6-7: Effects of Secondary Air Injection Upon the Fluidization Characteristics of the Lower Stage in a Two-Stage, Variable-Area Fluidized Bed Riser
J. Shabanian, F. Fotovat, J. Bouffard, J. Chaouki, Ecole Polytechnique de Montreal, Canada
11:42 - 11:56 am
11:56 - 12:10 pm
E.K. Johnson, S.L. Rowan, West Virginia University, USA
5-8: Particle to Gas Heat Transfer in a Circulating Fluidized Bed Riser
6-8: Gas-Solids Hydrodynamics in a CFB with 6 Cyclones and a Pant Leg
Y.T. Makkawi, Aston University, U.K.
L. Cheng, X. Zhou, C. Wang, Z. Wang, Z. Luo, K. Cen, L, Nie, C. Wu, Q, Zhou Zhejiang University, China
5-9: Fast Pyrolysis Process 6-9: The Research of CFB Boiler Intensification: Study of the Gas Phase Operation for Oxygen Enhanced Dried Residence Time Distribution and Lignite Combustion Backmixing in a Downer Reactor W. Muskala, J. Krzywanski, T. Czakiert, M. Huard, F. Berruti, C. Briens, The University W. Nowak, Czestochowa University of Technology, Poland of Western Ontario, Canada
Tuesday, May 3, 2011 (continued) 12:30 - 2:00 pm
LUNCH
2:00 - 3:40 pm
WORKSHOP A Panel Discussion on Energy Chairman: D. Keairns
3:45 - 5:20 pm
WORKSHOP B Chemical Looping Chairman: L. S. Fan
WORKSHOP C WORKSHOP D Instrumentation for Fluid PSRI/NETL Challenge Particle Systems Problem Chairman. J. R. van Ommen
5:30 - 5:44 pm
5:44 - 5:58 pm
SESSION 7 Mathematical Modeling II Co-Chairs: J. Li H. Arastoopour 7-1: DEM-CFD Modeling of a Bubbling Fluidized Bed and a Wurster Coater
SESSION 8 Industrial Operation of Fluidized Beds Co-Chairs: P. Gauville J. de Jong 8-1: Commissioning of a 0.8 MWth CFBC for Oxy-Fuel Combustion
L. Fries, S. Antonyuk, S. Heinrich, S. Palzer Hamburg University of Technology Germany
L, Jia, Y. Tan, D. McCalden, Y. Wu, I He R. Symonds, E.J. Anthony Canmet ENERGY Canada
7-2: Elutriation from Fluidized Beds: Comparison Between Experimental Measurements and 3D Simulation Results
8-2: High Sulfur Lignite Fired Large CFB Boilers-Design and Operating Experience
R. Ansart, H. Neau, O. Simonin, IMFT P. Accart, A. de Ryck, CNRS Universite de Toulouse, France
5:58 - 6:12 pm
M. Lakshminarasimhan, B Ravikumar, A. Lawrence, M. Muthukrishnan, Bharat Heavy Electrical Limited, India
7-3: Fluidized Bed Gasification of Mixed 8-3: Research on Heat Transfer Inside Plastic Wastes: A Material and a the Furnace of Large Scale CFB Boilers Substance Flow Analysis M.L. Mastellone, U. Arena Second University of Naples, Italy
6:12 - 6:26 pm
Co-Chairs: L. Shadle R. Cocco
R. Zhang, H. Yang, H. Zhang, Q. Liu, J. Lu Y. Wu, Tsinghua University, China
7-4: Circulating Fluidized Bed 8-4: Design and Operation of Biomass Combustion-Build-Up and Validation of Circulating Fluidized Bed Boiler with a Three-Dimensional Model High Steam Parameter M. Palonen, V. Yla-Outinen, Metso Power Oy, J. Laine, Tampere University of Technology Finland D. Pallares, A. Larsson, F. Johnsson Chalmers University of Technology Sweden
S. Li, S. Bao, Q. Lu, D. Wang, H. Teng Chinese Academy of Sciences Y. Peng, Z. Liu, B. Hong Changsha Boiler Plant Co., Ltd. China
Tuesday, May 3, 2011 (continued) 6:26 - 6:40 pm
6:40 - 6:56 pm
7-5: 3D CFD Simulation of Combustion 8-5: Operating Experience and Latest in a 150 MWe Circulating Fluidized Bed Developments of Alstom Power's 300 Boiler MWe Class CFB Boilers N. Zhang, B. Lu, W. Wang, J. Li, Chinese Academy of Sciences, China
B. Wilhelm, P. Gauville, I. Abdulally, C. Enault Alstom Power, France
7-6: Comparison Between Measurements and Numerical Simulation of Particle Flow and Combustion at the Duisburg CFBC Plant
8-6: UOP FCC Innovations Developed Using Sophisticated Engineering Tools L. Wolschlag, K. Couch UOP LLC USA
M. Weng, Aixprocess, J. Plackmeyer, Consulting Engineer Germany
6:56 - 7:10 pm
7-7: Hydrodynamics of a Cluster 8-7: Co-Gasification of Biomass and Descending at the Wall of a CFB Riser - Coal in an 8MW Dual Fluidized Bed Numerical Study Steam Gasifier S. Vashisth, J. Grace University of British Columbia Canada
7:10 - 7:24 pm
C. Pfeifer, I. Aigner, H. Hofbauer Vienna University of Technology Austria
7-8: Characteristics of the Solid Volume 8-8: Coal and Biomass Co-Gasification Fraction Fluctuations in a CFB in a Circulating Fluidized Bed Reactor S. Kallio, J. Peltola, V. Taivassalo VTT Technical Research Centre of Finland Finland
A. Czaplicki, M. Sciazko Institute for Chemical Processing of Coal Poland
7:30 - 9:15 pm
DINNER
9:15 - 11:00 pm
POSTER SESSION and SOCIAL HOUR for Papers Presented in Sessions 5, 6, 7 and 8
Wednesday, May 4, 2011 7 - 8:30 am
Breakfast PLENARY SESSION 3 Chairman: L. S. Fan
8:30 - 9:30 am
P-3: Evolution of FCC Technology-Past, Present and Future and the Challenges of Operating a High-Temperature CFB System Ye Mon Chen, Shell Global Services PLENARY SESSION 4 Chairman: L. S. Fan
9:30 - 10:30 am
P-4: Putting Structure Into Fluidized Beds - From Concept to Industrial Applications Fei Wei, Tsinghua University
10:30 - 11:00 am
COFFEE BREAK
11:00 - 11:14 am
SESSION 9 Particle Dynamics Co-Chairs: C. Pfeifer R. Karri
SESSION 10 HT/HP Research
9-1: Sulfur Uptake by Limestone-Based Sorbent Particles in CFBC: The Influence of Attrition/Fragmentation on Sorbent Inventory and Particle Size Distribution
10-1: The Variation of the Bubble Phase Properties of a FCC Fluidized Bed at High Temperature
Co-Chairs: K. Wirth S. Moffatt
R. Girimonte, B. Formisani University of Calabria, Italy
F. Montagnaro, P. Salatino, F. Scala, M. Urciuolo Universita degli Studi di Napoli Federico II, Italy
11:14 - 11:28 am
9-2: Study of Standpipe and Loop Seal 10-2: A Study of Solids and Gas Mixing Behavior in a Circulating Fluidized Bed in a Partitioned Fluidized Bed for Geldart B Particles A.R. Bidwe, A. Charitos, H. Dieter, A. Wei, M. Zieba, G. Scheffknecht University of Stuttgart, Germany
11:28 - 11:42 am
J-H. Moon, Y-J. Seo, S. Kang, S-Y. Lee, Y-C. Park, H-J. Ryu, G-T. Jin Korea Institute of Energy Research, Korea
9-3: Observation of Flow Regime 10-3: Effect of Temperature Field on the Transition in a CFB Riser Using an LDV Coal Devolatilization in a Millisecond Downer Reactor P. Yue, J. Mei, L. Shadle National Energy Technology Laboratory, USA
B. Yan, L. Zhang, Y. Jin, Y. Cheng Tsinghua University, China
Wednesday, May 4, 2011 (continued) 11:42 - 11:56 am
9-4: Bench-Scale Investigation of 10-4: Effect of Bed Temperature, Fuel Limestone Size Evolution in a Fluidized Density and Particle Size on Bed Combustor Hydrodynamic Parameters of 10 MW Fluidized Bed Combustion Power Plant X. Yao, N. Hu, H. Yang Using Riser Waste Tsinghua University, China J.H. Chiu, P. Gauville, S.G. Kang Alstom Power Inc., USA
11:56 - 12:10 am
R..I. Singh Jassar Guru Nanak Dev Engineering College S.K. Mohapatra Thapar University, India
9-5: Catalyst Attrition in the CFB Riser 10-5: Transient Temperature and Char Conversion Profiles in Wood During A. Thon, M. Kramp, E-U. Hartge, S. Heinrich, Devolatilization in a Fluidized Bed J. Werther Combustor Hamburg University of Technology Germany
12:10 - 12:24 am
D.R. Sudhakar, A.K. Kolar Indian Institute of Technology Madras India
9-6: The Relationship Between Fluidization Velocity and Segregation in Two-Component Fluidized Beds: A Preliminary Analysis B. Formisani, R. Girimonte, V. Vivacqua University of Calabria, Italy
12:30 - 2:00 pm
LUNCH
2:00 - 5:30 pm
FREE TIME SESSION 11 Mathematical Modeling III Co-Chairs: R. Cocco T. Shimizu
5:30 - 5:44 pm
11-1: Comparison of Entrainment Rate 12-1: Time-Resolved X-Ray in Acrylonitrile Reactors Using Plant Tomography of a Fluidized Bed of Data and CFD Simulations Geldart A Particles S. Moffatt, S. Ramchandran\ Ascend Performance Materials P. Zhao, K. Williams CPFD-Software, LLC
5:44 - 5:58 pm
SESSION 12 Measurement Techniques Co-Chairs: J. R. van Ommen M. Palonen
R. Mudde, Q. Ricoux, E. Wagner, J.R. van Ommen Delft University of Technology The Netherlands
11-2: Critical Evaluation of Euler-Euler 12-2: A New Approach for Modeling of and Euler-Lagrangian Modelling a Fluidized Bed by CFD-DEM Strategies in a 2-D Gas Fluidized Bed F. Hernandez-Jimenez, A. Acosta-Iborra University Carlos III Madrid Spain J.R. Third, C.R. Muller ETH Zurich Switzerland
S. Karimi, H. Chizari, N. Mostoufi, R. Sotudeh-Gharebagh University of Tehran Iran
Wednesday, May 4, 2011 (continued) 5:58 - 6:12 pm
11-3: Particle-Fluid Flow Simulation of 12-3: Characterization of Fluidization an FCC Regenerator and Mixing of Binary Mixtures Containing Biomass at Low Velocities S. Clark Through Analyzing Local Pressure CPFD Software Fluctuations USA
F. Fotovat, J. Shabanian, J. Chaouki, J. Bergthorson Ecole Polytechnique de Montreal, Canada
6:12 - 6:26 pm
11-4: CFD Simulation of CO2 Sorption in a Circulating Fluidized Bed Using Deactivation Kinetic Model E. Abbasi, H. Arastoopour Illinois Institute of Technology USA
6:26 - 6:40 pm
6:40 - 6:54 pm
F. Wang, Q. Marashdeh, L-S. Fan The Ohio State University, USA
11-5: CFD Modeling of Fluidized Bed Reactor for the Synthesis of Dimethyl Ether
12-5: Description of Pressure Fluctuations in a Circulating Fluidized Bed by Statistical Analysis
R. Kalluri, N. Akunuri, A. Jamal, R. Gupta RTI International USA
R. Coetzer, A. Mostert, A. Luckos Sasol Technology, South Africa
11-6: DEM Study of Fluidized Bed 12-6: Dynamics of Gas-Solids Fluidized Dynamics During Particle Coating in a Beds Through Pressure Fluctuations: Spouted Bed Apparatus A Brief Examination of Methods of Analysis S. Antonyuk, S. Heinrich, A. Ershova Hamburg University of Technology Germany
6:54 - 7:08 pm
12-4: ECVT Imaging of 3-D Flow Structures and Solids Concentration Distributions in a Riser and a Bend of a Gas-Solid Circulating Fluidized Bed
S. Sasic, F. Johnsson Chalmers University, Sweden M-O. Coppens Renssalaer Polytechnic Institute, USA J. van der Shaaf, Eindhoven University of Technology, The Netherlands S. Gheorghiu, Center for Complexity Studies Romania J. R. van Ommen, Delft University of Technology, The Netherlands
12-7: Dynamic Characteristics of Bubbling and Turbulent Fluidization Using a Hurst Analysis Technique H. Azizpour, N. Mostoufi, R. Zarghami R. Sotudeh-Gharebagh University of Tehran, Iran
Wednesday, May 4, 2011 (continued)
7:30 – 10:30 pm
CONFERENCE DINNER at the High Desert Museum
10:45 - 11:45 pm
POSTER SESSION and SOCIAL HOUR for Papers Presented in Sessions 9, 10, 11 and 12
Thursday, May 5, 2011 7 - 8:30 am 8:30 am
Breakfast END OF CONFERENCE
REFLECTIONS ON MATHEMATICAL MODELS AND SIMULATION OF GAS-PARTICLE FLOWS Sankaran Sundaresan Department of Chemical & Biological Engineering Princeton University Princeton, New Jersey 08544 USA
ABSTRACT Examples of complex flow characteristics observed in circulating fluidized beds and turbulent fluidized beds are presented. Different gas-particle modeling and simulation approaches that are being pursued to probe these flow characteristics are summarized. Major advances that are likely to emerge within the next decade are discussed. 1. INTRODUCTION Circulating fluidized beds (CFBs) and turbulent fluidized beds (TFBs) are applied widely in chemical process and energy conversion industries (1). They have been in use in fluid catalytic cracking of gas oil for nearly seven decades, and for lesser duration in many other processes; new processes, such as synthesis of olefins from methanol (2), coal and biomass gasification (3), and CO2 capture by solid sorbents (4, 5), are under development at the present time. Although the long history of use has led to a wealth of design and operational experience with these systems, confidence to design and build commercial plants without significant levels of pilot scale testing at various intermediate scales is still lacking. This is due to an incomplete understanding of the origin and nature of the inherently complex flow structures observed in these devices, and uncertainties as to how they would change upon scale-up. Advanced experimental characterization and rigorous modeling studies are being pursued to unravel the complexities of these flows in both pilot and commercial scale systems. This article presents briefly the author’s perspective on the current status of modeling these flows and the advances that can be expected to emerge in the near future. Section 2 outlines a few illustrative examples of intriguing behavior of CFBs and TFBs, and what one would like to model and understand. This is followed by a brief discussion of why modeling them is difficult. Section 3 attempts to explain why effective fluid-particle drag force model is a critical element in accurate and yet affordable simulations. Section 4 is devoted to advances being made in different modeling approaches. Section 5 touches very briefly on role of gas turbulence. Section 6 outlines some additional data that can benefit modeling efforts. Section 7 provides an outlook of what advances in modeling and simulations can be expected in the next 5-10 years.
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2. SOME FLOW CHARACTERISTICS TO UNDERSTAND AND MODEL In its simplest form, a CFB consists of a riser tube where particles are transported up by co-flowing gas, a device to separate the gas and particles at the top, a standpipe to return the particles to the bottom of the riser and a suitable valve to control the delivery of the particles to the bottom of the riser. The volume fraction of particles in the riser is generally small enough that the particles interact with each other primarily through collisions, while in standpipes it is usually high enough that stress transmission can occur through collisions as well as sustained frictional contact between the particles and between the particles and the wall. More elaborate CFBs would include additional devices such as fluidized beds (e.g., FCC regenerator), leading to more complex flow loops for the particles. Let us briefly review a few flow characteristics that one would like to be able to understand and model. a) In tall CFBs, operating at near atmospheric pressures, the gas pressure can increase appreciably from the top of the standpipe to the bottom resulting in loss of gas volume through compression; to compensate for the adverse effect of this compression, aeration gas is added at a number of elevations along the standpipe. At low aeration levels, stick-slip flow is often observed in the standpipe. Increasing the aeration level enables smoother flow and improved solids circulation rate. However, beyond some threshold aeration level, the flow becomes unstable and the circulation rate becomes very erratic, which is unacceptable (6). How does the onset of this instability depend on the manner in which aeration is administered and the scale of the CFB? b) The flow characteristics in the riser are complex even under stable operating conditions. Risers typically operate in the so-called fast-fluidization regime where there is a denser bottom region, transitioning to a more dilute flow at the top. Furthermore, the time-averaged particle volume fraction and gas and particle mass fluxes manifest significant lateral variations; particle volume fractions generally tend to be high near the riser walls where the mass flux of particles is frequently negative (i.e. downflow) even though the crosssectionally averaged mass flux of particles is positive (7). The particles tend to drag the gas downward in the wall region, and so there can be significant internal recirculation of both particles and gas in the riser. At very high gas velocities, the downflow disappears and one can even get a higher mass flux of particles at the wall region than the core (8). How well can we capture these trends in models and how confident are we in predicting the flow pattern changes that will come about upon scale-up, flow rates or modifications to the flow device? c) Since risers are often used to carry out (catalytic or non-catalytic) chemical reactions involving the gas and particles, one can anticipate that these persistent macro-scale non-uniformities would affect the effective contact between particles and the gas, and hence the conversion and selectivities. How well can we model these effects and propose design choices to maximize conversion and/or selectivity? d) Gas by-passing is a common concern in the operation of turbulent fluidized beds and the beds are extensively baffled to mitigate this problem. Deep beds operating at low (say, near-atmospheric) pressures are particularly
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susceptible to local defluidization and gulf-streaming (9). Can we capture such flows in models and simulations, and predict how they will change upon scale-up and process modifications (such as the introduction of baffles)? When such inhomogeneous flows arise in chemical reactors, they can affect conversions and selectivities; they can also lead to reactant breakthrough to the top of the bed and cause unwanted freeboard reactions. For example, in FCC regenerators, oxygen breakthrough causes CO combustion in the freeboard leading to high temperatures that favor NOx formation (10). Can such problems be detected in simulations in a reliable manner? e) Cyclones play vital functions in CFBs in capturing and returning the particles and minimizing particle emissions. The mass loading of particles in the stream entering the cyclones varies appreciably from stage to stage. The separation efficiency of cyclones is determined by the competition between the swirling flow which aids particle separation and turbulent dispersion which results in re-entrainment of particles into the gas (11, 12). Mass loading of particles affects both the strength of the swirl and turbulent intensity (13-15); how well can we model and simulate these effects? f) In some processes carried out in CFBs and TFBs, liquid is intentionally injected (16) either as a reactant or for coating purposes. In such systems, one can readily expect that in some regions of the bed (e.g., close to where the liquid is injected), the particles will be coated with a liquid and this can lead to agglomeration of the particles. These agglomerates are likely to induce local defluidization and cause secondary flow structures in the CFBs and TFBs (17). How well do we understand these secondary flow structures and their effects on conversions and selectivities (or coating uniformity)? The above list, though incomplete, illustrates some characteristics that one would like to understand and model with confidence, so that the models can then be used as tools to test design options for new plants as well modifications to existing units. More specifically, the models should help us understand the macroscale flow behavior and allow us to perform computational experiments exploring means of manipulating the flow to maximize a desired set of objectives, such as conversion, selectivity and operational stability. What makes modeling difficult? One can readily list a number of reasons, a few of which are described below. a) Circulating fluidized beds typically consist of a number of devices, as mentioned above, and regions with widely different particle volume fractions are encountered in the flow loop. As a result, the manner in which stress is transmitted through the particles changes significantly from location to location. For example, such stress transmission occurs predominantly by collisions in the riser, while in the standpipe and slide (or “L”) valves stresses transmitted through enduring contact become important. As the overall performance involves a complex interaction of various devices in the circulation loop, a good model should account for the effect of stress transmission through particles via collisions as well as enduring contact. b) Meso-scale structures form as a result of the instability of two-phase fluidized flow when the particle volume fraction becomes too small to support sustained force chains (18); the point at which this occurs depends on particle roughness, size (which affects the importance of cohesion) and
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shape. These meso-scale structures take the form of bubble-like voids at high mean particle volume fractions (approximately greater than 0.40) and clusters and streamers at low mean particle volume fractions (approximately less than 0.25) (19). In the intermediate region, a turbulent state exists where the meso-scale structure changes rapidly between bubble-like voids and clusters and streamers. These structures are generally difficult to resolve in computations; yet, they affect the gas-particle interactions (such as the effective fluid-particle drag, the heat and mass transfer rates, and the stress transmission in the particle and fluid phases). c) The particles invariably have a distribution of sizes that evolve naturally through attrition, or develop because of reactions occurring in the fluidized beds. Accounting for the particle size distribution (PSD) is critical to predict accurately the rate of elutriation from turbulent fluidized beds, cyclone efficiency, etc. 3. THE FLUID-PARTICLE DRAG It is easy to understand that one must include gravity (which pulls the particles to the bottom of any device), pressure gradient (which establishes motion of the gas relative to the particles) and fluid-particle drag (which is the principal means by which particles can be suspended against gravity) in any model to capture the flow of fluidized suspensions. The accuracy with which the fluid-particle drag can be determined is critically important in modeling of fluidized suspension flows. A number of empirical constitutive models for the fluid-particle drag in homogeneous suspensions of uniformly sized spherical particles are available in the literature (20); a prominent example is the widely used correlation due to Wen & Yu (21). A practical difficulty comes about when we apply such correlations developed for (nearly) homogeneous suspensions to flows of fluidized gas-particle mixtures. As noted earlier, fluidized suspensions readily form inhomogeneities that span a wide range of length and time scales. As a result of these inhomogeneities, flows in turbulent fluidized beds and risers are invariably multi-dimensional. Furthermore, when the particles and the gas move around from one location to another in a device, inertia should be included in the models. Inertia – especially, the particle phase inertia – is important to capture the formation of flow inhomogeneities such as bubbles, clusters and streamers. As a result of the multi-dimensionality and inclusion of inertia, the models are invariably solved numerically on suitable spatial grids (more on solution methods later). Such computations resolve the flow at scales larger than the grid resolution, but not those occurring at a sub-grid scale. Using extremely fine grid resolution to resolve all the flow structures is often not practical. The challenge in accounting for the effective drag force accurately can be illustrated as follows. Consider a zero-dimensional (0D) model for a turbulent fluidized bed, i.e. the entire bed is simulated using a single numerical grid cell. Such a 0D model ignores all the flow structures present in the bed and reduces to a force balance over a uniformly fluidized bed. If drag force correlations intended for homogeneous suspensions are employed, one readily concludes that the superficial gas velocity must remain well below the terminal settling velocity (vt) of the particles in the bed; however, this is almost never the case and most turbulent fluidized beds operate at velocities in excess of vt. This difference is primarily due to the fact that the inhomogeneities, which were not resolved in this 0D analysis, result in a decrease in
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the fluid-particle drag and it has not been properly accounted for in the analysis. To capture the particle volume fraction in the bed correctly using such a 0D model, one must modify the drag force correlation to reflect the effects of unresolved flow inhomogeneities. If one analyzes the same system as an unsteady flow problem using several numerical grid cells, then some of the flow inhomogeneities will be resolved, and so the modification to the drag force correlation will now be different; as the number of grids increases, the required modification to the drag force correlation decreases. Knowing what inhomogeneous flow structures have not been resolved and how one should account for their effects on the effective fluid-particle interaction (drag) force is a challenge. This issue has been addressed in the literature. O’Brien & Syamlal (22) and Heynderickx et al. (23) corrected the drag coefficient at very low particle volume fractions to account for the consequence of clustering. McKeen & Pugsley (24) used an apparent cluster size in an effective drag coefficient closure. Li and coworkers (25) deduced corrections to the drag coefficient using an Energy Minimization Multi-Scale approach. Filtered models, where the effects of sub-filter scale inhomogeneities on the drag force are modeled by introducing a filter size dependent drag law, are being developed (26, 27); Parmentier et al. (27) have presented an additional advance where the filter size dependent drag force is dynamically corrected in simulation of filtered model simulations. The development of these filtered models is still in the early stage, and many more validation studies are needed to test and refine these models. All the modifications to the drag law that have been described in the literature, which are intended to correct for unresolved structures, are for uniformly sized particles. Drag laws for homogeneous suspensions of particles having a distribution of sizes are described in the literature (28); however, little has been published in the literature on modifying these drag force correlations to correct for unresolved structures. 4. FORM OF THE MODEL FOR GAS-PARTICLE FLOWS The above discussion touched upon numerical computations without making specific reference to the form of the models for gas-particle flows. All the models for gasparticle flows solve the Eulerian form of the continuity and momentum balance equations for the gas phase on a fixed spatial grid, and so the unresolved structures discussed above are obviously relevant. When solving for the particle phase, there are multiple options. 4.1.
Eulerian treatment of the particle phase(s)
In two-fluid models, Eulerian continuity and momentum balance equations are formulated for the particle phase as well, and are solved using the same grids (as for the gas phase). This approach (also referred to as the Eulerian-Eulerian model) has a long history of development and analysis. When multiple types of particles are present, they can be generalized as multi-fluid models, where each particle type is treated as a separate phase, interacting with all the other phases. Two-fluid models have served well in our quest to understand the underlying mechanisms leading to inhomogeneous structures. For example, one can readily find the solution of two-fluid model equations corresponding to the state of uniform fluidization analytically, and examine its linear stability to pinpoint the origin of
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instability leading to bubble-like voids in dense suspensions, as well as clusters and streamers in dilute suspensions (19, 29, 30), and the characteristic length and time scales associated with the dominant instability mode. It has also helped advance simple qualitative arguments explaining why particles segregate towards the walls in riser flows (31). The two-fluid model equation for the particle phase allows for stress transmission through the particle phase. Over the past three decades, researchers have adapted the kinetic theory of dense gases and developed constitutive models for the rheology of assemblies of monodisperse, spherical and inelastic particles interacting through binary collisions (32, 33). Such models, generally referred to as kinetic theory of granular materials, require solution of an additional scalar equation for the kinetic energy per unit mass associated with the fluctuating motion of the particles relative to the local average velocity of the particle phase (a.k.a. granular temperature); the particle phase stress is then expressed in terms of local particle volume fraction, granular temperature and local rate of deformation of the particle phase. The kinetic theory models have also been generalized for mixtures of different types of particles (32-37). These multi-fluid models can take one of two forms: (a) Separate continuity, momentum and granular energy balance equations are formulated for each particle phase, and solved. In this approach, if there are N different particle phases, one has to solve (N+1) continuity equations, d(N+1) momentum balances (where d denotes the number of spatial dimensions involved in the problem) and N granular energy balance equations (34, 35). (b) Continuity equations are formulated for the N different particle species, along with a single momentum balance equation and a single granular energy balance equation for the particle mixture; these are supplemented by algebraic models for the granular temperatures of the different particle species and the “diffusive” flux of each particle species relative to the mixture flow. In this approach, one has to solve (N+1) continuity equations 2d momentum balances, one granular energy balance equation, along with a set of algebraic equations to determine the diffusive fluxes (36, 37). A recent study comparing these two approaches found that both approaches yield similar predictions for binary particle mixtures, with the latter approach requiring less computational time (38); the advantage is likely to be more significant when the number of particle species increases. While the kinetic theory has given us a good handle on particle phase stress resulting from particle streaming and collisions, models for stress in the dense, quasi-static flow regime where the particles make enduring contacts with multiple neighbors and stress is transmitted largely through force chains are by and large phenomenological (39-41). Distribution of particle sizes is handled in the Eulerian modeling approach in several different ways. In one approach, the particles are divided into a number of cuts, each representing a size range and each cut is treated as a separate particle phase. Multifluid models are solved to determine the flow behavior. In the other approach – discrete quadrature method-of moment – the actual PSD is replaced by a sum of
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delta functions placed at judiciously selected particle sizes (quadrature nodes); these quadrature nodes are allowed to change temporally and spatially to capture agglomeration and break-up, change in size by chemical reactions, etc. (42). The number of different particle phases that are needed to capture the effect of PSD will clearly depend on the nature of the PSD; studies addressing how the predictions change as more and more particle phases are used to approximate a given PSD are beginning to appear in the literature (43). The development of software platforms for solving multi-fluid models has progressed appreciably over the past two decades. The open-domain code MFIX developed at NETL (22) and commercial software (e.g., ANSYS Fluent®) are widely used by many research groups around the world to study reacting multiphase flows. Many research groups also employ in-house codes to solve such models (e.g., Neptune in the research group of Olivier Simonin). Application of multi-fluid models to simulate gas-particle flows does raise questions and also poses a number of challenges. Let us consider the two-fluid model where the all the particles are treated as a single particle phase and examine the issues: a) A basic question that one can raise concerns the validity of treatment of the particle phase via a continuum model. In writing such a model, it is presumed that the particles interact with each other rapidly, thus endowing the particle phase with a pressure and a viscosity. In normal fluids, we are able to do this for low Mach number flows as there is a clear separation of scales between the random motion of the molecules that gives rise to pressure and viscosity, and the mean velocity of the fluid phase. It is not at all obvious that such a separation of scales exists for the particle phase in most gas-particle flows where the particles interact via binary collisions. In such situations, the low order moments of the particle velocity distribution function - namely, mass, momentum and fluctuation energy - which are evolved through the particle phase continuity, momentum and granular energy balance equations - may not adequately define the full flow problem. b) High resolution simulations of gas-particle flows via two-fluid models yield fine structures at length scales as small as 10 particle diameters, and it is argued by some that these fine structures are not real features of gas-particle flows and that it is manifestation of the inadequacy of the continuum treatment of particle phase in the two-fluid model. c) The existence of such fine structure raises the issue on the required grid resolution. Resolving all the fine structures contained in the two-fluid model equations requires numerical computations using extremely fine grids. These are simply unaffordable. This necessitates development of filtered two-fluid models where the fine structures are smoothed out and their effects on the resolved flow are modeled. While some progress has been made on the hydrodynamic aspect of filtered two-fluid models, corresponding thermal energy and species balance equations have not yet been developed and validated. d) Handling PSD using multi-fluid models, especially when the PSD is changing due to reactions, break-up, etc., remains a challenge. e) Three-dimensional simulations using two-fluid models of large process units remain expensive (unless one uses filtered models that permit coarse grids); multi-fluid models increase the computational cost significantly.
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f) Boundary conditions for multi-fluid models at solid surfaces are still primitive. g) The continuum hydrodynamic model for the particle phase is obtained by taking the low-order moments of the Boltzmann equation for the particle distribution functions; such an approach usually emphasizes dominant aspects of flow. In some engineering applications, one would like to understand relatively rare events (such as formation of hard particle agglomerates in fluid cokers); two-fluid models are not useful for such inquiries. 4.2.
Lagrangian treatment of the particle phase(s)
Here one formulates and solves Newton’s equations for the motion of particles. Also, the particles are not restricted to be on an Eulerian mesh (e.g., the one used to solve for the gas phase variables). The fluid velocity and pressure gradient at the particle locations, required to solve the particle momentum balance, are readily obtained from the Eulerian (fluid phase) mesh via interpolation. Similarly, the force on the fluid due to the particles can readily be mapped to the Eulerian mesh from the particle locations. Such Lagrangian treatment offers several advantages, while also placing some limitations, as discussed below. At very low volume fractions where inter-particle collisions are rare and unimportant, one can formally ignore collisions and employ a point-particle approximation. Such an approach is used extensively in the literature in studies on particle-turbulence interactions. In the context of CFBs, it is employed in (secondary and tertiary) cyclones. Typically 1-50 million point particles can be tracked in practical simulations and so it is possible to follow all the particles in modestly sized devices only at extremely low particle volume fractions. 4.2.1. Parcel-based approach To circumvent this limitation on the number of particles that can be simulated, parcels of point particles are simulated. Here each test particle being tracked represents a large number of particles having the same characteristics as the test particle (e.g., see Andrews and O’Rourke (44), or Pantakar and Joseph (45). Such a parcel based approach can appreciably expand the range particle volume fractions that can be handled. The approach using parcels of point particles must be modified when the particle volume fraction becomes sufficiently large that interactions between particles via collisions, sustained force chains, cohesion, etc. become important. In the multiphase particle-in-cell (MP-PIC) method, the interaction of other particles with a test particle (parcel) is modeled by a force on the test particle that is proportional to the prevailing particle phase volume fraction gradient at the location of the test particle (46-49). Clearly, even though one tracks point particles, it is recognized that each particle has a finite volume; the particle phase volume fraction and its gradient at the location of the test particle affects both the fluid-particle drag and the effective force due to the interaction between particles. Conceptually, the parcel-based MP-PIC method and the multi-fluid model are equivalent; this has been illustrated recently by direct comparison of the two approaches on a model flow problem (50). Nevertheless, there are clear differences
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in the advantages offered by the two approaches. As noted earlier, the two-fluid model approach is readily amenable to stability and simple macroscopic analyses, which have been useful to develop better understanding of the competing forces leading to complex flow structures. The MP-PIC approach always requires a numerical solution and is not well-suited for simple mathematical analyses. On the other hand, the parcel-based MP-PIC approach does have major attractions: a) Particle size distribution is much more easily handled than in multi-fluid models. b) In dilute flows where the interaction of particles with bounding surfaces occurs mainly by collisions, boundary conditions are easily implemented. c) Changing particle properties and size are easily handled. d) As a large number of parcels are tracked, there is a possibility that rare events can be detected and analyzed. The parcel-based MP-PIC approach [available commercially, CPFD®] has indeed rapidly emerged as very powerful and is being used more and more in industries. Preliminary versions of this approach are available in MFIX as well. It is now being offered as an option in Fluent® as well. In the opinion of this author, parcel-based MP-PIC method will likely emerge over the next decade as a preferred approach for reasons mentioned above. At the same time, it should be noted that this approach has not been tested as extensively as the two-fluid models. There are relatively few interrogations of the properties of solutions obtained by this approach; for example, studies investigating the influence of grid resolution on the solutions for various classes of flow problems – fluidized beds, risers, etc. – are needed. A limited investigation (50) performed recently shows that at fine grid resolution the parcel based approach yields similar microstructure as the two-fluid model, and so it appears that MP-PIC simulation of flows in large devices using coarse grids will need filtered fluid-particle drag force models (and possibly modifications to the effective particle interaction force as well) – as in the case of multi-fluid models. If this is indeed the case, and if so, what filtered fluid-particle drag force model is appropriate for the parcel-based approach, are not clearly understood at the present time. It would also be useful to perform more simulations of classical problems to gain better understanding of the parcel-based approach itself, as well as the underlying flow physics. One example would be simulations of fluidization in a vertical pipe over a wide range of gas velocities and particle fluxes, thus generating a map of average pressure gradient vs. gas velocity at different particle mass fluxes. The general character of such phase diagrams are well known experimentally: choking, multiplicity of states and carrying capacity of the gas have been widely studied experimentally (51, 52). Demonstrating that such complex phase diagrams can be robustly captured would greatly increase the confidence of the method in the minds of the users, than simply testing the method against a few operating conditions. 4.2.2. Discrete Element Method (DEM) for spherical particles One can, in principle, circumvent the need to postulate a phenomenological model for the force on a test particle due to interactions with other particles (in the parcel approach) by directly simulating all the finitely sized particles in a region and their interaction via collisions and enduring contacts. Though these simulations may allow
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for arbitrarily shaped particles, let us focus first on spherical particles, which have been studied the most using granular/molecular dynamics. In assemblies at low particle volume fraction, where the particles interact largely through binary collisions, grains are conveniently modeled as hard particles that experience instantaneous collisions, which are detected using an event-based algorithm. At high volume fractions, where particles tend to make enduring contact, DEM is the preferred approach, where the particles are modeled as soft spheres that can overlap slightly and exert both normal and tangential forces on each other (53). DEM simulations, however, are computationally expensive. In the early 1990’s, DEM simulations were limited to about 103 particles (54, 55); simulations of millions of soft-sphere particles are now feasible using CPUs with higher clock frequency, as well as computer clusters. Also, significant improvements in commercial (e.g. PFC3D (56)), as well as open-source (e.g., LAMMPS (57)) software make DEM simulations more common. Recent applications of DEM-based simulations of particle flows also include a coupling to computational fluid dynamics (CFD) of the fluid phase, e.g., cyclone separators under high mass loads (58), or the DEM-CFD model recently proposed for fluidized bed reactors including heat, and mass transfer, as well as chemical reactions (59). Computations using Graphic Processing Units (GPUs) have become fashionable after software to use these powerful co-processors was published (60). Tailored applications focusing on single-GPU computations have been developed, enabling a roughly 100-fold speed-up compared to conventional single-CPU calculations (61, 62). Thus, the application of DEM-CFD models is poised to grow rapidly in the years ahead. DEM is also widely used as a tool to study rheological behavior of dense particle assemblies (63-65). However, most of these studies have been only for spherical particles. Also, these simulations tend to use simple interaction models (e.g., the linear spring-dashpot model of Cundall and Strack (66)). A comprehensive overview of more sophisticated contact force models, e.g. accounting for rolling and twisting resistance between particles, is given by Luding (67). Rarely do all details of these sophisticated contact forces and torques significantly impact granular flow behavior in most of industrial applications of interest; instead, the effect of particle shape has a more severe impact on the static and dynamic features of a granular assembly (53). 4.2.3. DEM for non-spherical particles The effect of particle shape on flow behavior is currently an active area of research – both from an experimental, as well as modeling point of view. Campbell (68) investigated the flow of prolate spheroidal particles and their effect on granular flow transition; he found that force chain formation, and consequently the stresses in a quasi-static flow situation, depend strongly on particle shape. A specialized algorithm for cylindrical objects has recently been published by Kodam et al. (69, 70). In this latter work the particles are described as true cylinders (as opposed to spherical particles glued together). Also, Kodam et al. provided experimental verification of their approach, as well as a comparison of their simulation with a glued-sphere approach. DEM simulation of non-spherical particles requires significantly more computational resources than a similar simulation of spherical particles. Although various strategies to approximate the true shape of particles exist, they currently cannot compete with the accuracy and details of particle interaction force modeling available for spherical particles. This point is even true for specialized algorithms, e.g., the one used by Kodam et al. (70), as the latter did not include rolling or twisting
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resistance. Thus, there will be always a compromise between particle shape, contact force modeling, as well as time and resources available for the simulation. While the effect of shape on dry granular flow without interaction with the gas phase has been summarized recently (53), there is much less published on fluidized suspensions; more studies would be useful to fully expose the role of particle shape on fluidization. Recently, Liu et al. (71) measured lower fluidization velocities of nonspherical particles compared to sphere packings. Hilton et al. (72) simulated nonspherical Geldart group D particles using a DEM-CFD approach. Rosendahl and Mando (73) recently reviewed the status of models for non-spherical particle motion in gas-solid flows, and highlighted the importance of the alignment of particles with turbulent vortices. The effect of particle shape on fluidization is likely to be a concern in fluidized beds used to gasify biomass, where degassing of the particles may also alter the effective fluid-particle drag considerably. 4.2.4. More on parcel-based methods Even with advances in computational power, DEM simulations will remain prohibitive for large process devices. Therefore, a parcel-based approach will remain the method of choice for large scale problems. As noted earlier, in the parcel-based MPPIC method the particle interaction force is modeled through an empirical particle pressure, while in DEM simulations they are resolved. Researchers have examined if the parcel-based method could be configured in a way that the need for empirical pressure model can be eliminated. Sakai et al. (74) as well as Mohktar et al. (75) assume that the parcel is represented by a sphere with a volume equivalent to the sum of the volumes of the particles making up the parcel. This requires contact detection between parcels, and hence is computationally more expensive than the parcel-based MP-PIC approach discussed earlier. A primitive form of such contact detection has been already used in the work of Patankar and Joseph (45). Bierwisch et al. (76) have shown recently that a parcel-approach with contact detection, when using appropriately scaled interaction parameters, yields simulation results independent of the number of particles making up the parcel. In this approach, one would be performing DEM simulations of the pseudo-particles representing the parcels, where the characteristics of these pseudo-particles are chosen (based on dimensional analysis) such that important features of the flow remain equivalent to the flow of the original particles. Specifically, they show that it is possible to obtain stresses in the quasi-static regime and parcel velocities in all regimes of granular flow, that are independent of the scaling of the system. Analogous scaling can be identified for a linear spring-dashpot model as well (77), whereby properly scaling the spring stiffness, the damping coefficient, as well as cohesive forces, a parcel-based approach can be made to yield the same quasi-static flow behavior, stresses, and particle velocities as the original particle system; however, the parcel-based approach with contact detection overestimates the stresses in the inertial regime (78) where stresses are primarily transmitted through collisions and so a correction is needed. In their latest work, O’Rourke and Snider (46) propose a method to relax the parcel velocities to their local mean value which can be adapted to the parcel-based approach with contact detection to obtain the same particle phase pressure as in the original system of particles (Radl et al. (77) ). Thus, it seems possible to have a discrete particle method based on parcels that can closely approximate the stresses in the original system of particles across different flow regimes. In the opinion of the
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author, this approach holds promise for simulations of flows in standpipes, hoppers, spouted beds, dense phase pneumatic conveying, etc. 5. ROLE OF GAS TURBULENCES IN CFBs AND TFBs In turbulent and fast fluidized beds, where the mass loading of particles is often one or more orders of magnitude larger than that of the gas, gas turbulence has only a secondary effect on the flow in most of the regions (in the opinion of this author). Its effect is more likely to be localized in regions where the particle volume fraction is low (for example, in cyclones where turbulent dispersion of particles lead to loss of separation efficiency) and at the interface separating dense and dilute region where it plays an important role in entrainment of particles into the dilute stream (for example, particle pickup by turbulent eddies in pneumatic conveying, and entrainment of particles into jets). Adequate resolution of gas-phase turbulent fluctuations (e.g., via Large Eddy Simulations, or Direct Numerical Simulations) in industrial-scale devices and jets, especially at high particle volume fractions, does not seem feasible for the foreseeable future. We will continue to rely on sub-grid models for the role of gas turbulence in inducing fluctuations in the particle phase. Such models can readily be included in the granular energy equation of the two-fluid model and in parcel-based models (79, 80). This seems adequate for modeling gasphase stresses in CFB applications, where the mass loading (i.e., the ratio of particle mass flux to gas mass flux) is relatively high. 6. MODEL VERIFICATION AND VALIDATION It is clear from the discussion above that a variety of different models are being applied to study gas-particle flows, and simulations are based on discretized versions of these models. It is important that the models and the simulators based on these models be subjected to careful verification as well as validation with experimental data. What constitutes verification and what is validation have been discussed in some detail by Grace (81). Verification is an essential first step and it can take several different forms depending on the model being tested: a) It is important to demonstrate that simulators based on any model be compared (if possible) with analytically obtainable results for some test problems, even if the problems are highly idealized. For example, in two-fluid models for gasparticle flows, the growth rate of instability modes (starting from a uniformly fluidized state) can be determined readily through linear stability analysis; verifying whether numerical codes can reproduce the analytical results (and if so at what grid resolution and time steps) is a natural test of the fidelity of the code [for example, see ref. (30)]. b) When parcel-based model simulations with collision tracking are formulated, it is important to verify that they yield the expected trends in predictions as one changes parcel size [For example, see ref. (77)]. c) When a filtered two-fluid model is developed by coarse-graining some (say, kinetic theory based) two-fluid model equations, one should verify that simulations of the filtered model yield the same coarse flow structures as the underlying two-fluid model (82).
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d) Models invariably involve approximations; frequently different models tend to capture different aspects of physics more accurately. Yet, there must be situations where the different models agree with each other reasonably well; and so, it makes eminent sense to compare predictions of various models. Such predictions fall in the category of verification and not validation. For example, demonstration that two-fluid model and a parcel-based model yield nearly the same results in simulations of a highly idealized flow problem (50) is a useful verification step as it enhances the credibility, establishes equivalence between models and exposes how ideas from one approach can be adapted for the other. Comparison of CFD-DEM and multi-fluid models is also in the same spirit. e) Comparison of the simulation results obtained at different grid resolutions is also an important verification step. Although this is indeed done in most published articles in an empirical manner (i.e. presenting results obtained at different grid resolutions for one or two test simulations), concrete guidelines on grid resolutions needed to get grid-size independent results are generally not available, with a recent study by Parmentier et al. (27) being a welcome exception. As a result even when grid size independence is demonstrated and the simulation is validated against experimental data in a pilot scale unit, practical challenges regarding grid resolution requirements when applying that simulation approach to large scale devices are not fully appreciated. Good verification studies should strive to bring forward simulation issues at different scales. Even though most of the simulation studies solve the unsteady equations governing the flow, attempts to validate have invariably focused on time-averaged flow characteristics such as axial pressure profile and lateral variation of particle volume fraction and mass flux. Indeed, these quantities arise naturally as the most important ones. Since the flows manifest persistent fluctuations, comparing the power spectra of fluctuations between the models and experiments makes eminent sense (of which differential pressure is the easiest to measure). Since the extent of contact between the gas and the particles is intimately linked to gas dispersion characteristics, they are also important metrics. The challenge problems issued as a part of this CFB-10 conference do indeed focus on validation of models with experimental data on these quantities. A large number of early studies compared time-averaged results obtained from 2D simulations with experimental data; with increasing computing power, more and more 3D simulations are being done. As one would expect, there are quantitative differences between the results obtained with 2D and 3D simulations, and so true validation does require 3D simulations. However, 3D simulations are very expensive and so demonstrating grid independence of solutions is often prohibitive; in the opinion of this author, it is not at all obvious if some of the published simulation results are truly grid independent. This point is particularly clear from the simulation study of turbulent fluidized beds by Parmentier et al. (27) who estimated the grid size needed for nearly grid independent solution of standard two-fluid models used by most researchers; such resolution is often not feasible in commercial scale devices. Based on a recent study (50) comparing the two-fluid model and a parcel based approach, it appears that the grid resolution requirement for the latter approach is also similar. Given this concern (as to whether the computed results are truly grid independent), there is a lingering doubt as to whether favorable comparison of model predictions with experimental data is really indicative of successful validation or a coincidence for the chosen grid resolution. Researchers engaged in simulations
13
certainly understand the need to seek grid independent results, and so the above comment is not intended as a criticism; instead, it is presented as a practical limitation imposed by the size of the problem that one can simulate within the available resources. As clearly demonstrated by Parmentier et al. (27), the grid resolution needed to get a grid-independent solution changes appreciably with particle size. Therefore, experimental data on riser flows and turbulent fluidized beds for particles of different sizes would be valuable. The challenge problem (#3) discussed in this conference considers riser flow data for 59 m (group A) and 802 m (group B) particles. It would be useful to generate riser flow results for several intermediate sizes as well. The challenge problems include turbulent fluidized bed data for a single particle size (~75-80 m, with different fines contents); data for somewhat larger particles would be useful as well. Even if a particular model is validated successfully using pilot scale data (at a certain grid resolution), is there a basis for trusting the simulation results on commercial scale device performance obtained using this model and necessarily coarser grids? This question has been repeatedly posed to the author of this article by researchers in industries (who use the simulation tools to evaluate performance of commercial scale devices). A major concern in scale-up from pilot scale to commercial scale has always been whether the flow characteristics would change qualitatively upon scaleup and lead to serious shortfall in performance. With this in mind, it is suggested that one should compare simulation results obtained at different scales with experimental data. For example, the current challenge problem (#3) considers data obtained in a 30 cm diameter riser; researchers will continue to use these data for many years to further refine their models and simulators. (The data from earlier challenge problems continue to be used for validation studies even today.) It would be useful to collect analogous data on a larger scale unit (say 75 cm diameter riser) for future challenge problems, so that one can evaluate how well the various models and simulation approaches capture both sets of data (30 and 75 cm). A great deal of current research is aimed at incorporating PSD into models and simulations. To better understand the role of PSD and also validate these models, it would be useful to have data for different PSDs (particularly for group B particles in the size range of commercial interest). Risers tend to operate in the fast fluidization regime, where (sometimes) there is a dense phase at the bottom transitioning to a dilute phase at higher elevations. Phase diagrams for riser flows suggest that nearly the same combination of riser gas velocity and particle mass flux can yield different pressure drops across the riser depending on the height of the dense region at the bottom. In such situations it is more sensible to specify one of the fluxes and the pressure gradient and calculate the other flux as an output. Most simulators do not perform such computations and part of the reason for poor validation may be due to this. This suggests that it would be useful to have (at least skeletal) performance data at different riser gas velocities (while fixing the solids flux) and generating results akin to the phase diagram mentioned in section 4.2.1). One would then ask how well models and simulations capture a continuous spectrum of operating conditions – this can help assess if the departure between models and experiments is qualitative or quantitative.
14
In the not too distant future, simulations of the entire CFB loop will become common. It would be of interest to develop good data sets on standpipe operation for models to compare against. The standpipe plays a critical role in stable operation of the CFB loop. Plant operators often seek to increase circulation rate (which is usually tied to productivity of the unit) by improving aeration. Unacceptable failure of the standpipe upon excessive aeration is of practical concern and models and simulations can play a valuable role in understanding this problem and identifying means of improving performance without causing instability. Good data to validate simulations of standpipe flow will help improve confidence in simulations of the full CFB loop. 7. FORWARD LOOK Within the next 5-10 years, significant advances can be expected in simulation of CFBs and TFBs, because of improved computer resources, as well as better modeling approaches. Three-dimensional simulations of full CFB loops will become more common, and these will pave the way for better understanding of global phenomena such as loop instability. Instead of performing simulations at a small number of operating conditions (in individual units such as risers), researchers will map out model predictions over a range of conditions and examine the robustness of trends and quantify uncertainties in the simulation results. At a more fundamental level, coarse-grained drag laws for polydisperse systems will emerge along with better understanding of how they should be constructed for the different simulation approaches (multi-fluid models vs. parcel based method). Parcel based methods (with and without collision detection) will likely emerge as the more preferred approach, and it will be studied in greater detail by academic researchers as well, leading to further improvements in the method. Although not discussed in this article, better understanding of models that one would use for wet systems (such as fluid cokers) where the particles can form agglomerates will also emerge (83). These, in conjunction with flow simulators, will lead to better understanding of secondary flows in such devices. Recent experimental findings, as well as small-scale simulations are a promising starting point to refine our understanding of liquid transport in fluidized beds (84, 85). ACKNOWLEDGEMENTS The author gratefully acknowledges the help of Stefan Radl, William Holloway and Sebastian Chialvo in the preparation of this article. Ted Knowlton’s critique of an initial draft of this manuscript is much appreciated. REFERENCES 1. 2. 3.
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ELECTROSTATIC PHENOMENA IN FLUIDIZATION SYSTEMS: CURRENT STATUS OF UNDERSTANDING AND FUTURE RESEARCH NEEDS Xiaotao T. Bi Fluidization Research Centre, Department of Chemical and Biological Engineering The University of British Columbia, Vancouver, BC, Canada ABSTRACT Electrostatic charging of dielectric powders in gas-solids fluidized beds has received increased attention in recent years, due to its impact on particle agglomeration, dispersion and reactor fouling in polymerization and pharmaceutical processes, and charging and coating uniformity in powder coating processes. This paper provides a critical review on the measurement techniques for characterizing powder charging, distribution of charged powders and the factors influencing the powder electrification in gas-solids fluidization systems. INTRODUCTION The electrification phenomenon in fluidized beds was first observed in late 1950s, and particle charging has been considered as one of the key factors causing particle agglomeration and reactor wall fouling in gas-solids fluidized beds dealing with fine dielectric powders [1]. Electrostatic forces induced by the charges carried by particles can also change the hydrodynamics of gas-solid fluidized beds. Despite these negative effects of electrostatic charging, the mechanisms of charge generation and dissipation are still poorly understood because of the lack of accurate measurement techniques for determining particle charge distributions inside the fluidized beds and the lack of applicable theories for charge separation between similar dielectric materials. Due to the non-homogeneous flow structure in gas-solids fluidized beds, the electric forces between adjacent particles cannot be considered as isotropic in the region close to the gas bubbles. Similarly the electric field of the charged particle bed cannot be treated as a homogeneous medium. Instead, heterogeneous flow structure at the bubble scale has to be considered in order to properly characterize the electrostatic forces and fields associated with charged particles. The reactor performance is impacted by electrostatics as reflected by the particle agglomeration, particle segregation, wall fouling and non-uniform temperature distribution. A proper understanding of charge generation and dissipation
mechanisms at the particle scale or even the molecular scale coupled with the distribution of charged particles at the bubble scale is required to properly predict the impact of electrostatic charges on the reactor hydrodynamics and to develop effective tools for mitigating electrostatic charges. Electrostatic phenomena in industrial fluidized bed polymerization reactors and the mitigation strategies were reviewed comprehensively by Hendrickson [1], while the triboelectric charging of dielectric powders was recently reviewed by Matsusaka et al. [2]. This paper attempts to provide a critical review on measurement techniques, charge generation and spatial distribution in fluidized beds, and the factors determining the degree of particle electrification in gas-solids fluidized beds. MEASUREMENTS OF STATIC CHARGES IN FLUIDIZED BEDS Various techniques have been used to quantify the electrification phenomenon and to measure particle charge density in gas-solids flow systems. The most commonly used method is to capture certain amount of charged particles into a Faraday cup so that the charge density of particles (Q/m) can be obtained. Some methods measure the current or the electric potential from the metal surface of an electrostatic probe either in direct contact with the bed particles (ball probes, collision probes or capacitance probes) or exposed to the electrical field induced by the pass-by particles (induction probes). Particle charge density can also be deduced by analyzing the trajectory of a charged particle subject to an electric or electromagnetic field. Table 1 summarizes the techniques used to measure electrification in gassolids fluidized beds. Direct Methods: Measurements of Charge Density by Faraday Cup and Particle Trajectory Tracking The sign and density of charges on each particle will provide crucial information on the degree of particle charging and the magnitude of electrostatic forces acting on individual particles. Charged particles in the fluidized bed can be removed by a sampling tube or a scooper, then poured into a Faraday cage, as illustrated in Figure 1. Such a sampling method has been applied to measure the particle charge density in the dense bed region [3, 4, 5, 6, 7] and the freeboard region [8] of bubbling/ slugging fluidized beds or CFB risers [9, 10]. Such a method can be made localized by taking samples from different locations of the fluidized bed [5]. Since particles with size distributions have been commonly used in the fluidized bed, it will be important to know the differential charging of particles of different sizes in the bed. To determine the charge density on removed particle samples of different sizes from the fluidized bed, multi-compartment Faraday cup systems were used which separated particles of different charge densities into several Faraday cups arranged either horizontally [11] or vertically [6], as shown in Figure 2.
Table 1. Electrostatics measurement techniques for gas-solids fluidized beds. Researchers Ciborowski and Wlodarski (1962) [12] Boland et al., (1969) [13]; Boland and Geldart (1971) [14] Bafrnec and Bena (1971) [15] Tardos and Pfeffer (1980) [3] Fasso et al. (1982) [8] Wolny and Opalinski (1983) [16]
Probe Ball probe
Signal Potential
Bubbling
Particles Sand, polystyrol, vinyl polyacetate Glass beads
Induction probe
Potential
Bubbling
Glass beads
Ball probe
Potential
Bubbling
Porcelain
Bubbling Bubbling
Glass beads Polystyrene
Q/m; Current Q/m Q/m
Fujino et al. (1985) [4]
Bubbling
Gajewski (1985) [17] Rojo et al. (1986) [18] Wolny and Kazmierczak (1989) [19] Gidaspow et al. (1986) [25] Guardiola et al. (1992; 1996) [20,21] Napier (1994) [22]
Bubbling Bubbling Bubbling
Glass bead, Neobead, PMMA Polyethylene Glass bead Polystyrene
Bubbling
Coal
Faraday cup; Ball probe Faraday cup Faraday cup (Single particle sampling) Ball probe; Faraday cup Multi ring probes Ball probe Tracking of particle trajectory Ball probe
Bubbling
Jiang et al. (1994) [9]
Riser
Coronella and Deng (1997) [23] Mountain et al. (2001) [24] Tucholski and Colver (1998) [10]
Riser
Glass beads, steel Glass beads, sugar FCC, Polyethylene Sand
Bubbling Riser
Polymers Glass beads
Bubbling
Ali et al. (1998; 1999) [11, 5]; Zhao et al. (2003) [6] Servais and Bernot (2000) [26] Park et al. (2002a, 2002b) [27,28] Revel et al. (2003) [7] Murtomaa et al. (2003) [29] Yao et al. (2002) [30] Mehrani et al. (2005; 2007) [31, 32] Chen et al. (2006, 2007) [33, 34, 35] Demirbas et al. (2008) [36] Wang et al. (2008) [37] Moughrabiah et al. (2009) [38]; Liu et al. (2010) [39] Sowinski et al. (2009, 2010) [40, 41] Omar et al. (2010) [42]
Bed Bubbling
Potential; Q/m Current Potential Q/m Current
Ball probe
Potential
Ball probe; Field meter Faraday cup
Q/m (No details) Q/m Potential
Polyamide
Electrostatic field meter Faraday cup Faraday cup; Ball probe; Metal wall Faraday cup
Bubbling
Polyethylene
Ball probe
Potential
Single bubble Slugging Bubbling
Glass beads
Ball probe
Current
Polyethylene Glass beads, lactose, cellulose
Q/m Potential & Q/m
Bubbling Bubbling Bubbling
Glass beads Glass beads, Polyethylene Glass beads
Bubbling
Glass beads
Bubbling Bubbling (P, T) Bubbling
Polyethylene Polyethylene, glass beads Polyethylene
Faraday cup Multi-scan Induction ring probe Ball probe Faraday cup (insitu) Multi induction probes Multi Induction probes Multi ball probes Multi ball probes
Bubbling
Polyethylene
Bubbling
Faraday cup Faraday cup (insitu)
Q/m Q/m Probe current; Wall current. Q/m
Current Q/m Current & Q/m distribution Charge Potential Current distribution Q/m Transient Q/m
Powder in
FB Faraday Cup Grounded Cup Electrometer
Faraday cup Electrometer
+ + ++ + ++++ Insulation
Figure 1. Schematic of a Faraday cup charge density measurement system.
Fluidized bed Plug Powders Metal sampling tube 1 2 3 4 Vertical array of Faraday cup sensors
5 6 7
(a)
(b)
Figure 2. (a) Horizontal and (b) vertical multi-compartment Faraday cups [6, 11]. The charge density of individual particles in the fluidized bed can also be measured using the single particle sampling method [16] or the single particle trajectory method [19]. For single particle sampling, a small vacuum sampling tube (0.5 mm in diameter) is inserted into the bed to remove single polystyrene particles (~ 1 mm in diameter) into a Faraday cup so that the charge density of individual particles can be directly obtained. The particle trajectory method is a non-contact measurement method by which two parallel metal plates are installed in the freeboard region of a transparent fluidized bed. When single particles are ejected into the space between the parallel plate by a single gas nozzle located inside the dense bed right beneath the space of the parallel plates, the trajectory of single particle movement was captured by a high speed video camera which enabled the determination of the
charge density of individual particles by analyzing the trajectory of the particles subjected to an electrical field. The accuracy of this method will be impacted by the interference of the electric field imposed by the charged column walls and the bed particles unless the space between the parallel plates are well isolated. The Faraday cup method can provide the charge density directly, but the measurement accuracy is affected by the additional charging or discharging of samples during the sampling process due to the contact of sampled powders with the sampling tube or device. A novel in-situ Faraday cup fluidized bed method was developed by Mehrani et al., [31, 32] to measure the charge density of entrained fine powders from an electrically isolated copper fluidized bed, which serves as a Faraday cup. As shown in Figure 3, when charged fine particles are elutriated from the fluidized, an equal but opposite charge will be registered by the electrometer connected to the isolated copper fluidized bed. To monitor the change of charge density of entrained fine powders with time, the entrained particles can be captured into a bag filter and weighed by a sensitive balance so that the transient charge density of the entrained fines can be obtained [42]. On the other hand, improvement has been made in minimizing the additional electric charging by discharging bed materials directly into a Faraday cup installed beneath a fluidized bed using a quickopen distributor for the measurement of the average charge density of bed materials at the end of each fluidization test [40, 41], as shown in Figure 4. C o pp e r p ip e
G a s o ut
C op p er c yc lon e
T ef lo n
In n er c o pp e r c olum n
P le x ig las s s ea le d c ha m b er Fine s c olle c te d In a c up
S ca le
O u ter c op p er c olum n
E le ct ro m et e r
D at a ac qu is itio n by c om pu te r
T ef lo n
F lu id iz a tio n g a s
Figure 3. A Faraday cup fluidized bed unit for transient charge measurement of entrained fines [31, 42].
Figure 4. A novel fluidized bed unit with a quick-opening distributor for measurement of powder charging [40, 41]. Indirect Methods: Measurements of Current and Voltage Potential by Electrostatic Probes When particles in the fluidized bed are charged, no matter whether the reactor wall is grounded or not, an electric field will be created in the fluidized bed. By placing a metal probe inside the fluidized bed with the probe connected to an electrometer, a potential against a grounded reference probe (either the reactor wall, the metal distributor or another metal probe) will be registered by the electrometer, as illustrated in Figure 5. The magnitude of the potential against a grounded metal wall or distributor generally increases with fluidization time, and reached a final steady state value, Vs, after a few minutes to an hour, see Figure 5. The final potential is thus believed to reflect the degree of electrification of fluidized particles at steady state when the equilibrium between charging and discharging is reached. An equivalent electric circuit could be used to relate the final potential to the charge behaviour in the fluidized bed [4]. As illustrated in Figure 5 the charged bed particles can be treated as a condenser which captures charges generated in the fluidized bed. The total electrical resistivity of the whole system consists of three parts: bed resistivity accounting for current flow from the bed to the probe, the leakage resistance from the reactor wall and the internal resistance of the electrometer. The final potential is thus related to the resistances by:
Vs I 0 Rt
I 0 Rbed Rwall Rm Rbed Rwall Rbed Rm Rwall Rm
The final potential between the probe and the grounded metal distributor can also be analyzed using a capacitance model, with the final potential being related to the charge in the capacitor by, 4H Vs Q f Dc D p where H is the vertical distance between the probe and the distributor, Dc is the diameter of the distributor and Dp is the diameter of the electrostatic probe. The final potential is then seen to be influenced by the dielectric properties of the bed particles, which depend on the bed voidage, relative humidity etc.
Im Q/m
Rbed
E
C
Rwall
Rm
FB V
Electrometer
Potential
V V s (1 e t / )
Vs
Potential probe Time Figure 5. A typical electrostatic potential probe, its equivalent circuit and measured signals. The other type of electrostatic probes is the collision type current probes, or socalled ball probes. The probe is installed in the fluidized bed and connected to a resistor, with the current measured by an electrometer, see Figure 6. Different from the potential probe which treats the fluidized bed as a charged continuum medium, the current ball probe receives charges both transferred from particles colliding with the probe surface and induced when the particles pass by the probe [3, 27]. For a single bubble passing a current probe in a two-dimensional fluidized bed, Park et al. [27] and Chen et al. [43] proposed a combined charge transfer and induction model to interpret the recorded transient current signal. The net current change is related to the charge transfer from particle-probe collisions while the fluctuations are induced by the passing bubble. Chen et al. [44] further demonstrated that the charge density of the particles in the dense phase surrounding the bubble could be obtained by fitting the model to the measured current signals from a single bubble, for given particle electrical properties and a uniformly charged dense phase. To eliminate probe interference with the motion in the bed and charge transfer due to collisions between particles and the probe, shielded induction probes have also been
used to characterize the electrification of fluidized beds. Boland and Geldart [14] embedded an induction probe onto the Plexiglas wall of a 2-D column, and registered the dynamic response of the probe to the passage of a single injected gas bubble into a fluidized bed of glass ballotini particles. They speculated for the first time that the static electrification is generated by the motion of particles around the gas bubble, particularly in the region of the wake. Single or double ring induction probes have been commonly used to measure particle velocities in pneumatic conveying lines based on probe calibration and cross-correlation of signals from two rings separated by a given distance [45-47] by assuming a uniform particle flow in the pneumatic conveying lines.
Figure 6. A typical collision current probe installed in a 2-D column [27]. Chen et al. [33-35] used multi-induction probes aligned horizontally to measure the charge distribution around rising gas bubbles in a two-dimensional Plexiglas column. Using four induction probes placed flush with the outer surface of the column, signals received from the four probes are shown in Figure 7(a), and used in conjunction with the bubble position captured by a synchronized digital camera to reconstruct the charge density distribution around rising gas bubbles in gas-solids fluidized beds. Figure 7(b) shows the reconstruction results for the induced charges shown in Figure 7(a). It can be seen that the charge inside the air bubble is almost zero, and the charge density increases gradually toward the dense phase region outside the bubble, with the sign of charges in the dense phase remote from the bubble being negative. There is a more negatively charged wake, confirming the postulation based on measurements with a single collision probe [23, 43, 44]. The charge density outside the bubble in the dense phase was approximately –3.6x10-7 C/kg and the charge in the wake was about–6.8 x10-7 C/kg. Murtomaa et al. [29] used a multi-scan induction ring probe to measure the induced potential at different bed height by moving the ring probe vertically. The average particle charge density of the bed under steady state was obtained by signal reconstruction, with the assumption of a uniform charge density on homogeneously distributed particles. As will be discussed later, the non-uniform charge density distribution in the fluidized bed will make this technique difficult to be used for fluidized beds without a priori charge distribution information. The disadvantage of induction probes is that the signals received by those probes
can be interfered by electric fields induced by charged column walls, especially for non-conductive walls such as Plexiglas.
inside bubble
1200
Induced charge, pC
1000
800
600
probe 4 400
line: measured scatter: reconstructed
probe 1
200
0
probe 2 100
50
probe 3 0
-50
-100
Distance from the center of the bubble, mm
(a) 80
3
charge density, pC/mm
60
-2.0 -1.7
40
-1.5
bubble
z, mm
20 probe 1
-1.2
probe 2
probe 3
probe 4
0
-0.90 -0.63 -0.35 -0.075
-20
0
-40 -60 -80 0
10
20
30
40
x, mm
(b) Figure 7. (a) Measured induced charge signals and simulated induced charge signals based on reconstructed charge distribution and (b) reconstructed charge distributions (Hmf = 0.7 m, DB=0.08 m, UB=0.45 m/s, dp=565 μm, ρp=2500 kg/m3, Qair=1.78 m3/s ) [34].
CHARGE GENERATION AND DISTRIBUTION IN FLUIDIZED BEDS Charge Generation and Bipolar Charging Tribo-electrification between dissimilar materials can be characterized by the surface work function, defined as the work/energy needed to pull an electron away from the surface of a material. In general, metal has a lower work function, thus easier to loss electrons when contact with other materials. Work function also closely correlates with the dielectric constant of the material, higher for materials with a higher dielectric constant. When two dissimilar materials are in contact, electrons will flow from the surface of lower work function to that of a higher work function. Thus, in contact with a neutral metal surface, a neutral dielectric particle will extract electrons from the metal surface. On separation, the dielectric particle becomes negatively charged. When a charged particle contacts with a metal surface, however, the net charge exchange will depend on the pre-charge level, collision speed and the collision angle etc. As reviewed in Matsusaka et al. [2], extensive work has been reported on the effect of particle pre-charge level, surface pre-charge level, collision angle and collision speed on the degree of charge transfer and separation. The charge generated by colliding a dielectric particle with a metal surface generally increases with the collision speed and is at the maximum for a head-on collision with the surface. In fluidized beds of dielectric particles, particles are expected to be charged via collision with the reactor walls, either grounded metal wall or poorly conductive plastic walls. Most studies on electrostatic charging of fluidized beds assumed a single polarity of bed materials before 1990s, although bipolar charging was identified as early as in 1950 [48, 49]. Based on the measurement of the charge density of individual particles, it was found that for glass beads of narrow size distributions, particles are approximately evenly divided between positively and negatively charged although the net charge is close to zero. Charging particles through a vibrating chute and then falling them through air between parallel vertical electrode plates to separate particles into eight groups using 8 Faraday pales aligned horizontally beneath the electrode plates with each unit connected to an electrometer, Turner and Balasubramanian [49] confirmed that both positive and negative charges could arise on particle surfaces for three narrowly sized glass bead samples, 45-53 m, 63-75 m and 75-90 m. Cartwright et al. [50] tested polyethylene (PE) powders in a pneumatic conveying system and showed that fine PE powders were charged negatively and coarse powders charged positively. Similar to the tests of Turner and Balasubramanian [49], the ratio of the positively and negatively charged powders strongly depended on the relative humidity, suggesting that the moisture on the particle surface might play an important role during the charging process. Similar bipolar charging phenomenon was also reported by Singh and Hearn [51] and Mazumder et al. [52, 53] for PVC and tonner powders based on the measured charges on individual particles in samples of size distributions using a microprobe system and an electric single particle aerodynamic relaxation time (E-SPART) analyzer, respectively. The first direct evidence of bipolar charging in fluidized beds was given in the paper by Wolny and Kazmierczak [19] who measured the charge density of individual particles using a particle trajectory tracking technique installed in the freeboard of the fluidized bed and reported a probability distribution of charge density, although his
results were not noticed until now. Ali et al. [11] used a horizontal array Faraday cup system (see Figure 2) to measure the charge density of particles charged differently whereby a small sample of charged powders removed from a fluidized bed by a scooper was poured from a height and separation occurred between the majority and the minority particles of opposite charges. The degree of separation is a function of charges on the individual particles and the height at which the sample is poured. Bipolar charging of the fine and coarse fluidized particles was identified in which the fine particles are charged negatively, while coarse particles are charged positively for two polymer samples tested, while the third polymer sample showed the opposite charge signs. Further tests by Zhao et al. [6] using a vertical array Faraday cup system confirmed the bipolar charging behaviour for three polymer powders, all showing negatively charged fines. Mehrani et al. [32] measured the charge density of fines entrained from a copper fluidized bed of glass beads and polyethylene powders. Bipolar charging was identified with fines charged positively for all glass beads samples, but negatively charged fines for polyethylene resin powders at both zero and 60% relative humidity. Further study of Omar et al. [42] using polyethylene resins of different grades from different manufacturers showed consistent results of negatively charged fines. The addition of different fines to the polyethylene powder [32] also showed a strong dependence of the charge level and signs on the relative humidity of the fluidizing gas. The fine Larostat 519 powder was negatively charged at dry nitrogen, but positively charged at 60% relative humidity. The opposite signs reversal phenomenon was observed for fine catalyst powder and silver-coated glass beads. The identification and confirmation of bipolar charging changed the perception of a uniformly charged fluidized bed of particles of singular polarity, and thus opened the door for examining the charge distribution inside the fluidized beds. Charge Distribution Ali et al. [5] studied the charge distribution in fluidized beds of polymer powders with the samples removed from different locations of the fluidized bed by a scooper and poured into a Faraday cup. Their results showed that the charge density inside the bed was quite uniform, except in the near wall region where some fine powders deposited onto the walls. The wall deposit was further analyzed on its mean size and the charge density. It was found that the wall deposit was positively charged in the dense bed region, with the charge density increased with increasing the distance from the distributor [Figure 8(a)]. Above the bed surface, however, the deposit reversed the polarity to negative. In the region where the charge density crosses zero, there was no deposit on the wall. The maximum charge density of the deposit somehow corresponded to the maximum field potential for a uniformly charged bed [Figure 8(b)]. Also, the mean particle size in the deposit of the dense bed was generally larger than that in the freeboard region [Figure 8(c)]. Recently, Sowinski et al. [40, 41] carried out similar tests in a novel fluidized bed unit in which the entrained fine powders were captured into a Faraday cup to have the charge density measured, and the charge density of the bed material, polyethylene powders, was measured by dropping the bed particles into a Faraday cup located beneath the gas distributor right after the fluidizing gas was cut off. The fine powders deposited on the column wall was visually inspected and sampled to have the
charge density and the particle size distribution determined. Their results showed that the entrained fines were highly positively charged, while the bed materials were negatively charged. The particles deposited on the wall were overall highly negatively charged, with most deposit residing in the dense bed region, and minor deposit above the bed surface. Again there was a clean wall region between the dense bed and the freeboard region. Their results are in general consistent with Ali et al. [5], although the deposits at different region were not examined.
Figure 8. Axial distribution of (a) charge density of wall deposit, (b) field intensity of charged bed and (c) volume mean particle size of wall deposit. [5]. Gajewski [17] measured the axial profiles of the average electrostatic current from multiple isolated copper rings embedded inside a glass fluidized bed with polypropylene powders as the bed material. Positive current was measured in the bottom dense bed region, and negative current was found in the upper bed and freeboard region, showing a reversal of current flow in the system. Moughrabiah et al. [38] and Liu et al. [39] also measured the axial distribution of electrostatic current from 8 ball probes located at both the axis and near the wall of the fluidized bed using polyethylene powders. The average currents from those probes were found to be always negative in the lower dense bed region, and positive in the upper and the freeboard region, confirming the current sign reversal phenomenon in the bed. Rojo et al. [18], Servais and Bernot [26] and Wang et al. [37] measured the axial distribution of electrical potentials using potential probes immersed in the fluidized
bed. Wang et al. [37] showed that negative potentials always presented in the lower dense bed region, and positive potentials in the upper bed and freeboard region, appearing to be consistent with the reported current distribution. The new experimental evidence on non-uniform distribution of bipolarly charged powders inside the fluidized beds and the likely induction charging of bed particles from the highly charged wall deposit [5] revealed the complexity of the particle charging and accumulation mechanisms in the fluidized bed. All bulk measurement techniques relying on the assumption of uniform spatial charge distribution would not be able to capture the local characteristics of the charging phenomenon. Localized measurements using sampling tubes in conjunction with cascaded Faraday cups or small current probes and localized current induction tomography in combination with advanced signal analysis and reconstruction methods may provide the opportunity to improve our understanding of local charging behaviour of fluidized beds. FACTORS INFLUENCING POWDER CHARGING Tardos and Pfeffer [3] and Fujino et al. [4] found that the particle charge density was not sensitive to the superficial gas velocity. Revel et al. [7], however, showed that the charge density increased with increasing the superficial gas velocity using polyethylene powders. Note that all those tests were conducted in bubbling fluidized beds. In circulating fluidized risers, both Jiang et al. [9] and Tucholski and Colver [10] showed that the particle charge density was insensitive to the solids circulation rate and the superficial gas velocity. One likely explanation is that the increase in gas velocity and particle circulation rate promotes both charge generation and dissipation via particle-wall collisions, with the net change remaining unchanged. Fujino et al. [4] showed that the relative humidity has little impact on the particle charge density in bubbling fluidized beds. Tardos and Pfeffer [3] showed that the charge density in the bed with a humid gas (RH=36-42%) was almost 50% lower than that at lower humidity (RH=21-25%). Wolny and Kazmierczak [19] also showed that the charge density of a 0.475 mm diameter polystyrene bubbling bed decreased substantially when the relative humidity increased from 30% to 70%. In a charge dissipation test carried out in a packed bed separately, they also demonstrated that the charge dissipation rate at 70% relative humidity was much higher than at 30% relative humidity. The increase in relative humidity was also found to be effective in reducing the particle charge density in circulating fluidized risers [9]. It is also noted that the reduction in average current and final potential measured by static probes has been reported in all tests in the literature, supporting the effectiveness of relative humidity in lowering the particle charge density. In general, such a reduction in bed charge level at high humidities can be attributed to the increased surface conductivity of moisture-coated particles [13]. Also, the increase in the humidity of the fluidizing gas may also lower the resistivity and the break-down potential of the gases, helping the dissipation of particle surface charges. Antistatic agents have been used in the industry for powder charge mitigation. Wolny and Opalinski [16] reported that the addition of 0.1% fine powders of either conductive (active coal and TiO2) or insulating materials (pigment A-extra) was effective in reducing the buildup of particle charge density in a fluidized bed of ~1 mm polystyrene particles. Wolny and Kazmierczak [19] later reported that the addition of fine aluminium and NaCl powders to a bubbling fluidized bed of
polystyrene beads significantly reduced the charge density. It was also observed that all NaCl particles in the bed were negatively charged and that all polystyrene particles were coated with NaCl fines. The effectiveness of fine powders, no matter conductive or not, has also been reported in many studies based on reduced current and potential from electrostatic probes [15, 20, 28]. Servais and Bernot [26] examined the effect of antistatic powders (Larostat 519) on both the axial and radial electrical potential profiles in a fluidized bed of polyethylene and polypropylene powders using a potential probe. The results showed that both the axial and radial electric potential profiles were significantly modified in the presence of antistatic powders. The signs of the potential could even be flipped with the addition of antistatic powders. They also showed that the potential increased with increasing the fines content in polyethylene samples, inferring that higher charges were generated for powders containing more fines, as evidenced by more fines sticking to the column wall. Their results suggest that the addition of fine powders may not only change the bed particle charge density but also altered the spatial distributions of bipolarly changed particles in the fluidized bed. One possible explanation is the lubrication of the fine powders as “spacers” attached to bed particles so that charge dissipation by particle-to-particle contact is enhanced due to the increased particle contacts. The difference between antistatic powders and fine powders is still unclear and requires further investigation in the future. Particle size distribution may also influence the electrostatic behaviour of powders because of the bipolar charging between fine and coarse particles and the effect of the addition of fines on the reduction of charge buildup. The implication is that a proper design of particle size and size distribution may potentially reduce electrostatic charge generation and build-up in fluidized beds. FUTURE RESEARCH NEEDS Understanding the impact of electrostatic charging of particles on gas-solids fluidized bed reactor performances related to particle agglomeration and reactor fouling requires the exploration of electrostatic charge generation and dissipation mechanisms and segregation patterns of charged particles, which, in turn, relies on the development of reliable and accurate measurement techniques. Advanced techniques which can provide transient local particle charge density as well as charge density distribution across a spectrum of particle sizes of the fluidized particles will enable researchers to improve the understanding of the bipolar charging behaviour and segregation of particles of different sizes and charge polarities in the fluidized bed. Resolution of charged particle distribution surrounding gas bubbles in the bubbling beds and particle clusters or particle streamers in the annulus region of risers may provide key information for unlocking particle charging and segregation mechanisms in fluidization systems. ACKNOWLEDGEMENT I am grateful to final supports from NSERC, Mitsubishi Chemicals, Japan Polychem, Nova Chemicals, Chevron-Philip Chemicals and contributions from John Grace, Alissa Park, Poupak Mehrani, Li Yao, Aihua Chen, Flip van Willigen, Kwang-Seok Choi, Wajeeh Moughrabiah, Zhengliang Liu, Amy Yang and Muammar Omar.
NOTATION DB = bubble diameter, m Dc = diameter of gas distributor, m Dp = diameter of electrostatic probe, m dp = mean particle diameter, μm H = vertical distance between probe and distributor, m Hmf = packed bed height, m I = electric current, A m = particle mass, kg Q = electrostatic charges, C Q/m = particle charge density, C/kg Qair = fluidizing air flow rate, m3/s RH = relative humidity of gases Rbed = Electric resistance of bed, Rm = External electric resistance, Rwall = Electric resistance of wall, UB = bubble rise velocity, m V = voltage potential, V Vs = steady state voltage potential, V X = horizontal coordinate, m z = vertical coordinate, m f = dielectric constant ρp = particle density, kg/m3 REFERENCES 1. Hendrickson, G., Electrostatics and gas phase fluidized bed polymerization reactor wall sheeting, Chem. Eng. Sci., 61, 1041 – 1064, 2006. 2. Matsusaka, S., H. Maruyama, T. Matsuyama and M. Ghadiri, Triboelectric charging of powders: A review. Chem. Eng. Sci., 65, 5781–5807, 2010. 3. Tardos, G., and R. Pfeffer, A method to measure electrostatic charge on a granule in a fluidized bed. Chem. Eng. Comm., 4, 665-671, 1980. 4. Fujino, M., S. Ogata and H. Shinohara, The electric potential distribution profile in a naturally charged fluidized bed and its effects. Int. Chem. Eng., 25(1), 149-159, 1985. 5. Ali, F.S., I.I. Inculet and A. Tedoldi, Charging of polymer powder inside a metallic fluidized bed. J. Electrostatics, 45, 199-211, 1999. 6. Zhao, H., G.S.P. Castle, I.I. Inculet, and A.G. Bailey (2003). Bipolar charging of poly-disperse polymer powders in fluidized beds. IEEE Transactions Industry Applications, 39, 612-618. 7. Revel, J., C. Gatumela, J.A. Dodds and J. Taillet, Generation of static electricity during fluidisation of polyethylene and its elimination by air ionisation. Powder Technol., 135– 136, 192– 200, 2003. 8. Fasso, L., B.T. Chao and S.L. Soo, Measurement of electrostatic charges and concentration of particles in the freeboard of a fluidized bed. Powder Technol., 33, 211-221, 1982. 9. Jiang, P.J., J.P. Zhang and L.S. Fan, Electrostatic charge effects on the local solids distribution in the upper dilute region of circulating fluidized beds, CFB?? 10. Tucholski, D. and G.M. Colver, Charging of glass powder in a circulating fluidized bed, IEEE Trans. Ind. Applica. 1906-1912,1998. 11. Ali, F.S., M.A. Ali, R.A. Ali and I.I. Inculet, Minority charge separation in falling particles with bipolar charge. J. Electrostatics, 45, 139-155, 1998. 12. Ciborowski, J. and A. Vlodarski, On electrostatic effects in fluidized beds. Chem. Eng. Sci., 17, 23-32, 1962. 13. Boland, D., Q.A.W. Al-Salim and D. Geldart, Static electrification in fluidised beds, Chem. Eng. Sci., 24, 1389-1390, 1969. 14. Boland, D. and D. Geldart. Electrostatic charging in gas fluidized beds. Powder Technol., 5, 289-297, 1971/2. 15. Bafrnec, M. and J. Bena, Quantitative data on the lowering of electrostatic charge in a fluidized bed. Chem. Eng. Sci., 27, 1177-1181, 1972.
16. Wolny, A. and L. Opalinski, Electric charge neutralization by addition of fines to a fluidized bed composed of coarse dielectric particles. J. Electrostatics, 14, 279289, 1983. 17. Gajewski, A., Investigation of the electrification of polypropylene particles during the fluidization process. J. Electrostatics, 17, 289–298, 1985. 18. Rojo, V., J. Guardiola and A. Vian, A capacitor model to interpret the electric behaviour of fluidized beds. Influence of apparatus geometry. Chem. Eng. Sci., 41, 2171-2181, 1986. 19. Wolny, A. and W. Kazmierczak, Triboelectrification in fluidized bed of polystyrene. Chem. Eng. Sci., 44, 2607-2610, 1989. 20. Guardiola, J., G. Ramos and A. Romero, Electrostatic behaviour in binary dielectric/conductor fluidized beds. Powder Technol., 73, 11-19, 1992. 21. Guardiola, J., V. Rojo and G. Ramos, Influence of particle size, fluidization velocity and relative humidity on fluidized bed electrostatics. J. Electrostatics, 37, 1-20, 1996. 22. Napier, D.H., Generation of static electricity in a fluidized bed and in powder conveying. Proc. 2nd World Congress Particle Technology, 1994. 23. Coronella, C.J. and J.X. Deng, Electrostatic effects in cold-model circulating fluidized beds, 1997. 24. Mountain, J.R., M.K. Mazumder, R.A. Sims, D.L. Wankum, T. Chasser and P.H. Pettit, Triboelectric charging of polymer powders in fluidization and transport processes, IEEE Transactions on Industry Applications, 37(3), 778-784, 2001. 25. Gidaspow, D., D. Wasan, S. Saxena, Y.T. Shih, R. Gupta, A. Mukherjee, Electrostatic desulfurization of coal in fluidized beds and conveyors, AIChE Symp. Ser. 83 (255), 74–85, 1986. 26. Servais, T. and C. Bernot, Measurement of electrostatic effects in a fluidized bed reactor. First European Conference on the Reaction Engineering of Polyolefins, Lyons, July 3–6, 2000. 27. Park, A., H.T. Bi, J.R. Grace and A.H. Chen, “Modeling electrostatic charge transfer in gas-solids fluidized beds,” J. Electrostatics, 55, 135-168, 2002a. 28. Park, A., H.T. Bi and J.R. Grace, “Reduction of electrostatic charges in fluidized beds,” Chem. Eng. Sci., 57, 153-162, 2002b. 29. Murtomaa, M., E. Räsänen, J. Rantanen, A. Bailey, E. Laine, J. Mannermaa, J. Yliruusi, Electrostatic measurements on a miniaturized fluidized bed, J. Electrostatics. 57, 91-106, 2003. 30. Yao, L., H.T. Bi and A.H. Park, “Electrostatic charges in freely bubbling fluidized beds with dielectric particles,” J. of Electrostatics, 56, 183-197, 2002. 31. Mehrani, P., H.T. Bi, and J.R. Grace (2005). Electrostatic charge generation in gas-solid fluidized beds. J. Electrostatics., 63, 165-173. 32. Mehrani, P., X.T. Bi and J.R. Grace, Electrostatic behavior of different fines added to a Faraday cup fluidized bed, J. Electrostatics, 65, 1–10, 2007. 33. Chen, A.H., H.T. Bi and J.R. Grace, Effects of Probe Numbers and Arrangement on the Measurement of Charge Distributions around a Rising Bubble in a TwoDimensional Fluidized Bed, Chem. Eng. Sci., 61, 6499-6510, 2006a. 34. Chen, A.H., F. Kleijn van Willigen, H.T. Bi, J.R. Grace, R. van Ommen, Measurement of charge distribution around a single rising bubble in a twodimensional fluidized bed, AIChE J., 52, 174-184, 2006b. 35. Chen, A.H., H.T. Bi and J.R. Grace, Charge distribution around a rising bubble in a two-dimensional fluidized bed by signal reconstruction, Powder Technology, 177, 113-124, 2007. 36. Demirbas, B., J. Nijenhuis, C.U. Yurteri and J.R. van Ommen, Towards
Monitoring Electrostatics in Gas–Solid Fluidized Beds, Can. J. Chem. Eng., 86, 493-505, 2008. 37. Wang, F., J.D. Wang and Y.R. Yang, Distribution of Electrostatic Potential in a Gas-Solid Fluidized Bed and Measurement of Bed Level. Ind. Eng. Chem. Res., 47, 9517–9526, 2008. 38. Moughrabiah, W., J.R. Grace and X.T. Bi, Effect of pressure and temperature on electrostatics in fluid beds of PE particles, Ind. & Eng. Chem., 48, 320-325, 2009. 39. Liu, Z.L., X.T. Bi, J.R. Grace, Electrostatic charging behaviour of dielectric particles in a pressurized gas-solid fluidized bed. J. Electrostatics, 68, 321-327, 2010. 40. Sowinski, A., Salama, F., Merhani, P., New technique for electrostatic charge measurement in gas–solid fluidized beds. Journal of Electrostatics 67, 568–573, 2009. 41. Sowinski, A., L. Miller, P. Mehrani, Investigation of electrostatic charge distribution in gas–solid fluidized beds. Chem. Eng. Sci., 65, 2771–2781, 2010. 42. Omar, M., K.C. Choi, X.T. Bi, J.R. Grace, Effect of particle size and residence time on charging behaviour of fine polymer powders in fluidized beds. Fluidization XIII, Korea, 2010. 43. Chen, A.H., H.T. Bi and J.R. Grace, “Effect of charge distribution around bubbles on charge induction and transfer to a ball probe in gas-solid fluidized beds,” J. of Electrostatics, 58, 91-115, 2003a. 44. Chen, A.H., H.T. Bi and J.R. Grace, “Specific charges of particles in fluidized beds,” Powder Technology, 133, 237-276, 2003b. 45. Yan, Y., B. Byrne, S. Woodhead and J. Coulthard, Velocity measurement of pneumatically conveyed solids using electrodynamic sensors. Meas. Sci. Technol., 6, 515-537, 1995. 46. Ma, J. and Y. Yan, Design and evaluation of electrostatic sensors for the measurement of velocity of pneumatically conveyed solids. Flow Measurement and Instrumentation. 11, 195-204, 2000. 47. Armour-Chelu, D.I. and S.R. Woodhead, Comparison of the electric charging properties of particulate materials in gas-solids flows in pipelines. J. Electrostatics, 56, 87-101, 2002. 48. Kunkel, W.B., The static electrification of dust particles on dispersion into a cloud. J. Appl. Phys., 21, 820-832, 1950. 49. Turner, G.A. and M. Balasubramanian, The frequency distribution of electrical charges on glass beads, J. Electrostatics, 2, 85-89, 1976. 50. Cartwright, P., S. Singh, A.G. Bailey and L.J. Rose, Electrostatic charging characteristics of polyethylene powder during pneumatic conveying. IEEE Trans. Industry Applicat., IA-21 (2), 541-546, 1985. 51. Singh, S. and G.L. Hearn, Development and application of an electrostatic microprobe. J. Electrostatics, 16, 353-361, 1985. 52. Mazumder, M.K., R.E. Ware, T. Yokoyama, B.J. Rubin and D. Kamp, Measurement of particle and electrostatic charge distributions on tonners using E-SPART analyzer, IEEE Trans. Industry Applicat., 27(4), 611-619, 1991. 53. Mazumder, M.K., S. Banerjee, R.E.Ware, C. Mu, N. Kaya and C.C. Huang, Characterization of tribocharging properties of powder paint, IEEE Trans. Industry Applicant, 30(2), 365-369, 1994.
EVOLUTION OF FCC – PAST PRESENT AND FUTURE – AND THE CHALLENGES OF OPERATING A HIGH-TEMPERATURE CFB SYSTEM Ye-Mon Chen Shell Global Solutions (US) Inc. ABSTRACT The fluid catalytic cracking (FCC) process is one of the most important circulating fluidized bed processes. Although the FCC process has been in commercial operation for over 60 years, the technology continues to evolve in order to meet new challenges, which include processing more difficult feedstock and meeting more stringent environmental regulations. This paper presents selected snap-shots of a few challenges (high temperature erosion, corrosion and emission control) that the FCC process faces today and the new challenges yet to come in the near future. INTRODUCTION The fluid catalytic cracking (FCC) process is one of the most important circulating fluidized bed processes, with more than 400 units in operation worldwide today. The FCC unit is the primary conversion unit in a refinery, which converts, or cracks, low value heavy ends of crude oil into a variety of higher-value, light products, such as gasoline and LPG. The unit consists of a reactor and a regenerator, as shown in Figure 1.
Figure 1: A typical FCCU configuration Historically, the FCC unit and its downstream units, such as the alkylation unit, supply about 50% of the gasoline supply in the US. Although FCC is a mature process commercially deployed for over 60 years, the technology continues to evolve because of following unique capabilities:
The FCC process is the only continuous catalytic process in the refinery business, which can adjust or replace catalyst on the run without a shutdown, The FCC catalyst is relatively robust to handle a wide variety of feedstock, and The FCC process can be operated over a wide range of conditions.
The focus of this paper is NOT on the historical evolution [1, 2, 3, 4] of FCC technology. Instead, the paper focuses on selected snap-shots of issues, such as high temperature erosion, corrosion and emission control, that exemplify the challenges that the FCC process faces today and the new challenges in the future. EXAMPLES OF CHALLENGES THAT FCC FACES TODAY It might sound strange at first that keeping Examples of Today’s Challenges an FCC unit running, without having the unit `fall apart unexpectedly, is in fact the 1. Cyclone reliability biggest challenge today for a process that A. Erosion has been around for over 60 years. To put this in the right perspective, the average B. Corrosion run length between two scheduled 2. Emission Control – NOx reduction maintenance shutdowns for an FCC unit was about 2 years in the 1970’s/80’s, whereas the current average run length is now being stretched to between 4 to 5 years. Considering the fact that an FCC unit, on average, circulates about 50 tons of catalyst per minute between the reactor and the regenerator; keeping an FCC unit running continuously for 4 or 5 years means that the equipment will experience the traffic of over 100 million tons of catalyst without falling apart, which is by no means a small task. On the other hand, the incentive of stretching the FCC run length is also enormous because the average costs of an FCC maintenance shutdown is on the order of 10’s of millions of dollars. Cyclone Reliability The FCC unit relies on reactor and regenerator cyclones to keep the catalyst within the unit while circulating catalyst between the two vessels. Two recent industry surveys reveal the pervasive problems of cyclones used in FCC operation today. Table 1 summarizes the survey results from Grace Davison as presented at their 2002 Dublin FCC conference. The results indicate that catalyst losses from cyclones were the number 1 problem in FCC operation, identified by the participants of the meeting. The 2006 Solomon survey (Figure 2) again revealed that FCC cyclone reliability was the number 1 limitation of FCC unit run length today, with more than 41% of unscheduled shutdowns of FCC units in US caused by cyclone problems. Two common cyclone problems that challenge FCC units today, high-temperature cyclone erosion and corrosion will now be discussed. Cyclone Reliability – Erosion Problem Problem Statement One major contributor to unscheduled FCC unit shutdowns is unexpected cyclone failure due to high temperature erosion. Figures 3a and 3b show two examples of high temperature cyclone erosion problems. Figure 3a shows a cyclone which was eroded
through, from inside out, with holes on the cyclone body. Figure 3b shows multiple cyclones that had severe erosion into the cyclone diplegs such that several diplegs were cut off and fell to the bottom of the vessel. Table 1: Grace Davison 2002 Survey
2006 Solomon FCC Survey Events Determining TAR Timing (Outside of Planned Maintenance)
12%
Rx Cyclones Regen Cyclones Rx Refractory Rg Refractory Rotating Equipment Slide Valves Regulatory Other
13%
14%
28%
3% 2% 12% 16%
Figure 2: 2006 Solomon Survey Chart
(a)
(b)
Figure 3. FCC cyclone damage a) Inside out erosion; b) Diplegs cut off due to erosion What is Happening in the Cyclone? The most pervasive problem is high temperature erosion in the secondary cyclone, particularly in the lower cone and in the transition to the dipleg, which is the focus of the study. There is a fundamental difference between erosion patterns in first and second stage FCC cyclones. Highly-loaded first stage cyclones normally experience little to no cone erosion, whereas the lightly-loaded second stage cyclones can have severe cone erosion. This seems to be counter-intuitive at first. However, the key difference in erosion pattern lies in the differences in the solids flow patterns and vortex formation, as shown in Figure 4. Due to high solids loading and low gas velocity in a typical FCC primary cyclone, the gravitational force plays a key role; as a result, the solids appear to drop (or fall) rapidly down into the cyclone cone and dipleg, as shown in the figure on the left side of Figure 4, taking only one to two full turns before exiting the cyclone bottom. The vortex length in the highly-loaded primary cyclone is much shorter because the high solids loading dampens the formation of a robust vortex. Therefore, the vortex does not “whip” the solids at a high velocity around the cone in the primary cyclone. In a typical FCC second stage cyclone, the solids loading is approximately 1/1000 to 1/10,000 of the loading in the first stage cyclone. Due to the light solids loading and high gas velocity, the vortex is relatively long, energetic and, more importantly, moving asymmetrically about its axis. As the swirling solids in the outer vortex approach the cone in a second stage cyclone, the long, rapidly-rotating vortex accelerates the solids stream and causes it to intensify its rotation (i.e., the solids spin faster similar to the motion of a figure skater pulling inwards).
Figure 4: Schematic Depiction of First and Second-Stage Cyclone Operation The outer vortex in a second-stage cyclone typically takes from four to six turns before exiting the bottom cone, as shown in the figure on the right in Figure 4, and the spinning continues into the top portion of the dipleg below the cone. The concentrated solids stream rotates at a high velocity, and the unstable, continuous movement of the vortex causes the significant erosion observed in the cone and the top of the dipleg of secondstage cyclones. Solution to the Cyclone Erosion Problem Most FCC units in US rely on cyclone vendors to provide cyclones for FCC applications, which will be categorized as “conventional cyclones” in this paper. Shell Global Solutions, on the other hand, has developed a cyclone technology, which is different from conventional cyclone in that it uses a vortex stabilizer. Particulate Solids Research, Inc. (PSRI), an independent, industrial consortium, recently studied and benchmarked different FCC cyclone technologies [5], since high temperature cyclone erosion and cyclone reliability were highlighted as the major concerns of FCC operation for companies in recent surveys. The PSRI cyclone test program was structured to benchmark three different possible solutions to mitigate the damaging erosion occurring in FCC second-stage cyclones: 1. Increasing cyclone length (L/D) of a conventional cyclone 2. Increasing the angle of the cone of a conventional cyclone 3. Adding a vortex stabilizer to a conventional cyclone Air was used as the conveying gas in the test unit. The solids used were equilibrium FCC catalyst with a median (Dp50) particle size of approximately 75 µm. The fines
(material <44 µm) concentration in the catalyst was approximately 8 wt.%. The particle density of the catalyst was 1488 kg/m3. Loadings to the second stage cyclone were varied between 0.001 to 0.16 kg/m3. Multiple coatings of drywall joint compound were added to the inside of the cyclone before each test. The amount of erosion occurring in the cyclone was measured by the weight loss of the drywall compound occurring over a certain period of time. Effect of Increased Cyclone Length The study found that the erosion took place primarily in the bottom 1/3 of the cone of a secondary conventional cyclone. A photograph illustrating this effect is shown in Figure 5. This figure shows that the drywall coating was completely eroded from the bottom 1/3 of the cone, whereas the remaining drywall was mostly intact. Cyclone lengths were increased by increasing the length of the conventional cyclone barrel with L/Ds of 3.1, 4.1 and 5.1. In these tests, the inlet gas velocity to the cyclone was 19.8 m/s and the outlet gas velocity was 26.8 m/s. The results of the testing to determine the effect of cyclone length are shown in Figure 6. As can be seen, the erosion rate decreased with increasing cyclone length. The measured erosion rate at an L/D of 5.1 was about seventy percent (70%) of the erosion rate of the cyclone with an L/D of 3.1. Barrel erosion rates were also measured in the tests and were found to be much lower than the erosion rates in the cone at the test conditions, as also shown in Figure 6. The measured barrel erosion ranged between 85 to 105 g/h, which is approximately fifteen percent (15%) of the cone erosion rate for the cyclone with an L/D of 3.1, and approximately twenty percent (20%) for the cyclone with an L/D of 5.1. Effect of a Longer Cone Conventional Cyclone The effect of a longer cone of a conventional cyclone on cyclone cone erosion was tested by adding a longer cone so that the cone angle from the horizontal increased from 79 to 84º. This increased the cone length from 0.8 m to 1.7 m. When comparing the two cone configurations, the overall length of the cyclone was held constant. As shown in Figure 7, the longer cone had a higher erosion rate at lower gas velocities than the shorter cone (but longer barrel). However, the erosion rate became approximately equal to the erosion rate of the shorter cone at the highest gas velocity. The trend of the two curves was exactly opposite. For the short cone, the erosion rate increased with gas velocity, whereas for the longer cone the erosion rate decreased with increasing gas velocity. For an outlet gas velocity of approximately 26.8 m/s, the erosion rate for the short cone was approximately 800 g/h, while the erosion rate for the longer cone was approximately 1800 g/h—a factor of 2.25. However, even at the highest gas velocities, which are outside typical operating conditions of the secondary cyclones, the longer cone did not have a significant advantage over the shorter cone in regard to cone erosion
Figure 5: Photograph of Erosion of Drywall Joint Compound in the Cone of a Second Stage Cyclone The Effect of Adding a Vortex Stabilizer to a Conventional Cyclone To determine the effect of adding a vortex stabilizer to a conventional cyclone on cone erosion, a flat-disk vortex stabilizer was added to the cyclone, approximately 1/3 of the cone length up from its bottom to simulate the improved cyclone technology. The effect of adding the vortex stabilizer disk to a conventional cyclone, simulating the improved cyclone technology, on cone erosion is shown in Figure 8 for cyclones with an L/D of 3.1. It was found that cone erosion for a cyclone with the vortex stabilizer was significantly lower than that for a conventional cyclone without a vortex stabilizer. Cone erosion increased linearly with increasing gas velocity for a conventional cyclone without a vortex stabilizer. However, cone erosion of a cyclone with a vortex stabilizer decreased slightly with increasing gas outlet velocity. The decrease in erosion is counter-intuitive at first; however, this can be explained by the fact that the vortex diameter is smaller when the diameter of the outlet tube is decreased to increase the gas outlet velocity. This increases the distance between the vortex and the cone wall, which then reduces the centrifugal force (and, therefore, the solids velocity) on the solids rotating in the cone. This reduction in force on the solids would explain the decrease of cone erosion vs. gas outlet velocity for a cyclone with the vortex stabilizer as shown in Figures 8.
Figure 6: The Effect of Second-Stage Cyclone L/D on Cone Erosion and Barrel Erosion
3 Figure 7: The Effect of Cone Length on Second-Stage Cyclone Cone Erosion
0
20
Ugo , m/s
40
60
3000 w/o disk w/ disk
00
-
-
2500 2000 1500 1000 500 Cone Ero sion Rat e, g /h
0 0
50
100 Ugo , ft/s
Material: FCC Eq. Catalyst Ugi: 65 ft/s (19.8 m/s) Cyclone Size: 17 -in (43 cm)
150
200
Inlet Type: Tangential L/D b: 3 3 Li: 14 grains/ft 3
Figure 8: The Effect of Gas Outlet Velocity on Second-Stage Cyclone Cone Erosion for Cyclones With and Without a Flat-Plate Vortex Stabilizer (Cyclone L/D = 3.1) For the shorter cyclone, the cone erosion rate was approximately 2100 g/h for the conventional cyclone without a vortex stabilizer at a gas velocity in the outlet tube of 15.2 m/s. The corresponding cone erosion rate for the cyclone with a vortex stabilizer at the same gas velocity was about 1400 g/h. The cone erosion rate with the vortex stabilizer was about 67% of the cone erosion for the cyclone without the vortex stabilizer. However, at an outlet gas velocity closer to actual practice (~ 46 m/s), the cone erosion rate for a cyclone with the vortex stabilizer was only about 600 g/h. The corresponding cone erosion rate for the conventional cyclone without the vortex stabilizer was about 2900 g/h. This is a factor of about 5. For the cyclone with an L/D of 5.1, the overall cone erosion rates were lower. This was to be expected because the tests with a longer cyclone described above gave lower cone erosion rates than shorter cyclones. As with the shorter cyclone, the trend lines of cone erosion rate vs. outlet gas velocity were linear. Similarly, the curve for the conventional cyclone erosion without a vortex stabilizer increased with increasing gas velocity, and the curve for the cyclone erosion with the vortex stabilizer decreased slightly with increasing gas velocity. However, as with the shorter cyclone, the cyclone with the vortex stabilizer was found to have much lower erosion rates than the conventional cyclone without the vortex stabilizer. Comparing the cone erosion rates at an outlet gas velocity of 46 m/s, the conventional cyclone without a vortex stabilizer had a cone erosion rate of approximately 1200 g/h, while the cyclone with the vortex stabilizer had a cone erosion rate of about 240 g/h. This is a factor of approximately five, which is similar to what was found for the shorter cyclone.
Why does the vortex stabilizer decrease cone erosion? It appears that the stabilizer prevents the vortex from "whipping" the solids around at high velocities below the stabilizer in the region where high cone erosion rates are experienced for a conventional cyclone. Below the stabilizer, the high-velocity central vortex does not really exist. Therefore, this reduction of spinning solid velocity in the outer vortex leads to significant reduction in erosion. A comparison of the cone erosion rates for various second-stage cyclone configurations is given in Table 2. Table 2: Comparison of cone erosion rates for different cyclone configurations
L/D (-)
Velocity Inlet, m/s
Velocity Outlet, m/s
Erosion Reduction Factor
Cone Erosion Rate, g/h
Short Cyclone
3.1
19.8
46
Base
2850
Long Cyclone
5.1
19.8
46
>2
1200
Long Cone
5.1
19.8
46
>2
1200
Vortex Stabilizer
3.1
19.8
46
>4
650
Vortex Stabilizer
5.1
19.8
46
>11
240
Drywall joint compound was also added to the disk to see if the upper surface of the vortex stabilizer would be eroded by the vortex. However, essentially no erosion was measured on the upper surface of the disk. No erosion was found on the supporting rods as well. Commercial Bench-Marking In the 1980’s, Shell had over 30 FCC units within the system, mostly with Conventional Cyclones, which were found to be the number one cause of all FCC unscheduled shutdowns. Shell made a conscious decision in developing improved cyclone technology, using the vortex stabilizer, and started the implementation of the technology in the early 90’s. Figure 9 shows the result of how this improved cyclone technology reduced overall FCC unscheduled down time in Shell refineries. Using 1992 data as the base line, Figure 9 shows that the improved cyclone technology, with the vortex stabilizer, reduced the total unit down time of all FCC units in Shell system by a factor of 10. Cyclone Reliability – Corrosion Problem Problem Statement In recent years, several FCC units have encountered unscheduled shutdowns due to high temperature corrosion failure of the cyclone refractory system. These refractory systems failed unexpectedly in some cases within only 4 to 5 years. Figure 10 (a) [6] shows examples of a failed refractory system which resulted in sheets of refractory
peeling off from the walls of the regenerator cyclones. Figure 10 (b) shows that the fallen refractory sheets were caught above the primary cyclone termination device.
Severity(Incl. nearmisses)[Numberof Events* Duration] 1992=100%
Total Severity of Cyclone Problems 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1992
1993
1994
1995
1996
1997
1998
1999
Figure 9: Severity of cyclone related issues in Shell FCCUs
Regenerator Hexmesh Corrosion
Figure 10. (a) Failed refractory, in sheets, due to Hex Mesh corrosion [6]; (b): fallen refract sheets caught above the primary cyclone termination device
Figure 10: Hex Mesh Refractory Structure
What is Happening to the Cyclone Refractory System? The FCC regenerator cyclones are typically made of SS 304H material for high temperature (~ 1400 ºF) operation. In order to protect cyclones from high temperature erosion damage, as discussed previously, the regenerator cyclone internal surface is commonly lined with SS 304H hex mesh, approximately 2.5 cm deep which is welded on to the cyclone interior and packed with refractory within the hex mesh. The structure of the hex mesh/refractory looks like a honeycomb, as shown in Figure 11. The refractory is a concrete-like material which has high resistance to erosion. Historically, the hex mesh/refractory system has served the FCC industry well in providing protection against high temperature erosion. However, a series of unexpected failures of the hex mesh/refractory system, due to corrosion of the SS304H hexmesh, has surfaced very recently as reported by a number of operating companies as well as FCC licensors [6, 7]. As shown in Table 3 and based on current known information, a common pattern involves the application of a “calcium-rich” refractory in the regenerator section of FCC units and failure appears to be related to a corrosion mechanism that attacks the SS304H hex mesh support system for this refractory. It is more prevalent in complete combustion mode (full burn).
Table 3: Summary of refractory analysis [7]
Refractory Analysis – Sample Summary Effect of exposure to process conditions
Refinery Refinery X Bad
Good
Reported Refractory Type
Location
Instalation Date
SO2 Level (ppm)
S
Na
Volatiles
Ca-rich binder
Dipleg - 1st stage cyclone
2005
2500
1.80
0.41
2.55
1.34
0.21
1.38
-
-
-
1.43
0.62
1.43
Refinery Y
Ca-rich binder
Regen Primary Cyclone
Refinery Z
Ca-rich binder
primary cyclone
Refinery U
Ca-rich binder
1997
Refinery X
Ca-rich binder
Regen cyclone
2002 (original)
2500
1.69
0.27
2.89
Refinery T
Ca-rich binder
Regen Cyclone
2002
900
0.01
0.716
0.33
phos-bonded
Regen Cyclone Dipleg
0.07
0.11
0.27
?
Regen Cyclone
0.16
0.11
0.4
phos-bonded
flue gas expansion joint
1.12
0.07
5.66
Refinery H Refinery Y
900
• Both Ca-rich binder and phos-bonded materials appear to absorb sulfur from the process gas • No clear relation between S, Na and volatile content and “good/bad” rating 124
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Although there are some minor variations, this particular type of corrosion has very specific patterns that are strikingly similar from all reported failures:
The attack of the 304H SS hex mesh was identified primarily as sulfidation/oxidation corrosion of the metal support structure. The corrosion occurs preferentially on the underside of the hexmesh metal lining where it is welded to the base steel, such as at the cyclone wall, whereas little corrosion is observed on the process side of the hexmesh lining where it is exposed to the bulk of flue gas, as shown in Figures 12 and 13 The corrosion on the process side is mild oxidation with a well protected Cr-O layer, shown in Figure 14. The corrosion on the underside is a combination of oxidation, sulfidation and carburization where Cr in SS304H no longer forms a tight formation of Cr-O protection layer. The hexmesh/refractory detached from the wall in sheets, as shown in Figure 10, due to weakening of the corroded hexmesh. The corrosion can be very aggressive. Some units reported that newly installed regenerator cyclones could have total refractory system failure within 4 years.
Figure 11: Refractory on process side vs. underside of the hex mesh lining
Corrosion Characterization General Trends
Process Side Tab
• Process gas itself does not appear corrosive. - Conditions are different within/behind refractory. • Accelerated corrosion seemingly always associated with carburization of base metal.
120
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Figure 12: Assessment of general trends in refractory corrosion characterization [7]
Regenerator Hexmesh Corrosion • SEM/EDS analysis of a corroded hexmesh ligament – Exposed side – oxidation – Creviced region – carburization – Underside – carburization & sulfidation/oxidation
Q&A Session 2010 Cat Cracker Seminar
Figure 13: Possible corrosion mechanism: Process side vs. Underside [6] Possible Solutions The root cause of the high temperature corrosion to the refractory metal support structure is currently being discussed in the FCC industry. Several teams in the industry are actively working on the problem. The common belief is that the hexmesh on the underside is under a sub-environment that is more reducing than the bulk flue gas (more oxidizing), and hence more prone to sulfidation and carburization. However, the exact mechanism of how local reducing, sub-environment is formed is still being debated. Possible solutions are a work in progress at the moment. Emission Control – NOx Reduction Problem Statement The FCC regenerator is a major NOX emission source from the US refineries. Several existing technologies are available to reduce NOX emissions from an FCC regenerator, which include SCR, or selective catalytic reduction. Even though SCR is quite effective, it has several issues:
It is quite expensive, on the order of 10’s of millions of dollars, and even more so, on the order of hundred million dollars, when the CO boiler needs to be replaced due to the increase of back pressure caused by the catalyst bed of SCR. It requires a higher a residual flue gas temperature. Unless a second stage heat recovery system is included (which means additional cost), the unit loses energy efficiency
There is a potential cost-effective solution to this challenge.
What is Happening Inside the Regenerator? Most of the NOX emissions from FCC units comes from nitrogen in the feed. The contribution of direct oxidation of N2 to NOX is negligible, particularly for full combustion FCC regenerators. For partial combustion regenerators, the contribution of direct oxidation of N2 to NOX is relatively small if low-NOX burner technology is applied in the CO boiler/incinerator. A recent study [8] shows that about 50% of the nitrogen in the feed exits the FCC unit on the reactor side and the remaining 50% of nitrogen in the feed exits as coke on the spent catalyst sent to the regenerator. Of the 50% of the feed nitrogen exiting from the reactor, about 10% of the feed nitrogen ends up as ammonia, which is collected in sour water, and the other 40% of the feed nitrogen ends up in various streams of the reactor liquid products. This section focuses on the remaining 50% of the feed nitrogen, which enters the regenerator in the form of coke on the spent catalyst. As the spent catalyst is regenerated and coke is burned off in the regenerator, the nitrogen species on the coke are released into the flue gas. Recent studies [8, 9] further show that only a small fraction, less than 10%, of the feed nitrogen on coke is released in the form of NO X emissions in the flue gas. The majority, or more than 40%, of the feed nitrogen on coke is initially released in the form of NOX or other intermediates, but is eventually converted in-situ to N2 in the regenerator. The recent study [9] of batch regeneration of spent FCC catalyst with oxygen and helium reveals a close interaction between the combustion of carbon and the release of nitrogen in the coke. Figure 15 shows the concentrations of CO, CO2, and O2 in the flue gas as a function of time as coke on the catalyst is burned off in the batch regeneration experiment. The amount of coke on catalyst was not directly measured, but Figure 15 implies that coke on catalyst was removed continuously, converted to CO/CO 2, and became negligible after 26 minutes as both CO and CO2 concentrations fell to negligible levels. For the first 9 minutes, the O2 concentration remained low and the CO concentration was higher than CO2, indicating a reduction environment in this period of the batch regeneration. As O2 broke through the unit around the 10-minute mark and its concentration continued to rise afterwards, coinciding with sharp drop of CO concentration and rise of CO2, the batch regeneration shifted gradually from a reduction environment to an oxidation environment. Figure 16 shows the concentrations of NO, HCN, and N2 in the flue gas as a function of time as coke nitrogen is released in the same batch regeneration experiment. Note that most of the coke nitrogen was released as N2, which peaked around the 13.5-minute mark at about 200 ppm, under a reducing or a slightly oxidizing environment. A fraction of the coke nitrogen was released as HCN, which peaked around the 10.5-minute mark at 35 ppm, under the same environment. Note that the NO concentration was below 20 ppm for the first 14 minutes under a reducing or a slightly oxidizing environment when both coke on catalyst and CO were present. The NO X level increased sharply afterward, and peaked around the 18-minute mark at 190 ppm when the CO concentration fell sharply and the O2 concentration increased beyond 1%, as shown in Figure 15.
Figure 14: CO, CO2 and O2 in the Flue Gas as a Function of Time [9]
Figure 15: NO, HCN and N2 in the Flue Gas as a Function of Time [9] The proposed reaction kinetics [9] for the release of coke nitrogen in the FCC catalyst regeneration process involves initial volatilization of coke nitrogen as HCN, which could be hydrolyzed to another intermediate, NH3. Both intermediates, HCN and NH3, can be oxidized to NO, which can be reduced by the presence of CO or/and coke on catalyst to N2.
Solution to the NOx Emission Problem The FCC regenerator design has a direct impact on the effectiveness of in-situ reduction of NOX to N2, and hence reduction of the final NOX emissions in the flue gas. The new Shell Global Solutions’ low NOX regenerator technology [10] involves an improved process consisting of the strategic design of catalyst and air distributions, as shown in Figure 17, which enables the unit to operate in both full and partial combustion modes with low NOX emission. As shown in Figure 17, the regenerator system 1 includes a single regenerator vessel 10 having an upper end 12 and a lower end 14. The regenerator vessel 10 includes a dilute phase catalyst zone 16 above and a dense phase catalyst zone 18 below with a transition surface 20 between the two. The dense phase catalyst zone 18 further includes a high velocity central region 22, located in the central portion 26 of the dense phase catalyst zone 18, and a low velocity annular region 24, located in the annular portion 28 of the dense phase catalyst zone 18. It is a significant aspect of the new regenerator technology that the high velocity central region 22 and the low velocity annular region 24 are formed within the dense phase catalyst zone 18, without the use of a structural element such as a vertical baffle or a partition. The two fluidization regions are instead formed within the dense phase catalyst zone 18 by the introduction into the dense phase catalyst zone 18 of more than one fluidization gas stream, each of which is directed and controlled in such a manner as to cause the formation of multiple fluidization regions. Introduced into the central portion 26 of the dense phase catalyst zone 18 is a high velocity fluidization gas stream that passes through the fluidization gas distribution ring 32 near the bottom of the regenerator vessel 10. Introduced into the annular portion 28 of the dense phase catalyst zone 18 is a low velocity fluidization gas stream that passes through the fluidization gas distribution ring 38 located within the annular portion 28 near the bottom of the regenerator vessel 10. The controlled introduction of the various fluidization gas streams at the different fluidization gas flow rates along with the directed introduction of the fluidization gas streams to desired locations induces a desired circulation of the FCC catalyst within the dense phase catalyst zone 18, as depicted in Figure 17 by the bold arrows 40 that show the general direction and circulation of the FCC catalyst within the dense phase catalyst zone 18. As shown by the bold arrows 40, catalyst particles in the high velocity central region move in a generally upward direction, and catalyst particles in the low velocity annular region move in a generally downward direction. Catalyst from the bottom end 42 of the low velocity annular region 24 flows into the high velocity central region 22 and most of catalyst from the top end 44 of the high velocity central region 22 flows into the low velocity annular region 24, thereby forming the catalyst circulation within the dense phase catalyst zone 18.
Figure 16: A Schematic Diagram of the Low NOX Regenerator System The regenerator system 1 further includes the introduction of spent catalytic cracking catalyst into the high velocity central region 22 through conduit 50, which can be a riser or a standpipe. Connected to the end of conduit 50 is a spent catalyst distributor 52 that introduces spent FCC catalyst into the high velocity central region 22 in a generally horizontal direction and mixes with catalyst circulating from the bottom end 42 of the low velocity annular region 24. The regenerated catalyst is removed from the low velocity annular region 24 by way of conduit 54, which removes regenerated catalyst from the annular portion 28 of the dense phase catalyst zone 18. One advantage of the new regenerator system is that the induced catalyst circulation pattern distributes partially regenerated spent catalyst to the proximity of the surface 20, which results in reducing NOx emissions from the regenerator. Another advantage of the new regenerator system is that the location and the spent catalyst distributor design induce intimate mixing between catalyst and transport air, thus preventing transport air or entrained hydrocarbon from breaking through the dense bed, and resulting in reduced afterburn. Commercial Experience The new low NOX regenerator technology was implemented as an integrated part of a recent major FCC revamp, as presented in the case study below [10].
The original FCC unit was a large side-by-side unit with a regenerator diameter > 15 m, as shown to the left in Figure 18. The scope of the revamp, as shown to the right in Figure 18, included:
A new stripper,
Catalyst Circulation Enhancement Technology (CCET) [4] at the stripper outlet,
A new, larger air blower,
Additional pairs of regenerator cyclones for handling higher air flow, and
The low NOX regenerator technology, consisting of a new spent catalyst distributor, new regenerator outlets with CCET, new air distributors, and the controlled system.
The unit performance was measured before and after the revamp. Key performance improvements were observed: The unit was able to operate in both full and partial combustion modes with low NO X emission after the revamp. Figure 19 shows the NO X level in partial combustion mode under 4 different air distribution conditions. As shown in the figure, NO X emission is the lowest with Case D, with 40 ppm @ CO concentration of 2.4%. The unit can reach NO X level lower than 40 ppm under either lower CO concentration or full combustion mode with O2 concentration < 1% (not shown in the figure); however, partial combustion with CO concentration in the range of 2 to 3.5% is the preferred mode of operation because of its capability to increase the unit feed rate.
Figure 17: Scope of the FCC Revamp in the Case Study
Stack NOx versus Rgn OH CO 100 90 80
NOx @ 0% O2, ppm
70 60 50 40 30 20 10 0 1.5
2.0
2.5
3.0
3.5
4.0
CO, pct Case A
Case B
Case C
Case D
Figure 18: NOx emission in partial combustion under four cases of air distribution What the Future of FCC Might Look Like Shifts of Product Demands and Feedstock Historically, the FCC unit and its downstream units provide the majority of gasoline supply in the world. However, the landscape of the demand from FCC products is shifting and is region-specific as shown in Figure 20. The demand for propylene from FCC products has increased, and in some regions, the light olefins have become the premier products from FCC. In addition, in many regions the demand for diesel outpaces the demand for gasoline. Figure 21 shows the demand shift in the US. The motor gasoline demand has reached a plateau and the long term projection is a decreasing demand trend. One the other hand, demand for gasoil and diesel is increasing. This shift from motor gasoline to gasoil/diesel follows a similar pattern as was seen in Europe 10 years ago. In parallel, the demand for propylene in the US is also growing so rapidly that the traditional source for propylene steam cracking - cannot keep up with the demand. From the available technologies to bridge the gap, FCC is by far the best.
4.5
Figure 19: Current market demands of FCC products
Figure 20: Road transport fuel demand trends in the United States Given these demand shifts, the refining industry needs a redefinition of the FCC process to enable the following process objectives to be satisfied:
Lower gasoline yield; Higher LCO yield, with better product quality characteristics (lower density and higher cetane number); Higher propylene yield; Flexibility to switch seamlessly between these different production modes and conventional FCC operation depending on the market demand.
There are several new FCC process technologies available that address these expected shifts in market demands. One of them is the Shell MILOS process [11, 12], or Middle Distillates and Lower Olefins Selective process. A relative new direction of FCC is to co-process bio-liquid feedstock. The FCC unit is the single largest contributor, accounting for 30 to 50%, to the overall CO2 footprint of a refinery. Beside the benefit of a potential, inexpensive, alternative feedstock, processing bio-liquid feedstock has the added benefit of a tax credit in a region where CO2 emissions are regulated. Processing bio-feedstock presents totally different challenges, yet to come, in FCC operation such as extremely high oxygen content in the feedstock. MILOS – Process Background Recycling cat-cracked gasoline to the bottom of the riser (where the temperature is typically in the range of 1250-1320 ºF) has been widely practiced in the refining industry with the objective of increasing propylene yields. This has the desired effect of producing more propylene, but with significant penalties of producing large amounts of coke, dry gas, butylenes, LCO+ and it would produce little iso-butane. To confirm this, experiments were conducted in which light and heavy cat cracked gasoline were recycled in the Shell Global Solutions large FCC riser pilot plant. The most favorable condition for the production of propylene was found when light CC gasoline was injected below the VGO feed such that the residence time for cracking pure light CC gasoline was approximately 0.5 to 1 second prior to the VGO injection point. However, it had a large impact on VGO conversion, which dropped at constant coke rate. As a result the net LCO+ flow rate at riser exit increased significantly. Such large increases in LCO+ material were also observed in all other tests. Additionally, injecting light CC gasoline with or above the VGO feed yielded a net decrease in propylene yields due to quenching in that section of the riser reactor. For heavy gasoline, the results were even more disappointing in all cases. The flaw of this option is that it is impossible to achieve optimal conditions for both feeds (both the recycled gasoline and the VGO) in one single riser.
Figure 21: MILOS concept MILOS Process Concept The MILOS concept, shown in Figure 22, consists of adding a separate riser to the FCC unit, in which gasoline or other suitable streams are cracked under process conditions tailored specifically to maximize propylene yields, to maintain or increase the iso-butane yield (which is desirable for the alkylation unit) without producing excessive amounts of dry gas, coke and butylenes. The ideal temperature is relatively low (1050-1150 ºF) in order to minimize thermal reactions. At temperatures lower than these, high gasoline conversions cannot be easily obtained. The catalyst used is the same as in a conventional cat cracker with ZSM-5 added to boost the propylene yield. Typical yields obtained by cracking catalyst cracked gasoline in a MILOS riser are 15.5 %wt propylene, 7.5 %wt dry gas and 5%wt iso-butane. Case Study – Diesel Mode A case study focusing on diesel-mode implementation is presented to demonstrate one aspect of implementing MILOS in existing FCC units. The base-case is a Shell Global Solutions’ designed long residue CCU, processing feed with a Conradson Carbon content of 3.2%wt. Shell’s rigorous heat-balanced Catalytic Cracking Process model was used. This model was tuned to the actual operating conditions of the specific FCC unit, including realistic unit constraints and feed properties. The base-case (Case #1) represents the average operating conditions and average feed properties of the unit over the recent years. Besides this case, the following cases were explored:
Cases #2 and #3: the maximum diesel and the maximum propylene cases based on the conventional FCC-unit without the addition of a separate MILOS riser Case #4: a MILOS riser is added to the FCC unit to achieve maximum diesel and propylene Case #5: a sensitivity case with the same MILOS-FCC unit, maximizing propylene only.
The results of the case studies are presented in Table 4.
The comparison between the base case FCCU (Case #1) versus the diesel mode FCCU (Case #2) or propylene mode FCCU (Case #3) is straightforward. If we operate the unit in diesel mode (Case #2), the LCO yield is boosted by more than 3%wt (Table 4) and the cetane index is increased by 5 points. However, propylene yield suffers a reduction of 1.2%wt. Valuable LPG (total C3s and C4s) also suffers a reduction of 3.5%wt. On the other hand, if we operate the unit in propylene mode (Case #3), the propylene yield is boosted by 3%wt, and the LPG (total C3s and C4s) is also increased by 9%wt. (The increase in butylenes Cases #3, 4 and 5 is a direct result of ZSM-5 addition). However, the cetane index of LCO suffers a big hit of about 6 points reduction. LCO yield is slightly reduced by 1%wt. The situation above is a typical dilemma faced by FCC that is supplying a diesel and propylene driven market. The diesel mode and propylene mode represent the opposite extreme ends of operation and depending on market forces, the operator swings from one mode to the other. Some operators might choose to operate in the middle of the two modes and not make either maximum propylene or maximum diesel in terms of quantity and quality. However, after revamp with MILOS, the operator can produce more propylene than the standalone FCC propylene mode and more LCO than the standalone FCC diesel mode, with even a higher cetane index, all achieved at the same time (Case 4 in Table 4). Table 4: Diesel revamp case study results Cas e number Case
#1
#2
#3
#4
#5
Base
FCC on ly Di esel mod e
FCC onl y Prop ylen e mode
FCC Di esel mo de + MILOS
FCC Pro pyle ne mode + MIL OS
Typ ical o pera ti ng co ndi ti ons in rece nt ye ars
Case d escrip ti on
Fre sh fe ed rate to FCC riser
t/d
Existing FC C re vamp Existing FCC re vamp Existin g unit op erated Exi sti ng u nit o pe ra te d with a ddi ti on o f wi th a ddi ti on o f to pro duce ma ximum to prod uce maxi mu m MIL OS tech nology. MIL OS tech nol ogy. LCO p ropyl en e FCC i s o per ati ng in FCC i s o pera ti ng in diesel mod e p rop yl en e mo de
100 00
10 000
10 000
10 000
1 000 0
N/A
N/A
N/A
2 50 0
25 00
Base
-19
+5
-30
+5
ZSM-5 a dditive in catal yst in ventory
C % wt
0
0
10
10
10
LCO Ce tane Index
-
2 5.9
31 .0
24.9
3 3.3
2 4.8
% wt
4.3
3.2
4.4
4.7
6.3
%wt
1.1
0.8
1.2
1.9
2.4
% wt
8.2
6.3
12.2
1 3.7
1 7.1
Propylene
%wt
5.2
4.0
8.2
9.8
1 1.9
Prop ane
% wt
3.1
2.3
4.0
4.0
5.1
%wt
1 0.2
8.6
13.7
1 6.0
1 7.8
i-Bu ta ne
% wt
3.2
2.8
3.8
5.5
5.6
n-Bu ta ne
% wt
1.2
0.9
1.5
1.7
2.0
Total C4 Ole fins i-Bu tylene s
% wt % wt
5.8 1.3
4.9 1.1
8.4 2.9
8.9 2.9
1 0.2 3.5
Li gh t cat cra ck gasol in e (C5 - 142 de gC ) % wt He avy cat crack g asolin e ( 142 - 22 1 d eg C) % wt
3 4.7 1 1.2
32 .8 12 .4
30.3 9.4
1 4.4 1 2.0
1 6.1 1 0.8
LCO (221 - 370 degC)
%wt
1 5.8
18 .9
14.7
1 9.3
1 5.2
HC O (3 70 - 4 25 de gC)
% wt
3.4
4.4
3.4
4.5
3.5
SO (4 25 d egC+)
% wt
4.5
6.7
4.4
7.5
4.5
Co ke
% wt
7.6
6.8
7.5
7.9
8.8
wt
2.9
2.4
2.7
1.4
1.8
Recycl e of ligh t cat crack gaso lin e to MILOS t/d FCC riser ou tlet temp erature
o
Ove rall yie ld C2 mi nus Ethyle ne Total C 3
Total C4
Gas oline /C ycle Oil ratio
It is clear from the results above that a revamp with MILOS (Case #4) on a conventional FCC unit brings the benefits of maximising propylene make, maximising LCO make and increasing LCO Cetane quality, all at the same time. This is achieved by allowing the FCC to operate in diesel mode to achieve the desired high LCO yield and high LCO Cetane. Directing the cooler MILOS spent cat (cooler relative to regen temperature) to the FCC riser also plays an important role in improving the LCO yield and quality. On the other hand, the MILOS riser is focusing on maximising propylene by cracking recycled light cat-cracked gasoline. With the same FCC and MILOS unit, we have studied a sensitivity case to see if the propylene make can be boosted further. In this study, the conventional FCCU is operated towards maximum propylene make instead of operated in the diesel mode (Table 4). It is clear that with this MILOS-FCC configuration (Case #5), the propylene yield can be further boosted if the operator is ready to accept the same level of LCO yield and quality as they are getting during the conventional FCC propylene mode operation. The propylene yield can be increased to almost 1.5 times compared to the standalone FCC propylene mode operation. MILOS vs. other process technologies The MILOS process has significant advantages compared to technologies licensed by Shell’s competitors (Tables 5 and 6). Most importantly, the operational flexibility offered by MILOS is a key advantage. As MILOS is integrated in an FCCU, it can even be reverted on the run to regular FCC operation if this is required. DCC and PetroFCC do not have this flexibility. A DCC/PetroFCC implementation requires many more significant changes to an existing FCC unit. Overall, the revamp of a conventional FCC unit to a Diesel MILOS-FCC is a very attractive option to refiners, especially for those units located in Europe or other regions where both diesel and propylene demand are expected to grow rapidly. The operating flexibility provided by MILOS helps set a refinery up for long term success with changing market environments. CONCLUSIONS The FCC process is one of the most important circulating fluidized bed processes. Through a few examples, some current challenges of high temperature erosion, corrosion and NOx emission in operating a high temperature CFB process like FCC have been highlighted. Although the FCC process has been in commercial operation for over 60 years, the technology continues to evolve. A new FCC technology, MILOS, for producing more light olefins and diesel in light of the market demand shift has been proposed and the new trend of co-processing bio-feedstock has been discussed. These new applications will present new challenges to the operation of FCC.
Table 5: Typical features of MILOS relative to Deep Catalytic Cracking technology
Table 6: Typical features of MILOS relative to PetroFCC technology
REFERENCES 1. Fluid Catalytic Cracking hits 50 year mark on the run, A.D. Reichle, OGJ, May 18, 1992, P. 41. 2. Evolutionary design changes mark FCC process, J. R. Murphy, OGJ, May 18, 1992, P. 49. 3. FCC is far from being a mature technology, A.A. Avidan, OGJ, May 18, 1992, P. 59. 4. “Fluid Catalytic Cracking”, in Handbook for fluidization and fluid-particle systems, Y. Chen, Wen-Ching Yang, ed. (2003) 5.
“Keeping FCC units on track, winning the operation race with an innovative cyclone technology”, Y. Chen, et al, 2010 NPRA meeting
6. 2010 NPRA FCC seminar 7. 2010 NPRA Q&A 8. Rosser, F.S., et al, “ Integrated view to understanding the FCC NOx puzzle “, 2004 AIChE Annual meeting. 9. Stevenson, S. A. et al., “Model of NOx emission from laboratory regeneration of spent fluid catalytic cracking catalyst”, Ind. Eng. Chem. Research, 2005, 44, 2966-2974. 10. Y. Chen and D. Brosten “A New Technology for Reducing NO X Emission from FCC Regenerators”, 2008 NPRA meeting 11. W. Mo, F. H. H. Khouw and G. A. Hadjigeorge, US2006/0231461A1 – “Method and Apparatus for making a Middle Distillate Product and Lower Olefins from a Hydrocarbon Feedstock”; US2006/0178546A1; US2006/0191820A1. 12. M. Nieskens, “MILOS – Shell’s ultimate flexible FCC technology in delivering diesel/propylene”, NPRA Annual Meeting, AM-08-54, March 9-11, 2008.
PUTTING STRUCTURES INTO FLUIDIZED BEDS - FROM CONCEPT TO INDUSTRIAL APPLICATIONS Qiang Zhang, Weizhong Qian, Guohua Luo, Yao Wang, Fei Wei* Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China Corresponding author. Fax: +86-10-62772051; E-mail:
[email protected] ABSTRACT Structures of particles, particle agglomerates, distributors, and internals have significantly influence on hydrodynamics and transfer behaviors of the dense gas-solid fluidized bed. For nanomaterial production, the particle surface and their agglomerated structures directly influence the fluidization behaviors; while for coal to chemical process, the distributors, internals play an important role in regime transient, and hydrodynamics. Carbon nanotubes mass production, coal to chemicals process. and fuel production were employed as examples to describe the concept of putting structures into fluidized bed, and then to put these structures into industrial applications. The asymmetric surface of solids plays a very important role in material functions, such as its reactivity, catalytic performance, adsorption, and transfer behaviors, which were rapidly developed with the growth of nanotechnology and information technology. Recently, nano-structured granular materials, which are consisted of various flakes, tubes, and rods, possess unique mechanical, electrical, optical, electrochemical, and thermal properties. Novel advanced functional nanomaterials for energy conversion and storage, environmental, catalysis, materials engineering, biology, drug, sensors, devices, information technology are highly concerned. Those particles usually belong to group C particles according Geldart classification, which were extremely fine powders and therefore the most cohesive particles. The fluidization of Group C particles was difficult to achieve, which may require the application of some external forces, such as mechanical agitation. Recently, with the fast development of nanotechnology, nanomaterials with tunable structures and extraordinary properties were widely investigated.
In fluidized bed, the particles become fluid-like dense flow. Thus, the heat transfer between fluid and solid phase is over thousand times of that on porous catalysts, which is an important step for high-efficiency solid processing. For fluidization
1
behavior, the symmetric spherical particles and cylinder fluidized beds always the first and simplest choice. While for the academic researches and industrial applications, it is quite important to let materials or catalysts well fluidize, and to control their fluidized states as well as transfer behaviors. Structured internals in a fluidized bed reactor have significantly influences on regime transition. Recently, they were also widely used in a fluid catalytic cracking (FCC) stripper. To get high yield of intermediate products, plug flow fluidized bed with high efficient heat transfer were highly required. Structured distributors and internals can also play an important role in control of the backmixing and heat transfer of the fluidized bed. When the fluidized bed reactor is coupled with special structures for various functions, excellent heat and mass transfer properties as well as the multifunctional properties were provided by the particles. This significantly enlarges fluidized bed reactor applications for chemical or material production, or even device manufacture. When putting those structures into fluidized bed, a series of scientific questions were proposed: 1) Can those structures be smoothly fluidized? 2) Can we use fluidized bed to produce structures in large scale? 3) Can we use structure to improve the efficiency of fluidized bed process? To answer these questions, we briefly review our recent progress on putting structures into fluidized bed reactor, and demonstrate several examples in mass production of carbon nanotubes (CNTs), high efficient catalytic reaction for coal to chemicals and particle sensors for detection of fluidization state. FLUIDIZATION SCIENCE: FLUIDIZATION
NANOPARTICLE AGGLOMERATION AND ITS
Nanomaterials with morphological features on the nanoscale (smaller than 100 nm in at least one dimension) have become the focus of science because of many interesting properties that make them attractive for various applications. Interactive forces between nanosized particles significantly increase with decreasing particle size, so nanoparticles coalesce easier than micron-sized particles. The properties of the primary particles determine the properties of the agglomerates that control the behavior of the two-phase flow in a fluidized bed. For instance, as shown in Fig. 1, the SiO2 aerogels with a size distribution of 7 to 16 nm form agglomerates, called multi-stage agglomerates (MSA), by several steps with different bonding mechanisms.[2] The cohesive force between nanoparticles and the gravity force on the agglomerates are effectively diminished by the porous MSA structure, thus, even with a fairly high bed expansion, these SiO2 agglomerates still form bubbleless fluidized beds that have a texture much closer to the particulate fluidization of a liquid–solids system and which obeys the Richardson–Zaki relation, as opposed to the aggregative bubbling regime in many gas–solids systems. In the region of Ug =
2
0.01–0.08 m/s, the average hydrodynamic diameters of the fluidized complex agglomerates are 230–331 μm. The structure of the agglomerates as well as their size, apparent weight and the interactive forces between them significantly influence the hydrodynamic behavior of agglomerating fluidization. Except nanoparticles, 1D nanomaterials, such as CNTs which consist of graphene sheet wrapped around to form cylindrical tubes, were selected as model to demonstrate their fluidization behavior. The dependences of bed expansion and pressure drop on gas velocity in a CNT nano-agglomerate fluidize bed is shown in Fig. 2.[3] A smooth and highly expanded fluidization was achieved, but a strong hysteresis exists in the CNT fluidization curve. On the defluidization branch, similar to Geldart-A particles, particulate fluidization, ABF, turbulent, and fast fluidizations can be successively observed. However, Uc and Use of the CNTs were relatively lower than that of Geldart-A particles, due to the weak interaction among the CNT agglomerates and their highly porous structure. This indicated that good fluidization behavior can be achieved only with strong turbulence of the fluidized bed, because of strong interaction between agglomerated CNTs.
4
Increasing gas velocity Decreasing gas velocity
H/H0
3
2
1 U 0.0
mf
Umb=0.038 =0.017
Uc=0.11
0.1
Ut=0.205
0.2
Ug (m/s)
Fig. 1. Nanoparticle agglomeration behavior on Geldart classification.
Fig. 2. Dependence of bed expansion and pressure drop on gas velocity in a CNT nano-agglomerate fluidized bed.[3]
Fig. 3. (a) Design of prototype PMS. (b) A PMS prototype.[4]
3
To identify a particle trace in a fluidized bed, an intelligent particle spy capable of detecting, transferring, and storing data, is proposed under the name of Particle Measurement Sensor (PMS) (Fig. 3).[4] The mobile PMS can be appended to the measurement system to sense movement information, including acceleration, velocity, distance between particles, and so on. A prototype 60-mm-dia PMS was tested and served as a particle spy in a fluidized bed delivering the in situ acceleration information it detects. With increasing superficial gas velocity in the fluidized bed, the acceleration felt by PMS was observed to increase. The variance of the signals, which reflect the fluctuation, increased at first and then reached a maximum at the gas velocity (Uc), which marks the transition from bubbling to turbulent fluidization. The probability density distribution peak can be divided into the emulsion phase peak and the bubble phase peak. The average acceleration of emulsion and bubble phase increased, while the variance of both phases reached a maximum at Uc, at the same time. However, the difference between the variances of two phases reached the maximum at Uc. The solids mixing behavior of nanoparticle in a fluidized bed were also investigated.[5] The axial and radial solids dispersion coefficients of nanoparticles were two orders of magnitude lower than those in fluid catalytic cracking (FCC) catalyst systems. The axial solids dispersion coefficient increased with increasing superficial gas velocities, and ranged between 9.1×10−4 and 2.6×10−3 m2/s. There was a step increase in the axial solids dispersion coefficient between the particulate fluidization regime and bubbling and turbulent fluidization regimes. As the superficial gas velocity increased, the radial solids dispersion coefficient increased gradually, from 1.2×10−4 to 4.5×10−4 m2/s. The much smaller Da and Dr, compared to regular fluidized systems, is mainly due to the reduced density difference between the fluidized particles and fluidizing medium. PARTICULOGICAL DESIGN: FLUIDIZED BED
NANOSTRUCTURE PRODUCTION IN A
The fluidization technique has been an efficient route for materials production. However, how to produce tunable nanostuctured materials in a fluidized bed is still an open area. We selected CNTs as a typical advanced nanostructured materials to describe their mass production via fluidization bed chemical vapor deposition (CVD).[1, 6] CNTs possess extremely high tensile strength, high modulus, large aspect ratio, low density, good chemical and environmental stability, and high thermal and electrical conductivity. It is a new type of carbon nanomaterials with high performance that are in demand for different potential applications, including both the large-volume applications (such as supercapacitor or battery electrodes, battery electronic additives, conductive, high-strength composites, etc.) and limited-volume applications (such as electronic devices, etc). Recently, CNTs have been used as fillers in advanced battery electronic additives, supercapacitor or battery electrodes,
4
and light high-strength composites at a scale of hundreds of tons. Mass production of CNTs with desired structure at a low cost is the first step.
Fig. 4. The pilot plant facility for MWCNT production.[1] Among various ways to synthesis CNTs, the CVD method has the advantages of mild operation, low cost, and controllable process, and is the most promising method for the mass production of CNTs. In CVD methods, the carbon source is deposited with the assistance of a catalyst at temperatures lower than 1200 oC. Tubular CNTs are deposited at the catalyst site. Fe, Co, Ni particles, always show high activity for CNT growth. With sustainable growth of CNTs, the as-grown tubes will cluster into agglomerated or aligned CNTs. The CNT aggregates were nanocarbon products in which CNTs are randomly entangled with each other, while the CNT arrays were nanocarbon products in which CNTs are nearly parallel to each other. The agglomerated CNTs are 3-dimensional network structure composed with large amounts of CNTs. They are very easy to be synthesized for the reason that CNTs are
5
prone to entangle with each other during the growth on powder catalysts.[7] If the wall number of CNTs decreased and became double/single-walled, then the as-obtained CNTs will be very flexible. S/DWCNTs were confined grown in the porous catalyst.[8] S/DWCNT growth needs not only a good dispersion of the active metal components on the catalyst support and a suitable large BET surface area, but also a proper catalyst structure. Layered double hydroxides (LDHs), also known as hydrotalcite-like materials, which are a class of Fig. 5. CNT-array double helices grown on two-dimensional nanostructured anionic clays Fe/Mg/Al LDH flakes. a) As-obtained Fe/Mg/Al whose structure is based on LDH flakes; b) a large number of CNT-array brucite (Mg(OH)2)-like layers, double helices; c) dextrorotatory CNT-array can be used as a novel double helices grown on LDH flakes; d,e) catalysts for SWCNT growth calcined LDH flakes and middle section of (c); with a huge BET surface area of 1289 m2/g.[9] Based on catalyst concept design, multiphase flow of CNTs, and process scale up rules, A pilot plant for producing high quality and purity MWCNTs was designed (Fig. 4).[1] Based on the fluidized bed technologies developed, we realized a commercial production of MWCNTs with a capacity of 560 tons per year, and SWNCTs with a capacity of 8 tons per year in the middle of 2009. The aligned structures of CNTs were always obtained via a bottom-up self-assemble process during thermal/floating CVD. The synchronous growth of a CNT forest induced by stress was found.[10] Based on CNT growth and agglomeration mechanism, various effective strategies for VACNT mass production have been proposed. It is commonly reported that aligned CNTs can be synthesized on a flat surface. However, the surface area of the flat substrate is often limited and its mobility is poor. Only 1 g/h VACNT arrays can be obtained with flat silica as substrate. If a substrate with a larger surface area is used, such as spheres, more VACNT
6
arrays can be produced. We have recently large scale synthesized VACNT arrays on spheres,[11] quartz fibers[12] and flakes.[13] Left or right handed CNT arrays were twisted into a double helix on a LDH flake through a self-organization process during growth by chemical vapor deposition (Fig. 5).[13a] The wall number and diameter of CNT in the double helix can be tuned by chemical precursor mediated process. [13b] To avoid the damage caused by the collisions among CNT arrays during the transport or fluidization process, a strategy of intercalating VACNTs into layered compounds and directly constructing a layered hybrid nanocomposite composed of alternate CNT films and inorganic sheets was proposed (Fig. 6).[14] This is a successful way for the mass production of VACNT arrays in a fluidized bed reactor. A 3.0 kg/hr VACNT array productivity was realized in a fluidized bed reactor with a diameter 500 mm. [15] The CNTs in the as-grown arrays were with good alignment, and can be easily purified by facile acid treatment.
Fig. 6. Illustration of the formation of hybrid composites by intercalating vertically aligned CNT films into layered inorganic compounds, showing stacked layers in the original vermiculite (left panel), catalyst particles adhering to the surface of the layers after impregnation (middle), and aligned CNTs between the layers after the CNT growth process (right); d) SEM image showing an enlarged view of a single interlayer with aligned CNTs and an interlayer distance of 20mm. e) SEM image showing CNT growth on both sides of a vermiculite layer. f) Transmission electron microscopy image of a multiwalled CNT.
7
For the horizontally aligned CNT formation, the gas flow, which makes the CNTs grow in a way similar with a flying kite, is efficient for super long (20 cm) CNT growth at a fast rate (80-90 μm/s).[16] The growth rate of CNT is very high, and the weight space velocity can reach 108 gCNT/gcath, which is millions of times of that of agglomerated CNTs on porous catalyst. The synthesis of HACNTs on movable or free substrates in a fluidized bed to obtain long CNTs is still an important issue. Based on particuological concept design, the agglomerated CNTs and aligned CNTs have been successfully mass produced in ton scale via fluidized bed process, and are available in the market. This will greatly promote the bulk applications of CNTs, and provide a sustainable route for the development of the novel advanced materials. INDUSTRIAL APPLICATION: MULTI-FUNCTIONAL ADVANCED CATALYTICAL FLUIDIZATION TECHNIQUE
STRUCTURE
FOR
The last level of structure in fluidized bed is in macro-scale of fluidized bed, such as the distributors, internals or bluffs in a fluidized bed reactor. Chemical looping concept for high efficient fossil fuel conversion by a family of configurations with different reactors or regions of fluidized bed have also been explored. [17] Many applications of internals in fluidized bed have been employed for acrylonitrile, aniline, vinyl acetate and vinyl chloride monomer synthesis,[18] and fluid menthol to propylene. The influence of internals on hydrodynamics and mixing in fluidized bed and related reactor performance will also be presented. Due to the worldwide crude oil shortage and the rapidly increasing demands for light olefins, the methanol to olefins (MTO) process and dimethyl ether to olefins (DTO) process were selected as the alternative ways for the production of light olefins. Because of the strongly exothermicity of MTO/DTO reactions and fast deactivation of SAPO-34 zeolite catalyst, a fluidized bed was the preferred reactor for industrial application.[20] Consequently, the MTO/DTO catalysts needed to be prepared for facile fluidization. Herein, a hierarchical SAPO-34 zeolite with high crystallinity and excellent hydrothermal stability was directly synthesized in the nanoscale confined environment provided by the natural layered material kaolin (Fig. 7). [19] The hierarchical sample showed a much higher conversion than the conventional zeolite. The overall olefin selectivity was as much as 96.7%, which was 4–7% higher than that of the conventional zeolite. The presence of the mesopore structure shortened the diffusion path, thus, the primary products could easily diffuse out of the zeolites and secondary reactions were avoided. Therefore, higher propylene selectivity and overall olefin selectivity were obtained. This expected to be a facile and economically
8
feasible way to prepare more effective catalyst for fluidized MTO/DTO process. A fluidized bed methanol to propylene (FMTP) industrial technology was developed. The industrial practice of fluidized bed for FMTP process at Huainan with a 470 hours of continuous operation at full capacity were carried out, and high yield of propylene was reached (Fig. 7c).
Fig. 7. a) As synthesized hierarchical SAPO-34 zeolite; b)DME conversions and overall olefin selectivities (Conversion on hierarchical (■) and conventional (□) SAPO-34; selectivity on hierarchical (▲) and conventional (Δ) SAPO-34.[19] c) 300kT/a multistage fluidized bed FMTP unit Polyvinylchloride (PVC) is second largest general plastics in the world, the very low space velocity makes gas-phase catalytic hydro-chlorination of C2H2 on HgCl2/activated carbon (AC) catalyst only can be carried out in packed bed at high conversion. However, if a multistage fluidized bed (MSFB) is applied into this process, a high conversion of C2H2 at 130-140 °C at a high space velocity of C2H2, while maintaining high selectivity to vinyl chloride monomer (VCM), could be obtained by efficiently inhibiting the back-mixing of gases between stages. The new catalyst with a coconut-shell-type AC as the support shows much higher mechanical strength for stable fluidization with a proper pore structure for the dispersion of HgCl2 and a high thermal stability, as compared to a catalyst with coal-based AC as the support in
9
conventional packed bed reactors. The catalyst lifetime estimated by simulation and a rapid sublimation experiment fits in well with the data from the pilot plant test. These results suggest that the combination of a MSFB and the new catalyst described here is a novel technology for producing VCM on a large scale at low cost. The scale-up of MSFB through a series of small hot model, 3000 ton/a pilot-plant and 100 kT/a industrial plant was demonstrated and reactor modeling of the scale up of the MSFB was carried out, as shown in Fig. 8.
Fig. 8. Top: Internal structured fluidized bed reaction; Bottom: Multistage fluidized bed reactor for VCM production scale-up process: Left: small hot model test unit; Middle: 3 kT/a pilot plant; Right: 100kT/a multistage fluidized bed CVM unit The flow structure of gas-solid can significantly regulated the flow directions. The gas-solid downward flow (downer) reactor can reduce axial gas and solids backmixing by 30 times in comparison with upward flow riser reactor. Downer is therefore acknowledged as a novel multiphase flow reactor with great potential in high-severity operated processes, such as the short contact time reactions with the intermediates as the desired products.[21] Compared with the riser reactor with the same feeds and catalysts, the LPG and propylene yield increased by 8.15 and 4.30 wt%, respectively. The gasoline octane number likewise reached 94.8 with 28 wt% olefin content. However, dry gas is significantly suppressed, and the coke has little
10
change in yield even with the increased catalyst to oil ratio. With some gasoline recycling, the LPG and propylene yield increased by 11.45 and 5.06 wt %, respectively. The olefin content in gasoline significantly decreased to 22 wt %; the high octane number (95.4) is maintained. The computational fluid dynamics (CFD) coupled with a 6-lump kinetic model is also applied to simulate the FCC process for the industrial trials. The yield of propylene and butylene and the temperature profile along the axis direction demonstrated consistency between the simulation results and the experimental data. The axial solid mixing mechanisms in gas-solids cocurrent upflow and downflow circulating fluidized bed systems revealed that among the many influencing factors, flow direction has the most profound influence on the axial solids mixing. When the flow is in the direction of gravity (downflow in the downer), axial solids dispersion is very small and the flow pattern approaches plug flow; when the flow is against gravity (upflow in the riser), axial solids dispersion is significantly larger and the flow pattern deviates significantly from plug flow. Solid mixing is found to be mainly due to the dispersion of dispersed particles in the downer; however, in the riser, both the dispersion of dispersed particles and particle clusters were co-existed in the riser.. In both the riser and the downer, the dispersion of the dispersed particles is very small, indicating that dispersed particles pass through the system in a near plug flow pattern. Dispersion due to particle clusters in the riser, on the other hand, is very significant, contributing to the large axial solids backmixing and the bimodal solids residence time distribution in the riser. CONCLUSIONS Development of fluidization technique has spanned several decades, attesting to the importance of this technique in chemical engineering, thermal engineering, and metallurgy. When nanostructured granular materials were used for fluidization, some kinds of nanoparticles (such as SiO2, carbon nanotubes) are agglomerated into multi-stage agglomerates for stable fluidization. The structure of the agglomerates as well as their size, apparent weight and the interactive forces between them significantly influence the hydrodynamic behavior of agglomerating fluidization. The concept of a particle spy was tested in the form of an encapsulated prototype PMS was proposed to in situ detect the phase structure of fluidized bed. The axial and radial solids dispersion coefficients were both two orders of magnitude lower than those in FCC systems. Based on the scientific understanding of nanomaterials fluidization, the fluidization process was successfully employed for nanomaterial (both agglomerated and aligned CNT) production. Multi-functional nanostructures, such as hierarchical SAPO-34 zeolite, were employed as advanced catlaysts for catalytic fluidized bed route for methanol to olefin process. Novel fluidized bed reactor, such as the novel riser-downer coupling reactor for the FCC process and a two-stage fluidized-bed for gas-phase catalytic hydrochlorination of acetylene, were
11
also proposed. More efforts should be pain on new scientific challenges for nanomaterials fluidization and novel industrial process for nanomaterial production and (nano)structure enhanced fluidization technology for advanced materials, novel chemical production, and energy conversion. REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
F. Wei, et al., Powder Technol 2008, 183, 10-20. Y. Wang, et al., Powder Technol 2002, 124, 152-159. H. Yu, et al., AIChE J 2006, 52, 4110-4123. Q. Zhang, et al., Particuology 2009, 7, 175-182. a) C. Huang, et al., Powder Technol 2006, 161, 48-52; b) C. Huang, et al., Powder Technol 2008, 182, 334-341. Y. Wang, et al., Chem Phys Lett 2002, 364, 568-572. Y. Hao, et al., Carbon 2003, 41, 2855-2863. Y. Liu, et al., Carbon 2008, 46, 1860-1868. a) M. Q. Zhao, et al., Carbon 2010, 48, 3260-3270; b) M. Q. Zhao, et al., Adv Funct Mater 2010, 20, 677-685. a) Q. Zhang, et al., J Phys Chem C 2007, 111, 14638-14643; b) J. Q. Huang, et al., Nanotechnology 2008, 19, 435602; c) J. Q. Huang, et al., Nanoscale 2010, 2, 1401-1404. Q. Zhang, et al., Carbon 2008, 46, 1152-1158. a) Q. Zhang, et al., Mater Chem Phys 2008, 107, 317-321; b) K. Zhou, et al., Nanoscale Res Lett 2010, 5, 1555-1560. a) Q. Zhang, et al., Angew Chem Int Ed 2010, 49, 3642-3645; b) M. Q. Zhao, et al., J Am Chem Soc 2010, 132, 14739-14741. Q. Zhang, et al., Adv Mater 2009, 21, 2876-2880. a) Q. Zhang, et al., Carbon 2009, 47, 2600-2610; b) Q. Zhang, et al., Carbon 2010, 48, 1196-1209. a) Q. Wen, et al., Adv Mater 2010, 22, 1867-1871; b) Q. Wen, et al., Chem Mater 2010, 22, 1294-1296. L. S. Fan, Chemical Looping Technology for Fossil Energy Conversions, John Wiley, 2010. X. B. Wei, et al., Ind Eng Chem Res 2009, 48, 128-133. J. Zhu, et al., Chem Commun 2009, 3282-3284. H. Q. Zhou, et al., Appl Catal A-gen 2008, 348, 135-141. a) R. S. Deng, et al., Ind Eng Chem Res 2005, 44, 1446-1453; b) H. Liu, et al., Ind Eng Chem Res 2005, 44, 733-741; c) F. Liu, et al., Ind Eng Chem Res 2008, 47, 8582-8587; d) Y. Cheng, et al., Powder Technol 2008, 183, 364-384.
12
A MODELING STUDY OF GAS STREAMING IN A DEEP FLUIDIZED BED OF GELDART GROUP A PARTICLES Shayan Karimipour, Todd Pugsley1 Department of Chemical Engineering, The University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, Canada S7N 5A9 1
Corresponding author (
[email protected]), Tel.: (+1-306) 966-4761, Fax: (+1-306) 966-5205
ABSTRACT Gas streaming has been modeled in a deep fluidized bed of 5 m depth and 0.3 m inside diameter. The model results suggest that the lower pressure drop of the stream zone compared to the remainder of the bed is the reason for severe streaming flow in deep beds. The effects of different parameters such as bed depth, gas velocity and particle size on the severity of the streaming flow are also evaluated with the model. INTRODUCTION Several studies in the past decade have demonstrated that in sufficiently deep fluidized beds (i.e. beds approaching a depth of 1 m or greater) of Geldart Group A particles (1), gas bypassing may occur by increasing the superficial gas velocity (2-6). When this phenomenon occurs, the fluidizing gas bypasses the bed in the form of streams of gas, leaving a large fraction of the bed unfluidized or poorly fluidized. The concept of gas streaming was first reported in the literature by Wells (2). He performed several experiments in large scale units with up to 2.5 m diameter and 5 m bed depth and observed streaming flow under conditions that were expected to lead to operation in the bubbling regime. He attributed the streaming phenomenon to gas compression, caused by the pressure head of the deep solids bed over the distributor. Karri et al. (3) investigated the formation of streaming flow in a column of 0.3 m inner diameter and 4.9 m height. They found that for all combinations of operating conditions investigated, the addition of a sufficient amount of fines to the bed of Geldart Group A particles was able to delay the streaming. In another work, Issangya et al. (4) used several pressure transducers mounted at various radial positions to detect the presence of streaming flow. Recently, Karimipour and Pugsley (5) have performed a systematic study on the streaming flow in deep beds of FCC particles. They discussed the effects of streaming flow on the pressure fluctuations time series measured in the fluidized bed for different combinations of bed depth, gas velocity, particle size, and distributor design. They concluded that streaming flow does not appear suddenly, but emerges gradually in the bed by increasing the bed depth. They found that although changing parameters such as superficial gas velocity and/or fines content can
1
reduce the severity of the streaming flow, streaming is the dominant phase for deep fluidized beds operating at gas velocities where a fully bubbling bed regime would normally be anticipated. The only mathematical model to predict the onset of gas streaming is that of Wells (2). Wells (2) concluded that when the ratio of the density at minimum fluidization to the density of the emulsion phase becomes less than a critical value for a given bed depth, streaming occurs. The model of Wells (2) was tremendously valuable for improving the understanding of streaming, but it was not a direct function of operating conditions such as bed depth and gas velocity. The objective of the present work is to develop a simple phenomenological model for the streaming flow and to use the model to evaluate the effect of bed depth, gas velocity, and particle size. MODEL DEVELOPMENT Based on our finding from a separate experimental campaign (e.g. 5), the deep fluidized bed is divided into two adjacent regions in which the smaller region is occupied by the stream flow and the other region is assumed to be at minimum fluidization conditions. It is assumed that by increasing the superficial velocity the gas in excess of that required for minimum fluidization is directed into the stream zone. Also based on our experimental observations, the cross section of the gas stream is assumed to be circular and its diameter to be less than one fourth of the bed diameter. The stream therefore forms a vertical cylinder of constant diameter along the fluidized bed. A small lateral zone above the distributor is reported to be better fluidized (2) and gas and particles from other parts of the distributor find their way towards the stream and move upward through the stream. As such, particles can be assumed to move upward only in the stream and after discharging at the surface of the bed slowly return to the bottom through the non-streaming region. Similar to the acceleration zone of a circulating fluidized bed riser (6, 7), the stream can be modeled by a force balance over a single particle inside the stream:
ρ pV p
2
⎞ 1 ⎛u = ρ g ⎜ st − υ p ⎟ Ap CD − ( ρ p − ρ g )V p g ⎟ 2 ⎜⎝ ε g dt ⎠
dυ p
(1)
Assuming the particles as spheres of constant diameter, and incorporating Eq. 2 from the derivative theory, the force balance equation can be re-written as Eq. 3:
dυ p dt
=υp
dυ p
(2)
dz 2
⎞ 3ρ g CD ⎛ ust g = (ρ p − ρg ) ⎜⎜ − υ p ⎟⎟ − dz 4d p ρ pυ p ⎝ ε g ⎠ ρ pυ p
dυ p
(3)
We have estimated the drag coefficient, CD, in Eq. 3 based on the correlation of Mostoufi and Chaouki (8). The porosity in these equations is calculated from the solids mass balance equation as follows: Gp = ρ p (1 − ε g )υ p (4) The initial value of the particle velocity at the bottom of the stream is obtained from the solids mass balance. Thus, Eq. 3 will be solved subject to the following initial condition:
υp
z =0
=
Gp
(5)
ρ p (1 − ε mf )
2
Once the axial profile of particle velocity in the stream is determined from Eq. 3, the corresponding solids holdup can be calculated from ε p = 1− ε g (6) The axial profile of the pressure drop along the stream can be determined from the momentum balance over the stream. The momentum balance could be expressed as follows:
dp ⎛ dp ⎞ ⎛ dp ⎞ ⎛ dp ⎞ = ⎜ ⎟ +⎜ ⎟ +⎜ ⎟ dz ⎝ dz ⎠ head ⎝ dz ⎠ acceleration ⎝ dz ⎠ friction
(7)
where
⎛ dp ⎞ = ρ pε p g + ρ g ε g g ⎜ ⎟ ⎝ dz ⎠ head
(8)
dυ p u d ⎛ ust ⎛ dp ⎞ = ρ pε pυ p + ρ g ε g st ⎜ ⎜ ⎟ dz ε g dz ⎜⎝ ε g ⎝ dz ⎠ acceleration
⎞ ⎟⎟ ⎠
(9)
The pressure drop caused by friction includes two sources, i.e., gas-wall and particle-wall frictions:
⎛ dp ⎞ ⎛ dp ⎞ ⎛ dp ⎞ =⎜ ⎟ +⎜ ⎟ ⎜ ⎟ ⎝ dz ⎠ friction ⎝ dz ⎠ gas − wall ⎝ dz ⎠ particl − wall
(10)
These pressure losses are defined by the Fanning equation as
ust2 1 ⎛ dp ⎞ ρ = f g g ⎜ ⎟ εg 2d st ⎝ dz ⎠ gas − wall 1 ⎛ dp ⎞ ρ pε pυ p2 = fp ⎜ ⎟ 2d st ⎝ dz ⎠ particle − wall
(11) (12)
Since gas-wall and particle-wall frictions form a minor portion of the overall pressure drop, type of the friction factor does not have a major effect on the results. Here, the gas-wall friction factor, fg, has been calculated from the Blasius formula (9)
fp =
0.316 , Reg0.25
Reg ≤ 105
(13)
and the particle-wall friction factor has been estimated using the correlation of Kanno and Saito (10)
fp =
0.057 1/ 2 ( gd st ) 2υ p
(14)
The wall in our case corresponds to the “wall” of the cylindrical stream in the bed. In order to solve these equations, the solid circulation rate (Gp) is needed as an input. Since the system is not a real circulating fluidized bed, a pseudo-circulating rate may be calculated from the correlations proposed for the internally circulating fluidized bed. An internally circulating fluidized bed resembles the current case in that both of the systems involve flow of gas and solids between a fluidized bed at minimum fluidization conditions and a dilute bed (a riser in an internally circulating fluidized bed and a stream in the current case). The net rate of the particle exchange between two zones along the fluidized bed is considered to be trivial. The correlation of Jeon et al. (11) has been used for this purpose:
⎛u ΔPor = 5.327 × 10 ⎜ st ⎜u ⎝ mf 3
⎞ ⎟⎟ ⎠
0.795
⎛ dp ⎞ ⎜ ⎟ ⎝ d or ⎠
0.728
(15)
3
Gp = Cdis
Sor 2 ρ p (1 − ε mf )ΔPor Sst
(16)
In the above equations, the orifice refers to that point at the bottom of the bed that allows for the exchange of gas and particles between the stream and non-streaming zones. For the pressure drop through the none-streaming zone which is considered to be at minimum fluidization conditions, the pressure drop is assumed to be due to the mass of the particle bed:
dp = ρ p g (1 − ε mf ) dz
(17)
RESULTS AND DISCUSSIONS The model predictions of pressure drop along the fluidized bed for a bed depth of 5 m are provided in Fig. 1. As can be seen in the figure, the model predicts a lower pressure drop immediately above the distributor for the non-stream zone compared to the case of the stream zone. Therefore, streams do not form in this region. However, the stream pressure drop decreases dramatically with increasing distance from the distributor, which makes the streams a preferable pathway for the gas. The higher pressure drop of the stream immediately above the distributor is due to the much higher flow of gas and particles in the stream compared to the non-stream zone. Similar trend of pressure drop has been reported for the bottom of FCC risers (7, 8). As illustrated in the figure, as the upper surface of the bed is approached, the difference between the pressure drop of the streaming and non-streaming zones decreases. The result of this would be that preferential flow of gas through the stream would be diminished, allowing gas to diffuse into other parts of the bed and provide more uniform fluidization at upper regions. This is consistent with visual observations from experiments, which showed improved fluidization at the upper regions of the bed. 6
Axial Position (m)
5
Non-Stream Zone Stream Zone
4 3 2 1 0 0
5000
10000
15000
20000
25000
30000
35000
Pressure Drop (Pa) Figure 1. Axial profile of the pressure drop in the fluidized bed, Bed depth = 5 m, Superficial gas velocity = 0.2 m/s, Particle diameter = 84 microns
4
Effect of Bed Depth Fig. 2 illustrates the differences between the pressure drops of stream and non-stream pathways at the bottom of the fluidized bed for different bed depths. As can be seen, the difference in the pressure drops of the two zones, which is considered to be the motivation for the formation and stability of the streams, increases with increasing bed depth. Experimentally we found that the onset of streaming flow occurred gradually in the fluidized bed as bed depth was increased. According to the model results, this can be attributed to the gradual increase of the difference in pressure drop between the streaming and non-streaming zones. This difference is probably low enough in shallow beds that the gas is able to fluidize all of the cross section and prevents the formation or permanence of streaming flow. Effect of Gas Velocity
Difference between the pressure drop of Stream and Non-Stream zones at the bottom of the bed (Pa)
Fig. 3 provides the axial profile of the pressure drop in the fluidized bed for different superficial gas velocities. As Fig. 3 illustrates, two changes occur in the fluidized bed by increasing the gas velocity. Firstly, the difference between the pressure drops of the streaming and non-streaming zones decreases and secondly, the region expands above the distributor where streaming is not preferred or present. The positive influence of increasing the gas velocity on diminishing the streaming flow has been emphasized in all of the previous experimental works in the literature (2-6). As the figure indicates, at gas velocities higher than 1 m/s streaming flow is not preferred anywhere in the fluidized bed and uniform fluidization would be possible throughout the bed. 20000
16000
12000
8000
4000
0 0
1
2
3
Bed Depth (m)
4
5
6
Figure 2. Difference between the pressure drop of Stream and Non-Stream zones at the bottom of the bed for different bed depths, Superficial gas velocity = 0.2 m/s, Particle diameter = 84 microns
5
Axial Position (m)
6 5
Non-Stream Zone Stream Zone, U0 = 0.2 (m/s)
4
Stream Zone, U0 = 0.4 (m/s) Stream Zone, U0 = 0.6 (m/s)
3
Stream Zone, U0 = 0.8 (m/s) Stream Zone, U0 = 1 (m/s) Stream Zone, U0 = 1.2 (m/s)
2 1 0 0
10000
20000
30000
40000
50000
Pressure Drop (Pa) Figure 3. Axial profile of the pressure drop in the fluidized bed for different superficial gas velocities, Bed depth = 5 m, Particle diameter = 84 microns Effect of Particle Size Fig. 4 illustrates the axial profile of the pressure drop in the fluidized bed for different particle sizes and a constant particle density of 1400 kg/m3. As can be seen, the pressure drop in the stream increases by increasing the particle size. Thus, its preference as an alternative pathway with lower pressure drop for gas decreases gradually. According to the literature, streaming flow has only been reported for Geldart Group A particles; it does not appear to exist for coarser Geldart B particles (2-6). Thus, as the model predicts, the fluidized bed of these particles display uniform fluidization. The results show that the model is able to predict this directional effect of increasing particle size. Effect of Stream Size The effect of the size of stream zone (i.e. stream diameter) on the axial profile of pressure drop has also been investigated (results not shown due to space constraints). Our model predicts that decreasing the stream size from 1/4 to 1/8 of the bed diameter reduces the preference of streaming as an alternative pathway for gas flow. CONCLUSIONS In the present work, gas streaming flow has been modeled in a deep fluidized bed of 5 m bed depth and 0.3 m diameter. The trend of the model predictions have been qualitatively compared and validated with the experimental findings. The model is based on the assumption that the stream already exists in the bed. The initiation of streaming flow has been discussed in our previous work (6). According to that work, the potential for streaming always exists in a fluidized bed. The results of the present work suggest that what causes a severe streaming flow with increasing bed depth is probably the gradual increase of the difference between pressure drop of two zones: that smaller portion of the bed where streaming becomes preferred and the remainder of the bed at minimum fluidization. Our model results show that increasing the bed
6
depth favors the streaming flow, while increasing the gas velocity increases the uniformity of the bed and decreases the streaming severity. Streaming flow was found to be less severe for larger particle sizes. All of these findings are in conformity with experimental investigations reported previously in the literature. 6
Non-Stream Zone Stream Zone, Particle Ave. Diam. = 42 microns
Axial Position (m)
5
Stream Zone, Particle Ave. Diam. = 84 microns Stream Zone, Particle Ave. Diam. = 168 microns
4
Stream Zone, Particle Ave. Diam. = 252 microns
3 2 1 0 0
5000
10000
15000
20000
25000
30000
35000
Pressure Drop (Pa) Figure 4. Axial profile of the pressure drop in the fluidized bed for different particle sizes, Bed depth = 5 m, Superficial gas velocity = 0.2 m/s NOTATION Ap Ar
cross-sectional area of particle (m2) Archimedes number ( d 3p ρ g ( ρ p − ρ g ) g / μ 2 )
Cdis CD dp dst D f fp fg g Gp p ∆Por Reg Sor Sst t umf ust vp
gas discharge coefficient effective drag coefficient particle diameter (m) stream diameter (m) fluidized bed diameter (m) drag coefficient correction factor solid-wall friction factor gas-wall friction factor acceleration of gravity (m/s2) solids flux (kg/m2s) pressure (Pa) orifice pressure drop (Pa) gas Reynolds number (D U0 ρg/μg) orifices cross sectional area (m2) stream cross sectional area (m2) time (s) minimum fluidization velocity (m/s) gas velocity in stream (m/s) particle velocity (m/s)
7
Vp z
particle volume (m3) fluidized bed height above distributor (m)
Greek Letters εg gas voidage gas voidage εp εmf voidage at minimum fluidization ρg gas density (kg/m3) ρp particle density (kg/m3) μ gas viscosity (Pa∙s) REFERENCES [1] D. Geldart, Types of gas fluidization, Powder Technology 7 (1973) 285-292 [2] J. Wells, Streaming flow in large scale fluidization, Paper presented at the AIChE annual meeting, Particle Technology Forum, Reno, Nevada, USA, 2001. [3] S. B. R. Karri, A. S. Issangya, M. Knowlton, Gas bypassing in deep fluidized beds, In Fluidization XI, U. Arena, R. Chirone, M. Miccio, P. Salatino, eds., Ischia (Naples), Italy, 9-14 May 2004. [4] A. Issangya, T. Knowlton, S. B. R. Karri, Detection of gas bypassing due to jet streaming in deep fluidized beds of group A particles, In Fluidization XII, F. Berruti, X. Bi, T. Pugsley, eds., Vancouver, British Columbia, Canada, 13-17 May 2007. [5] S. Karimipour, T. Pugsley, Study of gas streaming in a deep fluidized bed containing Geldart’ Group A particles, Chemical Engineering Science 65 (2010) 3508–3517. [6] T. S. Pugsley, F. Berruti A predictive hydrodynamic model for circulating fluidized bed risers, Powder Technology 89 (1996) 57-69 [7] Sh. Karimipour, N. Mostoufi, R. Sotudeh-Gharebagh, Modeling the hydrodynamics of downers by cluster-based approach, Ind. Eng. Chem. Res. 45 (2006) 7204-7209 [8] N. Mostoufi, J. Chaouki, Prediction of effective drag coefficient in fluidized beds, Chemical Engineering Science 54 (1999) 851-858 [9] R. W. Fox, A. T. McDonald, P. J. Pritchard, Introduction to fluid mechanics, 6th ed.; Wiley: New York, 2003 [10] H. Kanno, S. Saito, Pneumatic conveying of solid through straight pipes, J. Chem. Eng. Jpn. 2 (1969) 211-217 [11] J. H. Jeon, S. D. Kima, S. J. Kim, Y. Kang, Solid circulation and gas bypassing characteristics in a square internally circulating fluidized bed with draft tube, Chemical Engineering and Processing 47 (2008) 2351-2360
8
EFFECTS OF PARTICLE PROPERTIES ON CLUSTER CHARACTERISTICS IN A 2-D CFB RISER Jing Xu and Jesse Zhu* Particle Technology Research Centre, Department of Chemical & Biochemical Engineering, The University of Western Ontario, London, Ontario, Canada N6A 5B9 *Corresponding author. Tel.: +1 519 661 3807; fax: +1 519 850 2441; email address:
[email protected] (J. Zhu)
ABSTRACT The characteristic of particle clusters was studied in a 2-D circulating fluidized bed riser by using a digital image system and optical fiber probes. A new parameter, cluster number fraction, was proposed to characterize the clusters. The results indicated the smaller and lighter particles have higher potential to aggregate, while the effects of particle sphericity were less significant than those of particle density and size.
INTRODUCTION The analysis of the micro structure of the gas-solid two phases is considered complex and remains unclear. The micro flow structure is usually identified as the behavior of clusters, which are defined as dense clouds of particles having significantly more particles per unit volume than the surrounding dilute regions (1). Since the particles in CFB tend to aggregate and form clusters, which flow quite differently from a single particle, the gas-solid flow in CFBs is often characterized by the existence of particle aggregates or clusters (1, 2). Most of the researchers (1-5) used the visualization technique and intrusive probe to obtain the micro flow behaviors. However, the visualization technique they used was restricted to dilute flow. In addition, almost all of the former studies appear to conduct the investigation within one type of particles, and very few references can be found clarifying the effects of particle properties on the cluster characteristics. Since previous studies (6, 7) have revealed that a strong dependence of the particle properties on the solids concentration and particle velocity, the particle properties, including particle density, size and sphericity may play an non-negligible role in affecting the cluster properties. In this study, various types of particles with typical different density, size or sphericity were taken into consideration. Moreover, very few studies have been carried out to investigate the cluster properties by combining the visualization with intrusive probes. In this study, we applied high-speed video camera and optic fiber probes in a 2-D circulating fluidized bed, to connect the two different measurements and make both function better. This study also realized the visualization technique to be effectively used under high solids concentration (Gs is up to 200 kg/m2s).
EXPERIMENTAL APPARATUS All experiments were carried out in a rectangular circulating fluidized bed which is illustrated schematically in Figure 1. The riser is a rectangular column with 7.6 m height and 19 mm × 114 mm (0.75 in × 4.5 in) cross-section. The visualization system was self-designed and set up. To eliminate the entrance and exit effects, the system was mounted focusing on the upper fully developed region, where Z = 5m. The system consists of a light source, a high-speed video camera and programs for digital image analysis (Figure 2). The speed of the highspeed video camera is up to 16,000 fps. A MATLAB program was used to allow the images to be analyzed frame by frame. The probe chosen to measure the local solids concentration in this study was optical fiber probe, which is capable of measuring the solids concentration with a reliable pre-calibration (1, 8, 9). The solids concentrations were measured at 9 lateral positions and 6 axial levels along the entire riser. The instantaneous solids concentration was analyzed by a selfdeveloped FORTRAN program which introduces sensitivity analysis to identify the clusters from solid concentration signals.
Images Monitor
Face Wall
Diffuser Panel
Video Camera
Images Analysis
Lamp CFB Riser
Figure 1 Circulating fluidized bed unit and schematic of riser cross section
Side Wall
Figure 2 Sketch of Visualization system
The properties of the particles used in this study are listed in Table 1. The materials were selected in order to investigate the effects of particle size, density and sphericity, respectively. In this study, the solids concentration measurement was conducted under the operating conditions of Ug = 3 ~ 8 m/s, and Gs = 50 ~ 200 kg/m2·s. The high-speed videos were recorded simultaneously under the corresponding operating conditions at 2000 fps.
Table 1 Properties of particles used Particles
FCC
Glassbeads #1
Glassbeads #2
Glassbeads #3
Sand
Particle Sauter mean diameter, μm
67
76
134
288
138
1877
2453
2403
2498
2467
1125
1434
1421
1475
1453
Sphericity, -
0.95
~1
~1
~1
0.65~0.75
Particle terminal velocity, m/s
0.26
0.42
1.19
3.73
N/A
Particle density, 3 kg/m Bulk density, kg/m
3
RESULTS AND DISCUSSION Observation and Description of Clusters Generally, the solids flowing in the riser were observed to distribute dense in wall regions and dilute in the center. The particles were moving faster in the column center and slower towards the wall. Nearly no appearance of solids buildup or fallingdown was observed on either of the face walls. Cluster Forms Figure 3 shows an image sequence with a solid cluster moving upwards in the center region of the riser. The frames (window size of 46.6 mm × 13.5 mm) were taken at a recording rate of 2000 fps and an exposure time of 500 μs. The movement and development of the cluster can be recognized as the darker structure which is visualized directly within the observed area. The sequence of images clearly show that a U-shape cluster is formed with a round nose facing downward and a core on the “nose tip”, where it is much darker than the peripheral area. Initially, the cluster is moving as a longish core with blurred boundary. Continuously, the core is stretched to be longer and crotched. Since the cluster is moving slower, the dispersed particles passing by are blocked and adhere to the cluster. Therefore, the U-shape outline is clearer and the core is darker and bigger with the aggregating of particles. In this study, the U-shape cluster is the most common form of clusters observed in the riser center. As observed, the clusters exist as U-shape, longish strand and other cluster structures. The pictures of the clusters shown in Figure 4 are for different particles under several typical operating conditions. As mentioned above, the U-shape is the most common structure viewed in the riser center. Some of them have a core with particular high solids concentration at the nose tip, while the others don’t. The opening of the U-shape cluster faces upward or downward with a size range from 5 cm to 30 cm, most are smaller than 20 cm. The longish strand cluster is the cluster form also often observed in the core region of the riser. The size of the strand cluster is approximately 10 mm in width and 10 cm to 50 cm in length. The motion of this form of cluster is slower than other particles in the vicinity. Occasionally, some clusters in small size (1-2 cm), and in shapes of circle, short stripe (as shown in Figure 4(c)) or crescent are observed. They are always seen to flow as fast as the adjacent non-aggregative region. In addition, the shape of these types of clusters is nearly constant during the motion in the observation window. Besides the forms of individual clusters aforementioned, clusters in different structures may adhere
together to form floc-like clusters. The most common combination seen is between the U-shape and the strand clusters, typically shown in Figure 4(d), (e) and (f).
Figure 3 Sequence of 12 images taken in the core region with a U-shape cluster
(a)
(b)
(c)
(d)
(e)
(f)
Figure 4 Cluster structures observed in the riser center under different operating conditions: (a) Glassbead #3, Gs = 100 kg/m2·s, Ug = 5 m/s; (b) Glassbead #3, Gs = 100 kg/m2·s, Ug = 8 m/s; (c) Sand, Gs = 100 kg/m2·s, Ug = 5 m/s; (d) Glassbead #1, Gs = 100 kg/m2·s, Ug = 5 m/s; (e) Glassbead #1, Gs = 150 kg/m2·s, Ug = 5 m/s; (f) FCC, Gs = 100 kg/m2·s, Ug = 5 m/s
Effects of Particle Properties on Cluster Forms Five different types of particles were employed in this study. The effects of particle density, size and sphericity on the clusters behaviors were compared. All the images in Figure 4 are in the size of 256 pixel ×880 pixel with a scale of 1.99×10 -4 m/pixel. In Figure 4(a) and (b), Glassbead #3 with a mean size of 288 μm is seen to form clusters typically in U or strand shape and with a large cluster size. The Sand particles having particle diameter 134 μm and approximate 0.7 sphericity are observed to have the flow structure indistinguishable from the Glassbead #3, while small size clusters can be found occasionally. With further decreasing of particle size to 76 μm for Glassbead #1, the form of clusters turns to be more complicated and interconnective. It is seen that the clusters are smaller in size than that of the larger particles, and tend to connect together with each other. Therefore, the individual cluster is difficult to be identified in small size particle flow. Compared with the flow of Glassbead #1 (2453 kg/m3), the structure of clusters is more intricate and irregular when lighter FCC particles (1877 kg/m3) were used, as shown in Figure 4(f). The
area with darker color, which is occupied by the clusters, is obviously larger than the area in lighter color. In other words, - a great amount of particles are seen moving in aggregating form and few particles are left to move individually. The size and form of individual cluster is unlikely to be identified since the interconnection among the highly populated clusters is very intense. The observation indicates that the smaller and the lighter particles have higher potential to aggregate than the larger and heavier particles, whereas, the effect of particle sphericity cannot be distinguished here. Characterizing Clusters with Optical Fiber Probe To identify clusters with reflective probes applied, a set of criterion must be satisfied. Based on the previous studies, it is suggested that (3, 10): (1) the solids concentration inside the cluster must be n-times the standard deviation of the sampled signal over the local time-mean solids concentration; (2) the number of consecutive samples above the critical solids concentration, Ns, must be set to determine the minimum time interval for the perturbation caused by a cluster; (3) the sampling volume must be greater than one to two orders of particle diameter. Furthermore, it is found in this study that the sampling frequency of solids concentration signal is another sensitive criterion to establish the cluster properties. The sampling frequency in this study is placed very high, at 100 kHz, which allowed high sensitivity to the solids concentration. 1.0
Ns = 30
Large size cluster fraction
FCC, y/Y = 0 FCC, y/Y = 0.5 FCC, y/Y = 0.98 GB #1, y/Y = 0 GB #2, y/Y = 0 GB #3, y/Y = 0 Sand, y/Y = 0
Ug = 5.5 m/s
0.8
2
Gs = 100 kg/m s 0.6
0.4
0.2
0.0 0
50
100
150
200
250
300
350
400
Ns
Figure 5 Cluster number fraction vs. Ns
Subjecting to the difference of sampling frequency among various studies, the optimal values for both n and Ns are required to be determined individually. The sensitivity analysis, firstly introduced by Manyele et al. (3), is adopted in this study to optimize the value for n. Consequently, n=2 is finalized as optimal critical solids concentration. Then, such method is further improved to identify Ns. A new parameter, cluster number fraction, Flc, is proposed for the first time to characterize clusters. Flc is defined as Flc Nlc N c , where Nc is the total number of perturbations with the solids concentration higher than the critical value over the time series studied; while Nlc is the number of perturbations with Ns consecutive samples above the critical solids concentration. Considering the cluster number fraction as a sample parameter for the sensitivity analysis, an increase/decrease of Ns would lower/raise the fraction of clusters with existence time longer than Ns sampling time interval.
Some of the perturbations of solids concentration are very small and with short time intervals, therefore, they are classified as part of the dispersed particulate phase. Since such small perturbations are numerous, there tends to be a decrease of Flc with the increase of Ns. As shown in Figure 5, there is a sharp change at Ns = 30, which is the critical value that demarcates the particulate phase and the clusters. In addition, single critical Ns is also applicable to different types of particles and multiple lateral positions under specific operating condition. Cluster Number Fraction (Flc) It is found that Flc can not only be used to determine the optimal value for Ns, but also characterizes the effects of particle properties on the aggregates. Since Ns is the number of consecutive samples, with a sampling frequency, it can be easily transfromed to the minimum time interval, Tc, set for the perturbation caused by a cluster. Since the clusters are irregular in shape and vary both laterally and axially within a riser, the accurate definition of the cluster size is impossible. Therefore, the vertical length is usually used to identify the size of clusters. Since the time interval of the solids concentration perturbation measured by the optical fiber probe represents the traveling time of a cluster passing through a probe tip, it is feasible to calculate the cluster vertical length with dvl Vc Tc , where Vc is the vertical velocity of cluster (11). With the evaluation of the cluster velocity, it is possible to obtain the size of clusters in the riser. To some extent, the cluster number fraction reflects the distribution of cluster size. Figure 6 plots Flc against minimum time interval to elucidate the cluster characteristics of different particles. Figure 6(a) interprets the fractions of cluster number at different lateral positions. The Flc at the riser center region (y/Y = 0), middle region (y/Y = 0.5) and wall region (y/Y =0.98) are seen decrease with the increase of minimum time interval. In other words, with increasing Tc set for a cluster, the number of clusters with the transit time longer than the criterion decreases. It can also be seen that the decreasing curves of y/Y =0 and y/Y = 0.5 are almost overlapping and cross approximately at Tc = 1.1×10-3 µs. Below this value, the cluster number fraction at y/Y =0 is larger than that at y/Y = 0.5, while smaller when beyond the point. The curve for the wall region with y/Y = 0.98 is obviously above the other two, meaning that the population of clusters in the wall region is larger than that in the center or middle region. Due to the wall friction, the particle velocity is low on the wall, which increases the tendency of forming clusters and the probability of existence for clusters. Figure 6(b) compares Flc of particles with different mean size. It is found that under identical Tc, the Flc of smaller size particles is greater than that of larger particles. It shows that the finer particles are prone to aggregate and incline to form clusters. It is agreed well with the observations in the section 3.2. The finer particles (GB #1 or FCC particles) were seen to form numerous clusters interconnecting with each other, while only several huge size clusters were observed occasionally in the coarse particles (GB #3 or Sand).
Large size cluster fraction, Flc
1.0
(a)
1.0
0.8
0.8
0.6
0.6
(b) Ug = 5.5 m/s 2
Gs = 100 kg/m s Z = 5.33 m
GB #1 (dp = 76m), y/Y = 0
FCC, y/Y = 0 FCC, y/Y = 0.5 FCC, y/Y = 0.98
0.4
0.2
GB #2 (dp = 138m), y/Y = 0
0.4
GB #3 (dp = 288m), y/Y =0 0.2
-3
Tc=1.110 s
0.0
0.0 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1.0
(c)
-3
Tc=0.5510 s
Large size cluster fraction, Flc
4.0
0.0
0.6
0.6
3
1.5
2.5
3.0
3.5
4.0
(d)
GB #2 ( s 1), y/Y = 0
0.4
Sand ( s 0.7), y/Y = 0
3
GB #1 (p = 2453kg/m ), y/Y = 0 0.2
2.0
-3
0.8
FCC (p = 1877kg/m ), y/Y = 0
1.0
Tc=0.4510 s
0.8
0.4
0.5
1.0
0.2
0.0
0.0 0.0
0.5
1.0
1.5
2.0
2.5
3.0
Minimum time interval, T c, ( s)
3.5
4.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Minimum time interval, T c, ( s)
Figure 6 Cluster number fraction for different particles.
For the particles with different density, the curves of Flc against Tc cross approximately at Tc = 0.55×10-3 µs, as the dotted line shown in Figure 6(c). Under the given small time criterion, the fraction of clusters number for FCC particles is larger than that of GB#1. With the increase of Tc, the clusters for heavier particles (GB #1) are greater in fraction than for lighter particles (FCC) when Tc passes over the marked dotted line. It indicates that the lighter particles tend to form smaller clusters than the heavier particles. It is matched with the observations by high-speed films in section 3.2. The curves for particles with different sphericities are also observed in Figure 6(d) when Tc = 0.45×10-3 µs. Same interpretation can also be applied to the different sphericities particles: the more spherical particles incline to aggregate larger size clusters than the irregular particles when Tc is beyond the value of the intersection point. Below the marked line, the two curves are nearly overlapping, indicating that the clusters fraction in each cut for both types of particles are almost the same. In other words, the sphericity of particles has only minor effects on the small size clusters but more obvious effects on the larger size clusters.
CONCLUSION The particle aggregation characteristics were studied in a narrow rectangular CFB riser with a 19 mm × 114 mm cross-section and a 7.6m length. The study was conducted by operating FCC, glassbeads and sand particles under various operating conditions. Forms of clusters were observed by employing a visualization system with a high-speed video camera. The lighter and smaller particles tended to
aggregate and form interconnected clusters, while the effects of particle sphericity on the form of clusters are less evident. The aggregation characteristics were also studied with the optical fiber probe. A new parameter, the cluster number fraction, was originally defined. The results obtained by analyzing the instantaneous solids concentration agreed well with the observations and also indicate that the low particle density and small particle size contribute to form clusters. It shows that the particle sphericity plays a limited role in affecting the particle aggregation.
NOTATION dp Flc Tc Vc y Nc Z Φs
Sauter mean diameter of particle, m Cluster number fraction Minimum time interval for cluster, s Cluster vertical velocity, m/s Lateral coordinates Number of perturbations higher than critical solids concentration Height from riser bottom, m Particle sphericity
dvl Gs Ug Y Nlc Ns ρp
Cluster vertical length, m Solids circulation rate, kg/m2·s Superficial gas velocity, m/s Half of riser width, m Number of clusters Number of consecutive samples above critical solids concentration Density of particle, kg/m3
REFERENCES 1. Bi, H.T., J.X. Zhu, Y. Jin, and Z.Q. Yu. Forms of particle aggregations in CFBs. in Proceedings of the Sixth Chinese Conference on Fluidization. 1993. Wuhan, China. 2. Soong, C.H., K. Tuzla, and J.C. Chen. Experimental determination of cluster size and velocity in circulating fluidized bed. in Fluidization VIII. 1995. New York: Engineering Foundation. 3. Manyele, S.V., J.H. Pärssinen, and J.-X. Zhu, Characterizing particle aggregates in a high-density and high-flux CFB riser. Chemical Engineering Journal, 2002. 88: p. 151-161. 4. Li, H., Y. Xia, Y. Tung, and M. Kwauk, Micro-visualization fo clusters in a fast fluidized bed. Powder Technology, 1991. 66: p. 231-235. 5. Hatano, H. and N. Kido, Microscope visualization of solid particles in circulating fluidized beds. Powder Technology, 1994. 78: p. 115-119. 6. Mastellone, M.L., U. Arena, The effect of particle size and density on solids distribution along the riser of a circulating fluidized bed. Chemical Engineering Science, 1999. 54: p. 5383-5391. 7. Xu, J. and J. Zhu, Effects of particle properties on flow structure in a rectangular circulating fluidized bed: solids concentration distribution and flow development. Submitted to Chemical Engineering Science, 2011. 8. Zhang, H., P.M. Johnston, J.-X. Zhu, H.I. de Lasa, and M.A. Bergougnou, A novel calibration procedure for a fiber optic solids concentration probe. Powder Technology, 1998. 100: p. 260-272. 9. Liu, J., J.R. Grace, and X. Bi, Novel multifunctional optical-fiber probe I: Development and validation. AIChE Journal, 2003. 49: p. 1405-1420. 10.Soong, C.H., K. Tuzla, and J.C. Chen. Identification of particle clusters in circulating fluidized bed. in Circulating Fluidized Bed Technology IV. 1994. New York: AIChE. 11.Li, H., Q. Zhu, H. Liu, and Y. Zhou, The cluster size distribution and motion behavior in a fast fluidized bed. Powder Technology, 1995. 84: p. 241-246.
OXY-COMBUSTION OF DIFFERENT COALS IN A CIRCULATING FLUIDIZED BED Monika Kosowska-Golachowska1, Adam Luckos2, Konrad Klos1, Tomasz Musial1 1 Czestochowa University of Technology Institute of Thermal Machinery Armii Krajowej 19C, 42-201 Czestochowa, Poland e-mail:
[email protected] 2
Sasol Technology R&D 1 Klasie Havenga Road, Sasolburg PO Box 1, Sasolburg 1947, South Africa e-mail:
[email protected] ABSTRACT Combustion of three Polish and one South African bituminous coal particles in air versus O2/CO2 mixtures with oxygen concentrations in the range from 21% to 60% vol. was conducted at temperature of 850°C in a 12 kW bench-scale CFB combustor. Combustion in air was proceeded at ~50˚C higher centre temperatures and was slightly shorter in time compared to combustion in O2/CO2 mixture with 21% vol. O2. Larger heat capacity of CO2 compared to that of N2 also retards the ignition of volatiles in O2/CO2 mixtures with 21% O2. However, when the concentration of oxygen in O2/CO2 mixtures is larger than 30%, the ignition time decreases and surface and centre temperatures increase significantly with increasing O2 content. INTRODUCTION Nowadays, greenhouse gases emissions from coal-fired systems, particularly CO2, become more and more important. Oxy-fuel combustion is one of the promising options for power generation with carbon dioxide capture. This technology can reduce significantly emissions of NOx and improve the thermal efficiency of the combustion process through the reduction of flue gas volume. In the oxy-fuel combustion, coal particles are burnt in a mixture of pure oxygen and recycled flue gas. Because nitrogen is eliminated from the oxidizing gas, the flue gas leaving the combustion chamber is highly enriched in CO2 which means that the combustion process takes place in an O2/CO2 environment. Partial recycling of flue gas helps to control the flame temperature in the combustion chamber. Extensive studies in both pilot-plant and lab scales have pointed out the pronounced influence of gas composition (air versus O2/CO2) on coal combustion performance. The heat transfer and temperature distribution in a furnace are greatly affected by the large specific heat capacity of CO2. Coal ignition is delayed in O2/CO2 in comparison to in O2/N2 with the same O2 concentration. To match the flame/particle temperature in air, a large amount of O2 in CO2, typically around 30%, is required. Coal conversion rate, char properties, and reactivity are also affected by the replacement of air with an O2/CO2 mixture, Zhang et al. (1). Buhre et al. (2) and Toftegaard et al. (3)
summarized the literature on oxy-fuel combustion of pulverized coal and discussed a number of operational concerns, including ignition, heat transfer, environmental issues and flame stability. Oxy-fuel combustion has now been well studied for pulverized coal combustion, but to date has received relatively little attention for oxyfuel circulating fluidized bed combustion (CFBC), Jia et al. (4). Work in this field has been conducted by: Foster Wheeler Energia Oy and VTT (5), ALSTOM (6), CANMET (4) and Czestochowa University of Technology (7). In the present work, oxy-fuel CFB combustion tests were conducted in a 12-kW bench-scale CFB combustor. The main objective of this study is to investigate the combustion behaviour of three Polish and one South African bituminous coal particles, in air and O2/CO2 mixtures, in terms of particle temperature profiles, ignition time, devolatilization time and the total combustion time. EXPERIMENTAL Oxy-CFB Combustor Oxy-fuel combustion tests were conducted in a 12-kW bench-scale CFB combustor shown schematically in Figure 1. The bench-scale CFBC consists of a combustion chamber (1), a cyclone (2) a downcomer (3) and a loop seal (4). The electricallyheated rectangular combustion chamber (riser), 680×75×35 mm, is the main component of the unit. The front wall of the riser is made of transparent quartz through which the combustion process can be directly observed.
Fig. 1. Schematic diagram of the experimental apparatus for oxy-CFB combustion 1-combustion chamber, 2-cyclone, 3-downcomer, 4-loop seal, 5-coal particle, 6-insulation, 7-drain valve, 8-preheater, 9-card, 10-computer, 11-temperature measurement and control system, 12-gas cylinders, 13-air compressor, 14-pressure regulators, 15-rotameters, 16-valves, 17-mixer, 18-gas analyser, 19-ventilation duct, T1–T3-S-type thermocouples
Particles of silica sand between 100 and 400 μm, with d50=210 μm (d50, median sand particles diameter, represents the size at which 50%, of the sand particles, by weight, are smaller than the specified diameter), constitute the inert bed (see Figure 2 for the particle size distribution). Total mass of circulating solids is 0.3 kg. The gases to make up gas mixtures are supplied from cylinders (12) to a mixer (17) and then transferred via a preheater (8) directly into the combustion chamber. Flow rates of gases are controlled by valves (16) and measured by rotameters (15). During combustion tests, the superficial gas velocity was kept at a constant level of about 5 m/s. The temperature was held at 850°C by means of microprocessor thermoregulators (11). S-type thermocouples (T1–T3) measured the temperature at three different levels inside the combustion chamber with an accuracy of ±2°C. A single coal particle (5) was introduced into the combustion chamber and positioned stationary in the bed. To measure the temperatures in the centre and at the surface of the coal particle a special stand was constructed. It provides a support for two S-type thermocouples. The tip of the first thermocouple was located inside the particle, while the second thermocouple measured the surface temperature and served as a basket in which the coal particle was places. The thermocouples were connected via a card (9) to a computer (10) in order to record the temperature measurements. Ignition time and devolatilization time were measured by stopwatch with an accuracy of 0.1s. The intraparticle temperature, the surface temperature, ignition time and devolatilization time were measured simultaneously. The experiments were carried out in air (base case) and mixtures of O2/CO2 with oxygen concentrations in the range from 21% to 60% vol. Video and digital cameras were used to record the progress of combustion. a)
b)
Cumulative mass fraction, %
100 90 80 70 60
50 40 30
d50 = 210 μm ρs = 2620 kg/m3
20 10 0 0
100
200
300
400
500
600
700
dp, μm
Fig. 2. The bed material (silica sand): a) particle size distribution, b) SEM picture Coals Tested Particles of three Polish and one South African bituminous coal were used in this study. Table 1 shows proximate, ultimate and petrographic analyses of these coals. Spherical 10-mm particles were produced from coal lumps through mechanical grinding.
Table 1. Proximate, ultimate and petrographic analyses of the coals tested Coal Proximate analysis (%, air-dried basis) Volatile Matter Moisture Ash Fixed carbon Calorific value (HHV), MJ/kg Ultimate analysis (%, dry, ash-free basis) Carbon Hydrogen Nitrogen Sulphur Oxygen (by difference) Petrographic analysis, % Vitrinite Liptinite Inertinite Mineral matter
A Polish
B Polish
C Polish
D South African
30.9 2.7 2.4 64.0 32.38
30.9 4.3 8.2 56.6 31.31
28.9 10.1 11.1 49.9 24.66
23.7 3.8 25.0 47.5 22.44
83.73 4.59 1.34 0.33 10.01
82.97 5.14 1.28 0.68 9.93
75.94 4.59 1.48 2,17 15.82
81.90 3.44 2.44 2.71 9.51
52 9 39 0
88 2 6 4
59 12 21 8
14 0 68 18
RESULTS AND DISCUSSION Results of proximate and petrographic analyses (Table 1) reveal that ash and inertinite (maceral that is less reactive than vitrinite) contents in the South African are much higher than those in Polish coals. Therefore, it can be expected that the combustion behaviour of these coals may differ significantly. Thus, the main objective of our study was to investigate the combustion behaviour of these coals, in air and O2/CO2 mixtures, in terms of particle temperature profiles, ignition time, devolatilization time and the total combustion time. Figure 3 shows temperatures measured at the surface and in the centre of C and D coal particles burned at 850˚C in air and in O2/CO2 mixture with 21% vol. O2. In both cases, after an initial delay, the centre temperature exceeds the surface temperature and stays approximately 100˚C during the course of combustion. Lower surface temperatures can be explained by intensive heat transfer between burning coal particles and bed material. Combustion in air proceeded at ~50˚C higher centre temperatures and was slightly shorter in time compared to combustion in O 2/CO2 mixture with 21% vol. O2. b) coal D 1400
1200
1200
Temperature, C
Temperature, C
a) coal C 1400
1000 800 600 air - surface
400
air - centre 21%O2 + 79%CO2 - surface
200
1000 800 600 air - surface
400
air - centre 21%O2 + 79%CO2 - surface
200
21%O2 + 79%CO2 - centre
0
21%O2 + 79%CO2 - centre
0 0
200
400
600
800
Time, s
1000
1200
1400
0
200
400
600
800
1000
1200
1400
Time, s
Fig. 3. Temperature profiles for coal C (a) and coal D (b) combusted in CFB in air and 21%O2+79%CO2
As expected, the total combustion time, in both air and O2/CO2 mixture, for coal D was much longer (~90%) than that for more reactive coal C. Figure 4 illustrates the effect of oxygen content in O2/CO2 mixtures on surface and centre temperatures measured for coals C and D. For both coals the trends are similar; these temperatures increase significantly with increasing O2 concentration. In the case of coal D, the maximum difference between the centre and surface temperatures is larger than for coal C. b) coal D 1400
1200
1200
1000
1000
Temperature, C
Temperature, C
a) coal C 1400
800 600 30%O2 + 70%CO2 - surface
400
30%O2 + 70%CO2 - centre 60%O2 + 40%CO2 - surface
200
800 600 30%O2 + 70%CO2 - surface
400
30%O2 + 70%CO2 - centre 60%O2 + 40%CO2 - surface
200
60%O2 + 40%CO2 - centre
60%O2 + 40%CO2 - centre
0
0
0
100
200
300
400
0
500
100
200
300
400
500
600
700
800
900
1000
Time, s
Time, s
Fig. 4. Temperature profiles for coal C (a) and coal D (b) particles combusted in CFB in various mixtures of O2/CO2 Figure 5 shows the effect of gas composition on the ignition time of volatiles. Ignition time was characterized by the time required to achieve a visible flame. Larger heat capacity of CO2 compared to that of N2 retards the ignition of volatiles in O2/CO2 mixtures with 21% O2. However, when the concentration of oxygen in O2/CO2 mixtures is larger than 30%, the ignition time decreases with increasing O2 content. The trends are consistent with those found in the literature, Toftegaard et al. (3) and Molina and Shaddix (8). 30
Air 21%O2 + 79%CO2 30%O2 + 70%CO2 40%O2 + 60%CO2 50%O2 + 50%CO2 60%O2 + 40%CO2
Ignition time, s
25
20
24 19
16
15
13 11
10 5
11
9 5
6
8 6
4
3
2
5
4
9 7
6
5
3
10
4
1
0 Coal A
Coal B
Coal C
Coal D
Fig. 5. Ignition time for bituminous coal particles combusted in various atmospheres The influence of oxygen concentration in O2/CO2 mixtures on the devolatilization time is shown in Figure 6. Devolatilization time was the duration of the visible flame
(from ignition of volatile matter to the end of combustion of volatile matter). The devolatilization time decreases with increasing O2 concentration for coals A, B and C and increases for coal D. Different devolatilization behaviour in the case of coal D can be associated with differences in its internal structure (porosity and pore size) and lower reactivity.
Devolatilization time, s
65
Coal A
60
Coal B
55
Coal C Coal D
50 45 40 35 30 25 10
20
30
40
50
60
70
Oxygen concentration, %vol
Fig. 6. Devolatilization time for coal particles combusted in mixtures of O2/CO2 Figure 7 shows the influence of O2 concentration in O2/CO2 mixtures on the total combustion time. For all coals tested the total combustion time decreases with increasing oxygen concentration. The largest combustion times have been recorded for coal D. They are approximately 100% higher compared to those for coal C. Figure 8 shows images of A coal particle at different stages of combustion.
Total combustion time, s
1500
Coal A Coal B
1300
Coal C
1100
Coal D
900 700 500 300 100 10
20
30
40
50
60
70
Oxygen concentration, %vol
Fig. 7. Total combustion time for coal particles combusted in mixtures of O2/CO2 CONCLUSIONS Oxy-fuel tests were conducted for 10-mm three Polish and one South African bituminous coal particles in a 12 kW bench-scale CFB combustor at temperature of 850°C. The experiments were carried out in air (base case) and mixtures of O2/CO2 with oxygen concentrations in the range from 21% to 60% vol. Results of proximate and petrographic analyses reveal that ash and inertinite (maceral that is less
reactive than vitrinite) contents in the South African are much higher than those in Polish coals. Therefore the combustion behaviour of these coals was different in air and O2/CO2 mixtures. Combustion in air proceeded at ~50˚C higher centre temperatures and was slightly shorter in time compared to combustion in O 2/CO2 mixture with 21% vol. O2. As expected, the total combustion time, in both air and O2/CO2 mixture, for South African coal was much longer (~90%) than that for more reactive Polish coal C. Larger heat capacity of CO2 compared to that of N2 also retards the ignition of volatiles in O2/CO2 mixtures with 21% O2. These trends are consistent with those found in the literature. However, when the concentration of oxygen in O2/CO2 mixtures is larger than 30%, the ignition time decreases with increasing O2 content. Surface and centre temperatures increase significantly with increasing O2 concentration. The devolatilization time decreases with increasing O2 concentration for Polish coals A, B and C and increases for South African coal D. Different devolatilization behaviour in the case of coal D can be associated with differences in its internal structure (porosity and pore size) and lower reactivity. The total combustion time decreases with increasing oxygen concentration for all coals tested. air
21%O2 + 79%CO2
40%O2 + 60%CO2
60%O2 + 40%CO2
Heating and drying
Ignition of volatile matter
Combustion of volatile matter
Combustion of char
Fig. 8. Visualisation of A coal particle in CFBC in air and O2/CO2 mixtures
This paper included fundamental research and it is only the first step in studying and modeling of the oxy-fuel combustion process. The next step will be combustion of a portion of coal not only single coal particles. It will allow us to answer a question how a specific coal influences operation of oxy-CFB combustor like temperature profile in combustion chamber, real particles distribution of fluidized bed (fragmentation and erosion processes) and emission of pollutants. Next research will also answer a question how much oxygen in mixture of O2/CO2 is needed to have similar conditions like in the air combustion. Concentration of oxygen in O2/CO2 mixture will differ for various types of coal. It is very important for design and operation of oxy-fuel CFB units. ACKNOWLEDGMENT This work was supported by the Polish Ministry of Science and Higher Education from sources for science in the years 2009-2010 under Research Project No. N N513 396336. The support is gratefully acknowledged. NOTATION dp d50 ρ
– particle diameter, μm – median particle diameter, μm – density, kg/m3
REFERENCES 1.
2. 3. 4. 5.
6. 7. 8.
Zhang L., Binner E., Chen L., Qiao Y., Li C.Z., Bhattacharya S., Ninomiya Y.: Experimental Investigation of the Combustion of Bituminous Coal in Air and O2/CO2 Mixtures: 1. Particle Imaging of the Combustion of Coal and Char. Energy Fuels 24, p.4803-4811, 2010. Buhre B.J.P., Elliott L.K., Sheng C.D., Gupta R.P., Wall T.F.: Oxy-fuel combustion technology for coal-fired power generation. Progress in Energy and Combustion Science 31, p.283-307, 2005. Toftegaard M.B., Brix J., Jensen P.A., Glarborg P., Jensen A.D.: Oxy-fuel combustion of solid fuels. Progr. in En. and Comb. Scien 36, p.581-625, 2010. Jia L., Tan Y., Anthony E.J.: Emissions of SO2 and NOx during Oxy-Fuel CFB Combustion Tests in a Mini-Circulating Fluidized Bed Combustion Reactor. Energy Fuels 24, p.910-915, 2010. Kuivalainen R., Eriksson T., Hotta A., Sacristan A.S.B., Jubitero J.M., Ballesteros J.C., Lupion M., Cortes V., Anthony B., Jia L., McCalden D., Tan Y., He I., Wu Y., Symonds R.: Development and Demonstration of Oxy-fuel CFB Technology. Proc. of the 35th International Technical Conference on Clean Coal & Fuel Systems, Florida, USA, 2010. Fiveland W.: Advanced Combustion Technology: Oxy-Firing to Enable CO2 Capture. 2nd Workshop Intern. Oxy-Combustion Res. Network, USA, 2007. Czakiert T., Sztekler K., Karski S., Markiewicz D., Nowak W.: Oxy-fuel circulating fluidized bed combustion in a small pilot-scale test rig. Fuel Processing Technology 91, p.1617-1623, 2010. Molina A., Shaddix C.R.: Ignition and devolatilization of pulverized bituminous coal particles during oxygen/carbon dioxide coal combustion. Proc. Combust. Inst. 31(2), p.1905-12, 2007.
FLUIDIZED BED MEMBRANE REACTOR FOR STEAM REFORMING OF HIGHER HYDROCARBONS: MODEL SENSITIVITY M.A. Rakib*, J.R. Grace and C.J. Lim University of British Columbia, Vancouver, Canada V6T 1Z3 * Corresponding author (
[email protected]), currently with SABIC T&I, Riyadh 11551, Saudi Arabia ABSTRACT A fluidized bed membrane reactor (FBMR) was built and operated at temperatures <600°C to reform higher hydrocarbons like propane and heptane. A two-phase reactor model is utilized to simulate the FBMR with hydrogen withdrawn from both phases. The superficial gas velocities in the reactor change because of variations in molar flow due to reaction and hydrogen withdrawal through the membranes, as well as variations in temperature, pressure and cross-sectional area. Sensitivity studies show that the FBMR performance is primarily controlled by chemical equilibrium and hydrogen permeation through the membranes, while being insensitive to errors in accurately characterizing the chemical kinetics and hydrodynamics. INTRODUCTION The increasing demand for hydrogen as an industrial commodity and future energy carrier has intensified research on alternative methods of hydrogen production. Steam reforming is the favoured process for making hydrogen (1). Natural gas is the most widely used feedstock due to its widespread availability. Liquid hydrocarbon feedstocks like naphtha and LPG can be used when natural gas is not available. Higher hydrocarbons like LPG, naphtha, kerosene and diesel are preferable for making hydrogen in mobile applications. In addition, feedstock flexibility is desirable for refineries, which often have seasonal surpluses of some of these hydrocarbons. The main reactions for steam reforming of higher hydrocarbons are (1): CnHm + nH2O D nCO + (n + m/2)H2 DH°298 = 499 kJ/mol (for n = 3) (1) CH4 + H2O D CO + 3 H2 DH°298 = 206 kJ/mol (2) CO + H2O D CO2 + H2 DH°298 = - 41 kJ/mol (3) CH4 + 2H2O D CO2 + 4H2 DH°298 = 165 kJ/mol (4) The yield of hydrogen is limited by the reversibility of reactions (2)-(4). In general, reaction (1) is irreversible for higher hydrocarbons, but methane appears in the reaction mixture due to the reversibility of reactions (2) and (4). In traditional steam reformers, this is overcome by operating at high temperatures. However, elevated temperatures lead to limited pressure ratings of the containment material, catalyst sintering, and high steam-to carbon ratios to minimize catalyst deactivation. A fluidized bed membrane reactor (FBMR) for steam reforming of natural gas is a potential low-temperature alternative to achieve high hydrogen yields by continuous shifting of equilibrium in the forward direction (2-4). An FBMR has been shown to be flexible; able to process different alkane hydrocarbons (CnHm) as the feedstock (5,6), leading to complete conversion of the hydrocarbons to produce maximum hydrogen: CnHm + 2nH2O ® nCO2 + (2n + m/2)H2
(5)
EXPERIMENTAL SETUP Steam reforming was conducted in a pressure vessel designed to withstand 10 bars gauge at a maximum temperature of 621°C. The dense catalyst bed is contained in a rectangular channel, 1.88 x 10-3 m2 and height of 1.87 m, with an extended circular cross-section, 4.26 x 10-3 m2, above this. Figure 1 portrays the FBMR pressure vessel. Vertical membrane panels (231.8 mm x 73.0 mm x 6.4 mm), with 25 µm thick Pd77Ag23 membrane foils bonded on either side, divide the rectangular channel into two equal sections. Six membrane panels could be inserted from alternate sides, supported on rectangular flange covers, and passed through vertical slits on the reactor wall. Consecutive membrane panels were separated by a vertical distance of 22 mm. There is a horizontal gap of 5 mm between the reactor wall and the unsupported end of the membrane panel. To evaluate the effects of the hydrogen withdrawal through the membranes, the reactor was also operated with no membrane panels, one membrane panel (fifth panel from the bottom), and six membrane panels, with dimensionally identical stainless steel dummy plates replacing the active membrane panels to keep the reactor internal geometry identical wherever the latter were not installed. The static bed height for all runs was 1.7 m. The FBMR was operated under different operating conditions with a feed of steam mixed with methane, propane or heptane, with varying steamto-carbon molar ratios. Experimental details are available elsewhere (5,6). The operating conditions for the runs compared with the simulations of this paper are given in Table 1. The reactor was heated by internal cable heaters along the height of the dense catalyst bed at all four corners of the rectangular channel, in addition to external band heaters in the semi-circular spaces between the successive side openings for inserting and supporting membrane panels. The small scale of the reactor, and heat losses, especially close to the flanges, made the temperature profiles strongly dependent on the heater distribution. Table 1: Experimental conditions for steam reforming of propane Feed Steam-to-carbon molar ratio Average temperature Reactor pressure Permeate pressure Total feed rate
Propane, Steam 5.0 500°C 600 kPa abs 25 kPa abs 0.614 mols/min
Figure 1: Fluidized bed membrane reactor showing six membranes for removal of pure hydrogen.
Commercial naphtha steam reforming catalyst particles, RK-212, of mean diameter from Haldor Topsoe A/S were crushed and sieved to a mean diameter of 179 µm.
2
REACTOR MODEL The FBMR was modeled as two phases in parallel: a high-density dense phase, containing most of the particles, and a low-density bubble phase including a small number of particles, with the solids volume fraction assumed to be: (6) fb = 0.001e b The minimum fluidization velocity was estimated from the correlation of Grace (7). Model assumptions are: (1) Steady state conditions. (2) Ideal gas law. (3) Catalyst temperature equal to local gas temperature. The axial variation in temperature is the same in each phase and follows the measured profile. (4) Catalyst internal mass transfer resistance ignored. (5) The membranes are infinitely selective, i.e. only H2 passes through them. (6) Catalyst deactivation is neglected. (7) The gas in both the dense and bubble phases are assumed to be in plug flow. (8) Visible bubble flow = flow in excess of that at minimum fluidization. For each phase, the mole balance equation has four components: (a) Reaction terms: The reactions are given by equations (1) – (4), with rate equations as listed in an earlier paper (8). (b) Interphase diffusional mass transfer: The interphase mass transfer coefficient is estimated by the correlation of Sit & Grace (9), with the effective diffusivity of gas components based on the average composition of the bubble and the dense phases, using the correlation of Wilke (10). The bubble size is estimated based on Darton’s equation (11). (c) Hydrogen permeation: The immersed membrane panels withdraw hydrogen from both phases, with the hydrogen flux governed by Sieverts’ equation (12): æ - EH 2 ö P ÷ PH ,j - PH ,m (7) QH 2 ,j = AP M 0 exp çç 2 2 ÷ δH 2 è RT ø (d) Interphase convection to maintain with the two-phase theory of fluidization. For this, at a given height, the flow required in the dense phase is written as: Qd , req = U mf A(1 - e b ) (8)
(
)
Hence, the volumetric bulk convective terms can be written as: when when
NC
å Fi ,d > Qd ,req , Qd ®b =
R.T P
å Fi ,d £ Qd ,req , Qb®d = Qd ,req -
i =1 NC i =1
R.T P
NC
R.T P
åF
i,d
i =1
R.T P
- Qd ,req , NC
åF i =1
i,d
,
Qb®d = 0
(9)
Qd ®b = 0
(10)
This way of maintaining the flow in the dense phase is consistent with CFD predictions for small particles (13). Thus, the mole balance equation for the separation side can be written as:
dFH 2, p dL
(
= a e b Qm H ,b + (1 - e b )Qm H , d 2 2
)
(11)
where α, (≤1), the overall permeation effectiveness factor, is an adjustable parameter to fit the simulated hydrogen permeation yields to the experimental results.
3
For the ith species in Bubble Phase:
dFi, b dh
= fb r p A
NR
åg ij R j,b + kiq abe b A(Ci, d - Ci,b ) - ae b Qmi,b j =1
dQbd dQdb Ci , b + Ci , d dh dh
with i = C7H16, C3H8, CH4, H2O, CO, CO2, and H2
(12)
th
A similar mole balance is written for the i species in the dense phase. The flux of all components other than hydrogen through the membranes is zero. To predict the reactor offgas composition, it is also necessary to account for catalytic reaction in the freeboard. An amount of catalyst equivalent to 0.8 mm of static bed depth was assumed to be distributed uniformly in the freeboard region, based on least squares error minimization with respect to the experimental concentrations of methane, CO2, and H2 in the reformer off-gas for all of the experimental runs. The freeboard was then modeled as a single-phase dilute suspension, with
dFi , fb dh fb
NR
= f fb r p Aå g ij R j , fb
(13)
j =1
The following quantities are calculated to assess the reactor performance: Permeate hydrogen yield =
molar flow of pure H2 extracted via membranes
(14)
molar flow of hydrocarbon in feed stream
Total hydrogen yield = molar flow of pure H2 extracted via membranes + molar flow of H2 in retentate stream (15) molar flow of hydrocarbon in feed stream
Carbon oxides yield = Methane yield =
total molar flow of CO and CO in retentate stream 2 molar flow of carbon (in hydrocarbon) in feed stream
molar flow of methane in retentate stream molar flow of carbon (in hydrocarbon) in feed stream
(16) (17)
RESULTS AND DISCUSSION Temperature profiles for the experiments, greatly affected by the heater arrangement, are shown in each plot below. The model was used (8) to simulate previous experimental results (5,6), and good agreement was achieved with α as the only adjustable parameter. Fitting of experimental data to the model for hydrogen permeation through the membranes gave a = 0.248 as a correction to a membrane permeation equation provided by the suppliers of the membrane panels. The decline in permeation flux relative to that in tests in a permeation test rig without particles was likely due to formation of a thin coating of catalyst fines on the membrane foils. Figure 2 plots the superficial gas velocities for propane steam reforming with six membrane panels. Four factors caused the variations in superficial velocity: (1) Intermittent abrupt variations of the superficial gas velocity due to changes in cross-sectional area in the spaces between adjacent membrane panels. (2) The superficial gas velocity is affected by the temperature variations. (3) The steam reforming reactions lead to a net increase in molar flow. This caused steep increases in U near the FBMR entrance, where propane conversion is completed. Subsequent methanation (reverse reactions from equations (2) and (4)) can result in the opposite trend.
4
Superficial velocity also varies owing to hydrogen removal via the membranes.
Superficial Gas Velocity (m/s)
Local Temp. (oC)
(4)
570 520 470 420
0.08
0.06
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
Height above Distributor (m)
Figure 2: Gas superficial velocities for propane steam reforming.
The sensitivity of the reactor model was tested (8) to understand the relative importance of the various phenomena inside the FBMR, as well as the effect of uncertainties in estimating parameters in the model. The bulk mass transfer was found to be negligible compared to the other three components of the mole balance equations. Similar observations apply to steam methane reforming in an FBMR (14). The kinetic rate constants for all reactions included were first varied upwards and downwards by a factor of 10 compared with those based on the literature values. Some variations in performance occurred near the reactor entrance, affected mainly by the propane steam reforming kinetics. However, over most of the height, there was very little difference in the local yields of methane, carbon oxides or hydrogen. To test the importance of hydrodynamics and interphase mass transfer, Figure 3 shows the reactor performance with the interphase mass transfer coefficient increased and decreased a factor of 10 relative to those from the Sit and Grace (9) correlation. The higher coefficient results in almost immediate transfer of propane from the bubbles to the dense phase, whereas, slower mass transfer retains more propane in the bubbles, delaying its conversion, Since methane is an intermediate component, it appears more slowly in the reactor, and its overall conversion is also delayed. With delayed transfer of hydrogen from the dense phase, where it is produced, to the bubble phase, where negligible hydrogen is produced, the net removal of hydrogen via membranes is reduced. While the effects of tenfold upward and downward changes in the interphase mass transfer coefficient are discernible, these effects are not very significant. Hence, interphase mass transfer, while not a negligible factor, plays a secondary role with respect to overall reaction. Since the bed hydrodynamics mostly enter the model through the interphase mass transfer, one may also conclude that accurate portrayal of bed hydrodynamics is of secondary importance for this process and for the operating conditions investigated. To explore the effect of permeation capacity variation of the membranes, the membrane permeation effectiveness factor was set at a = 0.15, 0.248 ( fitted value), and 0.35. As shown by Figure 4, the FBMR performance depends strongly on the hydrogen permeation through the membranes.
5
Local Temp. ( o C)
570 520 470
C)
570
o
420
Local Temp. (
520 470 420
0.7
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
0.5 0.3 0.7
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
0.7
a = 0.15 a = 0.2484 a = 0.35
0.5 0.3 0.7
0.3 0.1
a = 0.15 a = 0.2484 a = 0.35
0.5
Methane Yield
Methane Yield
0.5
0.9
Carbon Oxides Yield
Carbon Oxides Yield
0.9
1.00
0.50
0.1
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
0.25
Yield
Propane Conversion
0.3
0.75
7 5
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
3 1 10
a = 0.15 a = 0.2484 a = 0.35
3 1
10 8
Yield
8
6
2
6 4
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
2
Total H
Total H
2
Yield
Permeate H
Yield
5
Permeate H
7
2
2
0.00
4
a = 0.15 a = 0.2484 a = 0.35
2 0
0 0.0
0.5
1.0
1.5
2.0
Height above Distributor (m)
Figure 3: FBMR performance with variations in interphase mass transfer coefficient
2.5
0.0
0.5
1.0
1.5
2.0
2.5
Height above Distributor (m)
Figure 4: FBMR performance with variations in permeation effectiveness factor.
CONCLUSIONS A fluidized bed membrane reactor is modeled to simulate its performance for producing hydrogen from propane. Model sensitivity studies show that the chemical kinetics are fast enough at all temperatures tested for their role to be insignificant in determining the FBMR performance. The interphase diffusional mass transfer rate is somewhat more significant in affecting reactor performance, but again plays a secondary role. From these results, it is evident that the FBMR performance is primarily controlled by chemical equilibrium and by the rate of hydrogen permeation through the membranes. Hence the model is sensitive to accurately characterizing
6
the chemical equilibrium and hydrogen permeation, but insensitive to the chemical kinetics, interphase mass transfer and hydrodynamics, at least for the temperature range of interest (450-550°C). ACKNOWLEDGEMENT Financial support from the Canada Foundation for Innovation and the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged. M.A.R. thanks NSERC for a two-year doctoral scholarship. NOTATION
ab A
AP C i ,b
Specific surface area of gas bubbles (m2/m3) Cross-sectional area of bed (m2) Membrane permeation area per unit length of membrane (m2/m) Molar concentration of species i in bubble phase (mol/m3)
C i ,d
Molar concentration of species i in dense phase (mol/m3)
E H2
Activation energy for permeation (J/mol)
Fi ,b
Molar flow rate of species i in bubble phase (mol/s)
Fi,d
Molar flow rate of species i in dense phase (mol/s)
Fi , fb
Molar flow rate of species i in freeboard (mol/s)
h
h fb
Vertical coordinate measured from distributor (m) Vertical co-ordinate from dense catalyst bed surface (m)
k iq
Interphase mass transfer component for species i (m/s)
m, n NC, NR P
Stoichiometric constants (-) Number of components, reactions (-) Pressure (Pa) Partial pressure of species i (bar)
Pi PH 2 ,b , PH 2 ,d Partial pressure of hydrogen in bubble, dense phase (atm)
PH 2 , p
Partial pressure of hydrogen on permeate side (atm)
PM 0
Pre-exponential factor for permeation (mole/(m.min.atm0.5))
Qbd Qdb
Cross-flow from bubble to dense phase per unit length (m3/(m.s)) Cross-flow from dense to bubble phase per unit length (m3/(m.s))
Qd ,req
Flow requirement for dense phase to prevent de-fluidization (m3/s)
Qmi ,j
Membrane permeation rate of species i for j phase (mol/(m.s))
R Rj
Universal gas constant (J/mol/K) Rate of jth reaction (mol/kg catalyst/s) Superficial gas velocity (m/s) Thickness of hydrogen selective membranes (m)
U
dH eb fb , f d f fb 2
Volume fraction of catalyst bed occupied by bubble phase (-) Bed volume fraction occupied by particles in bubble, dense phase (-) Volume fraction of freeboard occupied by solid particles (-)
7
g ij rp
Stoichiometric coefficient of component i in j th reaction
DH
Heat of reaction (kJ/mol)
Density of catalyst particles (kg/m3)
Subscripts b, d fb i in j m
j
Bubble, dense phase Freeboard Species i At reactor inlet Reaction j Membrane side Phase j
REFERENCES 1. Rostrup-Nielsen, J., Catalytic steam reforming. In Catalysis Science and Technology, Andersen, J.R.; Boudart, M., Eds. Springer-Verlag: 1984; pp 1-117 2. Adris, A.M.; Lim, C.J.; Grace, J.R., The fluidized bed membrane reactor system: A pilot scale experimental study. Chem. Eng. Sci. 1994, 49, 5833-5843. 3. Boyd, T.; Grace, J.; Lim, C.J.; Adris, A.M., Hydrogen from an internally circulating fluidized bed membrane reactor. Int. J. Chem. React. Eng. 2005, 3, A58. 4. Patil, C.S.; Annaland, M.V.S.; Kuipers, J.A.M., Fluidised bed membrane reactor for ultrapure hydrogen production via methane steam reforming: Experimental demonstration and model validation. Chem. Eng. Sci. 2007, 62, 29893007. 5. Rakib, M.A.; Grace, J.R.; Lim, C.J.; Elnashaie, S.S.E.H., Steam reforming of heptane in a fluidized bed membrane reactor. J. Power Sources 2010, 195, 57495760. 6. Rakib, M.A.; Grace, J.R.; Lim, C.J.; Elnashaie, S.S.E.H.; Ghiasi, B., Steam reforming of propane in a fluidized bed membrane reactor for hydrogen production. Int. J. Hydrogen Energy 2010, 35, 6276-6290. 7. Grace, J.R., Fluidized-bed hydrodynamics. In Handbook of Multiphase Systems, Hetsroni, G., Ed. Hemisphere: Washington, 1982; pp 8-5 - 8-64. 8. Rakib, M.A.; Grace, J.R.; Lim, C.J.; Elnashaie, S.S.E.H., Modeling of a fluidized bed membrane reactor for hydrogen production by steam reforming of hydrocarbons. Ind. Eng. Chem. Res., 2011, In Press. 9. Sit, S.P.; Grace, J.R., Effect of bubble interaction on interphase mass transfer in gas fluidized beds. Chem. Eng. Sci. 1981, 36, 327-335. 10. Wilke, C. R., Diffusion properties of multicomponent gases. Chem. Eng. Prog. 1950, 46, 95-104. 11. Darton, R.C.; Lanauze, R.D.; Davidson, J.F.; Harrison, D., Bubble growth due to coalescence in fluidized-beds. Trans. IChemE 1977, 55, 274-280. 12. Sieverts, A.; Zapf, G., The solubility of deuterium and hydrogen in solid palladium. Zeitschrift für Physikalische Chemie 1935, 174, 359-364. 13. Li, T.W.; Mahecha-Botero, A.; Grace, J.R., Computational fluid dynamic investigation of change of volumetric flow in fluidized bed reactors. Ind. Eng. Chem. Res. 2010, 49, 6780-6789. 14. Adris, A.M.; Lim, C.J.; Grace, J.R., The fluidized-bed membrane reactor for steam methane reforming: Model verification and parametric study. Chem. Eng. Sci. 1997, 52, 1609-1622.
8
CRITICAL EVALUATION OF EULER-EULER AND EULERLAGRANGIAN MODELLING STRATEGIES IN A 2-D GAS FLUIDIZED BED F. Hernández-Jiméneza, J.R. Thirdb, A. Acosta-Iborraa, C.R. Müllerb a Universidad Carlos III of Madrid, Department of Thermal and Fluid Engineering, ISE research group. Av. de la Universidad, 30, 28911, Leganés, Madrid, Spain . b ETH Zürich, Institute of Energy Technology, Laboratory of Energy Science and Engineering, Leonhardstrasse 27, 8092 Zürich, Switzerland. Abstract Two-phase granular systems are commonly encountered in industry, and fluidized beds are particularly important due to their excellent heat and mass transfer characteristics. Here, we critically evaluate the differences between two modelling strategies, Euler-Euler and Euler-Lagrangian models. Euler-Euler simulations were performed using MFIX and an in-house code was used for Euler-Lagrangian simulations. A 2D bed of width, height and transverse thickness of respectively, 0.2 m, 0.5 m and 0.01 m, served as a test case. The settled bed height was H 0 = 0.2 m. Particles of density ρ = 1000 kg/m³ and diameter dp = 1.2 mm were fluidized with air. The drag-law proposed by Benyahia et al. (10) was used in both models. Comparison between the simulation results was based on both instantaneous and time-averaged properties. A particular focus of this study was the influence of the coefficients of restitution and friction on the simulation results. INTRODUCTION Fluidized beds have various applications in industry, such as fluid catalytic cracking (FCC), gasification and combustion of coal, and Fischer−Tropsch synthesis (Kunii and Levenspiel (1)). Despite the fact that fluidized beds have been used in industry since the 1920s and good progress has been made in numerical simulations using two-fluid (Gidaspow (2)) or discrete element models (Tsuji et al. (3)), some aspects of fluidized bed hydrodynamics, such as bubble splitting, are still far from fully understood. Numerical modelling of fluidized beds has advanced significantly over the last two decades, the most popular modelling approaches being the Euler-Euler and EulerLagrangian models. The Euler-Lagrangian approach combines an Eulerian description of the fluid-phase with a Lagrangian particle simulation, in which the trajectory of each particle is calculated based on Newton's second Law. The gassolids interaction is computed through semi-empirical closure models (Deen et al. (4)). Although very promising, the Euler-Lagrangian approach is very computationally expensive and is, therefore, currently unable to simulate the large number of particles encountered in medium- or large-scale fluidized beds. In the Euler-Euler approach (Gidaspow (2), Wachem and Almstedt (5)) the particulates and the fluid phase are treated as inter-penetrating continua (two-fluid model). As in
the case of the Euler-Lagrangian approach, two-fluid simulations of fluidized beds require closure relationships for the gas-solids interaction. However, since the particle motion is not modelled in detail, the two-fluid model also requires closure relationships for the particle-particle interactions. These closure relationships may be empirical in nature or may be derived from theoretical relations that are linked to the kinetic theory of granular gases (Gidaspow (2)). The aim of this work is to compare the Euler-Euler and Euler-Lagrangian approaches for a specific test case, consisting of a two-dimensional (2D) gas fluidized bed. In addition, the effect of parameters such as the inter-particle and particle-wall coefficients of friction, and the coefficient of restitution, will be studied for both models. DEM APPROACH A Discrete Element Model (DEM) has been constructed based on the work of Tsuji et al (3), which combines the discrete element model of Cundall and Strack (6) to simulate the particulate phase, with the volume-averaged Navier-Stokes equations for the fluid phase, as derived by Anderson and Jackson (7). For each particle, the linear and angular momenta are governed by Newton’s second law:
d vs V p d s =−V p ∇ p vg− vs F c Ip =Tp dt 1− g dt s , V p , vg , , F c , Tp and I p are the mass, linear and angular where m p , vs , mp
velocities of the particle, the particle volume, the velocity of the gas phase, the interphase momentum exchange coefficient, the force and torque resulting from the collision of the particles, and the moment of inertia of the particle, respectively. To model the collision between contacting particles the soft-sphere approach was used, in which the particles are allowed to overlap by a small amount, δ. For the fluid the volume-averaged continuity and Navier-Stokes equations are given by Anderson and Jackson (7):
∂ g g ∇ · g g vg =0 ∂t
∂ g g vg ∇ · g g vg ²=− g ∇ p−∇ · g g − F p g g g ∂t is the viscous stress tensor and F p is the rate of exchange of
here, g momentum between the particulate and the fluid phases. The fluid was assumed to be Newtonian. The rate of momentum exchange between the particulate and fluid phases was calculated by adding up the fluid forces acting on the N p individual particles in a fluid cell of volume V cell : Np
F p=
Vp V cell
∑ vg− vs n=1
1−g
TWO-FLUID MODEL APPROACH The two-fluid model, based on the conservation equations of mass, momentum and granular temperature, was solved using the MFIX code (Multifluid Flow with Interphase eXchanges) (Syamlal et al (8), Benyahia et al (9)). The kinetic theory of
granular gases was used for the closure of the solids pressure stress terms. The governing equations can be summarized as follows. Mass conservation of the gas (g) and solid (s) phases:
∂ g g ∇ · g g vg =0 ∂t
∂ s s ∇ · s s vs =0 ∂t
Momentum conservation of the gas and solids phases:
∂ g g vg ∇ · g g vg =−g ∇ p∇ · g g g g −K gs vg −vs ∂t ∂ g g vg ∇ · s s vs =−s ∇ p−∇ p s∇ · s s s g K gs vg −vs ∂t where g , s , g , s , vg , vg correspond to gas and solids volume fraction, gas and solids density and gas and solids velocity respectively, p is pressure, g , s the g is the acceleration due to the stress tensors for gas and solids respectively, K gravity and gs is the gas-solids momentum exchange coefficient. The balance equation for the granular temperature, Θ, is given by:
3 ∂ ∇ · s s vs =− p s I s :∇ vs ∇ · k ∇ −−3K gs 2 ∂t s s where − p s I s : ∇ vs is the generation of Θ by the solids stress tensor, ∇ ·k ∇ is the diffusion of Θ energy, is the collisional dissipation of energy and 3K gs is the transfer of kinetic energy between phases. A second order accurate scheme (Superbee) was used to discretize the convective derivatives in the balance equations. NUMERICAL SIMULATIONS The gas-fluidized bed studied was of 0.2 m width, 0.01 m transverse thickness and 0.5 m height, filled with spherical particles of density ρ = 1000 kg/m³ and diameter d p = 1.2 mm. The static bed height was H0 = 0.2 m and the gas inlet velocity was U = 0.6 m/s, corresponding to U/Umf = 2 . Several cases were studied to evaluate the effect of the properties of the particles and walls. Table 1 summarizes the cases studied in this work. The parameters that are varied are the inter-particle and particle-wall coefficients of friction, and the restitution coefficient. Case 1 is taken to be the base case incorporating commonly used parameters. The inlet has been modelled as a homogeneous velocity inlet and the outlet as a constant pressure outlet for both models. The computational domain for the two-fluid model simulations comprised 57 × 141 × 8 cells in the x- (width), y- (height) and z- (thickness) directions, respectively. This creates a mesh with a 3.5 mm cell size, which is below 10 particle diameters and ensures grid-independent results. A partial slip boundary condition was applied at the walls of the fluidized bed, with a partial slip coefficient of Ф=0.6. The fluid computational domain for the DEM model comprised 58 × 148 × 3 cells in the x-, y- and z- directions. The fluidized bed contained 265650 particles. Interactions between particles are modelled using a damped Hertzian spring with an E-modulus of 1.2×106 N/m2. Both models use the drag law proposed by Benyahia et al. (10). For the time-averaged results, 40 seconds are employed for the EulerEuler model and 28 seconds for the Euler-Lagrangian model.
Model
Two-fluid model
DEM
Parameter
Case 1 Case 2 Case 3 Case 4
Restitution coefficient
0.9
0.9
0.9
0.5
Coefficient of friction between particles
0.57
0.1
0.57
0.57
Walls boundary conditions
Partial slip
Partial slip
Free slip
Partial slip
Restitution coefficient
0.9
0.9
0.9
0.5
Coefficient of friction between particles
0.57
0.1
0.57
0.57
Friction between particles and walls
0.57
0.1
0
0.57
Table 1: Simulation parameters for the two-fluid and DEM simulations. RESULTS DISCUSSION Figure 1 shows instantaneous snapshots of the solids volume fraction for case 1 simulated using the two models. Both snapshots were taken after the transient fluidization that occurs during start-up. The snapshots show the characteristic pattern of 2-D beds: small and narrow bubbles appearing in the bottom of the bed, and bigger and less numerous circular bubbles reaching the bed surface. Here bubbles are located where the solids volume fraction reaches a value close to zero. The solids volume fractions presented have been averaged over the entire bed thickness.
Figure 1. Instantaneous snapshot of the bed showing αs: a) two-fluid model; b) DEM. Figures 2 and 3 show the solids volume fraction averaged over the width and thickness of the bed, as a function of time, for the two fluid model and DEM respectively. Both models show the creation of small, slow-moving bubbles close to the distributor and the coalescence and eruption of faster bubbles at distances around y = 0.1 m above the distributor. Figure 4a and 4b show the power spectra obtained from the data presented in Figures 2 and 3 at two different heights, y = 0.005 m (close the distributor) and y = 0.217 m (close to the top of the bed). For both models the maxima in the power spectra occur at higher frequencies at y = 0.005 m than at y = 0.217 m. This is expected because bubbles coalesce as they rise through the bed, leading to a reduction in the number of bubbles that cross a horizontal section.
It should be noted, however, that the frequency depicted in Figure 4 is a 'bubble coherence frequency' because several bubbles may cross a horizontal section at any instant of time. Therefore, the frequencies of Figure 4 cannot be interpreted as a single bubble frequency unless the size of the bubble is comparable to the bed width, i.e. near the bed surface. The bubble coherence frequency near the distributor defines the principal frequency of bubble formation. This frequency of bubble formation is qualitatively similar in both models, namely ~ 6 Hz. The principal frequencies at y = 0.217 m, i.e. the frequency of bubble eruption, are also similar for both simulation strategies. In particular, Figure 4 shows that the peak of the power spectrum at y = 0.217 m occurs at ~ 2.5 Hz, which is in agreement with the bed oscillation frequency due to bubble eruption given by Baskakov et al. (11), f = g/ H o /=2.23 Hz .
s
Figure 2. XZ-averaged αs, two-fluid model. Case 1.
s
Figure 3. XZ-averaged αs, DEM. Case 1. The average solids volume fraction in an x-z plane located at y = 0.22 m is shown in Figure 5 for the two fluid model and DEM. This y position is close to the freeboard of the bed. Figure 5a reveals that the amplitude of the fluctuations in the solids volume fraction is smaller in the two-fluid simulations when compared with the DEM results. This is expected since the two-fluid approach tends to smear the distinction between the bubble and particulate phase. For the DEM a sharper, and more realistic, transition between the bubble and particulate phase is modeled. Figure 5b plots the dominant frequencies, extracted as the peak-frequency from the power spectra, as a function of vertical position, y. In both simulation strategies, the
profiles of peak-frequencies are in good agreement. In particular, high frequencies (around 6 Hz) are observed near the distributor and there is a transition zone in 0.05 m < y < 0.1 m. Near the freeboard both simulations show a region where the frequency stabilizes due to big bubbles passing at a frequency around 2.5Hz. Figures 2 and 3 reinforce this observation: both figures indicate a large number of slow-moving bubbles close to the distributor and a smaller number of faster bubbles after the transition zone.
Figure 4. Power spectra of XZ-averaged αs, a) two-fluid model, b) DEM. y = 0.005 m (solid line); y = 0.217 m (dash line). Case 1.
Figure 5. a) XZ-averaged αs at a height of 0.22 m b) Vertical profile of peak frequency for XZ-averaged αs: two-fluid model (solid line); DEM (dash line). Case 1. The effect of the wall friction is demonstrated in Figures 6a and 6b. Here, the solids velocity and solids volume fraction, averaged with respect to time and transversal thickness, are presented at a height y = 0.01 m for both simulation strategies. In case 1, both models predict very similar magnitudes for the solids velocity, however the bed hydrodynamics are substantially different. In the two-fluid model there are two preferential bubble paths at a distance of ~ 0.05 m away from the lateral walls (Figure 6b). On the other hand in the DEM there is only one path in the middle of the bed. For case 3, which employs a free slip condition at the walls, the time-averaged velocities within the bed are an order of magnitude greater than those obtained for case 1. Furthermore, there are substantial discrepancies between the two-fluid and DEM results obtained for case 3: the two-fluid model predicts velocities that are approximately twice those predicted by the DEM and also predicts higher solids volume fractions, i.e. smaller bed expansion.
Finally, Figure 7 compares the solids velocity in both models for cases 1, 2 and 4. For the two-fluid model only small changes in the profile of the solids velocity can be observed for the case that the coefficients of friction and restitution are reduced. However, for the DEM the coefficient of friction plays an important role. Reducing the coefficient of friction in the DEM from 0.57 to 0.1 leads to a substantial increase in the time-averaged solids velocities, as seen in Figure 7b. Furthermore, it is observed that for the two-fluid model reducing the coefficient of restitution decreases the gradient along x-direction in the solids velocity profile; only very small variations were observed in the DEM results.
Figure 6. Time averaged values of a) solids vertical velocity and b) αs at a height of 0.1 m: two-fluid model case 1 (solid line); DEM case 1 (dash line); two-fluid model case 3 (dot line); DEM case 3 (dash-dot line).
Figure 7. Time averaged values of solids vertical velocity at a height of 0.1 m, a) two-fluid model b) DEM: case 1 (solid line); case 2 (dash line); case 4 (dot line). CONCLUSIONS DEM and two-fluid model simulations of 2D bubbling fluidized beds have been compared in this work. For the base case, in which the coefficient of friction was set to 0.57, both simulation strategies yield time-averaged velocities with similar magnitudes, however the agreement of the characteristics of the velocity profiles is disappointing, especially for the case using zero friction for the particle-wall contact. The two-fluid model predicts that the highest velocities within the bed are located at a distance of ~ 0.05 m away from the side wall, whereas the DEM predicts that the highest velocities are located at the centre of the bed. For both simulation techniques, the time-averaged solids volume fractions show minima that are coincident with the maxima in the velocity profiles. This is consistent with the
hypothesis that bubbles preferentially pass through these locations. The behaviour of bubbles has been examined by averaging the solids volume fraction over horizontal cross sections of the bed. Both the two-fluid and DEM simulations predict a coherence bubble frequency of 6 Hz close to the distributor and a frequency of 2.5 Hz close to the surface of the bed. Furthermore, the influence of the coefficients of friction and restitution on the simulation results has been investigated. The time-averaged solids velocity and solids volume fraction profiles suggest that, within the range examined here, the behaviour of the bed, using two-fluid and DEM models, is relatively insensitive to the particle-particle coefficient of friction and, for the DEM results, to the coefficient of restitution. However, setting the particle-wall coefficient of friction to zero was found to have a pronounced effect on the particle motion within the bed. Under these conditions both models were found to give time-averaged solids velocities an order of magnitude larger than those predicted for simulations with particle-wall friction. Nevertheless, further work is required to establish the causes of the discrepancies between the DEM and two-fluid models highlighted here. Acknowledgement This work has been co-funded by the Spanish Government (Project DPI2009 -10518) and the Autonomous Community of Madrid (Project S2009/ENE-1660). References 1. D. Kunii, O. Levenspiel, Fluidization Engineering: Butterworth-Heinemann: Newton, MA, 1991. 2. D. Gidaspow, Multiphase flow and Fluidization: Continuum and kinetic theory descriptions; Academic Press: San Diego, CA. 1994. 3. Y. Tsuji, T. Kawaguchi, T. Yanaka, Discrete particle simulations of 2-dimensional fluidized-beds. Powder tech. 77 (1993) 79-87. 4. N.G. Deen M. van Sint Annaland, M.A. van der Hoef, J.A.M. Kuipers, Review of discrete particle modelling of fluidized beds. Chem. Eng. Sci. 62 (2007) 28-44. 5. B.G.M. van Wachem, A.E. Almstedt, Methods for multiphase computational fluid dynamics. Chem. Eng J. 96 (2003) 81-98. 6. P.A. Cundall, C.D.L. Strack, A discrete numerical-model for granular assemblies. Geotechnique 29 (1979) 47-65. 7. T.B Anderson, R. Jackson, A fluid mechanical description of fluidized beds. Ind. Eng. Chem. Fund 6 (1967) 527-539. 8. M. Syamlal, W. Rogers, T.J. O'Brien, MFIX Documentation: Theory guide, U.S. department of Energy (DOE), Morgantown Energy Technology Center, Morgantown, West Virginia, 1993. 9. S. Benyahia, M. Syamlal, T.J. O'Brien, Summary of MFIX equations 2005-4, 2007. 10. S. Benyahia, M. Syamlal, T.J. O'Brien, Extension of Hill-Koch-Ladd drag correlation over all ranges of Reynolds number and solids volume fraction. Powder Tech. 162 (2006) 166-174. 11. A. P. Baskakov, V. G. Tuponogov, N. F. Filippovski. A study of pressure fluctuations in a bubbling fluidized bed. Powder Tech. 45 (1986) 113-117.
DESCRIPTION OF PRESSURE FLUCTUATIONS IN A CIRCULATING FLUIDIZED BED BY STATISTICAL ANALYSIS Roelof L.J. Coetzer, Andre Mostert and Adam Luckos Sasol Technology, Research and Development 1 Klasie Havenga Road, Sasolburg, 1947 South Africa ABSTRACT In this paper we evaluate different methods for statistically analyzing the variability in pressure fluctuations measured at three locations in an 80-mm-ID, 5-m-tall CFB model operated with natural rutile particles and air at ambient conditions. The methods evaluated are the Shannon entropy, Fischer information matrix together with kernel density estimation, and an estimation of the magnitude of the pressure amplitudes. The accuracy of the different methods is estimated by the bootstrap method. We illustrate how informative statistics from these methods can be used to quantify the effect of the process variables on fluidization at different bed locations. Depending on the interest of the experimenter, the method and statistic can be selected which explains fluidization operation most accurately. INTRODUCTION The continuous monitoring of gas-solid fluidized-bed reactors is an important issue in the industrial practice because of the complex dynamical behaviour characterizing these systems. Failures and difficulties experienced in the operation of fluidized-bed reactors are usually attributed to an un-sufficient understanding of the physics of gassolid fluidization (1). In particular, our knowledge on systems with irregularly shaped particles with wide size distributions operated at higher gas velocities (in the turbulent regime and in the fast fluidization regime), elevated temperatures and pressures is still relatively poor. In the last three decades, several techniques have been developed to describe the dynamic phenomena that take place within the bed. Among these techniques, the pressure fluctuation measurements are the most popular owing to their low costs and direct relation to the bed dynamics (2). Pressure measurements sampled at frequencies 20–1000 Hz can be used to describe important fluidized-bed characteristics such as the quality of fluidization, size and frequency of bubbles, transition from bubbling to turbulent regime and minimum fluidization velocity.
In our previous studies, several important process parameters such as critical velocities, distributions of solid concentration and pressure fluctuations were measured in a CFB cold model (3–6). The variability in the pressure fluctuations were previously evaluated by using the standard deviation and the coefficient of variation (7). In this paper, we extend the analysis of the data by applying Shannon entropy and Fisher information matrix (8). Our analysis should establish the relationship between two entities, (1) process variables, and (2) pressure fluctuations at different levels in the riser. This relationship will provide a basis for controlling the operation of a CFB reactor. TEST APPARATUS AND PROCEDURE Measurements of pressure fluctuations were carried out in the riser of the 80-mm, 5-m tall CFB cold model made of transparent PVC. Data acquisition units recorded the signals (sampled at 200 Hz) from three pressure transducers located at the bottom (0.2 m above the distributor), in the middle (at 2.46 m), and at the top of the riser (at 4.47 m). All tests were conducted with air at ambient conditions. At each stable condition signals were collected over a period 40 s, an interval producing 8192 (i.e. 213) pressure readings. The solid material used was natural rutile (TiO2). Its particles fall into group B of Geldart’s classification. They are sub-rounded, fine (80–165 µm) and dense (4085 kg/m3). A more detailed description of the apparatus and test procedure can be found in an earlier paper on the subject (4). RESULTS The concentration of solids in the riser adopts a ‘C’ shape, which becomes less pronounced as the solid-circulation rate, Gs, at a given superficial fluidizing velocity, U, decreases (4–6). Concomitant with the decrease is a shift in solids concentration at each point in the riser to lower values, and move to greater solids concentrations at the top of the riser than at the bottom. A higher suspension density at the top of the riser—a consequence of the rebounding of particles from the plate closing the top of the riser— is a phenomenon that is well known in small-scale (<0.2 m) CFB units (9−12). In tests at a comparatively low U (3.5 m/s), higher solids concentrations span the top half of the riser (~2.5 m). The suspension density decreases gradually from the top of the riser. At higher fluidizing velocities (U=4.9 and 7.4 m/s) higher solids concentrations are confined to a shorter length of the riser (≤1 m), and the profile is much steeper. As Jin and co-workers showed (13), higher superficial gas velocities increase the velocity of upwardly moving particles, which increases the exchange of momentum between particles moving in opposite directions. As the influence of upwardly moving particles grows stronger (at high gas velocities), the region of momentum exchange shortens. A shortening of the region of higher suspension densities would accompany this change. Pressures in the riser fluctuate over a range of about 1.2 kPa. The patterns of fluctuations along the length of the riser are similar and synchronized (6). Pressure fluctuations are irregular, and their peak intensities vary. The distribution of pressure at each tap is skewed towards higher values; fluctuations are more pronounced above the mean than below it. Expressing the amplitude of fluctuations over a scanned interval by the standard deviation of pressure readings, one can readily see that (7):
• The average amplitude of pressure fluctuations increases with increasing Gs and U. • The average amplitude of fluctuations is largest at the top of the column; the
amplitudes of fluctuations at the middle and bottom of the column are similar—yet the solids concentration is similar at the top and bottom, and different from that in the middle. • There is an exception to this pattern at high superficial gas velocities. The measurements may be problematic as solids flow bordered on being unstable. Figure 1 depicts the pressure fluctuations (normalized with respect to the mean pressure) in the middle of the riser at U=3.57 m/s and Gs=11.52 kg/m2·s. It can be observed that pressure fluctuates in a fairly narrow band about zero.
800
800
600
600
400
400
Pressure, Pa
Pressure, Pa
Figure 2 shows the pressure fluctuations in the middle of the riser at U=3.57 m/s and Gs=19.24 kg/m2·s. It is immediately evident that pressure fluctuates significantly more about zero for the higher Gs of 19.24 kg/m2·s compared with the lower Gs of 11.52 kg/m2·s depicted in Fig. 1. Specifically, the variability in the pressure fluctuations is significantly higher for Gs=19.24 kg/m2·s compared with Gs=11.52 kg/m2·s.
200 0 -200
200 0 -200
-400
-400
-600
-600
-800
-800 0
5
10
15
20
25
30
Time, s
35
40
0
10
20
30
40
Time, s
Fig. 1. Pressure fluctuations (normalized) Fig. 2. Pressure fluctuations (normalized) in the middle of the riser at U=3.57 m/s in the middle of the riser at U=3.57 m/s and Gs=19.24 kg/m2·s and Gs=11.52 kg/m2·s However, the statement of “significantly higher” variability should be substantiated and statistically quantified. Therefore, statistical methods need to be applied to quantify the difference in variability. This becomes even more relevant when more than two reactor conditions are being evaluated at different values. In the study, three different superficial gas velocities and three different solids fluxes were evaluated for differences in pressure fluctuations along the riser. STATISTICAL METHODOLOGY The superficial gas velocity and solids flux are referred to as the reactor variables. Three values or conditions for each reactor variable were tested and the pressure fluctuations recorded. The pressure fluctuations will be evaluated in terms of its variability at the different reactor conditions (see Table 1). Note that pressure fluctuations were measured at three different positions in the fluidized bed i.e. at the bottom, middle and top of the riser.
The standard deviation has been used in a previous study by the authors for quantifying the variability of pressure fluctuations (7). In this paper, we introduce the Shannon entropy, HX, and Fisher information matrix, IX, as methods for evaluating the variability of pressure fluctuations. Table 1. Experimental conditions Condition
U, m/s
Gs, kg/m2·s
1 2 3 4 5 6 7 8
3.57 3.57 3.57 4.17 4.17 4.17 4.78 4.78
11.52 14.78 19.24 17.79 21.75 26.76 29.65 34.17
The Shannon entropy is a well-known tool for investigating the degree of disorder in dynamical systems. The Shannon entropy will be high if the degree of disorder in the system is high. Let x = ( x1 , x 2 , K, x N ) T denote the sample of pressure fluctuations. The ¯
Shannon entropy (differential entropy) is given by the following formula (8): ∞
H X = − ∫ f ( x) log f ( x) dx
(1)
−∞
where f (x) is the probability density function (PDF) of x. The Fisher information is a tool that can be used to accurately describe the behavior of dynamic systems and to characterize the complex signals generated by these systems (8). The Fisher information is defined as follows: 2 ∞ (2) ∂ dx I X = − ∫ f ( x) ∂x f ( x) −∞ In this paper we approximate the PDF with the kernel density estimation technique. Specifically, the PDF is approximated by: (3) 1 N x − si fˆN ( x, λ ) = K ∑ λN i =1 λ where si is the i-th pressure measurement, K is the kernel function and λ is the chosen bandwidth (14). A popular choice for the kernel is the Epanechnikov kernel given by: (4) 3 (1 − u 2 ) if u ∈ [−1,1] K (u ) = 4 0 if u ∉ [ −1,1] The kernel, K(u), in eq. (4) is a continuous non-negative and symmetric function satisfying
∫
∞
−∞
K (u )du = 1 . The bandwidth, λ, is estimated by minimizing the integrated
mean squared error (IMSE):
IMSE (λ ) =
∫ ( f ( x) − fˆ ∞
N
)
2
( x, λ ) dx
−∞
(5)
Faraway and Jhun (14) proposed the estimation of the optimal λ with the bootstrap. As an example, Fig. 3 illustrates the estimation of the PDFs for the different conditions of U and Gs in Table 1 at the bottom of the reactor.
Fig. 3. PDF estimations for conditions 1 and 2 in Table 1 at the bottom of the reactor STATISTICAL RESULTS Figure 4 shows the relationship between the calculated Shannon entropy, HX, and the U/Gs ratio for all conditions. The HX increases with an increase in U and Gs. Several observations can be made from this figure; first, there is a significant drop in the entropy with an increase in the U/Gs ratio for all locations in the reactor. Second, there exists a significant difference between the entropy at the top of the reactor compared to that at the middle and bottom of the reactor. Therefore, there is significantly less variation or chaotic behavior of the fluidization process at the top of the reactor compared to the middle and bottom of the reactor. Figure 5 shows the relationship between the IX and the U/Gs ratio. Similar trends to the HX are observed for the IX; IX decreases for an increase in the U/Gs ratio. Again, the IX illustrates that there is significantly less variation at the top of the reactor compared to the middle and bottom of the reactor. 7.0
0 top middle
6.5
-2
IX×10
Hx
4
bottom
6.0
5.5
-4
-6
top middle bottom
5.0 0.10
0.15
0.20
0.25
0.30
0.35
3
U/Gs, m /kg
Fig. 4. Shannon entropy, HX, as a function of U/Gs
-8 0.10
0.15
0.20
0.25
0.30
3
U/Gs, m /kg
Fig. 5. Fischer information, IX, as a function of U/Gs
0.35
250 top middle
σ, Pa
200
bottom
150
100
50 0.10
0.15
0.20
0.25
0.30
0.35
3
U/Gs, m /kg
Comparing with previous work (7), Fig. 6 shows the relationship between the standard deviation, σ, and U/Gs. Trends are similar to the HX and the IX; the σ decreases with an increase in U/Gs. Again, σ illustrates that there is significantly less variation in the fluidization process at the top of the reactor compared to the middle and bottom sections of the reactor.
Fig. 6. Standard deviation, σ, as a function of U/Gs
The differences in the measures of variability, i.e. HX, IX, σ (also referred to as responses) between the locations in the reactor, as well as the relationship with U/Gs, can be quantified by constructing a statistical model to predict the measure of variability as a function of the location and U/Gs (15). Specifically, the linear model is of the form: (6) yi = τ i z i + βx where yi is the response variable for the i-th point or height in the reactor where the pressure measurements were made, i.e. top, middle or bottom, respectively; τi is the effect of the i-th location in the reactor on the response, and zi is the dummy variable indicating the location in the reactor, i.e. zi = 1 for the i-th location and zero otherwise. In Eq. (6) x is the U/Gs ratio, and τi (i=1, 2, 3) and β are parameters to be estimated from the least squares minimization. Table 2. Parameters for linear models describing HX, IX and σ Measure
IX
HX
σ
Location
bottom middle top bottom middle top bottom middle top
Regression information Intercept, τ 0.00032 0.00031 0.00005 7.447 7.403 6.803 293.62 288.09 211.44
Slope, β -0.00216
Standard error 0.00007
p-value 0.0001
-4.873
0.13
0.0001
-594.74
20.05
0.0001
Table 2 shows the results of fitting the Eq. (6) to the three measures of variability, HX, IX and σ. The standard error of the model, i.e. the square root of the sum of squares of errors divided by the number of observations minus 2, is very small for each model (note the standard error of the model is in the same units as the response). The p-value indicates the significance of the model, i.e. a p-value smaller than 0.05 indicates a 95%
confidence in the relationship between the variables and the measure of variability. Clearly, all three models in Table 2 are highly significant. 7.0
2 bottom middle top
IX ×10
4
6.5
HX
bottom middle top
0
6.0
-2
-4
5.5 -6
5.0 0.10
0.15
0.20
0.25
0.30
0.35
3
-8 0.10
0.15
0.20
0.25
0.30
0.35
3
U/Gs, m /kg
U/Gs, m /kg
Fig. 7. Predicted entropy, HX, as a function of U/Gs
Fig. 8. Predicted Fischer information, IX, as a function of U/Gs
Equation (6), with the parameters in Table 2, can be used to predict the 200 measures of variability for a given U/Gs 150 and location in the reactor. As an illustration, Fig. 7 shows the predicted 100 entropy, HX, as a function of U/Gs and the location in the reactor. The 95% 50 confidence bands are also indicated for 0 each model. The 95% confidence band 0.10 0.15 0.20 0.25 0.30 0.35 U/G , m /kg indicates the area about the regression Fig. 9. Predicted standard deviation, σ, as a model that captures the true relationship with 95% confidence. Note the function of U/Gs significant difference between the relationship at the top of the reactor to the relationship at the bottom and middle of the reactor. Figures 8 and 9 show the predicted Fischer information, IX, and the standard deviation, σ, respectively as a function of U/Gs and the location in the reactor. Similar trends to the entropy are observed. 250
σ, Pa
bottom middle top
3
s
CONCLUSIONS The Shannon entropy analysis and Fisher information matrix analysis of pressure fluctuations are promising techniques to study the dynamics of gas-solid flow in fluidized beds. In this study, effects of two operating variables namely the superficial gas velocity, U, and solids circulation flux, Gs, on the Shannon entropy and Fisher information at different bed locations were determined. Both HX and IX follow the same trend; they decrease with increasing U/Gs. This result suggests that the fluidization process in CFBs with lower solids concentrations can be less chaotic than that in CFBs with high solids concentrations. The analysis also shows less variation at the top of the riser compared to its middle and bottom sections.
Simple linear statistical models have been developed to quantify the relationships between the measures of variability and U/Gs. The standard errors for these models are very small indicating that they are highly significant. The results confirm that both techniques can be use as tools to understand the complex dynamic behavior of gassolid flows in CFBs. ACKNOWLEDEMENT Experimental data utilized in this paper were collected in the CFB cold model at Mintek. NOTATION Gs HX IX K(u) N
– solids flux, kg/m2·s – Shannon entropy – Fisher information – kernel function – number of pressure measurements
U x β λ σ
– gas velocity, m/s – pressure vector – parameter in Eq. (6) – band width – standard deviation
REFERENCES 1. M. Hartman, O. Trnka and K. Svoboda (2009). Ind. Eng. Chem. Res. 48: 6830. 2. L. de Martin, J. Villa Briongos, J.M. Aragon and M.C. Palancar (2010). Chem. Eng. Sci. 65: 4055. 3. A. Luckos and P. den Hoed (2004). Ind. Eng. Chem. Res. 43: 5645. 4. A. Luckos and P. den Hoed (2005). In K. Cen (ed.) Circulating Fluidized Bed Technology VIII: 231–238. International Academic Publishers, Beijing. 5. A. Luckos and P. den Hoed (2005). In A. Luckos and P. Smit (eds.) IFSA 2005, Industrial Fluidization South Africa: 345–355. SAIMM, Johannesburg. 6. A. Luckos, Q.G. Reynolds and P. den Hoed (2007). In X. Bi, F. Berruti and T. Pugsley (eds) Fluidization XII: 145–152. ECI, New York. 7. R.L.J. Coetzer, A. Luckos and P. den Hoed (2008). In T. Hadley and P. Smit (eds.) IFSA 2008, Industrial Fluidization South Africa: 428-444. SAIMM, Johannesburg. 8. L. Telesca, R. Caggiano, V. Lapenna, M. Lovallo, S. Trippetta and M. Macchiato (2008). Physica A 1: 6. 9. U. Lackermeier and J. Werther (2002). Chem. Eng. Process. 41: 771. 10. C.M.H. Brereton and J.R. Grace (1993). In A.A. Avidan (ed.) Circulating Fluidized Bed Technology IV: 137–144. AIChE, New York. 11. Q.-Y. Zheng and H. Zhang (1995). In Fluidization VIII, Preprint, Vol. 2: 657–664. Progep, Toulouse, France. 12. T. Pugsley, D. LaPointe, B. Hirschberg and J. Werther (1997). Can. J. Chem. Eng. 75: 1001. 13. Y. Jin, Z. Yu, C. Qi and D.-R. Bai (1988). In M. Kwauk and D. Kunii (eds.) Fluidization’ 88 Science and Technology: 165–173. Science Press, Beijing. 14. J.J. Faraway and M. Jhun (1990). J. American Statistical Association 85: 1119. 15. D.C. Montgomery (2001). Design and analysis of experiments. John Wiley & Sons, Inc.
TIME-RESOLVED X-RAY TOMOGRAPHY OF A FLUIDIZED BED OF GELDART A PARTICLES R.F. Muddea , Q. Ricouxb , E.C. Wagnera and J.R. van Ommenb a Kramers Laboratorium voor Fysische Technologie (
[email protected]) b Department of Chemical Engineering Delft University of Technology Pr.Bernhardlaan 6, 2628 BW Delft, The Netherlands. ABSTRACT This paper discusses the influence of fines on the size of bubbles moving through a 23 cm ID fluidized bed of Geldart A particles imaged with an X-ray Tomographic Scanner. In earlier work [1], the bubble distribution in a fluidized bed of Geldart B particles was shown. The current study using Geldart A particles is more challenging to the reconstruction algorithm, since there are more bubbles, and they are smaller in size. We study the influence of adding fines (i.e. particles ≤ 45 micron) to the system. When adding a mass fraction of fines of 24%, we find a decrease of the average bubble of 40% of the size for the original powder, in line with earlier results from pressure probes and optical probes [2]. We find that the entire distribution of the bubble sizes shifts to smaller values.
INTRODUCTION Many catalyzed gas reactions are performed in fluidized beds. The small particle size in combination with the low pressure drop are favorable for several applications. For higher gas through puts, part of the gas will flow through the fluidized bed in the form of void or bubbles. Although these bubbles are important when it comes to mixing of the powder, they have negative effects on the conversion as not all gas is now always in contact with the catalytic material. As shown in [2], a reduction of the bubble size by 40% may lead to an increase in conversion by 50%. Various papers have discussed the possibilities of reducing the bubble size. For instance, in [3] different gas injection strategies were employed. In the same article, the authors used electrical fields to influence the interparticle forces. In both cases a reduction of the bubble size was observed. The addition of fines is another possibility to improve the conversion [4], [5]. Beetstra et al. ([2]) reported experiments on the influence of fines on the fluidization behaviour of porous alumina particles. They added up to 50% fines to their powder and find, from pressure fluctuation analysis, a decrease of the average bubbles size with increasing mass fraction of fines. However, for the lower superficial gase velocities they investigated an increase was observed. Nevertheless, the trend was clear: for higher superficial gas velocities the average bubble size decreased monotonically with increasing mass fraction fines. From video images taken in a pseudo 2D bed, the authors found a clear shift in the bubble size distribution: for the higher gas velocities the entire distribution moved to smaller values with a smaller standard deviation (in absolute values). In the present paper, we report experiments using our three source fast X-rays tomographic scanner and a 23cm fluidized bed. Tomographic systems have the advantage of being nonintrusive, while at the same time giving bubble properties such as size and shape. A few different tomographic techniques are in use, such as Electrical Capacitance Tomography (ECT, [6], [7]) and nuclear densitometry. ECT has the advantage of being fast and relatively cheap. However, its spatial resolution is problematic due to the soft nature of the electric fields used. The trajectory of the field lines is influenced by the gas-solid distribution, leading to a more cumbersome reconstruction ([8]). On the other hand, γ and X-rays are hard fields that, in prin1
ciple provide a better spatial resolution. They are, however, costly and due to inherent noise usually slow ([9], [10], [11], [12]). Recently, the time resolution of X-ray tomographic scanners has been considerably improved ([13], [14], [15], [16]). We study the bubble size distribution of a Geldart A powder to which in steps a certain mass fraction of fines (particle size smaller than 45µm) is added. We reconstruct individual bubbles and measure their size. At the same time, we obtained the bubble frequency, by counting the number of bubbles passing the measuring plane in a given time interval.
EXPERIMENTAL SETUP A 23cm inner-diameter perspex tube (wall thickness 5mm, height 71cm) is partially filled with powder. As the base case, the column is filled with alumina particles (mean particle size = 146 µm; particle density 1300 kg/m3 ). These particles form a Geldart A powder close to the border of the A-B line. The minimum fluidization velocity is 0.008m/s. Additionally, different amounts of fines (also alumina) have been added during the experiments creating powders with a mass fraction of 0%, 8%, 16% and 24% of fines. The total amount of particles in the bed was kept at 14.5 kg, which gives a packed bed height of 40cm. The particle size distributions are given in table 1; the minimum fluidization velocity for each powder in table 2. cumm. fraction (%) base material (µm) fines (µm)
10 104 19.3
25 121 26.0
50 143 33.7
75 169 42.1
90 195 50.3
mean 146 34.0
Table 1: Particle size distribution. fraction fines (%) Umf (cm/s)
0 0.8
8 0.6
16 0.4
24 0.12
Table 2: Minimum fluidization velocity for the powders investigated. Air (room temperature) is used as fluidization gas. It is either supplied via a wind box to the bed through a porous plate (sintered bronze, pore size 30 to 70µm, plate thickness 7mm), or via a single capillary (diameter 3.7mm) inserted in the bottom part of the bed. In the former case, the wind box is filled with 2mm glass beads to generate an as even as possible air flow into the bed. In the latter case the bed is not gassed through the porous plate: all air comes out of the capillary. On top of the bed an expanded section is placed (diameter 44cm, height 79cm) acting as a free board. The air leaving the set up first passed a filter before being vented to the surroundings. A schematic of the fluidized bed with the densitometer is given in Fig.1. fluidized bed
upper & lower detector array 3 X-ray source 1
upper detector array
X-ray source 2 fluidized bed
er low y 2 & er arra upp ector det
lower detector array
up det per & ect or lowe arr ay r 1
X-ray source 1
X-ray source 3
Figure 1: Schematic of the X-ray scanner. Left: side view showing for clarity only one source with its two detector arrays, right: top view.
2
The X-ray sources are placed at 120◦ around the fluidized bed. Each source generates a fan beam that is detected by two sets of 32 sensors placed opposite of the source. By using 2 sets, two measuring planes are formed located at 34.1cm and 36.0cm above the distributor plate, resp. The distance between the planes is 1.9cm. Comparing the raw signals allows estimation of the velocity of passing bubbles and from that the bubble size in the vertical direction. The distance from the X-ray-target to the center of the fluidized bed is 68.5cm, from the X-ray target to the detectors is 138.6cm. The fluidized bed is placed on a table that can be moved up and down in the vertical direction. This way we can adjust the height of the measuring plane in the column. The X-ray sources used are manufactured by Yxlon International GmbH. The maximum X-ray energy is 150keV. We operate the sources at a low energy flux with a tube current less than 1mA. The detectors all consist of a CdWO4 scintillation crystal optically coupled to a PIN photo diode. They are manufactured by Hamamatsu (type: S 1337 - 1010BR). Their crystal size is 10mm*10mm*10mm. A plastic casing in the form of an arc holds two horizontal arrays of 32 detectors. The curvature of the array is such that the distance to the focal point of the source is equal for all detectors. The data are collected simultaneously at a sampling frequency of 2500Hz, both for the upper and lower ring of detectors. The measured data are read out using a 12 bit ADC-card. The entire process is controlled via a workstation that sends out the trigger signals to the sources and reads out the detectors.
TOMOGRAPHIC RECONSTRUCTION Measuring Principle Each detector measures the attenuation of the X-rays in a thin cone. In the reconstruction this is treated as a thin line. If such a line of mono-energetic γ- or X-rays is transmitted through a closed system containing a particle-gas two-phase mixture, the number of photons registered per second, R, follows from the Lambert-Beer law: R = R0 exp [− ((1 − α)µp + αµg ) d]
(1)
where R0 is the number of photons registered per second when the system is in vacuum; µp and µg denote the linear absorption coefficient of the particle and gas phase; α is the volume fraction of the gas phase; d is the inner diameter of the system. It should be noted that the attenuation characteristics of the fluidized bed wall is incorporated in R0 . X-ray sources generate a wide spectrum of X-ray energies. The attenuation coefficients, µp and µg , are functions of the photon energy E. Therefore, a two-point calibration is inadequate, as the absorption of the photons of lower energy is much faster. In order to deal with this, we have calibrated all detector individually by placing various amounts of packed powder in between a source and its detectors. The amount of packed powder is put inside the column to ensure that the calibration encompasses the effects of the walls of the column. Furthermore, the calibration includes a completely empty and a completely filled bed. These two calibration points provide the upper and lower limit of the signal. We fitted a smooth function of the form Acal + Bcal · exp (−x/Ccal ) to the data, with x the distance traveled by the beam through the powder phase. For every detector, an individual curve is obtained, see [13]. Reconstruction We use an Algebraic Reconstruction Technique. Although significantly slower than e.g. Linear Back Projection, algebraic methods offer more flexibility in terms of limited data sets and are more appropriate for the CT configuration system under consideration here. Detailed accounts of reconstruction techniques can be found in [17], [18] or [19]. We use the calibration curve to convert the measured line-averaged attenuation into a line averaged solids fraction. We will reconstruct the solids fraction α(x, y) in a pixel representation of the cross-section of the fluidized bed. Here, we use a square pixel array of 55*55 pixels. The cross-section of the fluidized bed exactly fits in this square. All pixels outside the circle have a 3
solids fraction of zero. For a given ray, traveling through the object, the total solids fraction on the line, pi , referred to as ray sum, can be estimated as p˜i =
N X
Wik αk
(2)
k=1
with αk the pixel-based value of the solids fraction distribution and Wik the weighing factor for pixel k for the ith ray through the object. We use a linear weighing matrix W . Hence, the weighing factor Wik is the length of ray i through pixel k. To reconstruct the image we need to solve the unknown pixel-averaged solids fraction αk from eq.(2) for M different rays on N pixels. As the number of independent measurements is only 3*30=90 and the number of unknown pixels is easily 1000 or more, the problem is ill-posed. Moreover, there will be measuring noise in the data. The Algebraic Reconstruction Techniques (ART, see e.g. [17]) are designed to minimize the mismatch between the data p~ and W · α ~ . They are iterative methods that solves p~ = W · α ~ . We use the Simultaneous Algebraic Reconstruction Technique (SART) [20]. Instead of sequentially updating the pixels on a ray-by-ray basis, SART simultaneously applies to a pixel the average of the corrections generated by all rays. This offers a reduction in the amplitude of the salt and pepper noise that is usually present in ART. However, it goes at the expense of the computation time. Still, pepper and salt noise will be present in the images. This can be reduced by using a so-called one-step-late algorithm (see [21]). We invoked an algorithm based on the median root function (suggested first by [22]). It effectively removes pepper and salt noise, but keeps the edges of larger objects sharp enough, see e.g. Mudde et al. [13] for full details about the reconstruction algorithm.
RESULTS AND DISCUSSION X-ray sources are inherently noisy. This noise corrupts accurate reconstruction of the images of the bubbles as they are cut out by the measuring plane. Therefore, we have averaged the data first over a period of 10 data points and moved in steps of 10 time steps through the data series for each new reconstruction. This obviously reduces the time resolution of the images from 2500 frames per second to 250 frames per second. For the reconstructions a square pixel array of 55*55 pixels is used. The fluidized bed just fits in this square. Each pixel has a size of 4.188mm. Pixels outside the cross-sectional area of the fluidized bed are during the reconstruction set to zero. Fig.2 shows a quasi-3D reconstruction of the bubbling bed. The reconstructed images (at a frame rate of 250Hz) are stacked vertically. Note that the data are gathered in one plane: the vertical axis in the figure is time rather than the vertical coordinate (the two horizontal axis are in pixels; the unit for the vertical axis is 4ms). Note that the rectangular box shown in Fig.2 does not coincide with the outer dimensions of the fluidized bed, which is cylindrical in shape: a horizontal cross-section of the fluidized bed is a circle that exactly fits in a horizontal cut through Fig.2. Bubble travelling along the wall region of the fluidized bed, therefore look like moving internal through this box. However, they actually tough the wall of the bed. It is noted here, that with the X-ray scanner and the reconstruction algorithm used we can confidently detect bubbles with a diameter of 2.5cm and above. Smaller bubbles can still be detected, but not with the same accuracy as the bigger ones. The influence of noise and small misalignments as well as the uncertainty in the calibration data put a lower limit to the diameter of the bubbles. To estimate the accuracy of small bubbles we performed a test on static phantoms. For this, we filled the bed with the Geldart A powder and places hollow cylinders of various diameters in the bed. In the measuring planes these cylinders should be visible as circular voids. As this is a steady state experiment there is no motion blurring and the measured data should yield the best possible reconstructions. For a cylinder with a diameter of 1.28cm, i.e. an area of 1.29cm2 , we reconstruct an area of 1.03cm2 , thus a diameter of only 1.14cm. if we put in more cylinders at the same time, reconstruction of the smaller ones
4
Figure 2: Reconstructed images stacked to form a quasi 3D image of the bubbles in the bed. The rectangular box does not show the wall of the fluidized bed, but is the stacking of the square reconstruction grid used. becomes more problematic: occasionally ghost images of non-existing small cylinders show up. This is caused by noise in the data and by slight misalignment of the detectors. Note, that the value of each pixel is between 0 (fully packed) and 1 (no particles in pixel). To find the edge of teh bubbles, we used a threshold value of 0.5. Gas Jet Experiments Two types of experiments are performed. In the first one all air is sparged via a single capillary. This way a bubble train is generated. This has the advantage that we know a priori that only one bubble a time will be present in the reconstructed planes, making interpretation of the reconstructed solids distribution straight forward. The bubble diameter is obtained from the reconstructed images by taking the image in which a bubble shows its maximum area for each indimax −ymin vidual bubble. From that we estimate the bubble size according to Db = xmax −xmin +y . 2 Here, xmax , xmin denote the maximum, respectively minimum x-coordinate of all pixels comprising the bubble cross-sectional area, in the x-direction of the reconstruction grid. Similarly, ymax , ymin denote those in the y-direction. Subsequently, this bubble diameter is converted to centimeters using the known column diameter (23cm) and the number of grid points per direction (i.e. 55). Experiments are run at two different gas velocities: Ujet = 46.5 cm/s and 93cm/s, i.e. a superficial velocity of 1.20cm/s and 2.41cm/s, resp. The averaged bubble size is computed from 10 different runs, each of 5 seconds data collecting and separated by 15 minutes between two subsequent runs. In total at least 140 bubbles are detected and used to compute the average bubble size. The effect of the mass fraction of fines is shown in Fig.3(a). It is clear from the data that the bubble size significantly decreases when increasing the mass fraction fines. From the measurements, we also obtain the bubble frequency, i.e. the number of bubbles passing the measuring plane per unit time, see Fig.3(b) where we show the bubble frequency for the high velocity jet case. The bubble frequency increases when increasing the mass fraction of fines. However, in the case of jet gassing, the increase of the frequency is only 30% for the lower gas velocity and 50% for the higher gas velocity case. Fig.3(a) shows a decrease of 40% of the mean bubble diameter. Consequently, based on the mean size, the bubble volume is only 22% of the bubble volume at 0% fines. Hence, the visible bubble rate, calculated as
5
φb = fb Vb , decreases to 28% of the visible bubble rate without the addition of fines for the lower gas inlet velocity and to 33% for the higher gas velocity used in the experiments. Apparently, considerably more gas is moving through the powder phase when introducing fines in the system. It should, however, be noted here that the bed is not fully fluidized; a considerably amount of the dense phase is in the packed state. 5
10 4
jet, 93cm/s
Db (cm)
8 p, 10cm/s
4
3 (Hz) 2
2
1
6
0
p, 6cm/s
5
p, 10cm/s
fb
jet, 46.5cm/s
10 15 20 mass fraction fines (%)
jet, 93cm/s
0
25
5
10 15 20 mass fraction fines (%)
25
(b) Relative bubble size as a function of the mass fraction fines. Filled squares: jet Ujet = 93 cm/s, filled circles porous pate: Usup = 10 cm/s.
(a) Evolution of the bubble size as a function of the mass fraction fines: squares for the jet, black square Ujet = 93 cm/s, open square for Ujet = 46.5 cm/s; circles for the porous plate (denote by p), grey circle for Usup = 10 cm/s, open circle for Usup = 6 cm/s.
Figure 3: Average bubble size and bubble frequency. Fig.4 shows the bubble size distribution for Ujet = 46.5 cm/s for the original powder (0% fines) and for the 24% mass fraction fines case. Clearly the entire distribution has shifted to smaller values. Similar findings have been reported for a pseudo 2D bed, based on video images by [2]. For both cases shown in Fig.4 the standard deviation of the distribution is about 1.5 cm.
fraction (-)
0.4
24% 0%
0.3
0.2
0.1
0
1
2
3
4
5
6
7
8
9
10 11
12
bubble size (cm)
Figure 4: Bubble size distribution for two cases (Ujet = 46.5 cm/s): black bars 0% fines, white bars 24% fines.
Uniform Gassing Similar experiments as described in the previous section have been performed using the porous plate as gas distributor. Although we tried to get the gas distribution as uniform as possible of the cross-section of the distributor of the plate, we observed that there was a slight tilt in the spatial bubble distribution. In Fig.3(a) also the average bubble size for gassing via the porous plate is shown as a function of the mass fraction fines for two superficial gas velocities, Usup = 6.0 cm/s, 10.0 cm/s resp. Again, a clear decrease in bubble size is found. In this case the bubble size drops to only 60% of the original one. The values found are in reasonable 6
agreement with the Darton et al. equation [23], which predicts 5.7 and 4.5 cm diameter, respectively. However, the Darton et al. equation predict a slight increase in bubble diameter with increasing fines fraction (since the mimimum fluidization velocity is decreasing), while we find a strong decrease in bubble diameter. Fig.3(b) shows the increase in bubble frequency. For these conditions the increase in bubble frequency is much higher than for the jet-case. Consequently, the visible bubble rate drops less than in the jet experiments, but the drop is more relevant in this case, since the dense phase is fully fluidized at all fines fractions. At the highest mass fraction of fines (24%) the relative visible bubble rate is only 76% of that without fines. Clearly, a larger fraction of the gas flows through the particle phase when introducing fines. This is very important for catalyzed gas phase reactions, in which this will lead to higher conversions and often to higher selectivities.
CONCLUSIONS In this paper, we used our fast X-ray tomographic scanner to investigate the influence on the bubble size when adding fines to a Geldart A powder (alumina, average particle size 146.4 µm; close to the B-border). We performed two experiments. In the first one, all gas is sparged through a single capillary, mounted in the bottom of the bed, into the powder. In the second one, the gas is introduced via a porous plate over the entire bottom of the fluidized bed. In both case the mass fraction of fines added was 8, 16 and 24%. We find a clear decrease in the bubble size and an increase in the bubble frequency. The average bubble size may decrease to only 60% of the mean bubble size without adding fines. The increase of the bubble frequency is insufficient to make up for this volume decrease and we conclude from the measurements that more gas flows through the powder mass when fines are present. Our data agree with those reported by [2] based on pressure analysis and optical probes. The advantage of the X-ray scanner over pressure sensing is a more direct assessment of the bubble properties. Compared to the optical probe, the X-ray technique is, obviously, nonintrusive. Moreover, with the tomographic reconstruction the entire bubble is imaged, whereas the optical probe can only measure the chord length, which needs to be converted back, via a probabilistic approach, into the bubble size distribution. We can conclude that, due to its measuring speed, the X-ray tomographic scanner is a welcome new technique to experimentally study bubbling fluidized beds.
NOTATION Acal , Bcal , Ccal Db d fb pi R R0 Ujet Usup Vb v Wik x x, y α ∆t φb µg µp
calibration coefficient estimated bubble diameter path length of beam through material bubble frequency total solids fraction on X-ray beam (ray sum) photon count rate photon count rate in vacuum gas velocity through capillary superficial gas velocity bubble volume velocity weighing matrix path length of beam through powder phase Cartesian coordinates in measuring plane gas volume fraction time interval visible bubble rate linear absorption coefficient of gas linear absorption coefficient of particle
7
m m −1
s−1 s−1 m/s m/s m3 m/s m m s m/s3 m−1 m−1
REFERENCES 1. Mudde, R.F., Time-resolved X-ray tomography of a fluidized bed, Powder Techn., 199, 55-59 (2010). 2. Beetstra, R., Nijenhuis, J., Ellis, N. and Van Ommen, J.R., The influence of the particle size distribution on fluidized bed hydrodynamics using high-throughput experimentation, AIChE J., 55 (8), 2013-2023. 3. van Ommen, J.R., Nijenhuis, J., van den Bleek, C.M. and Coppens, M.O., Four ways to introduce structure in fluidized bed reactors, Ind. & Eng. Chem. Res., 46, 4236-4244 (2007). 4. Yates, J.G. and Newton, D., Fine particle effects in a fluidized-bed reactor, Chem. Eng. Sci., 41, 801-806 (1986). 5. Sun, G. and Grace, J.R., The effect of particle size distribution on the performance of a catalytic fluidized bed reactor, Chem. Eng. Sci., 45, 2187-2194 (1990). 6. Beck, M. S., Dyakowski, T., and Williams, R. A., Process tomography - the state of the art, Trans. Inst. Meas. and Control, 20(4), 163-177 (1998). 7. Warsito, W. and Fan, L.-S., Dynamics of spiral bubble plume motion in the entrance region of bubble columns and three-phase fluidized beds using 3D ECT, Chem. Eng. Sci., 60, 60736084 (2005). 8. Van Ommen, J.R. and Mudde, R.F., Measuring the Gas-Solids Distribution in Fluidized Beds - A Review, Int. J. Chem. Reactor Eng., 6, Article no. R3 (2008). 9. Dudukovi´c, M.P., Opaque Multiphase Reactors: Experimentation, Modeling and Troubleshooting, Oil & Gas Sci. Techn., 55 (2), 135-158 (2000). 10. Kumar, S.B., Moslemian, D. and Dudukovic, M.P., A γ-ray tomographic scanner for imaging voidage distribution in two-phase flow systems, Flow Meas. Instr., 6 (1), 61-73 (1995). 11. Kumar, S.B., Moslemian, D. and Dudukovic, M.P., Gas holdup measurements in bubble columns using computed tomography, AIChE J., 43, 1414-1425 (1997). 12. Mudde, R.F., Harteveld, W.K., Van den Akker, H.E.A., Van der Hagen, T.H.J.J. and Van Dam, H., Gamma radiation densitometry for studying the dynamics of fluidized beds, Chem. Eng. Sci., 54, 2047-2054 (1999). 13. Mudde, R.F., Alles, J. and Van der Hagen, T.H.J.J., Feasibility study of a time-resolving X-ray tomographic system, Meas. Sci. & Techn., 19, 085501 (2008). 14. Mudde, R.F., Bubbles in a fluidized bed: a fast X-ray scanner, AIChE J., online: 29 Nov. (2010). 15. Bieberle, M. and Hampel, U., Evaluation of a limited angle scanned electron beam x-ray CT approach for two-phase pipe flows, Meas. Sci. Technol., 17, 2057-2065 (2006). 16. Bieberle, M., Fischer, F., Schleicher, E., Hampel, U., Koch, D., Aktay, K.S.D.C., Menz, H.J. and H.-G. Mayer, Ultrafast limited-angle-type x-ray tomography, Appl. Phys. Lett., 91 (12), 123516 (2007). 17. Brooks, R. A. and DiChiro, G., Principles of computer assisted tomography (CAT) in radiographic and radioscopic imaging, Phys. Med. Biol., 21, 689-732 (1976). 18. Herman, G. T., Image reconstruction from projections - the fundamentals of computerized tomography, Academic Press, 1980. 19. Kak, M. and Slaney, M., Principles of computerized tomographic imaging, IEEE Press, New York, 1988. 20. Andersen, A. H. and Kak, A. C., Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm, Ultrasonic Imaging, 6, 81-94 (1984). 21. Green, P. J., Bayesian reconstruction from emission tomography data using a modified EM algorithm, IEEE Trans. on Med. Imag., 9, 84-93 (1990). 22. Alenius, S. and Ruotsalainen, U., Bayesian Image reconstruction for emission tomography based on median root prior, Eur. J. of Nucl. Med., 24, 258-265 (1997). 23. Darton, R.C., La Nauze R.D., Davidson, J.F. and Harrison, D., Bubble growth due to coalescence in fluidised beds, Trans. I. Chem. E., 55, 274-280 (1977).
8
ENERGETIC OPTIMIZATION OF THE LIGNIN PYROLYSIS FOR THE PRODUCTION OF AROMATIC HYDROCARBONS Miika Franck, Ernst-Ulrich Hartge, Stefan Heinrich, Bea Lorenz, Joachim Werther Institute of Solids Process Engineering and Particle Technology Hamburg University of Technology, Hamburg, Germany
ABSTRACT The energy supply of the endothermic lignin pyrolysis process in a circulating fluidized bed is to be achieved by recirculation of bed material, which is heated by combustion of the by-products char and permanent gases in a separate combustor. The feasibility of this concept is examined by the investigation of the yield of the byproducts in pyrolysis experiments, char combustion kinetics, the pyrolysis energy demand and the combustion energy of the char and the non-condensable gases. It is shown that the integrated pyrolysis-combustion process is feasible and even produces an energy surplus of 4 MJ/kg raw lignin, which can be used in the downstream processing of pyrolysis products.
INTRODUCTION As a waste material in the bioethanol production and the celluloses industry lignin is a source for aromatic hydrocarbons. It is built up from three-dimensionally connected phenylpropane units, which upon thermal degradation are a rich source for phenolic compounds for synthesis of chemicals (1) to (3). One of the most promising concepts for the thermal decomposition of lignin is to use a circulating fluidised bed (CFB) reactor for pyrolysis. The benefits of the use of a CFB system are the short residence time of the pyrolysis vapours (the desired product) and a high heating rate of the lignin, which are necessary to prevent further degradation reactions and to achieve a high yield of liquid product. By-products of the endothermic pyrolysis are non-condensable gases and char (4). The aim of the present project is to optimize the endothermic pyrolysis process energetically by integrating the combustion of the by-products into the solids circulation loop of the CFB, by means of a bubbling fluidized bed. Furthermore, as char components have been reported to catalyze secondary reactions of the products to form lighter compounds (1, 5 and 6), the effect of catalysis shall be minimized by the integrated combustion of the char.
EXPERIMENTAL To determine the design parameters for an integrated pyrolysis-combustion system, experiments in a CFB pyrolysis reactor (Figure 1) with riser diameter of 80 mm and a height of 1700 mm have been carried out at different operating conditions (Table 1). The lignin was pneumatically fed into the reactor through a nozzle with 6 mm diameter at a velocity of 40 m/s. The superficial velocity directly above the distributor
plate u0,b is given in Table 1 as well as u0,t which is the superficial velocity at the riser top, which includes the lignin conveying N2 as well as the pyrolysis gas and the steam from vaporization of the lignin humidity. The solids flux in the riser could not be measured. The composition of the non-condensable, permanent gas has been monitored continuously throughout the experiments. The bed material (BM) as well as the material from the secondary cyclone (2C) have been collected after each test. The material properties, i.e. composition, particle morphology, particle size distribution and particle density were analyzed.
Electrostatic precipitator
Demister
Pyrolysis reactor
Scrubber
1C
2C
These properties have been used to determine the char combustion kinetics in a laboratory-size fluidized bed reactor. At the temperatures 700, 750, 800, 850 and 900°C the samples of the materials with known mass mS and carbon content γC were injected batchwise into the reactor. The gas composition of the flue gas was monitored and used to calculate the conversion Xchar(t). Figure 1: Experimental pyrolysis plant (80 mm riser diameter) Moreover, the gross calorific values of the gas and the char together with their yields were compared with the required heat for pyrolysis. The latter was estimated by a simultaneous differential scanning calorimetry – thermogravimetric analysis (DSCTGA) experiment of lignin. In the experiment two lignin samples were heated in an argon environment at a heating rate of 10 K/min in a thermal analysis apparatus, type NETZSCH STA F1 Jupiter between room temperature and 900°C. The gross calorific value of the char was determined by experiments with an adiabatic calorimeter. Table 1: Pyrolysis conditions Exp. No. 1 2
Lignin Feed rate kg/h 2.76 2.57
Fluidisation by steam uN2 m/s 40 40
H2O kg/h 24 24
u0,b m/s 5 5
Gas velocity at
Pyrolysis
riser top u0,t m/s 5.7 5.7
temperature °C 650 600
THEORY Due to the nature of the bed material particles, i.e. the char layer on the quartz sand, the shrinking particle model (7) is modified, so that the particle shrinks upon
combustion until the inert sand core is reached. The modified model is subsequently derived and called shrinking particle inert-core model.
Shrinking Particle Inert Core-Model The shrinking of a particle can be described as follows:
− ρ char
dR = kCOn 2 dt
(1)
Therein ρchar is the density of the char, RP the mean radius of the char laden particle at the entrance of the fluidized bed combustor, k the reaction rate constant, C O 2 the mean oxygen concentration in the whole bed during the reaction, t the time and n the reaction order. With the burnout time t = τ and the radius of the quartz sand core R = RQS, the integration gives
τ=
ρchar ⋅ ( RP − RQS )
(2)
kCOn 2
Analogously for an arbitrary time t < τ :
t=
ρchar ⋅ ( RP − R)
(3)
kCOn 2
Dividing Eq. (3) by Eq. (2) results in
R = K1 ⋅ 1 − τ RP t
(
(4)
)
with K 1 = 1 / 1 − RQS / RP . For a particle with inert core the conversion Xchar can be expressed as
1 − X char
(
4 3 π R3 − RQS volume unreacted char 3 3 = = = K 2 ⋅ R 3 − RQS 4 total char volume 3 π RP3 − RQS 3
(
)
(
)
(
)
(5)
)
3 with K 2 = 1 / RP3 − RQS . Solving Eq. (5) for R and replacing R by the result in Eq. (4)
with subsequent differentiation results in 2
dX char dt
=
=
3 3 3R K 2 1 − X char RQS ⋅ + τ K1 R 3P K 2 RP 3 P
2 P
3R K 2 ⋅ kC
ρchar
n O2
1− X R ⋅ 3 char + QS R P K 2 RP 3
2 3
(6)
It should be noted that with RQS = 0 the equation reduces to the shrinking particle formulation, that can be found in (7). The radius of the inert quartz sand core RQS is calculated by the difference RQS = RP − d .
RESULTS Pyrolysis Conditions and Solids Properties For the determination of combustion kinetics it is necessary, that the composition of the solid fuel (proximate and ultimate analysis), its density and morphology as well as particle size distribution are known. The proximate as well as ultimate analysis for the bed material (BM) and the material collected by the secondary cyclone (2C) are listed, in Table 2. The difference of the inert and the ash fraction is equal to the sand Table 2: Proximate and ultimate analysis as well as gross calorific value of solid materials used for experiments Sample
Lignin BM Exp. 1 2C Exp. 1 BM Exp. 2 2C Exp. 2
Sample composition (wt.-%) Proximate analysis Ultimate analysis VolaWater CFix Inert Ash C H N S O tiles 5.3 66.0 27.5 1.2 1.2 58.8 5.3 0.4 1.7 27.3 0.7 1.5 6.9 90.9 12.1 9.3 0.3 0.0 0.3 0.0 1.1 1.9 11.1 85.9 12.1 16.8 0.4 0.0 0.3 0.0 0.8 1.4 7.3 90.5 7.2 8.2 0.3 0.0 0.2 0.0 1.0 2.1 9.9 87.0 7.2 10.6 0.3 0.0 0.3 0.8
HHV MJ/kg 25.2 29.0 29.7 30.2 28.6
Reference state: raw except HHV: inert free; Water content of lignin at 60°C (due to reactions at temperatures above 66°C)
fraction in the sample. The ash contents of the bed material as well as the secondary cyclone material have been calculated by the ash content of the lignin divided by the char yield YC. Latter is calculated by dividing the integral char mass after an experiment by the integral of the fed raw lignin mass during the experiment:
YC =
mbed material after exp. + m2C material after exp. − mbed material before exp. mlignin fed during exp.
(7)
Cumulative mass in %
100 80 60 40 20 0 0
Figure 2: SEM of the surface structure of a bed material particle’s coating fracture, scale bar is 10 µm
2C Exp. 2 QS F36 BM Exp. 2 100 200 300 400 Particle diameter in µm
Figure 3: Exemplarily particle size distribution of BM and 2C of Exp 2 in comparison with quartz sand
A SEM image of the bed material after pyrolysis experiment is shown in Figure 2. On the picture a sand particle coated with a char layer can be recognized, with part of the char layer broken apart. The particle size distribution of the secondary cyclone
material (2C) is bimodal, while Table 3: Sauter mean diameter DS, char layer the distributions of the quartz thickness d and densities ρQS of the quartz sand sand and the bed material (BM) and ρP of the bed material and the corresponding are monomodal (Figure 3). This ρchar of the char DS d ρQS/ ρP ρchar is due to the fragments of the Sample 3 g/cm3 µm µm g/cm char coating shells, which are Quartz sand 39.2 -2.63 -mainly found in the secondary BM Exp. 1 78.8 9.0 2.50 0.81 cyclone material. The char 2C Exp. 1 35.8 ---density ρchar was calculated BM Exp. 2 129.5 15.5 2.50 1.03 from the composition of the bed 2C Exp. 2 45.0 ---material and the densities of bed material and quartz sand. The average char layer thickness d has been calculated, using the assumption of spherical particles. These calculations have been validated by dimensioning of char layer fragments in several SEM images (0.6 to 18.1 µm). The results are listed in Table 3.
Combustion Kinetics The conversion of the char Xchar at an arbitrary time t is defined as the conversion of carbon at that time. Therefore, the measured carbon content in the flue gas, integrated over the time, is divided by the initial carbon mass of the sample:
X char =
mC (t ) in CO 2 mS ⋅ γ C
(8)
1
Temperature in °C 900
0.4 0.2 0 0
850
800
750
700
n
0.6
-4.5
2
Xchar
0.8
ln(k) with k in kg/(m s (mol / l) )
-4
10
SPIC-model Experimental data Shifted SPIC-model 20 30 40 t in s
Figure 4: Conversion of a BM Exp. 1 sample in a combustion experiment at 850°C and the correlative Shrinking Particle Inert Core kinetic model conversion
BM Exp. 2: n = 0.5 ln(k) = -914.06/T - 3.443 BM Exp. 1: n = 0.5 R2 = 0.5443 ln(k) = -1157.8/T - 3.9193 2
R = 0.7833
-5
2C Exp. 2: n = 0.5 ln(k) = -1143.2/T - 3.9937 2C Exp. 1: n = 0.5 -5.5 R2 = 0.8871 ln(k) = -1028.4/T - 4.8865 2 R = 0.9816 -6 0.852406 0.890353 0.931836
0.977374
1.02759
-1
1000/T in K
Figure 5: Arrhenius-plot of the analysed materials
The measured values Xchar(t) were fitted by the Shrinking Particle Inert Core-model (Eq. (6)) with the fitting parameters k and n. An example of both curves for BM Exp. 1 is given in Figure 4. Other than predicted by the Shrinking Particle Inert Core-model, the slope of the conversion-curve is not maximal at the beginning and also much smaller at higher reaction times than expected. Because of the experimental setup, which comprises a quite long duct between the reactor and the online gas measurement as well as a gas filter, dispersion effects cause the deviation. This was checked by the introduction of a known square pulse to the reactor. The pulse response needed about 8 s to reach the pulse amplitude and the decay took about 16 s. These durations approximately relate to the response and
decay times in Figure 4. Therefore, the conversion curve computed by the Shrinking Particle Inert Core-model was shifted by 8 s (Figure 4). The resulting Arrhenius-plots for the four tested materials are shown in Figure 5 in the temperature interval of 700 to 900°C with the obtained values for n and k.
Energy Balance To establish an energy balance for the pyrolysis system with integrated by-product combustion, the total heat release of the by-products related to the fed raw lignin mass and the mass specific heat demand for lignin pyrolysis are specified below. The combustion of the by-products shall take place at 850°C. Heat losses and auxiliary energy demand, e.g. for feeding of lignin and preheating of the fluidisation gas, have not been considered.
100
1.2 DSC
TG in %
80 0.4
60
DSC in W/g
0.8
0 TG 40 0
200
400 600 Temperature in °C
-0.4
800
Figure 6: Thermal analysis of lignin (DSC and TGA)
Pyrolysis energy requirement q in MJ/kg raw lignin
Pyrolysis Energy Requirement The results of the thermal analysis of lignin are shown in Figure 6. It is very probable that the first mass loss step belongs to the loss of moisture, whereas the other steps are caused by devolatilization and pyrolytic cracking of the sample. 2.5 2 1.5 1 0.5 0 0
200
400 600 800 Temperature in °C
Figure 7: Approx. pyrolysis requirement per kg raw lignin
1000
energy
According to the mass loss endothermic or exothermic effects can be seen in the DSC curve. The enthalpy of the single effects is difficult to determine, because the effects interfere with each other. In the temperature interval of 200°C to 350°C it can not be differentiated between endothermic or exothermic behaviour. To calculate the energy demand for the pyrolysis the integral of the DSC signal qɺ is used (Eq. (9)).
q=
1
β
T
t
⋅ ∫ qɺ dT = ∫ qɺ dt T0
(9)
t0
The results are shown in Figure 7. The specific heat is related to the initial mass of raw lignin. For the pyrolysis at a temperature of 650°C a mean value of about 1300 kJ/kg of raw lignin for the energy demand can be calculated.
Energy Supply The yield of permanent gases, which contribute to a heat release upon combustion (CO, CO2, H2, C1-C4 as CH4) rises with rising temperature. This result coincides with the observations of Wei et al. (8), Scott and Piskorz (9), Zheng (10) and Di Blasi (11). As with rising temperature secondary cracking reactions are favoured, the more complex char and liquid molecules are converted to gases. Our measurements have shown, that the composition of the permanent gas mixture does not change considerably. This trend can also be found for the total heat release by the combustion of permanent gas per mass of raw lignin fed to the pyrolysis. The results are shown in Figure 8. 5
6 5 4 3 2 1
0 600
12 kg/h Steam 24 kg/h Steam 650 700 750 800 Pyrolysis temperature in °C
Figure 8: Total heat release of permanent gas combustion per kg raw lignin (combustion at 850°C)
Total heat release in MJ / kg raw lignin
Total heat release in MJ/kg raw lignin
7
4 3 2 1
BM Exp. 1 & 2 2C Exp. 1 & 2 0 600 625 650 Pyrolysis temperature in °C
Figure 9: Total heat release of char combustion per kg raw lignin (combustion at 850°C)
On the other hand, because of secondary reactions as explained above, the yield of char decreases with rising temperature, while the gross calorific value does not change significantly (see Table 2). Therefore, the lignin specific total heat release of char combustion decreases with rising temperature. This can be seen in Figure 9 and is in good agreement with the above cited literature. At a pyrolysis temperature of 650°C, the energy demand for lignin pyrolysis is about 1500 kJ/kgL. The combustion of the by-products at this temperature releases about 3000 kJ/kgL for the permanent gases and 2500 kJ/kgL for the char. In the integrated pyrolysis-combustion process this gives an energy surplus of 4000 kJ/kgL.
SUMMARY AND CONCLUSIONS To couple a pyrolysis CFB reactor with an integrated combustion of the pyrolysis byproducts, char properties, combustion kinetics and the energy balance have been investigated. The quartz sand particles, which make up the bed material, are coated with char. Therefore, a model for combustion kinetics has been derived, which accounts for the fact that only a char layer with certain thickness is combusted. For the investigated materials, the combustion kinetics have been determined by combustion experiments in a laboratory scale fluidized bed. The combustion kinetics model does fit the experimental data well.
The energy demand for lignin pyrolysis has been estimated by differential scanning calorimetry. Pyrolysis experiments in a CFB reactor (80 mm diameter) were carried out to identify the lignin specific energy in the by-products. At the pyrolysis temperature of 650°C the combustion of permanent gases and char generates an energy surplus of 4000 kJ/kgL. The surplus can be used for preheating the fluidization gases and balancing the heat losses. The obtained data is currently used to design a circulating fluidized bed pyrolysis reactor with integrated fluidized bed combustor.
ACKNOWLEDGEMENT This work has been carried out within the framework of the cluster Biorefinery2021, funded by the German Federal Ministry of Research and Technology (FKZ 0315559A). The responsibility for the content of this work lies with the authors.
NOTATION C d DS k K m n q
qɺ R t T X Y
Concentration Char layer thickness Sauter mean diameter Reaction rate constant Constant Mass Reaction order Mass specific energy Mass specific heat flux Radius time Temperature Conversion Yield
µm µm n kg/(m²s(mol/l) ) -kg -kJ/kg W/kg µm s K ---
β γ υ ρ τ
Heating rate Mass fraction Temperature Density Burnout time
K/min -°C kg/m³ s
Subscripts char Char C Carbon L raw Lignin P Particle QS Quartz sand S Sample, Sauter
REFERENCES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Amen-Chen C., Pakdel H., Roy C. (2001), Bioresource Technology 79, 3, 277–299. Bridgwater A. V., Meier D., Radlein D. (1999), Organic Geochemistry 30, 12, 1479–1493. Bridgwater A. V. (2003), Chemical Engineering Journal 91, 2-3, 87–102. Basu P. (2010) Biomass gasification and pyrolysis, Elsevier/AP. Amsterdam Park H., Park Y., Dong J., Jeon J., Kim S., Kim J. et al. (2009), Fuel Processing Technology 90, 2, 186–195. Asadullah M., Anisur Rahman M., Mohsin Ali M., Abdul Motin M., Borhanus Sultan M., Robiul Alam M., Sahedur Rahman M. (2008), Bioresource Technology 99, 1, 44–50. Levenspiel O. (1999) Chemical reaction engineering, Wiley. Hoboken, NJ. Wei L. G., Xu S. P., Zhang L., Zhang H. G., Liu C. H., Zhu H., Liu S. Q. (2006), Fuel Processing Technology 87, 10, 863–871. Scott D. S., Piskorz J. (1982), Canadian Journal of Chemical Engineering 60, 5, 666–674. Zheng J. L. (2007), Journal of Analytical and Applied Pyrolysis 80, 1, 30–35. Di Blasi C. (2009), Progress in energy and combustion science 35, 2, 121– 140.
THE INFLUENCE OF CARBON STRIPPER EFFICIENCY ON CO2-CAPTURE RATE IN A CHEMICAL-LOOPING COMBUSTION PROCESS FOR SOLID FUELS Marvin Kramp, Andreas Thon, Ernst-Ulrich Hartge, Stefan Heinrich, Joachim Werther Institute of Solids Process Engineering and Particle Technology Hamburg University of Technology Denickestrasse 15 21073 Hamburg, Germany ABSTRACT In the present work a Chemical-Looping Combustion process for solid fuels is simulated on the 100 MW th scale. The coal is gasified inside the fuel reactor by recirculated CO2 and H2O. A carbon stripper downstream of the fuel reactor is used to reduce the carryover of char from the fuel to the air reactor. The influence of the carbon stripper on the CO2 capture rate is investigated. The results demonstrate the significance of the carbon stripper in this process. INTRODUCTION Chemical-Looping Combustion (CLC) is an interesting variant for the inherent separation of carbon dioxide inside a power generation process. It has recently attracted much attention by numerous research groups (e.g. 1-4). Most research focuses on the realization of CLC in two interconnected fluidized bed reactors. In between these two reactors solids are circulated which transport chemically bound oxygen taken up from the air inside the air reactor to the so called fuel reactor. The oxygen carrier (OC) particles provide the fossil fuel (e.g. natural gas or coal) with oxygen and will themselves be reduced. Reduced oxygen carrier particles are then cycled back towards the air reactor for re-oxidation. CLC has the advantage that the carbon dioxide of the off-gas will not be diluted by nitrogen. After condensation of water, almost pure carbon dioxide can be obtained and transported to its designated storage location. If solid fuels are to be used in CLC, the complexity increases compared to gaseous fuels. A scheme for a solid fuels CLC process is shown in Figure 1. The direct reaction between the solid carbon and the oxygen bound to the solid oxygen carrier particles will not proceed at a significant rate. Thus the solid carbon has to be gasified. In CLC it is self-evident to recycle the fuel reactor off-gases consisting of H2O and CO2 to use them as gasifying agents. During carbon gasification H2 and CO will be produced. The OC particles will oxidize these intermediate products further towards H2O and CO2. Gasification is a rather slow process compared to the reaction of CO and H2 with the OC (5) and thus sufficient residence time of the coal particles in the fuel reactor must be
provided. On the other hand to yield a sufficient flow of oxygen for fuel oxidation a rather large circulation flow of OC particles is needed. The solids flow leaving the fuel reactor consists of a mixture of unreacted char particles, ash and oxygen carrier particles. The char particles must not enter the air reactor since they would combust with the air-oxygen present. The formed CO2 would not be captured and thus decrease the CO2 capture rate of the plant. To reduce the amount of carbon slip towards the air reactor a carbon stripper can be used. The OC particles and the char particles can be separated according to their terminal settling velocity. The aim of this investigation is to investigate how Figure 1: Scheme of a CLC the CO2 capture rate is influenced by the carbon process for solid fuels stripper. THEORY Char Gasification In CLC both CO2 and H2O can be used for gasification of the coal char. It is repeatedly reported in literature that steam gasification proceeds at a higher rate than carbon dioxide gasification (e.g. 6). The net gasification reactions are shown in the following equations: C + β ⋅ H 2O → β ⋅ H 2 + ( 2 − β ) ⋅ CO + ( β − 1) ⋅ CO2 (1)
C + CO2 → 2CO (2) The factor β in equation (1) was introduced by Matsui et al. (7) and summarizes the following two reactions: (3) C + H 2O → CO + H 2 C + 2 H 2O → CO2 + 2 H 2
(4)
For this investigation β is set to 1.2 according to (7). A set of kinetic equations was chosen that describes the gasification of coal char by CO2 and H2O (7,8). Fuel Reactor Model The fuel reactor is a bubbling fluidized bed, where the solid phase is assumed to be ideally mixed. It was stated above that the fuel reactor should be fluidized by its own recirculated off-gas. Therefore the gas composition at steady-state is the same at the inlet and the outlet of the fuel reactor. Accordingly the fuel reactor can be described reasonably well by continuous stirred tank reactor characteristics. A corresponding model has been set up that is able to handle multiple reactions and considers changes in volume flow due to reaction. The release of volatiles is assumed to occur
instantaneously upon fuel introduction in the reactor. The composition of the volatiles is calculated according to the model of Jensen (9). The model has been slightly modified in order to neglect the formation of nitrous oxides and the sulfur content of the fuel. All fuel nitrogen is therefore released as gaseous nitrogen. It is assumed that the particle size distribution (PSD) of the char does not change during the initial devolatilization process. During gasification a size reduction of the char particles is considered. It is assumed that shrinkage is a process of external surface reaction:
dR m = −k s dt s
(5)
The PSD of the char at the exit of the fuel reactor is then calculated according to Levenspiel (10). The reaction of volatiles and the gasification products with oxygen carrier particles is usually much faster than the gasification of char. Accordingly the concentrations of the aforementioned gases in the fuel reactor are set to zero due to their reaction with oxygen carrier particles. Carbon Stripper Model The carbon stripper is simulated as a classifier. The grade efficiency is described by the Rogers expression (11), which has been adapted for usage with settling velocities instead of particle diameters and to neglect bypass of fines:
1
G ( ut ,i ) =
u 1 + t ,50 ⋅ e ut ,i
3
ut ,i ut ,50
α ⋅1−
,
(6)
with the grade efficiency G(ut,i ), defined as the ratio of mass fraction of the particle settling velocity interval i in the coarse product and the mass fraction of the same interval in the feed. The parameter ut,50 designates the cut terminal velocity (50 %-value of the grade efficiency curve) and ut,i is the terminal settling velocity of the particles in size interval i. The sharpness of the separation is defined by α, which can vary between 0.3 (diffuse separation) and 6.6 (analysis-sharp separation). A more common description of the separation sharpness is (12):
χ=
ut ,25 ut ,75
,
(7)
where ut,25 and ut,75 are the terminal velocities that belong to the values of the grade efficiency curve at G(ut,i) = 0.25 and 0.75, respectively. An ideal separation would have a value of χ = 1 (usual technical sharpness: 0.3 < χ < 0.6, technically sharp: 0.6 < χ < 0.8, analysis sharp: 0.8 < χ < 0.9 according to 12). Reactions are not considered in the carbon stripper. Simulation Environment The simulations have been carried out in SolidSim (13), a steady-state flowsheet simulation environment for solids processes.
RESULTS AND DISCUSSION Test Case The flowsheet of the test case is shown in Figure 2. To prevent unburnt char from entering the air reactor a carbon stripper (sifter) is located downstream of the fuel reactor. This device separates the mixture of OC and char by differences in settling velocity. It is desired to facilitate this separation as much as possible. Coal particles will naturally decrease in size during combustion and therefore it is chosen to grind the coal fine and have larger oxygen carrier particles in comparison. This strategy is reflected by the flowsheet. The stream of fine particles leaving the carbon stripper is reintroduced to the fuel reactor while Figure 2: Simulated flowsheet of the CLC process for solid fuels. the OC rich stream of coarse particles is transported to the air reactor. The feed denoted by OC-refill is necessary to ensure that the target circulation flow of OC particles can be reached during the iterative solution procedure. As fuel a Columbian anthracite coal from the El Cerrejon mine has been selected. Proximate and ultimate analysis results are shown in Table 1 and Table 2. Table 1: proximate analysis results of coal ‘El Cerrejon’ (14)
LHV [MJ/kg] (waf) 31.98
Water [wt.-%] (raw) 15.39
Ash [wt.-%] (wf) 10.3
Volatiles [wt.-%] (waf) 41.9
Table 2: ultimate analysis results of coal ‘El Cerrejon’ (14)
C [wt.-%] (waf) 81.0
H [wt.-%] (waf) 6.01
O [wt.-%] (waf) 10.70
N [wt.-%] (waf) 1.50
S [wt.-%] (waf) 0.79
El Cerrejon coal char has an apparent density of 1500 kg/m³ (14). The total flow of coal should represent a fuel input of 100 MW th at complete combustion. Dividing the coal feed into separate flows 1.528 kg/s char, 0.536 kg/s H2O, 0.780 kg/s CH4, 0.567 kg/s CO, 0.126 kg/s N2 and 0.036 kg/s CO2 are fed to the fuel reactor (sulfur content is neglected). The fate of the ash is not tracked in this investigation. The initial PSD of the coal is the same as a state of the art coal mill for pulverized coal boilers delivers (15).
cumulative mass, -
cumulative mass, -
1 A copper based OC was selected that consists of 10 wt.-% of active char 0.8 CuO. The inert phase is Puralox NWa155 from Sasol, Germany, 0.6 which is a porous aluminum oxide oxygen carrier of medium size and a possible 0.4 inert support for impregnation (16,17). Since the particle size 0.2 distribution of synthetic OC 0 prepared by impregnation is defined by the support particles, 0 200 400 600 800 the particle size distribution of the dp, µm Puralox (measured by laser diffraction) was chosen for the OC Figure 3: Particle size distributions of char (dots) particles of this investigation. The and oxygen carrier particles (empty squares). apparent density is taken to be 1 1800 kg/m³ for the oxidized state. char 0.8 The particle size distributions of char and OC are shown in Figure 0.6 3. It can be observed, that there is a certain overlap of the two 0.4 distributions. The difference in particle density is not very large, 0.2 oxygen carrier thus the settling velocity 0 distributions of the two solids in 0 0.5 1 1.5 2 Figure 4 shows a similar overlap. Figure 4 shows only a small part terminal settling velocity, m/s of the distribution of OC particles. At 8 m/s the distribution Figure 4: Settling velocity distributions of char approaches finally the value 1. The (dots) and oxygen carrier particles (empty squares) settling velocity distributions are with marked area of overlap at fuel reactor calculated for fuel reactor conditions. conditions. According to Figure 4 the cut separation velocity should be chosen between 0.2 m/s and 1.0 m/s. Fuel reactor operation is carried out at 900°C and the solids entering the fuel reactor have a residence time of 240 s (18,19). For fluidization and gasification a mixture of steam and carbon dioxide is fed to the fuel reactor. The CO2 fraction is assumed as 74 % by weight which would correspond to complete gasification of the char. For the reoxidation of the OC particles a global excess air ratio of 1.2 is assumed. The circulation mass flow of OC particles is 650 kg/s.
Simulation results Simulation results are compared on CO2 capture rate (CCR) basis:
CCR =
CO2 flow from fuel reactor total CO 2 flow based on fuel input
(8)
The aforementioned results are shown in Figure 6 in terms of CO2 capture rate for χ = 0.5 and χ = 0.8 (corresponding to α = 0.84 and α = 3, respectively). The lower boundary indicated by the dashed line represents the case of the CLC process without a sifter. In this case a CCR of 0.36 is achieved, which corresponds to the reaction of volatiles with the oxygen carrier particles and the
CO2 Capture Rate, -
solid flow to sifter, kg/s
char flow in coarse, kg/s
The CCR is decreased by CO2 leaving the process through the air reactor. This is the case, when char is transported to the air reactor where it will combust with air oxygen. In order to minimize the flow of char from the fuel reactor to the air reactor a sifter / carbon stripper was introduced in-between both reactors (Figure 2). The carbon stripper divides the mixed solids flow into a flow of fines and coarse. Because of the aforementioned overlap of the settling velocity distributions the separation of the two types of solids will never return two pure streams of only one species. It is possible though to minimize the flow of char within the stream of coarse particles flowing to the air reactor. This can be achieved by the choice of a high cut velocity. Figure 5 shows the result of a variation of the cut velocity in terms of char flow in the coarse flow for a separation sharpness of α = 0.84 which corresponds to χ = 0.5. The 1.6 3000 flow of char in the coarse flow from the carbon stripper 1.2 decreases with increasing cut 2000 velocity. Increasing the cut velocity does though also 0.8 increase the flow of OC particles 1000 in the flow of fines that is returned 0.4 to the fuel reactor. If this flow increases, the carbon stripper will 0.0 0 have to handle a larger flow of mixed solids. This can generally 0 1 2 3 only be achieved by a larger unit. cut velocity ut,50, m/s In Figure 5 the flow entering the Figure 5: Char flow towards the air reactor in carbon stripper unit is shown on dependence of the cut terminal settling velocity and the secondary axis. For 3 m/s for the corresponding total flow of solids entering the instance it can be observed that sifter unit (χ = 0.5). the flow entering the sifter unit 1 reaches approximately 2500 kg/s χ =0.8, finer char 0.8 which is 3.8 times the flow of OC χ =0.8 particles circulating between the 0.6 reactors. χ =0.5 0.4 0.2
without carbon stripper
0 0
1 2 cut velocity ut,50 ,m/s
3
Figure 6: CO2 capture rate in dependency of the cut terminal settling velocity for χ = 0.5 and χ = 0.8. Additionally the value of CCR is shown for a CLC process without a carbon stripper. The influence of initial char particle size distribution is shown for χ = 0.8 and 1.5 m/s cut velocity.
fraction of char that can be gasified in a single pass through the fuel reactor. Linking the results shown in Figure 6 with those of Figure 5 it can be observed that corresponding to the decreased flow of char towards the air reactor at higher cut velocities the CCR increases at higher cut velocities. For a χ = 0.5 (usual technical sharpness) the CCR is significantly lower than for χ = 0.8, which represents a technically sharp separation. The maximum CCR for χ = 0.8 is 0.74. This value is still rather low but higher CCRs are possible in an optimized process. For instance smaller particle sizes for the coal would facilitate the separation from the oxygen carrier particles in the sifter. In Figure 6 the result of a single simulation at χ = 0.8 and 1.5 m/s cut velocity with finer coal (dp,63 = 50 µm) is additionally shown. This simulation reaches a CCR of 0.83. This value is still not satisfactory but there is still room for improvements. In general there are two ways to improve the CCR. First the carbon stripper can be improved and second the char conversion in the fuel reactor can be increased. This can be achieved in various ways and the first one was already mentioned above, a further simplification of the separation of char and OC. Other options are: • Increased residence time of the solids in the fuel reactor • Higher temperatures in the fuel reactor • Elevated steam content in the gasification gas • Usage of special oxygen carriers that release gaseous oxygen in the fuel reactor Finally, there is a certain spread in the literature concerning char gasification rates with H2O and CO2. The chosen gasification kinetics are a rather conservative choice compared to Ye et al. (6) which differ up to one order of magnitude. CONCLUSIONS The influence of the carbon stripper on the CO2 capture rate of a Chemical-Looping Combustion process for solid fuels was investigated. It can be concluded that the carbon stripper is an important unit operation in a CLC process for solid fuels. Higher cut velocities lead to a decreased slip of char towards the air reactor. This increases the carbon capture rate but on the other hand also increases the load on the carbon stripper. The simulations show rather low CCRs for the chosen process setup and operation. Yet, it is possible to achieve higher CCRs either by improvements regarding the carbon stripper or by increasing the char conversion in the fuel reactor. ACKNOWLEDGEMENT The present work is part of a joint research project of the Institute of Combustion and Power Plant Technology of the University of Stuttgart, the Institute of Energy Systems of the Hamburg University of Technology and the Institute of Solids Process Engineering and Particle Technology of the Hamburg University of Technology. This project received financial support of the German Federal Ministry of Economics and Technology (FKZ 0327844B / CLOCK) with additional funding from BASF SE, EnBW Kraftwerke AG, E.ON Energie AG, Hitachi-Power Europe GmbH, RWE Power AG and Vattenfall Europe Generation AG. The responsibility for the content of this report lies with the authors.
NOTATION
CCR CLC OC
Split factor for CO / CO2 production in H2O gasification, Separation sharpness, CO2 capture rate, Chemical-Looping Combustion Oxygen carrier
PSD
Particle size distribution
G
Separation grade efficiency,-
β
ks R t ut,i
Shrinkage rate, m s-1 Particle radius, m Time, s Terminal settling velocity of particles in class i, m s-1 Separation sharpness (Rogers),-
χ
α
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
Jerndal E., Mattisson T., Thijs I., Snijkers F., Lyngfelt A. (2010), Int. J. Greenh. Gas Con. 4, 1, 23–35. Gayán P., Forero C. R., Diego L. F. de, Abad A., García-Labiano F., Adánez J. (2010), Int. J. Greenh. Gas Con. 4, 1, 13–22. Xiao R., Song Q., Song M., Lu Z., Zhang S., Shen L. (2010), Combust. Flame 157, 6, 1140–1153. Hossain M. M., Lasa H. I. de (2008), Chem. Eng. Sci. 63, 4433–4451. Berguerand N., Lyngfelt A. (2008), Int. J. Greenh. Gas Con. 2, 2, 169–179. Ye D. P., Agnew J. B., Zhang D. K. (1998), Fuel 77, 11, 1209–1219. Matsui I., Kunii D., Furusawa T. (1985), J. Chem. Eng. Jpn. 18, 2, 105–113. Matsui I., Kunii D., Furusawa T. (1987), Ind Eng. Chem. Res. 26, 1, 91–95. Jensen A. (1996), Nitrogen Chemistry in Fluidized Bed Combustion of Coal. Ph.D. thesis. Technical University of Denmark. Lyngby, Denmark. Levenspiel O. (1989), The chemical reactor omnibook, OSU Book Stores. Corvallis, Or. Rogers R. S. C. (1982), Powder Technol. 31, 1, 135–137. Rumpf H. (1990), Particle technology, Chapman and Hall. London. SolidSim Engineering GmbH (2010), SolidSim. Version 1.2. http://www.solidsim.com/. Ratschow L. (2009), Three-Dimensional Simulation of Temperature Distributions in Large-Scale Circulating Fluidized Bed Combustors. Ph.D. thesis. Hamburg University of Technology. Hamburg, Germany. Baumeister W., Bischoff W., Pannen H. (2002), VGB PowerTech 82, 9, 54–60. Adánez J., Gayán P., Celaya J., Diego L. F. de, García-Labiano F., Abad A. (2006), Ind Eng. Chem. Res. 45, 625–645. Diego L. F. de, Gayán P., García-Labiano F., Celaya J., Abad A., Adánez J. (2005), Energ. Fuel. 19, 1850–1856. Markström P., Berguerand N., Lyngfelt A. (2010), Chem. Eng. Sci. 65, 18, 5055– 5066. Leion H., Mattisson T., Lyngfelt A. (2008), Int. J. Greenh. Gas Con. 2, 2, 180–193.
CATALYST ATTRITION IN THE CFB RISER Andreas Thon, Marvin Kramp, Ernst-Ulrich Hartge, Stefan Heinrich, Joachim Werther, Institute of Solids Process Engineering and Particle Technology Hamburg University of Technology Denickestrasse 15 21073 Hamburg, Germany ABSTRACT Catalyst attrition in the CFB riser was experimentally investigated in a pilot scale CFB system consisting of a 400 mm diameter riser with a height of 15 m, a return leg and a two-stage cyclone separation. The catalyst loss of the CFB system was measured. In order to discern between attrition occurring in the cyclones and in the riser the system was simulated by a population balance approach which takes the separation efficiency of the cyclone system into account. On the basis of the experimental investigation an empirical correlation for catalyst attrition in the CFB riser has been developed which accounts for the influence of the gas velocity and the catalyst mass in the riser. INTRODUCTION Attrition plays a major role in fluidized bed processes where catalyst particles are used, e.g. in the Fluid Catalytic Cracking (FCC) process. In this process the fluidization of the catalyst inside and the transport between the reactors together with the gas solid separation in the solids recovery system (e.g. cyclones) causes a considerable mechanical stress on the catalyst material. This stress leads to particle attrition. In the process a number of attrition sources can be identified. Usually, the gas jets issuing from the gas distributor into the fluidized bed, the bubble motion in the fluidized bed and the cyclones are regarded as the most relevant attrition sources (1). Under normal operating conditions the attrition in a fluidized bed process occurs as surface abrasion, which means that asperities and edges are removed from the surface of particles (2). The primary consequence of attrition is the production of fine particles which can not be kept in the process by the solid recovery system. Thus, attrition leads to a loss of valuable catalyst material (3). Catalyst attrition is therefore a major issue and efforts have to be made to produce sufficiently attrition resistant catalyst in order to reduce the costs for make-up catalyst (4). Furthermore, the prediction of the catalyst loss in a fluidized bed process is an important issue for process design. Many works deal with the experimental investigation of the attrition sources in a fludized bed process e.g. in the cyclone (5), jet-induced attrition (6) and bubble-induced attrition (7)). At Hamburg University of Technology test procedures were established to characterize the material’s attrition propensity under mechanical stress conditions in an isolated cyclone and in a bubbling fluidized bed with and without submerged gas jets.
/
Based on the experimental investigations of each attrition source in isolation, attrition models have been developed. An overview of various attrition tests is given in (3). Unlike the before mentioned attrition sources, practically no experimental investigation regarding the attrition occurring in a circulating fluidized bed (CFB) riser can be found in the open literature. The difficulty in the experimental investigation of attrition in the CFB riser is that it can not be continuously operated in isolation. This is, because the catalyst which is entrained from the riser with the gas, has to be separated from the gas and returned to the bottom of the riser for continuous operation. Hence, the catalyst loss and changes in the particle size distribution (PSD) in a CFB system will originate from the combination of attrition occurring in the riser and attrition occurring in the cyclone and will be affected by the separation efficiency of the cyclone. The present work now focuses on the experimental investigation of the attrition occurring in the CFB riser. THEORY In this study the attrition occurring in the CFB riser and in the cyclone are considered. The jet-induced attrition occurring above the gas distributor is neglected, because a bubble cap gas distributor with low gas exit velocities is installed in the pilot scale CFB system. Furthermore, the attrition in the return leg is neglected. The catalyst attrition occurring in the cyclone is simulated according to the model developed by Reppenhagen (5). The rate of attrition rc,i in the particle size class dp,c,i occurring in the cyclone is given by
rc ,i
2 ɺ attr,c,i m uc,in = = Cc ⋅ dp,c,i ɺ c,in,i m µc
(1)
with ṁattr,c,i the mass flow of fines generated by attrition in the particle size class i, ṁc,in,i the catalyst mass flow entering the cyclone, Cc is the size-independent attrition rate constant, dp,c,i the particle size, uc,in is the gas velocity at the cyclone inlet and µc is the solids loading of the incoming gas flow,
µc =
ɺc m ρc ⋅ uc ⋅ Ac
(2)
where ρc is the density of the inflowing gas and Ac is the inlet cross-sectional area of the cyclone. In contrast to the attrition in the cyclone no specific attrition model for attrition occurring in the CFB is available in the open literature. However, the attrition due to bubble motion in fluidized beds was intensively investigated by various authors. For example Merrick and Highley (7) and Ray et al. (8) found that the bubble-induced attrition rate rb, which is the mass flow of attrition generated fines in the bubbling fluidized bed ṁattr,b related to the bed material mass mb is given by
rb =
/
ɺ attr,b m mb
= Cb ⋅ (u − umf )
(3)
with Cb the attrition rate constant for bubble induced attrition, u the superficial gas velocity in the fluidized bed and umf the minimum fluidization velocity. The experimental investigations of the bubble induced attrition, cited above, were carried out at excess gas velocities (u-umf) below 2.16 m/s (7) and 1.1 m/s (8), respectively. Hence, the application to attrition occurring in a CFB riser, which is operated at much higher gas velocities, means a significant extrapolation. However, as a first approach it is assumed here that the same general relationship describes the rate of attrition occurring in the riser rr,
rr =
ɺ attr,r m mr
= Cr ⋅ (u − umf )
(4)
with ṁattr,r the mass of attrition generated fines in the CFB riser, mr the mass of catalyst in the riser as indicated by the pressure drop, u the gas velocity in the riser Cr the attrition rate constant for attrition occurring in the CFB riser.
EXPERIMENTAL Material In this study equilibrated cracking catalyst (FCC) was used which has been used for long time in the industrial process. The particle size distribution, shown in Fig. 1, has developed from a feed material as result of solids separation and attrition processes. The mean diameter dp50 of the catalyst was 85 µm.
cumulative mass Q3, -
Attrition rates rc and rr cannot be measured individually in the CFB loop. What is measured is the overflow of the cyclone which is leaving the system. In order to get access to rr the population balance for the whole system has to be solved. Therefore, the flowsheeting software SolidSim (9) is used. In SolidSim the system is simulated by connecting unit models for the cyclones and the CFB riser. The CFB riser is modelled by a fluidized bed module developed by Püttmann (10). It accounts for the hydrodynamic as well as the attrition induced changes in the particle size distribution. Based on the population balance modelling approach developed by Werther and Hartge (e.g. 11) the effect of attrition on the PSD in the system is calculated. The attrition process generates fines, which are added to the smallest size class i = 1. Moreover, the mother particles shrink due to surface abrasion, which leads to a mass transfer from the size class i to i-1. 1 0.8 0.6 0.4 0.2 0 0
50
100
150
200
250
300
particle diameter, µm
Figure 1. Particle size distribution of the catalyst
Attrition test in an isolated cyclone The material specific attrition constant Cc for attrition occurring in the cyclone was measured for the FCC catalyst in an isolated cyclone on the lab scale. The diameter of the cyclone was 90 mm. The tests were conducted under ambient conditions with air for gas velocities at the cyclone inlet between 10 and 20 m/s and solids loadings at the
/
cyclone inlet between 1 and 2 kg/kg. Detailed information about the test procedure can be found in (12). CATALYST LOSS IN THE CFB SYSTEM The CFB system shown in Fig. 2 was used for the experimental investigation of catalyst attrition occurring in the CFB riser. The riser has a height of 15.6 m and an inner diameter (ID) of 0.4 m. A bubble cap gas distributor is installed. The experiments were conducted under ambient conditions with air as fluidizing gas in the riser and syphon. The crosssectional average gas velocity in the riser was varied between 3 and 5 m/s and the catalyst mass in the riser between 30 and 110 kg. The catalyst entrained from the riser with the gas is separated by a primary and a secondary cyclone with diameters of 1 m and 0.8 m, respectively. According to the cyclone’s separation efficiency some catalyst leaves the secondary cyclone with the gas through the overflow and is then collected in a subsequent bag filter. The latter catalyst is not returned to the process and therefore designated here as catalyst loss. The recovered catalyst is recycled via a syphon to the riser. In the return leg the circulation rate is measured. Therefore, a section of the downcomer is separately supported and decoupled from the system by compensators. In this section a butterfly valve is installed. The circulation rate can be determined by measuring the weight change of the section with time after the butterfly valve is closed.
Figure 2. Pilot scale CFB system
Figure 3. Measurement system
The CFB system’s catalyst loss is assessed by measuring a side stream which is withdrawn in a section between the secondary cyclone’s overflow and the bag filter. The measurement system used is shown in Fig. 3. The side steam is withdrawn via a suction probe. In the tests two different probes, one with a diameter of 6 mm and the second with a diameter of 20 mm, were used. The catalyst entrained by the withdrawn gas is subsequently separated by a filter. The catalyst mass flow is determined by differential measurement of the filter weight per measurement time. The volume flow through the probe is controlled manually by valves and a rotameter. It is adjusted such that the gas velocity
/
in the probe is the same as the average gas velocity in the measurement section of the pipe. The measurement setup allows the measurement at different radial positions to investigate the solids concentration profile. RESULTS AND DISCUSSION Material specific cyclone attrition rate constant In an isolated cyclone the catalyst loss ṁc,loss, i.e. the catalyst which is entrained with the gas in the cyclone overflow, is measured for multiple passes of the catalyst through the cyclone. Under steady-state conditions it is assumed that the measured ṁc,loss is equal to the mass flow of fines generated by attrition inside the cyclone and the attrition rate rc is given by
rc =
ɺ c,loss m u2 = Cc ⋅ c,in ⋅ dp,c,mean ɺ c,in m µc
(5)
with the mean particle size dp,c,mean defined as n
dp,c,mean = ∑ dp,c,i ⋅ q3,i ∆dp,c,i
(6)
i =1
q3 denotes the mass density distribution of the particles entering the cyclone. The mean particle size is calculated with the PSD measured for the FCC catalyst.
6
steady state attrition rate -5 rc , 10 kg/kg
-5
attrition rate rc,10 kg/kg
In Fig. 4 the attrition rate rc against the passes through the cyclone is shown for two tests at different operating conditions. The results illustrate that the attrition rates measured for the first passes through the cyclone are higher than for the following. This can be explained by fines which are present in the feed material and are sifted off in the first passes through the cyclone. Thereafter the attrition rate decreases to a stationary value, the so called steady state attrition rate indicated by the dashed lines in Fig. 4. The steady state attrition rate measured for each operating condition plotted against the product of dp,c,mean·uc,in²·µc-0.5 is shown in Fig. 5. According to the attrition model equ. (1) the slope of the straight line represents the material specific steady state attrition rate constant Cc, which is 8.8·10-4 s²/m³ in this case. u=10 m/s µ=1 kg/kg u=20 m/s µ=1 kg/kg
5 4 3 2 1 0 0
5
10
15
20
number of passes trough the cyclone
Figure 4. Attrition rates rc measured over 15 passes through the cyclone for two different operating conditions
/
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0
0.01
0.02
0.03
0.04
0.5
dp,c,mean·uc,in²/µc , m³/s²
Figure 5. Steady state attrition rates versus the operating conditions according to the attrition model in equ. (1)
Attrition test in the CFB system The CFB system was operated over 200 h under different operating conditions. For each measurement condition the plant was filled with a predetermined mass of solids (8-200 kg). Then the plant was run for two hours in order to elutriate the fines of the initial inventory. After this time the cyclone loss contains only solids generated by attrition, i.e. the fines attrited from the solids and the particles which have shrunk by attrition. This is a quasi-steady state. A real steady state would require addition of fresh bed material compensating the loss. Since the measurements show that the catalyst loss during one hour is less than 0.05 % of the bed material this latter effect was neglected. The catalyst mass flow in the side stream of the cyclone is measured using the measurement setup shown in Fig. 3. Under each operating condition the measurement was repeated more than four times. In order to isolate the attrition effect in the riser it must be considered that the measured catalyst loss results from several mechanisms, i.e. attrition occurring in the cyclones, attrition in the riser, particle shrinkage and the separation in the cyclones. The difficulty is that the latter mechanisms are influenced by the catalyst’s particle size distribution and concurrently affect the catalyst’s PSD themselves. Hence, the CFB system has to be simulated in consideration of the catalyst’s PSD.
Simulation
cross sectional average mass flow Gs , kg/m²s
70
In Fig. 6 the cross sectional average catalyst mass flow in the riser Gs measured for the three different gas velocities versus the catalyst mass in the riser is shown. Obviously, the circulation rate increases with increasing gas velocity and catalyst mass in the riser. The Gs values measured under different operating conditions vary from 15 and 60 kg/m²s.
u = 3 m/s u = 4 m/s u = 5 m/s
60 50 40 30 20 10 0 0
20
40
60
80
100 120
catalyst mass in thecatalyst riser, kg mass Figure 6. Influence of the and gas velocity u on the cross sectional average solid mass flow in the riser Gs
The simulation flowsheet representing the pilot scale CFB system is shown in Fig. 7. The process is simulated based on the catalyst properties (i.e. PSD and attrition characteristics), the operating conditions and the geometry of the CFB system. Unlike the attrition constant Cc the constant for attrition occurring in the riser Cr (c.f. equ. (4)) is unknown. Hence, this parameter is fitted to all measurements using the simulation. The material specific attrition constant Cr is found to be 3.2·10-8 1/m. Compared to the attrition rate constants for FCC catalysts measured previously in a stationary fluidized bed Cb = 0.45·10-8 1/m and Cb = 1.37·10-8 1/m (12) the attrition rate constant Cr is higher. The exemplary result for the simulation conducted with a catalyst mass in the riser of 89 kg and a superficial gas velocity in the riser of 4 m/s are also shown in Fig. 7. The
/
results display that attrition in the riser is ṁattr,c2 = 0.23 g/h responsible for a major part of the catalyst loss. ṁattr,c1 = 14.7 g/h The rate of attrition occurring in the riser rr, operated at a superficial gas velocity of 4 m/s is 1.1 % of the catalyst inventory in the riser per day. The influence of the catalyst mass in the riser on the catalyst loss rate was experimentally investigated. Additionally, simulation runs with varying catalyst mass in the riser are conducted. A comparison between the measured and simulated catalyst loss rate is shown Fig. 8. The catalyst loss rate increases with increasing catalyst mass in the riser. The influence of the gas velocity in the riser on the catalyst loss rate is shown in Fig. 9. The gas velocity has a significant influence on the catalyst loss rate in the CFB system, with increasing gas velocity in the riser the catalyst loss increases. The results indicate that with the approach for attrition in the CFB riser, equ. (4), a satisfactory description of all measured values is possible.
61.6 g/h
ṁattr,r = 45 g/h
61.6 g/h
Figure 7. SolidSim flowsheet representing the CFB system with simulation results for mr = 89 kg and u = 4 m/s
CONCLUSION
6 mm probe 20 mm probe simulation
45 40 35 30 25 20 15 10 5 0
100 catalyst loss rate, g/h
catalyst loss rate, g/h
The catalyst loss of a pilot scale CFB system was measured. The influence of the catalyst inventory in the riser and the gas velocity in the riser was investigated. Based on a population balance approach the attrition in the CFB system was simulated. The simulation considered the attrition occurring in the cyclone and in the CFB riser in the mode of pure abrasion. As a first approximation the mass produced by attrition in the riser was assumed to be linear proportional to the excess gas velocity (u-umf) and the solids mass mr in the riser, respectively. The attrition constant for attrition occurring in the CFB riser was then fitted to the measured values using the simulation. The
60 40 20 0
0
20 40 60 80 100 catalyst mass in the riser, kg
120
Figure 8. Influence of the catalyst mass in the riser on the catalyst loss rate for u = 3 m/s
/
measurement simulation
80
0
1 2 3 4 5 gas velocity in the riser, m/s
6
Figure 9. Influence of the gas velocity in the riser on the catalyst loss rate for mr = 50 kg
comparison of the simulation results and the measured catalyst loss rates for varying solids mass in the riser and gas velocities indicates that the simulation predicts the tendencies without significant deviations. Additionally, the simulation allows the investigation of the individual attrition sources. NOTATION Ac Cb Cc Cr dp Gs ṁ m
cross-sectional cyclone inlet area, m² bubble induced attrition constant, 1/m constant for attrition occurring in the cyclone, s²/m³ constant for attrition occurring in the CFB riser, 1/m particle diameter, m average cross sectional solids mass flow in the riser, kg/m²s mass flow, kg/s mass, kg
q3 r uc,in umf µc ρc CFB PSD
mass density distribution, 1/m attrition rate, rc, - ; rb and rr, 1/s gas velocity in the cyclone inlet, m/s minimum fluidization velocity, m/s solids loading of the gas, kg/kg gas density at the cyclone inlet, kg/m³ circulating fluidized bed carticle size distribution
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Püttmann A., Hartge E. -U, Werther J. (2008), Modeling of Fluidized Bed RiserRegenerator Systems. In: Circulating Fluidized Bed Technology IX, Werther J.; Nowak W.; Wirth K-E; Hartge E. -U (eds.), TuTech Innovation GmbH, Hamburg.
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EFFECTS OF GAS VELOCITY AND SOLID HOLD-UP ON THE SUB-GRID BEHAVIOR OF RISER FLOWS Christian Costa Milioli 1 and Fernando Eduardo Milioli 2 University of São Paulo, School of Engineering of São Carlos, Dept. of Mech. Engng., Av. Trabalhador São-carlense, 400, 13566-590, São Carlos, SP, Brazil. 1
[email protected]; 2
[email protected] ABSTRACT Two-fluid highly resolved sub-grid simulations (SGS) of riser flows were developed under realistic gas velocities and solid hold-ups, for a solid phase derived from high Stokes number particles. The results showed that both gas velocity and solid hold-up considerably affect the effective hydrodynamics of the solid phase. INTRODUCTION Large scale simulations (LSS) with two-fluid models are expected, in time, to provide accurate predictions of real scale riser flows. One related aspect requiring attention is the proposition of sub-grid closures for solid phases. Along this last decade some researchers have been trying to draw those closures from sub-grid scale simulations (SGS) with two-fluid models. Among the relevant works on that matter are those of Sundaresan (1), Agrawal et al. (2), Andrews IV et al. (3), van der Hoef et al. (4) and Igci et al. (5). Those works take advantage of the fact that, regarding solid phases, highly resolved simulations are feasible in computational domains that are large enough to fit LSS numerical cells. Under suitable grid refinements a single SGS step can directly provide closures for LSS of real riser flows. The micro-scale description of the solid phase that is required in SGS is brought from the kinetic theory of granular flows (KTGF) (6,7,8,9). The common SGS computational experiment is performed in small periodic domains which are thought to repeat themselves throughout the whole volume of a riser. As periodic boundaries are applied an additional gas phase pressure gradient is introduced in the gravitational direction to account for the flow driving force. Such additional term is chosen to exactly match the gravity acting on the average gas-solid mixture, so that the simulations give rise to low velocity gas-solid flows. In spite of that, the clustering mechanism that prevails is believed to be relevant to rapid gas-solid flows (10,11). In a recent work by Benyahia (12), a filtered drag model derived from the sub-grid data of Igci et al. (5) was applied to the LSS of a riser flow. The author’s comparison of predictions against experiment showed considerable discrepancies, indicating that SGS under periodic boundaries requires enhancement. The present article is an attempt to contribute to the discussion on that matter by performing SGS under riser realistic conditions of gas velocity and solid hold-up. It is proposed, differently from all the previous work, to apply an additional gas phase pressure gradient in excess of that required to match the gravity acting on the local gas-solid mixture. In this case
1
the flow becomes accelerated and instantaneous field predictions are found through a range of gas velocities. While those predictions at any particular mesh point should not be regarded as significant in view of the instability of the flow, the domain average results, on the other hand, are thought to be quite representative. Simulations were developed for a range of domain average solid volume fractions, and results were analyzed in a range of gas phase axial velocities typical of riser flows. The effect of those parameters were evaluated over the flow topology, the average slip velocity, the effective stresses of the solid phase, and the effective drag. A solid phase was considered which is derived from a high Stokes number particulate typical of circulating fluidized bed combustors and gasifiers. MATHEMATICAL MODELLING Multiphase flow two-fluid models stand on the major hypothesis of continuum for all of the phases, no matter fluid or particulate. The phases are treated as inter-penetrating dispersed continua in thermodynamic equilibrium. The theory of two-fluid models has been developed by many researchers. Some classical references on this matter are due to Gidaspow (9), Anderson and Jackson (13), Ishii (14), Drew (15), Enwald et al. (16), among many others. The hydrodynamic two-fluid models comprise a basic set of average mass and momentum conservative equations plus closure laws for stress tensors, viscosities, pressures and drag. The present two-fluid model is formulated to perform SGS, which remains LSS alike regarding the gas phase, but is required to become highly resolved regarding the solid phase so that all the scales of clusters are captured. In this way, the gas phase would require closures at both the micro and the meso-scales. Literature shows that under high Stokes numbers, which is the present case, the turbulence of the fluid phase has little effect over the solid (2,17). As the concern in the present analysis is the behavior of the solid phase, no turbulence model is applied for the gas phase. The micro-scale closure for the solid phase is established by applying the kinetic theory of granular flows (KTGF), where solid phase micro-scale properties are derived as a function of a granular temperature determined from a pseudo thermal energy balance. In this work, for the sake of simplicity, the algebraic approach of Syamlal et al. (18) is applied, where the pseudo thermal energy is assumed to be locally generated by viscous stress and dissipated by inelastic collisions. Table 1 presents the sub-grid scale hydrodynamic formulation that was applied, where the gas phase continuity and momentum equations come from Favre averaging over the respective filtered equations. Periodic conditions are applied at entrance and exit, i.e. in the horizontal boundaries normal to the vertical gravitational direction. An additional gas phase pressure gradient is enforced in that direction to account for the flow driving force. Free slip is applied in all of the vertical boundaries. Agrawal et al. (2) showed that the application of either free slip, partial slip, or periodic conditions to vertical boundaries gives rise to the same flow topology. In the present work the simpler free slip condition was applied. Solid phase’s effective stresses and effective drag are determined from the SGS predictions by applying the relations in Table 2. The effective stress tensor is derived by Favre averaging over the filtered solid phase momentum equation. As a filter size is applied that exactly fits the sub-grid domain, the filtered parameters become equal to their volume averages. Following literature (4,5) the filtered drag force was expressed as a function of an effective drag coefficient and the filtered slip velocity, thereby providing a relation for the effective drag coefficient.
2
Table 1. Sub-grid scale hydrodynamic formulation of the two-fluid model. —————————————————————————————————————
(
)
(
)
∂ ρ g α g + ∇ ⋅ ρ g α g u~ g = 0 ∂t ∂ ( ρs α s ) + ∇ ⋅ ( ρs α su s ) = 0 ∂t ~ ∂ ~ ρ g α g u~ g + ∇ ⋅ ρ g α g u~ g u~ g = − α g ∇ Pg + ψ∇ Pg* + ∇ ⋅ α g τ~g − τ ge + ρ g α g g + M gI ∂t ∂ ( ρs α su s ) + ∇ ⋅ ( ρs α su su s ) = − α s ∇P~g + ψ∇Pg* + ∇Ps + ∇ ⋅ (τ s ) + α s ρs g + M sI ∂t α g + αs = 1
(
)
(
(
)
)
(
[ (
)]
)
∇Pg* = (ρs α s + ρ g α g ) g
[∇u~ + (∇u~ ) ] − µ (∇ ⋅u~ )I = µ [∇u + (∇u ) ] + (λ − µ ) (∇ ⋅ u ) I
τ~g = µ g
τs
g
s
T
g
s
s
2 3
T
g
2 3
s
4 Θ µs = α s2 ρs d p g 0 (1 + e ) 5 π
1
g
s
s
1
4 Θ 2 λs = αs2 ρs d p g 0 (1 + e ) 3 π
2
(Gidaspow, 9)
− 2.5 α
s ,max α s g 0 = 1 − (Lun and Savage, 19) α s ,max Ps = ρ s α s Θ[1 + 2(1 + e )g 0 α s ] (Gidaspow, 9)
Θ
1
2
=
[
( )]
− K1 α s tr (Ds ) + K12tr 2 (Ds ) α s2 + 4 K 4 α s K 2 tr 2 (Ds ) + 2 K 3 tr Ds2
1 2
Ds =
[∇u
s
+ (∇u s )T
2α s K 4
]
K1 = 2(1 + e )ρs g 0
K2 =
(Syamlal et al., 18)
4d p ρs (1 + e ) α s g 0 3 π
(
−
2 K3 3
)
d p ρs 12 1 − e 2 ρs g 0 π [1 + 0.4(1 + e )(3e − 1) αs g 0 ] + 8αs g 0 (1 + e) K4 = 2 3(3 − e ) 5 π dp π ~ M sI = − M gI = − β u s − u~ g ρ g α s u~ g − u s α s2 µ g β = 150 + 1.75 for αs > 0.2 (Ergun, 20; Gidaspow, 9) 2 αg d pφp d pφ p ~ ρg αsα g u g − u s − 2.65 3 β = C Ds αg for α s ≤ 0.2 (Wen and Yu, 21; Gidaspow, 9) 4 d pφ p K3 =
(
(
)
(
C Ds
)
(
)
)
( )
24 0 ,687 for R e 1 + 0.15 R e p p = for 0.44
R e p < 1000
Rep = R e p ≥ 1000
u~ g − u s d p ρ g α g µg
(Rowe, 22)
—————————————————————————————————————
3
SIMULATIONS The simulations were performed for a solid phase derived form a high Stokes number monodisperse particulate typical of low density risers (520 µm diameter, 2620 kg/m3 density), and for solid phase average volume fractions of 0.015, 0.03, 0.05, 0.07 and 0.09. Accelerating flows were generated and the results were analysed for increasing gas velocities from about 3 to about 9 m/s. A 2x2 cm wide and 8 cm tall vertical hexahedral domain was considered, applying a 1x1x1 mm uniform hexahedral numerical mesh. The flow entered the domain through the bottom and exited at the top. The density and viscosity of the gas phase were, respectively, 1.1614 kg/m3 and 1.82 x 10-5 N.s/m2. A solid phase volume fraction at maximum packing of 0.38 was applied following Gidaspow and Ettehadieh (23), and a restitution coefficient of 0.9 was taken following Agrawal et al. (2). Table 2. Solid phase’s effective stresses and effective drag. ————————————————————————————————————— = ρs α su su s
α s u su s ~ ~ τ se = ρ s α s ( u~ s u s − u su s ) = ρ s α s α
αu αu − s s⋅ s s αs αs
M sI = − β (u s − u~ g ) = − β (u s − u~ g )
M sI = − βe u s − u~ g
s
α su s ⋅ α su s − αs ~ β us − u g βe = u s − u~ g
(
)
————————————————————————————————————— The driving force factor ( ψ ) was set to 1.5. This value allowed the simulations to go along a suitable range of gas axial velocities in a reasonable computing time. Initial conditions for the accelerating runs were obtained by running previous simulations applying ψ = 1 , departing from uniform quiescent suspensions with fixed uniform solid volume fractions. A time step of 5x10-5 s was applied which is suitable for solid phase highly resolved simulations. The lower characteristic time scale of clusters of the order of 10-2 s (24). Also, for the present 520 µm particulate size the smaller clusters on the flow are expected not to be larger than 5.2 mm (following 2). Therefore, regarding the solid phase, both the spatial and temporal meshes which were applied are suitable for highly resolved simulations. The convergence criterion for the numerical procedure was a rms of 1x10-5. The simulations were carried out using the software Cfx (25). RESULTS The effects of the domain average solid volume fraction and gas phase axial velocity over the flow effective hydrodynamics were evaluated. The greyscale plots of solid phase fraction in Figure 1 show that the topology of the flow considerably changes by changing the concerning parameters. By increasing the average solid fraction, for a particular gas velocity, larger clusters are formed. By increasing the gas velocity, for a particular average solid fraction, the clusters become stretched in the axial direction. Figure 2 shows plots of the effective stresses of the solid phase. Even though the results are very scattered, it is possible to observe that higher solid fractions give rise to higher stresses. The effective shear stresses seem not to change with gas velocity, while the normal stresses show considerable variations (e.g. drawn line in Fig. 2 (b)).
4
0.015
0.05
0.09
0.015
∼ 3 m/s
0.05
0.09
0.015
0.05
∼ 6 m/s
0.09
∼ 9 m/s
> 0.25
0
Figure 1. Solid volume fraction in an axial section of the domain for αs = 0.015,
v g ≅ 3, 6, 9 m/s.
0.05, 0.09, and
|τxx,se|, |τyy,se|, |τzz,se| (N/m )
(b) 2
2
|τxy,se|, |τxz,se|, |τyz,se| (N/m )
(a) 1
1E-3
1E-6
3
4
5
6
7
8
9
< vg > m/s
10
0.1
1E-3
3
4
5
6
7
8
9
< vg > (m/s)
Figure 2. Effective shear (a) and normal (b) stresses of the solid phase as a function of v g , for αs = 0.015 (S); 0.03 (z); 0.05 (Â); 0.07 (U) and 0.09 ({). Figure 3 shows the behavior of the slip velocity and the effective drag coefficient. As seen, the higher the solid fraction, the lower the slip velocity, and the higher the effective drag coefficient. Both the parameters resulted little affected by the gas velocity, except for the slip velocity at higher solid fractions, where an oscillating behavior is also observed. This is possibly due to the formation of larger clusters in comparison to the size of the domain (see Fig. 1). Those oscillations are expected to disappear at sufficiently enlarged domains, that would always hold a considerable number of clusters throughout the whole range of gas velocities in an accelerating run. This issue, of course, requires verification. Figure 4 brings some of the predictions compared to empirical data of Luo (26). This author performed experiments in a riser column with the same conditions applied in the current simulations. From the measurements, Luo determined effective shear stresses and effective drag coefficients for various average solid fractions. A few of those solid fractions, for a gas velocity close to 5 m/s, fall in the ranges considered in
5
the present simulations. As seen in Figure 4, Luo’s results for those cases compare reasonably well with the present predictions, which is fine considering that Luo’s results apply to regions close to the column wall, while the predictions are volume averaged over a free slip walls domain. (a)
(b)
1000
βe (kg/m s)
4.5
750
3
< vg - vs > (m/s)
5.0
4.0 3.5
500 250
3.0 5
6
7
8
0
9
5
6
7
8
9
< vg > (m/s)
< vg > (m/s)
Figure 3. Slip velocity (a) and effective drag coefficient (b) as a function of
v g , for
αs = 0.015 (S); 0.03 (z); 0.05 (Â); 0.07 (U) and 0.09 ({). (b)
900
2
|τxy,se|, |τxz,se|, |τyz,se| (N/m )
(a)
3
βe (kg/m s)
1
0.01
1E-4 0.00
0.02
0.04
0.06
0.08
600
300
0 0.00
0.10
< αs >
0.02
0.04
0.06
0.08
0.10
< as >
Figure 4. Effective shear stresses of the solid phase (a) and the effective drag coefficient (b) as a function of αs , for v g ≅ 5 m/s; (X) empirical, Luo (26). CONCLUSION Two-fluid SGS was developed to investigate the sub-grid behavior of riser flows for a solid phase derived from a high Stokes number monodisperse particulate. Accelerated flow simulations were performed for a range of average solid fractions and gas velocities typical of risers. The effects of those parameters over the flow topology, the effective hydrodynamics of the solid phase and the effective drag were analyzed. The effects of both the gas velocity and the solid hold-up were found to be significant. A comparison was made of predictions against a few empirical data, and a reasonable agreement was found.
6
ACKNOWLEDGEMENTS This work was supported by The State of São Paulo Research Foundation (FAPESP), The National Council for Scientific and Technological Development (CNPq), and The Coordination for the Improvement of Higher Level Personnel (CAPES). NOTATION CD
drag coefficient (nd)
P
pressure (Nm-2)
dp
particle diameter (m)
∇P*
additional pressure gradient (Nm-3)
D e g
strain rate tensor (s-1) restitution coefficient (nd) gravity acceleration (ms-2) radial distribution function (nd) unit tensor (nd) interface drag force (Nm-3)
Re p
particle Reynolds number (nd) time (s) velocity vector (ms-1) Cartesian velocities (ms-1) SGS domain volume (m3) Cartesian coordinates (m)
g0 I M
t u u , v, w
V x , y, z
Greek letters α β Θ
λ
µ
ρ
volume fraction (nd) friction coefficient (kgm-3s-1) granular temperature (m2s-2)
τ τe φp ψ
bulk viscosity (Nsm-2) dynamic viscosity (Nsm-2)
density (kgm-3) viscous stress tensor (Nm-2) effective stress tensor (Nm-2) particle sphericity (nd) driving force factor (nd)
Subscripts e g I
meso-scale or effective gas phase interface
max
s
x , y, z
maximum solid phase Cartesian directions
Others
~
LSS filtered (resolved)
...
volume average,
f
=
1 V
~ αf Favre average, f = α
∫ f dV V
REFERENCES 1. Sundaresan, S. Modeling the hydrodynamics of multiphase flow reactors: current status and challenges. AIChE J. Vol. 46-6, pp. 1102-1105 (2000). 2. Agrawal, K., Loezos, P. N., Syamlal, M. and Sundaresan, S. The role of meso-scale structures in rapid gas-solid flows. J. Fluid Mech. Vol. 445, pp. 151-185 (2001). 3. Andrews IV, A. T., Loezos, P. N. and Sundaresan, S. Coarse-grid simulation of gas-particle flows in vertical risers. Ind. Eng. Chem. Res. Vol. 44-16, pp. 6022-6037 (2005). 4. van der Hoef, M. A., Ye, M., van Sint Annaland, M., Andrews IV, A. T., Sundaresan, S. and Kuipers, J. A. M., Multiscale modeling of gas-fluidized beds, Adv. Chem. Eng. Vol. 31, pp. 65-149 (2006). 5. Igci, Y., Andrews IV, A. T., Sundaresan, S., Pannala, S. and O’Brien, T. Filtered two-fluid models for fluidized gas-particle suspensions. AIChE J. Vol. 54-6, pp.
7
1431-1448 (2008). 6. Bagnold, R. A. Experiments on a gravity-free dispersion of large solid spheres in a Newtonian fluid under shear. Proc. R. Soc. Vol. A225, pp. 49-63 (1954). 7. Jenkins, J. T. and Savage, S. B. A Theory for the rapid flow of identical, smooth, nearly elastic spherical particles. J. Fluid Mech. Vol. 130, pp. 187-202 (1983). 8. Lun, C. K. K., Savage, S. B., Jeffrey, D. J. and Chepurniy, N. Kinetic theories for granular flows: inelastic particles in Couette flow and singly inelastic particles in a general flow field. J. Fluid Mech. Vol. 140, pp. 223-256 (1984). 9. Gidaspow, D. Multiphase flow and fluidization. San Diego. Academic Press (1994). 10. Goldhirsch, I., Tan, M. -L. and Zanetti, G. A molecular dynamical study of granular fluids I: the unforced granular gas in two dimensions. J. Sci. Comput. Vol. 8-1, pp. 1-40 (1993). 11. Tan, M. -L. and Goldhirsch, I. Intercluster interactions in rapid granular shear flows. Phys. Fluids Vol. 9-4, pp. 856-869 (1997). 12. Benyahia, S., On the effect of subgrid drag closures, Ind. Eng. Chem. Res., Vol. 49, pp. 5122-5131 (2010). 13. Anderson, T. B. and Jackson, R. Fluid mechanical description of fluidized beds. Equations of motion. Ind. Eng. Chem. Fund. Vol. 6, pp. 527-539 (1967). 14. Ishii, M. Thermo-fluid dynamic theory of two-phase flow. Paris. Eyrolles (1975). 15. Drew, D. A. Averaged field equations for two-phase media. Stud. Appl. Math. Vol. 1-2, pp.133-136 (1971). 16. Enwald, H., Peirano, E. and Almstedt, A. -E. Eulerian two-phase flow theory applied to fluidization. Int. J. Multiphase Flow. Vol. 22, pp. 21-66 (1996). 17. Fede, P. and Simonin, O. Numerical study of the subgrid fluid turbulence effects on the statistics of heavy colliding particles. Phys. Fluids Vol. 48, pp. 045103 (2006). 18. Syamlal, M, Rogers, W. and O´Brien, T. MFIX documentation theory guide. West Virginia. U.S. Department of Energy (1993). 19. Lun, C. K. K. and Savage, S. B. The effects of the impact velocity dependent coefficient of restitution on stresses developed by sheared granular materials. Acta Mech. Vol. 63, pp. 15-44 (1986). 20. Ergun, S. Fluid flow through packed columns. Chem. Eng. Prog. Vol. 48-2, pp. 89-94 (1952). 21. Wen, C. Y. and Yu, Y. U. Mechanics of fluidization. Chem. Eng. Prog. S. Ser. Vol. 62, pp. 100-111 (1966). 22. Rowe, P. N. Drag forces in a hydraulic model of a fluidized bed. Part II. Trans. Inst. Chem Eng. Vol. 39, pp. 175-180 (1961). 23. Gidaspow, D. and Ettehadieh, B. Fluidization in two-dimensional beds with a jet. Part II. Hydrodynamic modeling. Ind. Eng. Chem. Fund. Vol. 22, pp. 193-201 (1983). 24. Sharma, A. K., Tuzla, K., Matsen, J. and Chen, J. C. Parametric effects of particle size and gas velocity on cluster characteristics in fast fluidized beds. Powder Technol. Vol. 111, pp. 114-122 (2000). 25. Cfx, Discretization and solution theory. In Solver theory manual. Release 10. Waterloo. Ansys Canada (2005). 26. Luo, K. M., Experimental gas-solid vertical transport, PhD Thesis, Illinois Institute of Technology, Chicago, Illinois, (1987).
8
A GENERALIZED FLOW REGIME DIAGRAM FOR FLUIDSOLID VERTICAL TRANSPORT Xiaotao T. Bi Fluidization Research Centre Department of Chemical and Biological Engineering The University of British Columbia, Vancouver, Canada ABSTRACT An ideal generalized flow regime diagram was proposed for fluid-solids vertical transport systems with no bottom and top restrictions. Such an ideal flow regime diagram was further extended to shed light onto the understanding of the flow regimes and instabilities encountered in bottom- restricted bubbling and circulating fluidized bed systems. INTRODUCTION Flow patterns and flow regimes in gas-solids two-phase fluidization and vertical flow systems have attracted a great attention in the multiphase research community since the 1940s. A number of flow regime maps have been proposed to distinguish different unique flow patterns. Although it has been commonly agreed that there exist distinct flow patterns in gas-solids fluidized beds and vertical transport lines, such as the bubbling and slugging fluidization and dilute phase transport based on extensive research from 1940s to 1960s. Controversies still exist on the existence of turbulent fluidization, which was first proposed in late 1960s, fast fluidization, which was first proposed in late 1970s. The transition from pneumatic conveying to fast fluidization or dense suspension upflow is still not well defined, as reflected in the debates on the definition of choking in Fludization X in Beijing and CFB-7 in Naragra Falls. Further work on this topic is warranted in order to develop a generalized flow regime diagram for the flow pattern identification. In this work, attempt was made to elucidate the flow patterns in free or non-restricted gas-solids vertical flow systems in hope that such an analysis will shed some light on the understanding of the bottomrestricted fluidized bed systems and the dense suspension upflow system in which the solids feeding system is coupled with the flow in the riser. FLOW PATTERNS IN FREE GAS-SOLIDS VERTICAL FLOW SYSTEMS In gas-solids vertical flow systems with gas flowing upward, particles can travel up or down, giving rise to two possible flow modes: co-current upflow and counter-current flow. The termination of counter-current flow occurs when solids can no longer fall downward (i.e. at the flooding point) and the gas-solids co-current upflow ceases when the gas velocity is lower than particle terminal settling velocity.
If we feed solids from the middle section into a vertical tube with open top and bottom in which gas is flowing from bottom to top, both co-current upward flow in the upper section above the feeding point and counter-current flow in the lower section below the feeding point are possible depending on the gas velocity and the solids feeding rate. At a gas velocity lower than the particle terminal velocity, all feed particles will fall downward at a low feed rate, forming a counter-current flow in the lower section of the tube and a single-phase gas flow in the upper section, as shown in Figure 1. However, with the increase in solids feed rate, flooding will be reached when particles discharge rate from the bottom end of the tube becomes smaller than the solids feed rate. As a result, solids start to build up upward into the upper section, forming a dense fluidized bed in the upper section. This flooding phenomenon is in analogy to the flooding in gas-liquid counter-flow systems. 1400
II
V
Solids feed rate, kg/m2s
1200
1000 Saturation 800
IV 600
400 Flooding
I 200
Ut
III
Use
0 0
2
4
6
8
10
12
Superficial gas velocity, m/s
Figure 1. A flow regime diagram for non-restricted vertical transport lines. FCC particles in ambient air: mean particle size, 60 μm; particle density, 1800 kg/m3. Let us now consider the case when the gas velocity in the tube is higher than the particle terminal settling velocity. At a low solids feed rate all fed particles are carried upward giving a co-current upward flow in the upper section, and a single-phase gas flow in the lower section, shown in Figure 1. When the solids feed rate is increased to such an extent that the solids feed rate exceeds the saturation particle carrying capacity of the gas, excess amount of particles will fall downward and leave the tube from the bottom, forming a counter-current flow in the lower section as well as a cocurrent upward flow in the upper section. If the solids feed rate is further increased to such an extent that the downflowing particle rate exceeds the flooding rate which corresponds to the maximum discharge rate from the bottom end of the column, a
dense suspension starts to build up above the solids feed point, forming a co-current dense suspension upflow in the upper section and a flooded counter-current flow in the lower section. A flow regime diagram for a given vertical tube, gas and particle properties can be constructed based on the flooding velocity and the gas velocity corresponding to the saturation carrying capacity, estimated from two correlations: Equation (1) from Papa and Zenz [1], which was modified from the Sherwood equation originally developed to predict flooding in packed towers, is selected to predict flooding point:
⎡ U g ⎛ ρ g ⎞1 / 2 ⎤ ⎜⎜ ⎟⎟ ⎥ ⎢ ⎢⎣ gD ⎝ ρ D ⎠ ⎥⎦
2/3
1 ⎛G /ρ ⎞ + 1/ 3 ⎜ s p ⎟ 2 ⎜⎝ gD ⎟⎠
2/3
1/ 3
⎛ 1 ⎞ =⎜ ⎟ ⎝ 2 tan θ ⎠
(1)
where Ug is the superficial gas velocity, Gs is the solids flux, D the column diameter, θ is the angle of internal friction and is typically around 70 degrees for round-shaped particles. Equation (2) developed by Bi and Fan [2] based on experimental data in CFB risers is selected to predict the saturation carrying capacity:
U CA / gd p = 21.6 Ar 0.105 (Gs / ρ gU CA ) 0.542
(2)
Figure 1 shows such a flow regime diagram for a gas-solids vertical transport line with an upward gas flow. It is seen that there exist five unique flow regimes in the tube, as summarized in Table 1. Table 1. Flow regimes and corresponding flow patters in a vertical tube with open ends. Regime Ug Gs Upper section Lower section I
Gs,f Dense co-current Dense counter flow flow * III >Ut Ut Gs*Ut >Gs*+Gs,f Dense co-current Dense counter flow flow FLOW PATTERNS IN BOTTOM-RESTRICTED CIRCULATING FLUIDIZED BED SYSTEMS If the bottom of the tube is restrained by a distributor to prevent particles from escaping from the bottom of the system, a circulating fluidized bed forms as shown in Figure 2(b). Thus, two types of flow systems can be distinguished, with the free system corresponding to transport operation as shown in Figure 2(a), while the bottom-restricted system corresponds to (circulating) fluidized bed operation as indicated in Figure 2(b). A circulating fluidized bed can be operated in either a cocurrent upward flow (pneumatic transport) mode or fast fluidization mode, depending
on the gas velocity and solids circulation rate. It can be visualized that the flow pattern in the bottom-restricted CFB riser should be identical to the upper section above the solids feeding point of a free pneumatic vertical transfer line when the solids feeding/circulating rate is lower than the saturation carrying capacity of the gas. When the solids feeding rate is higher than the saturation carrying capacity, a dense bed forms at the bottom of the riser and develops upward with further increase in the solids feeding rate under steady state operation. The global flow patterns in the riser then resembles the “fast fluidization” as commonly accepted in the literature, with a dense region in the lower section and a dilute region in the upper section of the riser.
Gs Gs
Ug
(a) Free system
Ug
(b) Bottom-restricted
Figure 2. Illustration of (a) free vertical transport system and (b) bottom-restricted CFB system. A dense fluidized bed is typically operated at the saturation carrying capacity point, with entrainment rate equal to the saturation carrying capacity. The transition from bubbling to turbulent fluidization would thus be better defined by the flow pattern difference in the dense fluidization region. As generally agreed, such a transition corresponds to the balance between bubble coalescence and splitting, as reflected by the maximum pressure fluctuations in the dense bed, denoted by transition velocity Uc. The transition from turbulent fluidization to fast fluidization has still not been well defined. Some considered a critical velocity, Use, corresponding to the onset of significant particle entrainment as the transition form turbulent to fast fluidization [3, 4]. This critical velocity can be considered as a hindered or apparent terminal settling velocity of bed particles, reflecting the existence of particle clusters or agglomerates in the dense fluidized beds for Group A and fine Group B particles. For Group D particles, Use is essentially the same as the terminal settling velocity of single particles. Others proposed a transport velocity, Utr, beyond which the sharp change of vertical pressure drop gradient with increasing solids circulation rate disappears to quantify the transition from turbulent to fast fluidization [5]. An examination of pressure gradient profiles reveals that Utr varies with height. Utr may indicate a transition of axial voidage profiles in the riser [6]. Below this velocity, a distinct interface exists between the top-dilute and bottom-dense regions. Beyond this velocity, the interface becomes relatively diffuse. For Group A powders, another transition velocity, Uk, defined as the level-off point in pressure fluctuations with
further increase in gas velocity, was proposed in early years to represent the disappearance of bubbles in the fluidized bed [5]. Such a transition velocity was not consistently identified in later studies because of the strong influence of solids return system design [4, 7]. Quantitatively, reported Uk values were found to be very close to the critical velocity Use [6], suggesting some linkage between the breakdown of bubbles in the dense bed and the onset of significant entrainment of particles from the dense fluidized bed. If the transition from turbulent fluidization to fast fluidization is considered as corresponding to the flow pattern changes in the dense fluidized beds, e.g. the disappearance of regular shaped bubbles or voids, then such a transition can be demarcated by either Uk or Use. On the other hand, if such a transition is considered as the disappearance of a distinct dense-dilute interface around the upper bed surface, then Utr can be used to demarcate such a transition. Quantitatively, Utr appears to be slightly higher than Use and Uk, but generally around 1 to 2 m/s for Group A powders. Once substantial solids entrainment occurs at a gas velocity well above the transport velocity, the flow pattern in the CFB riser now depends on not only the superficial gas velocity but also the solids feeding rate, with the standpipe being coupled with the riser to establish a circulation loop. As a result, the circulation rate in the CFB system is now also influenced by the solids inventories due to the global pressure balance over the whole solids circulation loop [8]. Such a pressure balance becomes a key in understanding the “choking” phenomenon defined as the critical condition when the CFB riser terminates its stable operation, either because of the gas blower limitation to support a dense flow in the riser or the insufficient pressure head buildup in the standpipe to feed particles from the standpipe side into the riser due to a lower solids inventory [9], see Figure 3. For an ideal system with no “slugging” in the riser flow, there is no reason that prevents the riser to be operated at a full dense suspension flow at high solids circulation rates when the limitations from the gas blower and the standpipe solids return line are eliminated. The riser can thus be operated in a “dense suspension upflow” regime [10], similar to those identified in Figure 1. Decreasing superficial gas velocity (Gs = constant)
Severe slugging
UCC
Blower/standpipe UCB limitation
Bubbly/slug flow
Choking transitions
CFB FFB
Turbulent flow
VCA
Core-annular dilute-flow
Vmp
Homogenous dilute-flow
Vc Non-choking systems
Fast fluidization
CFB FFB
Circulating Fluidized Beds Fast Fluidized Beds
Figure 3. Flow patterns and termination of stable operation in a bottom-restricted CFB system.
FLOW REGIME MAPS FOR BOTTOM-RESTRICTED CIRCULATING FLUIDIZED BEDS The first attempt concerning the gas-solids co-current upward flow appears to be made by Zenz [11] with the pressure drop over a unit length (dP/dz) plotted versus the superficial gas velocity (Figure 4). He tried to develop a unified flow diagram combining experimental findings from traditional low velocity fluidized beds and pneumatic transport lines. The co-current gas-solids flow region in the diagram spans the flow regimes encountered in the circulating fluidized beds with the lower limit set by the “choking” velocity. The lower velocity fluidization is divided into a "dense phase" fluidization (likely to correspond to the bubbling fluidization) and "turbulent" fluidization (may be the same as the slugging/turbulent fluidization used nowadays) regions. The missing linkage between the lower velocity fluidization and the co-current upflow was attributed to “choking”. Such a regime diagram has been further extended to incorporate more sub-regions for the co-current upflow [12], including at least the homogeneous dispersed flow, core-annulus flow and fast fluidization. Since the pressure gradient (dP/dz) is proportional to the solids fraction if the friction and acceleration/deceleration are neglected, one can alternatively plot solids fraction [13] or bed voidage [3, 14] versus the superficial gas velocity (U) or the normalized superficial gas velocity (U/vt).
De n se P has e
"Turbulent" Fluidization
Log(dP/dz)
Slugging or Choking Velocity
W3 W2
Tu
be
Slug Flow
C G ocu as rr -S en ol t id sF lo w
Fix ed
Be
d
"Quiescent" Fluidization
mp ty
W1
sF ric tio Ga
W2
Countercurrent Gas-Solids Flow
ni nE
W3
W1
ut
Log(Ug ) Figure 4. Flow regime diagram of Zenz (1949) for both free and bottom-restricted vertical transport lines. W is the solid flux rate and ut is the terminal velocity. Another group of diagrams plots solids circulation rate (Gs) or solids loading ratio [Gs/(ρgU)] versus the superficial gas velocity or normalized superficial gas velocity, with one typical diagram shown in Figure 5. It is seen that the flow patterns in CFB
riser could be divided into the dilute phase flow, refluxing flow, fast fluidization and, ideally, turbulent and bubbly flow regimes if the severe slugging and blower and standpipe limitations are absent. 14 10 10
Ump
Use UCA
A reCo
8
la nnu
rF
low
11
ly F bb u B
-1 0.1 10
6
low
Inoperable
BD boundary
Approximate AB boundary
2
Ut Typical AC boundary
-2 0.01 10
Pa ck
UCC
4
0 0.00
Umf
Fast Fluidization
Uc
Flow
V*
Ug /u t
10
as e -Ph e t Dilu t ulen Turb
ed Be dF low
Homogeneous Flow
12
rt spo n a Tr
-3
0.05
0.10
0.15
0.20
0.25
0.30
Gs/ρp, m/s Figure 5. Flow regime diagram for co-current upward bottom-restricted CFB risers [12].
0.001 10
1
3
10 10
Ar
30 1/3
2
100 10
300
Figure 6. An ideal flow regime map for both fluidized beds and co-current upflow risers [12]. V* is the relative velocity between the superficial gas and particle velocities.
Grace [15] incorporated the typical operation regions of pneumatic transport lines and fast fluidized beds into a phase diagram plotted with the dimensionless superficial gas velocity versus the dimensionless particle diameter. The typical operation region for bubbling fluidized beds and the spouted beds are also identified in this diagram. Bi and Grace [12] extended this diagram by plotting the dimensionless relative velocity between the gas and particles against the dimensionless particle diameter, see Figure 6. Such a diagram is thus believed to be able to identify the ideal flow regimes without the solids circulation rate Gs provided in the diagram by assuming that the flow pattern will be primarily determined by the relative motion between the gas and particles in co-current upflow systems. To summarize, each diagram has its particular use and advantages. Those developed by Grace [15] and others mostly apply to dense fluidized beds with limited solids entrainment/circulation. The other two types of diagrams developed for vertical upflow in the riser, on the other hand, can provide detail quantitative boundaries between flow regimes for each riser-particle-gas system. The one developed by Bi and Grace [12] attempted to extend the Grace [15] diagram to the vertical co-current upflow riser system, without considering the limitations from the blower/standpipe.
Therefore, the severe slugging, blower and standpipe limitations are not captured in almost all of these ideal phase diagrams, but can be identified by using appropriate analyses and approaches as demonstrated in [8] for specific CFB systems. CONCLUSION A flow regime diagram for the non-restricted vertical transport lines includes at least 5 different flow patterns in the riser below and above the solids feeding level, with flooding limiting the maximum solids downflow through the lower section and saturation carrying capacity limiting the upward solids flow rate. In a bottomrestricted CFB riser, the same flow patterns exist in the upper section, except that there are now no particles leaving the riser from the bottom of the riser. The coupling of the riser and the standpipe makes the maximum solids circulation rate now being determined by the pressure balance over the whole CFB loop and the capability of the standpipe and the gas blower to withstand pressure fluctuations induced by severe slugging in the riser for slugging systems. REFERENCES 1. Papa, G.; Zenz, F. A. Optimize performance of fluidized-bed reactors. Chem. Eng. Prog. 1995, 91(4), 32. 2. Bi HT, Fan LS. Regime transitions in gas-solid circulating fluidized beds. Paper #101e, AIChE Annual Meeting, Los Angeles, Nov. 17-22, 1991. 3. Li Y, Kwauk M. The dynamics of fast fluidization. In: Grace JR, Matsen JM, eds. Fluidization. New York: Plenum, 1980, pp.537-544. 4. Bi HT, Grace JR, Zhu JX. Regime transitions affecting gas-solids suspensions and fluidized beds. Chem Eng Res Des 73:154-161, 1995. 5. Yerushalmi J, Cankurt NT. Further studies of the regimes of fluidization. Powder Technol 24:187-205, 1979. 6. Bi, H.T., Transition from turbulent to fast fluidization, Chem. Eng. Comm., 189, 942-958, 2002. 7. Rhodes, M.J. and D. Geldart, Transition to turbulence? Fluidization V, K. Ostergaard and A. Sorensen eds., Science Foundation, New York, pp.281-288, 1986. 8. Bi HT, Zhu JX. Static instability analysis of circulating fluidized beds and the concept of high-density risers. AIChE J 39:1272-1280, 1993. 9. Bi HT, Grace JR, Zhu JX. On types of choking in pneumatic systems. Int J Multiphase Flow 19:1077-1092, 1993. 10. Grace JR, Issangya AS, Bai DR, Bi HT, Zhu JX. Situating the high-density circulating fluidized beds. AIChE J 45:2108-2116, 1999. 11. Zenz FA. Two-phase fluidized-solid flow. Ind Eng Chem 41:2801-2806, 1949. 12. Bi HT, Grace JR. Flow regime maps for gas-solids fluidization and upward transport. Int J Multiphase Flow, 21:1229-1236, 1995. 13. Squires, A.M., M. Kwauk and A.A. Avidan, Fluid beds: at last, challenging two entrenched practices, Science, 230, 1329-1337, 1985. 14. Avidan, A.A. and J. Yerushalmi, Bed expansion in high velocity fluidization, Powder Technol., 32, 223-232, 1982. 15. Grace JR. Contacting modes and behaviour classification of gas-solid and other two-phase suspensions. Can J Chem Eng 64:353-363, 1986.
GAS-SOLIDS HYDRODYNAMICS IN A CFB WITH 6 CYCLONES AND A PANT LEG Leming Cheng a, Xinglong Zhou a, Chao Wang a, Qinhui Wang a, Zhongyang Luoa, Kefa Cen a, Li Nie b Chaogang Wu b, Qi Zhou b a State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China b Dongfang Boiler Group Co., Ltd. Chengdu 611731, China ABSTRACT Solids volume fraction and particle velocity profiles were measured with a fiber optical probe in a cold circulating fluidized bed test rig with 6 parallel cyclones and a pant leg. Results in the pant leg zone, main bed zone and exit zone of the furnace are reported. The work also includes the influences of superficial gas velocity, secondary air rate and static bed height on the gas-solids hydrodynamics. INTRODUCTION Circulating fluidized beds (CFBs) are commercially employed in a number of gas–solids contacting processes such as fluid catalytic cracking, fossil fuel combustion and gasification (1). Knowledge of the gas-solids hydrodynamics is the key to understanding and predicting chemical interactions between gas and particles, heat transfer performance and erosion in the furnace (2). The majority of previous experimental studies of solid flux profiles have been carried out in CFBs with single furnaces (3-5). In single furnace, a typical core-annulus flow pattern has been observed by many researchers in laboratory and industrial scale units (5-7). However, the structure of a CFB furnace is becoming more and more complex during scale-up, especially in recent development of a 600MW CFB boiler. Large-scale CFB furnaces have new characteristics, i.e. multiple outlets, pant legs and internal walls. Few studies have been conducted and reported on lateral solids flow in the riser with a pant leg structure. Therefore, further study on the gas-solids hydrodynamics in a large-scale CFB furnace is required to provide more understanding of the flow structure. In this study, solids volume fraction and particle velocity profiles in a CFB with 6 cyclones and a pant leg furnace were investigated by using a fiber optical probe (FOP). The work focused on three zones in the furnace, the lower pant leg zone, the main bed zone and the exit zone of the furnace. Effects of superficial gas velocity, secondary air rate and static bed height on the solids volume fraction and particle
velocity distributions were examined. This was a part of the work conducted to develop the 600MW CFB boiler. EXPERIMENTAL The experiments were done in the CFB cold model system shown in Fig. 1. The test rig was scaled down from a 600 MW CFB boiler (8) following the scaling law proposed by Glicksman et al (9). The riser had a height of 5.5 m and the bottom section of the riser looks like a pair of pants (pant leg structure). The internal walls of the pant leg were 1 m high. Above 1 m from the gas distributors, the riser had a cross-sectional area of 0.422 × 0.727 m2. 6 parallel cyclones were located asymmetrically on the left and right walls of the riser (Fig. 2). Circulating loops were formed with the pant leg riser, 6 cyclones, 6 standpipes and 6 loopseals. Most of the apparatus was made of Plexiglas for convenient observation. The measurements were conducted at atmospheric pressure and ambient temperature.
Fig. 1. Experimental CFB cold test rig
Fig. 2. Layout of the 6 parallel cyclones
During operation, the solids fluidization in the individual pant legs was controlled by independent primary air flow. Bed pressures in the two pant legs were kept in balance by a pressure control unit (PCU). The Fiber optical probe (FOP) method (10-12) was used to measure the solids volume fraction and the particle velocity in the riser. The probe measurement system (PV6D) was composed of an optical fiber probe, a control system, an A/D converter and a data acquisition system. It was calibrated before the measurements were made (13).
The FOP measurements were taken at seven different height levels (level 1: 0.25, level 2: 0.65, level 3: 1, level 4: 1.45, level 5: 2.55, level 6:3.65 and level 7: 5.25 m) above the air distributors as shown in Fig. 1. At locations from level 1 to level 6, the fiber optic probe was traversed from the left wall to the right wall. It was traversed from the front wall to the rear wall at level 7, as shown in Fig. 2. It should be noted that a positive value for the particle velocity means the particle moves upward in the riser at levels 1 ~ 6 and flows toward the cyclones at level 7. Glass beads with a density of 2364 kg/m3 and an average particle diameter of 0.114 mm were used as bed materials. The effects of superficial gas velocity (u0), secondary air rate (RSA) and static bed height (Hs) on the solids volume fraction and particle velocity profiles were studied in the experiments. The bed material properties and the operational parameters of the boiler and model are shown in Table 1. Table 1. Bed material properties and operational parameters Parameter Unit Boiler Temperature, Tb °C 900 3 Gas density, ρg kg/m 0.301 2 1.55E-04 Gas viscosity, Vs m /s mm 0.400 Mean particle diameter of, dp kg/m3 2000 Density of particle, ρs m/s Superficial gas velocity, u0 m/s 0.050 Minimum fluidization velocity, umf 2 kg/m s Solid flux, Gs
Model 50 1.093 1.80E-05 0.114 2364 1.7 0.016 6.00
RESULTS AND DISCUSSION Solids volume fraction and particle velocity profiles in the pant leg, main bed and exit zone Fig. 3 shows the solids volume fraction (εs) and particle velocity (up) profiles at level 2 (within the pant leg section, 0.65 m above the gas distributors) for u0 = 1.7 m/s, RSA = 10 % and Hs = 500 mm. Corresponding results for the left and right pant leg show similar distributions of εs and up although the particles in the two pant leg furnaces are fluidized independently. The solids volume fraction increased near the walls since it requires less energy for the bubbles to pass through the center of the bed (14). Particle velocity decreased near the walls, and a negative axial solids velocity appeared near the wall. Lateral profiles of εs and up at level 3 (top- section of the pant leg, 1 m above the gas distributors) at the same conditions of Fig. 3 are shown in Fig. 4. A W-shaped
distribution of the solids volume fraction and an M-shaped distribution of the particle velocity are seen in Fig. 4. Solid boundary layers exist near the side walls and the internal walls of the pant leg. The solids volume fraction in the boundary layer is higher than in the core zone. The maximum solids volume fraction in the boundary layer near the internal walls was even higher than in the boundary layer near the side walls. It was found that the downflow of particles in the boundary layers had different downflow velocities at different locations. The downflow particles near the enclosure walls had higher velocities (1.5 ~ 2 m/s) than that near the pant leg internal walls (below 1 m/s).
Fig. 3. Profiles of εs and up at level 2
Fig. 4. Profiles of εs and up at level 3
The above results on εs and up profiles in Fig. 3 and Fig. 4 give a general picture of the gas-solids flow pattern in the pant leg zone. Within the pant leg zone, two core-annulus flow structures are formed independently. These two cores are merged into one core at the top of the pant legs and then a W-shaped profile of solids volume fraction and an M-shaped profile of particle velocity are formed. Above the pant leg section, the riser is a single furnace. Fig. 5 shows εs and up profiles at level 4 (1.45 m above the air distributors) and shows a transitional flow pattern from the two core-annulus structures to a traditional core-annulus structure. The particle velocity profile has completely transitioned, but the solids volume fraction profile still is higher in the center of the furnace. Fig. 6 shows εs and up profiles of a traditional core-annulus flow structure in the upper dilute section of the riser (at level 5, 2.55 m above the air distributors). The boundary layer thickness is defined as the distance from the wall to the position of zero particle velocity, i.e. the position separating the particle upflow in the bed core and the particle downflow near the wall (7). From the areas marked out by the dotted lines in Figs 4 to 6, the boundary layer thickness is about 5 to 15 mm which decreases slightly with the height of the riser. This is in agreement with results from literature (6).
Inside the core zone, the εs and up profiles are both flat with an average εs of approximately 0.017 and an average up of about 1 m/s. The particles in the boundary layer have a downflow velocity which varies from 0.7 to 1.5 m/s depending on location. The downflow velocity reaches a lower value of about 0.7 m/s near the wall. This suggests a momentum transfer between the falling film and the wall, which is likely to depend on the properties of the wall surface (surface roughness of wall, inclination of surfaces from the vertical direction, etc.). The resulting solids volume fraction and particle velocity distributions are similar to measurements reported by Zhang et al. (7) .
Fig. 5. Profiles of εs and up at level 4
Fig. 6. Profiles of εs and up at level 5
The εs and up distributions at the exit section presented in Fig. 7 were measured at level 7 (near the horizontal ducts, 5.25 m above the air distributors). Due to the complicated structure of the exit section and the asymmetric layout of the cyclones, solids volume fraction and particle velocity were not evenly distributed across the furnace. Of the three outlets indicated by the dotted lines in Fig. 7, a slightly higher particle velocity and a lower solids volume fraction were measured for the middle outlet (outlet B). Effect of superficial gas velocity, secondary air rate and static bed height The evolution of the εs and up profiles as a function of superficial gas velocity (u0) is shown in Fig. 8. The results were measured at level 5 for a secondary air rate of 10 % and a static bed height of 500 mm. The solids volume fraction in the core zone increased with increasing superficial gas velocity. When u0 increases more particles are entrained into the upper section of the riser. However, the superficial gas velocity did not show a significant impact on solids volume fraction in the boundary layer. Moreover, in spite of increasing superficial gas velocity, it did not have a significant effect on particle velocity.
Fig. 9 shows εs and up profiles at a constant superficial gas velocity (u0 = 1.70 m/s) and static bed height (Hs = 500 mm) but at different secondary air rates (RSA). It can be seen that at level 6, the solids volume fractions in both the core zone and the boundary layer decrease when secondary air rate increases from 10 % to 20 %. The difference in the solids volume fraction is most likely, because of the barrier effect of the secondary air (15). The particle velocity in the core decreases when RSA increases due to the reduction in the acceleration of the particles due to primary air (16).
Fig. 7. Profiles of εs and up at level 7
Fig. 8. Profiles of εs and up for different u0
Fig. 9. Profiles of εs and up for different Rsa at level 6
Fig. 10. Profiles of εs and up for different Hs at level 6
Fig. 10 shows εs and up profiles at level 6 in the furnace for different static bed heights (Hs = 400 and 500 mm). The solid volume fractions in both the core zone and the boundary layer when Hs = 500 mm were higher than that for a lower Hs. This is reasonable because a higher Hs results in a higher average solids volume fraction in the whole furnace. Comparing the up profiles in Fig. 10, it can be seen that up in the core zone increases slightly with Hs while no clear influence is observed in the boundary layer.
CONCLUSIONS The results of the investigation show that within the pant leg section, two core-annulus flow structures are formed independently. These two cores are merged into one core above the pant leg section and the solids flow pattern makes a transition to a traditional core-annulus structure. The boundary layer thickness is about 5 to 20 mm which decreases slightly with riser height. The particles in the boundary layer have a downflow velocity which varies from 0.7 to 1.5 m/s. Due to the asymmetric layout of the three riser outlets on each side wall, a higher particle velocity and a lower solids volume fraction were observed in the middle outlet. The solids volume fraction in the upper section of the riser increases with superficial gas velocity and static bed height but decreases with secondary air rate. Particle velocity in the core zone of the upper riser section increases with static bed height and decreases with secondary air rate. There was no significant influence of bed height on particle velocity in the boundary layer. ACKNOWLEDGEMENT The authors are grateful for financial support of the National Key Technologies R & D Program of China (No. 2006BAA03B02-08, 2006BAA03B01-09). NOTATION dp: Mean diameter of particle [mm] 2 Gs: Solid flux [kg/m s] H: Height of the riser [m] Hs: Static bed height [mm] D: Depth of the riser’s cross-section [m] X: Radial position from left wall [m] RSA: Secondary air rate [%] Tb: Bed temperature [°C]
u0: Superficial gas velocity [m/s] umf: Minimum fluidization velocity [m/s] up: Particle velocity [m/s] Vs: Gas viscosity [m2/s] εs: Solids volume fraction [-] ρs: Solids density [kg/m3] ρg: Gas density [kg/m3]
REFERENCES 1.
2. 3.
Cheng LM, Chen B, Liu N, Luo ZY, Cen KF. Effect of characteristic of sorbents on their sulfur capture capability at a fluidized bed condition. Fuel 2004: 83: 925-932. Basu P. Combustion of coal in circulating fluidized-bed boilers: a review. Chemical Engineering Science 1999: 54: 5547-5557. Ye S, Qi XB, Zhu J. Direct Measurements of Instantaneous Solid Flux in a
4.
5. 6.
7. 8.
9. 10.
11. 12.
13.
14. 15. 16.
Circulating Fluidized Bed Riser using a Novel Multifunctional Optical Fiber Probe. Chem Eng Technol 2009: 32: 580-589. Malcus S, Cruz E, Rowe C, Pugsley TS. Radial solid mass flux profiles in a high-suspension density circulating fluidized bed. Powder Technology 2002: 125: 5-9. Salvaterra A, Geldart D, Ocone R. Solid flux in a circulating fluidized bed riser. Chem Eng Res Des 2005: 83: 24-29. Kim SW, Kirbas G, Bi HT, Lim CJ, Grace JR. Flow structure and thickness of annular downflow layer in a circulating fluidized bed riser. Powder Technology 2004: 142: 48-58. Zhang WN, Johnsson F, Leckner B. Fluid-Dynamic Boundary-Layers in CFB Boilers. Chemical Engineering Science 1995: 50: 201-210. Li Y, Nie L, Hu XK, Yue GX, Li WK, Wu YX, Lu JF, Che DF. Structure and performance of a 600MWe supercritical CFB boiler with water cooled panels. In: 20th International Conference on Fluidized Bed Combustion. Xian, China, 2009: 132-136. Glicksman LR, Hyre M, Woloshun K. Simplified Scaling Relationships for Fluidized-Beds. Powder Technology 1993: 77: 177-199. Li SH, Yang HR, Zhang H, Yang S, Lu JF, Yue GX. Measurements of solid concentration and particle velocity distributions near the wall of a cyclone. Chem Eng J 2009: 150: 168-173. Werther J. Measurement techniques in fluidized beds. Powder Technology 1999: 102: 15-36. Zhu JX, Li GZ, Qin SZ, Li FY, Zhang H, Yang YL. Direct measurements of particle velocities in gas-solids suspension flow using a novel five-fiber optical probe. Powder Technology 2001: 115: 184-192. Zhang H, Johnston PM, Zhu JX, de Lasa HI, Bergougnou MA. A novel calibration procedure for a fiber optic solids concentration probe. Powder Technology 1998: 100: 260-272. Guo QJ, Werther J. Flow behaviors in a circulating fluidized bed with various bubble cap distributors. Ind Eng Chem Res 2004: 43: 1756-1764. Koksal M, Hamdullahpur F. Gas mixing in circulating fluidized beds with secondary air injection. Chem Eng Res Des 2004: 82: 979-992. Ersoy LE, Golriz MR, Koksal M, Hamdullahpur F. Circulating fluidized bed hydrodynamics with air staging: an experimental study. Powder Technology 2004: 145: 25-33.
PRODUCTION OF GASOLINE AND GASEOUS OLEFINS: CATALYTIC CO-CRACKING OF PYROLYSIS OIL RESIDUE P. Bielansky, A. Reichhold, A. Weinert Vienna University of Technology, Institute of Chemical Engineering A 1060 Vienna, Getreidemarkt 9/166-3
ABSTRACT Co-processing of biomass in petroleum refineries is a promising approach for biofuel production. In this work fluid catalytic cracking of residue from a co-pyrolysis with sawdust and VGO (1:2) was investigated. The pyrolysis oil residue with a boiling range bigger than 350 °C was mixed in different ratios with VGO and could be processed successfully up to 20 m%. Crack gas amounts increased while gasoline and total fuel yields decreased compared to VGO cracking. The gasoline obtained has a high octane number and is oxygen free.
INTRODUCTION For the last decades crude oil was the most important raw material for the production of transport fuels. This leads to several problems like the dependence on politically unstable countries and the emission of huge amounts of fossil CO2. Many scientists see a correlation between global warming and the increase in CO2 concentration in the atmosphere (1). As a consequence new ways to substitute fossil fuels by renewable fuels have been investigated in the last years. Currently the production of so called first generation biofuels like bio ethanol and biodiesel can be considered as state-of-the-art technologies with considerable production capacities worldwide (2, 3). A disadvantage is that these fuels are derived from agricultural products which lead to a food vs. fuel dilemma (4). Second generation biofuels made of lignocellulosic biomass from forestry as well as agricultural and industrial waste are not in competition with food. Gasification with downstream Fischer Tropsch synthesis has been investigated extensively (5-7). However, high investment costs lead to high fuel prices. In contrast, co-processing biomass with FCC-plants in existing crude oil refineries requires only little additional investments. Due to the very large scale the conversion process is highly efficient and existing downstream facilities for product upgrading can be used (8). The suitability of the FCC-process for vegetable oils and used cooking oils has been shown by several researchers with promising results (9-11). The use of
lignocellulosic feed requires prior liquefaction. Bio oil obtained from flash pyrolysis is an interesting possibility for co-processing. Due to high oxygen and water content it is not miscible with VGO and needs upgrading associated with additional costs (12). Alternatively, bio oil can be in situ upgraded by introducing catalysts during pyrolysis (8). In literature some experimental results with different pyrolysis oils as FCC feedstock can be found. All show the tendency to high coke formation and reactor plugging if they are processed in high concentrations (8, 12). A new approach is co-pyrolysis. VGO is heated up in a stirred tank reactor and biomass is added. Released inorganic substances from biomass decomposition act catalytically. Thereby a part of the VGO is cracked mainly to diesel as well as gasoline and gases. These products leave the reactor. In this paper the suitability of a co-pyrolysis residue (boiling range 350°C plus) in admixtures with VGO in an FCC-plant is investigated.
EXPERIMENTAL Small scale pilot plant An FCC plant consists of the two main parts: reactor (usually constructed as riser), and regenerator. All experiments were conducted in a fully continuous FCC pilot plant at Vienna University of Technology. An internal circulating fluidized bed was used which means that regenerator and reactor are arranged concentrically in one apparatus. In Figure 1 a scheme of the apparatus and the periphery is shown, Table 1 comprises some benchmark data. To chimney
Gas
Natural Gas
Product
Gas-sampling tube for GC-analysis
N2 Cat-cooler
Air
Gas meter M
M
Condensate
Feed
Barrel 50l
Heated and stirred beaker
Flue gas
M
M
Gear pump
Gas cooler Gas analyzer
M
Figure 1: Scheme of the FCC pilot plant and periphery
Diaphragm pump
Feed is dosed by a gear- or a peristaltic pump and preheated in a tubular oven to a temperature barely under boiling temperature (approximately 280–320 °C) and enters the apparatus at the bottom of the riser. Due to the contact with the hot catalyst it evaporates instantaneously resulting in a strong upwards expansion. Thereby catalyst is sucked into the riser and pneumatically transported to the top. All cracking reactions as well as coke formation and deposition on the catalyst surface occur in the riser within a mean residence time of approximately 0.9 s. At the particle separator catalyst and products are separated. The product gas leaves the apparatus at the top. Due to the large difference in diameter from the riser to the upper part of the apparatus the fluidization velocity decreases under transportation velocity. Thus, the catalyst flows down the return flow tube and enters the regenerator through a nitrogen fluidized siphon which acts as a gas barrier. In the regenerator coke is burned with air whereby the catalyst is regenerated. Emerging flue gas leaves the reactor laterally. The heat generated is required for the endothermic cracking reactions. It is transported via the hot bed material as well as direct heat transfer to the riser. Feedstocks which yield more coke, and thus to a high catalyst temperature in the regenerator, require a cooler in the bottom part to adjust the riser temperature. Table 1: Benchmark data of the FCC pilot plant Height Riser length Riser diameter Regenerator diameter Catalyst Catalyst mass Catalyst spectrum Pressure
2.5 m 2.022 m 0.0205 m 0.18 m Commertial E-Kat Shape selective zeolite 9 - 11 kg 20 - 200 μm Ambient
Riser temperature Regenerator temperature Feed flow Riser residence time Fluidization bottom Fluidization syphon Fluidization regenerator Fluidization velocity Flue gas oxygen
550 - 600°C 590 - 650°C 1 - 3 l/h ca. 0 9 s 1.5 Nl/min 8 Nl/min 29 Nl/min 16 vmf 1 - 2 vol%
Sufficient siphon fluidization is required in order to maintain circulation. Interrupting this fluidization results in a breakdown of the circulation. As a result the level in the return flow tube increases while it decreases in the regenerator. Pressure measured at the bottom of the reactor decreases proportionately with the bed height whereby the circulation rate can be calculated during operation. The product gas is burned in a flare. For analyses purposes a branch current is sucked off before the flare by a diaphragm pump and condensed in three intensive coolers. The incondensable crack gas flows through a gas sampling tube and a gas meter and is then combusted with the rest of the product. The regenerator flue gas is determined by a gas analyzer. Analysis Gaseous and liquid chromatographs.
products
were
analyzed
separately
with
two
gas
The gas chromatograph used for crack gas analysis consists of two capillary columns and two detectors. Hydrocarbons are detected by a flame ionization detector (FID), nitrogen and carbon dioxide are detected by a thermal conductivity
detector (TCD). Liquid products were analyzed conducting a simulated distillation (SimDist) using a gas chromatograph with a capillary column and an FID. Additionally a PIONA analysis was conducted to obtain detailed information of the product composition and quality. RON and MON were calculated out of these results. Feedstock and Catalyst The experiments were conducted with different mixtures of VGO and residue from a co-pyrolysis from VGO and lignocellulosic biomass. Both feeds were provided by the OMV AG. VGO is the top product of the vacuum distillation with a boiling range between 350 and 650°C. It consists mainly of paraffins, naphtenes und aromatics. A low sulfur content to obtain long catalyst lifetime is achieved by hydro treating (Figure 2). The co-pyrolysis was conducted in a batch stirred tank reactor under atmospheric pressure. VGO was heated to approximately 350 °C and 33 m% biomass (sawdust) was added. The lignocelluloses started to decompose immediately to gaseous and liquid products, coke, and inorganic compounds. These inorganic substances (mainly salts) act catalytically and enable cracking of a part of the VGO. All substances with a boiling range below reactor temperature (mainly diesel with a cetane number of approximately 40 as well as gasoline, gases, and water) leave the reactor. The remaining residue and coke are separated by a centrifuge. According to C14 analysis the residue contains a certain amount of biomass derived substances. Only small amounts of the oxygen from the biomass remain in the residue. Table 2 shows the elementary analysis. Detailed analyses of the co-pyrolysis residue are confidential and cannot be published. In this paper the suitability of this residue (boiling range 350°C plus) in admixtures with VGO in an FCC-plant is investigated. Table 2: Composition of the co-pyrolysis residue Nitrogen Carbon Hydrogen Sulfur Oxygen Ash Water
0.3 84.7 11.1 <0.1 1.8 1.576 <0.1
[m%] [m%] [m%] [m%] [m%] [m%] [m%]
Figure 2: The main components of VGO The commercial FCC equilibrium catalyst E-Space from the company Grace Davison was used. It is an acidic spray dried REUSY-catalyst which is partially coated with ZSM-5-zeolite crystals. It was already in use at the OMV refinery in Schwechat and extracted during the process from the FCC-plant. Thus there was no need to steam it to obtain a certain conversion level.
Definitions and calculations For product characterization a lump model was used (Table 3). Gaseous and liquid fractions were separated by condensation. Water and liquid organic products can be easily separated by phase separation. The liquid organic phase was divided furthermore according to the boiling range in gasoline, light cycle oil (LCO) and residue. In order to determine the coke lump the flue gas was analyzed by a gas analyzer for O2 (paramagnetic measurement method) as well as CO and CO2 (infrared measurement method). The amount of coke is calculated out of these values. Table 3: The six Lump Model Fraction Gas Fraction Liquid fraction
Solid fraction
Lump Crack gas Gasoline LCO Residue Water Coke
Composition, Boiling range C1-C4 <215°C 215 - 320°C >320°C
Analysis method GC SimDist SimDist SimDist Gravimetric Flue gas composition
The total fuel yield X is defined as: TFY =
mCrack
gas
+ mGasoline
mFeed
RESULTS Experiments lasted about six hours each in steady state operation. Three sample periods of 15 minutes were made per run and analyses values were averaged. The riser temperature was set to 550 °C. 15
80
60 50
Total fuel yield Crack gas Gasoline
40 30 0 5 10 15 20 Amount co-pyrolysis residue [m%]
Figure 3: Influence of co-pyrolysis amount on total fuel yield, crack gas, and gasoline
Amount [m%]
Amount [m%]
70
LCO Residue
10
5
0
0 5 10 15 20 Amount co-pyrolysis residue [m%]
Figure 4: Influence of co-pyrolysis amount on light cycle oil and residue
Figure 3 depicts the total fuel yield. It decreases from approximately 82.5 m% for VGO to 77.5 m% with 20 m% admixture of pyrolysis residue. The gasoline yield decreases significantly from approximately 52 m% to 39 m% with a pyrolysis residue content of 17.5 m% and increases to 40 m% with 20 m% pyrolysis residue. Crack
gas rises clearly from 31 m% to a maximum of 38 m% between 10 and 17.5 m% pyrolysis residue addition and decreases slightly to 37.5 m% at a 20 m% admixture. LCO increases slightly with a maximum at 17.5 m% pyrolysis residue addition. Approximately 4 m% residue is formed with a clearly higher value at 20 m% pyrolysis residue admixture (Figure 4). 15
Amount [m%]
Coke [m%]
8
6
4
10
Ethene Propene
5
0 0 5 10 15 20 Amount co-pyrolysis residue [m%]
Figure 5: Influence of co-pyrolysis amount on coke
0 5 10 15 20 Amount co-pyrolysis residue [m%]
Figure 6: Influence of co-pyrolysis amount on ethene and propene
Coke amounts increase strongly with higher pyrolysis residue ratios with a maximum at 17.5 m% addition (Figure 5). Ethene and propene increase with increasing pyrolysis residue admixture. This is mainly caused by higher crack gas yield, concentration of gas components stays roughly constant (Figure 6). The C/O ratio for experiments with pyrolysis residue addition was higher than for VGO experiments (Figure 7). Figure 8 shows the gasoline composition for three samples with different pyrolysis residue amounts in the feedstock. Naphtenes and iso-paraffins concentrations are less with admixtures, more aromatics are formed. N-paraffins and olefins stay roughly constant. 60 50 Amount [m%]
30
C/O Ratio [-]
25 20 15
0 m% Pyrolysis residue 10 m% Pyrolysis residue 20 m% Pyrolysis residue
40 30 20 10 0
10 0 5 10 15 20 Amount co-pyrolysis residue [m%]
Figure 7: Influence of co-pyrolysis amount on the catalyst/oil ratio
es ins ins ins ins ins tics hten araff araff Olef i-Olefn-OleAf roma Nap i-P n-PCyclic
Figure 8: Gasoline composition with different amounts of co-pyrolysis residue
Table 4 depicts some gasoline characteristics. Research octane numbers (RON) are generally at a high level with bigger values for experiments with pyrolysis residue
addition. Motor octane numbers (MON) show an opposite trend. Benzene (like aromatics in general) increases at higher pyrolysis residue ratios. The caloric value for all samples is at a similar level. Gasoline from feedstock with addition contains less hydrogen. Further on, density and thus average molecular weight are slightly higher. Table 4: Gasoline characteristics Amount pyrolysis oil RON MON Benzene Caloric value C:H Ratio Density Average molecular weight
0 101.8 90.5 0.99 42.85 0.57:1 763.5 104.2
10 104.4 89.5 1.64 42.19 0.62:1 791.2 106.3
20 103.2 88.2 1.56 42.28 0.61:1 788.0 106.4
[m%] [-] [-] [m%] [MJ/kg] [-] [kg/m³] [g/mol]
CONCLUSIONS Admixtures of VGO and co-pyrolysis residue up to 20 m% could be converted successfully in a fully continuous FCC pilot plant for several hours in steady state operation. No major adaption was necessary. Higher pyrolysis residue contents led to more crack gas and less gasoline, resulting in a decrease in total fuel yield. The product quality was very high with RON clearly over 100 and MON around 90. Due to the chemical similarity (the product is oxygen free) it can substitute regular gasoline in any percentage without limitation. VGO experiments could be conducted without catalyst cooling in the regenerator bottom. Due to the bigger coke yield with pyrolysis oil admixtures the regenerator temperature increases and cooling was necessary to reach the required riser temperature. The VGO experiments showed a decrease in temperature from riser bottom to the top. Experiments with pyrolysis oil admixtures had a different trend due to relatively low catalyst temperature in the bottom part. This led to higher reaction temperature in the upper part of the riser thus promoting secondary cracking. Higher C/O-ratios for experiments with pyrolysis oil addition also supported secondary cracking. These two process parameters may have enhanced gas formation. For admixtures with more than 20 m% pyrolysis oil no stable operation point could be found. The feed tended to strong coking in the feed inlet area after a few minutes of operation. As a result the riser clogged and circulation collapsed. One of the big advantages of the presented technology is the possibility of coprocessing in existing petroleum refineries. The large scale of these facilities leads to high efficiency in the conversion and product upgrading process. Additionally, considerable amounts of propene and ethene are formed which can be used to produce polymers out of renewable sources.
ACKNOLEDGEMENT This work was supported by OMV AG.
REFERENCES 1 IPCC, Climate Change 2007 - The Physical Science Basis: Working Group I, Contribution to the Fourth Assessment Report of the IPCC, Cambridge, University Press, (2007) 2 EU, Panorama of Energy – Energy Statistics to Support EU Policies and Solutions, (2007) 3 IEA, Biofuels for Transport – An International Perspective, 2004 4 R. Rathmann, A. Szklo, R. Schaeffer, Land use competition for production of food and liquid biofuels: An analysis of the arguments in the current debate, Renewable Energy 35 (2010) 14–22 5 M.J.A. Tijmensen et al., Exploration of the possibilities for production of FischerTropsch liquids and power via biomass gasification, Biomass and Bioenergy 23 (2002) 129–152 6 T. Takeshita, K. Yamaji, Important roles of Fischer-Tropsch synfuels in the global energy future, Energy Policy 36 (2008) 2773–2784 7 S. Fürnsinn et al., Diesel aus Holz – die Fischer- Tropsch Synthese als zukunftsweisende Technologie zur Gewinnung flüssiger Brennstoffe aus Biomasse, 4, Internationale Energiewirtschaftstagung an der TU-Wien, 2005 8 A.A. Lappas, S. Bezergianni, I.A. Vasalos, Production of biofuels via coprocessing in conventional refining processes, Catalysis Today 145 (2008) 55– 62 9 R.O. Idem, S.P.R. Katikaneni, N.N. Bakhshi, Catalytic conversion of canola oil to fuels and chemicals: roles of catalyst acidity, basicity and shape selectivity on product distribution, Fuel Processing Technology 51 (1997) 101–125 10 T.L. Chew, S. Bhatia, Catalytic processes towards the production of biofuels in a palm oil and oil palm biomass-based biorefinery, Bioresource Technology 99 (2008) 7911–7922 11 P. Bielansky, A. Reichhold, C. Schönberger, Catalytic cracking of rapeseed oil to high octane gasoline and olefins, Chemical Engineering and Processing 49 (2010) 873–880 12 G. Fogassy, Biomass derived feedstock co-processing with vacuum gas oil for second-generation fuel production in FCC units, Applied Catalysis B: Environmental 96 (2010) 476–485
COMMISSIONING OF A 0.8 MWTH CFBC FOR OXY-FUEL COMBUSTION L. Jia, Y. Tan, D. McCalden, Y. Wu, I. He, R. Symonds and E.J. Anthony CanmetENERGY, 1 Haanel Drive, Ottawa, ON, Canada, K1A 1M1 ABSTRACT Oxy-fuel fluidized bed combustion (FBC) is a new technology being developed for power production from carbonaceous fuels while producing a nearly pure steam of CO2 ready for sequestration or storage. Unlike oxy-fuel pulverized fuel combustion technology, oxy-fuel FBC offers the opportunity to use poor quality coals, hydrocarbon residues and a range of other materials including biomass. In Canada, pitches, tars and bottoms, in particular, are available in large quantities in western Canada, and this technology offers an opportunity to deal with many of these waste feedstocks in an environmentally benign manner. In addition, oxy-fuel circulating FBC (CFBC) can be fired at lower flue gas recycle ratio, offering potentially smaller plants for any given power output, and can capture sulphur in situ. CanmetENERGY has been operating a 75 kW oxy-fuel CFBC since 2006 with full flue gas recycle. Initial results were very encouraging and in order to further study oxy-fuel FBC technology, a 0.8 MW th CFBC unit has been retrofitted for oxy-fuel research. The facility is used to emulate commercial oxy-fuel CFBC performance. The modifications included adding oxygen supply, flue gas recycle train, airtight fly ash discharge, flue gas compressor for baghouse pulsing and system purge, etc., as well as upgrading the control and instrumentation for oxy-firing. The most major challenge has been to properly seal the entire CFBC unit to prevent air ingress. Fuels fired during the commissioning phase included bituminous coal and petroleum coke from the US, and lignite from Saskatchewan. Combustion under oxy-fuel conditions has proved to be very stable and the transition from air firing mode to oxyfuel firing mode and vice versa were quick and presented little operational difficulties. This work has demonstrated that the retrofitted oxy-fuel CFBC can produce a stream of flue gas containing 80% to 90% CO2. The NOx emissions were significantly lower compared to air firing in CFBC with the same fuel. SO2 capture was in the range of 70% to 75%, but limestone utilization is lower than in air-firing mode, and research is on-going to better understand sulphation under oxy-firing conditions. INTRODUCTION Anthropogenic CO2 production is primarily driven by fossil fuel combustion. To reduce greenhouse gas emissions from fossil fuel combustion in the electric utility industry, CO2 capture and storage (CCS) appears to be among the most promising methods (1). Oxy-fuel pulverized coal combustion technology had been studied for many years (1, 2, 3). The commissioning of a 30 MW th oxy-fuel demonstration plant at Schwarze Pumpe in Germany by Vattenfall is an important milestone for the
development of pulverized fuel (PF) oxy-fuel technology. The Schwarze Pumpe oxyfuel plant has been running initial tests since mid 2008 (4). Oxy-fuel circulating fluidized bed combustion (CFBC) is a relatively new technology compared with oxy-fuel PF combustion, but has several advantages. It can be fired at lower flue gas recycle ratio; it can capture sulphur in situ; and can co-fire biomass. CanmetENERGY-Ottawa has been working on this technology since 2006, using a 75 kW th CFBC facility with full flue gas recycle and has obtained very positive results (5, 6). Eriksson et al. (7) have also reported on their initial oxy-fuel CFBC results in 2007 using a similar facility to that at CanmetENERGY and their results also confirm the same advantages with the technology. At this point the technology is receiving attention in Canada (5, 6), Finland (7), Poland (8), China (9), USA (10) and Spain, where a 20 MW th demonstration oxy-fuel CFBC plant is being built (11), among other countries. In order to advance studies in this area, a 0.8 MW th CFBC unit at CanmetENERGY has been retrofitted for oxy-fuel research. The facility is used to simulate commercial oxy-fuel CFBC performance. The modifications included adding oxygen supply, flue gas recycle train, airtight fly ash discharge, flue gas compressor for baghouse pulsing and system purge, etc., as well as upgrading the control and instrumentation for oxy-firing. This paper describes the pilot oxy-fuel CFBC plant and initial results obtained during the commissioning of the unit. CanmentENERGY’s 0.8 MWth OXY-FUEL CFBC The 0.8 MW th pilot-scale CFBC research facility at CanmetENERGY was built in the 1990s to generate a database useful for the design and process optimization of fullscale units, study emissions of pollutants and predict the combustion performance of feed stocks. The main components of the facility comprise the refractory-lined combustor and cyclone, an inclined L-valve, four retractable bayonet-type cooling tubes, flue gas cooler, baghouse, fuel and sorbent feed system, stack and air supply system. The combustor has an inside diameter of 0.406 m and an internal height of 6.5 m. The CFBC has comprehensive instrumentation, control and data acquisition systems and is also equipped with an 0.5 MW start-up burner fired with natural gas to preheat the unit as well as to provide an additional heat source if needed. Modifications to the pilot-scale CFBC began in 2007 for oxy-fuel CFBC research. Oxygen supply and flue gas recycle were installed, and much work related to preventing leakage throughout the entire plant has been undertaken. The oxy-fuel CFBC is shown schematically in Fig. 1. Components shown in red are the existing parts, and components shown in blue are the new additions. The primary fluidizing gas, secondary gas and loop gas are all supplied from the main gas header. The supply pressure of the main gas header is typically maintained at 55 kPag. Secondary gas may be introduced into the combustor at up to 5 different locations along the height of the combustor. The positions of these inlets are at 765 mm, 1549 mm, 2159 mm, 2769 mm and 4521 mm above the distributor.
Figure 1. CanmetENERGY 0.8 MW th oxy-fuel CFBC research facility The recycled flue gas is controlled using a Tuthill Competitor GT series tri-lobe blower capable of delivering up to 800 kg/h of recycled flue gas through the main air header at a discharge pressure of 55 kPa. The flow, temperature and header pressure of recycled flue gas are measured and logged. Oxygen is added to the main header after the loop seal gas header. Oxidant level in the gas header can be brought up to 29% oxygen (vol. wet basis). Oxidant level in the main air header is measured with a Siemens Oxymat 61. The Oxymat 61 measures oxygen using the paramagnetic alternating pressure method. Both the primary and secondary gases have the same oxidant level at the present time. A planned upgrade will see a second oxygen train installed, allowing the primary and secondary fluidizing gases to be operated at different oxygen levels if desired. Oxygen for the enrichment of the recycled flue gas is provided from a Linde-supplied 1500 US gal. bulk liquid oxygen tank. The oxygen is vaporized and brought to the pilot facility at 50 psig. The maximum design flow of oxygen using this system is 220 kg/h. Isolation between the air system and recycle gas system is achieved using two pneumatically actuated proportionate slide gate valves. The fuel feeding system is comprised of a fuel storage bin, vibratory bin dispenser and belt-type gravimetric weigh feeder. The maximum feed rate of the weigh feeder is 342 kg/h with a 20:1 turndown ratio. The sorbent feeding system has a similar arrangement as the fuel system. Maximum sorbent feed rate is 64.8 kg/h. The combustor is equipped with four bayonet cooling tubes that can be inserted or raised during operation. Each bayonet tube is 7160 mm in length and is fabricated with a 30 mm schedule 40 outer tube and a 16 mm 18 BWG inner tube (both 304SS). The bayonet tubes were designed for a total heat duty of 546 kW with a water flow rate of 217 L/min. Fly ash removal is accomplished with a negative pressure fabric filter dust collector with a pulse jet bag cleaning system. The dust collector uses Nomex bags with
maximum temperature rating of 200C. The design gas volume is 0.59 Am3/s with a normal operation inlet temperature of 177ºC and a dust loading of 48-79 g/Am3. Ports are available for solid and gas sampling from the combustor. In addition, CanmetENERGY has the equipment and trained staff for stack sampling of volatile organic compounds (VOCs) and isokinetic sampling for semi-volatile organic compounds (SVOCs), metals and polyaromatic hydrocarbons (PAHs) using US EPA or Environment Canada standard sampling techniques. Under air firing conditions, the pressure balance point is usually at the furnace exit. Under oxy-fuel mode, the combustor is run under slightly positive pressure to prevent air ingress. Some typical operating parameter ranges are shown in Table 1. Table 1. Range of operating conditions Input Range Combustion temperature up to 980ºC Heat input up to 0.8 MW th Excess O2 2-10% Ca/S molar ratio 0-3 Primary air 30-100% Secondary air 0-70% Loop seal air 1-5% Superficial velocity 2-7 m/s O2 in primary gas up to 29% O2 in secondary gas up to 29% Table 2. Fuel analysis, wt% (as analyzed) Petroleum coke Proximate analysis Moisture 0.63 Ash 0.48 Volatile 10.17 Fixed carbon 88.72 Ultimate analysis Carbon Hydrogen Nitrogen Sulphur Oxygen (diff) LHV, MJ/kg
86.84 3.42 1.48 5.61 1.59 32.46
Anthracite 8.3 30.7 6.4
54.6 1.7 0.9 1.0 2.8 19.89
COMMISSIONING TESTS Bituminous coal, lignite, petroleum coke and anthracite were all used in the commissioning tests. Table 2 gives analysis of some of the fuels. Limestone was not used in the first several tests since the primary goal was to first obtain the desired CO2 levels in the flue gas. In later tests, Calpo limestone (98.6% CaCO3) from Canada was used.
The first several tests under oxy-fuel mode revealed that transition from air firing to oxy-fuel firing was quick and smooth. The transition took less than 30 min to complete. Combustion under oxy-fuel mode was stable. Transition from oxy-fuel to air firing mode was faster. No operational difficulties were encountered. However, CO2 concentrations in the flue gas were only about 50-60% in these initial tests. Repeated efforts were made to seal the flue gas recycle train and all components which are under negative pressure during oxy-fuel operation (baghouse, flue gas cooler, etc.). It was a very difficult task. Once the system was properly sealed, CO2 concentration in the flue gas reached ~85%+ consistently in the following tests. Figures 2 and 3 show the first successful oxy-fuel test in the 0.8 MW th CFBC unit firing Pine Bend coke. CO2 concentration remained at ~90% for the entire duration of the oxy-fuel firing period. Temperature distribution along the axis of the combustor was similar to that of air firing with the same fuel. Figure 2 shows the transition from air to oxy-fuel firing was completed in about 30 min, and the transition from oxy-fuel to air firing was very fast indeed. Figure 3 shows the concentrations of SO2 and CO. Note the SO2 concentration was over the range of the SO2 analyzer (1.5%) during oxy-fuel firing conditions. Later a new analyzer with a much higher range was purchased and installed. CO concentration was about the same for both air firing and oxy-fuel firing periods. The increase of CO at time 16+ h was apparently caused by the decrease of oxygen concentration as shown in Fig. 2.
Concentration, %
100 90
CO2
80
O2
70 60 50 40 30 20 10 0 10
11
12
13
14
15
16
17
18
time, hour
Figure 2. Oxy-fuel combustion of Pine Bend coke. Note the transition from air firing to oxy-fuel mode in about 30 min.
14000 SO2 CO
Concentration, ppm
12000 10000 8000 6000 4000 2000 0 10
11
12
13
14
15
16
17
18
time, hour
Figure 3. SO2 and CO concentrations during oxy-fuel combustion of Pine Bend coke. As the commissioning process progressed, a full week test using anthracite with limestone addition was carried out. Figure 4 shows CO2 and O2 concentrations for the entire duration of the test. 100 90 CO2 O2
80
CO 2, %
70 60 50 40 30 20 10 0 0.5
1
1.5
2
2.5 Time , day
3
3.5
4
4.5
Figure 4. Oxy-fuel CFBC combustion of anthracite. Results shown in Fig. 4 and Table 3 demonstrate that the retrofitted 0.8 MW th oxyfuel CFBC unit was reliable. It was extremely stable during operation. This test proved it can be run continuously for long periods of time under oxy-fuel CFBC conditions to generate useful data for industrial purposes. It should be noted that since the pilot CFBC does not have an ash cooler (external or integrated) in the return loop, the recycle ratio was higher, at 70.7%. Fuel nitrogen to NO was low, 1.13% under oxy-fuel conditions. However, it is in line with our earlier results on the much smaller 10 cm (75 kW th) oxy-fuel mini-CFBC plant (5, 6). Sulphur capture was poorer than would be expected based on experience with air firing. This poor performance may also have been exacerbated by the fact that the limestone used was too fine. It is of interest that Scala and Salatino (12) also noted a lower degree of sulphation in batch tests carried out in a 40 mm ID oxy-fired bubbling bed reactor,
which they relate to the well known phenomenon that direct sulphation is slower than indirect sulphation until conversions reach a level around 30-40% (13). However, a complicating factor is that all comparative evidence for this difference has been developed using TGA and differential reactors using dry gas mixtures (14). However, while it is recognized that direct sulphation is influenced by water (15), the assumption that indirect sulphation is not itself strongly influenced by water is not well founded (16). Therefore, while sulphation in oxy-fuel FBC systems needs further study, the evidence for the superiority of conversions achieved by direct sulphation, for longer sulphation periods is not supported by these results, and needs to be reassessed with tests carried out with water levels typical of combustion (10-20%). Table 3. Oxy-fuel CFBC combustion of anthracite at nominal temperature of 900C Anthracite Average freeboard temperature, C 905.5 ± 37 Fuel, kg/h 96.5 ± 1.4 Limestone, kg/h 9.5 ± 1 Oxygen, kg/h 138.7 ± 4 Nominal Ca/S 3 O2, % 3.85 ± 0.55 CO2, % 86.9 ± 0.6 CO, ppm 1143 ± 300 SO2, ppm 2628 ± 592 NO, ppm 108.4 ± 14 Superficial velocity in riser, m/s Recycle ratio, % Sulphur capture, % Fuel N to NO, %
5.9±0.4 70.7 ± 5 43.8 ± 4 1.13 ± 0.1
CONCLUSIONS A 0.8 MW th pilot-scale CFBC unit at CanmetENERGY had been successfully retrofitted for oxy-fuel CFBC research. The unit can fire a wide range of fuels including coal, petroleum coke and lignite. The operation of oxy-fuel CFBC was stable and reliable. Transition from air firing to oxy-fuel firing was fast and smooth. CO2 concentrations in the flue gas reached 85%+ during the entire oxy-fuel firing period. NO emissions were extremely low with only 1.13% fuel nitrogen converted to NO. Sulphur capture was poorer in the initial series of commissioning tests. Part of the reason may have been that the limestone used was too fine. REFERENCES 1. Buhre, B.J.P., Elliot, L.K., Sheng, C.D, Gupta, R.P., Wall T.F., Oxy-fuel Combustion Technology for Coal-Fired Power Generation, Progress in Energy and Combustion Science 31 (2005) 283-307. 2. Wall, T., Liu, Y., Spero, C., Elliott, L., Khare, S., Rathnam, R., et al., An overview on oxyfuel coal combustion--State of the art research and technology development, Chemical Engineering Research and Design 87 (2009) 1003-1016. 3. Toftegaard, M.B., Brix, J., Jensen, P.A., Glarborg, P., Jensen, A.D., Oxyfuel combustion of solid fuels, Progress in Energy and Combustion Science 36 (2010) 581-625.
4. Vattenfall CCS Newsletter, http://www.vattenfall.com/en/ccs/pilot-plant.htm 5. Jia, L., Tan, Y., Anthony, E.J., Emissions of SO2 and NOx during Oxy-Fuel CFB Combustion Tests in a Mini-Circulating Fluidized Bed Combustion Reactor, Energy & Fuels 24 (2009) 910-915. 6. Jia, L., Tan, Y., Wang, C., Anthony, E.J., Experimental Study of Oxyfuel Combustion and Sulfur Capture in a Mini-CFBC, Energy & Fuels 21 (2007) 31603164. 7. Eriksson, T., Sippu, O., Hotta, A., Myohanen, K., Hyppanen, T., Pikkarainend, T., Oxy-fuel CFB Boiler as a Route to Near Zero CO2 Emission Coal Firing, Power Generation Europe, Madrid, Spain, June 26-28, 2007. 8. Czakiert, T., Bis, Z., Muskala, W., Nowak, W., Fuel conversion from oxy-fuel combustion in a circulating fluidized bed, Fuel Processing Technology 87 (2006) 531-538. 9. Fang, M., Yang, L., Mao, Y., Luo, Z., Cen, K., Experimental Study on O2/CO2 Combustion in a CFB Test Facility, 2007 International Conference on Coal Science and Technology, Nottingham, United Kingdom, August 28-31, 2007. 10. Victor, R., Bonaquist, D., Shah, M., Hack, H., Wagner, D., Leathers, D., Kulig, S., The Jamestown Oxy-Coal Project, Power-Gen International 2008, Orlando, Florida, December 2-4, 2008. 11. http://www.fwc.com/publications/pdf/PowerNews_Q42007.pdf 12. Scala, F., Salatino, P., Flue Gas Desulfurization under Simulated Oxyfiring Fluidized Bed Combustion Conditions: The Influence of Limestone Attrition and Fragmentation, Chemical Engineering Science 65 (2010) 556-561. 13. Chen, C., Zhao, C., Liu, S., Wang, C., Direct Sulfation of Limestone Based on Oxy-Fuel Combustion Technology, Environmental Engineering Science 26 (2008) 1481-1487. 14. Hu, G., Dam-Johansen, K., Wedel, S., Hansen, J.P., Review of the Direct Sulphation Reaction of Limestone, Progress in Energy and Combustion Science 32 (2006) 386-407. 15. Hu, G., Dam-Johansen, K., Wedel, S., Hansen, J.P. Direct Sulfation of Limestone, AIChE Journal 53 (2007) 948-960. 16. Wang, C., Jia, L., Tan, Y., Anthony, E.J., The Effect of Water on Sulphation of Limestone, Fuel 89 (2010) 2628-2632.
A NEW APPROACH FOR MODELING OF A FLUIDIZED BED BY CFD-DEM S. Karimi1, H. Chizari2, N. Mostoufi*1, R. Sotudeh-Gharebagh1 Process Design and Simulation Research Center, Department of Chemical Engineering, Universityof Tehran, P.O. Box 11155/4563, Tehran, Iran 2 Department of Mechanical Engineering, Universityof Tehran, Tehran, Iran * Corresponding author, Tel.: (+98-21)6696-7797, Fax: (+98-21)6646-1024, E mail: [email protected]
1
ABSTRACT Numerical studies of 3D cylindrical fluidized bed by means of combined computational fluid dynamics (CFD) and discrete element method (DEM) were carried out. For motion of particles, Newton's second law and 3D compressible Navier-Stokes equations in generalized curvilinear coordinates in its conservative form were used. Navier-Stokes equations were solved with high order compact finite difference scheme by fully implicit flux decomposition method. Non-reflecting boundary conditions (NRBC) were used for the outflow boundary. The convergence of this method, especially at high Reynolds number, is significantly better than the SIMPLE method. INTRODUCTION Gas-solid fluidized beds have been widely utilized as reactors in the chemical and petrochemical industries. Successful design and operation of fluidized bed reactors requires proper prediction of the performance of reactor. Whereas experiment studies in these systems are quite tedious and expensive, recently modeling of fluidized bed reactors has been extensively used to study these systems. Mathematical models for modeling fluidized bed reactors can be grouped into two main categories: Eulerian-Eulerian (EE) and Lagrangian-Eulerian (LE) .In the EE model, both particles and gas phase are considered as a continuum phase (1, 2, 3 4). Recently, many researchers (5, 6) have adopted the LE model, which is also called discrete element method (DEM), for modeling the phenomena in particle-fluid systems. Using this model, trajectories of individual particles can be traced by solving Newton's second law for each particle while the flow of gas, which is treated as a continuum phase, is described by Navier-Stokes equation. Finite volume method is usually used to solve the Navier-Stokes equation. The SIMPLE method with staggered grid is suitable for solving Navier-Stokes equation. However, using this method of solution for problems with high Reynolds number is typically unstable. Furthermore, implementing the high order SIMPLE method is so difficult in comparison with finite difference schemes. One of the finite difference methods that can be used for high Reynolds flow is the flux decomposition method. In this method, convective fluxes are decomposed based on eigenvalues which results in a better agreement with the physical properties of the fluid, especially at high Reynolds numbers.
1
Although the LE approach is more appropriate to study the hydrodynamics of fluidized beds. This model requires large computational resources for large scale 𝐿 systems ( > 4.4𝑅𝑒1/6 must be chosen to prevent wave reflection). In recent years, 𝐷 non-reflecting boundary conditions (NRBC) were used by some researchers (7, 8) to mitigate wave reflection problems. The concept of NRBC was proposed by Thompson (9) where the idea of specifying the boundary conditions according to the inward and outward propagating waves was introduced. Thompson showing that wave reflections, and therefore, can be used as a fictitious boundary. By using this boundary condition, computational domain and computational time for solving Navier-Stokes equation is decreased. The amount of time saving depends on the gas inlet velocity. As the gas inlet velocity increases the free domain which is required for particles motion increase and therefore the effect of NRBS reduces. But using NRBS almost half the computational time decreases. In the present work, a CFD-DEM technique was used to investigate the hydrodynamics of a 3D cylindrical fluidized bed. Newton's second law and 3D compressible Navier-Stokes equation in generalized curvilinear coordinates in conservative form was solved for particle and gas phase respectively. In spite of previous studies that usually used SIMPLE method for solving Navier-Stokes equations, these equations were solved with high order compact finite difference scheme by fully implicit flux decomposition method. Using curvilinear coordinates, the physical domain was changed from cylindrical to semi-Cartesian coordinates in computational domain. Furthermore, NRBC was used for outflow boundary to reduce computation time. Hydrodynamics of the bed was investigated and the results were in good agreement with the expected behavior of gas and solids in the fluidized bed. Governing equations Equation of motion In the present work, the flow of sphere particles in a 3D cylindrical fluidized bed was investigated. Newton’s second law was applied to each particle. The translational and rotational motion of the particles can be described by following equations (10): 𝑘𝑖
𝑑𝑉𝑖 𝑚𝑖 = = 𝑓𝑓,𝑖 + ��𝑓𝑐,𝑖𝑗 + 𝑓𝑑,𝑖𝑗 � + 𝑓𝑔,𝑖 𝑑𝑡 𝐼𝑖
𝑘𝑖
𝑑𝜔𝑖 = � 𝑇𝑖,𝑗 𝑑𝑡
(1)
𝑗=1
(2)
𝑗=1
Soft sphere method (11) was used for simulation Inter-particle and particle-wall contact forces. U
U
For the gas phase, three-dimensional compressible Navier-Stokes equations in generalized curvilinear coordinates (𝜉, 𝜂, 𝜁 )were written in conservative form: 𝜕𝜀𝑄 𝜕(𝐸 − 𝐸𝑣 ) 𝜕(𝐹 − 𝐹𝑣 ) 𝜕(𝐺 − 𝐺𝑣 ) (3) + + + =𝑃 𝜕𝑡 𝜕𝜉 𝜕𝜂 𝜕𝜁
2
𝜌𝑈𝜀 𝜌𝑉𝜀 𝜌𝑈𝑢𝜀 + 𝑃𝜉𝑥 𝜌𝑉𝑢𝜀 + 𝑃𝜂𝑥 ⎞ ⎞ 1⎛ 1⎛ 𝜌𝑉𝑣𝜀 + 𝑃𝜂𝑦 𝜌𝑈𝑣𝜀 + 𝑃𝜉𝑦 𝐸= ⎜ 𝐹= ⎜ ⎟ ⎟ 𝐽 𝐽 𝜌𝑉𝑤𝜀 + 𝑃𝜂𝑧 𝜌𝑈𝑤𝜀 + 𝑃𝜉𝑧 ⎝𝑈(𝐸𝑡 + 𝑃)𝜀 + 𝑈𝑃 𝑃 (1 − 𝜀 )⎠ ⎝𝑉 (𝐸𝑡 + 𝑃)𝜀 + 𝑉𝑃 𝑃(1 − 𝜀 )⎠ 0 𝜌𝑊𝜀 𝜀�𝜏 𝜉 + 𝜏 𝜉𝑦 + 𝜏𝑥𝑧 𝜉𝑧 � 𝑥𝑥 𝑥 𝑥𝑦 𝜌𝑊𝑢𝜀 + 𝑃𝜁𝑥 ⎞ ⎞ 1⎛ 1⎛ � 𝜀�𝜏 𝜉 + 𝜏 𝜉 + 𝜏 𝜉 𝜌𝑊𝑣𝜀 + 𝑃𝜁𝑦 ⎜ ⎟ 𝐺= ⎜ 𝐸 = 𝑦𝑥 𝑥 𝑦𝑦 𝑦 𝑦𝑧 𝑧 𝑣 ⎟ ⎟ 𝐽 𝐽⎜ 𝜌𝑊𝑤𝜀 + 𝑃𝜁𝑧 𝜀�𝜏𝑧𝑥 𝜉𝑥 + 𝜏𝑧𝑦 𝜉𝑦 + 𝜏𝑧𝑧 𝜉𝑧 � ⎝𝑊 (𝐸𝑡 + 𝑃)𝜀 + 𝑊𝑃 𝑃 (1 − 𝜀 )⎠ ⎝ 𝑄𝑥 𝜉𝑥 + 𝑄𝑦 𝜉𝑦 + 𝑄𝑧 𝜉𝑧 ⎠ 0 0 𝜀�𝜏𝑥𝑥 𝜂𝑥 + 𝜏𝑥𝑦 𝜂𝑦 + 𝜏𝑥𝑧 𝜂𝑧 � 𝜀�𝜏𝑥𝑥 𝜁𝑥 + 𝜏𝑥𝑦 𝜁𝑦 + 𝜏𝑥𝑧 𝜁𝑧 � ⎞ ⎞ 1⎛ 1⎛ � � 𝜀�𝜏 𝜂 + 𝜏 𝜂 + 𝜏 𝜂 𝜀�𝜏 𝜁 + 𝜏 𝜁 + 𝜏 𝜁 ⎜ ⎟ ⎜ 𝐹𝑣 = 𝐺𝑣 = (4) 𝑦𝑥 𝑥 𝑦𝑦 𝑦 𝑦𝑧 𝑧 𝑦𝑥 𝑥 𝑦𝑦 𝑦 𝑦𝑧 𝑧 ⎟ ⎟ ⎟ 𝐽⎜ 𝐽⎜ 𝜀�𝜏𝑧𝑥 𝜂𝑥 + 𝜏𝑧𝑦 𝜂𝑦 + 𝜏𝑧𝑧 𝜂𝑧 � 𝜀�𝜏𝑧𝑥 𝜁𝑥 + 𝜏𝑧𝑦 𝜁𝑦 + 𝜏𝑧𝑧 𝜁𝑧 � ⎝ 𝑄𝑥 𝜂𝑥 + 𝑄𝑦 𝜂𝑦 + 𝑄𝑧 𝜂𝑧 ⎠ ⎝ 𝑄𝑥 𝜁𝑥 + 𝑄𝑦 𝜁𝑦 + 𝑄𝑧 𝜁𝑧 ⎠ 0 𝑆1 𝐹𝜉 ⎞ 1⎛ 𝐿∞ 𝐿∞ 𝑆1 𝐹𝜂 𝑃= ⎜ , 𝑆1 = , 𝑆2 = , ⎟ 2 ⎜ ⎟ 𝐽 𝜌∞ 𝑈∞ 𝜌∞ 𝑈∞3 𝑆1 𝐹𝜁 𝜌 𝜌𝑢 1⎛ ⎞ 𝑄 = ⎜ 𝜌𝑣 ⎟ 𝐽 𝜌𝑤 ⎝ 𝐸𝑡 ⎠
⎝𝑆2 �𝐹𝜉 𝑈𝑃 + 𝐹𝜂 𝑉𝑃 + 𝐹𝜁 𝑊𝑃 � + 𝑆2 𝑄𝑃 ⎠
The contra-variant velocity components 𝑈, 𝑉, and 𝑊 are defined as: 𝑈 = 𝑢𝜉𝑥 + 𝑣𝜉𝑦 + 𝑤𝜉𝑧 , 𝑉 = 𝑢𝜂𝑥 + 𝑣𝜂𝑦 + 𝑤𝜂𝑧 , 𝑊 = 𝑢𝜁𝑥 + 𝑣𝜁𝑦 + 𝑤𝜁𝑧 ,
Other equations used in Navier-Stokes equations are introduced as follow: 𝑄𝑥 = −𝑞𝑥 + 𝜀�𝑢𝜏𝑥𝑥 + 𝑣𝜏𝑥𝑦 + 𝑤𝜏𝑥𝑧 � �𝑄𝑦 = −𝑞𝑦 + 𝜀�𝑢𝜏𝑦𝑥 + 𝑣𝜏𝑦𝑦 + 𝑤𝜏𝑦𝑧 �
𝑄𝑧 = −𝑞𝑧 + 𝜀�𝑢𝜏𝑧𝑥 + 𝑣𝜏𝑧𝑦 + 𝑤𝜏𝑧𝑧 � 1 � ℎ(4𝜋𝑟𝑃2 )�𝑇𝑃 − 𝑇�� , 𝑇� = 𝑇 × 𝑇∞ , 𝑄𝑃 = 𝑉𝑐𝑒𝑙𝑙 𝐹𝜉 =
𝜏𝑖𝑗 = 𝑘𝑒𝑓𝑓
𝐹𝑥 𝜉𝑥 + 𝐹𝑦 𝜉𝑦 + 𝐹𝑧 𝜉𝑧 �𝜉𝑥2 + 𝜉𝑦2 + 𝜉𝑧2
,
𝐹𝜂 =
ℎ=
𝐹𝑥 𝜂𝑥 + 𝐹𝑦 𝜂𝑦 + 𝐹𝑧 𝜂𝑧 �𝜂𝑥2 + 𝜂𝑦2 + 𝜂𝑧2
𝜇 𝜕𝑢𝑖 𝜕𝑢𝑗 2 𝜕𝑢𝑘 �� � − 𝛿𝑖𝑗 �, + 𝑅𝑒 𝜕𝑥𝑗 𝜕𝑥𝑖 3 𝜕𝑥𝑘 𝑘�𝜀 + 𝑘�𝑃 (1 − 𝜀 ) = 𝑘∞
𝑞𝑖 = −
𝑁𝑢 × 𝑘� , 2𝑟𝑃 𝐹𝜁 =
𝑘𝑒𝑓𝑓 2 𝑅𝑒𝑀𝑟 𝑃𝑟(𝛾
𝑘� = 𝑘 × 𝑘∞ ,
𝐹𝑥 𝜁𝑥 + 𝐹𝑦 𝜁𝑦 + 𝐹𝑧 𝜁𝑧 �𝜁𝑥2 + 𝜁𝑦2 + 𝜁𝑧2
𝜕𝑇 − 1) 𝜕𝑥𝑖
(5)
(6)
(7)
(8) (9) (10)
For solving Navier-Stokes equations with flux decomposition method, first the Jacobean matrices must be evaluated: 𝜕𝐸𝑖 𝜕𝐹𝑖 𝜕𝐺𝑖 𝐴𝑖𝑗 = , 𝐵𝑖𝑗 = , 𝐶𝑖𝑗 = (11) 𝜕𝑄𝑗 𝜕𝑄𝑗 𝜕𝑄𝑗
3
By considering𝑄 𝑛+1 = 𝑄𝑝 + 𝛿𝑄𝑝 Navier-Stokes equations are changed to: 𝜕𝑄 𝑛+1 𝜕𝜀 𝑛+1 𝜕𝐴 𝑝 𝜕𝐵 𝑝 𝜕𝐶 𝑝 𝑛+1 � 𝑛+1 � � � 𝜀 +𝑄 + 𝛿𝑄 + 𝛿𝑄 + 𝛿𝑄 𝜕𝑡 𝜕𝑡 𝜕𝜉 𝜕𝜂 𝜕𝜁 𝜕𝐸𝑣 𝜕𝐹𝑣 𝜕𝐺𝑣 𝑛 𝜕𝐸 𝜕𝐹 𝜕𝐺 𝑛 � −� + = 𝑃𝑛 + � + + + � 𝜕𝜉 𝜕𝜂 𝜕𝜁 𝜕𝜉 𝜕𝜂 𝜕𝜁 The Jacobean matrices are decomposed using eigenvalues (𝜆): 1 𝐴 = 𝐴+ + 𝐴− 𝐴± = (𝐴 ± 𝜆𝐼) 2 𝜕𝐴+ 𝜕𝐴− + − = 𝐴+ = 𝐴− 𝑖+1,𝑗,𝑘 − 𝐴𝑖,𝑗,𝑘 𝑖,𝑗,𝑘 − 𝐴𝑖−1,𝑗,𝑘 , 𝜕𝜉 𝜕𝜉
(12)
(13) (14)
Finally, flow field obtain by solving following equation: 2∆𝑡 𝜕𝐴 𝜕𝐵 𝜕𝐶 �𝐼 + 𝑛+1 � + + �� 𝛿𝑄𝑝 = 𝑅𝑖𝑚𝑝 𝑛 𝑛−1 6𝜀 − 4𝜀 + 𝜀 𝜕𝜉 𝜕𝜂 𝜕𝜁 𝜀 𝑛+1 (3𝑄𝑝 − 4𝑄𝑛 + 𝑄𝑛−1 ) + 𝑄𝑝 (3𝜀 𝑛+1 − 4𝜀 𝑛 + 𝜀 𝑛−1 ) 𝑅𝑖𝑚𝑝 = − 6𝜀 𝑛+1 − 4𝜀 𝑛 + 𝜀 𝑛−1 2∆𝑡 𝜕(𝐸 − 𝐸𝑣 ) 𝜕(𝐹 − 𝐹𝑣 ) 𝜕(𝐺 − 𝐺𝑣 ) � � − 𝑛+1 + + 6𝜀 − 4𝜀 𝑛 + 𝜀 𝑛−1 𝜕𝜉 𝜕𝜂 𝜕𝜁 2∆𝑡 + 𝑛+1 𝑃 6𝜀 − 4𝜀 𝑛 + 𝜀 𝑛−1
(15)
NON-REFLECTING BOUNDARY CONDITIONS If 𝑄 is the conservative variable,𝑞 is the primitive variable and 𝑀 is the conversion matrix as below: 1 0 0 0 0 𝜌 𝑢 𝜌 0 0 0 ⎛ ⎞ 𝑢 𝑣 0 𝜌 0 0 𝜕𝑄 ⎛𝑣⎞ ⎟ 𝑀= =⎜ (16) 𝑤 0 0 𝜌 0 ⎟, 𝑞 = 𝜕𝑞 ⎜ 𝑤 1 2 1 ⎝𝑃⎠ (𝑢 + 𝑣 2 + 𝑤 2 ) 𝜌𝑢 𝜌𝑣 𝜌𝑤 ⎝2 𝛾 − 1⎠ NRBC in the 𝜉-direction in conservative form can be written as 𝜕𝑄 𝜕𝑞 𝜕𝑞 + 𝐷𝑖 + 𝑀𝑏� + 𝑀𝑐̿ = 𝐽𝑅𝑣 𝜕𝑡 𝜕𝜂 𝜕𝜁 where 𝑊 𝜌𝜁𝑥 𝜌𝜁𝑦 𝑉 𝜌𝜂𝑥 𝜌𝜂𝑦 𝜌𝜂𝑧 0 𝜂𝑥 ⎛0 𝑊 0 ⎛0 ⎞ 𝑉 0 0 𝜌 ⎜ ⎜ ⎟ 𝜂𝑦 ⎟ ⎜ ⎜ 0 𝑉 0 𝑏� = ⎜ 0 , 𝑐̿ = ⎜ 0 0 𝑊 ⎟ 𝜌 ⎜ ⎟ ⎜ 𝜂𝑧 ⎜0 ⎟ 0 0 𝑉 ⎜0 0 0 𝜌 ⎝0
𝛾𝑃𝜂𝑥
𝛾𝑃𝜂𝑦
𝛾𝑃𝜂𝑧
𝑉⎠
⎝0
4
𝛾𝑃𝜁𝑥
𝛾𝑃𝜁𝑦
(17) 𝜌𝜁𝑧 0
0
𝑊
𝛾𝑃𝜁𝑧
0 𝜁𝑥 ⎞ 𝜌⎟ 𝜁𝑦 ⎟ 𝜌⎟ ⎟ 𝜁𝑧 ⎟ 𝜌 𝑊⎠
(18)
𝑑1 𝐷1 𝑢𝑑1 + 𝜌𝑑2 ⎛ ⎞ ⎛𝐷2 ⎞ ⎜ 𝑣𝑑1 + 𝜌𝑑3 ⎟ 𝐷 𝐷 = 𝑀𝑑 = ⎜ 3 ⎟ = ⎜ ⎟ 𝑤𝑑1 + 𝜌𝑑4 ⎜ ⎟ 𝐷4 1 2 𝑑5 2 2 (𝑢 + 𝑣 + 𝑤 )𝑑1 + 𝜌𝑢𝑑2 + 𝜌𝑣𝑑3 + 𝜌𝑤𝑑4 + ⎝𝐷5 ⎠ ⎝2 𝛾 − 1⎠ 𝜌 (𝐿 4 + 𝐿 5 ) 𝜉̃𝑥 𝐿1 + 𝜉̃𝑦 𝐿2 + 𝜉̃𝑧 𝐿3 + √2𝑐 ⎛ ⎞ 𝜉̃𝑥 ⎜ (𝐿4 − 𝐿5 ) ⎟ −𝜉̃𝑧 𝐿2 + 𝜉̃𝑦 𝐿3 + 𝑑1 ⎜ ⎟ √2 ⎜ ⎟ 𝑑 ⎛ 2⎞ 𝜉̃𝑦 ⎟ ̃ ̃ 𝑑 = 𝑃𝐿 = ⎜𝑑3 ⎟ = ⎜ (𝐿4 − 𝐿5 ) 𝜉𝑧 𝐿1 − 𝜉𝑥 𝐿3 + ⎜ ⎟ √2 𝑑4 ⎜ ⎟ ̃ 𝜉𝑧 ⎝𝑑5 ⎠ ⎜ (𝐿4 − 𝐿5 ) ⎟ −𝜉̃𝑦 𝐿1 + 𝜉̃𝑥 𝐿2 + √2 ⎜ ⎟ 𝜌𝑐 (𝐿 4 + 𝐿 5 ) ⎝ ⎠ √2 𝜕𝜌 𝜕𝑣 𝜕𝑤 𝜉̃𝑥 𝜕𝑃 � 𝑈 �𝜉̃𝑥 + 𝜉̃𝑧 − 𝜉̃𝑦 − 𝜕𝜉 𝜕𝜉 𝜕𝜉 𝑐 2 𝜕𝜉 ⎛ ⎞ ⎜ ⎟ 𝜕𝜌 𝜕𝑢 𝜕𝑤 𝜉̃𝑦 𝜕𝑃 𝑈 �𝜉̃𝑦 − 𝜉̃𝑧 + 𝜉̃𝑥 − 2 � ⎜ ⎟ 𝐿1 𝜕𝜉 𝜕𝜉 𝜕𝜉 𝑐 𝜕𝜉 ⎜ ⎟ 𝜕𝜌 𝜕𝑢 𝜕𝑣 𝜉̃𝑧 𝜕𝑃 ⎟ ⎛𝐿 2 ⎞ ⎜ 𝑈 �𝜉̃𝑧 + 𝜉̃𝑦 − 𝜉̃𝑥 − 2 � 𝐿 = ⎜𝐿 3 ⎟ = ⎜ ⎟ 𝜕𝜉 𝜕𝜉 𝜕𝜉 𝑐 𝜕𝜉 𝐿4 ⎜ ⎟ ̃ ̃ ̃ ⎝𝐿5 ⎠ ⎜ (𝑈 + 𝐶 ) � 𝜉𝑥 𝜕𝑢 + 𝜉𝑦 𝜕𝑣 + 𝜉𝑧 𝜕𝑤 + 1 𝜕𝑃� ⎟ ⎜ √2𝜌𝑐 𝜕𝜉 ⎟ √2 𝜕𝜉 √2 𝜕𝜉 √2 𝜕𝜉 ⎜ ⎟ 𝜉̃𝑥 𝜕𝑢 𝜉̃𝑦 𝜕𝑣 𝜉̃𝑧 𝜕𝑤 1 𝜕𝑃 (𝑈 − 𝐶 ) �− � − − + ⎝ √2 𝜕𝜉 √2 𝜕𝜉 √2 𝜕𝜉 √2𝜌𝑐 𝜕𝜉 ⎠
(19)
(20)
(21)
RESULTS AND DISCUSSION In this numerical study, a cylindrical fluidized bed with 7.5 cm diameter and 50 cm height, filled with 10000 particles was considered. The diameter and density of particles were 2 mm and 1100 kg/m3, respectively. A jet of gas was introduced into the bed via a central hole with jet velocity of 8 m/s (u~5umf). Fig. 1 shows snapshots of particles positions in the bed. As can be seen in this figure, particles that are at the center of the bed accelerate very fast and move vertically in the bed. The initial acceleration of the particles is so high that they travel almost 50 cm in the bed. This high acceleration is due to the high vertical drag force exerted on particles due to high gas velocity in the central area of the bed. However, particles in the annulus of this area do not move very noticeably in the vertical direction. This is mainly due to the low gas velocity in this region. The gas velocity in this region is low for two reasons. First, the gas is not introduced into the bed from near the walls. Second, wall effects induce a velocity boundary layer in which the gas velocity is low near the wall.
5
t= 0.1s
t= 0.3s
t=1s
Fig.1. Snapshot of particle motion
Over time by particles movement from distributor, the void fraction in that region increase and the gas velocity decrees. Void fraction increasing in distributor center is more than the distributor corner. So reduction in the gas velocity in the distributor center holes is more than distributor corners. The gas velocity affects particles motion that caused to occurring two peaks in particles motion. If the simulation has been permitted to continue for a longer time, the transient state of the bed would have been vanished and a better solid flow pattern would have been obtained. Since the time required for the calculation is very high (around 1 month), this results has not been obtained yet. Fig. 2 shows velocity vector and density contour of the gas in the bed. The bed is divided into four sections that are perpendicular to each other to able to show the behavior of the flow field in 3D cylindrical bed. However, this figure shows only two sections of the bed. For low Mach number flows, changes in density are low, but as it can be seen in these figures, with upcoming the particles the density of fluid before them increases. Moreover, the velocity vectors in center of the cylinder are high. This flow pattern is in accordance with a gas flow pattern in a cylinder with boundary layer. According to the continuity relation, while the gas velocity increases, the density of gas decreases. This result shows that the presented model and the implemented method can predict compressibility behavior of the gas very well.
6
t = 0.1s
t = 0.3 s
t=1s
Fig.2.velocity vectors and density contour of gas
CONCLUSIONS A combination of CFD and DEM was used to study the behavior of the high velocity fluidized bed reactor. The conservative form of Navier-Stocks equations in curvilinear coordinate was derived and used to obtain flow field of the gas in the bed. As described before, flux decomposition method is more compatible with high Reynolds numbers and high order solving methods than SIMPLE method; therefore in this work flux decomposition method was used for modeling high Reynolds fluidized bed. It must be emphasized that solving high order Navier-Stokes equations caused to seeing the physical behavior of the bed much better. NOTATION d D E Ev fc fd ff fg F
amount of wave for each primitive Navier-Stokes equation amount of wave for each conservative Navier-Stokes equations inviscid flux vector (𝜉 direction) viscous flux vector (𝜉 direction) contact force, N Damping force, N particle-fluid interaction force, N acceleration force, N inviscid flux vector (𝜂 direction) 7
Fv G Gv I J L M q Q U V W
viscous flux vector (𝜂 direction) inviscid flux vector (𝜁 direction) viscous flux vector (𝜁 direction) moment of inertia, kg m2 Jacobian of the coordinate transformation, amount of each characteristic wave conversion matrix vector of primitive variables vector of conservative variables contra-variant velocity(x direction), m s-1 contra-variant velocity(y direction) , m s-1 contra-variant velocity(z direction) , m s-1
REFERENCES 1. D. Gidaspow, Y. C. Seo, B. Ettehadieh, Hydrodynamics of fluidization: Experimental and theoretical bubble sizes in a two-dimensional bed with a jet, Chemical Engineering Communication, 22,253-272, 1983. 2. J. A. M. Kuipers, K. J. vanDuin, F. P. H. van Beckum, W. P. M. van Swaaij, Computer simulation of the hydrodynamics of a two-dimensional gas-fluidized bed, Computer and Chemical Engineering, 17(8), 839-858,1993. 3. C. Guenther and M. Syamlal, The effect of numerical diffusion on simulation of isolated bubbles in a gas-solid fluidized bed, Powder Technology,116, 142-154, 2001. 4. F. Gevrin, O. Masbernat, O. Simonin, Granular pressure and particle velocity fluctuations prediction in liquid fluidized bed, Chemical Engineering Science, 63, 2450-2464, 2008. 5. H. P. Zhu, Z.Y. Zhou, R. Y. Yang, A. B. Yu, Discrete particle simulation of particulate systems: a review of major application and findings, Chemical Engineering Science, 63(23), 5728-5770, 2008. 6. M. Ye, M. A. van der Hoef, J. A. M. Kuipers, A numerical study of fluidization behavior of Geldart A particles using a discrete particle model, Powder Technology, 139, 129, 2003. 7. L. H. Jiang, Shan, C. Liu, M. Visbal, Non-Reflecting Boundary Conditions For DNS in Curvilinear Coordinates, International Conference, Rutgers, New Jersey, June 7-9.1999. 8. T. J. Poinsot, and S. K. Lele, Boundary Conditions for Direct Simulations of Compressible Viscous Floes, Journal of Computational Physics, 101, 104-129, 1992. 9. K. W. Thompson, Time-Dependent Boundary Conditions for Hyperbolic Systems II, Journal of Computational Physics, 89, 439-461, 1990. 10. B.H. Xu and A. B. Yu, Numerical simulation of the gas-solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics, Chemical Engineering Science, 52 (16), 2785-2809,1997. 11. P. A. Cundall and O. D. L. Strack, A discrete numerical model for granular assemblies, Geotechnique, 29, 47-65, 1979.
8
THE RESEARCH OF CFB BOILER OPERATION FOR OXYGEN-ENHANCED DRIED LIGNITE COMBUSTION Waldemar Muskala, Jaroslaw Krzywanski, Tomasz Czakiert, Wojciech Nowak Czestochowa University of Technology, Faculty of Environmental Protection and Engineering Dabrowskiego 73, 42-200 Czestochowa, Poland, tel./fax: +48 343250933 ABSTRACT The paper presents the research of CFB boiler operation for oxygen-enhanced dried lignite combustion. The combustion in oxygen-enhanced conditions generally leads to reducing the emissions of CO and NOx and N2O due to reduced volume of flue gas. The maximum oxygen content for oxygen-enhanced combustion in O2/N2 conditions should not exceed 60%, however, the maximum drying extent of fuel should not be higher than 50% of the initial moisture content in an examined lignite. INTRODUCTION The technology of coal combustion in circulating fluidized bed (CFB) boilers and its chemical and process engineering may be ranked among the technologies referred to as clean combustion technologies including reduced emission of pollutants. Such technologies are crucial as the most favorable conditions for the combustion of solid fuels in oxygen-enhanced conditions appears to be the circulating fluidized bed. This technology brings about a number of advantages such as reducing the pollutants emission, increasing the effectiveness of CO2 separation from the flue gas owing to higher CO2 content, decreasing the flue gas volume. The technical knowledge of an operation of circulating fluidized bed boilers is still insufficient within the modeling and simulation of the processes of combustion in such boilers. However, as this new technology has been developing rapidly one may expect a lot of additional data from experiments to appear concerning the operation of CFB boilers under such conditions. The theoretical computations which have been carried out provide the results that satisfactorily simulate the boiler operation parameters and some of those parameters were possible to be determined during the operation of a real facility. ASSUMPTIONS FOR THE MATHEMATICAL MODEL OF FUEL COMBUSTION IN OXYGEN-ENHANCED CONDITIONS The model consists of several sub-models which enables the description of crucial processes associated with combustion of solid fuels under circulating fluidized bed conditions. The model considers hydrodynamics of bed material, fuel devolatilization stage, volatiles and char combustion, flue gas desulfurization as well as heat and mass transfer. The core of the model was based on the fluidized bed coal combustion model (1, 2). The structure was developed by adding additional data from experiments on the combustion in oxygen-enhanced conditions (3-5). This paper presents some computations and validations performed for an existing 670 t/h CFB boiler operated at nominal load. The computing data refer to the boiler operation taking into account the initial drying extent of fuel before it was fed to the combustion chamber. The combustion is assumed to occur in oxygen enhanced conditions within the range of 21-100% under O2/N2 and O2/CO2 conditions. The
simulation computations were carried out assuming the constant heat flux within fuel fed to the boiler containing initially 43% of the moisture content and in the dried fuel the moisture content being 30%, 20% and 10% respectively. Table 1. presents the properties and fluxes of fuel in the CFB boiler depending on the drying extent of fuel. Table 1. The properties of fuel depending on the drying extent of fuel Moisture [%]
Carbon (c) [%]
LHV [kJ/kg]
43.5 30 20 10
28 34.7 39.6 44.6
10991.2 14214.7 16602.5 18990.3
The flux of coal [kg/s] 63.0 48.7 41.7 36.5
Two different simulations of the boiler operation as well as the assumptions for the mathematical model of fuel combustion in oxygen-enhanced conditions were thoroughly analyzed in the passage referring to modeling of solid fuels combustion under oxygen-enhanced conditions in circulating fluidized bed boiler (5). The sorbent was also fed to the boiler. The sorbent volume is ms = 2.02 kg/s. The simulation computations for two different boiler operations are presented taking into account variations in the oxygen contents in the mixtures during coal combustion in oxygen-enhanced conditions. The overall results are calculated using Runge-KuttaIV type Equation. After entering input data, determining the initial conditions and convergence conditions, the computations of bed hydrodynamics and chemical reactions are carried out. Distribution and concentrations of solids and gaseous components concentrations in the whole volume of combustion chamber are established on the basis of solids and gas balances. The physicochemical properties of fuel are the average properties of lignite from the Turow Coal Mine, combusted in fluidized- bed boilers in the Turow Power Plant in Poland. The fluidizing reacting gases were O2/CO2 and O2/N2 mixtures with increased oxygen content (zo2) The primary gas to the secondary gas ratio was 60/40 and the secondary gas was supplied onto two levels: 0.75 and 1.25 m above the grid. In order to retain the regime of the circulating fluidized bed with the variable oxygen content in a supplied gas mixture, the modification of the overall dimensions of combustion chamber is necessary. In a presented model, only one of the three characteristic chamber dimensions , i.e. the chamber depth, was subjected to changes that ranged from 9.9 m for zo2= 21% to 2.1 m for zo2 = 100%, which corresponded to the reduction of the chamber volume to 33,6% of its initial volume. It allows maintaining a constant gas velocity in the whole volume of combustion chamber at a level of w=5.4 m/s. COMPUTATION RESULTS AND THEIR ANALYSIS The computational model served for the simulation and assessment of the concentration in dry flue gas (Fig. 1 - 2) and (Fig. 3 - 4) their volume flux of CO, CO2 NO and N2O as a function of oxygen content in the inlet gas at the exit of the combustion chamber during combustion. The obtained results presented in (Fig. 1) show that the increase of the oxygen content leads to an increasing carbon dioxide content from 15 % with 21 % of oxygen content in O 2/N2 mixture to about 98 % during the oxy-combustion, whereas the CO content decreases (within 3 to 0,8 ppm) thus reaching almost zero value. Similar results were reported by other researchers
(6) Decline in the moisture content (W r) of combusted fuel from 43% to 10 % causes a slight increase of about 2-5 % of CO2 and CO contents in dry flue gas. 16
3,0
14
2,5 2,0
10
21 % O2
8
1,5
6
zCO [ppm]
zCO2 [%]
12
1,0
4
CO2
2
CO
0,5
0
0,0 5
15
25
35
45
Wr [%]
120
1,60 1,40
100
zCO2 [%]
1,00
60
0,80 CO2
100 % O2
40
0,60
zCO [ppm]
1,20 80
CO 0,40
20
0,20
0
0,00 5
15
25
35
45
Wr [%]
Fig.1. CO and CO2 concentration in dry flue gas as a function of moisture content in fuel for different oxygen content in the inlet gas of O2/N2 Reduction of NOx and N2O concentration (Fig. 2) occurs as the moisture content in the inlet gas decreases. For NOx the reduction is about 30%, from the value of about 110-140 ppm to 90-100 ppm regardless of the oxygen content in the mixtures of O2/N2. Reduction of N2O concentration of about twice (that is from about 8 - 10 ppm to 4 - 5 ppm) is noticed. Decline in moisture content in flue gas results in slight increase of CO concentration in combustion chamber, which promote NOX reduction. Fig. 3 and Fig.4. show the comparison of CO2, CO, NO and NO2 flux in dry flue gas as a function of oxygen content in the inlet gas for oxygen-enhanced combustion in O2/N2 conditions. The CO2 and CO flux little depends on the drying extent of solid fuel and its values are as follows: for CO2 from 1,3 to about 1,8 kmol/s and for CO within the range of 2,5 10-5 - 1,5 10-6 kmol/s. However, CO2 flux increases whereas CO flux declines as a result of oxygen content increase in O2/N2 mixtures.
Fig.2. NO and N2O concentration in dry flue gas as a function of moisture content in fuel for different oxygen content in the inlet gas of O2/N2 Decline of NOx and N2O flux (Fig.4) of about 40 % was found as a result of decreasing moisture content in fuel from 43 to 10 %. Decline of N2O and NOx flux with O2 content in an inlet gas occurs as oxygen concentration in O2/N2 mixtures increases. The general conclusion could be drawn that the combustion in oxygenenhanced conditions leads to reducing the pollutants emission of NOx and N2O which makes it possible to obtain the required reduced emission of pollutants and no additional technologies need to be implemented. As it is in the case of oxygen enhanced combustion in O2/N2 conditions, the CO2 and CO concentrations are regardless of the drying extent of fuel. The concentration of CO 2 is estimated at about 95-98%. The CO concentration increases as the moisture content in combusted fuel decreases and they range within 1-3 ppm. Decline on average of about 30 % of NO and N2O with O2 content in an inlet gas was also found, which could be explained by the effect of increasing CO concentration in dry flue gas.
Fig.3. CO2 and CO flux in dry flue gas as a function of moisture content in fuel for different oxygen content in the inlet gas of O2/N2 CO2 and CO fluxes little depend on the drying extent of fuel and they range within: for CO2:: 9,0 – 1,6 kmol/s, for CO: 2,8 10-5 – 1,6 10-6 kmol/s, higher concentrations is noticed when there is a decline of oxygen contents in O2/CO2 mixtures. Similarly as it is in case of O2/N2, there is a decline of NOx and N2O concentrations in O2/CO2 conditions as well as decline of moisture content of about 40%. Besides increase in oxygen concentration from 21% to 100 % for oxygenenhanced combustion in O2/CO2 conditions results in NOx and N2O 4-times reduction. As it was in the case for oxygen-enhanced combustion in O2/N2 conditions, it may be supposed that combustion of initially dried fuels in oxygenenhanced conditions could lead to reducing pollutants emissions of NOx and N2O.
7,0E-05
1,4E-03
6,0E-05
4,0E-05 3,5E-05
5,0E-04
3,0E-05
5,0E-05 4,0E-05
8,0E-04 3,0E-05 6,0E-04
21 % O2
4,0E-04 2,0E-04
15
25
2,0E-05 1,5E-05
2,0E-04
2,0E-05
N2O
1,0E-05
1,0E-04
0,0E+00
0,0E+00
35
2,5E-05
3,0E-04
NO
0,0E+00 5
4,0E-04
nNO [kmol/s]
1,0E-03
nN2O [kmol/s]
40 % O2
45
NO
1,0E-05
N2O
5,0E-06 0,0E+00
5
15
25
Wr [%]
35
45
Wr [%]
4,00E-04
3,0E-05
3,50E-04
3,5E-04
2,5E-05
3,0E-04
2,0E-05
2,5E-04
2,5E-05
2,00E-04
1,5E-05
1,50E-04
1,0E-05
1,5E-04
1,0E-05
NO
60 % O2
5,0E-06
N2O
5,00E-05 0,00E+00
0,0E+00 5
1,5E-05
2,0E-04
15
25
35
45
1,0E-04
NO
80 % O2
0,0E+00
0,0E+00 0
Wr [%]
10
20
30
40
50
Wr [%]
3,0E-04
2,5E-05
2,5E-04
nNO [kmol/s]
5,0E-06
N2 O
5,0E-05
2,0E-05
2,0E-04 1,5E-05 1,5E-04 1,0E-05
NO
nN2O [kmol/s]
1,00E-04
nNO [kmol/s]
2,0E-05
2,50E-04
nN2O [kmol/s]
nNO [kmol/s]
3,00E-04
nN2O [kmol/s]
nNO [kmol/s]
1,2E-03
6,0E-04
nN2O [kmol/s]
1,6E-03
1,0E-04 N2O
100 % O2
5,0E-05
5,0E-06
0,0E+00
0,0E+00 5
15
Wr25[%]
35
45
Fig.4. NO and N2O flux in dry flue gas as a function of moisture content in fuel for different oxygen content in the inlet gas of O2/N2 The extent of fuel drying largely influences the overall flue gas flux which is shown in Fig.5 for oxygen-enhanced combustion with various moisture contents in fuel combusted under O2/N2 and O2/CO2 conditions and various fraction of oxygen. There is a decline of about 15-20% of wet gas concentration as the moisture content declines from 43 to 10 %. The increase of oxygen content both in O 2/N2 and O2/CO2 mixtures results in 4-times reduction of pollutant emissions. On the one hand it may reduce chimney loss due to the reduction of flue gases in a volume, however, it may also result in limiting the heat transfer in combustion chamber, which may make difficult to keep the temperature to 1173 K required in CFB boilers.
Fig.5. Flux of wet flue gas as a function of moisture content in fuel for different oxygen content in the inlet gas of O2/N2 Combustion in an oxygen-enhanced conditions leads to an increase in the adiabatic temperature. The variations in the heat duty are the result of the changes in the temperature distribution in the combustion chamber. The variable temperature distribution, in turn, results from an increase in the adiabatic temperature, which is mainly caused by the reduction of the flue gas volume with the increase in the oxygen content of the gas supplied to combustion. Slight increase of CO2 flux and decrease in CO flux during combustion in oxygen-enhanced conditions prove higher efficiency of the process under these conditions. Maximum oxygen content in O2/N2 and O2/CO2 mixtures should not be higher than 60 %, however, the maximum drying extent of fuel should not exceed 50 % of the initial moisture content, which means the value of 20 - 25 % for the examined brown coal. SUMMARY AND CONCLUSIONS The research of CFB boiler operation for oxygen-enhanced dried lignite combustion under O2/N2 and O2/CO2 conditions allow the following general conclusion to be drawn: The CO2 concentration in dry flue gas little depends on the drying extent of fuel but depends on the oxygen content in mixtures of O2/N2 and O2/CO2 and its concentration ranges within 15 - 96 % depending on the increasing oxygen content. The CO2 flux little depends on the drying extent of fuel in the mixture of O2/N2, however, it declines about six times as oxygen content in the mixture O 2/CO2 increases. Decrease in CO concentration in dry flue gas is close to zero (about 1 - 5 ppm) for oxygen-enhanced combustion in O2/N2 and O2/CO2 conditions, however, it slightly increases as the decline of the initial moisture content is noticed. Slight increase in CO flux (about 10%) for oxygen enhanced combustion in O2/N2 and O2/CO2 conditions is noticed as the moisture content decreases, however, there is a significant decline of CO flux of about one degree as the oxygen content increases. Decrease in NO concentration in dry flue gas of about 30 % for oxygen enhanced combustion in O2/N2 and O2/CO2 conditions is noticed as the initial moisture content declines. Decrease in NO flux of about 40 % for oxygen-enhanced combustion in O2/N2 and O2/CO2 conditions is noticed as the initial moisture content declines.
N2O concentration in dry flue gas decreases about twice for oxygen enhanced combustion in O2/N2 and O2/CO2 conditions as the initial moisture content declines. Decrease of N2O flux on average of about 40 % for oxygen-enhanced combustion in O2/N2 and O2/CO2 conditions is noticed as the initial moisture content declines. The analysis of operation of a circulating fluidized bed boiler show that combustion in oxygen-enhanced conditions cause an increase in the adiabatic temperature. The variation in the heat duty is the result of the changes in the temperature distribution in the combustion chamber. It mainly refers to membrane walls of the combustion chamber, superheaters SH I and SH II and heat exchangers located in the 2nd pass. Coal combustion in oxygen-enhanced conditions causes an increase of heat transfer between fluidized bed, membrane-walls and superheaters. That increase in the heat transfer is the result of the changes in the temperature distribution in the combustion chamber. The research show that the maximum oxygen content for oxygen enhanced combustion in O2/N2 and O2/CO2 conditions should not be higher than 60 %, however, the maximum drying extent of fuel should not exceed 50 % of initial moisture content, which means the 20-25 % for the examined coal. NOTATION ni ngas w Wr zi
– – – – –
i-th gas component flux, kmol s-1 wet flue gas flux, kmol s-1 superficial gas velocity, m s-1, moisture content, % concentration of i-th gas component, %, ppm
REFERENCES [1] Muskała W., Krzywański J., Sekret R, Nowak W. Model research of coal combustion in circulating fluidized bed boilers. Chemical and Process Engineering 2008; 29:473-492. [2] Krzywanski J., Czakiert T., Muskala W., Sekret R., Nowak W., Modeling of Solid Fuels Combustion in Oxygen-Enriched Atmosphere in Circulating Fluidized Bed Boiler – Part I. The mathematical model of fuel combustion in oxygen-enriched CFB environment, Fuel Processing Technology, 2010, Vol. 91, pp. 290-295. [3] Klajny T., Krzywański J., Nowak W. Mechanism and Kinetics of coal combustion in oxygen enhanced conditions. 6th International Symposium on Coal Combustion , Wuhan, China, 1-4 December 2007: 148-153. [4] Nowak W., Klajny T., Krzywański J., Czakiert T., Mechanism of Coal Combustion in Oxygen Enhanced Conditions, Procedings of the 20th International Symposium on Chemical Reaction Engineering, Kyoto 7-10.09.2008, Book of Abstracts, p.410- 411. [5] Klajny T., Krzywański J., Nowak W., Mechanism and kinetics of biomass combustion in oxygen enhanced conditions, XX-th International Symposium on combustion processes, Pultusk, Poland, September 3-5, 2007. [6] Hu, Y., Naito, S., Kobayashi, N., Hasatani, M., 2000. CO2, NOX and SO2 emissions from the combustion of coal with high oxygen concentration gases. Fuel. 79, 1925-1932
COLD FLOW MODEL STUDY ON INTERCONNECTED FLUIDIZED BED REACTORS FOR MULTI-GENERATION SYSTEMS AND CHEMICAL LOOPING PROCESSES G. A. Ryabov*, O. M. Folomeyev, D. A. Sankin, K. V. Khaneyev All-Russian Thermal Engineering Institute, 14/23 Avtozavodskaya St., Moscow, Russia, Phone (495) 675-3239, Fax (495) 234-7427, e-mail: [email protected], [email protected] ABSTRACT Interconnected fluidized bed reactors (DCFB) were implemented in multi-generation systems (pyrolysis FB reactor and CFB boiler), chemical looping combustion (CLC) systems (double metal oxides or carbonate oxide reactors) and three reactor chemical looping gasification processes. The presented data focus on the solids circulation rate and on pressure profiles of the DCFB depended on selected operating parameters such as fluidization gas flow rate, loop seal fluidization, and solids inventory. Most work was devoted to studying standpipe and valve operation. INTRODUCTION The important feature of the DCFB concept is the inherent stabilization of solids hold-up obtained by the direct hydraulic link between the two CFB systems, i.e. the loop seal connection in the bottom region of the risers. Only the air reactor entrainment is responsible for the global solids circulation between the reactors, while the fuel reactor operation can be optimized. The hydrodynamics of interconnected reactor systems and loop seal (standpipe and non-mechanical valves) are very important in that optimization. Modern multi-generation technologies can be subdivided into three categories: - Multi-generation systems based on complete coal gasification, used by Shell Co.; - Multi-generation systems based on partial coal gasification (combination of coal combustion and gasification); - Multi-generation systems based on pyrolysis of coal. Multi-generation systems based on pyrolysis of coal include many of configurations. An example is the shale processing units in the USSR. The resulting technology was implemented in industrial units. Two high-capacity units of this type (UTT – 3000, 139 tons of shale per hour each) were erected at in the Estonian thermal power plants and they with still show good performance. VTI has an agreement on scientific cooperation with the Institute for Thermal Power Engineering, Zhejiang University in this field. In studies carried out by Zhejiang University (1), (2)) data were received from their tests on a 1 MW pilot plant. The first results showed that a pilot unit can produce gas with a heating value of about 12 to 14 MJ/m3 with a 5 to 6 % tar content at 500 to 600 оС in the fluidized bed (FB). It was established that
solids circulation is a critical parameter for stable plant operation. The investigation was financed by the Russian Ministry of Science and Education as a part of a China – Russia scientific cooperation project. Another aim of the investigation of the interconnected FB and CFB reactors is to study the hydrodynamics of the CLC (double metal oxides or carbonate oxides reactors) and the three reactor chemical looping gasification processes (3) and (4). RESULTS It is important for interconnected units containing CFBs to provide a stable circulation rate between reactors as well as an efficient solids separation. Such a study has been connected in VTI`s experimental unit shown in Fig. 1. This unit consists of a CFB reactor and a transportation reactor (TR) that are interconnected with pneumatic valves and standpipes to a FB reactor. The system consists of three interconnected fluidized bed reactors: The CFB (0.2*0.3 m cross section, 5 m height), the FB reactor (0.4*0.4 m cross section, 1.6 m height) and the transport reactor (0.1 m diameter, 4.8 m height). Standpipes and J or L valves connected the FB reactor and the bottom parts of the CFB and transport reactors with the cyclones of the CFB and transport reactors. The CFB reactor consisted of a vertical column 5.4 m high, made of 9 sections 600 mm high each with a 300*200 mm cross-section. The upper section was connected to the cyclone inlet. The cyclone outlet was removable and Fig. 1 –Drawing of the VTI cold model of was located 50 mm away interconnected reactors from the cyclone central axis. The cyclone cone was connected to a standpipe, consisting of 3 sections, each of 100*100 mm crosssection. The lower section of the standpipe was connected to a pneumatic valve. The design of the valve allowed solids to flow in different directions. Part of the flow could be directed back to the bottom part of the CFB reactor, and the other flow to the FB reactor. The transportation reactor consisted of a vertical column 5.4 m high, made of 3 sections of 100 mm diameter and each were 1600 mm high. The reactor and standpipes were equipped with taps to measure pressure along their heights.
The FB reactor consisted of a vertical vessel with a square (400*400 mm) crosssection and 1500 mm in height. There was a nozzle grid at its bottom for distributing the air. The reactor was subdivided into two equal 200*400 mm sections by a partition. Solids were discharged from the FB reactor through pipes equipped with a pneumatic L-valve. The L-valve connected the bottom of the first section of the FB reactor to the bottom part of the transportation reactor. In the first section of the FB reactor, air flowed counter-currently to the flow of solids. In the second section flow ws co-current. In the second section, solids were discharged through the top part of the section, and then fell into the CFB reactor. The VTI aerodynamic test unit has been described in other references (5) and (6). Local mass flows were measured with isokinetic S-type probes, and local heat exchange with gauges while heating. Also mini-turbine flowmeters and differential concentration-measurement gauges were employed. Based on these tests it was established that: - pressure profiles and vertical solid concentrations in the CFB and FB reactors are depended on gas velocity and solids loading; -pressure drops in the unit depended on operating parameters and on design features of the units and return legs. VTI studies carried out on a similar unit showed that particle size determined the value of the dimensionless criteria (Аr, Re, Fr), used for hydrodynamic and heat exchange calculations. It was established that the change in average particle size with respect to unit width or diameter was insignificant. The studies were basically directed to calculate the circulating mass flow rate in the external circuit (Gr), as it Fig. 2 – Typical solid circulation rate as function of is the critical operational velocity and inventory parameter for the ireactors. The solid flow rate increased with gas velocity in the CFB unit and with unit inventory. This relationship is shown in Fig. 2. This relationship was obtained at stable unit operation when unit load increases were possible. Stable system performance was made possible with good loop seal operation. A separated solids return system is necessary for reliable operation of the CFB unit. Usually a return system consists of a vertical standpipe, pneumatic valve and a short return leg back to the furnace. It is important to choose the right dimensions (diameters and height) of the standpipes and have them operate in the correct fluidization regime. Observation of the solids
motion in the standpipe indicated that in the bottom part was in dense phase flow, but it was in dilute phase flow at the top. During several tests with sand (at a low bed level in the standpipe and a large mass of sand in the unit) a pulsating regime was established with bubble generation and gas overshoot to the upper part of the standpipe. The amplitude of the standpipe solids level oscillations reached 1 m with a frequency of 0.1 Hz. The limiting significant pulsation case could be estimated using Equation (1):
⎛ ΔP ⎞ ⎜ ⎟ ⋅ Hs ≈ Hv ⋅ (1− εv ) ⋅ ρp ⋅ g + ΔРft + ΔРc ⎝ L ⎠mf
(1)
At normal conditions the pressure drop in the upper part of the loop seal H v ⋅ (1 − ε v ) ⋅ ρ p ⋅ g was equal to or exceeded the pressure drop at the bottom part of the furnace ΔРfb=ΔРf-ΔРft, so this guaranteed zero furnace gas overshoot into the valve or the standpipe. The limiting bed level in the standpipe was estimated using Equation (2): ΔР f + ΔРc (2) (Н s )lim = ⎛ ΔР ⎞ ⎜ ⎟ ⎝ L ⎠ mf In the tests, the limiting bed level in the standpipe ranged from 0.15 to 0.5 m. This increased with increasing bed mass and increasing particle size. For an industrial CFB boiler this limiting value was about 2-3 m. It was suggested (7) to increase the standpipe height to double its normal height. Fig. 3 shows the dependence of standpipe bed level with constant aeration. Small changes in aeration to the standpipe caused a significant bed level change in the standpipe (Fig. 3a), but the aeration to the valve had little affection on the standpipe bed level (Fig. 3b).
1 – full cross-section bed material reduced speed Wms=0,013 m/s; 2 – Wms=0,02 m/s; 3 – Wms=0,05 m/s; 4 – Wms=0,1 m/s; 5 – Wms=0,15 m/s Fig. 3 – Standpipe bed level dependence of aeration to the standpipe (a) and to the valve (b) Thus, parameters shown in Fig. 3 are critical for return system regulation. The best operational mode is with continuous light aeration to the standpipe and regulating the aeration to the valve.
A master curve of standpipe bed height (Нs/H0) relative to aeration velocity (Wsv/Wmf) is shown in Fig. 4. Solids inventory in the unit (riser + return system), gas flow rate in the riser and in the solids upflow section of the loop seal were constant, as aeration to the standpipe was varied. A similar curve was given by (8), which says that the minimum bed height level was obtained when the air velocity is about 5 Wmf. If the velocity was less than 4 Wmf there was an increase in the height of the solids level in the standpipe. If the air velocity was greater than 6 Wmf, then the steandpipe height increased with increases in gas velocity. At low velocities – there was a dense bubbling downflow mode and at high velocities air 1 –Knowlton data (8), 2 – VTI experimental data bubbles moved Fig. 4 – Relative standpipe bed material height level upwards to the dependence of relative air velocity in the standpipe cyclone. Our data show that the cyclone efficiency decreased when that occurred. From the data in Fig. 4 it is clear that it was necessary to maintain the bed level in the standpipe so that it did not exceed 0.4 of its height with simultaneous limitation of aeration to the standpipe at la value of 3-6 Wmf. Within the recommended regime, the solids flow rate reached 1800 t/h⋅m2 (there was no opportunity to reach higher values because of cyclone overload). It is recommended a velocity of 0.1 m/s be used for standpipe diameter calculations. In the normal operating mode of the ash return system, a dense flow occurs in the bottom part of the standpipe. In this case, the Ergan Equation pressure gradient will be equal to:
ΔP 150 ⋅ μ g = L (ϕ ⋅ d )2
2 1,75 ⋅ ρ g ⎛1− ε ⎞ ⋅⎜ ⎟ ⋅ VR + ϕ ⋅d ⎝ ε ⎠
⎛1− ε ⋅⎜ ⎝ ε
⎞ ⎟ ⋅ VR ⋅ / VR / ⎠
(3),
A linear dependence of voidage on relative velocity was suggested by Knowlton (8):
ε = ε v + (ε mf − ε v ) ⋅ Relative bed velocity is equal to:
VR ⋅ ε mf Wmf
(4)
VR = Wp −Wg =
Gp
ρ p ⋅ (1 − ε ) ⋅ Fs
−
Qg
ρg ⋅ ε ⋅ Fs
(5)
From Equations (3), (4) and (5) voidage as a function of pressure gradient Δ P in L the standpipe can be calculated as: ΔP 2 ⎛ 1 − ε mf ⎛1− ε ⎞ L ⋅⎜ ⎜ ⎟ ⋅ (ε − ε v ) = ⎜ ε P Δ ⎛ ⎞ ⎝ ε ⎠ mf ⎝ ⎜ ⎟ ⎝ L ⎠ mf
2
⎞ ⎟ ⋅ ( ε mf − ε v ) ⎟ ⎠
(6)
The standpipe voidage dependence on relative gas velocity (Wg/Wmf) is represented in Fig. 5a. Standpipe voidage increased with increasing of the aeration, but was still less than the voidage a minimum fluidization. A comparison of the relative particle velocity from Equation (5) and form the observed solids velocity (Fig. 5b) shows that the calculated relative velocity was lower than the observed velocity in most cases. This indicates that gas flowed down with the bed material. In the pulsation regime, the relative velocity was much higher than the observed velocity, indicating that the gas was flowing up the standpipe.
1-particle velocity in standpipe Wms=0,06 m/s; 2 – Wms=0,1 m/s, 3 - Wms=0,13 m/s Fig. 5 – Relative air velocity dependence of standpipe voidage (a); Particle velocity in the standpipe dependence of its relative velocity (b) Thus, calculations confirmed the experimental data from the unit and indicate that this method can be used to design industrial units.
CONCLUSIONS The studies on solids circulation and solids return systems of interconnected reactors found: - pressure profile and vertical solids concentrations in interconnected FB and CFB reactors were typical for reactors of this type, and depended on gas velocity and bed mass; - the axial pressure profile depends on the fluidization velocity and the design features of unit`s structure and return system; - the recirculating solids flow rate between interconnected FB and CFB reactors depends on gas velocity and solids inventory in the CFB reactor, on highefficiency cyclones and sufficient capacity of the return system for a certain particle size; - the solids return system (standpipe and pneumatic valve) must have a reserve capacity and be able to regulate aeration to the standpipe and to the upflow part of the valve. Otherwise, the recirculation solids flow rate is determined by the operational mode of the valve. ACKNOWLEDGEMENT Financial support of this work by the Federal Agency of Science and Innovation is gratefully acknowledged. NOTATION Gc – solid circulation rate Ho – standpipe height Hs – bed height in the standpipe Hv – height of the upper part of the valve M – unit inventory ΔPft – pressure differential at the top of the furnace ΔP c – cyclone pressure drop ΔPfb – pressure differential at the bottom part of the furnace ΔPf – pressure differential in the furnace u – gas velocity in the riser ρp – particle density εv – vibrated bed porosity (≈ 0.37) εu – voidage in the upper part of the valve Wmf – velocity at minimum fluidization Wms – particle velocity in the standpipe (downflow particle velocity) φ – shape coefficient (ϕ ≈ 0.73) VR – relative particle velocity ε – voidage εmf – minimum voidage Gp – solids flow rate Qg – flow rate of gas Fs – cross-section of the standpipe
Wg – relative gas velocity Wsv – standpipe fluidization air velocity
⎛ ΔP ⎞ ⎜ ⎟ - pressure gradient in the standpipe at minimum fluidization parameters ⎝ L ⎠ mf ΔP - pressure gradient
L (H S )lim - limiting bed level in the standpipe d - average particle diameter REFERENCES 1. M. Fang, Q. Wang , C. Yu, Z. Shi, Z. Luo, K. Cen, Development of Gas Steam and Power Multi-Generation System, Proc. of 18th Int. Conf. on FBC, May 22-25, 2005, Toronto, Ontario, Canada. 2. M. Fang, J. Cen, Q. Wang, K. Cen,. Z. Cao, X. Zhang, J. Shu, X. Rao, G. Dong, J. Wang, Y. Wang, Study on Coal Combustion and Pyrolysis Gas Tar and Steam Polygeneration System, Proc. of 9th Int. Conf. on CFB, May, 2008, Hamburg, Germany. 3. A. Lyngfelt, M. Johansson, T. Mattisson, Chemical looping combustion – Status of development, Proc. of CFB 9, May 13-16, Hamburg, Germany, pp 39-53 4. T. Proll, K. Rupanovits, P. Kolbitsch, J. Bolhar-Nordenkampf, H. Hofbauer, Cold Flow Model Study on a Dual Circulating Fluidized Bed (DCFB) System for Chemical Looping Processes, Proc. of CFB-9, Germany, Hamburg, May 2008. 5. O. M. Folomeyev, S. N. Truhachev, G. A. Ryabov, Hydrodynamics and heat exchange study of overbed area functional in the CFB units, “Teploenergetica” №10, 2000., pgs 27-32. 6. G. A. Ryabov, O. M. Folomeyev, D. A. Shaposhnik, Solid separation in upper part of CFB riser, Proc. of the 8-th Int. Conf. on CFB, Hangzgou, China, May 10-13, 2005. 7. Chong I.O., O'Dea D.P., Leung L.S. at el. Design of standpipe and nonmechanical V-valves for a circulating fluidized bed. 3-rd Int. Conf. on CFB Technology, Pergamon Press, 1988. 8. Knowlton T.M. Non-mechanical solid feed and recycle devices circulating fluidized beds. 3-rd Int. Conf. on CFB Technology, Pergamon Press, 1988.
FLOW RATE OF SOLIDS IN L-VALVES Duvvuri Subbarao Department of Chemical Engineering, Universiti Teknologi PETRONAS, Tronoh 31750, Malaysia ABSTRACT Movement of solids in a L-valve by fluid drag due to external aeration is opposed by frictional resistance at the wall/non-moving solids. Model equations developed for the threshold aeration rate and solids flow rate as a function of aeration rate compare well with the literature data. INTRODUCTION Transport of solids into and out of process equipment is of critical importance for smooth continuous operation of process units handling particulates such as circulating fluidized beds. Non-mechanical valves such as L-valve (L–shaped pipe) are widely used to control solid flow by fluid drag. Solids discharging from a cyclone at a pressure PS flow and accumulate as packed bed of height HU in the vertical standpipe of a L-valve as the drainage at the bottom is arrested by the presence of a horizontal pipe of length HD. External aeration to the bottom section of the stand pipe at a pressure P1 control solids flow through the horizontal pipe into the riser at pressure PR. Pioneering work reported by Knowlton and Hirsan (1) on the characteristics of L-valves has excited further investigations (2 - 8) to explore the effect of particle properties and geometry of the valve on the particle flow rate W as a function of gas flow rate Q. It is to be noted that in industrial units PS > P1 >PR while in laboratory research units PS = PR = P2. Some of the empirical correlations proposed to relate solid flux to gas velocity are summarized in Table 1. Table 1: Correlations reported in the literature for sold flux through L-Valve Reference Correlation Geldart and Jones (2) Gs u
Dt
= 3354
u mf
− 2965
Smolders and Baeyens (6)
⎛ u Gs = 79600⎜ ⎜u Dt ⎝ mf
Daous , Al-Zahrani (10)
Gs u = 1.08 − 5450 Dt Dp
Chan et l.(12)
⎡ ⎛ u ⎞⎤ Gs ⎟⎥ = 0.0002 ⎢ln⎜ Dt ⎢⎣ ⎜⎝ D p ⎟⎠⎥⎦
2
⎞ 0 .6 ⎟ Dp ⎟ ⎠
8.9
Yang and Knowlton (9) presented a model to estimate solid flow rate in a Lvalve assuming no slip between flowing gas and particles; the net gas flow was assumed to include external aeration introduced along with the gas flowing through the solids in the standpipe; they adopted the equation of Jones and Davidson (10) (developed for particle discharge through an orifice from fluidized beds) for solids flow rate through an active flow area which increased with L-valve pressure drop; however, the correlation proposed by them reflects the universally observed need of a threshold aeration rate to initiate flow of solids. Daos and Al-Zahrani (11) and Tong and Zheng (12) considered gas-particle slip velocity to be described by Ergun’s equation for flow through packed beds. Tong and Zheng (12) considered mechanics of particulate media flow in modeling gas and particle flows in a L-valve; they noted that external aeration rate can split into two streams – one flowing out horizontally through the bend and the other through the vertical stand pipe – depending on the relative resistance in each section for gas flow. Asymmetry in the introduction of gas flow and two phase flow through a three dimensional 90o bend in a L-valve makes particle flow visualization difficult. In an interesting piece of effort, Chan et al. (13) investigated particle motion by positron emission particle tracking technique to identify particle flow structure and observed solid flow to be stable for u/umf less than 6. They observed that maximum solid flow rate in a hopper fed laboratory L-valve is limited by the hopper discharge pipe diameter. Agarwal (14) investigated particle flow structure in a 2-dimensional L-valve made of 0.6 cm thick Perspex sheets with a cross sectional size of (13.8 cm x 2 cm). Effect of standpipe height (i150 to 100 cm), length of horizontal section (i30 to 50 cm) and particle size on solid discharge rate as a function of aeration rate were investigated. A typical solid flow trajectories are shown in Fig 1. For gas flow rates below threshold aeration, solids were stagnant; at and around the threshold aeration rate first movement of solids was observed near the 900 bend at the upper edge of the L-valve; coordinates of points farthest from Y-axis upto which solids velocity is zero (y) were noted for each (x) coordinate; these points were plotted as shown in Fig. 1.
cms
Zone of Stagnant Solids
Zone of Solid flow
cms
Fig.1 Solid flow trajectories in a 2-D L-valve (Agarwal(14))
At an aeration rate of 422 cc/s with a low solid flow rate of 10 kg/hr, particles movement was restricted to a rather narrow region very near the inside of 900 turn and along the top edge of the horizontal section over a stagnant layer; at the exit, particles rolled out over the slope dictated by angle of repose; cross sectional area of particle down flow in the stand pipe increased with distance away from the 90o bend. At 750 Kg/hr, the stagnant region decreased in size and at 1700 Kg/hr it decreased further but still a substantial portion of the particles in the bend remained stagnant. Similar solid flow profiles were observed at different aeration point locations with the other particles. A simple equation for solid flow rate as a function of the affecting parameters is needed for rational design of L-valves. In the present work, an attempt is made to develop equations for a L-valve operating with PS = PR = P2 considering that a threshold aeration rate is needed to initiate solid flow and solids flow rate increases with fluid drag on particles while their movement is resisted by the drag due to wall / stationary particles on moving solids. THE MODEL The aeration rate Q provided to a stand alone L-valve containing particles in the standpipe and horizontal pipe in a packed bed form gets split into an up flow stream QU (through the standpipe) and downflow stream QD (through the horizontal pipe) depending on their relative packed bed resistance for gas flow. At a threshold aeration rate QTh, solids flow rate is initiated through a “small throat” at upper wall 90o bend of the horizontal pipe as the gas drag force overcomes the friction between particles to wall/particles to push the particles to the edge where they roll over by gravity. Particles in the standpipe descend by gravity to the extent of solid flow through the horizontal section. As the particles descend in a section of the standpipe against the up flow aeration rate of QU, the gas to particle relative velocity in that section will be at the incipient fluidization condition as the bed voidage is around 0.5. The “small throat” diameter needs to be greater than 5xDp to 10xDp to overcome arching tendency. Further increase in external aeration through the L-valve increases the gas drag force on particles to increase solid flow rate and throat area near 90o bend near the upper wall. Due to the gas-particle slip velocity and increased sold flow rate, most of the external aeration ends up in the down flow stream assisting solid flow rate. Together, the solid flow rate depends on aeration rate Q above the threshold aeration rate QTh, standpipe diameter D, height of the standpipe above the aeration point HU, length of downstream solid flow path HD through which solids flow out, particle diameter Dp, particle density !p, gas density !g and gas viscosity . Based on this hypothesis, equations are developed in the following sections. Model for Threshold Aeration Rate QTh: Threshold aeration rate is the aeration rate at which the drag by gas can just overcome the friction between particles and particles/wall to let the solids flow along with the gas. Aeration flow introduced into the standpipe at a pressure P1 gets split into upward flow through the standpipe and downward/horizontal flow through the horizontal section depending on the resistance in each section. As both sections are filled with particles in a packed bed form, assuming laminar flow and exit pressure P2 to be same, from Ergun’s equation, the upflow and downflow components can be expressed as
QU = QD =
πDt2
ε3
πDt2
ε3
D p2 (P1 − P2 )
4 150 ( 1 − ε )2
μHU
1
μH D
2
D p2 (P1 − P2 )
4 150 ( 1 − ε )2
From ratio of these two flows
QU H D = QD H U
3
and the two components add upto the aeration rate Q
QU + QD = Q
4
From equations 3 and 4, flow in the horizontal section can be obtained as
⎛ H ⎞ Q D ⎜⎜1 + D ⎟⎟ = Q ⎝ HU ⎠
5
From equations 2 and 5
D p2 (P1 − P2 ) ⎛ H U + H D ⎜⎜ Q= 4 150 ( 1 − ε ) 2 μH D ⎝ HU
πDt2
ε3
⎞ ⎟⎟ ⎠
6
At the point of particle flow initiation, particles flow through a critical throat area (fU ( Dt2/4)). Relative velocity between gas and particles in the moving section of the standpipe will be around umf. Hence, pressure drop for gas upflow in that section can be expressed as
P1 − P2 = H U (ρ p − ρ g )(1 − ε )g
7
With this approximation, threshold gas velocity can be obtained as
D p2 (ρ p − ρ g )g (H U + H D ) QTh = fU 4 150 ( 1 − ε ) HD μ ( HU + H D ) πDt2 = fU umf = fU X HD 4
πDt2
ε3
8
with X defined as
X =
πDt2 4
u mf
HU + H D HD
9
Threshold Aeration Model Validation: Experimental observations on QTh reported in the literature are compared with parameter X, defined by equation 9 in Fig. 2. The correlation is reasonably good and an average value for the factor fU at the initiation of particle flow is estimated to be 0.07.
QTh = 0.07
πDt2 4
umf
HU + H D HD
10
350 300
QTh, mL/s
250 Knowlton
200
Arena 150
Geldart Zheng
100
QTh=0.07 X 50 0 0
1000
2000
3000
4000
5000
6000
Parameter X
Fig.2 Correlation of data on threshold aeration rate reported in literature with parameter X (Eq.9) Model for Particle Flow Rate: Let the interstitial gas velocity be u and solids velocity be v in the horizontal pipe through which solids flow out of L-valve. The flow of solids due to fluid drag is resisted by the friction at the non moving particles/pipe walls.
πD 2 H D 6(1 − ε ) πD p2 Np FD = c Dm ρ g (u − v )2 3 4 πD p 4
=
f πDH D ρ p (1 − ε )ε
= Friction at the wall
Fluid drag on particles
v2 2
11
For aeration rates Q greater than the threshold QTh, area ( D2/4) of solid flow increases with gas flow upto ( Dt2/4) where the solid flow rate will be Wmax.
W D 2 − DTh2 = Wmax Dt2
≈
D2 Dt2
12
Assuming laminar flow conditions, the drag coefficient and friction factor can be taken as
c Dm
∝
μg
D p ρ g (u − v )
; f
∝
μb Dρ b v
13
Rearranging equations 11 with 12 and 13
u
2 ⎛ k μ b D p ⎞⎟ ⎜ = 1+ v ⎜ 1 − ε μ D2 ⎟ g ⎝ ⎠
14
where k is a constant. Combining equation 14 with 12
(Q − QTh )
⎛ D2 W ⎞ ρ p (1 − ε ) = ⎜⎜1 + kv p2 max ⎟⎟W Dt W ⎠ ε ⎝
15
where kv is a constant incorporating gas viscosity, moving bed viscosity, bed voidage, k and other constants.
Yang and Knowlton (8) suggested that maximum flow rate of solids may be estimated using the equations proposed by Jones and Davidson (10) developed for particle discharge rate from fluidized beds through an orifice
= 0.56 ρ p (1 − ε )g 0.5 H U0.5 ( Dt − D p ) 2
Wmax
16
However, particles move as a packed bed in a section of the horizontal pipe. Hence, maximum flow rate of solids may be dictated by the diameter of the horizontal pipe and may be estimated by the equation of Beverloo et al. (15) given as
= 0.58ρ p (1 − ε )g 0.5 ( D t − 1.5D p ) 2.5
Wmax
17
Validation of the Model for Particle Flow Rate: Data of Knowlton and coworkers (1), Zeng et al. (4) and Arena et al. (7) on solid flow rate as function of aeration rate are compared with the present model equation 15 in Fig.3 with and kv assigned values of 0.4 and 2640. Table 2 summarizes the details of the properties of the experimental systems. The estimates of maximum solid flow rate by the equation 16 of Jones and Davidson (10) and equation 17 of Beverloo et al.(15) are included in Table 2. Experimentally observed maximum solid flow rates are in the range suggested by the Beverloo equation. Table 2. Details of the systems used for the evaluation of the equation 15 Reference Knowlton 1, (1) Knowlton 2, (1) Knowlton 3, (1) Zheng, (4) Arena 1, (7) Arena 2, (7) Arena 3, (7) Arena 4, (7)
D cm 7.62 5.08 7.62 4.7 2.7 2.7 2.7 2.7
HS cm 854 854 854 295 270 270 270 270
HD cm 45.72 45.72 45.72 32.6 31.7 31.7 31.7 31.7
Dp cm 0.02609 0.02609 0.02609 0.0605 0.0073 0.0156 0.0341 0.017
!p g/cm3 2.611 2.611 2.611 1.392 2.55 2.55 2.55 4.46
W(1+[kvDp2Wmax)/(Dt2W)], g/s
10000
1000 Knowlton 1 Knowlton 2 Knowlton 3
100
Zheng Arena 1 Arena 2
10
Arena 3 Arena 4 Parity line
1 1
10
100
1000
10000
(Q‐QTh)!p((1‐ε)/ε), g/s
Fig.3. Comparison of equation 15 with the literature data.
Wmax. g/s Eq.16 Eq.17 38820 3798 17253 1378 38820 3798 4629 605 2678 277 2678 277 2678 277 4683 485
Data of Knowlton and coworkers (1) Zeng et al. (4) and Arena et al.(7) are resasonably well represnted by the equation 15 as shown in Fig.3.. Using equations 15 and 17, maximum solid flow rate and corresponding maximum aeration rate Qmax upto which the L-valve can be operated can be estimated. CONCLUSIONS 1). A model for solid flow rate in a stand alone L-valve as a function of external aeration rate is developed considering that a minimum threshold aeration rate QTh is necessary to initiate solids flow rate in L-valves. 2). Threshold aeration rate increases with increase in L-valve diameter and particle minimum fluidization velocity. The model equation 10 gives a reasonable description to the experimental observations reported in the literature as shown in Fig.2. 3). Equation 15 with kv assigned a value of 2640 could correlate the data of Knowlton and coworkers (1), Zeng et al. (4) and Arena et al. (7) on solid flow rate as a function of external aeration rate as shown in Fig.3.
⎛ D p2 Wmax ⎞ ρ p (1 − ε ) ⎟W (Q − QTh ) = ⎜1 + 2640 2 ⎜ ⎟ D W ε t ⎝ ⎠
18
ACKNOWLEDGEMENT The author is thankful to University Teknologi PETRONAS for the encouragement and support for carrying out this work. NOTATION cDm D Dp Dt f fU g Gs HD HU Hs P1 P2 Q QD QTh Qu
Gas-particle drag coefficient Diameter of throat through which solids flow (cm) Diameter of particles (cm) L-valve diameter (cm) Friction factor for flow of solids Fraction of standpipe particles set in motion Acceleration due to gravity (cm/s2) Particle mass flux, (kg/m2/s) Length of down flow section (cm) Height of upflow section (cm) Standpipe height (cm) Aeration point pressure (g/cm/s2) Discharge point pressure (g/cm/s2) Aeration flow rate (cm3/s) Downward aeration flow through Horizontal section (cm3/s) Threshold Aeration Rate (cm3/s) Upward aeration flow through standpipe (cm3/s)
u’, v’ u umf W Wmax
Actual gas and particle velocities (cm/s) Superficial gas velocity (cm/s) Minimum fluidization air velocity (cm/s) Solids flow rate (gm/s) Maximum solid flow Rate (gm/s)
Greek Symbols ,!g !b !P b
Bed voidage Gas density (g/cm3) Density of moving bed, Particle (g/cm3) Gas viscosity (g/cm/sec) Viscosity of moving bed (g/cm/sec)
REFERENCES 1. 2. 3.
4.
5.
6. 7. 8. 9.
10. 11. 12. 13.
14. 15.
T. M. Knowlton, I. Hirsan, L-valves characterized for solids flow, Hydrocarbon process, 3(1978) 149-156 D. Geldart, P. Jones, The behavior of the L-valves with granular powders, Powder Technol, 67 (1991) 163-174. M.Ozawa, S.Tobita, T Mii, Y.Tomoyasu, T.Takebayashi, K Suziki, Flow pattern and flow behaviour of solid particles in L-valve, CFB Technology III, (Eds) P.Basu, M. Horio and M.Hasatani, Pergamon, Oxford (1991), 615-620. Q.Zheng, Z.Ma, A.B.Wang, Experimental study of the flow pattern and flow behaviour of gas-solid two phase flow in L-valve, in:A.A.Avidan, (ed). CFB Technology IV, Hidden Valley, USA (1993) 246-252 M. Rhodes, H. Cheng, Operation of an L-Valve in a circulating bed of fine solids, in:A.A.Avidan, (ed). CFB Technology IV, Hidden Valley, USA (1993) 240-245. K.Smolders and J.Baeyens, “ The operation of L-Valves to control standpipe flow” Advanced Powder Technology, vol. 6 (1995) 163-176 U. Arena, A. Cammarota, “L-valve behavior with solids of different size and density”, Powder Technol, 98 (1998) 231-240. W.Yang, T. M. Knowlton, L-valve equations, Powder Technol, 77 (1993) 4954 T. M. Knowlton, “Standpipes and return systems”, in: J.R.Grace, A.A.Avidan and T.M.Knowlton (Eds.), Circulating Fluidized Beds, Chap 7, Blackie Academic & Professional, an imprint of Chapman & Hall, London, 1997, 214260. D.R.M. Jones, J.F. Davidson, The flow of particles from a fluidized bed through an orifice, Rheologica Acta 4 (1965) 180– 192. M. A. Daous, A. A. Al-Zahrani, “Modeling solids and gas flow through an Lvalve”, Powder Technol, 99 (1998) 86-89. H. Tong, Q. Zheng, “Hydrodynamic modeling of the L-valve”, Powder Technol, 129 (2003) 8-14. C.W.Chan, Jonathan Seville, Xianfeng Fan, Jan Baeyens, “Particle motion in L-valve as observed by positron emission particle tracking”, Powder Technology 193 (2009) 137-149. Amit Agarwal, “Solid flow in a 2 D Lvalve”, M.Tech Thesis IIT Delhi (2004). W.A. Beverloo, H.A.Leniger, J, van de Velde, The flow of granular solids through orifices, Chem.Eng.Sci., 15 (1961)260-269.
PROCESS DECOUPLING OF PLASMA ENHANCED SYNTHESIS OF CHLORINATED POLYVINYL CHLORIDE (CPVC) PARTICLES IN A CIRCULATING FLUIDIZED BED Wei Lu, Tengfei Cao, Yi Cheng* Department of Chemical Engineering, Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Tsinghua University Beijing 100084, P.R. China PH: +86-10-62794468; FAX: +86-10-62772051; e-mail: [email protected] ABSTRACT Plasma enhanced synthesis of CPVC particles in a gas-solid plasma circulating fluidized bed reactor (PCFBR) is proposed as a novel CPVC synthesizing method. The chlorination process is decoupled into a fast initiation step in a plasma riser and a slow chlorination process in the accompanying bed. The CPVC product has good properties in terms of chlorine content and microstructure. INTRODUCTION Chlorinated polyvinyl chloride (CPVC) is produced by chlorination of polyvinyl chloride (PVC) particles. In recent years, CPVC has received much attention as a kind of high-performance thermoplastic which gives better performance in heat stability, mechanical properties and flame retardant ability because of the increase of polarity compared with PVC. Therefore, CPVC can be widely used in hot and cold water pipes, industrial liquid handling and other commercial applications (1). For the rapid development of the chlor-alkali industry in China, a large amount of excess chlorine has caused a serious problem, i.e., how to balance the noxious chlorine gas or the chlorine ion as in HCl from a sustainability viewpoint. PVC is a major solution to this problem, CPVC provides a further valuable route to immobilize chlorine. By further chlorinating the PVC particles to CPVC, more chlorine gas can be fixed into the solid products. It is known that the chlorine content of PVC is about 56.7 wt%. Most commercial CPVC resins have a chlorine content ranging from 63 wt% to 69 wt%, and even to 74 wt% by special treatments. For example, about 400 kg of chlorine is consumed by chlorinating 1,000 kg of PVC to CPVC with a 69 wt%
chlorine content. Accordingly, the CPVC industry not only consumes the excess chlorine, but also converts PVC into another high-value material. Among the reported approaches to synthesizing CPVC, the aqueous suspension method is the main production technology at the commercial scale (2-3). However, a gas-solid method is acknowledged as a cleaner process, considering the ease to control and to separate the CPVC products from waste gases, etc. The main challenge of this method is to improve the gas-solid contact efficiency and find an effective initiator. In general the approach is carried out in fluidized beds, utilizing UV light as the initiator. But, the UV light is too easily shielded by PVC particles and the capability of initiation is weakened. So, finding a more effective and cleaner initiator for the gas-solid chlorination method is of great importance. It is reported that the typical mechanism of PVC chlorination is a series of free radical reactions (4). It was proposed that cold plasma could be an effective initiator instead of UV. Cold plasma can be comprised of many kinds of ions, radicals, UV light, etc. Researchers have shown that cold plasma can be widely applied in the surface treatment of polymer materials (5-6). The mechanism showed that plasma could activate the surface of polymers and reactive gas simultaneously. However, the chlorine absorbed on the surface of a PVC particle must migrate into the core of the particle to chlorinate the bulk of the particle, which is assumed to be a slow diffusion-like process (7). Therefore, the chlorination process can be decoupled into two steps: the first step is the initiation of chlorination by plasma, and the second step is the chlorine diffusion from the surface to the inside of particles in a chlorine atmosphere. In this work, we proposed a novel CPVC synthesizing method in the Plasma Circulating Fluidized Bed Reactor (PCFBR). EXPERIMENTS First, a fixed-bed DBD reactor was employed to examine the feasibility of the CPVC synthesizing method. PVC particles were chlorinated with/without plasma at 100 C. During chlorination, the plasma power source was turned on for 1 minute and then turned off for 10 minutes to simulate the process decoupling method. The power density of the plasma was 8.1 W/cm3 at atmospheric pressure, and the working frequency was 13.8 KHz. A PCFBR was designed to synthesize CPVC as shown in Figure 1. The riser was assembled by two 0.5 m long straight coaxial quartz tubes with a gap of 4 mm, and the thickness of quartz tubes was ~2 mm. An iron bar was inserted into the inner tube as one electrode while some copper was wound outside the outer tube as the other.
One electrode was connected to the DBD power source (CTP-2000K, Nanjing Suman Electronics Co., Ltd), the other electrode was grounded. The plasma was generated by means of double–dielectric barrier discharge method inside the gap of two tubes. The accompanying bed was made of quartz, the same as the riser. The particle circulation rate was controlled by the flow rate of carrier gas. The chlorine gas had a purity of 99.999%, and the other gases including Ar and N2 had a purity of 99.9%. The waste gas from the reaction unit was treated by an alkali solution. Waste gas
Alkali Solution
Electrodes Plasma generator MFC Cl2 Thermostat
Ar Cl2 N2
Figure 1 Schematic drawing of the plasma circulating fluidized bed reactor (PCFBR) Approximately 70 g of PVC particles (SG-5 provided by Xinjiang Tianye Co. Ltd.) with ~150
m diameter were added to the accompanying bed. The whole apparatus was
purged with N2 gas in order to exhaust the oxygen in the system and fluidize the particles. The accompanying bed was heated up from room temperature to 70 oC, and then the reactant gas, e.g., the mixture of chlorine and N2, was injected into the accompanying bed from the bottom nozzle. The gas flows were fixed at 1 SLM Cl2 and 1 SLM N2. At the same time, a mixture of chlorine and Ar was injected into the riser from the bottom nozzle with two different flow rates of 0.3/2.0 SLM and 0/2.5 SLM. The Cl2/Ar plasma was generated inside the gap between the two tubes by the DBD method. The power density and frequency were 1.02 W/cm3, 12.7 KHz (Cl2/Ar=0.3/2.0) and 1.24 W/cm3, 13.6 KHz (Cl2/Ar=0/2.5, respectively). The particle circulation rate in both situations was ~0.2 g/s. After treating particles at 70 C for 20 minutes, the temperature was raised to 100 C for 40 minutes. After that, the particles were chlorinated for different times. At the end of the experiments, the plasma generator, chlorine flow and heating apparatus were turned off but N2 and Ar were kept flowing for several minutes to purge the remaining chlorine in the apparatus and that absorbed on the particles. Then the particles were exposed in the fume hood for
a day or more so that chlorine gas volatilized thoroughly. The CPVC products were characterized by several methods. The chlorine content was analyzed by oxygen combustion and electrification method. Each product was characterized for at least 3 times to get an average and the error was found to be less than 0.5%. The distribution of chlorine in CPVC particles was measured by SEM and EDS (JSM-6460LA and EDS were made by the Oxford Instrument Company). The microstructure of CPVC products was characterized by Raman spectrum analysis (RM2000 made in Renishaw, UK.). In the measurements, the laser was Ar-514, with 20X objective lens, 5 m diameter of facula, 4.7 mw and 30 s scanning time. RESULTS AND DISCUSSION Chlorination of PVC particles without a plasma initiator was carried out in the first series of experiments in the fixed bed reactor. The chlorine content of the CPVC particles rose to 69.7 wt% after 12 hours (see Figure 2). It proved that the chlorination process could take place by heating PVC particles at high temperature without any other initiator. However, it is not an effective method due to the low reaction rate. Then an atmospheric pressure DBD plasma was employed as the initiator in the second experimental series. It can be clearly seen that the chlorination process was accelerated significantly by the cold plasma and the chlorine content rose to 67 wt% in only 3 h. Considering that the working time of plasma was only 5 min/h under the intermittent operation, this proved that plasma was an efficient initiator in PVC chlorination.
68
64
60
o Heated PVC particles at 100 C
56
for chlorination o CPVC at 100 C with plasma as initiator
0
3
6
9
12
Chlorine Content /%
Chlorine content /wt%
72 the ratio of Ar/Cl2=2.5/0,
66
1.24 W/cm , 13.6KHz
63
1.02 W/cm , 12.7KHz
3
the ratio of Ar/Cl2=2.0/0.3, 3
60
57
15
Chlorination time /h
Figure 2 Chlorination curves of PVC particles with/without plasma as the initiator at 100 C
0
1
2
3
4
5
Chlorination Time /h
Figure 3 The chlorine content of CPVC by mass as a function of reaction time in PCFBR
The results obtained from fixed-bed DBD reactor proved that the cold plasma was
able to initiate the chlorination effectively. At the same time, the method of process decoupling was feasible and could be operated in a CFB reactor. That was, PVC particles were treated fast in the riser by cold plasma and the chlorination was initiated, then the chlorine slowly diffused into the core of the particles in the accompanying bed with a relatively long residence time. Particles were circulated in the PCFBR several times to be chlorinated sufficiently. The following discussion is based on the experiments in the PCFBR. The chlorine content of CPVC is shown as a function of reaction time in Figure 3. One series of the results shows the chlorine content increasing up to 65.0% when pure Ar was used as the carrier gas with plasma in the riser. These results revealed that PVC can be chlorinated in the PCFBR. The total residence time of particles in the plasma zone was only about 22.2 s during the 4-hr experiments. In fact, with appropriate optimization of the PCFBR operation, a successful single-run of the chlorination process would be ~1.5 hr, or even much less. Figure 3 also indicates that under the same power input of plasma, the composition of carrier gas in the riser is very important, because it provides the atmosphere in which plasma is generated, and the reactive species are different in different plasmas. So it is possible to adjust the property of plasma by changing the ratio of Cl2/Ar. At the same time, the mechanism of chlorination is changed. For example, chlorination can occur in the riser when Cl2 is introduced, which is different from the case of pure Ar as carrier gas. It was noticed that the chlorine content increases linearly as a function of reaction time in Figure 3 for the case of Cl2/Ar=2.0/0.3. This might indicate that when introducing chlorine gas with Ar to generate plasma, not only are the PVC particles activated, but also the surface of the PVC particles is chlorinated rapidly in the plasma zone. However, the chlorine content increased slowly when the chlorination time was lengthened. Another fact is that the discharging of Cl/Ar plasma was weaker than pure Ar plasma so that the initiation effect became weaker. Obviously, work is required for a deep understanding of the complex mechanism and for guiding this optimization of the process design. CPVC characterization SEM and EDS can give a semi-quantitative result by comparing two samples under the same conditions. The chlorine content shown in Figure 4 is the mass fraction of chlorine. The total amount of chlorine and carbon elements (hydrogen is not included because EDS cannot discern light elements). So this value is larger than the actual chlorine content of CPVC. Considering that the analysis error of the instrument is less than 5 %, in principle, the results of EDS shown in Figure 4 indicate that the CPVC synthesized in this work has a much higher chlorine content than PVC raw
material. In addition, the scanning area was the interior of the CPVC particles, which proved that chlorination occurred inside the PVC particles, not only on the surface.
Figure 4 Chlorine distribution in the particle of plasma synthesized CPVC (left), and PVC raw material (right) The microstructure of CPVC was analyzed by Raman spectral analysis. In Figure 5, five kinds of CPVCs were analyzed, including four samples of CPVC synthesized using PCFBR with Ar plasma as the initiator and one sample of commercial CPVC using the aqueous suspension method. The PVC SG-5 raw material was also analyzed for comparison. In the spectra, the characteristic peak of CPVC is at 300-500 cm-1.
25000
Intensity
20000 Commercial CPVC
15000
PECFB 4h
10000
PECFB 3h PECFB 2h
5000
PECFB 1h PVC SG-5
0
0
750
1500
2250
3000
-1
Wavenumber cm
Figure 5 Raman spectra of PVC or CPVCs
3750
In the chlorination process, the increase of the amount of -CCl- can be seen clearly. At the same time, there is essentially no -C=C- group in all of the samples, which has a signal at 1650 cm-1. This proves that almost all the CPVC samples have a fine microstructure. But with it is still evident that there is a very weak -C=C- signal in the spectrum of an every CPVC sample and no signal in the spectrum of PVC, which reveals the influence of Elimination-Addition mechanism. To sum-up, it was shown that chlorination occurred in the bulk of the PVC particles and that the microstructure of plasma synthesized CPVC was comparative to the commercial product by the Raman analysis. CONCLUSION In this article, a novel CPVC synthesizing method was proposed that employed plasma as the initiator and which operated in a circulating fluidized bed. The chlorination process consisted of two steps: a fast initiation step and a slow chlorination process. In the chlorination process, the residence time of particles in the plasma zone was only about 22 seconds in the total 4-h reaction time, which indicated that plasma had high initiation efficiency. In the experiments so far, the chlorine content of CPVC product reached 65.0%. Characterizations by SEM and Raman spectrum show that the bulk of the particles were chlorinated. Moreover, the particle product had a uniform chlorine distribution inside the particles and a fine microstructure. The objective of this work was to propose a novel CPVC production method with preliminary demonstration of feasibility. This would also open a specific, potential area for CFB applications. Still, there is lots of work to do in the future, for example, to investigate the operational performances of PCFBR, plasma-related design and optimization, influence of PVC particle properties on the chlorination process, etc. ACKNOWLEDGEMENT Financial support from the National Science and Technology Key Supporting Project (No.2009BAC64B09) and the Program for New Century Excellent Talents in University are acknowledged. REFERENCES 1. Liu, H., and Zhang, X.M. (2008). “Review on chlorinated polyvinyl chloride.” Polyvinyl Chloride, 36 (11), 9. 2. Alan, O. J., and Robert, V. G., Process for chlorination of PVC in water without use of swelling agents: US, 4412898 [P], 1983 3. Wakabayashi, T., Kobayashi, Y., and Tujii, I., Process for the proparation of
chlorinated polyvinyl chloride resin: US, 3534013 [P], 1970 4. Lukas, R., Svetly, J., and Kolinsky, M. (1981). “Structure of chlorinated poly (vinyl chloride) X. Conclusions on the chlorination mechanisms.” Journal of Polymer Science: Polymer Chemistry Edition, 19, 295. 5. Arpagaus, C., Sonnenfeld, A., and Von Rohr, P. R., (2005). “A downer reactor for short-time plasma surface modification of polymer powders.” Chemical Engineer Technology, 28 (1), 87. 6. Arpagaus, C., Rossi, A., and Von Rohr, P. R., (2005). “Short-time plasma surface modification of HDPE powder in a plasma downer reactor – process, wettability improvement and ageing effects.” Applied Surface Science, 252, 1581. 7. Wachi, S., Morikawa, H., and Inoue, H. (1988). “Conversion distribution in diffusion-governed chlorination of poly (vinyl chloride).” AICHE Journal, 34 (10), 1683.
A PRACTICAL MODEL FOR A DENSE-BED COUNTERCURRENT FCC REGENERATOR Yongmin Zhang, Chunxi Lu State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, 102249, P. R. China Abstract In this study, a new practical countercurrent regenerator model for in-situ FCC operation optimization was proposed. A three-zone-and-two-phase gas model and a new two-CSTR-with-interchange model were used to give better descriptions on the gas and solids flow patterns, addressing the region-dependent mass transfer rates and the freeboard effect on catalyst regeneration. The model coupled mass and heat balances, hydrodynamics and reaction kinetics. The modeled results are in reasonable agreement with the commercial data from an industrial FCC regenerator under both partial and full CO combustion modes. INTRODUCTION A regenerator is an indispensable part of a FCC unit, acting as a fluidized-bed reactor to burn the coke deposited in the spent catalyst and recover its cracking activity. An ideal FCC regenerator requires very low levels of carbon content in regenerated catalyst (CCR) (0.05~0.1 wt%) with minimized air consumption and maximized coke burning intensity (CBI) (usually defined as weight of coke burned for a given catalyst inventory and a given period). A practical regenerator model based on sound understanding of its intrinsic hydrodynamics, mixing and reaction kinetics is undoubtedly valuable to optimization of its design and operation. There have been several published studies on modeling dense-bed FCC regenerators (1-7). However, they all failed to describe the gas and solids flow patterns properly in the three zones (grid zone, dense-bed zone and freeboard zone) of a regenerator simultaneously, resulting in modeled results divergent largely from experimental facts and low reliability and predictability. Some of them (1-5) used the simple Orcutt fluidized-bed model (8) to model gas flow in the dense bed, which falsely modeled the reactant gas concentration in the emulsion phase to be a constant level. Otherwise, only Lu (5) and De Lasa et al. (7) considered the large amount of particles entrained in the freeboard and the associated reactions. However, Lu (5) improperly modeled the solid flow in the freeboard with a multiple-CSTR-in-series model, which over-predicted the freeboard reaction. The freeboard model of De Lasa et al. (7) was a particle-trajectory based model, which was too complex to use in engineering practice. The goal of this study is to establish a modified model for a countercurrent regenerator. This model has a modified hydrodynamic model that provides better
-1-
descriptions for gas and solid flows in both dense bed and freeboard. Otherwise, its structure still remains simple enough to be a practical engineering tool. MODEL SCHEME y O , out u2 At2 2
freeboard
freeboard
CCf
y O , z=H u1At1 2
K be
bubble void
Fs,df
Fs,df
dense bed
f
Fs0
emulsion
dense bed CCd
kj
jet
emulsion
y O , z=0 u1At1
Fs0
2
(a)
(b)
Fig. 1 Gas and solid flow patterns in the countercurrent FCC regenerator model: (a) gas flow pattern; (b) solid flow pattern
A countercurrent regenerator is usually the preferred choice in a FCC unit for its better performance, where catalysts are usually injected to the top of its dense bed by a specially designed catalyst distributor, and withdrawn through the bed bottom. Figure 1 illustrates the hydrodynamic models describing the gas and solid flow patterns in this study. For the gas flow in the dense bed, a simple two-phase bubbling-bed model proposed by Chavarie and Grace (9) is used. This is a two-phase model with a “stagnant” emulsion phase, i.e. gas in the emulsion phase coming only from mass transfer from the bubble phase and without axial dispersion. Different from the Orcutt model (8), there is an axial gradient for the reactant gas concentration in the emulsion phase in agreement with experimental facts. Axially, two zones were partitioned in the dense bed to address the different gas transfer rates between emulsion and voids in the bubbling zone and jets in the jet zone. In the freeboard, the gas phase becomes a continuous phase, where interphase mass transfer becomes less important than in the dense bed. Gas flows in the jets, voids and freeboard were all modeled as plug flow without back-mixing. For an irreversible first order reaction A B with negligible volume change, mole balances on A in the bubble phase and emulsion phase yield, respectively, u0
dC Ab + kbeab db (C Ab - C Ae ) + kr f sbC Ab = 0 , dz
(1)
kbeabdb (C Ae - C Ab ) = kr fseC Ae .
(2)
-2-
For the solids flow, a two-CSTR-with-interchange model shown in Fig. 1(b) was adopted. A distinct difference in this model lies in its different d t2 manipulation on solid flow in the freeboard. In a typical fluidized bed, particles in the freeboard come mainly from bubble eruptions on the bed surface. Particle concentration freeboard and upward flux decrease exponentially with increasing distance from the bed surface. Only a negligibly small fraction of particle leaves from the freeboard top and is Cs Fs0 C CHs u1At1 QH O captured by cyclones. This demonstrates that most solid inventory in freeboard exists within a small-height zone near d t1 the bed surface, i.e. the so called splash zone. In this zone, bubbling violent mixing due to strong gas flow turbulence and large zone solids exchange rate between the dense bed and the jet zone freeboard can be expected. Therefore, solids flow in F s0 CCr freeboard was modeled as a separate CSTR reactor with u1At1 solid exchange with the dense bed in this model. Physically, Fig. 2 Geometry model for freeboard in this model is to provide particles with additional an FCC regenerator with time to burn coke with negligible interphase mass transfer an expanded freeboard resistance. 2
Other simplifications are assumed to facilitate the modeling. First, the hydrogen content of the coke is assumed to combust instantly near the bed surface due to the much higher combustion rate of hydrogen (usually an order faster than carbon combustion) (10). Second, the structure of the FCC regenerator is simplified as showed in Fig. 2. The bottom bed section is always assumed to have the same height as the dense bed, Hf, whereas the expanded top section is assumed to be a cylinder of diameter dt2 and height Ht-Hf. MODEL SETUP Kinetic Model Due to the simplification for hydrogen combustion, only carbon combustion needs to be considered in this model. Carbon combustion can be described by .
C+
æ b ö÷ æ 1 ö÷ 2b +1 O2 çç ÷ CO2 + çç ÷ CO ÷ ç 2 (b +1) è b +1ø èç b +1ø÷
(3)
where is the ratio of CO2 to CO released. is affected by many factors including catalyst type, feedstock, temperature, contents of oxygen and CO promoter etc. In this model, is simply determined as the ratio of CO2 and CO concentrations in the flue gas of the modeled regenerator. This also simplifies the complex homogeneous and heterogeneous CO combustion procedures in actual conditions. The carbon combustion rate is estimated by (11)
-3-
æ 1.422´105 ö÷ ÷. kC = 2.967 exp çççè RT ø÷÷
(4)
Hydrodynamics Model Two important parameters in the grid zone, jet length and jet diameter, are determined by Lu‘s correlations (5). Lj 141.85d or (
p d p 0.273 g uor d or 0.654 uor2 0.408 , ) ( ) ( ) gd or g d or g
D j 0.388d or (
uor2 0.332 . ) gd or
(5)
(6)
The average bed density is also determined based on the measured industrial data as expressed by Eqs. (7) and (8). The derivative in Eq. (8) is derived from a correlation of Cai et al. (12) rB =rB,exp +
¶rB (u1 - u1e ) ¶u1
(7)
3n (rp - rB,exp ) ¶rB ¶ éêërp (1- e)ùúû = =u1 ¶u1 ¶u1
(8)
The dense bed height and the axial particle concentration profile are determined based on Zhang et al. (13), which considered the solid mass balance of the whole regenerator. The solid fraction in the freeboard is expressed as fs =f s* + ( f s0 -fs* ) exp (-azf ) ,
(9)
where fs* is the saturated solids fraction, determined by measured cyclone inlet concentration in this study, fs0 is the initial solid fraction at the bed surface and determined by 0.3 u1 umf 1 mf At1 . (10) fs0 At2ub
Here, ub is void rise velocity determined by the ratio of superficial gas velocity and bubble fraction in the dense bed, i.e. u1/ b; the exponent coefficient is determined by 0.7/u2 according to Zhang et al. (13). Based on mass balance in the regenerator,
B H f At1 p At2
H t Hf
fs dzf M s ,
0
(11)
the dense bed height Hf can be determined. Gas transfer coefficient between jet and emulsion is estimated by Lu’s correlation (7), -0.504
æ d u r ÷öæ u 2 ÷ö k j = 0.48 ççç or or g,j ÷÷ççç or ÷÷ èç Lj ÷øèç gLj ÷ø
-4-
æ Lj çç çè d
0.905
ö÷ ÷÷ ÷ or ø
0.068
æ ö çç d or uor rg,j ÷÷ ÷÷ çç u è ø g,j
.
(12)
Bubble-emulsion gas transfer coefficient is estimated by De Groot’ s correlation (14), K be = kbe ab =
u1 , 0.67 H f0.5 d t10.25
(13)
which omits the need to know the average bubble size, a very difficult parameter to estimate in large-scale industrial fluidized beds. Mass and Heat Balances To determine the profiles of gas components, carbon content in the catalyst and temperature in the regenerator, the oxygen balance, carbon balance and heat balance are needed in the model. Due to page limit here, these procedures are only briefly introduced in the following text. During the regeneration process, changes of gas compositions, carbon content and temperature are interrelated. Their values need to be solved together. Oxygen balance in the dense bed is based on Chavarie and Grace (14) with consideration of different mass transfer rates in the grid and bubbling zones. In the freeboard, interphase mass transfer is neglected, with reaction kinetics as the controlling factor. With oxygen concentration, concentrations of CO2 and CO are readily known according to the reaction formula shown in Eq. (3). The profile of carbon content is determined according to the solids raw data flow model and the consumption of data initialization oxygen. In this model, the carbon contents in the dense bed and Td0 Tf0 freeboard are constant due to the hydrodynamics completely mixed assumption. With higher mass transfer rate, the carbon CCd0 CCf0 content in the freeboard is a little gas component balance lower than in the dense bed. The heat balance needs to consider the carbon balance heat input from combustion of carbon CCd0 ≠ CCd1 CCd1 CCf1 and hydrogen, heat to heat up the CCf0 ≠ CCf1 influent air and spent catalyst, heat heat balance loss to atmosphere from the outside Td0 ≠ Td1 Td1 Tf1 shell, and heat removed from catalyst T ≠T f0
coolers.
f1
output
Solving Algorithm
Fig. 3
Flow chart of model program
This model is programmed in Matlab language using a modularized scheme and solved by an iterative method as shown in Fig. 3. There are seven modules and two iteration loops. To establish a model for optimizing the operation of a specified FCC regenerator, a calibration procedure is required to determine key unit-dependent parameters based on existing industrial data. Then, basic operating data can be
-5-
changed to see their effects on the performance of the regenerator and to determine optimized operating parameters.
MODEL VALIDATIONS AND DISCUSSION Table 1 A comparison of the modeled results and industrial data Items
Partial mode
Full mode
Catalyst inventory, ton
185
160
Superficial gas velocity in the dense bed, m/s
0.85
0.93
Superficial gas velocity in the freeboard, m/s
0.48
0.52
Items for comparison
Model
Bed height of dense bed, m Bed density, kg/m
Exp.
7.91
3
Model
Exp.
8.05
278
276
221
220
10.9
12
14.9
14
Dense bed temperature, °C
660
662
689
690
Freeboard temperature, °C
669
670
696
699
Carbon content of the spent catalyst, (wt) %
1.49
Carbon content of the regenerated catalyst, (wt) %
0.18
0.15
0.038
0.05
CBI, kg/(h.ton (cat.))
102.1
105.7
112.8
106.7
O2
0.89
0.8
3.31
3.1
CO
1.61
1.6
0.31
0.3
CO2
16.88
16.8
15.8
15.4
Freeboard density, kg/m
3
Components of flue gas (dry), v%
1.74
Industrial data from a FCC unit in Luoyang Petrochemical Corporation, Sinopec were used to compare with the modeled results. This FCC unit has a coaxial reactor-regenerator layout, processing 1.4 million tons of atmospheric residue per year. A single-stage countercurrent regenerator is used to regenerate the spent catalyst. The regenerator was first operated in the full CO combustion mode with a CO promoter. Later, in order to increase the processing capacity and decrease the main air flow rate, the regenerator was revamped to partial CO combustion mode with reduced air flow rate and without CO promoter. An advantage of this model is that only one fitting parameter, i.e. the interchanging solids flux between the dense bed and the freeboard, Fs,df, is used, which was determined based on the difference of temperature in the dense bed and freeboard. With a same Fs,df, both regeneration modes are modeled. The modeled results are compared with industrial data in Table 1. The main modeled hydrodynamic and performance results are in reasonable agreement with the industrial data, demonstrating the feasibility of this model. With this model, the axial profiles of voidage, temperature, gas components and carbon content can be predicted, as shown in Fig. 4 for a typical partial CO combustion mode. It can be seen that most of the solids inventory in the freeboard is
-6-
concentrated within a ~2 m high from the bed surface, where solids mixing is vigorous and a large solids exchange flux exists between the dense bed and freeboard. Therefore, there is only a small temperature increase in the freeboard, as seen in Fig. 4(b). Due to the different mass transfer rates, oxygen concentration decreases much sharply in the grid zone than in the bubbling zone. In the grid zone, the difference of oxygen concentration in the emulsion and dilute phases is much lower than in the bubbling zone. Due to higher mass transfer rate, carbon burns more efficiently in the freeboard, as indicated by the lower carbon content shown in Fig. 4 (d). 1.2
700
o
dense bed
1.0 0.9
freeboard 0.8 0.7
0
5
T
680
Temperature, C
Voidage, -
1.1
10
Height, m
15
660 640 620 600
20
freeboard
dense bed
0
5
10
Height, m
(a) 0.20
dense bed
yCO2
12
Carbon content, w%
16
Mole content, %
20
(b)
20
yH2O yb,O2
8
freeboard
4
ye,O2 0
15
0
CC
0.18
0.16
dense bed
0.14
freeboard
yCO 5
10
Height, m
15
20
0.12
0
5
10
Height, m
(c)
15
20
(d)
Fig. 4 Predicted profiles of (a) voidage, (b) temperature, (c) gas composition, and (d) carbon content under partial CO combustion mode
CONCLUSION In this study, a modified countercurrent FCC regenerator model is proposed based on modified gas and solids flow patterns. The gas flow pattern in dense bed employs the “two-phase bubbling bed model” proposed by Chavarie and Grace (8), which can predict gas concentration profiles in better agreement with experimental facts. The modification in solids flow patterns focuses on the solids flow in freeboard, which was modeled as another CSTR exchanging solid with the dense bed. The model was applied to an industrial FCC regenerator operated under both full and partial CO
-7-
combustion modes with agreeable modeled results obtainable with industrial data for both modes.
NOTATION At bed area, m2 C gas concentration, carbon content, CC diameter, m d bed diameters, m dt solid volume fraction, fs Fs,df interchange solid rate, kg/m2.s bubble-emulsion mass transfer kbe coefficient, m/s bubble-emulsion mass transfer Kbe coefficient, 1/s jet-emulsion mass transfer kj coefficient, kg/m2.s reaction constant, 1/s kf R gas law constant, kj/(kmol.K) T temperature, K dense bed height, m Hf jet length, m Lj M mass, kg u superficial gas velocity, m/s y concentration, z height, m
b
coefficient, 1/m interphase area per volume of bubble, m2/m3 CO2/CO, b bubble fraction, ε void fraction, density, kg/m3; Subscripts b bubble/bed e emulsion d dense bed s solid f freeboard g gas j jet mf minimum fluidization p particle or orifice 0 initial 1(2) dense bed (freeboard) * saturated
REFERENCES 1. Ford, F. D., Reinmen, R. C., Vasalos, I. A., and Fahrig, R. J., 1977. Chem. Eng. Prog. 73 (4): 92. 2. De Lasa, H. I., Errazu, A, Barreiro, E. And Solioz, S., 1981. Can. J. Chem. Eng. 59: 549-553. 3. Faltsi-Saravelou, O., Vasalos, I. A. And Dimogiorgas, G., 1991. Comp. Chem. Eng. 15: 647-656. 4. Filho, R. M., Batista, L. M. F. L. and Fusco, M., 1996. Chem. Eng. Sci. 51: 1807-1816. 5. Lu, C., 1996. Doctoral dissertation, China University of Petroleum, Beijing, China. (in Chinese) 6. Lee L. S., Yu, S. W., Cheng, C. T. and Pan, W. Y., 1989. Chem. Eng. J. 40: 71-82. 7. De Lasa, H. I. and Grace, J. R. 1979. AIChE J. 25: 984-991. 8. Orcutt, J. C., Davidson, J. F. and Pigford, R. L., 1962. Chem. Eng. Prog. Symp. Ser. 58: 1–15. 9. Chavarie, C. and Grace, J. R., 1975. Ind. Eng. Chem. Fundam. 14: 75-91. 10. Wang, G., Lin S., and Yang G., 1986. Ind. Eng. Chem. Proc. Des. Dev. 25: 626. 11. Dong, X. and Hao, X., 2006. Petroleum Refinery Engineering. 36 (6): 7-9. (in Chinese) 12. Cai, P., Jin, Y., Yu, Z. Q., and Wang, Z. W., 1990. AIChE. J. 36: 955-956. 13. Zhang Y. M., Lu C. X., Shi M. X., 2008. Chem. Eng. Tech. 31, 1735-1742.
-8-
INVESTIGATION ON THE HYDRODYNAMIC PROPERTIES IN THE EXTERNAL LOOP OF CIRCULATING FLUIDIZED BED WITH A LOOP SEAL Xuan Yao, Tao Wang, Hairui Yang, Hai Zhang, Qing Liu, Junfu Lv, Guangxi Yue Key laboratory for Thermal Science and Power Engineering of ministry of Education, Tsinghua University, Haidian District, Beijing 100084, China T: 86-1-62773384; F:86-10-62781743;E:[email protected] ABSTRACT The pressure balance and mass balance are influenced by the characteristics of different components in the loop of a circulating fluidized bed (CFB). Experiments were conducted in a 4.3 m high cold laboratory CFB test rig with a loop seal. With a fixed bed inventory and superficial gas velocity, the pressure drop of the loop seal decreased with increasing aeration, thus causing an increase in the solid circulation flux (Gs). Correspondingly, the pressure drop in the riser became higher with increasing Gs; the pressure drop of the cyclone had a non-linear relationship with Gs, and the transition point was determined in the experiment. Using the laser fiber and gas tracer method, hydrodynamic characteristics in the standpipe were directly measured. It was found that the pressure gradient, voidage, and solid height in the standpipe were affected by the pressure balance in the whole loop. By adjusting the gas flow rate and direction in the standpipe, the gas-solid slip velocity and pressure gradient changed correspondingly. Therefore, the standpipe could maintain the pressure balance and realize self-equilibrium of the loop by absorbing the pressure drop variations of other parts in the system. INTRODUCTION Circulating fluidized bed (CFB)normally consists of the riser, cyclone, standpipe and solid recycling valve. The standpipe and recycling valve can overcome the high pressure difference and recycle particles collected by cyclone to the bottom of the riser. The standpipe often has the function to prevent gas bypassing. Non-mechanical valves, such as loop seal, L valve, are commonly used in industrial CFB boilers as they can work under the harsh conditions of high temperature and pressure. The solid circulating flux is commonly controlled by changing the aeration rate in the valves. Though a number of studies have been conducted on gas-solid flow in the riser and cyclone [1-5], there are few studies on the hydrodynamic properties in the external loop of CFB system, especially for the influence of riser operation conditions on the performance of the recycle valve and standpipe. Basu [1] built a model to describe the CFB external loop pressure drop and analyzed the influence of aeration rate,
1
riser velocity and Gs on the standpipe pressure gradient. However, the gas flow direction and vodiage in the standpipe were not measured directly. Monazam[2] studied the influence of bed inventory, riser velocity and aeration rate on GS and confirmed that the performance of the loop seal and standpipe can’t be studied ignoring the influence of other components in the whole system. Standpipe is a special part of the CFB system. It has the function to absorb the pressure variation of other components in the system and the gas bypassing can be prevented by designing the standpipe correctly. However, there is very little literatures on the performance of the standpipe in the CFB system. The flow state in the standpipe is still in controversy and there is little evidence to validate the different viewpoints [1,6]. In this paper, experiments was conduced in a 4.5 m high CFB test rig with a loop seal to study the influence of operating conditions on the performance of hydrodynamic properties in the external loop, including the loop seal and standpipe. Laser fiber and gas tracer methods were applied to measure the flow behavior in the standpipe directly, including the voidage and gas flow direction. EXPERIMENTAL Experimental test rig Experiments were conducted in a CFB cold test rig as shown in Fig.1. The rig consisted of a distributor, a riser, a cyclone, a standpipe and a loop seal. The riser had a cross-section area of 0.1x0.1 m2 and a height of 4.5m. The cyclone was of high separation efficiency. The standpipe had a height of 3.0m and a diameter of 0.08m and connected the riser with a loop seal [7]. The dimension of the loop seal was shown in Fig.2. 20 pressure taps were installed at different heights along the solid circulation loop to measure the pressure drop online. The fluidizing gas rate and loop seal aeration rate, Q, were measured by gas flow meters. Two methods were used to measure the solids circulation flux, GS. One was based on the time for the recirculating solids to accumulate to a certain height in the standpipe after a sudden close of a butterfly valve installed in the standpipe. The other method used a self-designed measuring device placed under the
2
Fig. 1 Schematic diagram of experimental test rig
cyclone. All experiments were carried out at ambient temperature and atmospheric pressure. The bed material was quartz sand, its physical properties are listed in Table 1. Real density 2625 kg/m3
Table 1 Physical properties of the bed material Bulk Minimum Minimum Size range voidage voidage fluidization velocity 0.50 0.58 0.09 m/s 200-500μm
Sauter diameter 360μm
Flow behavior measurement in the standpipe and loop seal To study the gas flow behavior in the standpipe, high purity CO2 gas was used as the tracer. As shown in Fig. 2, CO2 was injected into the system at Point A, and CO2 concentration at Tap 1, 2 and 3 were simultaneously measured by a CO2 detecting system with 3 channels, each equipped with a sampling probe, a CO2 sensor (GSS-C20) and a vacuum pump. A laser fiber was used to measure the solid volumetric fraction at Taps 2 and 3. Signals from the CO2 sensors and laser fiber were recorded online through a data acquisition system.
Fig. 2 Measurement system of flow behavior in loop seal and standpipe
In the first test, it found that CO2 concentrations at different locations in the same cross section were nearly the same, so the gas and solids in the standpipe were regarded as well mixed horizontally. Shown in Fig.2, in the control volume enclosed by the dot lines and solid walls, the gas balance can be expressed as following equations when CO2 is injected with a certain volumetric flow rate q at Point A. According to Eqn.1and 2, QV and QH can be calculated with known Q, q, C1 and C3.
QV + QH = Q + q QV × C3 + QH × C1 = q
(1) (2)
DISCUSSION Riser pressure drop The solid circulation flux GS of the system has significant influence on the mass balance in the CFB reactor, and directly determines the performance of the system. As shown in Fig. 3, GS increases with the aeration rate, Q, in the loop seal. After Q increases to a critical value, GS no long changes with aeration rate and maintains at
3
the maximum rate about 45kg/m2s. GS can be estimated by the particle suspension density in the upper region of the riser. GS = ρ s(1 − ε ) (uriser − ut) .Therefore, GS can be used characterize the suspension density in the upper riser. Fig. 4 shows the solids hold up distribution in the riser at Uriser=5.0 m/s. With increasing Gs, particles hold up in the riser becomes denser. Former studies have shown that, with increasing Gs at a certain riser fluidizing velocity, the flow in the riser will transition from dilute phase pneumatic conveying to the fast fluidization state [8]. In the fast fluidized bed, the voidage in the dense zone and upper dilute zone are held stably, while the height of dense zone ascends gradually and the voidage in the transition zone increases. 50 2
4
Riser hieght (m)
bubbling transintion point
30
2
Gs (kg/m s)
40
20 10 0 5.0
5.5
6.0
6.5
7.0
7.5
3
Gs=15.7 Kg/m s ,△P=515 Pa 2 Gs=27.7 Kg/m s ,△P=950 Pa 2 Gs=38.1 Kg/m s ,△P=1500 Pa 2 Gs=39.8 Kg/m s ,△P=2542 Pa 2 Gs=45.6 Kg/m s ,△P=3670 Pa 2 Gs=45.7 Kg/m s ,△P=5020 Pa
2 Uriser=5.0 m/s
1
0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Solid hold up 1-ε
8.0
3
Areation rate (m /h)
Fig. 3 Variation of GS with Q of loop seal Fig. 4
solid hold up distribution in riser
The pressure drop of the riser △Pr is approximately equal to solids gravity pressure drop, which is determined by solids inventory in the riser. Fig. 5 shows the relationship of △Pr and Gs. △Pr and Gs represent proportional relationship, but after the △Pr is larger than 3.0KPa, Gs maintains at about 45kg/m3s. According to Figs 4 and 5, when Gs reaches the critical value 45kg/m2s, solid hold up of upper and bottom region of the riser remains unchanged. The increase of solids inventory in the riser only causes height of dense phase zone increase. Overall solids hold up distribution fit the S type curve of fast fluidization, and the saturated carrying rate under 5.0m/s in the paper is about 45 kg./m2s. Cyclone characteristic The pressure drop or resistance of the cyclone △Pcyc has a square relationship with the entering gas velocity. However, △Pcyc is also influenced by solids concentration CS. CS is defined as the solids mass in one cubic meter of fluidizing gas. It can be calculated with GS by CS = GS AS / (Ar uriser). In this paper, besides the traditional steady method, a transient method is used to
4
study the characteristic of the cyclone pressure drop. At a specific Uriser, after a steady state with a large Gs was maintained for a few minutes, the loop seal aeration was suddenly cut off, the bed inventory was then elutriated out of the riser and accumulated in the standpipe. The flow regimes in the riser changed from fast fluidization to dilute convey regime with decreasing Gs, until the riser was empty of particles. This method is simple and has better reproducibility than traditional steady method. Fig. 6 shows the characteristic of the cyclone at different Uriser. The comparison between the steady method and transient method further verifies the reliability of the transient method. 1000
6000 5000
800
△Pcyc (Pa)
△ Pr (Pa)
4000 3000 2000 1000 0
Uriser 6.0 m/s 5.0 m/s 4.0 m/s
steady transient method method
600
400
200 0
10
20
30
40
0
50
2
3
4
5
Cs(kg/m )
Gs (kg/m s)
Fig. 5
1
3
2
Fig. 6
Variations of △Pr with Gs
character of cyclone △Pcyc
Flow behavior of loop seal and standpipe It’s generally believed that all the aeration gas passes through the loop seal into the riser. However, industrial operation has verified that the gas-bypassing phenomenon in standpipe can’t be ignored. As shown in Fig. 7, the actual gas flow rate across the loop seal is not equal to the aeration rate. With a small aeration rate,Q, QH through the loop seal is close to the aeration rate, account for more than 95% percent of Q. However, with increasing Q, more gas goes up through the standpipe, QH/Q rapidly decreases. A loop seal can be divided into a horizontal part and a vertical part based on its physical structure, as shown in Fig.2. The gas-solid flow in the horizontal part belongs to the transport state, the resistance of which is mainly caused by the friction among particles and the wall. With increasing aeration rate, the pressure drop of the horizontal section (P17-P18) increases due to higher GS. The flow in the vertical section is in the bubbling bed regime. With higher aeration rate, as QH/Q decrease, the ejecting height of particle decreases rapidly due to less QH. This results in the reduction of △Plsl, as shown in Fig. 7.
5
Gas flow ratio (%)
120 110 100 90 80 70 60 2000 50
Voidage ε △P/L (kPa/m) Height (cm)
Therefore, the loop seal characteristics are closely related to the flow state in the
QH/Q
loopseal
0 5.0
solid height
40 12 0 8
standpipe pressure gradient
4 0
voidage
0.52
vertical hozirontal 5.5
0.3 U (m/s)
△P (Pa)
500
80
0.56
1500 1000
120
6.0
6.5
3
Areation rate (m /h)
7.0
7.5
0.2 0.1 0.0 5.0
Fig. 7 the performance of loop seal (Uriser=5.0 m/s)
Usl Ug Us
5.5
6.0
6.5
7.0
Areation rate (m 3/h)
7.5
Fig. 8 flow behavior of standpipe (Uriser=5.0 m/s) standpipe, which affects the actual gas passing through the loop seal. In this paper, the pressure drop gradient, voidage and gas flow were directly measured. As shown in Fig. 8, when Gs increases with increasing aeration rate, Q, at fixed Uriser and bed inventory, IV, the mass of solid and thereby solid height in the standpipe decrease because more solids accumulate in the riser. At the same time, the pressure gradient in the standpipe increases. This is a special feature of transient packed bed flow [9], which is related to the pressure gradient and gas-solid slip velocity USl. According to the experimental measurement of voidage by laser fiber and gas flow rate measured by gas tracer, the slip velocity USl can be calculated by the equation USl=GS/ρp(1-ε)+Ug/ε[9]. With increasing aeration rate, particle velocity, US, increases due to higher GS. At the same time, the upward gas flow rate in the standpipe, QV, also increases. Therefore, the slip velocity will increase with increasing aeration rate. Although the solid height decreases, the solid seal can provide a pressure head because of the increasing pressure gradient and slip velocity USl. With increasing aeration rate, the upward gas flow, QV, keeps on increasing and the voidage gradually approaches to the minimum fluidization voidage. With very high aeration rates, upward flowing bubbles can be visually observed and the flow reaches the bubbling state. Pressure balance of the CFB external loop As shown in Fig. 9, at fixed riser fluidizing velocity, more bed material accumulates in the riser with increasing aeration rate. As a result, △Pr increases. At the same
6
△Pr (Pa)
time, both the pressure drop gradient 6000 and total pressure drop, △Psp, in the standpipe increase. Under stable 5000 operation condition, the pressure balance of the CFB system can be 4000 expressed as △Pr + △Plsl + △Pcyc = △Psp △Psp. Whenever pressure drop of 3000 any other parts changes, the pressure gradient △P/L and Usl in the standpipe 2000 △Plsl △Pr change correspondingly to rebalance 1000 the system. Therefore, the standpipe △Pcyc has the ability to keep pressure 0 balance in the CFB by adjusting inner 5.0 5.5 6.0 6.5 7.0 7.5 3 Areation Rate (m /h) flow behavior. Its characteristic is greatly affected by the operation Fig. 9 Variations of pressure drops around conditions of the riser and other parts, the CFB loop and should be studied in the external loop of CFB system [10]. CONCLUSION The flow behavior of a CFB external loop, including loop seal and standpipe is schematically studied in this paper. It found that with a fixed bed inventory and fluidizing velocity, the pressure drop of the riser increases with solid circulation flux because more particles accumulate in the riser. When Gs reach a critical value, solids suspension distribution can be described by the S type curve of the fast fluidization. The pressure drop of a cyclone depends on the riser gas velocity and solids concentration. The cyclone pressure drop first decreases and then increases with suspension solid concentration. The actual gas flow ratio QH/Q through the loop seal decreases with increasing aeration rate, while the upward flow gas rate in the standpipe increases. Voidage in the standpipe increases and the flow state gradually transitions to the minimum fluidizing state from packed bed flow. The flow behavior in the standpipe adjusts to conditions in the whole CFB system. The standpipe maintains the pressure balance by changing the slip velocity and pressure gradient to provide the required pressure head with lower solids height. Because the CFB system used in this paper is an inventory constrained system, the results, especially for the transition points, may be different under different bed inventories. At same time, the solids properties also have an influence on the results. These factors will be studied in the future studies.
7
ACKNOWLEDGMENT Financial support of this work by High Technology R&D (863) (2009AA05Z302) is acknowledged. NOTATION Ar CS IV QH q Uriser US ρs P0~P19
section area of riser, m2 solids concentration, kg/m3 system bed inventory, kg gas flow cross loop seal, m3/h gas tracer(CO2) flow rate,m3/h riser fluidizing velocity, m/s actual particle velocity m/s particle density kg/m3 Number of pressure taps
△Plsl
pressure drop of loop seal, △Pr P17-P19 Pa pressure drop of standpipe, △Psp/L P17-P14,Pa
△Psp
As GS Q QV Ug USl Ut ε △Pcyc
section area of standpipe, m2 solid circulation flux, kg/m2s loop seal aeration, m3/h gas flow cross standpipe, m3/h superficial gas velocity m/s gas –solid slip velocity m/s actual solid velocity m/s voidage pressure drop of cyclone, P13-P14, Pa pressure drop of riser,P0-P13, Pa pressure drop gradient, (P16-P15)/0.1, Pa /m
REFERENCES 1. Basu P; Cheng L. An analysis of loop seal operations in a circulating fluidized bed. Chemical Engineering Research and Design,2000,78(7) :991-998 2. Monazam E R; Shadle L J ,et al. Impact of the circulating fluidized bed riser on the performance of a loopseal non-mechanical valve. Industrial & Engineering Chemistry Research,2007,46(6):1843-1850 3. Wang Qing,Sun Jian. A overall fluid flow process dynamic model of circulating loop for CFB boiler. Proceedings of the CSEE,1999,19(12):31-35 4. Hu Nan,Wang Wei,et al. Study on gas-solid s flow properties in the 38 m/54 m riser of circulating fluidized bed. Proceedings of the CSEE,2009,29(26):7-12 5. Lim K S, Zhu Jinxu, et al. Hydrodynamics of gas-solid fluidization. International Journal Multiphase Flow, 1995,21(1):141-193 6. Kim, S. W; Kim, S. D. Effects of particle properties on solids recycle in loop-seal of a circulating fluidized bed, Powder Technology, 2002,124(1) :76-84. 7. Yao, X; Yang, Set al; Experiment study of solids circulating rate's effect on the pressure loop in circulating fluidized bed. Proceeding of CSEE. 2010, 30(20), 1-6 8. Yerushalmi J, Cankurt N T．Further studies of the regimes of fluidization. Powder Technology,1979, 24(2): 187-205 9. Ergun S. Fluid flow through packed columns. Chemical Engineering Progress,1952, 48 (2): 89-94. 10. Yang, S; Yang, H. R et al. Impact of operating conditions on the performance of the external loop in a CFB reactor. Chemical Engineering and Processing: Process Intensification. 2009, 48(4), 921-926.
8
COAL IGNITION TEMPERATURE IN AN OXYGEN-ENRICHED CFB BOILER Junnan Chao, Hairui Yang, Junfu Lv, Hai Zhang, Qing Liu, Yuxin Wu Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Thermal Engineering, Tsinghua University, Beijing, 100084, China T: 86-1-62773384; F:86-10-62781743;E:[email protected] ABSTRACT The oxygen-enriched Circulating fluidized bed (CFB) combustion technology is a new method to reduce CO2 emissions. The coal ignition temperature, TiF, in an oxygen-enriched CFB boiler is an important parameter for designing the startup burner and for choosing the operating strategy during the startup process. The combustion of five types of coal under four different atmospheres (air, O2 27 %, O2 40%, O2 53%, CO2 as balance gas) was measured in a laboratory scale fluidized bed (FB) with an under-bed preheat system. Using thermocouples and a Gas Analyzer, the changes in bed temperature and the concentration of the different components, such as O2, CO2 and CO, in flue gas were directly measured to determine TiF. It was found that TiF decreased with increasing O2 concentration. The differences between the ignition temperatures determined in air and with 27 % O2 were not significant. At lower bed temperatures, for two coal types with higher volatiles, a two stage-ignition for volatiles and char was observed under a high O2 concentration. The time delay between the two stages decreased and finally merged into one with increasing bed temperature. Similar results were obtained in air. The coal with the higher volatile content had a lower ignition temperature in an oxygen-enriched CFB. Comparison of the ignition temperatures obtained by different methods and the feed temperatures in industrial CFB boilers showd that the measured result in a fluidized bed can be used as a reference for oxygen-enriched CFB boilers. INTRODUCTION As one of the “green house” gases, CO2 was considered as the one of the main reasons for causing climate changes. Oxygen-enriched combustion in a PC boiler is considered one potential technology to reduce CO2 emissions. Many researchers have focused on this area. By recycling the flue gas, the CO2 concentration in the exhaust gas can reach more than 90%, which is convenient for the application of Carbon Capture and Storage (CCS) technology.
An oxygen-enriched CFB is superior being studied as a new technology researchers. Recycling of the flue desulphurization sorbent, which
to a traditional CFB, Yuru Mao et al (1), and is to reduce CO2 emissions by more and more gas increases the contact of the SO2 and decreases the conversion rate of the
SO2 ,Zhongyang Luo et al,(2).The emissions of NOX is much lower because of the ‘time gap’ in the formation of nitrogen species and the lack of thermal NOx,Omasz Czakiert et al,(3). However, there are still many promblems to be solved for oxygen-enriched CFB, such as the coal ignition temperature and coal combustion characteristics in the CFB boiler. The Coal ignition temperature in CFB boilers, Ti, defined as the lowest bed temperature required for stable coal combustion, is an important parameter for burner design and automatic control during the startup process. When the coal is fed into a furnace at a temperature lower than Ti, the coal will not burn and the furnace temperature will decrease even more. Once the fuel concentration and temperature in the furnace reach critical conditions, the mixture will flash and the furnace temperature will suddenly increase, which leads to overheating. Feeding the coal in the furnace at temperatures higher than Ti is safer but consumes time and oil. The coal ignition temperature is influenced by the type of the reactor, the way of heating and particle size in the conventional CFB boiler, Hairui YANG et al,(4).The ignition characteristics of the coal can be described by an ignition index, which is defined as:
Fi =
ΔT (t1 + t2 )
where ΔT is the difference between the initial furnace temperature and the maximum temperature, t1 is the time to reach the lowest temperature, and t2 is the time to reach the maximum temperature, Hairui YANG et al,(4). In this work, ignition temperatures of five different types of coal under four different atmospheres (air, O2 27 %, O2 40%, O2 53%, CO2 as balance gas) were measured in a laboratory scale bubbling fluidized bed (FB) with an under-bed preheat system. By using thermocouples and a Gas Analyzer, bed temperature, O2 concentration and Fi were obtained to determine the factors influencing the coal ignition temperature and the combustion characteristics. EXPERIMENTAL SETUP The ignition temperature was measured in a laboratory-scale fluidized bed reactor with an inner diameter of 65 mm, which was heated with an under-bed preheat system. The sketch of the experimental setup is shown in Figure 1. Quartz sand was used as bed material. The size of the bed particles was in the range of 0.275mm-0.3mm.The height of the bed was 40mm. CO2 and O2 were mainly used as the component gases in this experiment to obtain three kinds of inlet gases with different concentration of O2. The component gases were supplied by gas cylinders. The simulated gas flowed through the pre-heater directly into the FB. The superficial gas velocity in the combustion chamber was about three times Umf. Since the operating conditions greatly influence the coal ignition characteristics, standard
operating conditions as a baseline were specified in Table 1.
Fig.1. Schematic drawing of the experimental setup Table 1 The standard conditions of coal particle ignition tests Fluidized gas O2/ CO2
No. 1 2 3 4 5
U/ Umf.
Coal particles (g)
Bed material
Bed height(mm)
3
2.0
Quartz sand
40
Table 2 Proximate analyses of different coals proximate analyses [wt%] Coal Type Vad Fc ad Aad Mt Long Yan 4.72 4.30 58.02 32.96 Lu An 2.12 10.31 61.03 26.54 Fu Gu 3.96 30.19 44.31 21.53 Yan Zhou 0.00 35.51 55.67 8.82 Xiao Long Tan
9.88
49.07
36.10
Bed material size(µm) 275-300
LHV,ar (MJ/kg) 19.07 25.05 22.58 21.38
4.95
12.43
Table 3 Ultimate analyses of different coals No. 1 2 3 4 5
Coal Type Long Yan Lu An Fu Gu Yan Zhou Xiao Long Tan
Cad 52.30 62.13 56.58 55.38 36.72
Had 1.04 2.67 3.61 2.04 1.87
Oad 0.83 7.33 14.28 6.42 12.59
Nad 0.71 1.18 1.12 1.12 1.01
Sad 1.12 0.15 0.42 0.50 1.66
Five different types of coal were used as fuel. Tables 2 and 3 list the proximate and ultimate analyses of each fuel. The size of the coal sample was in the size rang of 1-2mm and the mass in each batch experiment was about 2g. The temperature of the dense bed was measured by thermocouples during the experiment. A Gas Analyzer was used to measure the changing of concentration of different components. Procedure 1. After the furnace was electrically heated to the selected furnace temperature, Tb, the coal particles were injected into the furnace. 2. The furnace temperature was then recorded by the data acquisition system. Ignition of the coal particles was evaluated by observing the flame and sparks through a mirror. If the coal particles ignited, the furnace temperature was reduced to a lower temperature Ti+1 and steps 1 and 2 were repeated until the coal particles did not ignite. 3. If the coal particles did not ignite, TiF, was assumed to be half way between Ti and Ti-1 and the process was repeated. The process was terminated when |Ti - Ti-1 |<5℃.Then Ti was considered as the ignition temperature. RESULTS AND DISCUSSION Influence of furnace temperature on TiF Figure 2 shows the bed temperature history curves of coal type 2 with different initial bed temperatures at an oxygen concentration of 52.7 %(CO2 balance). 1.0
140
O2 52.7% CO2 balance
120
0.8
o
60 40 20 0 -20
0.6
o
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FI, C/s
1: 585 C o 2: 583 C o 3: 570 C o 4: 561 C o 5: 553 C o 6: 509 C
o
ΔΤ, C
100
52.7% 40.7% 27.8% air
0.4
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10
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t, s Fig.2. Bed temperature difference vs. time (coal 2, O2 52%, CO2 balance)
400
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600
o
T, C Fig.3. Fi of coal 2 vs. temperature
650
Obviously, the combustion characteristics of the same coal type were different even if the initial bed temperature, Tb, changes within a narrow range. The ignition index increased with increasing Tb, as shown in Figure 3, which was the same as the result obtained with air [Yang 2005]. This means the coal particle is easy to ignite at higher Tb. The initial bed temperature directly influences the heating rate of the particles, the volatile release rate, and the combustion kinetics. There is a point of the index when Tb reaches ignition temperature, which means when Tb is over the ignition temperature the reaction goes faster than it does below this temperature. Results obtained from different coal types had the same tendency. Influence of volatile content on TiF Figures 4 and 5 show a comparison of the temperature history curves of char and coal at 785℃, O2 20.9%, CO2 balance condition with coal types 1 and 5. For these conditions coal types 1 and 2 with low volatile contents had a higher ignition index than their char with the same mass. However, for the same conditions, for coal types 3, 4 and 5 with high volatile contents, the results were opposite. For a high furnace temperature, a small volatile content in the coal would encourage the ignition, yet large content would inhibit the ignition. 100
100
o
o
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1: char Tb=785 C 2: coal O2 20.9% CO2 balance
60
1: char 2: coal 1
40 o
ΔΤ, C
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Tb=784 C O2 20.9% CO2 balance
80
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t, s
Fig.5. Bed temperature difference vs. time Fig.4. Bed temperature difference vs. time (coal 1,785℃, O220.9% , CO2 balance,2.0g) (coal 5,785℃, O220.9% , CO2 balance,2.0g) Volatiles release from the coal particles immediately after the coals were injected into the furnace, which increases the porosity at the surface and inside the coal particles. Increased porosity of the particle enhances oxygen diffusion and increases the reactivity of char. Therefore, for coals with a high volatile content, TiF is lower. However, at a higher Tb, rapid volatile releases and combustion results in increasing local temperature, this causes the local reactant gases to increase in volume increasing too, which decreases the oxygen concentration. For coal with a high
volatile content, the positive influence caused by increasing contact area is restrained by the negative influence caused by the volatiles, so, at higher furnace temperatures, the influence of volatile content is not as significant as at a lower Tb, and even can have the opposite effect. Influence of oxygen concentration on TiF The tests results show that the ignition index increases with increasing oxygen concentration for all types of coal, as shown in Figure 2. With a high initial temperature, this tendency is more obvious. The combustion rate of the coal particles in a fluidized bed boiler is usually controlled by the chemical kinetics and the species diffusion. However, it is controlled most by chemical kinetics when the furnace temperature is low (under 500℃). When the oxygen concentration of the fluidizing media was 27%, the ignition curves were similar with the results obtained in air, which implies that the increase of the oxygen concentration from 20.9% to 27% did not improve the ignition process. Because of the replacement of N2 with CO2, the combustion reaction process was inhibited by the high concentration of CO2. Two stage-ignition
Fig.6.Two stage ignition vs. time, coal 4 (O2=27.8%)
Fig.7.Two stage ignition vs. time, coal 4 (O2=40%)
Two stage-ignition was observed in the tests with some high volatile coal types with a certain range of initial bed temperatures and oxygen concentrations, as shown in Figures 6 and 7. The volatiles released and were ignited after the coal particles were injected into the furnace, and then the char began to burn. This “two stage-ignition” became more conspicuous with increasing oxygen concentration, and the two stages would move closer when furnace temperature was increased. The two stages would
merge when the initial bed temperature was high enough, because of the high chemical kinetics of char. However, two stage-ignition was not found in the tests with coal 5 which had the highest volatile content. It was observed that the combustible content in the volatiles of coal 5 was much lower than that of coals 3 and 4 by using a mass spectrograph. This means the reactivity of the volatiles of coal 5 is not high enough to reach the first stage.Therefore,the ignition process is influenced not only by the volatile content, but also the combustible content in the volatiles. Comparison Figure 8 shows a comparison of the ignition temperatures obtained in laboratory-scale FB in air condition and oxygen-enriched condition, and the actual feed temperatures in CFB boilers of China. The actual feed temperatures in CFB boilers for bituminous coal and brown coal are similar with the ignition temperatures obtained in the laboratory-scale FB in air. However, for coal types with low volatile content and high ash content, such as anthracite, the feed temperatures are much higher than the ignition temperatures because the operators normally choose relative high feed temperatures for safe operation. The oxygen enriched technology is capable of decreasing the coal ignition temperature in compared with air, so it can save more oil during the ignition stage.
CFB plants in China 52% O2+CO2 40% O2+CO2 27% O2 +CO2 Air
Ti，oC
750
600
450
0
15
30
45
60
Vdaf,% Fig.8.Comparison of ignition temperature and actual feed temperatures in CFB. CONCLUSION The ignition temperatures and combustion characteristics of five different types of coal were determined in a laboratory-scale FB with a preheat system. Similar with
the results obtained in air, coals with high volatiles had lower ignition temperatures in an oxygen-enriched CFB. TiF decreased with increasing O2 concentration. The difference between the ignition temperatures determined in air and 27% O2 were not significant. Two-stage ignition appeared in the ignition process of some high volatile content coals in a range of oxygen concentrations and initial temperatures. This was also influenced by the combustible content in the volatiles. ACKNOWLEDGMENT Financial support of this work by High Technology R&D (863) (2009AA05Z302, 2007AA05Z303) are grateful acknowledged. NOTATION U— fluidizing air velocity, m/s Umf—minimum fluidizing air velocity, m/s TiF—coal ignition temperature in FB, ℃ Tb—bed temperature, ℃ Vdaf—volatile content as base of dry free of ash,% REFERENCES 1. Yuru Mao, Mengxiang Fang, Zhongyang Luo, et al. Experimental research on Coal/ char combustion in O2/ CO2 mixed atmosphere. Proceedings of International Conference on Energy and the Environment ,China , (2003). 2. Luo Zhong yang , Mao Yuru, WU Xue cheng et al ,Test study on and analysis of burning behavior for coal under atmosphere of O2/ CO2.,Thermal power generation, 33 ,(2004) 3. T Czakiert,Z.Bis,W Muskala,et al.Fluidized bed combustion in oxygen-enriched conditions,.The 19th inter-national Conference on Fluidized Bed Combustion.(2006.) 4. Hairui Yang,Lei Xue,Yuanxiong Guo et al,The ignition characteristic in CFB boiler, Journal of Combustion Science and Technology,11,(2005) 5. C.S. Zhao, L.B. Duan, X.P. Chen, C. Liang, Latest Evolution of Oxy-fuel Combustion Technology in Circulation Fluidized Bed, 20th International Conference on Fluidized Bed Combustion,(2009)
EFFECT OF TEMPERATURE FIELD ON COAL DEVOLATILIZATION IN A MILLISECOND DOWNER REACTOR Binhang Yan, Li Zhang, Yong Jin, Yi Cheng * Department of Chemical Engineering, Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Tsinghua University Beijing 100084, P.R. China PH: +86-10-62794468; FAX: +86-10-62772051; e-mail: [email protected] ABSTRACT A comprehensive CFD-DPM model was established to describe coal pyrolysis in millisecond downer reactors under high temperatures. The model predictions revealed the fact that the reactor performance is dominated by the design of the temperature field to guarantee fast, sufficient heating of coal particles in milliseconds. INTRODUCTION Coal pyrolysis to acetylene in thermal plasma provides a direct route to make chemicals from coal resources (1-3). Since the coal pyrolysis process is accommodated in a multiphase downer reactor operated under extreme conditions (e.g., an ultra-high temperature greater than 3000 K), multiple physical and chemical processes are completed in milliseconds of contact time, where the rapid heating and release of volatile matter in coal particles play the dominant role in the overall reactor performance. It has been acknowledged that thermal energy is the driving force for coal devolatilization. Therefore, the reactor design is actually directed to the appropriate design of the temperature field inside the reactor to guarantee sufficiently, fast heating of coal particles in milliseconds. A comprehensive computational fluid dynamics model with a discrete phase model (CFD-DPM) was developed to understand the complex gas-particle reaction behavior in the coal pyrolysis millisecond process. The model incorporated particle-scale physics such as heat conduction inside solid materials, diffusion of released volatile gases (4), coal devolatilization, and the tar cracking reaction (5-6). The chemical percolation devolatilization (CPD) model (7-9) was applied to describe the devolatilization behavior of rapidly heated coal based on the physical and chemical transformations of the coal structure. The predictions by the CFD-DPM method were validated by comparing the predicted volume fractions of the main species and light gas yields with the experimental data under a set of typical
operating conditions from the 5-MW coal pyrolysis plasma reactor (10). The results showed that the heating histories and the devolatilization of particles with the same diameter were mainly determined by the surrounding temperature field. That is to say, different heating histories experienced by the particle led to different heating rates as well as the heating time of the particles, and different yields of light gases. For further illustration of the effect of the temperature field on the heating histories of particles and coal devolatilization, different reactor designs were modeled using CFD-DPM. The same energy input to the downer reactor was assumed by fixing the pre-defined enthalpy streams of gases, the heat input through the reactor wall, and the coal feeding conditions. The results showed that coal particles exhibited different devolatilization performance when experiencing different temperature histories. Accordingly, reactor optimization can be determined with the guidance of the above simulation. MODEL DESCRIPTION The comprehensive CFD-DPM model includes the k-ε turbulence model for gaseous turbulent flow with heat and mass transfer, the mixture fraction approach with the probability density function (PDF) method of modeling the interaction of turbulence and chemistry, the chemical equilibrium model for high temperature gas-phase chemical reactions, the discrete phase model (DPM) for momentum, heat and mass transfer between gas and particles and sub-models for the devolatilization of coal particles.
Figure 1 Heat, mass and momentum transfer
Figure 2 Schematic of a coal particle with
between the discrete and continuous phases
heating gas
The numerical simulations of gas-particle flows follow the Eulerian-Lagrangian approach, as shown in Figure 1. The fluid phase is treated as a continuum by solving the Navier-Stokes equations, while the particle is considered as the dispersed phase, solved by tracking a large number of particles through the calculated flow field. As the trajectory of each particle is computed, the momentum, mass and energy
exchange between the particles and the continuous phase is added to the source term of the discretization equations for the gas continuum (see Chen and Cheng (11)). In addition, the heat transfer model inside a particle (as shown in Figure 2) is established based on the conduction equation with consideration of the heat of pyrolysis and the heat conduction in solid materials: (1) ( ρcp )eff ∂T∂(tr , t ) = r12 ∂∂r ⎛⎜ λeff r 2 ∂T∂(rr , t ) ⎞⎟ − Δr H ⋅ γ vol (r , t ) ⎝ ⎠ where (2) ( ρ c p ) = ερvol c p,vol + (1 − ε ) ρ pc p,p eff
λeff = ελvol + (1 − ε ) λp
(3)
In these equations, T(r,t) represents the local temperature at any radial position r and time t, ε is the porosity of particle; ρp and ρvol are the densities of the solid material and the volatile phases, respectively; cp,p and cp,vol are the specific heat capacities of the solid material and volatile phases, respectively; λeff is the effective local thermal conductivity; λp and λvol represent the thermal conductivities of the solid material and volatile phases, respectively; ΔrH is the heat of pyrolysis; and γvol(r,t) denotes the rate of devolatilization (kg/m3·s).The boundary conditions of Eq. (1) are given as:
⎧ ∂T 2 ⎪ 4π R λeff ∂r ⎪ ⎨ ⎪ ∂T =0 ⎪ ∂r r =0 ⎩
2
r =R
δ ⎞ ⎛ = 4π ⎜ R + m ⎟ h (Tg − Tw )θ + σε p (4π R 2 ) Tg4 − Tw4 2 ⎠ ⎝
(
) (4)
where R is the radius of the coal particle, Tw is the temperature at the surface of the coal particle, Tg is the local temperature of the continuous phase, σ is the Stefan-Boltzmann constant, εp is the black-body radiation coefficient for the pulverized coal, δm is the thickness of the gas film around the coal particle (estimated to be 2R at a relative small Reynolds number in this study), h is the gas-particle heat transfer coefficient, and θ is a factor related to the effect of volatiles’ release on heat conduction. The gas-particle heat transfer coefficient was calculated from the Nusselt number, Nu = hδm/λm, where λm is the thermal conductivity of the gas film. The factor θ reported by Spalding (12) was adopted in this study,
θ=
cp,g ⎛ dmvol ⎞ B = , B 2π dp λm ⎜⎝ dt ⎟⎠ eB − 1
(5)
where cp,g represents the gas specific heat capacity, dp is the particle diameter, and dmvol/dt denotes the formation rate of volatiles from coal (kg/s). The CPD model was employed to describe the devolatilization of coal particles, where the fractional change in the coal mass as a function of time was divided into
light gases, tar precursor fragments and char. The tar cracked through the following assumed paths: k1 k2 light gases ←⎯⎯ Tar ⎯⎯→ soot The mechanism utilizes the Arrhenius equation which is defined as: ki = Ai exp ( −Ei / RT ) i = 1,2
(6) (7)
The values of the kinetic parameters were obtained from the work of Ma (5) and Brown (6). The solution of the complex model described above was implemented using the commercial software FLUENT with self-developed user-defined functions (UDFs). RESULTS AND DISCUSSION Model validation
80 60
Prediction Experiment
80 60
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0 C 2H 2
CO
CH 4
H 2 gases ht i Lg
0
Yield of product (wt %, daf)
Volume fraction (%)
Figure 3 shows a schematic drawing of the 5-MW plasma downer reactor for coal pyrolysis, which was composed of the V-shaped plasma torch, the mixing zone, the reaction zone and the quench and separator.
Figure 3 Schematic drawing of the
Figure 4 Comparison of predictions with the
5-MW plasma downer reactor
experimental data of the 5-MW reactor
The predicted volume fractions of the main species and the yield of light gases using this practical geometry are plotted in Figure 4, together with typical experimental data. The predictions and the actual performance of the 5-MW pilot reactor are based on typical operating conditions (10). It can be seen from Figure 4 that the model predictions agreed well with the experimental data. Therefore, the comprehensive CFD-DPM model is qualified for describing the complex devolatilization process in the reactor under the extreme environmental conditions such as ultrahigh temperatures and the milliseconds reaction time. Meanwhile, the simulations can help to optimize the operating conditions and improve reactor performance.
Reaction process of coal particles
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Figure 5 shows the variations of the particle temperature and yields of light gases with particle residence time in the 5-MW downer reactor, together with the temperature of the heating gas around the particle trajectories. The unique structure of V-shaped torch causes the uniform temperature distribution and the corresponding uniform velocity field in the reactor. Therefore, Coal particles with a diameter of 50 μm injected from different positions experience different heating histories, which result in different devolatilization performances. It can be shown that the effect of the surrounding temperature field on the particle heating history and devolatiliation performance is significant.
Figure 5 Reaction process of a single representative particle in the 5-MW downer reactor
Effect of the specified temperature field For further illustration of the effect of temperature field on the particle heating history and devolatilization performance, variations of the particle temperature and devolatilization versus the particle residence time under different specified temperature fields are plotted in Figure 6. It is assumed that coal particles with a diameter of 50 μm passed through the preset temperature field and the temperature of the heating gas was not impacted by the discrete particles. When the particle residence time is long enough to ensure that the temperature of the particle is close to that of the heating gas, a higher surrounding temperature would lead to a faster heating rate, and therefore a better devolatilization performance. The devolatilization is almost competed once the particle reaches its peak temperature. After that, thermal energy is no longer the main driving force for the coal devolatilization process. Therefore, in order to get a better devolatilization performance and a higher energy utilization efficiency, the thermal energy should be used for maintaining a high temperature field to make sure the coal particle is heated to a higher temperature. When the surrounding temperature field is fixed, the particle heating histories and yields of light gases are observed to be very sensitive to changes in the heat up time,
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as shown in Figure 7. The optimal residence time in the high temperature zone should be more than 2 ms under this situation. When the heat up time is less than 1 ms, the thermal energy stored in the high temperature heating gas was not used effectively to achieve satisfactory reactor performance.
Figure 6 Effect of temperature field on particle heating history and devolatilization
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performance
Figure 7 Effect of heating time on the particle heating history and devolatilization
performance Effect of reactor design The reactor design is actually directed to the temperature Wall-1 2600 design of the temperature field inside the reactor, Wall-2 2400 Wall-3 2200 as shown in Figure 8. Three kinds of energy input Wall-4 2000 1800 designs are carried out to investigate the Wall-5 1600 influences of reactor design on the particle 1400 1200 Wall-6 heating history and the yield of light gases. The 1000 800 feed conditions to the downer reactors are fixed 600 400 and the same amount of energy is exerted into the different specified reactor wall, Figure 8 Temperature field of i) Case I: all the energy is inputted to the different reactors reactor only through wall-2; ii) Case II: all the energy is inputted to the reactor through wall-2 and wall-3 evenly; iii) Case III: all the energy is inputted to the reactor through wall-2, wall-3, wall-4 and wal-5 evenly;
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The temperature of wall-1 is fixed at 300K and the ones of other walls are fixed at 1600 K for all cases. The input energy density of each reactor design is: Case I, 1.4e6 W/m2; Case II, 7e5 W/m2; Case III, 3.5e5 W/m2. It is shown in Figure 8 that different reactor designs cause different temperature fields, which leads to a different devolatilization performance. Too concentrated an energy input (e.g., Case I) leads to a shorter heat up time as well as a lower peak temperature of the coal particle. As a result, a poor devolztilization performance is obtained. The appropriate energy input density is achieved when both a high temperature field and enough heat up time occurs, which leads to the higher yield of light gases, as shown in Figure 9.
Case III
Figure 9 Effect of reactor design on the particle heating history and devolatilization
performance The temperature field can be designed by altering the operating conditions. In order to get a better devolatilization performance, special attention must be paid to the design of the high-temperature heat source and the gas-particle mixing efficiency to ensure that the coal particles can be heated up rapidly have sufficient time in the high temperature zone. CONCLUSION A comprehensive CFD-DPM model was established to describe coal devolatilization in millisecond downer reactors with successful validation using experimental data. This model was further employed to explore the effect of temperature field on the particle heating history and devolatilization performance. The results indicate that the coal particles exhibit different devolatilization performances when experiencing different designed temperature histories. Faster heat up rates, higher gas temperatures and longer residence times lead to a better devolatilization performance. With the guidance of these simulations, the reactor design and operating conditions can be selected to obtain the best temperature field and excellent gas-particle mixing efficiency in order to achieve a better reactor performance.
ACKNOWLEDGEMENT Financial supports from National 863 Project of China under the grants of no. 2009AA044701, National Natural Science Foundation of China (NSFC) under the grants of no. 20976091 and no. 20990223, and Xinjiang Tianye Corporation are acknowledged. REFERENCES 1. Bond, R. L., Ladner, W. R., Mcconnell, G. I. T., and Galbraith, I. F. (1963). “Production of Acetylene from Coal, Using a Plasma Jet.” Nature, 200(491), 1313-1314. 2. Nicholso, R., and Littlewo, K. (1972). “Plasma Pyrolysis of Coal.” Nature, 236(5347), 397-400. 3. Fauchais, P., Bourdin, E., Aubreton, J., and Amouroux, J. (1980). “Plasma Chemistry and Its Applications to The Synthesis of Acetylene from Hydrocarbons and Coal.” Int. Chem. Eng, 20(2), 289-305. 4. Shuang, Y., Wu, C. N., Yan, B. H., and Cheng, Y. (2010). “Heat Transfer inside Particles and Devolatilization for Coal Pyrolysis to Acetylene at Ultrahigh Temperatures.” Energy & Fuels, 24(5), 2991-2998. 5. Ma, J. (1996). “Soot Formation and Soot Secondary Reactions During Coal Pyrolysis.” PhD thesis, Brigham Young University, Utah, USA. 6. Brown, A. L. (1997). “Modeling Soot in Pulverized Coal Flames.” MSc thesis, Brigham Young University, Utah, USA. 7. Grant, D. M., Pugmire, R. J., Fletcher, T. H., and Kerstein, A. R. (1989). “Chemical Model of Coal Devolatilization Using Percolation Lattice Statistics.” Energy & Fuels, 3(2), 175-186. 8. Fletcher, T. H., Kerstein, A. R., Pugmire, R. J., and Grant, D. M. (1990). “Chemical Percolation Model for Devolatilization. 2. Temperature and Heating Rate Effects on Product Yields.” Energy & Fuels, 4(1), 54-60. 9. Fletcher, T. H., Kerstein, A. R., Pugmire, R. J., Solum, M. S., and Grant, D. M. (1992). “Chemical Percolation Model for Devolatilization. 3. Direct Use of C-13 Nmr Data to Predict Effects of Coal Type.” Energy & Fuels, 6(4), 414-431. 10. Yan, B.H., Wu, C.N., Jin, Y., & Cheng, Y. (2010, May). “CFD simulation of the reacting flow process of coal pyrolysis to acetylene in hydrogen plasma downer reactors.” In S.D. Kim, Y. Kang, J.K. Lee, & Seo, Y.C. (Eds.), Fluidization XIII (p.725), Engineering Conferences International, Gyeong-ju, Korea. 11. Chen, J. Q., and Cheng, Y. (2009). “Process Development and Reactor Analysis of Coal Pyrolysis to Acetylene in Hydrogen Plasma Reactor.” J. Chem. Eng. JPN, 42, s103-s110. 12. Spalding, D. B. (1955). “Some Fundamentals of Combustion.” Butterworths Scientific Publications: London.
NUMERICAL SIMULATIONS OF A CIRCULATING FLUIDIZED BED WITH A SQUARE CROSS-SECTION Tingwen Li1,2, Sreekanth Pannala3, Chris Guenther2 1. URS Corporation, Morgantown, WV 26505, U.S.A. 2. National Energy Technology Laboratory, Morgantown, WV 26505, U.S.A. 3. Oak Ridge National Laboratory, Oak Ridge, TN 37831, U.S.A. ABSTRACT In this study, both 2D and 3D numerical simulations of a well-documented circulating fluidized bed with a square cross-section were conducted. With some assumptions, a series of 2D simulations was first carried out to study the influence of grid resolution, initial flow field, and boundary condition on the flow hydrodynamics. It was found that 2D simulations under-predicted the solids inventory even with the finest grid (10-particle-diameter grid size). On the other hand, a 3D simulation with relatively coarse grid was found in better agreement with the experimental data. Differences between 2D and 3D simulations were briefly discussed. INTRODUCTION Circulating fluidized beds (CFBs) have been widely utilized in a variety of industrial applications including coal combustion, gasification, fluid catalytic cracking, etc. To accomplish successful and reliable design and operation of CFBs, numerous investigations pertaining to different hydrodynamic aspects of CFBs have been undertaken over the past few decades e.g. (1, 2). Among various research tools, computational fluid dynamics (CFD) is playing an increasingly important role in studying the complex flow hydrodynamics in CFBs. Numerical simulation of gas-solids flow in CFBs, usually with two-fluid model (TFM) based on kinetic theory of granular flow, requires a very fine mesh (typically 10~100 particle diameters), and consequently, very small time steps (typically 0.1~1.0 milliseconds). However, due to the high computational cost of multiphase flow simulations of usually very long CFBs, such a high resolution simulation is prohibitive. An appropriate simplification of the computational domain is necessary to achieve a good balance between speed and accuracy of the CFB simulations. Axi-symmetric assumption of the flow was employed mainly in the steady flow simulations as this assumption is likely to result in unphysical accumulation of particles along the axis in unsteady simulations (1). Alternatively, most unsteady numerical studies were carried out with a two-dimensional flow assumption in which a cut plane along the axis of a cylindrical column was simulated. Two-dimensional flow assumption is a rough assumption of the flow in a cylindrical column as the transient riser flow has significant angular movements despite its wide applications in the literature and successes in predicting certain flow hydrodynamics, for example, the core-annular pattern in riser flows. Undoubtedly, a 2D numerical simulation is not able to accurately account for the 3D effects resulting from geometry and operation. In addition, comparing ratios of wall area to column volume, 2D simulations inherently under-estimate the wall effects that are usually important in lab-scale riser flows. Hence, it is necessary to quantify the differences between 2D and 3D simulations in order to conduct efficient numerical
1
modeling. In this study, the gas-solids flow in a well-documented circulating fluidized bed of square cross-section has been simulated. The influences of initial conditions and grid resolution on 2D simulations were investigated mainly with respect to solids loading to seek a way to accelerate numerical modeling. After that, a 3D numerical simulation was conducted to evaluate the differences between 2D and 3D simulations. NUMERICAL SIMULATION Numerical Model An open source software, Multiphase Flow with Interphase eXchanges (MFIX), was used to conduct the numerical simulations. In MFIX, a multi-fluid, Eulerian-Eulerian approach, with each phase treated as an interpenetrating continuum, was employed. Mass and momentum conservation equations were solved for the gas and solid (particulate) phases, with appropriate closure relations (3). Constitutive relations derived based on the granular kinetic theory were used for the solid phase. More information on MFIX as well as detailed documentation on the hydrodynamic model equations can be found in (4). Simulation Setup A cold-model CFB riser of 146×146 mm square cross-section and total height of 9.14 m was simulated. Sand of mean diameter 213 µm, particle density 2640 kg/m3 and loosely packed bed void fraction of 0.43 was used as the bed material. In this study, a superficial gas velocity of 5.5 m/s and a solids circulating flux of 40 kg/m2s were simulated. Detailed information on the experimental setup and measurements on void fraction and solids velocity profiles were provided in the literature (5, 6). In this study, both 2D and 3D simulations as schematically shown in Figure 1 were conducted. The simple geometry of square riser was discretized by Cartesian grid with uniform grid size in each direction. Uniform inflow boundary conditions were imposed at the bottom gas distributor and the solids side inlet and a constant pressure at the outlet was imposed. A partial slip wall boundary condition was applied for the solids phase and a no-slip wall boundary condition was used for the gas phase. RESULTS AND DISCUSSIONS A series of 2D simulations is first presented to evaluate the effects of grid resolution and initial flow condition. This is Figure 1. Schematic plots of 2D and 3D followed by the 3D simulation and the simulations.
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differences between 2D and 3D numerical simulations are briefly discussed. Finally, comparisons between numerical results and experimental data are presented. Grid Resolution Different grid resolutions of 15×456, 30×456, 30×912, 60×912, 60×1824 and 60×3648 were tested in the 2D simulations. The finest grid resolution (dy≈dz≈2.5 mm) corresponds to 12-particle-diameter grid size which is very close to the 10-particle-diameter criterion for grid independence in gas-solids flow simulations (1, 7). The simulation has been typically performed for 100 seconds of real-time for most grid resolutions reported here. The solids inventory inside the system was monitored in form of the overall solids holdup to characterize the flow development as shown in Figure 2. It can be seen from the plot that solids loading decreases with time and finally reaches a near-stationary value indicating the fully developed state. The grid resolution affects the history of solids loading inside the riser at the initial stage but has no significant influence on the solids inventory after reaching the fully developed state except for the coarsest grid. Compared to an estimation of solids holdup, which is greater than 0.08, based on the experimental measurements of cross-sectional void fraction (5), the current 2D simulations substantially under-predict the solids inventory. It seems that the under-prediction cannot be overcome by decreasing grid size even to 10-particle-diameter, at least for the current case. Similar finding was reported by Lu et al. (8) in their 2D simulations of CFB riser with FCC particles although the grid sizes in their study were greatly larger than 10-particle-diameter. Consistent to their study, refinement of grid does affect the predicted transient flow behavior for example the spatial distribution of solids. The transient flow fields suggest that a finer grid tends to predict higher gradient of concentration and lower void fraction in the clusters.
Figure 2. Temporal variation of the overall solids holdup predicted by the 2D simulations with different grid resolutions. Initial Condition For the cases shown above, a uniform solids concentration of 0.15 was assumed throughout the domain as the initial flow condition. It is expected that the initial flow condition has no influence on the final results when the flow is fully developed. However, it is still meaningful to evaluate if different initial conditions affect the duration for the flow to fully develop so that the computation time can be shortened. For this purpose, two additional cases with initial conditions of a partial filled packed
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bed with the same loading (solids holdup of 0.15) and a uniform solids concentration of 0.05 were conducted. Figure 3 presents the temporal variation of the overall solids holdup for cases with different initial conditions. As expected, the initial condition does not affect the solid inventory when the flow is fully developed. However, it does affect the time needed to reach the stationary state. These simulations indicate that it is helpful to set the solids loading closer to that at the developed state to decrease the simulation time and accelerate convergence through the initial transients. For real process simulations, this is possible since the solids inventory can be roughly estimated based upon the overall pressure drop.
Figure 3. Temporal variation of the overall solids holdup for the cases with different initial flow conditions (grid resolution: 30×456). Inflow Condition The yz plane cutting along the side inlet and outlet was simulated in the 2D simulations. Constant solids feed rate at the side inlet was set based on the whole column cross-sectional solids flux reported in experiments. An alternate way was also evaluated to set the solids inflow rate based on the solids flux at the side inlet along the intersection of the yz plane. With this method, the solids circulating rate was higher than that based on the riser cross-section. For example, the resulting cross-sectional solids flux of 51 kg/m2s was 25% higher than that of 40 kg/m2s reported experimentally. As shown in Figure 4, the solids holdup at the fully developed state increases with the solids circulating rate. However, this adjustment of inflow condition in the 2D simulations cannot overcome the significant under-prediction of solids loading.
Figure 4. Time variations of the overall solids holdup for cases with different solids inflow rates based on column solids flux and side inlet flux (grid resolution: 30×456). 4
3D Simulation A case with grid resolution of 30×30×456 is conducted for which 150 seconds simulation was completed to address the differences between 2D and 3D simulations. Temporal variation of the overall solids holdup predicted by both 2D and 3D simulations with comparable grid sizes is presented in Figure 5. There is a substantial difference between 2D and 3D simulations with respect to the solids loading. Differences in 2D and 3D simulations for both cylindrical and rectangular fluidized beds operated in bubbling, slugging, and turbulent fluidization regimes have been systematically discussed by Xie et al. (9, 10). Their results demonstrated that a 2D Cartesian simulation can be used to successfully study a bubbling fluidization regime close to Umf (minimum fluidization velocity) but needs extra caution for modeling other fluidization regimes with higher gas velocity. The substantial differences presented here indicate that 3D simulation should be used for modeling CFBs. Of course, this inevitably leads to an extremely large number of computational cells for a grid-independent simulation. Due to the prohibitively expensive computational cost, not many 3D unsteady simulations of CFB riser flows were reported. To overcome high computational cost, a coarse grid three-dimensional numerical simulation with appropriate sub-grid closure models is necessary, which has been addressed by many researchers, e.g. (8, 11). Nevertheless, 2D simulations might be used as an effective tool to conduct qualitative study.
Figure 5. Time variations of the overall solids holdup predicted by 2D (30×456) and 3D (30×30×456) simulations. Due to the computational cost of 3D simulation, no sensitivity study on the grid resolution and initial conditions was conducted so far. However, in future it might be helpful to conduct sensitivity studies on grid resolution, wall boundary conditions, and other physical and operational parameters in 3D unsteady simulations. Comparison with Experimental Data Figure 6 presents axial profiles of mean void fraction at different lateral positions predicted by the current numerical simulations when the flow is fully developed as well as the experimental data measured by a fiber optic probe (5). General profiles of low void fraction at the bottom of the riser and gradually increasing towards the top are predicted by the 3D simulation. However, the trend is not correctly predicted by the 2D simulation and the void fraction is substantially over-predicted almost everywhere. The results clearly indicate that 3D effects in a CFB riser are important in
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the case studied and assuming 2D flow can lead to under-prediction of solids inventory. Under-prediction of solids inventory with a fixed solids mass flux or over-prediction of solids flux with a fixed solids inventory has often been reported in the literature for 2D numerical simulations of CFB riser flows with fine FCC particles by using traditional inter-phase drag correlations. It is usually attributed to the unresolved meso-structure or clustering phenomena by insufficient grid resolution. The current results likely suggest that the three-dimensional effect is another reason for the discrepancies owing to the inherent differences between 2D and 3D simulations as stated before.
Figure 6. Axial profiles of mean voidage at different lateral positions. Lateral profiles of mean void fraction at two heights are shown in Figure 7. Again, results of the 3D simulation shows better agreement with the experimental data than the 2D simulation. Offset of the void fraction maxima from the axis of the column is predicted. The asymmetric lateral distribution of void fraction caused by the side exit is reasonably captured. By examining the cross-sectional distribution of void fraction and solids velocity, a core-annulus flow structure is observed for this riser of square cross-section similar to risers of circular cross-section. Different lateral profiles of the mean vertical solids velocity at z=5.13 m above the distributor predicted by the numerical simulations are also compared to the experimental data in Figure 8. Numerical predictions are consistent with the experimental measurements, with most particles traveling downwards close to wall and upwards in the central region except that the rising velocity in the core region is under-predicted. Overall, the 3D numerical simulation shows reasonably good agreement with the experimental data. Although the 3D simulation produces much improved results, there are some discrepancies in void fraction and velocity profiles between simulation and experiment. For example, good agreement is obtained for the axial voidage profiles near the wall, but relatively poor agreement is predicted along the central axis. One possible reason can be the coarse nature of the grid (much larger than the
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10-particle-diameter thumb-rule for grid independence) and probably a sub-grid correction for the inter-phase drag is needed to account for clusters. Another possible reason is the simplification of solids inlet and outlet configurations. For instance, the effect of solid side inlet cannot be accurately modeled by the simple uniform inflow condition upon entering the system. Fluctuations in solids concentration and velocity at the inlet have been predicted by numerical simulations with a short L-valve (12). While, the simplification of the horizontal duct connecting the riser exit to cyclone into a simple pressure outlet is not capable of predicting the solids accumulation in the duct and its influence on the riser flow observed in the experiments. Closer agreement with the experimental data is expected if more accurate inlet and outlet conditions are assigned for the simulations.
Figure 7. Lateral profiles of mean voidage at z=7.06, 8.98 m and x=0 cm.
Figure 8. Lateral profiles of mean solids velocity at z=5.13 m. CONCLUSIONS In this study, both 2D and 3D numerical simulations of a well-documented circulating fluidized bed of square cross-section were conducted. Effects of grid resolution and initial flow condition were studied in 2D simulations. It was found that the time needed by numerical simulations to reach the stationary state can be reduced by carefully choosing the initial bed loading. However, it was demonstrated that the 2D simulations under-predicted the solids inventory even with very high grid resolution (~10-particle-diameter grid size). A 3D simulation predicted a considerably higher solids inventory compared to the 2D simulation. Profiles of void fraction and solids
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velocity predicted by the 3D simulation were in reasonable agreement with the experimental data. Clearly, three-dimensional simulations are required to accurately represent a circulating fluidized bed system, at least for the system simulated. With this in mind, some conclusions obtained through 2D simulations might need further verification in 3D simulations. ACKNOWLEDGMENT This research was sponsored by the Fossil Energy, U.S. Department of Energy. The work was partly performed at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725. The authors thank Drs. Sofiane Benyahia, Tom O’Brien, and Madhava Syamlal at National Energy Technology Laboratory. This research was also supported in part by an appointment to the National Energy Technology Laboratory Research Participation Program, sponsored by the U.S. Department of Energy and administrated by the Oak Ridge Institute for Science and Education. REFERENCES 1. Guenther, C.; Syamlal, M.; Shadle, L.; Ludlow, C. A numerical investigation of an industrial scale gas-solids CFB, Proceedings of the 7th International Conference on Circulating Fluidized Beds, Falls, Ontario, Canada, 2002. 2. De Wilde, J., G.B. Marin, and G.J. Heynderickx. The effects of abrupt T-outlets in a riser: 3D simulation using the kinetic theory of granular flow. Chemical Engineering Science 2003, 58: 877-885. 3. Syamlal, M., W. Rogers, and T. J. O'Brien. MFIX documentation: Theory guide. Morgantown, West Virginia, U.S. Department of Energy (DOE), Morgantown Energy Technology Center, 1993. 4. Benyahia, S., M. Syamlal, and T.J. O'Brien. Summary of MFIX Equations 2005-4. 2007. 5. Zhou, J., J.R. Grace, S. Qin, C.M.H. Brereton, C.J. Lim, and J. Zhu, Voidage profiles in a circulating fluidized bed of square cross-section, Chemical Engineering Science, 1994, 49: 3217-3226. 6. Zhou, J., J. R. Grace, C. J. Lim, and C. M. H. Brereton, Particle velocity profiles in a circulating fluidized bed riser of square cross-section, Chemical Engineering Science, 1995, 50: 237-244. 7. Andrews, A. T., P. N. Loezos, and S. Sundaresan. Coarse-grid simulation of gas-particle flows in vertical risers. Industrial & Engineering Chemistry Research, 2005, 44(16): 6022-6037. 8. Lu, B., W. Wang, and J. Li, Searching for a mesh-independent sub-grid model for CFD simulation of gas-solid riser flows. Chemical Engineering Science, 2009, 64: 3437-3447. 9. Xie N, Battaglia F, Pannala S. Effects of using two- versus three-dimensional computational modeling of fluidized beds: Part I, hydrodynamics. Powder Technology, 2008; 182:1-13. 10. Xie N, Battaglia F, Pannala S. Effects of using two- versus three-dimensional computational modeling of fluidized beds: Part II, budget analysis. Powder Technology, 2008; 182:14-24. 11. Igci, Y., A. T. Andrews, S. Sundaresan, S. Pannala, and T. O'Brien, Filtered tow-fluid models for fluidized gas-particle suspensions. AIChE Journal, 2008, 54: 1431-1448. 12. Li, T., J. Dietiker, and M. Shahnam, Numerical modeling of NETL challenge problem 3. Poster at CFB10, Oregon, USA, May 1-5, 2011.
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HIGH-RESOLUTION SIMULATIONS OF GAS-SOLIDS JET PENETRATION INTO A HIGH DENSITY RISER FLOW Tingwen Li1,2, Chris Guenther1 1. National Energy Technology Laboratory, Morgantown, WV 26505, U.S.A. 2. URS Corporation, Morgantown, WV 26505, U.S.A. ABSTRACT High-resolution simulations of a gas-solids jet in a 0.3 m diameter and 15.9 m tall circulating fluidized bed (CFB) riser were conducted with the open source software-MFIX. In the numerical simulations, both gas and solids injected through a 1.6 cm diameter radial-directed tube 4.3 m above the bottom distributor were tracked as tracers, which enable the analysis of the characteristics of a two-phase jet. Two jetting gas velocities of 16.6 and 37.2 m/s were studied with the other operating conditions fixed. Reasonable flow hydrodynamics with respect to overall pressure drop, voidage, and solids velocity distributions were predicted. Due to the different dynamic responses of gas and particles to the crossflow, a significant separation of gas and solids within the jet region was predicted for both cases. In addition, the jet characteristics based on tracer concentration and tracer mass fraction profiles at different downstream levels are discussed. Overall, the numerical predictions compare favorably to the experimental measurements made at NETL. INTRODUCTION Gasification is a process converting carbonaceous materials, such as coal, biomass, and waste to syngas, a mixture of CO and H2 and other gases, which can be used for the production of liquid fuels and chemicals or for power generation. Recently, gasifier designs based on circulating fluidized bed-type technology have drawn attention from both academia and industries (1), as circulating fluidized beds (CFBs) possess a number of unique features that make them more attractive than other systems in energy industries. In gasification processes, the fuel particles with high energy density are usually injected into the gasifier as gas-solids jets. It is thus of great practical importance to understand the manner in which fuel particles disperse and mix with the bed material upon entering the system. Only limited studies on particle-laden jets in fluidized beds can be found in the open literature. Glicksman et al. (2) studied the mixing characteristics of horizontally injected particles in a one-quarter scale model of a pressurized bubbling fluidized bed combustor using a thermal tracer technique. Shadle et al. (3) studied the jet penetration of a gas-solids jet into a circulating fluidized bed riser by tracking phosphorescent particles illuminated immediately prior to injection. Wang et al. (4) reported the dynamic phenomena of horizontal gas and gas/solid mixture jets in a bubbling fluidized bed with an electrical capacitance volume tomography (ECVT) technique. Recently, a challenge problem was generated by NETL in collaboration with PSRI, in which experimental measurements of a gas-solids jet into a riser flow will be reported for validation of mathematical models (5). However, the aforementioned knowledge is far from enough to fully understand the flow behavior of particle-laden jets in a gas-solids flow system.
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In this study, CFD was employed to study the gas-solids jet penetration into a high density circulating fluidized bed riser flow (3). A gas-solids jet in a pilot-scale riser was simulated with the open-source Multiphase Flow with Interphase eXchanges (MFIX) code at https://mfix.netl.doe.gov. The general hydrodynamics of riser flow predicted by the numerical simulations were first compared against the available experimental data. Jet behaviors were studied through the numerical simulations and separation of jetting gas and particles was observed. Parameters characterizing the solids penetration were evaluated at different levels above the jet injection and comparison with the experimental data was reported. NUMERICAL MODELING In this study, the MFIX code was used to carry out the numerical simulations. MFIX is a multi-fluid, Eulerian-Eulerian code, with each phase treated as an interpenetrating continuum. Mass and momentum conservation equations are solved for the gas and solid (particulate) phases, with appropriate closure relations (6). The governing equations for the solid phase are closed by the kinetic granular theory Constitutive relations derived based on the kinetic theory for the solid phase stress tensor are used. More information on the code as well as detailed documentation can be found at the MFIX website. The pilot-scale cold flow circulating fluidized system available at NETL with 0.305 m diameter 15.9 tall is schematically shown in Figure 1. To simplify the simulation, only the riser section indicated in Figure 1 was simulated. The solids enter the riser from a side port 0.23 m in diameter and 0.27 m above the gas distributor. Solids exit the riser through a 0.20 m side port about 1.2 m below the top of the riser. A gas-solids jet was introduced through a small tube of 1.59 cm diameter at 4.3 m above the bottom distributor in the same azmuthal direction as the bulk solids feed to the riser. Gas and particles injected through the jet were treated as species in the gas and solid phases, respectively. Transport equations were solved for the mass fraction of each species. The high density polyethylene (HDPE) beads with an averaged diameter of 750 microns and a density of 863 kg/m3 used in the experiments were simulated. The material properties and operating conditions based on the experiments conducted at NETL were used in the simulation, as summarized in Table 1. Two cases with low Figure 1. Schematic of NETL CFB with and high jet gas velocities of 16.6 and 37.2 the gas-solids jet injection (Adapted from (3)) m/s, respectively, were simulated.
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Table 1. Material properties and operating conditions. Value High-resolution 3D numerical Property simulations of the riser flow Particle diameter (µm) 750 were performed. A cuboid Solid density (kg/m3) 863 domain was discretized with Interparticle restitution coefficient 0.8 a uniform grid size of 7.5 mm Particle-wall restitution coefficient 0.7 except at the jet injection Packed bed voidage 0.346 level where the grid was Angle of internal friction (deg)_ 30 slightly refined. The current Superficial gas velocity (m/s) 7.62 gird is believed to be fine Solids circulation rate (kg/s) 11.34 enough according to Gas viscosity (Pa.s) 1.8E-5 10-particle-diameter criterion Jet inlet diameter (cm) 1.59 for grid independence in Voidage at jet inlet (-) 0.97 gas-solids simulations (7, 8). Jet gas velocity (m/s) 16.6, 37.2 To represent the cylindrical Jet solids velocity (m/s) 6.93, 15.5 geometry of the riser, some Temperature (K) 298 cells were blocked so that a Pressure at top exit (Pa) 101325 stair-step surface was used 118000 to represent the column Pressure at bottom (Pa) Gas molecular weight (kg/kmol) 28.8 boundary. A total of 3 million computational cells was used. To better resolve the transient flow behavior of riser flow and gas-solid jet, a second order Superbee discretization scheme was employed for all equations (9). The computation was conducted on a high performance computing (HPC) system with 192 Xeon quad-core CPU running at 2.83 GHz. More information on the numerical modeling was provided in (10). The following boundary conditions were applied in the numerical simulations. At the bottom distributor, a uniform gas inflow was specified, with no particles entering the domain. While for the side solids inlet and the gas-solids jet inlet, constant inflow conditions were assumed. At the top abrupt exit, a constant pressure was used and particles were free to leave the system. At the wall, a no-slip boundary condition was adopted for both gas and solid phases for simplicity and it is believed to be appropriate for the stair-step boundary surface used in the current study. RESULTS AND DISCUSSION Approximately, a real time simulation of 25 seconds was completed for each case. Numerical results in the last 5 seconds were recorded at a frequency of 20 Hz for post-processing with the first 20 seconds simulation excluded to avoid the startup effect of such a large system. The flow was confirmed to be fully developed after 15 seconds by monitoring the overall pressure drop across the entire riser. The numerical results were visualized by an open-source, multi-platform data analysis and visualization application–Paraview. General hydrodynamics of the riser flow were compared to the experimental measurements first to validate the numerical models. Figure 2 presents the comparison between numerical simulation and experimental data on the axial pressure gradient. The experimental data shown in the figure are average of 12 duplicated runs and the error bar indicated the standard deviation. Reasonable agreement between simulation and experiment was obtained though the pressure gradient was slightly over-predicted presumably because of the no-slip wall boundary condition and possibly due to the compressive nature of the superbee discretization
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scheme used in the simulations (7). From the axial pressure gradient profile, it can be observed that the apparent solids holdup first decreases with increasing height at the lower region (0-5 m) and remains fairly constant in the middle region (5-10 m) and then, increases with height at the upper region of the riser (10-15 m). This profile is consistent with the experimental measurements and the effects of solids side inlet and abrupt exit were reasonably predicted (11). In addition, comparison with the experimental data on the solid velocity was made and reasonable agreement between simulation and experiment was obtained (10).
Figure 2. Axial profiles of pressure gradient for the case with low jet velocity (error bar indicates the standard deviation of 12 experimental data sets of duplicated runs). In the numerical simulations, gas and particles injected through the jet were tracked as species in the gas and solid phases, respectively. Transport equations were
solved for the mass fraction of each species. The tracer concentrations, ε g ,tr and
ε p ,tr , thus can be determined through ε g ,tr = ε g X g ,tr
(1)
ε p ,tr = ε p X p ,tr
(2)
where ε g and ε p are volume fractions of gas and solid phases, and X g ,tr and
X p ,tr are species mass fraction of tracer gas and particles, respectively. Distributions of gas and solid tracers were calculated and the behavior of gas-solids jet was studied by analyzing the transient results. Snapshots of voidage, tracer concentrations close to the jet injection in the lengthwise cross-section are shown in Figure 3. Clusters phenomenon, prominent near the wall, can be clearly observed from the voidage distribution in Figure 3(a). The gas-solids jet penetrates into the riser flow and bends upward because of the crossflow. It was also found that the jet was very unstable due to the strong interactions between the jet and the non-homogeneous riser flow. The clusters in the dense riser flow played the most important role in the interaction. Occasionally, the large falling clusters close to the wall dragged the jet downward. It is seen from the distributions of solid and gas tracer concentration in Figure 3 that the jetting particles with high momentum travel deeper into the crossflow and
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separate from the jetting gas flow. This is caused by the different dynamic responses of gas and particles to the crossflow. The separation of gas and solids from the jet flow was also observed when the mean tracer concentrations were examined for both low and high jet velocities. Hence, the penetration depths, which determine the length of the effective interaction zone of the jet and crossflow, are different for the jetting gas and particles. The gas-solids separation suggests that both jetting gas and particles need to be tracked in order to study the gas-solids jet behavior. In some gasifier processes, the air-coal mixture is expected to undergo combustion reactions to supplement the heat needed by endothermic gasification reactions and provide rapid release of volatile matter. Under such circumstances, the separation of coal particles from the air jet not only limits the desired combustion but also leads to a cool region of oxygen-rich gas. The cool oxygen-rich gas flow propagates upwards and might cause un-wanted combustion of the devolitization and gasification product gases. Hence, the separation of gas and solids should be taken into account in design and operation when they are injected together into the reactor crossflow (9). The depth of tracer particles penetrating into the crossflow increases slightly at higher levels. It is difficult to define the jet boundary due to fast mixing of the jetting gas and solids with the riser flow. Here, the analyses were focused on the penetration of tracer particles into the cross-flow. The penetration depth of tracer gas can be studied in a similar way.
Figure 3. Snapshots of (a) voidage, (b) volume fraction of tracer particles, and (c) volume fraction of tracer gas in the length-wise cross-section for the case with a jet velocity of 37.2 m/s. In the experiments, the solids particles were exposed to UV light before injection into the riser in form of gas-solids jet (3). The phosphorescent glow from the tracer solids was then detected by photo sensors at different radial positions along the jet direction
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within the riser 15 and 30 cm above the injection level. Relative concentration distribution of the jetting particles could be obtained through voltage signals of the photo detector probe. Each set of average radial voltage signals was normalized by dividing by the maximum voltage measured for that radial profile. Normalized concentration profiles of the phosphorescent particles ranged between 0 and 1 were then obtained by data fitting. The distance from the jet wall to the maximum peak and the width of the profile at half of the peak value were reported to characterize the jet-behavior as schematically shown in Figure 4.
Figure 4. Schematic of experimental measurements on jet characteristics. It was expected that the voltage signal from the photo sensor is proportional to the local concentration of the tracer particles. However, the light intensity of the glowing particles detected by the probe was also affected by the presence of opaque bulk particles which might block the light from glowing tracer particles. Hence, the concentration profile measured through this technique was an unknown combination of the absolute tracer concentration and the relative concentration of tracer in the solid phase. Without knowing the exact contributions of both concentrations to the final signal, it is necessary to compare the jet behaviors determined through the absolute and relative concentration profiles, which is straightforward in the numerical
simulations. For this purpose, profiles of the tracer particles concentration, ε p ,tr , and the tracer particles mass fraction in the solid phase, X p ,tr , at different downstream levels are plotted against the radial distance from the wall in Figure 5 for the case with a jet velocity of 37 m/s. Regardless of the magnitude, similar shape of profiles are predicted with a single peak corresponding to the jet position. However, it is seen that the locations of peak are slightly different. From both profiles, the radial location of peak and half-height width can be determined as summarized in Tables 2 and 3 for cases with low and high jet velocities. The experimental measurements are listed for comparison. The radial peak locations based on the absolute tracer concentration profiles are higher than those based on the relative concentration profiles. While the half-height widths based on the absolute concentration are lower than those based on the relative concentration. The difference can be very significant, especially for the high jet velocity. From a practical point of view, the absolute tracer concentration should be used as it characterizes the distribution of the jetting particles in the riser. On the other hand, it is important to make sure the numerical simulations and the experimental measurements are equivalent in order to interpret experimental finding and validate numerical models. In Figure 5, obvious differences between these profiles can be observed near the wall. The high tracer concentration is resulted from
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the strong solids backmixing and the dense solids flow close to the wall.
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Radial distance (m)
0 0.3
Figure 5. Radial profiles of tracer particle mass fraction in the solid phase and tracer particle concentration at (a)15 and (b) 30 cm above jet inlet for high jet velocity. Table 2. Jet characteristics at 15 and 30 cm above injection for low jet velocity. 15 cm above injection peak location (cm) half-height width (cm) Relative concentration 6 5.4 Absolute concentration 6.2 5.2 Experiment (3) 8 13 30 cm above injection peak location (cm) half-height width (cm) Relative concentration 6.4 6.7 Absolute concentration 6.4 4.8 Experiment (3) 8 15 Table 3. Jet characteristics at 15 and 30 cm above injection for high jet velocity. 15 cm above injection peak location (cm) half-height width (cm) Relative concentration 10.8 7 Absolute concentration 12.8 6.4 Experiment (3) 13 18 30 cm above injection peak location (cm) half-height width (cm) Relative concentration 12.3 9.1 Absolute concentration 15 7.2 Experiment (3) 14 17 Generally, the current numerical predictions compare favorably to the experimental measurements. However, the half-height width is greatly under-predicted in the simulations. The main reason might be the unstable gas-solids flow through the feed nozzle. In the experiments, the solids concentration and gas and solids velocities subject to strong fluctuations, which tend to increase the lateral fluctuations of jet movement, leading to a wide concentration profile with low peak value. This was not considered in the current numerical simulations where a stable jet inflow with constant velocity and solids concentration was assumed. CONCLUSIONS High resolution numerical simulations were conducted to study the gas-solids jet penetration into a high density riser flow. General flow hydrodynamics of a pilot-scale riser were predicted and behaviors of a gas-solids jet were studied. The gas-solids jet
7
was found to be very unstable because of the strong interactions between the jet and the non-homogeneous riser flow. There existed a significant separation of the jetting gas and solids when they entered the crossflow. Since experimental measurements of the absolute tracer concentration was difficult to obtain, the jet characteristics based on the radial profiles of absolute tracer concentration and relative concentration to the solids phase were compared against the experimental data. Generally, reasonable agreement between numerical simulations and experimental measurements on the gas-solid jet characteristics were obtained. ACKNOWLEDGMENTS The authors would like to thank Drs. Lawrence Shadle, Christopher Ludlow, and James Spenik for providing detailed experimental data and useful discussions. This research was supported in part by an appointment to the National Energy Technology Laboratory Research Participation Program, sponsored by the U.S. Department of Energy and administrated by the Oak Ridge Institute for Science and Education. NOTATION
X g ,tr mass fraction of tracer gas
X p ,tr mass fraction of tracer particles
ε g volume fraction of gas ε g ,tr concentration of tracer gas
ε p volume fraction of particles ε p ,tr concentration of tracer particles
REFERENCES 1. Higman, C., and M. van der Burgt. Gasification, Gulf publishing, 2003. 2. Glicksman, L., E. Carr, and P. Noymer. Particle injection and mixing experiments in a one-quarter scale model bubbling fluidized bed. Powder Technology, 2008, 180(3):284-288. 3. Shadle, L., J.C. Ludlow, J. Spenik, S. Seachman, and C. Guenther, Jet penetration into a riser operated in dense suspension upflow: Experimental and model comparisons. Circulating Fluidized Bed IX, Tutech Innovation, Hamburg, 2008. 4. Wang, F., Z. Yu, Q. Marashdeh, and L.S. Fan. Horizontal gas and gas/solid jet penetration in a gas-solid fluidized bed. Chemical Engineering Science, 2010, 65(11):3394-3408. 5. NETL/PSRI Challenge Problem 3. URL: mfix.netl.doe.gov/challenge/index.php 6. Syamlal, M., W. Rogers, and T.J. O’Brien. MFIX documentation: Theory guide. U.S. Department of Energy (DOE), 1993. 7. Guenther, C., M. Syamlal, L. Shadle, C. Ludlow. A numerical investigation of an industrial scale gas-solids CFB, Circulating Fluidized Bed VII, 2002. 8. Andrews, A.T., P. N. Loezos, and S. Sundaresan. Coarse-grid simulation of gas-particle flows in vertical risers. Industrial & Engineering Chemistry Research, 2005, 44(16):6022-6037. 9. Li, T., M. Syamlal, C. Guenther, A. Gel, and S. Pannala, High resolution simulations of coal jets in a gasifier, Industrial & Engineering Chemistry Research, 2010, 49(21):10767–10779. 10. Li, T., and C. Guenther, A CFD study of gas-solids jet in a CFB riser flow, AIChE Journal, 2011, (in review). 11. Mei, J. S., E. R. Monazam, and L. J. Shadle. Flow regime study of a light material in an industrial scale cold flow circulating fluidized bed. Journal of Energy Resources Technology-Transactions of the ASME, 2006, 128(2):129-134.
8
CFD MODELING OF FLUIDIZED-BED REACTOR FOR THE SYNTHESIS OF DIMETHYL ETHER Ranjeeth Kalluri, Nandita Akunuri, Aqil Jamal, and Raghubir Gupta RTI International, P.O. Box 12194, Research Triangle Park, NC 27709 2194, USA ABSTRACT The syngas-to-DME reaction is highly exothermic, and the catalyst temperature window is very narrow. The fluidized-bed reactor is, therefore, an ideal choice to carry out these reactions. RTI is developing a circulating fluidized bed design for DME synthesis. This paper discusses a two-phase CFD model and optimization of the solids circulation rate. INTRODUCTION In recent years, dimethyl ether (DME) has been generating broad interest as a promising alternative transportation fuel with great potential impact on society. DME is also a key chemical intermediate in the production of several petrochemicals such as dimethyl sulfate, synthetic gasoline, polymer-grade ethylene and propylene, and acetonitrile, a solvent used in the battery industry. Production of DME worldwide has increased from 30,000 tonnes/yr in 2000 to 545,000 tonnes/yr in 2006, and is expected to continue to rise over the next decade, due to the planned construction of multiple DME production facilities, especially in Asia(1). The conventional production of DME utilizes carbon monoxide (CO) from syngas. The recent recognition of DME synthesis from carbon dioxide (CO2) as a potential means to mitigate global CO2 emissions has further added to the growing interest in DME research. Commercially, DME is produced in a two-step process, where syngas is first converted to methanol, and the methanol produced is dehydrated to DME. This process mainly involves the following three reactions: H 98.744kJ / mol Methanol synthesis reaction: CO 2H2 CH3OH H 21.255kJ / mol Water gas shift reaction: CO H2O CO2 H2 Methanol dehydration reaction: 2CH3OH CH3OCH3 H2O H 40.9kJ / mol
(1) (2) (3)
Reactions (1) and (2) are catalyzed by a methanol synthesis catalyst (Cu/ZnO/Al2O3) and reaction (3) is catalyzed by an acid catalyst. All the above three reactions are reversible and exothermic, which results in a narrow catalyst operating temperature window. Combining the above reactions so that they occur simultaneously allows inhibiting products from one equilibrium reaction to be consumed in another, creating a strong driving force for the production of DME(2). The methanol produced in 1
reaction (1) is consumed by reaction (3), and the water (H2O) formed in reaction (3) is consumed in reaction (2), thereby driving reaction (3), and producing additional hydrogen (H2) required for the methanol production (reaction (1)). The net reaction for the direct syngas-to-DME process can be given as: Overall Reaction: 3CO 3H2 CH3OCH3 CO2
H 256.615kJ / mol
(4)
Numerous commercial DME production processes are available from companies such as Haldor Topsoe, Lurgi, Mitsubishi Gas Chemical, etc. However, most of these designs are two-step processes that are extensions of existing methanol synthesis facilities, and are not optimized for the production of DME. Recent developments for direct syngas-to-DME (single-stage) production include the use of slurry phase reactors such as that employed by JFE Holdings, Inc., Japan (3). Although the slurryphase reactor provides improved conversion efficiency due to efficient removal of the heat generated by reactions (1) - (3), the product recovery at typical operating conditions requires cryogenic separation (< 213 K). An alternative reactor design for direct DME synthesis is a fluidized-bed reactor. Lu et al. (4) have demonstrated experimentally that the per-pass CO conversion can be significantly improved using two-phase fluidized-bed reactors compared to slurry reactors. These authors have also shown the DME selectivity to be notably higher in fluidized-bed reactors compared to slurry reactors. Fluidized-bed reactors offer higher gas-solid mass transfer rates, which lead to higher DME yields compared to slurry reactors. Dynamic mixing of particles in a fluidized-bed also eliminates hot spot formation. Elimination of hot spots is critical for an extended catalyst life time (5), as the catalyst rapidly deactivates at higher temperatures (> 570 K). Further, at higher temperatures the equilibrium does not favor a high DME yield. These catalyst deactivation and equilibrium restrictions lead to a narrow catalyst operating temperature window. Effective temperature control in a fluidized-bed reactor can be achieved by circulating boiler feed water through internal coils within the reactor. The major challenge for this internal cooling scheme is the design of the reactor internals, which provide maximum heat recovery. To overcome these limitations, RTI is exploring a circulating fluidized-bed (CFB) reactor design with an external solids-cooler. In this approach, the catalyst is circulated between the fluidized-bed reactor and an external solids-cooler, where the solids act as a heat transport medium to carry heat out of the reactor, thereby allowing for control of the reactor temperature. A schematic of this process is shown in Figure 1. This process ensures high
Figure 1: Schematic of the CFB design
2
heat transfer rates and applicability of commercial solid cooling systems typically used in fluid catalytic cracking (FCC) processes. In this paper, a computational fluid dynamics (CFD) model for simulating the two phase (gas-solid) fluidized-bed DME synthesis process was developed using Fluent 12.1. The model developed was first validated with the experimental fluidized-bed results obtained by Lu et al. (4). This validated model was then used to study the benefits of a CFB reactor with external solids cooling for DME synthesis, and to optimize the solids circulation rate for maximizing CO conversion and DME yields. This CFD model can be further used for design and scale-up of the proposed CFB reactor for DME production. CFD MODEL The two different fluidized-bed geometries (described in detail in the next section) considered in this study were modeled using 2-D axisymmetric models using Ansys Fluent 12.1. The Eulerian-Eulerian approach has been used to model the fluid-solid flow dynamics. This model considers both the primary and the secondary (dispersed) phases to be interpenetrating continua. The equations considered (6) in the model are summarized below. The generalized continuity equation can be written as (i= g or s): i i t
i i vi 0
(5)
The momentum balance equations for gas and solid phases can be written as (i, k = g or s): i i vi t
.( i i vi vi ) i i g i P . i (vk vi )
(6)
The generalized energy conservation equation for the gas and solid phases is: ( i i H i ) .( i i ui H i ) (ki Ti ) hki (Tk Ti ) t
(7)
For the solid phase, the random granular motion resulting from particle collision is described by the following transport equation: 3 ( s s ) ( s s vs ) ( Ps I s ) : vs .( s ) gs 2 t
(8)
where the first term on the right hand side is the generation of energy by the solid stress tensor, the second term denotes the diffusion of energy, the third term represents the collisional dissipation of energy, and the fourth term represents the energy exchange between the solid and the gas phase. The generalized conservation equation for the various species in the gas phase is:
3
g gYg , j g g vgYg , j . g J g , j R j t
(9)
The rate expressions and parameters from Lu et al. (7) were used to model the reactions involved in the DME synthesis process. The activity of the catalyst was assumed to remain constant, with no deactivation occurring with time. The rate expressions used for the three individual reactions (1) - (3), are:
r1
1 K
k1KCO pCO pH3/22 pM / ( pH0.52 K P01 CO
pCO KCO2 pCO2 pH0.52 KW / K H0.52 pW
k2 KCO2 pCO pW pCO2 pH 2 / K P03 r2 0.5 1 KCO pCO KCO2 pCO2 pH 2 KW / K H0.5 pW 2 0.5 k3 pM pD pW / K P02 r3 0.5 2 0 1 K M p D pW / K P2
(10)
(11)
(12)
As will be further discussed in the next section, these kinetic rate expressions were validated against experimental data reported by Lu et al. (4). While the methanol synthesis catalysts used in both these studies (4 and 7) were similar (Cu-ZnO-Al2O3), the methanol dehydration catalyst was different, -alumina by Lu et al. (4) and HZSM-5 by Lu et al. (7). In view of this difference in the methanol dehydration catalyst used, kinetic constant k3 was varied to enable a better fit of the CFD model with the experimental results, while all the other rate and equilibrium constants used in the above rate expressions were left unchanged from those published by Lu et al. (7). An order-of-magnitude-lower value for the pre-exponential factor for k3, compared to that reported by Lu et al. (7), yielded a better fit of the CFD data with the experimental results, and hence it was used for this entire study. For the purpose of this study, the CO conversion and DME selectivity was defined as: ( NCO )in ( NCO )out ( NCO )in 2 N DME 2 N DME N MeOH
X CO
(13)
SDME
(14)
PDME
M DME M cat
(15)
CFD SIMULATIONS AND RESULTS Validation of the CFD Model As described above, the CFD model was first validated with the experimental CO conversion results obtained by Lu et al. (4). The reactor geometry and operating conditions used in this published experimental study are summarized in Table 1. Also shown in the table are the assumed catalyst thermal properties (conductivity and specific heat), which correspond to the alumina (catalyst support). A particle 4
sphericity of 0.8 was assumed in this study to account for the particle surface roughness. The experimental study by Lu et al. was conducted over a pressure range of 2 MPa to 4 MPa and a H2/CO ratio of 0.8 to 2.1. A fixed gas space velocity of 3000 ml/g cat/h (STP) was used in all these experimental tests.
Table 1: Operating conditions and parameters
Component mole fraction
Figure 3 shows the axial profiles of solids volume fraction, and mole fractions of CO, H2, and DME for the case of inlet H2/CO ratio of 1.0 and 3 MPa reactor pressure. For these conditions, the bed expanded to about 1.40 m in height. The solids fraction increased gradually along the length of the reactor, as the gas volume (molar) flow decreased due to the reactions. The concentrations of CO and H2 decreased, as they were consumed by the reactions, and methanol and DME concentrations increased along the
3
Component mole fraction
Gas vol. flow rate (m /h)
Reactor diameter (m) 0.026 Reactor length (m) 2.0 Bed height at min. fluidization (m) 1.0 Inlet & wall temperatures (K) 533 Packed bed voidage 0.428 Catalyst particle diameter (μm) 150 Catalyst sphericity 0.8 Catalyst density (kg/m3) 1983 Catalyst specific heat (J/kg K) 880 Transient CFD cases representing Catalyst thermal conductivity (W/m.K) 35 each of these experiments were simulated. An isothermal wall boundary 0.6 0.2 condition (533 K) was used in all these CO mole fraction simulations to simulate the DME mole fraction 0.5 experimental conditions. Each of the H mole fraction 2 0.15 Gas Vol. flow simulations was run for at least three 0.4 (gas) residence times, so as to obtain steady state outputs. The output 0.3 0.1 composition and flow rate were monitored and plotted. Figure 2 shows 0.2 the typical outlet mole fractions and 0.05 flow rate variations with flow time as 0.1 predicted by the CFD simulation. The outlet gas composition and flow rate 0 0 0 20 40 60 80 typically approached relatively constant Time (s) values in about one (gas) residence time from startup. Other process Figure 2: Outlet stream composition and flow variables such as temperature and rate variation with time (P=3 MPa, H2/CO=1.0, pressure also showed similar T=533 K, and SV=3000 ml/gcat/h) stabilization patterns. To eliminate the effect of small fluctuations present in 0.7 CO mole fraction these output flow variables, timeDME mole fraction averaged (10 s) values were used in 0.6 H2 mole fraction assessing the steady-state Solids volume fraction 0.5 performance of the reactor. 0.4 0.3 0.2 0.1 0 0
0.5
1
1.5
2
Reactor height (m)
Figure 3: Profile plots of component compositions and solid volume fractions at t=70 s (P=3 MPa, H2/CO=1.0, T=533 K, and SV=3000 ml/gcat/h)
5
length of the reactor. Similar patterns were seen for the other reactor pressures and H2/CO ratios studied. The maximum temperature rise in any of these cases was less than 5 K, due to the isothermal wall boundary conditions and small reactor diameter used. The mole fraction of water (not shown in the figure) remained low (< 0.01) throughout the reactor, as the water gas shift reaction consumed most of the water generated by the methanol synthesis and methanol dehydration reactions. Figure 4 and Figure 5 show a comparison of CFD and experimental (4) results of CO conversion as a function of inlet gas composition and pressure, respectively. The CO conversion values are in good agreement with the experimental results for the entire range of pressure and H2/CO ratios reported by Lu et al. (4). The figures also show DME selectivity and productivity for various inlet conditions. For the same gas space velocity, increase in H2/CO ratio led to higher CO conversion (Figure 4), as the methanol synthesis reaction has higher (order) dependence on H2 partial pressure (1.5) than on CO partial pressure (1.0). Also, an increase in pressure led to higher conversions and DME productivities (Figure 5), due to higher reactant partial pressures and effective gas residence times in the reactor. The DME selectivity decreased with increasing conversions, as the increasing DME concentrations deterred the methanol dehydration (equilibrium) reaction. 100
0.5
80
0.4
80
0.4
60
0.3
60
0.3
20
X
(CFD)
CO
S (CFD) DME P (CFD) DME
0 0.5
1
1.5 2 H2/CO ratio
(%) DME
or S CO
40
X
DME
or S CO
X
(Lu et. al.)
CO
0.1
X
(Lu et. al.)
X
(CFD)
CO CO
20
S
DME
P
0
DME
0.1
(CFD) (CFD)
0
2.5
0.2
(g/g cat/h)
0.2 X
(g/g cat/h)
40
DME
DME
P
(%)
0.5
P
100
0 1
2
3
4
5
6
Pressure (MPa)
Figure 4: Effect of feed composition on simulation and experimental results (P=3 MPa, T=533 K, and SV=3000 ml/gcat/h)
Figure 5: Effect of pressure on simulation and experimental results (H2/CO=1.0, T=533 K, and SV=3000 ml/gcat/h)
Circulating Fluidized-bed Optimization Lu et al. (4) demonstrated through experimental results that the optimum reactor temperature for maximizing CO conversion and DME yield lies in the range of 550 K to 570 K, for an inlet gas pressure of 3 MPa and H2/CO ratio of 1.0. The kinetics of methanol synthesis are limiting at lower temperatures, whereas the equilibrium restricts CO conversion at higher temperature. This leads to a narrow temperature window for optimal reactor operation and hence makes the ability to attain precise temperature control in the reactor critical. As described previously, in this study, benefits of using a CFB reactor with an external solids cooling loop (Figure 1) for effective temperature control in the DME synthesis process were explored. 6
A series of CFD simulations were conducted using the above validated CFD model on a modified fluidized-bed geometry to optimize the solids circulation rates for maximizing CO conversion and DME yield. The circulating fluidized-bed geometry used for this study had a length of 4.0 m and a diameter of 0.026 m. In these simulations, feed gas and catalyst particles entered the reactor at 533 K and 3 MPa. The feed gas flow rate was fixed at 90 SLPM, and the solids/gas ratio entering the reactor was varied between 10 and 30, by changing the solids mass flow rate entering the reactor. The reactor wall boundary condition in these simulations was set to the adiabatic (no heat flux) condition. All the exothermic heat released from the reactions was carried out of the reactor exclusively by the catalyst particles and product gases exiting the reactor. The catalyst, thereby, served dual functions – reaction rate promoter as well as heat transport medium. Further, the hot solids exiting the reactor were assumed to be separated and cooled to 533 K, before being recirculated to the reactor. Figure 6 shows the effect of inlet solids/gas mass ratio on CFB reactor performance. For maximum CO 0.9 600 X CO conversion, the optimum solids/gas ratio S DME appears to be around 20. As expected, 0.8 590 Outlet Temp with increase in solids/gas mass ratio, the 0.7 580 outlet temperature decreased significantly, as the increasing solids flow absorbs more 0.6 570 heat. The optimum solids/gas ratio (=20) for CO conversion also corresponds with 0.5 560 the outlet temperature of 565 K, which is in the optimum reactor operating 0.4 550 temperature range suggested by Lu et al. 0 5 10 15 20 25 30 35 40 (4). A lower (<20) solid/gas ratio leads to Solids/gas mass ratio excess reactor temperature, resulting in Figure 6: Effect of inlet solids/gas mass ratio equilibrium limitations, whereas a higher on performance of CFB reactor for DME (>20) solid/gas ratio leads to low reactor production (Inlet conditions: P=3 MPa, temperatures and kinetic limitations. DME
or S
CO
X
610
Outlet Temperature (K)
(%)
1
T=533 K. and H2/CO=1.0)
CONCLUSIONS The CFD model developed in this study was successfully validated with experimental DME synthesis fluidized-bed results from the literature. It was further used to demonstrate the benefits of using a CFB reactor with external solids-cooler to control the bed temperature. The model predicted an optimal solids/gas ratio for highest CO conversion and/or DME yield to be about 20 for the catalyst (kinetics) used in this study. In this concept, the catalyst particles act as a heat sink to carry heat out of the reactor, apart from catalyzing the DME synthesis reactions. The kinetic model used in this analysis was validated using literature data. Further validation of the CFD model using our own experimental data is under way.
7
NOTATION acceleration due to gravity (m/s2) hsg heat transfer coefficient (W/m2.K) H enthalpy (J/kg) Jg,j diffusion flux (kg/m2s) k rxn rate constant (units vary) K reaction equilibrium constant Mcat mass of catalyst in reactor (g) MDME DME outlet mass flow rate (g/h) NCO molar flow rate of CO (mol/s) NDME molar flow rate of DME (mol/s) NMeOH molar flow rate of CH3OH(mol/s) p partial pressure of species „j‟ P pressure (bar) PDME productivity of DME (g/g(cat)/h) SDME selectivity of DME in products, dimensionless T temperature (K) r reaction rate, mol/gcat/s Rj rate of production of species „j‟ g
mean velocity of the phase (m/s) XCO conversion of CO, dimensionless Greek Letters drag coefficient (kg/m3.s) dense phase voidage, dimensionless energy diffusion coefficient s density, kg/m3 stress tensor (bar) gs energy exchange between gas
vi
and solid phase granular temperature dissipation of fluctuating energy (W/m3) Subscripts i gas or solid phase k interacting phase j species „j‟ g gas phase s solid phase
REFERENCES 1. Reuters. (2008). “China domestic production of dimethyl ether is expected to reach 4.36 million tons in 2008.” [cited 2009 Aug. 26]; Available from: http://www.reuters.com/article/pressRelease/idUS140041+08-Aug2008+BW20080808 2. Wang, Z., Wang, J., Diao, J., and Jin, Y. (2001). "The synergy effect of process coupling for dimethyl ether." Chem. Eng. Tech., 24, 507-511. 3. Bourg, H. M. (2006). "Future Prospective of DME." 23rd World Gas Conference, Amsterdam. 4. Lu, W., Teng, L., and Xiao, W. (2004). “Simulation and experiment study of dimethyl ether synthesis from syngas in a fluidized-bed reactor.” Chem. Eng. Sci., 59, 5455-5464. 5. Cooper, M., (2010). “Direct synthesis of dimethyl ether from syngas using a novel fluidizable catalyst.” Internal R & D Report, RTI International, Durham, NC. 6. Ansys Inc. “Ansys Fluent Theory Guide - Ansys Fluent 12.0.” 7. Lu, W.Z., Teng, L.H., and Xiao, W.D. (2003). “Theoretical analysis of fluidizedbed reactor for dimethyl ether synthesis from syngas.” Intl. J. of Chem. Reactor Eng., 1, S2.
8
CFD SIMULATION OF CO2 SORPTION IN A CIRCULATING FLUIDIZED BED USING THE DEACTIVATION KINETIC MODEL Emadoddin Abbasi and Hamid Arastoopour Department of Chemical and Biological Engineering, Wanger Institute for Sustainable Energy Research (WISER), Illinois Institute of Technology, Chicago, IL 60616 ABSTRACT The Computational Fluid Dynamics (CFD) approach was used to simulate sorption of CO2 using solid sorbents in the riser section of a circulating fluidized bed. The simulation results were compared with the experimental data of Korea Institute for Energy Research (KIER) for continuous CO2 sorption using potassium carbonate in a circulating fluidized bed system. INTRODUCTION Coal-based power plants generate more than 50 percent of the today’s United States electric power (1). This means coal will continue to play a significant role in electricity generation for the foreseeable future. Therefore, the global emission of CO2 and its impact on climate change will continue to increase. Separation and sequestration of CO2 has been investigated by many researchers during the past decade including pre-combustion CO2 separation (gasification), oxyfuel combustion, and postcombustion CO2 separation. Post-combustion CO2 separation, which includes chemical and physical sorption of CO2 from flue gases, is a challenging process due to the low pressure and low concentration of CO2 in flue gas which requires high volumetric flow rates of flue gas to be processed. In addition, these processes generally use sorbents to capture the CO2 and these sorbents need to be regenerated and used continuously in the process. The regeneration of sorbents is an energy demanding process that reduces the overall efficiency of the power plant. Therefore, developing more efficient and economically feasible processes for CO2 removal has been one of our goals in recent years. Based on National Energy Technology Laboratory’s 2009 report (1), the goal for energy consumption of novel sorbents should be one third of energy consumption for today’s commercially available sorbents, which are basically amine-based liquid sorbents. Recent studies have shown that alkali-metal-based solids could be a promising sorbent for efficient and cost-effective CO2 removal from combustion gases (2, 3, 4). Ryu and coworkers (3) studied sodium- and potassium-based sorbents and concluded that they possess excellent features like superior attrition resistance, high CO2 sorption capacity, and high bulk density. Afterward, their group (5,6) at Korea Institute for Energy Research (KIER) used a potassium-based solid sorbent to
1
perform two sets of experiments in a 2 Nm3/hr and a 100 Nm3/hr facility for CO2 capture from flue gas. They found that using a circulating fluidized bed (CFB) ensures the continuous CO2 removal process from dilute flue gases at laboratory and bench scales. To scale up this CFB process, a state-of-the-art design tool based on CFD simulation is needed. However, to this point, few detailed simulations of this process have been conducted (7, 8) that can capture qualitatively the behavior of the system. In this study, we used experimental data provided by KIER (5) to validate our CFD simulation for the CO2 capture process in the riser part of a circulating fluidized
bed using a potassium-based solid sorbent. EXPERIMENT USED FOR SIMULATION For our simulation we used the experiments of Yi et al. (5), which includes a circulating fluidized bed consisting of a riser as carbonator and a bubbling fluidized bed as regenerator. Figure 1 shows the schematic of CO2 sorption using a circulating fluidized bed system. CO2 free gas
Carbonator
CO2 + H2O
Regenerator
Flue gas containing CO2, H2O, and N2 enters the carbonator and reacts with fresh (regenerated) solid sorbent containing 35% K2CO3 as shown below: CO2 + H2O + K2CO3 2KHCO3 + Heat CO2-free gas exits from the top of the riser while reacted sorbents go back to the regenerator to react with steam according to the following reaction: 2KHCO3 CO2 + H2O + K2CO3 – Heat
A very slender 2.5 cm ID and 6 m height riser, with an expanded 3.5 cm ID mixing zone at the first 0.6 m of the bottom of the riser as the carbonator and a 10 cm ID and Figure 1. Schematic of CO2 1.28 m height bubbling bed as the sorption process in a CFB regenerator reactor, were used. In this study, our focus has been on the simulation of the riser and the carbonation process. The operating condition is atmospheric pressure and 80o C in the riser. The flue gas inlet velocity was 2 m/s with 12% CO2 (dry basis) and 12.3% H2O composition. The solid circulating rate was controlled using a solid valve and was set to 21 kg/m2.s as the baseline operating condition. Flue gas
Regenerative Fluidization Gas
The potassium-based Sorb KX35 sorbent has a bulk density of 1100 kg/m3 and particle density of 2394 kg/m3 with average particle size of 98 µm. The attrition index (AI) of this sorbent has been reported as 0.1% at 10 std l/min (3). Differential pressure was measured at four different elevations along the riser. In addition, CO2 concentration at the riser outlet was monitored continuously.
2
NUMERICAL MODELING The CFD simulation of this work is based on a two-dimensional Eulerian-Eulerian approach in combination with the kinetic theory of granular flow (9, 10). To convert the real geometry to a reduced two-dimensional domain, the solid mass flux was kept constant as the basis for calculation. The assumptions of our numerical simulation include the isothermal condition for the process, and consider the gas phase as an ideal gas and the particles in the solid phase to be of uniform and constant size and density. Fluent 6.3 code was used to solve a set of governing equations including: Mass conservation For gas phase:
• ∂ (ε g ρ g ) + ∇.(ε g ρ g v g ) = m g ∂t
For solid phase:
• ∂ (ε s ρ s ) + ∇.(ε s ρ s v s ) = m s ∂t
Momentum conservation For gas phase:
∂ (ε g ρ g v g ) + ∇.(ε g ρ g v g v g ) = −ε g ∇P + ∇.τ g + ε g ρ g g − β gs (v g − v s ) ∂t For solid phase:
∂ (ε s ρ s v s ) + ∇.(ε s ρ s v s v s ) = −ε s ∇P + ∇.τ s + ε s ρ s g + β gs (v g − v s ) ∂t Species conservation For gas phase:
∂ (ε g ρ g y i ) + ∇.(ε g ρ g v g y i ) = R j ∂t
i=1, 2, 3
For solid phase:
∂ (ε s ρ s y i ) + ∇.(ε s ρ s v s y i ) = R j ∂t
i=1, 2, 3
And conservation of solid phase fluctuating energy:
3 ∂ [ (ε s ρ sθ ) + ∇.(ε s ρ sθ )v s ] = ( −∇p s I + τ s ) : ∇v s + ∇.(κ s ∇θ ) − γ s 2 ∂t Where κ s and γ s are conductivity of fluctuating energy and collisional dissipation of solid fluctuating energy, respectively. For the gas-solid inter-phase exchange coefficient β gs there are different correlations available in the literature. Garg et al. (7) showed that using an EMMS-based model as proposed by Li et al. (11) gives better results compared to the drag model proposed by Gidaspow (12). Nikolopoulos et al. (13) showed that the EMMS model increased the accuracy of the simulation at the lower part of the riser, resulted in better prediction for solid concentration and pressure distribution, and was able to account for heterogeneous solid structures and cluster formation in the riser.
3
The EMMS-based drag model has been used in this study as follows: 3 (1 − ε g )ε g ρ g u g − u s C D 0 ω (ε g ) 4 dp
β sg =
150
(1 − ε g ) 2 µ g
εgdp
2
+ 1.75
(1 − ε g ) ρ g u g − u s dp
ε g > 0.74 ε g ≤ 0.74
Where , ω (ε g ) is called the heterogeneity factor and is defined as
ω (ε g ) =
− 0.5760 +
0.0214 4(ε g − 0.7463) 2 + 0.0044
0.74 < ε g ≤ 0.82
− 0.0101 +
0.0038 4(ε g − 0.7789) 2 + 0.0040
0.82 < ε g ≤ 0.97
ε g > 0.97
− 31.8295 + 32.8295ε g
And
CD0 =
24 0.687 (1 + 0.15 Re p ) Re p
for Rep<1000
C D 0 = 0.44
for Rep>1000
In addition, k − ε turbulent model has been used to take care of turbulent fluctuations of the gas-solid mixture. Initially, there was no solid in the riser and the concentration of CO2 was zero as well. The summary of the boundary conditions is shown in Table 1. A second order discretization scheme was used to discretize the governing equation throughout the domain including 34x1200 uniform rectangular cells. In order to check the grid independence of the solution, the computations were also performed using two other coarser grids. The comparison between the calculated pressure drop using 34x1200 and 17x600 uniform grids showed an insignificant difference in the calculated pressure drop along the riser. REACTION KINETIC MODEL There is very little information available on the kinetics of the carbonation reaction of K2CO3 in the literatures. Onischak and Gidaspow (14) have proposed a first order homogenous reaction kinetic model which is dependent only on CO2 concentration and is independent of sorbent and H2O concentration. Recently, Park et al. (15) investigated different kinetic models including a Homogenous Model (HM), Shrinking Core Model (SCM), and Deactivation Model (DM). They concluded that the Deactivation Model explains fixed bed reactor experimental data better than the other two above-mentioned models. Garg et al. (7) used the proposed HM to simulate a similar process. Although their simulation was able to capture the CO2 concentration at the outlet of the riser, the simulation results were approximately three times more sensitive to the changes in
4
gas flow rate than observed in their experiments. It seems that the independence of the reaction model to gas velocity is one of the issues of HM. The Deactivation Model (DM) proposed by Park et al. includes gas volumetric flow rate in the kinetic model. The concept of this model is based on analogy between deactivation of catalyst particles by coke formation and deactivation of sorbent particles by carbonation. Table 1. Summary of Boundary Conditions for Baseline Operating Condition Solid inlet
Gas inlet 2
Solid mass flux = 21 kg/m s
Outlet
Gas velocity= 2 m/s
Solid volume fraction = 0.6
Wall No slip condition for gas phase
P = 1 atm Solid volume fraction= 0
Carrier gas mass 2
flux = 0.05 kg/m s Mass fraction K2CO3 = 0.35
Mass fraction CO2 = 0.1
Mass fraction KHCO3 = 0
Mass fraction H2O = 0.15
Mass fraction Inert = 0.65
Mass fraction N2 = 0.75
Partial slip condition for solid phase
IN the deactivation Model, the effect of the formation of a product layer on the surface of sorbent particles (which results in an additional diffusion resistance and reduction in available active surface area) is lumped into a reducing activation factor with an exponentially deactivation rate. R = k CCO2 a Where a is activity of the sorbent and defined as:
a = exp[
[1 − exp(τ .k s (1 − exp(− k d t )))] exp(− k d t )] 1 − exp(− k d t )
τ , called surface time, is defined as the ratio of available pore surface to the volumetric flow rate of flue gas. ks= 2.44x10-3m/s and kd = 1.42x10-4/s are surface reaction constant and deactivation constant, respectively. (15) RESULTS The simulation results for CO2 removal percentage and sensitivity analysis of the model to the changes in gas flow rate and pressure drop along the riser are presented in this section. Simulations ran for 200 seconds of the processing time and, as it takes about 90 seconds for the solid inventory in the riser to become stable, the first 100 seconds of the simulations were not considered in the calculation of the time averaged results. Our results are being compared with the experimental data of KIER provided by Yi et al. (5).
5
Time averaged CO2 removal percentage %
Time averaged CO2 mass fraction
Figure 2 shows the simulated axial profile of time averaged CO2 mass fraction and CO2 removal percentage at the different elevations in the riser at the baseline operating condition. 0.1 60 The CO2 removal percentage at the 0.09 50 outlet of the riser is 58%, which is very 0.08 40 close to the 0.07 30 reported 54% removal in the 0.06 20 experimental data. In addition, the axial profile of the time 0.04 0 averaged CO2 mass 0 1 2 3 4 5 6 Riser Height (m) fraction showed that around 60% of CO2 Figure 2. Simulated time averaged CO2 mass fraction and removal takes place CO2 removal percentage in the first 0.6 m of rise (mixing zone), which is due to the higher solid concentration and solid circulation in this region. 0.05
10
Furthermore, the effect of gas flow rate on the percentage of CO2 removal has been investigated. The results showed that the increasing inlet gas flow rate (inlet gas velocity) decreases the CO2 removal percentage, which is in-line with the experimental data. The higher gas flow rate means shorter residence time, which results in reduction of surface time, τ , in DM and, in turn, increases the deactivation factor and decreases the CO2 removal (see Figure 3). Moreover, our model was able to capture the sensitivity of the CO2 removal process to the gas flow rate much better than Garg et al. (7) who used the homogeneous reaction model. Sensitivity of CO2 removal to the changes in the solid circulation rate was also investigated. As was expected, increasing the solid circulation rate resulted in increased CO2 removal percentage. Similar to the experimental data and the Table 2. Differential Pressure at Different Elevations simulation of Garg et al. (7), Differential Pressure Differential Pressure the results of our simulation (mm H2O) (mm H2O) were also sensitive to the KIER Experiments Simulation variations in the solid circulation rate. However, Yi et al (5) due to some inconsistencies in the reported data by Yi et DP1 100 107 al. (5), no comparison with their data was made in this DP2 200-500 335 study. Table 2 shows the comparison between the DP4 70 67 pressure drop predicted using our simulation with the experimental results of Yi et al. (5) at 4 different elevations of 0.52 m, 2.27 m, 4.07 m, and 5.87 m which are referred to as DP1, DP2, DP3, and DP4, respectively. Our simulation closely predicted the pressure drop over the DP1, DP2, and DP4 sections DP3
100-210
270
6
CO2 Removal %
and over predicts the pressure drop over the Baseline 70 DP3 section. This could operation be due to a couple of 60 condition reasons. First, in the 20 50 hours of pressure drop profiles reported by 40 KIER, there is a sudden 30 reduction over the DP2 KIER and DP3 sections at Simulation 20 Garg et al around the 11th hour of operation that remains 10 up to the end of the 1 1.5 2 2.5 3 3.5 experiment. This Inlet Gas Velocity (m/s) reduction in pressure Figure 3. Effect of inlet gas velocity on CO2 removal drop is apparently due to percentage an undisclosed change in the operating condition that cannot be implemented in our simulation. The second possible reason could be due to the inaccuracy of the EMMS drag model in predicting a wide range of solid phase concentration and heterogeneities. CONCLUSION A 2-D Eulerian-Eulerian CFD simulation based on kinetic theory of granular flow in combination with deactivation kinetic model has been used to simulate the KIER experiments for CO2 sorption in a circulating fluidized bed using solid sorbent. The simulation was able to predict CO2 removal percentage at the riser outlet at different gas flow rate in good agreement with experimental data. Our simulation was also able to predict the pressure drop in the riser and dependency of the CO2 conversion to gas flow rate in line with the KIER experimental data. NOTATION a
Activation coefficient (-)
yi
Species i mass fraction
C D0
Drag coefficient (-)
γs
Collisional dissipation of solid fluctuating energy (kg/s-m3)
dp
Particle diameter (m)
κs
kd
Deactivation rate constant (1/s)
θ
Conductivity of solid fluctuating energy (kg/m-s) Granular temperature (m2/s2)
ks
Surface reaction constant (m/s)
β gs
Inter-phase mass transfer (kg/m3-s)
ρi
Rj
Heterogeneous reaction rate (kg/m3-s)
ui
Superficial velocity of phase i (m/s)
vi
Local velocity of phase i (m/s)
τi εi τ
•
m
7
Inter-phase drag coefficient (kg/s-m3) Density of phase i (kg/m3) Stress tensor of phase i (Pa) Volume fraction of phase i (-) Surface time (s/m)
REFERENCES 1. DOE/NETL Advanced Carbon Dioxide Capture R&D Program: Technology Update, 2009, http://www.netl.doe.gov/technologies/coalpower/ewr/index.html 2. Hayashi, H., Taniuchi, J., Furuyashiki, N., Sugiyama, S., Hirano, S., Shigemoto, N. and Nonaka, T., “Efficient Recovery of Carbon Dioxide from Flue Gases of Coal-Fired Power Plants by Cyclic Fixed-Bed Operations over K2CO3-on-Carbon”, Ind. Eng. Chem. Res., 37, 185-191, 1998 3. Ryu, C.K, Lee, J.B., Eom, T.H., Oh, J.M., Yi, C.K,”Development of Na and K-Based Sorbents for CO2 Capture from Flue Gas”, FOURTH ANNUAL CONFERENCE ON CARBON CAPTURE AND SEQUESTRATION DOE/NETL, May 2-5, 2005 4. Lee, S.C., Chae, H.J., Lee, S.J., Park, Y.H., Ryu, C.K, Yi, C, K., Kim. J.C., “Novel regenerable potassium-based dry sorbents for CO2 capture at low temperatures” Journal of Molecular Catalysis B: Enzymatic 56, 179–184, 2009 5. Yi, C., K., Jo, S., J., Seo, Y., Lee, J., B., Ryu, C. K., “Continuous operation of the potassium-based dry sorbent CO2 capture process with two fluidized-bed reactors”, International journal of greenhouse gas control, 1, 31-36, 2007 6. Park, Y., C., Jo, S., J., Ryu, C., K., Yi, C., K.,” Long-Term Operation of Carbon Dioxide Capture System from a Real Coal-Fired Flue Gas Using Dry Regenerable PotassiumBased Sorbents” Energy Procedia 1, 1235–1239, 2009 7. Garg, R., Shahnam, M. and Huckaby, E. D., “Continuum simulations of CO2 capture by dry regenerable Potassium based sorbents”, 7th International Conference on Multiphase Flow, ICMF 2010, Tampa, FL, May 30 – June 4, 2010 8. Chalermsinsuwan, B., Piumsomboon, P., Gidaspow, D., “A Computational Fluid Dynamics Design of a Carbon Dioxide Sorption Circulating Fluidized Bed”, AIChE Journal, Vol. 56, No. 11, 2010 9. Arastoopour, H., “Numerical simulation and experimental analysis of gas/solid flow systems: 1999 Flour-Daniel Plenary Lecture” Powder Technology, 119, 59-67, 2001 10. Benyahia, S., Arastoopour, H., Knowlton, T.M., Massah, H., “Simulation of particle and gas flow behavior in the riser section of a circulating fluidized bed using the kinetic theory approach for the particulate phase”, Powder Technology, 112, 24-33, 2000 11. Yang, N., Wang, W., GE, W., Wang, L., Li, J., “ Simulation of Heterogeneous Structure in a Circulating Fluidized Bed Riser by Combining the Two-Fluid Model with the EMMS Approach”, Ind. Eng. Chem. Res., 43, 5548-5561, 2004 12. Gidaspow, D., “Multiphase flow and fluidization”, Academic Press, 1994 13. Nikolopoulos, A., Atsonios, K., Nikolopoulos, N., Grammelis, P., Kakaras, E., “An advanced EMMS scheme for the prediction of drag coefficient under a 1.2MWth CFBC isothermal flow—Part II: Numerical implementation”, Chem. Eng. Science 65, 4089– 4099, 2010 14. Onischak M, Gidaspow D., “Kinetics of the reaction of CO2 with solid K2CO3”, 73rd National AIChE Meeting, Minneapolis, USA, 1972. 15. Park, S.W, Sung, D. H., Choi, B.S., Lee, J.W., and Kumazawa, H., “Carbonation Kinetics of Potassium Carbonate by Carbon Dioxide”, J. Ind. Eng. Chem. Vol 12, No. 4, 522-530, 2006
8
RESEARCH ON HEAT TRANSFER INSIDE THE FURNACE OF LARGE SCALE CFB BOILERS Ruiqing Zhang, Hairui Yang, Hai Zhang, Qing Liu, Junfu Lu, Yuxin Wu Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Thermal Engineering, Tsinghua University, Beijing, 100084, China T: 86-1-62773384; F:86-10-62781743;E:[email protected] ABSTRACT Field tests in one unit of 135MWe and two units of 300MWe commercial Circulating Fluidized bed (CFB) boilers (A&B) with different structures were carried out. The influence of operating conditions on the thermal boundary layer, local heat transfer coefficient and peripheral distribution of heat transfer coefficient were studied. It was found that, in the 135MWe and 300MWe-A CFB furnace, the thickness of the thermal boundary layer was almost constant, about 100mm, and independent of the height above the distributor and the boiler load. The local heat transfer coefficient increased with increasing load as well as the coal feeding rate and air volume in both the 135MWe and 300MWe-A CFB boilers. The boiler structure and heating surface layout had a great influence on the distribution of the heat transfer coefficient in the large-scale CFB boilers. In both the 135MWe furnace and the 300MWe-B CFB boilers, the heat transfer coefficient was lower in the center than near the corner due to higher suspension density in the corner. In the 300MWe-B CFB with heating surfaces in the furnace, because of the uneven layout of the heating surface and the mal-distribution of gas-solid flow caused by the asymmetric arrangement of cyclones, heat transfer coefficients tended to be higher in the middle part than at the walls. INTRODUCTION The distribution of the heat flux and the heat transfer coefficient inside a circulating fluidized bed (CFB) boiler, which is related to the arrangement of heating surfaces, is of great significance to the design and operation of the boiler. Many studies on heat transfer in CFB boilers have been conducted (Glicksman (1); Wirth (2); Andersson and Leckner (3); Basu and Nag (4)) and the results showed that the heating transfer coefficient from bed to heating surface was affected by the solids suspension density, particle size and bed temperature. Pagliuso, et al. (5) found that the profile of the heat transfer coefficient along the riser correlated well with that of the solid suspension density. They also found that the heat transfer coefficient increased with the increasing particle size, which became more obvious for smaller particles and higher suspension density. Breitholtz and Leckner (6) presented a correlation between heat transfer coefficient and suspension density based on measurements in six CFB boilers ranging from 12 to 300 MWth. In addition, with increasing bed temperature, Tb, thermal conductivity and radiation heat transfer of the gas increased accordingly, which lead to an increase of the heat transfer coefficient. This was confirmed by the test of Jestin, et al. (7) in a 125MWe CFB boiler and the experiment of Jeon, et al. (8). Many studies on gas-solid flow and heat transfer in CFB boilers were carried out in laboratory scale CFB combustors, which differ from industrial ones because of
smaller height to diameter ratios. Zhang, et al. (9) and Noymer, et al. (10) found the height to diameter ratio of a riser has a significant impact on the gas-solid flow inside the furnace. Besides, considering the difference in temperature, particle size distribution and suspension density between laboratory scale CFB combustors and industrial ones, previous test results may not totally represent those found in industrial CFB boilers. At the same time, the heat transfer coefficient is also affected by boiler load, which was proven by Zhang et al. (11). Therefore, it is necessary to measure the distribution of the heat transfer coefficient inside an industrial CFB boiler for proper design and arrangement of heating surfaces. In recent years, large-scale CFB boilers have been developed, however, only a few test results on heat transfer in large-scale CFB boilers have been published. To compare and analyze heat transfer characteristics in different large-scale CFB boilers, field tests in one unit of 135MWe CFB boiler and two units of 300MWe CFB boilers with different structures were conducted. The influence of boiler load on the local heat transfer coefficient and thermal boundary layer distribution were studied in this work. EXPERIMENTAL The tests were carried out in a 135MWe CFB boiler and two 300MWe CFB boilers, which are all reheat, natural circulation boilers. The 135MWe CFB boiler has single furnace with height of 38m from distributor to the roof and with a cross section of 6.6m×13.1m. Two refractory-lined cyclones were used as the gas solid separators. Lean coal was burnt during the experimental test. Two 300MWe CFB boilers, referred to as A and B respectively, have different furnace structures. Boiler A has a pants leg furnace structure with height of 36m and a cross section of 14.8m×12.6m. Four cyclones are arranged on both sides of the furnace. A higher bed temperature (890℃) was adopted for coal with a low volatile content to improve combustion efficiency. The boiler was operated with a high solids circulation rate. Boiler B has a single furnace with height of 40m from the distributor to the roof, and a cross section of 8.4m×28.2m. There were two water cooled wing walls hung in the furnace. Three steam-cooled cyclones are used as gas solid separators. Local heat transfer coefficient (a), heat flux (b), the peripheral distribution of the heat transfer coefficient (c), the temperature profile near the wall (d) and the vertical distribution of the solid suspension density along the furnace height at different loads (e) were measured respectively. The local heat transfer coefficient, K, was measured by a water-cooled conductive heat flux meter designed by Tsinghua University, shown in Fig.1. According to the heat conduction law, the local heat transfer coefficient can be calculated from the axial temperature gradient inside the probe-defined as heat flux method (HFM). The heat flux, q, was calculated based on the temperature difference measured with thermocouples installed in the fin-tube (shown in Fig.2) with a two-dimensional finite element calculation method (FEM), which has been investigated by many researchers (Andersson and Leckner (12); Zhang, et al. (13); Wang, et al. (14); Zhang et al. (15)). The heat flux profile was obtained by arranging a number of measuring points peripherally at different heights along the furnace. The relationship between the heat transfer coefficient and the heat flux can be expressed as: q=K△T
The temperature difference, △T, was determined by the difference between the furnace temperature and the stream temperature at each point. The former can be gained from the temperature distribution at certain boiler height. The stream temperature inside the tube is the saturated water temperature under drum pressure. So, the heat transfer coefficient can be derived from equation above by knowing the heat flux q and the temperature difference △T. The temperature profile near the wall was measured with a multi-thermocouple probe, shown in Fig. 3. Pressure sensors were placed at different level to monitor the vertical profile of the suspension density along the furnace height. Measurements (a)~(e) were all conducted in the 135MWe CFB boiler while measurements (a), (d), (e) were performed in the 300MWe-A CFB boiler and measurements (b), (c), (e) performed in the 300MWe-B CFB boiler.
Figure 1. Heat flux meter combustor chamber
1 2 3
Insulation
Figure 2. Thermal couple distribution on tube surface
Figure 3. Temperature measurement probe
RESULT AND DISCUSSION Thermal boundary layer Fig.4 shows the temperature profiles near the wall in the 135MWe CFB boiler at full load. Measurement points were located at three different heights. Fig.5 shows temperature profiles near the wall in the 300MWe-A CFB boiler at full load. Measurement points 1-4 were symmetrically located 3m below the cyclones, with 1 and 2 on the rear wall and 3 and 4 on the front wall. Point 5 was located 3m above the distributor, where the bed temperatures were lower than those measured at 1-4. The temperature profiles remained similar at different boiler loads. It was found that furnace temperatures increased significantly in the region near the wall, about
100mm, and then become almost constant, at a temperature considered to be the furnace core temperature. The thickness of the thermal boundary layer was defined as the distance to the tube wall where the temperature was 90% of the core temperature. Figs.4 and 5 show that the thicknesses of the thermal boundary layers measured in both boilers were about 100mm and were independent of the boiler load and furnace height. This was consistent with the study of Wang et al. (16). Local heat transfer coefficient
1000
1000
900
900
800
800
Temperature (oC)
Temperature (oC)
Fig.6 shows the measured local heat transfer coefficient in the 135 MWe CFB boiler. Heat transfer coefficients increase with increasing load and decrease with increasing height, which agrees well with previous work (Zhang, et al. (11)). When the boiler load increases, the coal feeding rate, gas velocity and air volume increase correspondingly, resulting in a higher particle velocity that results in more particles participating in contacting the water wall per unit area. As a result, the heat transfer coefficient increases. The solid suspension density gradually decreases in the furnace from the distributor to the roof. Therefore, the heat transfer coefficients decrease with the height for a constant boiler load.
700 600
14m 22.2m 26m
500 400 300
0
200
400
600
800
700 mearsuring point-1 mearsuring point-2 mearsuring point-3 mearsuring point-4 mearsuring point-5
600 500 400 300
1000
0
80
120
160
Figure 5. Temperature profile (300MWe-A)
160 Output 85MWe Output 100MWe Output 135MWe
120
80
40 12
200
Distance from fin (mm)
Distance from fin (mm)
Figure 4. Temperature profile (135MWe) Heat Transfer Coefficient (W/(m2.K))
40
16
20
24
28
Furnace Height From Distributor (m)
Figure 6. Local heat transfer coefficient (135MWe) Table 1．Local heat transfer coefficient at measurement point-4 (300MWe-A)
Measurement point 4
Boiler load MWe 260 300
Furnace temperature o C 908 915
Local heat transfer coefficient W/(m2K) 161 183
The measurement of the local heat transfer coefficients at point 1-4 in 300MWe-A boiler verified the conclusions above. Typical results are listed in Table 1. The heat transfer coefficient at point 4 increased from 161 W/(m2K) to 183 W/(m2K) when the boiler load was increased to 300MWe from 260MWe. Though the effect of height on the heat transfer coefficient is not proved directly, it is reasonable to assume that the coefficient would decrease with increasing height because the suspension density decreases with height. Peripheral distribution of the heat transfer coefficient The peripheral distribution of the heat flux at different levels, from which the heat transfer coefficient distribution can be derived, was indirectly measured in the 135 MWe and 300 MWe-B boiler by the finite element method discussed above.
190 23.0m 18.5m 12.0m
185 180 175 170 165 160 0.0
0.1
0.2
0.3
0.4
0.5
Dimensionless Distance to the Center of the Side Wall (a)
Heat Transfer Coefficient (W/(m2.K))
Heat Transfer Coefficient (W/(m2.K))
In the test of the 135MWe CFB boiler, measuring points were arranged in the left front wall and the front left side wall at heights of 12m, 18.5m, and 23m above the distributor. The bed temperature was about 882~894℃, the water temperature was about 353~354℃, the superficial gas velocity was about 5.78~5.86m/s, the tube dimension was 60×5mm and the tube pitch was 90 mm. Fig. 7 shows the heat transfer coefficient distribution over half of the front wall and the left side wall at different height levels. Consistent with the result by HFM, the heat transfer coefficients by FEM also decreased with the increasing height. In the cross section, the heat flux and the heat transfer coefficient were lower in the center than in the corner on both the front wall and the side wall. 190 23.0m 18.5m 12.0m
185 180 175 170 165 160 0.0
0.1
0.2
0.3
0.4
0.5
Dimensionless Distance to the Center of the Front Wall (b)
Figure 7. Peripheral distribution of heat transfer coefficient on side wall and front wall (135MWe) In the 300MWe-B boiler, the measuring points were arranged in the left half of the rear wall and the back half of the left side wall at heights of 13m (L1), 19m (L2), 25m (L3), 30m (L4), and 35m (L5). In particular, the entire rear wall and left side wall at L1 were fitted with measuring points. The water temperature was about 358.6~359.3℃,
the superficial gas velocity was about 3.6~3.9m/s, the water tube dimension was 57×6.5mm and the tube pitch is 87mm. As the heat transfer at different heights was similar, results in L1 were set as an example for the analysis. The heat flux and the heat transfer coefficient distribution along the rear wall are shown in Figs.8 and 9 respectively. It is seen that, the heat fluxes between the wing walls appear to be higher than between the side wall and the wing wall. Also, heat fluxes on the left side are higher than on the right side. For the correlation of the coefficient and heat flux by calculation, the heat transfer coefficient prolife trend was similar trend to the pattern of the heat flux profile. In actual operation, it is found that the solids mix well in the dense zone at the bottom of the furnace. In the upper furnace, the two wing walls divide the furnace into three parts, marked as left, middle and right. Three cyclones were also arranged asymmetrically. Therefore, the gas-solid flows in these three regions were relatively independent, which prevents solids in upper furnace from experiencing good lateral mixing. Furthermore, the layout of the heating surfaces was not uniform. A1 is the heating surface area in the left or right section, A2 is the heating surface area in the middle section. Calculating from the heating surface structure, A2/A1 is about 0.91, which means less heating surface is placed in the middle part compared with that in left or right part. As a result, the temperature between the wing walls tends to be higher than that between the side wall and the wing wall. This can explain the temperature non-uniformity. The uneven layout of the heating surface is the essential reason for the uneven heat flux distribution. between side and wing wall between wing walls Heat Flux KW/m2
90
60
30 left side wall
0 0.0
0.2
right side wall
0.4
0.6
0.8
1.0
Dimensionless Distance from Left to Right Side Wall
Figure 8. Peripheral distribution of heat flux in the whole rear wall (300MWe-B)
Heat Transfer Coefficient (W/(m2.K))
120
200
between side and wing wall between wing walls
160 120 80 40 0 0.0
left side wall
0.2
right side wall
0.4
0.6
0.8
1.0
Dimensionless Distance from Left to Right Side Wall
Figure 9. Peripheral distribution of heat transfer coefficient in the whole rear wall(300MWe-B)
It is clear that the heat flux and heat transfer coefficient have a close relationship with suspension density and bed temperature. The peripheral distribution of the bed temperature, shown in Fig.10, gives good agreement with that of heat flux with the fact that bed temperature in center section was greater. Also, the temperature on the left side seems to be lower than that of right side, contrary to the heat flux distribution. This is supported by the result that the section has a greater suspension density than right, as derived from the pressure profile during the experiment. It is the combined effects of the suspension density and the bed temperature, that result in the heat flux and heat transfer coefficient characteristics presented above.
Furnace Temperature oC
1000 960
between side and wing wall between wing walls
920 880 840 800 0.0
left side wall
0.2
right side wall
0.4
0.6
0.8
1.0
Dimensionless Distance from Left to Right Side Wall
Figure 10. Temperature distribution in the furnace (300MWe-B) In Fig.9 the heat transfer coefficient was found to increase near the corner formed by the rear wall and either side wall or the wing wall, which is consistent with what was found in the 135MWe boiler. It can be inferred that in large-scale CFB boilers, a corner effect does exist, causing the heat flux and the heat transfer coefficient near the corner to increase compared to the central area. Considering the relationship between the heat transfer coefficient and the suspension density, the suspension density in this area is likely to be higher, which is also indicated by the fact that tube erosion near the corner is greater than the central area in CFB boilers. CONCLUSIONS Field measurements mainly on heat transfer performance has been carried out in a 135 MWe CFB boiler and two 300 MWe CFB boilers with different configurations. It was found that: 1. The thickness of the thermal boundary layer is about 100mm and remains constant with height above with distributor and with boiler load. 2. The local heat transfer coefficient decreases with increasing furnace height and with decreasing boiler load. 3. The heat flux and the heat transfer coefficient have an uneven distribution in large-scale CFB boilers. Near the corner formed by rear wall with the side wall and wing wall, the heat flux and heat transfer coefficient is higher than that in the central area. In a 300 MWe CFB boiler with two wing walls arranged unevenly inside the furnace, heat flux and heat transfer coefficient tend to be higher in middle section than in the left or the right section at a particular level. ACKNOWLEDGMENT Financial support of this work by High Technology R&D (863) (2009AA05Z302, 2007AA05Z303) are grateful acknowledged NOTATION A1 A2 K q
heating surface area of the left or right part in 300 MWe-B, m2 heating surface area of the intermediate part in 300 MWe-B, m2 heat transfer coefficient, W/(m2K) heat flux, W/m2
△T
temperature difference, K Abbreviations CFB Circulating Fluidized bed FEM Finite element method HFM Heat flux method REFERENCES 1. Glicksman, L. R., “Circulating Fluidized Bed Heat Transfer”, in “Circulating Fluidized Bed Technology II”, P. Basu and J. F. Large, Eds., Pergamon Press, Oxford (1988), pp13-29. 2. Wirth, K. E., “Prediction of Heat Transfer” in “Circulating Fluidized Beds” in “Circulating Fluidized Bed Technology IV”, A. A. Avidian, Eds, (1994), pp291-296. 3. Andersson, B. A. and Leckner, B., “Local Lateral Distribution of Heat Transfer on Tube Surface of Membrane Walls in CFB Boilers”, in “Circulating Fluidized Beds” in “Circulating Fluidized Bed Technology IV”, A. A. Avidian, Eds, (1994), pp311-315. 4. Basu, P., and Nag, P. K., “A Review on Heat Transfer to Walls of a Circulating Fluidized Bed Furnace”, Chemical Engineering Science 1, 1-26 (1996). 5. Pagliuso, J. D., Lombardi, G., Godstein, L., et al, “Experiments on the local heat transfer characteristics of a circulating fluidized bed”, Experimental Thermal and Fluid Science 20(3-4), 170-179 (2000). 6. Breitholtz, C and Leckner, B, “Wall average heat transfer in CFB boilers”, Powder Technology 120(1-2), 41-48 (2001). 7. Jestin, L., Meyer, P., Schmitt, C., et al, “Heat transfer in a 125 MWe CFB boiler”, Proceedings of the Engineering Foundation Conference, Australia (1992). 8. Jeon, J. H., Kim, S. D., Kim, S. J. and Kang Y., “combustion and heat transfer characteristics in a square internally circulating fluidized bed combustor with draft tube”, Fuel 87 , 3710–3713 (2008). 9. Zhang, W., Johnsson, F., Leckner, B., “Fluid-dynamic boundary layers in CFB boilers”. Chemical Engineering Science 50(2), 201-210 (1995). 10. Noymer, P. D., Hyre, M. R., Clicksman, L. R., et al, “The effect of bed diameter on near-wall hydrodynamics in scale-model circulating fluidized beds”, International Journal of Heat and Mass Transfer 43(19), 641-3649 (2000). 11. Zhang, M., Xiao, P., Lu, H. A. and Jiang, M. H., “Experimental Investigation on Heat Transfer Coefficient of CFB Boiler in Gaoba Power Plant”, Thermal Power Generation 4, 35-37 (2002) (in Chinese). 12. Andersson, B A, Leckner, B, “Experimental Methods of Estimating Heat Transfer in Circulating Fluidized Bed”, Int. J. Heat Mass Transfer 35, 3353-3362 (1992). 13. Zhang, H., Lu, J. F., Yang, H. R., et al., “Heat Transfer Measurements inside the Furnace of a 135MWe CFB Boiler”, Proceedings of the 8th International Conference on Circulating Fluidized Beds Technology. Hangzhou, pp254-260 (2005). 14. Wang, Y., Lu, J. F., Yang, H. R., et al., “Measurement of Heat Transfer in a 465t/h Circulating Fluidized Bed Boiler”, Proceeding of the18th International Fluidized Bed Combustion Conference, May 2005, Toronto Canada, No.96 (2005). 15. Zhang, P., Lu, J. F., Yang, H. R., et al., “The heat transfer coefficient distribution in a 300MWe CFB boiler”, Proceeding of the 20th International Fluidized Bed Combustion Conference, Xi’an, China, pp167-171 (2009). 16. Wang, J., Zhao, X., Wang, Y., et al, “Thermal boundary layer in the CFB boiler riser”, China P articuology 4 (3-4), 131-135 (2006).
MANUFACTURE OF GRANULAR POLYSILICON FROM TRICHLOROSILANE IN AN INTERNALLY CIRCULATING FLUIDIZED BED REACTOR Chenjing Wang, Tiefeng Wang, Zhanwen Wang Beijing Key Laboratory of Green Reaction Engineering and Technology Department of Chemical Engineering, Tsinghua University, Beijing 100084, China T: 86-10-62797490; F: 86-10-62772051; E: [email protected] ABSTRACT A lab scale internally circulating fluidized bed (ICFB) with a centrally located draft tube was designed to make polysilicon from trichlorosilane. Experimental results and evaluations showed that particle circulation could carry enough heat for reaction and effectively decrease wall deposition. Well grown granular polysilicon was obtained in a stable fluidization state and particle circulation rate. INTRODUCTION Solar grade silicon is in ever-increasing demand due to the rapid development of the photovoltaic (PV) industry (1). One challenge the PV industry is currently facing is the development of cheap solar grade silicon feedstock (2). The traditional method for producing high purity polysilicon is the Siemens process that uses the decomposition of trichlorosilane (SiHCl3 or TCS) on a high purity silicon rod in a bell-jar reactor. However, the cost of polysilicon made by this method (30-45 $·kg-1) is too high for the PV industry (3,4). Therefore, several manufacturers have developed other new production technologies for solar grade polysilicon (5), which could be divided into two categories. The metallurgical routes produce solar grade silicon directly from metallurgical grade silicon by a purification process. The chemical routes are based on different reactor types such as a tubular reactor and fluidized bed reactor for the decomposition of a silicon-containing gas. The technology that makes polysilicon by decomposing a silicon-containing gas in a fluidized bed reactor is considered to be the most attractive alternative to the conventional bell-jar process (6). This technology produces polysilicon in the form of silicon granules instead of the traditional silicon rods (7). The purity level of the granular materials produced is slightly lower than that produced with the Siemens process, but it meets the requirement of the PV industry. The feasibility and operating costs have been estimated in a pilot scale reactor by Wacker-Chemie, who showed that the energy consumption of polysilicon from the fluidized bed process
1
can be decreased to below half that of the Siemens process. The low energy consumption advantage would be more pronounced in large scale reactors. The chemical vapor deposition (CVD) reaction of polysilicon from SiHCl3 is a highly endothermic reaction and is sensitive to operating temperature. In a traditional fluidized bed reactor, deposition on the inner reactor wall is significant. This phenomenon will limit the life time of the reactor and cause problems due to the different thermal expansion coefficients of silicon and quartz. One effective way to limit wall deposition is the design of a novel reactor with an appropriate heating method. This is the most important issue in future demonstration of making polysilicon in a fluidized bed reactor. In this work, a type of internally circulating fluidized bed (ICFB) with a novel gas distributor was developed for producing high purity polycrystalline silicon. The ICFB consisted of a downcomer with low gas velocity and a riser with high gas velocity. The separate aeration of gas for the riser and downcomer provides a more flexible operation. EXPERIMENTAL In order to determine optimized reaction conditions such as temperature, molar ratio of H2 to SiHCl3 and superficial gas velocity, a lab scale bubbling fluidized bed reactor (I.D.=0.025 m, H=0.6 m) was designed for producing granular polysilicon from SiHCl3. The schematic diagram of the experimental apparatus is given in Figure 1. The fluidized bed reactor was made of quartz and heated by a resistance furnace outside the reactor. Liquid SiHCl3 was vaporized and mixed with H2 in a specially designed heated mixer, and the gas mixture was fed into the reactor through a gas distributor at the bottom of the reactor. The fluidized bed had an expanded head of 0.08 m I.D. to reduce particle entrainment. The gas distributor was placed below the heated zone to avoid plugging that would occur if there was silicon deposition on it. At the beginning of the experiment, the fluidized bed reactor was loaded with silicon particles. The incipient fluidization velocity, Umf, was calculated with the following empirical correlation (8):
Umf =
de2 ( ρp - ρ )g 1650μ
The cold flow experiments were first carried out in an ICFB reactor (I.D.=0.12 m, H=0.80 m) made of Plexiglas to determine the appropriate superficial gas velocity in the real CVD reaction. A Schematic of the ICFB is shown in Figure 2. The draft tube (I.D.=0.07 m, H=0.25 m) with 24 orifices of 0.01 m in diameter was fixed in the bottom of the reactor. For gas supply to the reactor, two separate tubular gas
2
distributors were used: one was in the annular downcomer with 12 orifices, and the other was in the central riser with 24 orifices. All the orifices were 2 mm in diameter. An expanded section (I.D.=0.25 m, H=0.4 m) was installed in the top of the reactor to reduce particle entrainment. The bed was loaded with silicon beads (dp=300 μm, ρp=2.71 kg·m-3, Umf=0.051 m·s-1) at the beginning of the experiment. The real CVD reactor for making polysilicon from SiHCl3 on granular silicon is the same in structure as the cold flow reactor. It was made of quartz and heated by a resistance furnace outside the reactor.
Figure 1. Schematic of experimental apparatus
Figure 2. Schematic of cold flow ICFB
The product gas contained SiHCl3, SiCl4, SiH2Cl2 and HCl. The molar fractions of these species were determined by GC analysis using an empirical molar calibration ratio. The main reactions were
SiHCl3 +H2 → Si+3HCl
(1)
4SiHCl3 → Si+3SiCl4 +2H2
(2)
SiHCl3 +H2 → SiH2Cl2 +HCl
(3)
The conversion of SiHCl3 (X), the yield of silicon (Y) and the selectivity to silicon (S, which was defined as the molar ratio of the produced silicon to the converted SiHCl3) calculated from the molar fractions of silicon-containing products and HCl. The microstructure of the polysilicon deposited on the surface of seed particles was investigated by scanning electron microscopy (SEM, JEOL-7410). The particle size distribution of the silicon particles was obtained using Image-Pro Plus software by analysis of high-resolution digital photos of the silicon particles.
3
RESULT AND DISCUSSION The CVD in Bubbling Fluidized Bed Reactor The preparation of polysilicon from SiHCl3 was studied in a bubbling fluidized bed reactor under a wide range of reaction conditions in previous research with small size silicon particles (dp=300 μm). The effects of temperature (T), molar ratio of H2 to SiHCl3, superficial gas velocity (Ug) and particle seed loading were investigated. The temperature and molar ratio of H2 to SiHCl3 have notable effects on the reaction. The conversion was more sensitive to temperature below 900 oC. The optimized operating conditions were determined to be 950~1050oC for temperature, 4:1~6:1 for the molar ratio of H2 to SiHCl3, and 2.5~4.5 for fluidization number (Ug/Umf). The deposition reaction occurred by heterogeneous deposition to produce polysilicon, and this avoided the generation of fines which would occur in silane pyrolysis by homogeneous deposition (9). In this work, larger diameter granular silicon (dp=742 μm) was used to make polysilicon, the molar ratio of H2 to SiHCl3 was 5 and the fluidization number was 4.5. Well grown polysilicon product was obtained in the optimized operating conditions. The influence of different temperatures (Figure 3) is the same as was found with the deposition reaction with smaller size silicon particles (dp=300 μm). The average particle was about 912 μm after CVD reaction. The total reaction time was about 8 h. The growth rate was evaluated to be 10 μm/h, which was consistent with previous experience. 70
X, Y or S, %
50
40
X Y S
Size Distribution, %
60
40 30 20 10 0 750
Before CVD
30
After
CVD
20 10 0
800
850
900
950
1000
400
1050
o
600
800
1000
1200
dp, μm
T, C
Figure 3. Conversion of SiHCl3 (X), yield (Y), and selectivity (S) of Si (molar ratio of H2 to SiHCl3 is 5)
Figure 4. Particle size distribution of silicon before and after CVD reaction (dp, seed =742 μm, dp, product =912 μm)
Figure 5 shows the digital photos of the granular silicon before and after the CVD reaction. It shows that compared with the seed silicon particles, the surface of silicon
4
granules become bright after the CVD reaction. The cross-sectional microstructure of the silicon particle after reaction is shown in Figure 6. It shows the clear interface between the deposited polysilicon and seed silicon particle after the CVD reaction. The growth thickness was about 80μm, which was consistent with the average diameter change analyzed by particle diameter size statistics.
Figure 5. Digital photos of granular silicon before and after CVD
Figure 6. Cross-sectional microstructure of granular silicon after CVD
Cold Flow Experiments in ICFB A big problem in the manufacture of granular polysilicon from SiHCl3 in a fluidized bed reactor is the deposition of silicon on the inner reactor wall. The ICFB reactor has a centrally located draft tube to form separate heating and reaction zones in the fluidized bed reactor. In this work, the interior of the draft tube was used as the reaction zone and the exterior was the heating zone. The difference in superficial gas velocity was the driving force for particle internal circulation between the heating zone and reaction zone. In the ICFB, another important issue was to guarantee the quantity of heat carried by the high temperature granular silicon for the CVD reaction. One aim of the cold flow experiments was to estimate whether the particle solid circulation rate was enough to supply heat for the reaction zone, the other was to investigate whether the gas bypassing between the two zones was small enough to reduce the wall deposition of silicon in the heating zone. The same optimized operating conditions (T=1000 oC, H2:SiHCl3=5:1, fluidization number=4.5) in the reaction zone were used to estimate the minimum solid circulation rate in the ICFB. The conversion of SiHCl3 (X) at 1000 oC was about 63% and the selectivity to Si (S) was about 45%. According to the optimized operating conditions, the inlet amount of SiHCl3 was 0.008 mol·s-1. In consideration of the reaction selectivity, the needed reaction heat was about 40.16 kJ·mol-1 SiHCl3. The sensible heat of feed H2 and SiHCl3 from 25 oC to 1000 oC was about 192.35 kJ·mol-1
5
SiHCl3. So the total heat needed was 1.74 kJ·s-1. The heat capacity of silicon granules at 1000 oC was 27.52 kJ·mol-1·K-1. Assuming that the temperature difference between the heating zone and reaction zone was 25 oC, the needed minimum solid circulation rate was 0.071 kg·s-1. Taking into account the cross-sectional area of the heating zone, the needed minimum solid circulation rate per unit area, Gs,min, was about 10 kg·m-2·s-1. The cold flow experiments show that when UD/Umf>1.8 and UR/Umf>4.5, the solid circulation rate per unit area Gs (Figure 7) could satisfy the heat demand. Heat supply could also be increased with a larger heating zone or a larger temperature difference between the two zones. For example, if the temperature difference between the heating zone and reaction zone was 50 oC, Gs,min was about 5 kg·m-2·s-1.
-2
Gs, kg·m ·s
-1
24
80 UD/Umf
1.1 1.8 2.5
60
γDR and γRD, %
30
18 12
o
25 C
Gs,min
6
UD/Umf=2.2
40
γDR γRD
20
o
50 C
Gs,min 4.5
6.0
7.5
UR/Umf
9.0
0
10.5
4
5
6
7
8
9
10
UR /Umf
Figure 7. Effect of superficial gas velocity in downcomer (UD) and riser (UR) on solid circulation flux (GS)
Figure 8. Effect of superficial gas velocity in riser (UR) on gas bypassing fraction γRD and γDR
The purpose of designing the ICFB reactor was to restrict wall deposition of polysilicon during CVD reaction and prevent detrimental destruction of the reactor. In the ICFB reactor, the circulating solid particles were transported upwards in the reaction zone and moved downwards in the heating zone, providing heat transfer between the two zones. At the same time, gas bypassed between the heating zone and reaction zone occurred and should be investigated. In the cold flow experiments, the amount of reaction gas SiHCl3 and the H2 bypassing from the reaction zone to heating zone (γRD) was slight, while the amount of H2 bypassing from the heating zone to reaction zone (γDR) was more notable. For example, when the fluidization number of the two zones (UD/Umf and UR/Umf) was 2.2 and 7.0, the gas bypassing fraction γDR was 60% and γRD was 4% respectively. According to the molar ratio of H2 to SiHCl3 (5) and a ratio of the reaction zone area and heating zone area (1:2), the molar ratio of H2 to SiHCl3 after gas bypassing was 7 and 40. So the molar fraction of SiHCl3 in heating zone was less than one-fifth of the molar fraction in the reaction zone. According to the kinetics of CVD in the SiHCl3-H2 system (10,11), the reaction
6
is one-order reaction to the concentration of SiHCl3, so the deposition rate in the heating zone is less than one-fifth of the reaction zone. The CVD Reaction in ICFB On the basis of the optimized operating conditions in the conventional fluidized bed reactor and cold flow experimental results, tests to manufacture granular polysilicon from SiHCl3 were carried out in an ICFB reactor of 0.12 m I.D.. Well grown polysilicon product was obtained with a stable operation and depressed wall deposition. CONCLUSIONS 1. Well grown granular polysilicon product was obtained with optimized operating conditions in a bubbling fluidized bed reactor of 0.025 m I.D.. Compared with the seed silicon particles, the silicon granules surface became bright and showed a clear interface between the fresh polysilicon and the seed silicon particle after the CVD reaction. The growth rate on the silicon particle surface was about 10 μm/h. 2. The effect of superficial gas velocity was investigated in cold flow experiments in an ICFB reactor. When UD/Umf>1.8 and UR/Umf>4.5, the solid circulation flux Gs (10 kg·m-2·s-1) could satisfy the heat supply needed. The heat transferred could be increased with a larger heating zone or a larger temperature difference between the heating zone and reaction zone. Analysis of the gas bypassing showed that the ICFB reactor could effectively decreased the wall deposition. 3. Well grown polysilicon product was also obtained in an ICFB reactor of 0.12 m I.D. with stable operation and depressed wall deposition at the optimized operating conditions. ACKNOWLEDGMENT The authors gratefully acknowledge the financial support by the Beijing New Star Project on Science & Technology of China under Grant No. 2009B35. NOTATION de dp g Gs H S T
equivalent diameter, m diameter of particles, μm gravitational acceleration, m·s-2 solid circulation rate, kg·m-2·s-1 height of reactor, m selectivity to silicon, % temperature, oC
7
UD Ug Umf UR X Y γDR γRD μ ρ ρp
superficial gas velocity in downcomer, m·s-1 superficial gas velocity, m·s-1 incipient fluidization velocity, m·s-1 superficial gas velocity in riser, m·s-1 conversion of SiHCl3, % yield of silicon, % gas bypassing fraction from the downcomer to the riser gas bypassing fraction from the riser to the downcomer viscosity, Pa·s density of gas, kg·m-3 density of particles, kg·m-3
REFERENCES Hesse, K.; Schindbeck, E.; Freiheit, H. C. Challenges of solar silicon production. The 9th Silicon for the Chemical and Solar Industry, Norway, 2008. 2. Sarti, D.; Einhaus, R. Silicon feedstock for the multi-crystalline photovoltaic industry. Sol. Energ. Mat. Sol. C 2002, 72, 27. 3. Woditsch, P.; Koch, W. Solar grade silicon feedstock supply for PV industry. Sol. Energ. Mat. Sol. C 2002, 72, 11. 4. Odden, J. O.; Halvorsen, G.; Rong, H. M.; Gløckner, R. Comparison of energy consumption in different production processes for solar grade silicon. The 9th Silicon for the Chemical and Solar Industry, Norway, 2008. 5. Braga, A. F. B.; Moreira, S. P.; Zampoeri, P. R.; Bacchin, J. M. G.; Mei, P. R. New processes for the production of solar-grade polycrystalline silicon: a review. Sol. Energ. Mat. Sol. C 2008, 92, 418. 6. Weidhaus, D.; Schindlbeck, E. Trichlorosilane based silicon feedstock for the photovoltaic industry. The 7th Silicon for the Chemical and Solar Industry, Norway, 2004. 7. Müller, A.; Ghosh, M.; Sonnenschein, R.; Woditsch, P. Silicon for photovoltaic applications. Mat. Sci. Eng. B 2006, 134, 257. 8. Jiang, W. J.; Dai, Y. Y.; Gu, H. J. Principles of Chemical Engineering. Tsinghua University Press. 2003, 203. 9. Wang, C. J.; Wang, T. F.; Wang, Z. W. Manufacture of granular polysilicon from trichlorosilane in a fluidized bed reactor. Ind. Eng. Chem. Res. 2010, submitted. 10. Hitoshi, H.; Takatoshi, N.; Masanori, M.; Masatake, K.; Manabu, S.; Kikuo, O. Model on transport phenomena and epitaxial growth of silicon thin film in SiHCI3-H2 system under atmospheric pressure. J. Cryst. Growth 1996, 169, 61. 11. Hitoshi, H.; Yasuaki, A.; Shoji, A.; Toru, O.; Qu, W. F.; Manabu, S.; Kikuo, O. Chemical process of silicon epitaxial growth in a SiHCl3-H2 system. J. Cryst. Growth 1999, 207, 77. 1.
8
SIMULATION OF PARTICLE-GAS FLOW IN CYCLONE USING URANS Aku Karvinen and Hannu Ahlstedt Tampere University of Technology PO Box 589, FI-33101 Tampere, Finland Marko Palonen Metso Power Oy PO Box 109, FI-33101 Tampere, Finland
ABSTRACT Particle-gas flow in a cyclone separator used in a circulating fluidized-bed boiler is simulated using computational fluid dynamics software Fluent 6.2.36 and an Unsteady Reynolds-Averaged Navier-Stokes (URANS) method. A Lagrangian method is used for particle simulation and a one-way coupling between particles and gas is assumed. The effect of the turbulence model is studied using several turbulence models. Only the Reynolds stress model gives a physically reasonable flow field without adjusting parameters unknown beforehand. INTRODUCTION Cyclone separators (Fig. 1) occur in many industries, e.g. in oil and gas industry, power generation, incineration plants, cement plants, coking plants and the food industry. Compared to the other methods for particle removal from gases the main advantages of cyclone separators are: low capital investment and maintenance costs, applicability under extreme processing conditions, no moving parts, and robustness. According to (1), the first studies of the cyclone flow were undertaken in 1930-1950 (2) and (3). The first CFD (computational fluid dynamics) simulations were undertaken in the 80’s (4). In the 21st century, CFD simulations have been made for example by Derksen (5) and by Wang (6). A more extensive review can be found in (7) and (1).
z outlet
inlet y
x
Figure 1: Cyclone separator. Domain and coordinate system used in this study.
CASE STUDIED The schematic of the case studied and the coordinate system used is given in Fig. 1. The flow gas is hot air. The cyclone body Reynolds number based on an average inlet velocity and the cyclone diameter (7) is Re = 634,000. METHODS AND MODELS The Reynolds-averaged Navier-Stokes equations (RANS) for incompressible flow are ∂ui = 0, ∂xi ∂ui ∂u 1 ∂p ∂ 2 ui ∂ ′ ′ −ui uj , +ν + + uj i = − ∂t ∂xj ρ ∂xi ∂xj ∂xj ∂xj
(1) (2)
where the overbar denotes time averaging, and the prime denotes the fluctuating component. In the k-ε models and the k-ω models, the Boussinesq hypothesis is used in which the Reynolds stresses, −ui′ uj′ , are calculated from 2 1 ∂ui ∂uj ′ ′ + −ui uj = 2νt Sij − kδij , Sij = . (3) 3 2 ∂xj ∂xi In the standard k-ε model (8), the turbulence kinetic energy and its dissipation rate are obtained from the modeled transport equations, which are as follows: ∂k ∂ νt ∂k ∂k = + uj ν+ + νt S 2 − ε, (4) ∂t ∂xj ∂xj σk ∂xj ∂ε ∂ ε2 νt ∂ε ε ∂ε + uj = (5) ν+ + C1ε νt S 2 − C2ε , ∂t ∂xj ∂xj σε ∂xj k k p where S ≡ 2Sij Sij . The turbulent viscosity is computed from νt = Cµ k 2 /ε. Model constants used have the following values: Cµ = 0.09, C1ε = 1.44, C2ε = 1.92, σk = 1.0, and σε = 1.3. The transport equations for k and ε in the RNG k-ε model (9) are ∂k ∂k ∂ ∂k = + uj αk νt + νt S 2 − ε, ∂t ∂xj ∂xj ∂xj ∂ε ∂ ε2 Cµ η 3 (1 − η/η0 ) ε2 ∂ε ε ∂ε = + uj , αε νt + C1ε νt S 2 − C2ε − ∂t ∂xj ∂xj ∂xj k k k 1 + βη 3
(6) (7)
where η ≡ Sk/ε, η0 = 4.38, β = 0.012, and νt is obtained in a similar way as in the standard k-ε model. The model constants are: Cµ = 0.0845, C1ε = 1.42, and C2ε = 1.68. The RNG k-ε model in Fluent provides an option to account for the effects of the swirl in the mean flow by modifying the turbulent viscosity appropriately: k νt,modified = νt f αs , Ω, , (8) ε
where Ω is a swirl number evaluated within Fluent and αs is a swirl constant that assumes different values depending on whether the flow is swirl-dominated or mildly swirling. The exact function for describing this dependency is not revealed in (10). The transport equations for k and ε in the realizable k-ε model (11), (10) are ∂k ∂ νt ∂k ∂k = + uj ν+ + νt S 2 − ε, ∂t ∂xj ∂xj σk ∂xj ∂ε ∂ε ∂ νt ∂ε ε2 √ , + uj = ν+ + C1ε Sε − C2ε ∂t ∂xj ∂xj σε ∂xj k + νε
(9) (10)
where C1ε is now a variable. The turbulent viscosity is obtained in a similar way as in the standard k-ε model, but Cµ is no longer constant. The model constants are: C2ε = 1.9, σk = 1.0, and σε = 1.2. In the standard k-ω model (12), the turbulence kinetic energy, k, and the specific dissipation rate, ω, are obtained from the following transport equations: ∂k ∂k ∂ νt ∂k ∗ + uj = (11) ν+ + νt S 2 − β∞ fβ ∗ kω, ∂t ∂xj ∂xj σk ∂xj ∂ω ∂ νt ∂ω ω ∂ε + uj = (12) ν+ + νt S 2 − βi fβ ω 2 , ∂t ∂xj ∂xj σω ∂xj k and the turbulent viscosity is obtained from νt = k/ω. The model constants are: βi = ∗ = 0.09, σ = 2.0, and σ = 2.0. 0.072, β∞ ω k The SST k-ω model (13) has a form similar to that of the standard k-ω model. The transport equations are given as follows ∂k νt ∂k ∂k ∂ ∗ ∗ ν+ + min(νt S 2 , 10ρβ∞ kω) − β∞ kω, (13) = + uj ∂t ∂xj ∂xj σk ∂xj ∂ω ∂ νt ∂ω ∂ε + uj = ν+ + S 2 − βi ω 2 + Dω , (14) ∂t ∂xj ∂xj σω ∂xj where βi = F1 βi,1 + (1 − F1 )βi,2 and σk and σω are calculated in a similar way. The turbulent viscosity νt = (k/ω)/ max[1, SF2 /(a1 ω)]. The model constants are: a1 = 0.31, ∗ = 0.09, β β∞ i,1 = 0.075, βi,2 = 0.0828, σk ,1 = 1.176, σω,1 = 2.0, σk ,2 = 1.0, and σω,2 = 1.168. In the Reynolds stress model (RSM), there are the exact transport equations for the transport of Reynolds stresses, −ui′ uj′ (14): ∂ui′ uj′ ∂t
∂ ∂ ∂ ∂ p′ ′ ′ ′ ′ ′ ′ ′ ′ ′ uuu + ui uj δkj ui + δik uj + uk ui uj = − + ν ∂xk ∂xk i j k ρ ∂xk ∂xk {z } | Dijt
∂uj ∂ui − ui′ uk′ + uj′ uk′ ∂xk ∂xk
p′ + ρ |
! ∂ui′ ∂uj′ ∂ui′ ∂uj′ + − 2ν , (15) ∂xj ∂xi ∂xk ∂xk | {z } {z } ε φij
ij
where Dijt , φij , and εij are modeled, see (10) for more details. The model constants are: C1 = 1.8, C2 = 0.60, C1ε = 1.44, C2ε = 1.92, Cµ = 0.9, σk = 1.0, and σε = 1.3. Calculation Procedure and Computational Grid All the calculations are performed using commercial software Fluent 6.3.26 (10). All terms in all equations are discretized in space using second-order central differencing, apart from the convection term, which is discretized using a second-order upwind scheme. Pressure-velocity coupling is achieved using the PISO algorithm. Time integration is done using a first order implicit method and adaptive time stepping where the first time step is very small (∆t0 = 10−6 s) and next time steps are chosen as follows: If the solution has not converged after seven iterations, then ∆tnew = 0.9∆told , otherwise, ∆tnew = 1.1∆told . The simulation is initiated using a steady state solver. After several thousand iterations, the simulation is continued by running an unsteady solver and run until the flow becomes statistically steady (30 s). After that, simulation is continued until statistically stable data is gathered (30 s → 60 s). The computational grid consists exclusively of hexahedral cells (Fig. 2). Four different grid resolutions have been used such that the total number of control volumes used is 72,978, 260,808, 785,819 or 1,802,564. All grids are constructed so that the grid is finer for the wall-adjacent cells of all no-slip walls of the cyclone.
(a) In inlet and cyclone walls.
(b) In middle of cyclone barrel in positive quadrant.
Figure 2: Grid 785,819.
GRID INDEPENDENCY TEST The grid independency test shows that velocity profiles of the RSM in the middle of the cyclone barrel do not change significantly when the grid is made finer than 785,819
cells (Fig. 3). A grid of 785,819 cells is therefore used exclusively in the turbulence model comparison. A dimensionless wall unit y + in a cyclone barrel is in an acceptable range (within the log-law layer) when a 785,819 grid is used (Fig. 3(d)). 8
2.0 Grid 72,978 Grid 260,808 Grid 785,819 Grid 1,802,564
6
v /velinlet
u/velinlet
1.5
Grid 72,978 Grid 260,808 Grid 785,819 Grid 1,802,564
1.0
4
2
0.5 0
0
0
0.2
0.4
y /R
0.6
0.8
(a) Radial mean velocity in middle of cyclone barrel.
0
0.2
0.4
y /R
0.6
0.8
1.0
(b) Tangential mean velocity in middle of cyclone barrel.
Grid 72,978 Grid 260,808 Grid 785,819 Grid 1,802,564
3
75
2.5
2
2.0 1
75 100 100
75
75
inlet
125
1.5 z/R
w /velinlet
−2
1.0
100
1.0
150
0 0.5 −1
0
0.2
0.4
y /R
0.6
0.8
(c) Axial mean velocity in middle of cyclone barrel.
0.0
1.0
125 −5
−4
5 −3 12
−2 t/R
−1
0
1
(d) Dimensionless wall unit y + in cyclone barrel looked inside out. Grid 785,819.
Figure 3: Grid independency test.
TURBULENCE MODEL COMPARISON Several papers, e.g. (7) and (1), show that there is rigid body rotation in the core of the cyclone and almost friction free flow in the outer part of the cyclone. Tangential velocity profiles complying with these assumptions are shown in Fig. 4(b) by the dashed lines (constants are adjusted according to the results of the RSM). Because of the highly anisotropic turbulence caused by the high curvature of the streamlines and the high swirl intensity, two equation models cannot predict the flow field correctly – they predict almost rigid body rotation in the whole flow field. Only the RSM gives a physically reasonable flow field. The RNG k-ε model with swirl modification implemented in Fluent gives usable results, but the modification contains an adjustable parameter which was unknown before the simulations. The default value of the parameter is αs = 0.07, which turns out to be too small, as can be seen in Fig. 4(b). This makes the modification difficult to use.
10
2.5 Std. k -ε RNG k -ε (αs = 0) RNG k -ε (αs = 0.07) RNG k -ε (αs = 0.35) Real. k -ε Std. k -ω SST k -ω RSM
u/velinlet
1.5 1.0
8 6 v /velinlet
2.0
4
Std. k -ε RNG k -ε (αs = 0) RNG k -ε (αs = 0.07) RNG k -ε (αs = 0.35) Real. k -ε Std. k -ω SST k -ω RSM v = C1 y + D1 v = C2 /y + D2
2 0
0.5
−2 0 −4 −0.5 0
0.2
0.4
0.6
y /R
0.8
1.0
−6
0
0.2
(a) Radial mean velocity.
y /R
0.6
1.0
0.8
(b) Tangential mean velocity.
7
Std. k -ε RNG k -ε (αs = 0) RNG k -ε (αs = 0.07) RNG k -ε (αs = 0.35) Real. k -ε Std. k -ω SST k -ω RSM
6 5 4 w /velinlet
0.4
3 2 1 0
−1 −2
0
0.2
0.4
y /R
0.6
0.8
1.0
(c) Axial mean velocity.
Figure 4: Turbulence model comparison. PARTICLE TRAJECTORY SIMULATION Because the only turbulence model that gives a reasonable flow field without any adjustable parameter is the RSM, particle trajectory simulation is only done using that model. Particles of different size are released from the bed and the particle trajectories are calculated assuming one-way coupling (particles do not affect the flow field). The mean flow field is used and no turbulence effects on particles are taken into account. The collision chart in the cyclone barrel is shown in the Fig. 5(a). The collision area of the largest particle corresponds well with the largest measured erosion (Fig. 5(b)).
2.5
2.5
2.0
2.0
inlet
inlet 1.5
d d d d d
1.0 0.5 0.0
−5
z/R
z/R
1.5 < 50 µm = 50 ... 100 µm = 100 ... 150 µm = 150 ... 200 µm > 200 µm −4
−3
1.0 0.5
−2 t/R
−1
0
(a) Simulated collision chart. RSM.
1
0.0
−5
−4
−3
−2 t/R
−1
0
1
(b) Measured erosion in target zone. Darkest gray denotes largest erosion.
Figure 5: Simulated collision chart and measured erosion.
CONCLUSIONS An extensive grid independency test is achieved using the Reynolds stress model (RSM). The number of the cells in grids varies between 72,978 and 1,802,564. The grid independency test shows that a grid consisting of about 800,000 cells gives acceptable results. An effect of the turbulence model is studied using six different turbulence models. Because of the highly anisotropic turbulence caused by the high curvature of the streamlines and the high swirl intensity, two equation models cannot predict the flow field correctly. Only the RSM gives a physically reasonable flow field. Also the RNG k-ε model with a swirl modification implemented in Fluent gives usable results, but the modification contains an adjustable parameter which was unknown before the simulations. When the RSM is used, the collision area of the largest particle corresponds well with the largest measured erosion. ACKNOWLEDGMENT The authors gratefully acknowledge the support from Metso Power Oy and from The Finnish Graduate School in Computational Fluid Dynamics. NOTATION Cµ , C1 , C2 , C1ε , C2ε Dijt Dω F1 , F2 Pij R Re S d fβ ∗ , fβ k p t ∆t0 , ∆told , ∆tnew u, v , w velinlet y+ Ω αk , αε αs ∗ ,β ,β βi , β∞ i,1 i,2 δ ε εij
model constant in turbulence models turbulent diffusive transport tensor in RSM cross-diffusion term in RNG k-ε model blending functions in SST k-ω model stress production tensor in RSM radius of cyclone barrel cyclone body Reynolds number mean rate-of-strain tensor particle diameter auxiliary functions in standard k-ω model turbulent kinetic energy mean pressure time, tangential coordinate first, old and new time steps mean velocity components average mean velocity in beginning of inlet dimensionless wall unit mean rate-of-rotation, characteristic swirl number inverse effective Prandtl numbers swirl constant in RNG k-ε model model constants in turbulence models Kronecker delta dissipation rate of turbulent kinetic energy dissipation tensor in RSM
ν, νt νt,modified φij ρ σk , σε , σω , σk ,1 , σk ,2 , σω,1 , σω,2 σk , σω
molecular and turbulent kinematic viscosity modified turbulent kinematic viscosity pressure-strain tensor in RSM density model constants in turbulence models auxiliary functions in SST k-ω model
REFERENCES 1. C. Cortés and A. Gil. Modeling the gas and particle flow inside cyclone separators. Progress in Energy and Combustion Science, 33(5):409–452, 2007. 2. R. M. C. K. Alexander. Fundamentals of cyclone design and operation. Proceedings of the Australasian Institute of Mining and Metallurgy, 152:208–228, 1949. 3. A. J. Linden. Investigation into cyclone dust collectors. Proceedings of the Institution of Mechanical Engineers, 160:233–251, 1949. 4. F. Boysan, W. H. Ayers, and J. Swithenbank. A fundamental mathematical modelling approach to cyclone design. Transactions of the American Institute of Chemical Engineers, 60:222–230, 1982. 5. J. J. Derksen, S. Sundaresan, and H. E. A. van den Akker. Simulation of massloading effects in gas-solid cyclone separators. Powder Technology, 163:59–68, 2006. 6. B. Wang, D. L. Xu, K. W. Chu, and A. B. Yu. Numerical study of gas-solid flow in a cyclone separator. Applied Mathematical Modelling, 30:1326–1342, 2006. 7. A. C. Hoffmann and L. E. Stein. Gas Cyclones and Swirl Tubes: Principles, Design and Operation. Springer, Berlin, 2002. 8. B. E. Launder and D. B. Spalding. Lectures in Mathematical Models of Turbulence. Academic Press, London, 1972. 9. V. Yakhot and S. A. Orszag. Renormalization group analysis of turbulence. Physical Review Letters, 57:1722–1724, 1986. 10. Fluent Inc. FLUENT 6.3 User’s Guide. Lebanon, 2006. 11. T.-H. Shih, W. W. Liou, A Shabbir, Z. Yang, and J. Zhu. A new k-ε eddy viscosity model for high Reynolds number turbulent flows. Computers and Fluids, 24:227– 238, 1995. 12. D. C. Wilcox. Turbulence Modeling for CFD. DCW Industries, Inc., La Canada, California, second edition, 1998. 13. F. R. Menter. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA Journal, 32:1598–1605, 1994. 14. M. M. Gibson and B. E. Launder. Ground effects on pressure fluctuations in the atmospheric boundary layer. Journal of Fluid Mechanics, 86:491–511, 1978.
A STUDY OF STANDPIPE AND LOOP SEAL BEHAVIOR IN A CIRCULATING FLUIDIZED BED FOR GELDART B PARTICLES Ajay R. Bidwe, Alexander Charitos, Heiko Dieter, An Wei, Mariusz Zieba, Günter Scheffknecht Institute of Combustion and Power Plant Technology (IFK), University of Stuttgart, Pfaffenwaldring 23, 70569, Stuttgart, Germany. Email: [email protected] ABSTRACT The loop seal aeration effect on the supply side has been studied through small scale CFB experimentation. Parameters affected include inventory allocation and entrainment. The gas velocity in the standpipe is influenced by loop seal aeration and riser velocity. Variation in slugging behavior above and below solid downflow velocity of 0.025 m/s is analyzed and discussed here. INTRODUCTION Circulating fluidized bed (CFB) technology is used in various applications including combustion of solid fuels for electricity production (1). Recently, it has been applied in the field of solid looping cycles aiming at CO2 capture, such as calcium looping (2), and chemical looping combustion (3). For combustors standalone CFB’s are used, while for solid looping cycles dual fluidized bed (DFB) systems are utilized. In both cases the standpipe-loop seal arrangement is an important component. A typical standalone CFB consists of a riser, cyclone and standpipe-loop seal arrangement, shown in Fig. 1. The loop seal acts as a solid pumping device from the low pressure cyclone to the high pressure riser and ensures that riser gas does not take the short cut Figure 1- Stand alone CFB through the cyclone. In comparison, DFB systems consist commonly of two CFBs or a CFB and a BFB, depending on the facility purpose (2). In this case the role of a loop seal is to transfer solids from the low pressure cyclone of one reactor to a high or low pressure point of the other reactor, while disallowing gases from one reactor to enter the other. In both cases, standpipe-loop seal operation is indispensible. Solid downflow, within the standpipe and loop seal occurs in three modes as explained by Knowlton (4). Moving bed mode, bubbling fluidized mode are possible below the level of the particle bed, Lst. Above this level solid flow occurs in dilute mode. A bubbling or moving bed
c in CFB appliccations standpipe is common d is conside ered to be sttable. Howe ever, in and sma all scale fa acilities, slu ugging mayy occur as a result of small scale sta andpipe diameter and is threatening to operrational stability. The lo oop seal itself consistss of two secctions, the supply section s an nd the recycle section, as show wn in Fig. 2. The sup pply section n is fed with a particle flow from m the riser cyclone. This flow pro oceeds thro ough the slit s and the e recycle section s bacck to the riiser or, in the case of o DFB sysstems, to the e other reacctor. andpipe and d loop seal Forr above me entioned DF FB processses the Figure 2- Sta loop seal is an n important part but gasses used in loop seal aeration a cou uld be a sou urce of dilution d for product p gasses. Thereffore optimiz zing the loop p seal desig gn is imporrtant for these proccesses. Con nsiderationss for standp pipe, loop seal design and operattion, to which w this paper p aimss to contribu ute to, are summarize ed below: (i) the loop seal s aerration effectt on solid in nventory, risser entrainm ment (ii) the e split of this aeration flow f between the loop seal supply and d recycle chamber c (iii) hydrodyynamic stab bility thro ough avoid dance of slugging ope eration in the t standpipe. In norrmal loop seal s ope eration both h sections of o the loop p seal are fluidized. f However to investigate the aerration split systematica s ally it was decided d to aerate a onlyy in the supply section. By doing this the effect of loop seal aeration only in the su upply sectio on can also o be inve estigated. TES ST FACILIT TY AND EX XPERIMENT TAL PROCE EDURE The e test facilityy consists of o a CFB syystem, as Table 1 :D Dimensions of CFB sho own in Fig g. 1 and iss constructted from Riser tran nsparent plexi-glass. The dimen nsions of Riser diam meter [[mm] 70 the CFB are shown s in Ta able 1. The aeration Riser heig ght [[m] 4 nozzzles, locate ed only in th he supply section s of Standpipe e H / Dia [[m] 1/0 0.03 the loop seal are insertted sidewayys at an Loop sea al ang gle of 45°, to o prevent particles p clog gging the Height [[mm] 200 0 gass nozzles. The T fluidiza ation gas iss air and Supply Le ength [[mm] 35 parrticles are Illmenite min neral particle es with a Recycle Length L [[mm] 50 parrticle size diistribution of o 100-200 µm µ and a Width [[mm] 35 mean particle size of 14 43 µm. The e particle Weir height [[mm] 150 0 den nsity ( ) iss 4400 kg/m m³ and the e Geldart Slit opening [[mm] 30 classsification of o particles is class B (5). The is calculatted, as shown in (1), to o be equal 0.029 m/s. Pressure transducers are mounted at va arious locations of the CFB system. Rotame eters are ussed to meas sure the air supply to the riserr and loop seal. s The fa acility is fille ed with a kn nown total solid s inve entory (TSI). The parameters varied are the loop l seal ae eration ( ) in wide ra ange of 0.003 to 0.2 28 m/s and d riser velo ocity 3 to 5 m/s. The entrainmen nt is measu ured thro ough stoppiing loop seal aeration and measu uring the acccumulation n of the particle bed d height with h the standpipe for a given period of time.
Loop seal aeration flow path determination The data evaluation procedure is based on Basu & Cheng (7) and has been applied for every steady state. Goal of the analysis, presented in steps, is to define the gas flow path through the loop seal. (i)
Calculation of the standpipe slip velocity
The slip velocity (
Δ
∆
), expresses the relative velocity between gas and solid within 150
1
|
|
1.75
1
|
|
(1)
the standpipe and is calculated through Eq.1. The pressure drop through the and the particle bed height ( ) have been measured for every standpipe ( Δ steady state, thus defining also the pressure gradient .The ∆ is measured from the bottom of the loop seal to the bottom of the cyclone. Althought the areas of Δ is assumed to be standpipe and loop seal vary, but for simplification the constant and further calculations of gas and solid velocity are based on the area of standpipe. The voidage (ε) value, for packed bed column found to be equal to 0.48 and at minimum fluidizing conditions (εmf) is found to be 0.54. However the exact voidage while standpipe operation is difficult to estimate. Therefore an average voidage of 0.51 is considered for calculation. Eq.1 has been applied only when the fluidization regime in the standpipe was moving bed. The situations in which this standpipe showed bubbling or slugging mode of fluidization are not considered in the calculations. The sphericity of the particles ( ) has been taken as 0.75 from voidage-sphericity graphs in (6). (ii)
Calculation of the real solid downflow and gas velocity
The real solid downflow velocity ( ) and gas velocity ( ) is calculated through Eq. (2) and Eq. (3). The and are positive in the downwards direction. The is defined through measurement of the riser entrainment, based on the standpipe cross-section ( ). The real standpipe gas velocity ( ) is subsequently calculated through Eq. 3, which is the definition of the slip velocity. The superficial gas velocity ( ) in the standpipe is calculated from the voidage. 1 (iii)
(2)
(3)
Calculation of loop seal aeration split
The flow travelling through the particle column in the standpipe ( ) is given by Eq.4. If ( ) is positive than the loop seal aeration flow is split between the loop seal supply chamber-standpipe and the recycle chamber-riser. This split is quantified with the ratio of Eq.5 and is defined as the fraction of total volumetric flow of the loop seal aeration ( ) entering supply side ( ) or the recycle side ( ) of the loop seal. The aeration split in the supply section is calculated using Eq.5 and the recycle side is negative, the gas flow from the cyclone is carried given by Eq.6. If the
wnwards wiith the particles. This carried c gas from the particles entters the recycle dow cha amber ( ) in i addition to t the loop seal s aeratio on ( ). A =
(5 5)
(4)
A
(6 6)
1 Pre essure bala ance The e pressure balance b for the system m of Fig.1 is given by Eq q.7 (7) (9). ∆ ∆ ∆ ∆
(7 7)
The e pressure drop d terms of Eq.7 are e depicted in n Fig.1. RESULTS AND DISCUSS SION The e effect of th he loop sea al aeration in n the supply y section is examined, with respec ct to the parameterss below. Ris ser pressurre drop Fig.3 shows th he effect off the loop sea al aeration on o the riserr pressure dro op. The lo oop seal becomes functional whe en > 2. Itts velocity ( ) is calculated based on the tota al area of o the lo oop seal inclluding supply and recycle Table 1. in cha amber given Increasing lo oop seal aeration deccreases the e standpipe e particle bed d height ( ) and incrreases its pre essure dro op ( ). This is exp plainable du ue to the increase of Δ , as expected e fro om Eq.1. Forr constantt riser superficial s Figure 3-- Effect off loop sea al aeration on velo ocity, the in ncrease of loop seal standpipe height, pressure p drop and riser r aerration causses an inccrease in pressure drop d rise er pressure drop ( ). This is a result of mass m transffer from the e standpipe, noticed ass a decreasse of standp pipe heig ght ( ), to the riser. A similar observation is reportted by Cha aritos et al (2). How wever, mosst of the rise er inventoryy is located in the botto om part the erefore slope of incrrease of is bigger than that t of the standpipe pressure drop ( ), this follo ows the pre essure balan nce as per Eq.7. Ris ser entrain nment The riser entra ainment ( ) is plotte ed against the loop seal s aerration ratio
oth riser ve elocities rea alized during g experime ents, in Fiig.4. For bo
incrreasing loo op seal aeration resultts in an inc crease of
until a maximum m is
ached (5)((7)(8). Thiss can be rea exp plained since increassing loop sea al aeration n results in an incrrease of as shown in Fig. 3. For a riser velocity ( ) of maximum m is equal 3.6 m/s the to 23.5 kg/m2s at a lo oop seal aerration ratio of 3.6 6. For the higher
of 4.3 4 m/s the maximum m is equ ual to 28 8.7 kg/m2s occcurring at of 6.6 6. Further
slightly. A aerration reducces the num mber of works, e.g. (7) and (9), do not predicct a reduction of with h loop seall aeration. However, op seal aera ation on rise er Lee e et al. (10) has reported such Figure 4- Effect of loo ent at two o velocities. TSI -2.9 kg g. entrainme beh havior in a seal s pot for Geldart A parrticles. In this work the decrease of beyond its maximum m coincidess and may be b attributed d to the app pearance off the slug gging in the e standpipe e. Finally, fo or a given value, in ncreasing riser velocity y results in a hig gher
valu ue, as reporrted in many y works, e.g g. (2).
Gas s velocity through t the standpip pe Fig.5 and Fig g.6 depict the effect of the loo op seal aeration ratio o (
) on the
perficial gass velocity in i the stan ndpipe ( ) and the aeration a sp plit respectively sup deffined in Eq.5, at riser velocity v of 3.6 and 4.3 3 m/s. The superficial gas velocitty is calcculated as a product of o the real gas velocitty ( ) and the voidag ge (ε) show wn in Eq..4. The void dage consid dered for ca alculation is 0.51. As se een in Fig.5 5 an increas se of results in an increase e of for the t riser ve elocity ( ) of o 3.6 m/s almost linea arly. Forr higher rise er velocity of o 4.3 m/s and loop sea al aeration ratios r
<5 5.6,
is in n the
range of -0.02 2 m/s. The gas flow in n the standp pipe is movving downw wards, when n and d the aeration split values v are negative. For the lo ower off 3.6 m/s, this phe enomenon is i encounte ered for a smaller range, r nam mely when < 2.6. For a give en
value, the
iss higher at a
of 3.6 m/s m in comp parison to a
of 4.3 m/s. m
The ese observa ations can be explained based on o the high her value es, resulting in 3.6 higher values (see Eq q.3), occurring at a of 4.3 m/s in comparisson to a n in Fig. 4. This T is base ed on that higher h . Higher H va alues result in a m/ss, as shown higher resistan nce for gass up flow in the standp pipe. Howevver, when the ratiio is ough, then the t and d aeration split s become positive, thus indica ating incrreased eno that the gas flo ow in the sttandpipe is upwards. Itt can be observed thatt the downw ward have a limittation, i.e. -0 0.03 m/s. Basu B and Bu utler(8) have e the similar conclusion n.
The aeration split increases with increase in and is calculated from -
Figure 5 – Effect of loop seal aeration on gas velocity in standpipe ( ) at different (TSI =2.9 kg) riser velocities
8% to +6%. Up to 6% of the aeration gas is entering the standpipe. This concludes that remaining 94-100% of the aeration gas is entering the recycle chamber. In case of negative additional gas is entering the recycle side of the loop seal. If the calculated aeration in the recycle side is in the and enough to keep range of 2-6 the recycle side of the loop seal fluidized. Therefore the loop seal worked well even without the aeration in the recycle chamber. The deviation bars in Fig.5 and Fig.6 show the influence of voidage in the calculations of and aeration split. The lower deviation shows the value at a voidage of 0.48 and the upper deviation shows the value of voidage 0.54 close . As observed the voidage can to affect the results significantly. As discussed earlier, the assumption of a Δ , despite the two different constant
standpipe cross-sections, holds true to a limited extent. Therefore, to find out the exact gas flow pattern would implicate the use of tracer gases. Johansson et al Figure 6 – Effect of loop seal aeration on (3) reported aeration split values of 2 to the aeration split 7 % using tracer gases. In that work, for a separate downcomer of the same facility values of -0.05 m/s to + 0.1 m/s were recorded. Hence the results reported in this section are comparable with the data of (3). Slugging in the standpipe Fig.5 and Fig.6 show that the increasing loop seal aeration increases the gas velocity in the standpipe. At low or negative gas velocities the standpipe was in moving bed mode. With increasing aeration in the loop seal a stage is reached when bubbles start to appear in the standpipe. The bubbles could be equivalent to a standpipe diameter in such small standpipe used in this study. This situation is commonly referred as slugging, common phenomenon for such small scale units. Two types of slugging have been described by Wen (5). Type A- Round nose slugging and Type B - Flat nose slugging. The Type A is similar to normal bubbling bed where bubbles rise up in fluidized bed. The difference compared to bubbling bed is that a gas slug is nearly equal to the standpipe diameter. Typical Type B
gging is sh hown in Fig g.7 where gas slug completely c slug occcupies the cross c sectio on and lift th he solid chu unk above it. The T solid do ownward motion is only possible by raining dow wn as solid streamers or when the e chunk getts broken. The e Type B slugging occurs ma ainly with cohesive parrticles and is not a suittable mode e for the sta andpipe to ope erate becau use sometim mes the chu unks may be e lifted up to the cyclone and cau use frequen nt operation nal break dow wns. Succh Type B slugging occcurred durring the experiments at riser velocities of 3.6 and 4.3 m/s m and ha as caused reduction in th he entrainm ment rate (see Fig.4). Typically Typ pe B sluggin ng occurred d at > 0..03 m/s whiile Type A or bubbling occurred o at < 0.02 25. Sometim mes both type es occurre ed simultaneously, i.e. Type B in the standpipe and Type A in the t loop seal supply side where cross section is i larger tha an standpipe e. This clea arly shows the influence of solid velocity on th he Type of slugging. This fact can be explain ned as follo ows: Increa asing loop sea al aeration increases i th he gas velo ocity in the standpipe g in and d at lower solid s velocitty ( <0.02 25 m/s), the e particles Figure 7- Slugging standpipe e bed d is expand dable and allows a smoo oth bubble travel. At higher solid ve elocities ( >0.03 m/s) the particle es tend to flow f closely packed, att the me time inccreasing ga as velocity in the stan ndpipe tries to expand d the bed. This T sam cou unter acting g behavior of o gas and solid leads s to the Typ pe B slugging depicted in Fig.7. Since Tyype B is common to co ohesive parrticles, thuss at higher d down flow solid s velo ocity the pa articles exhibit cohesive e nature. In certain c app plications likke calcium looping (2), the prefe erred fluidizzation mode e in standpipe is bubbling to prevent p the calcium pa articles from m sticking to ogether (1). For larg ge scale faccilities such h slugging may m not be relevent. However H in small facilitties, for certain co onditions Tyype A slug gging is un navoidable. The abovve results give g sug ggestions how to prevvent the sta andpipe from operating in the Tyype B slugg ging mode. To avoid d Type B slugging it is suggested that in the standpip pe is kept lo ower an be achie eved either by reducing the riser entrainmen nt or than 0.025 m/ss. Which ca t standpip pe area . by increasing the CO ONCLUSION NS The e standpipe e-loop seal behavior in small scale e CFB is sttudied throu ugh variation of the loop seal aeration a of the t supply chamber c us sing Ilmenitte particles. The standp pipe gass velocity and aeration n split in the e standpipe e increases with increa asing loop seal s aerration. Incre easing the downflow solid veloc city decreasses the ga as velocity and aerration split for f a given loop seal aeration. a An n incrementt in the loop p seal aera ation incrreases the riser u to a limit and then up n decreases due to sslugging in the standpipe. The e standpipe e slugged ea asily due to o its small diameter. d Ho owever at solid s velo ocities up to 0.025 m/s m Round nose slugg ging occurrred while a at higher solid s velo ocity i.e. ab bove 0.03 m/s m Flat nose e slugging occurred. o F a stable CFB opera For ation Flat nose slugg ging should d be avoided d.
ACKNOWLEDGEMENTS This work is part of the ongoing CAL-MOD Project which is funded in part by the RFCS Research Program of the European Commission (RFCR-CT-2010-00013). NOTATIONS
Δpi
m²
Standpipe area
m/s
m kg/m2s mbar, Pa
Particle diameter riser circulation rate Pressure drop in a given CFB section i Riser superficial velocity Superficial gas velocity in standpipe
m/s m/s m/s
m/s
m/s
m/s
Real gas velocity in the standpipe Gas velocity in loop seal Slip velocity Real solid velocity in the standpipe Minimum fluidization velocity
m³/s,
Volumetric flow rate
ρg ρs
kg/m³ kg/m³
Gas density Particle density
Greek symbols ε µ
Pa.s
Voidage Gas viscosity Sphericity
Abbreviations CFB DFB LS
Circulating fluidized bed Dual fluidized bed Loop seal
St TSI
Standpipe Total solid inventory
REFERENCES Basu P., (2006)''Combustion and Gasification in fluidized beds''.Taylor & Francis Group. Charitos A. Hawthorne C., Bidwe A.R., Korovesis L., Schuster A. and Scheffknecht G. (2010), ''Hydrodynamic analysis of a 10 kWth Calcium Looping Dual Fluidized Bed for post-combustion CO2 capture'', Powder technology, 200(3), 117–127. 3. Johansson E., Lyngfelt A., Mattisson T. and Johnsson F. (2003), ''Gas leakage measurements in a cold model of an interconnected bed for chemical looping combustion.'' Powder Technology, 134(3), 210-217. 4. Knowlton T.M. Handbook of fluidization and fluid particle systems, Chapter 21, Standpipes and Nonmechanical valves. Taylor and Francis group LLC. 5. Wen-Ching Yang. Handbook of fluidization and fluid particle systems, Chapter 3, Bubbling fluidized beds, Taylor and Francis group LLC 6. Kunni D. and Levenspiel O.,(1991), Fluidization engineering, A Butterworth-Heinemann 7. Basu P. and Cheng L., (2000), ''An analysis of loop seal operations in a circulating fluidized bed.'', Chemical Engineering Research and Design, 78(7), 991-998. 8. Basu P. and Butler J., (2009),''Studies on the operation of loop seal in ciruculating fludized bed boilers.'', Applied energy, 86(9),1723-1731. 9. Kim S.W., Namkung W. and Kim S.D. (1999), ''Solid flow characteristics in loop seal of circulating fludized bed.'', Korean J. of chemical eng., 16(1), 82-88 10. Lee H.S., Lee Y., Park S.S., Chae H.J. Jeon S.Y and Lee D.H. (2010), ''Hydrodynamic characteristics of cold-bed circulating fluidized beds for methanol to olefins process.'', Korean J. of chemical eng., 27(4), 1328-1332 1. 2.
EFFECT OF BED TEMPERATURE, FUEL DENSITY AND PARTICLE SIZE ON HYDRODYNAMIC PARAMETERS OF A 10 MW FLUIDIZED BED COMBUSTION POWER PLANT USING RICE WASTE Ravi Inder Singh1 and S.K.Mohapatra2 Assistant Professor, Dept. of Mechanical Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India 2 Prof and Head, Mechanical Engineering Department, Thapar University, Patiala, Punjab, India * Corresponding Author (Email: ravis021@ yahoo.com ) 1
ABSTRACT The design and operation of boilers using rice waste present a number of challenges. The overall capacity and efficiency of the boiler are strongly dependent on the fuel, and the supplier has to be able to guarantee the capacity and efficiency within the whole range of the fuel mixture being burned. It is well known that the exit gas composition is strongly dependent on the fuel. Bed temperature, fuel density and particle size significantly affect the hydrodynamic properties of a fluidized bed combustor. The effect of bed temperature, fuel density, particle size on exit gas composition and other hydrodynamic parameters of 10 MW power plant is discussed in this paper, and a heat balance sheet for the 10 MW fluidized bed boiler based on rice waste is prepared. Heat release in the fluidized bed region is also calculated and the efficiency of fluidized bed boiler is found. 1.0 INTRODUCTION Energy consumption in the world in the form of petroleum-based fuels has increased several fold during the last 20 years. Fast depleting stocks of fossil fuel and steep increases in their prices may lead to an energy crisis in the future. With declining reserves and fluctuating prices of fossil fuels, the search for an alternative renewable raw material to replace petroleum has intensified. Rice waste is an agro residue which is found in regions where the demand for energy exists. Rice waste is primarily comprised of rice straw, rice husk and rice bran. Fluidized bed combustion a technology, which produces energy efficiently with a wide range of fuels, at low temperature and at isothermal conditions. Due to these reasons a number of commercial fluidized bed power plants of 10-20 MW capacities based on rice husk/rice straw have been installed in the last two decades for power generation. Rice husk and rice straw are the waste materials which are incinerated in a fluidized bed combustor (FBC) in Punjab (India) and adjoining areas. Due to high collection costs, feeding problems and agglomeration problems, the rice straw is not incinerated in most commercial fluidized bed combustors in its present form. Rice husk can be easily bought from commercial rice mill owners, and is the main fuel used in commercial atmospheric fluidized bed combustors based on rice husk/rice straw. A three-phase mathematical model for exit gas composition and solid population balance (2) (4) for shrinking particles in an atmospheric bubbling fluidized bed combustor using rice-husk was developed in previous studies. The main aim of this paper is to study the effects of fuel density, bed temperature and particle size on hydrodynamic features of the plant and to calculate the various heat transfer effedcts in the 10 MW power plant situated at Jalkheri, Patiala, Punjab, India. Figure 1.1 shows the schematic diagram of the boiler, and Figure 1.2 shows the layout of the biomass based plant at Jalkheri for which the study and model were developed.
Ravi Inder singh et al. (1) conducted a study of an atmospheric bubbling fluidized bed combustor for a 10 MW power plant based on rice husk. In his paper an environmental assessment, an exit gas composition model, an agglomeration problem and a model for a solids population balance for the 10 MW power plant at Jalkheri, Punjab, India using rice husk is discussed. Goo al (2) studied the effects of temperature and particle size on minimum fluidization and transport velocities in a dual fluidized bed. They found that the minimum fluidization velocity decreased with temperature due to the increase of gas viscosity. The transport velocity increased with increasing temperature. They proposed correlations in terms of Reynolds and Archimedes numbers.
Figure 1.1 Schematic diagram of Boiler
Figure 1.2: Schematic diagram of 10 MW Jalkheri power plant
2.0 MODEL FORMULATION A mathematical model was developed to calculate the exit gas composition and solids population balance for a 10 MW FBC power plant suitable for biomass particularly rice husk. The model was validated by collecting data from the 10 MW commercial FBC plant based on biomass (1). The model was based on the three-phase theory of fluidization and single film theory of carbon combustion. It was based on the assumption that essentially the C + O2 = CO2 reaction is taking place in the combustor. It was assumed that the fluidized bed consists of a number of stages and the height of each stage was equivalent to the average bubble diameter. Each stage consisted of the bubble, cloud, and emulsion phases. Bubbles were solids free and gas in bubbles were in plug flow, bubbles and the cloud-wake rise at the same velocity. In each stage the emulsion and cloud-wake phases were back mixed. The voidage in both these phases were the same at incipient condition. The assumption that combustion does not involve a change in number of moles and that char and volatiles burn at the same rate throughout the bed was assumed. The main features of these models were physical chemical processes occurring in a fluidized bed combustor based on rice husk, oxygen mass balance for exit gas composition and a solid population balance. The details of the models can be obtained from references (1) and (3). A brief description of model is given below. Oxygen balance around stage ‘n’ for the bubble phase Oxygen in by convection-Oxygen out by convection -Oxygen transfer to cloud-wake = 0 It is represented symbolically as; U bC b
n −1
− U bC b − ( K n
) εb bc b
Zn
∫
( C b − C cw ) d Z
(2.1)
n
Z n −1
Oxygen balance around stage ‘n’ for bubble-cloud wake phase ׀Oxygen in by convection ׀ – ׀Oxygen coming from bubble phase by transfer ׀ – ׀Oxygen going out from cloud –wake to emulsion by transfer ׀ – ׀Oxygen out from cloud-wake by convection ׀ – ׀Oxygen consumed in cloud-wake phase = ׀0 Symbolically, this can be represented as:
U cw C cw
n −1
Zn
∫
+ ( K bc ) b ε b
( C b − C cw ) dZ + K ce C e − (U cw + K cw + K ce ) C cw n
n
(2.2) n
Z n −1
Where
K ce = ( K ce )b ε b ΔZ
K cw = ( K )( f cw )(ε b )( ΔZ ) and ΔZ = Z n − Z n −1 = Db
,
(2.3)
Oxygen balance around stage ‘n’ for the emulsion phase ׀Oxygen in by convection׀ – ׀Oxygen out by convection ׀+ ׀Oxygen transfer from cloudwake phase ׀ = ׀Oxygen consumed by Combustion reaction ׀ Symbolically, this can be represented as: (2.4) U mf Ce + ( K ce )(Ccw ) − (U mf + K e + K ce )Ce = 0 n −1
n
n
Where K e = K [1 − ε b (1 + f cw )]ΔZ and K ce = ( K ce )ε b ΔZ
(2.5)
The the oxygen balance over a height (dZ) in the bubble phase is written as:
U b Cb − U b (Cb + dCb ) − ( K bc )b ε b (Cb − Ccwn ) dZ = 0
(2.6)
Rearranging and integrating the above equation, we get
Cb − Ccwn = (Cbn−1 − Ccwn ) exp[{−( K bc )b ε b ( Z − Z n −1 )}/ U b ]
(2.7)
At the bottom of the bed (n=0), the concentration of oxygen fed to each phase is the same as that of the incoming feed oxygen. Hence, the boundary conditions are: At n=0, Cbn = Ccwn = Cen = C0 (2.8) Equations 2.1-2.7, together with the boundary conditions in equation 2.8, make up a complete mathematical description of the system. Details could be referred from (1) and (3). The average gas composition leaving the nth stage, i.e at the top of the bed is: Oxygen (2.12) Cavg = (U b Cb + U cw Ccw + U mf Ce ) / U 0 n
n
n
Carbon dioxide CO2 = C0 − C avg Nitrogen
N 2 = (0.79 / 22, 400)(273 / Tb ) + [ XN (1 − XW )] / 28.U 0 . At
(2.13) (2.14)
3.0 HEAT BALANCE CALCULATIONS IN 10 MW PLANT The amount of heat entering the bed and various losses have been calculated. Finally thermal efficiency is calculated and the results shown in Table 4.3 and Figure 4.14. 3.1 Heat added to fluidized bed combustor Qha= mass of fuel * Calorific value of rice waste (3.1) 3.2 Heat lost to flue gases: The flue gases contain dry products of combustion as well as the steam generated due to combustion of hydrogen in the fuel. (3.2) Qfg = mg * Cpg * (Tg – Ta ) 3.3. Heat carried away by the steam from flue gases (3.3) Qsg = ms1 (hs1 – hf1) 3.4 Heat lost due to incomplete combustion Heat lost due to incomplete combustion (6) of carbon per kg of fuel (3.4) Qic= [CO × C / CO2 + CO] × 23680 KJ/kg of fuel 3.5 Heat lost due to unburnt fuel Qub = mf1× C.V (3.5) 3.6 Convention and Radiation losses The loss of heat due to convection and radiation losses (3.6) Qcr= Heat released per kg of fuel – (Qg + Qs + Qic + Qub) (3.7) 3.7 Equivalent evaporation (me) = ma * (h – hf1) / hfg 3.8 Boiler efficiency (3.8) ηb = ma * ( h – hf1 ) / C.V
3.9 Average Heat transfer Coefficient The overall bed heat transfer coefficient is given by hα = Heat Transfer flux / A. ∆t More details could be found in the work of (6).
(3.9)
4.0 RESULTS AND DISCUSSIONS The input data required for the exit gas model and solid population model (1) was taken from a 10 MW FBC plant at Jalkheri (Jalkheri Power Pvt. Limited, Fatehgarh Sahib, Punjab, India). The plant uses the rice husk (80%) and rice straw (20%) as feed stock. The average size of rice husk particle ranges from 0.25 to 0.625 cm. At one time the size of the rice husk particles was uniform. Bed temperature ranged from 925 K to 1125 K. The density of fuel varied from 0.09 to 1.2 g/cm3 as fuel consists of rice husk and rice straw. The proximate and ultimate analyses of rice husk were done at different times and the result of one such sample is given in Table 4.1. The results of these analysis, along with the other physical-chemical parameters taken from plant were the input parameters to the model in Section 3.0 and (1). Fuel density, Table 4.1 Proximate and ultimate analysis of rice waste sample Proximate analysis (Rice husk) Volatile Fixed Carbon Ash Moisture
58.03 16.65 17.82 7.5
Ultimate analysis (Rice husk) C H S O N
38.9 5.1 0.12 37.9 2.17
Table 4.2 Physico chemical parameters of Plant Area of fluidized bed
391000 cm2 Nozzle type tuyre 22500
20 × 106 cm3/s
Type of distributor Total no. of holes in distributor Specific fuel consumption
10830 g/s
Bed Voidage
400oC
Bed height at minimum fluidization Boiler capacity
Type of fuel used (at time of study) Feed rate of fuel Feed rate of water
Rice husk and rice straw 3200 g/s 11110 g/s
Air flow rate Rate of steam generation Main steam temperature Combustion efficiency
82% Value)
(Designed
1300 g of fuel /unit generation 0.48 48 cm 50 T/hr equivalent to 13888.88 kg/s
Figure 4.1 Composition of oxygen and carbon dioxide in flue gas vs. fuel density
Figure 4.2, 4.3 and 4.4 Minimum fluidization velocity, bubble diameter and expanded bed height vs. fuel density
Figure 4.5, 4.6 and 4.7 Minimum fluidization velocity, bubble diameter and shrinkage rate vs. Particle size
Figure 4.8 Carbon utilization efficiency Figure 4.9, 4.10 Shrinkage rate and diameter of Vs. Bed temperature bubble vs. Bed temperature
Figures 4.11 and 4.12 Expanded bed height and the composition of oxygen and carbon dioxide in the flue gas vs. Bed Temperature bed temperature and particle size are the important parameters which decide the various hydrodynamic properties of FBCs. The output of both models resulted in hydrodynamic parameters, exit gas composition, carbon utilization efficiency etc. of the FBC situated at Jalkheri. The data given in table 4.2 were used to prepare the heat balance sheet for the Jalkheri boiler, and to calculate the actual thermal efficiency of the boiler at the time of operation. The variation of oxygen and carbon dioxide in the flue gas with fuel density is shown in Figure 4.1. The model values closely match the actual plant data. The value of carbon dioxide increases and oxygen decreases. It is due to the fact that the combustion of dense fuel is more proper than less dense fuel. The oxygen supplied to the dense fuel is more than less dense fuel. The trend of Umf is shown in Figure 4.2. Umf increases with increasing density and is directly proportional to fuel density.
The trend of bubble diameter with fuel density is shown in Figure 4.3. Bubble diameter decreased with increasing fuel density because the bubble diameter is dependent on the difference between superficial velocity and Umf. As Umf increases, it causes the bubble diameter to decrease. The trend of expanded bed height with fuel density is shown in Figure 4.4 The expanded bed height increases is due to increase of bubble diameter. The trend of minimum fluidization velocity with particle size is shown in Figure 4.5. It increases with increasing particle diameter. The bubble diameter and shrinkage rate with particle diameter are shown in Figure 4.6 4.7. Both decrease with increasing particle diameter as these are inversely proportional to particle diameter and due to the fact that Umf increases as particle size increases causing the increase of bubble diameter. Carbon utilization efficiency increases with increasing particle diameter because large particles stay in the fluidized bed longer than smaller particles.
Figure 4.13 Variation of percentage of gases in flue gas with moisture Shrinkage rate, bubble diameter and expanded bed height with bed temperature are shown in Figures 4.9, 4.10 and 4.11. Bubble diameter is dependent on Umf which is further dependent on gas viscosity. Gas viscosity decreases with increasing bed temperature. This causes the minimum fluidization velocity to decrease which further increases the bubble diameter. Bubble diameter causes the expanded bed height to increase. Bed temperature causes reaction rates to increase which causes the shrinkage rate to increase. Table 4.3 Heat balance sheet of Jalkheri Power Plant Notation
Description
Calculated Value 48 MJ
Notation
Description
Qha Qfg
Heat added to fluidized bed Heat lost to flue gases
Qub
0.62 MJ
Qcr
Heat gained by steam
33.93 MJ
me
Heat lost due to incomplete combustion
0.015 MJ
ηb
Heat lost due to un burnt fuel Convention , Radiation and other losses Equivalent evaporation Calculated value
Qsg Qic
Calculated Value 0.125 MJ 13.31 MJ 4.697 kg/kg of fuel 70.68 %
The variation of oxygen and carbon dioxide in flue gas with bed temperature is shown in Figure 4.12. The model values are completely in accordance with plant results as shown in the figure. Carbon dioxide increases up to 975 K and thereafter decreases. After 975 K the problem of agglomeration predominates and that causes non-uniform combustion in the fluidized bed, which causes the carbon dioxide values to decrease and oxygen to increase.
The moisture in rice husk varies throughout the year. The effect of moisture on oxygen concentration and carbon dioxide emitted from the FBC is shown in Figure 4.13. The percentage of carbon dioxide increases with increasing moisture content and the percentage of oxygen decreases with increasing moisture content in the rice husk particles. Fuel with different moisture contents is checked and model predictions are within satisfying limits. The actual plant data noted at different moisture percentage of rice husk particles are shown in Figure 4.13. The variation of actual plant data is in accordance with the model values. It was described by Ganesh et al. (6) that in order to achieve complete conversion of carbon it is desirable to maintain a low temperature during pyrolysis and/or partial combustion of rice husk, followed by steam gasification. The increase in moisture content in fuel causes the plant bed temperature to decrease. Bed temperature decreases because more heat is needed to convert moisture from fuel into the vapor form since a phase change takes place. Therefore, the latent heat required to change the phase is greater which causes bed temperature to decrease. Due to the lower bed temperature, complete carbon conversion occurs and causes to increased carbon dioxide concentration as shown in Figure 4.13
Figure 4.14 Heat Balance Chart The heat balance boiler sheet is described in Table 4.3 and Figure 4.14. This was calculated with data collected from the plant as shown in Table 4.2 and from steam tables. The calculations were performed assuming the calorific value of rice waste is 15 MJ/kg. The total heat input to the fluidized bed boiler is 48 MJ for a mass rate of fuel entering in the combustor of 3200 g/s. From the total heat entering, 33.93 MJ of heat has been used in raising the steam. Remaining heat is gone in the losses mentioned in figure 4.14 and table 4.3. Major portion of heat loss around 13.31 MJ is gone in radiation, convection and other losses. The other losses are like leakage of steam etc. The actual boiler efficiency calculated at plant is found to be 70.68 % which is quite lower than the designed value. It is because of various losses present which need to be controlled. It is also found that some refractory plates which are used to prevent radiation losses are in damaged condition. Mass of equivalent evaporation of steam is found to be 4.697 kg/kg of fuel burnt. The average heat transfer 2 coefficient is found to be 1101.7 W/m .K in bed region. 5.0 CONCLUSIONS The above study conducted with respect to incineration of rice waste in an FBC leads to the following conclusions. 1. The CO2 percentage in the flue gas increased from 11 to 15.62 and the oxygen percentage from 10.9 to 6.4 with increases in fuel density from 0.09 to 0.12 g/cm2, respectively.
2. Umf increased from 21.64 to 28.05 cm/s, bubble diameter decreased from 20.2 to 19.65 cm/s, expanded bed height decreased from 84.7 to 82.9 cm with increases in fuel density from 0.09 to 0.12 g/cm2, respectively. 3. Umf increased from 9.44 to 43.6 cm/s, bubble diameter decreased from 21.6 to 18.8 cm, shrinkage rate decreased from 0.0462 to 0.0325 cm/s and carbon utilization efficiency increased from 96.8 to 98.75 with increases in particle size from 0.25 to 0.6 cm. 4. The shrinkage rate increased from 0.0256 to 0.0383 cm/s, bubble diameter from 19 to 20.83 cm and expanded bed height increased from 80.93 to 86.8 cm with increases in bed temperature from 925 to 1100 K. The maximum value of carbon dioxide in the flue gas was 12.55 percent and the minimum value of oxygen in the flue gas was 8.3 percent. 5. Increasing the moisture percentage in the fuel from 6.5 to 12.5 decreased the oxygen percentage from 7.3 to 6.2 in the flue gas and there was an increase in the percentage of carbon dioxide from 13.4 to 14.8 in the flue gas. 6. The actual boiler efficiency was 70.68% which is quite low, The majority of the heat losses were from radiation and convection. The average heat transfer coefficient was found to 2 1101.7 W/m .K in the bed region. ACKNOWLEDGEMENT The authors are thankful to the management and Er. S.Bandopadhya of Jalkheri Power Private Limited for providing the information and data required for the study. NOTATION Ub Ucw
Ucwn-1 Ucwn Ug (Kce)b (Kbc)b Z Umf Uo Ucwn Cbn Ccwn fcw εb
= Velocity of gas through bubble phase, cm/s
= Velocity of gas through cloud wake phase, cm/s
= Velocity of gas through (n-1)th stage of cloud wake phase, cm/s = Velocity of gas through nth stage of cloud wake phase, cm/s = Gas velocity, cm/s = Gas interchange co-efficient from cloud wake to emulsion phase, s-1 = Gas interchange co-efficient from bubble to cloud wake phase, s -1, = Bed height ,cm, = Minimum fluidization velocity/emulsion phase gas velocity, cm/s, = Superficial gas velocity, cm/s , = Velocity of gas through nth stage of cloud wake phase, cm/s, = nth stage oxygen concentration in the bubble phase, g-mol /cm3 , = nth stage oxygen concentration in the cloud wake phase, g-mol /cm3 , = Fraction of cloud wake phase in the bed, = Volume fraction of bubbles in the bed. For detailed notations refer (1) and (3).
7.0 REFERENCES [1] Singh Ravi Inder, Mohapatra S.K. ,Gangacharyulu D.,2008, “ Energy Conservation and Management, Elsevier, Vol 49 No.11, pp 3086-3103. [2] J. H. Goo, M. W. Seo, S. D. Kim, B. H. Song, “Effects of temperature and particle size on minimum fluidization and transport velocities in a dual fluidized bed”, 2009, Proceeding of the 20th International conference on fluidized bed combustion, Springer. [3] Singh Ravi Inder Singh, “Study of performance of an atmospheric bubbling fluidized bed combustor using rice waste”, PhD Thesis, 2010, Thapar university, Patiala, Punjab. [4] Kunni Daizo, Levenspiel Octave. Fluidization engineering. USA: Butterworth–Heinemann; 1991. [5] Rajput R.K, “Thermal Engineering” Laxmi Publications (P) Ltd. Delhi, ISBN 81-7008-8348. [6] Ganesh A, Grover P D and Ramachandra P V I, 1992, “Combustion and gasification characteristics of rice husk”, Fuel, 71, 889-894.
DYNAMIC CHARACTERISTICS OF BUBBLING AND TURBULENT FLUIDIZATION USING A HURST ANALYSIS TECHNIQUE H. Azizpour, N. Mostoufi*, R. Zarghami, R. Sotudeh-Gharebagh Multiphase Systems Research Lab, School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155/4563, Tehran, Iran * Corresponding author, Tel.: (+98-21)6696-7797, Fax: (+98-21)6646-1024, E mail: [email protected] ABSTRACT A non-intrusive vibration monitoring technique was used to study the flow behavior in a fluidized bed. This technique has several advantages compared to other techniques, such as pressure probes and optical fiber probes which may influence the measurement because they are intrusive. Experiments were conducted in a 15 cm diameter by 2 m tall fluidized bed using 470 micron sand particles. Auto correlation functions, mutual information function and Hurst exponent analyses were used to analyze the fluidized bed hydrodynamics near the transition point from bubbling to turbulent fluidization regime. These methods were able to detect the regime transition point using vibration signals. INTRODUCTION Fluidization is a process in which solid particles become suspended and fluidized at threshold gas or liquid velocity (minimum fluidization velocity), and the bed adopts fluid-like characteristics. Today, due to their many advantages, fluidized beds have industrial applications in many areas, e.g. oil, petrochemical, mineral, biochemical, pharmaceutical, food processing. The proper functioning of fluid bed reactors requires suitable means of measuring and monitoring the bed hydrodynamics. Various techniques have been used to measure signals in fluidized beds. Pressure probes and fiber optic probes are widely used techniques but they have a shortcoming of being intrusive. This work utilized a novel non-intrusive method that measures vibrations in fluidized beds. The most common methods for characterizing time dependent signals from fluidized beds are time, frequency domain and state space analyses. Time domain approaches include observation of the time sequence of the measured signal, standard deviation analysis and analysis of other statistical moments like skewness, kurtosis and flatness (1-5). Autocorrelation and mutual information functions are more frequently used in nonlinear state space analysis to determine time delay of reconstructed attractor (68). Hurst exponent analysis was developed for the first time by Hurst (9) to distinguish completely random time series from correlated time series. Hurst exponent was used by Fan et al. (10), Franca et al. (11), Drahos et al. (12), Cabrejos et al. (13), Briens et al. (14), Karamavruc and Clark (15) to assess the hydrodynamic status of the fluidized bed. Fast Fourier transform and wavelet transform are also the mathematical tools used to analyze the pressure fluctuations in fluidized bed, which express the behavior of a time series in the frequency domain.
1
In the present investigation, auto correlation functions, mutual information function and Hurst exponent analysis were applied to analyze vibration signals of a gas-solid fluidized bed and identify the hydrodynamics of the bed. EXPERIMENTS The experiment setup is schematically shown in Fig. 1. The experiments were carried out in a gas-solid fluidized bed made of Plexiglas of 15 cm inner diameter (D) and 2 m height (L). The gas distributor was a perforated plate containing 435 holes with 7 mm triangle pitch. Air was supplied by a compressor and its flow rate was measured by an orifice meter. A cyclone was placed at the column exit to return the entrained solids back to the bed. Sand particles with mean size of 470µm and particle density of 2600 kg/m3 were used in the experiments. The system was electrically grounded to decrease electrostatics effect. The experiments were carried out a static bed height of 22.5 cm. The same DJB accelerometer with a cutoff frequency of 25.6 kHz and sensitivity of 100 mV/ms-2 was used to measure vibration fluctuations signals. These measuring probes were mounted on the column 10 cm above the distributor plate by means of a magnet to minimize sudden shakes. To prevent wave interference and losing information, the sampling frequency of vibration signals was set to 25.6 kHz. All the measurements were repeated three times to ensure accuracy and reproducibility of signals.
Figure 1. Schematic of the experimental fluidized bed set-up
METHOD OF ANALYSIS R/S Analysis Rescaled range analysis (R/S analysis) was first introduced by Hurst (9) for studying long-term memory of a time series. Mandelbrot (16) showed that the R/S analysis is a more helpful tool in detecting long range dependence compared to more
2
conventional analysis like autocorrelation analysis and spectral analysis. In this method, first cumulative deviation from the mean of the time series x(i) in time window n is calculated: N
x (i ) = ∑ ( x (i ) − x n )
(1)
i =1
where
xn =
1 n ∑ x(i ) n i =1
(2)
Then, the range function R(n) is determined as maximum and minimum difference of time series x(i) in each time interval n:
R (n ) = max ( x (i, n )) − min ( x (i, n )) 1 ≤ i < n
(3)
Rescaled range function is obtained by dividing R(n) by the standard deviation S(n):
R R (n ) = S S (n )
(4)
where the standard deviation S(n) is:
S (n ) =
1 n ∑ (x(i ) − xn ) n i =1
(5)
It has been found that, for some time series, the dependence of R/S on the number of data points follows an empirical power law described as (10):
(R S ) ∝ n
H
n
(6)
where H is the Hurst exponent and varies between 0 and 1. The Hurst exponent is equal to 0.5 for stochastic (e.g., white noise) series, less than 0.5 for rough anticorrelated series and greater than 0.5 for positively correlated series known as persistence. For the persistent data set, if the trend or behavior in the data set is increasing or decreasing over a certain unit interval of time, it would have a tendency to persist in increase or decrease over such an interval. Hurst exponent can be estimated by linear regression of ln(R/S) versus ln(n). Autocorrelation function The autocorrelation function (ACF) from the mathematics compares linear dependence of two time series separated by delay and is defined as (7-8):
3
∑ [x (i ) − x ][x (i + τ ) − x ]
N −τ
ACF =
i =1
(7)
∑ [x (i ) − x ]
N −τ
2
i =1
where
x=
1 N ∑ x(i ) N i =1
(8)
The time delay for the attractor reconstruction is then taken at a specific threshold value of ACF where ACF is equaled to one half or zero or the first inflection point of that. Mutual information While the autocorrelation function measures the linear dependence of two variables, Fraser and Swinney (6) suggested using the mutual information I(τ) function to determine when the values of x(i) and x(i+τ) are independent enough of each other to be useful as coordinates in a time delay vector, but not so independent as to have no connection which each other at all. The mutual information of the attractor reconstruction co-ordinates is defined as:
I (τ ) =
N − ( d −1)τ
⎡ P(x(i ), x(i + τ ),", x(i + (d − 1)τ )) ⎤
∑ P(x(i), x(i + τ ),", x(i + (d −1)τ )) log⎢ P(x(i ))P(x(i + τ ))...P(x(i + (d − 1)τ ))⎥ i =1
⎣
⎦
(9)
where P(x(i)) refers to the individual probability of the time series variable. In general, the time delay provided by the I(τ) criteria is normally smaller than that calculated by the ACF(τ) and provides appropriate characteristic time scales for the motion. As mentioned above, I(τ) presents a kind of nonlinear correlation concept, while the ACF(τ) provides an optimum linear correlation criterion. RESULTS AND DISCUSSION Fig. 2 shows the Hurst diagram of the vibration signal which is measured at 10 cm above the distributor for particles size of 470 µm, initial aspect ratio L/D of 1.5 and different gas velocities. According to the figure, the fluidized bed has multifractal behaviour with three different Hurst exponents. For example, consider the graph related to the gas velocity equal to 1.0 m/s, it can be seen that for small values of n (time lag), the Hurst exponent is 0.8451, which is much larger than 0.5, indicating a highly persistent dynamic feature of the fluidized bed. On the other hand, Hurst exponent is 0.494 for larger values of n, which is less than 0.5, indicates a highly antipersistent dynamic feature of the fluidized bed. In general, as stated by Karamavruc and Clark (15), bubble motions correspond to higher Hurst exponents than particle motions do. It can be concluded that while Hurst exponent at small values of n or smaller fractal dimension represents the dynamic feature of macro structures, Hurst exponent at larger values of n or larger fractal dimension represents the dynamic feature of finer structures.
4
The Hurst exponent value of macro structures initially rises with increasing the gas velocity and then declines with further increasing of the gas velocity. This change in the trend of the Hurst exponent can be related to the regime transition of the fluidized bed. Increase in the Hurst exponent of the macro structures can be related to the growth of the bubble size due to the gas velocity increase in the bubbling regime. However, further increase in the gas velocity in the turbulent regime results in the decrease of the Hurst exponent because of the breakdown of large bubbles to voids and small bubbles. (Transition velocity from bubbling to turbulent regime was calculated equal to U=1.23 m/s by Bi and Grace Correlation). As pointed out by Fan et al. (17), the reciprocal of the break point in the Hurst profile is similar to the dominant frequency of the bed. As shown in Fig. 2, the break point occurs at n equal to 47 points which is equal to a time interval of about 0.00077 (s), this presents an equivalent dominant frequency of about 1396 Hz which is close to the value estimated from the power spectrum analysis of the bed vibration signal at the same conditions (9).
Figure 2. Hurst exponent diagram of the vibration signal measuring in tap height 10 cm above the distributor at different superficial gas velocity, particles size 470 µm, and L/D=1.5
The autocorrelation function ACF(τ) and the mutual information profile I(τ) of the vibration signal which is measured at 10 cm above the distributor, for particles size of 470 µm, initial aspect ratio L/D of 1.5 and different gas velocities is illustrated in Fig. 3. For U=1.0 m/s, the first pass of the autocorrelation function from one half and the time delay at which the ACF becomes zero occur at 2 and 9, respectively. The first minimum of the mutual information occurs at a delay time of 26. As can be seen in this figure, these points initially occur at higher values of time delay with increasing the gas velocity, on further increase of the gas velocity results that these points occur at lower time delay. This trend can be related to the regime transition of the fluidized bed. The growth of the bubble size by increasing the gas velocity in the bubbling regime means that the behavior of the system tends to a periodic system. In periodic systems, the first pass of the
5
autocorrelation function from one half and the time delay at which it becomes zero, and also the first minimum of the mutual information occurs at a higher delay time in comparison with stochastic systems. When the turbulent regime is reached by the further increase of gas velocity, time delays were found to occur at lower point time delays than for the bubbling regime. This shows that the systems tends to tends to a stochastic system because of the breakdown of large bubbles to voids and small bubbles.
Figure 3. The autocorrelation function and mutual information profile against delay time for vibration
CONCLUSIONS Two different Hurst exponents were identified from the vibration signals measured in the fluid bed suggesting that the fluidized bed had a multifractal behavior. Higher Hurst exponent is corresponded to macro structure in the bed, e. g. motion of large bubbles. The reciprocal of the break point in Hurst profile is similar to the main frequency of the bed. The value of the larger Hurst exponents increased with increasing gas velocity and was highest at the bubbling-to-turbulent regime transition point. The transition velocity was about 1.2 m/s, and system shows the highest periodical behavior in this point. The autocorrelation and the mutual information functions were also used to determine the turbulent transition point from the accelerometer data. The fluidized bed system at transition point from bubbling to turbulent, the first pass of the autocorrelation function from one half and the time delay at which it becomes zero, and also the first minimum of the mutual information occurs at a higher delay time in comparison with stochastic systems, and the values of time delays were highest at the bubbling-to-turbulent transition gas velocity. These findings were similar to those of the Hurst exponent analysis. NOTATION ACF d
6
autocorrelation function embedding dimension
D fs H I L N P R S t x
Bed diameter sampling frequency, Hz Hurst exponent mutual information bed height; number of windows total number samples individual probability Rescaled Range function standard deviation time, s vibration signal (m/s2)
Greek symbols τ Embedding time delay τ1 Embedding time delay related to ACF=0.5 τ2 Embedding time delay related to ACF=0.0 τ3 Embedding time delay related to minimum mutual information REFERENCES 1. G. S. Lee, S. D. Kim, Pressure fluctuations in turbulent fluidized beds, J. Chem. Eng. Japan, 28, 515, 1988. 2. F. Johnsson, R. C. Zijerveldb, J. C. Schoutenb, C. M. van den Bleek, B. Leckner, Characterization of fluidization regimes by time-series analysis of pressure fluctuations, Int.J. Multiphase Flow, 26, 663, 2000. 3. H. T. Bi, J. R. Grace, K. S. Lim, Transition from bubbling to turbulent fluidization, Ind. Chem. Res. Dev., 34, 4003-4008, 1995. 4. D. Bai, E. Shibuya, N. Nakagawa, and K. Kato, Fractal characteristics of gassolids flow in a circulating fluidized bed, Powder Technol, 90, 205, 1997. 5. R. Zarghami, Conditional Monitoring of Fluidization Quality in Fluidized Beds, Ph.D. Dissertation, University of Tehran, 2009. 6. A. Fraser and H. Swinney, Independent coordinates for strange attractors from mutual information, Phys. Rev. A, 33, 1134-1140, 1986. 7. H. Kantz, T. Schreiber, Ed., Nonlinear time series analysis. Cambridge University Press, 2002. 8. P. S. Addison, Fractals and Chaos: An Illustrated Course. IOP Publishing Ltd., 2005. 9. Hurst, H. E., Long-term storage capacity of reservoirs, Trans. Am. Soc. Civ. Eng., 116, 770, 1951. 10. L. T. Fan, D. Neogi, M. Yashima, R. Nassar, Stochastic analysis of a three-phase fluidized bed: Fractal Approach, AIChE J., 36, 1529, 1990. 11. F. Franca, M. Acikgoz, R. T. Lahey, and A. Clausse, The use of fractal techniques for flow regime identification, Int. J. Multiphase Flow, 17, 545, 1991. 12. J. Drahos, F. Bradka, M. Puncochar, Fractal behaviour of pressure fluctuations in a bubble column, Chem. Eng. Sci., 47, 4069, 1992. 13. F. J. Cabrejos and G. E. Klinzing, Characterization of dilute flows using the rescaled range analysis, Powder Technol, 84, 139, 1995. 14. C. L. Briens, L. A. Briens, J. Hay, C. Hudson, A. Margaritis, Hurst’s analysis to detect minimum fluidization and gas maldistribution in fluidized beds, AIChE J., 43, 1904, 1997
7
15. A. I. Karamavruc and N. N. Clark, A fractal approach for interpretation of local instantaneous temperature signals around a horizontal heat transfer tube in a bubbling fluidized bed, Powder Technol, 90, 235, 1997. 16. B. Mandelbrot, the Fractal Geometry of Nature. Freeman, San Francisco, 1982. 17. L. T. Fan, Y. Kang, D. Neogi, M. Yashima, Fractal analysis of fluidized particle behavior in liquid-solid fluidized beds, AIChE J., 39, 513, 1993.
8
CORRELATION OF THE MINIMUM SPOUTING VELOCITY FOR THE DESIGN OF OPEN-SIDED DRAFT TUBE CONICAL SPOUTED BEDS FOR THE TREATMENT OF FINE MATERIALS M. Olazar, H. Altzibar, G. Lopez, I. Estiati, J. Bilbao University of the Basque Country, Dept. Chemical Engineering, P.O. Box 644E48080 Bilbao, Spain. ABSTRACT - The hydrodynamics of conical spouted beds provided with open-sided draft tubes have been studied for the treatment of fine particles. A correlation has been proposed for the calculation of the minimum spouting velocity as a function of dimensionless moduli that take into account the geometric factors of the contactor and the draft tube, particle characteristics and operating conditions.
INTRODUCTION The spouted bed regime is an alternative contact method to fixed and fluidized beds. The conventional spouted bed contactor is a cylindrical contactor which has a conical base. This conventional spouted bed has limitations for operation with deep beds and solids that are coarse, sticky and have a wide size distribution. Different modifications of the original spouted bed (cylindrical with conical base) are proposed in the literature with the aim of improving its performance. These modifications mainly concern the geometry of the contactor and/or the gas inlet to the bed. Given the advanced knowledge of their hydrodynamics and applications, the spouted beds of rectangular section, also with rectangular gas inlet (Freitas and Dogan (1), Dogan et al (2)), the conical spouted beds (Olazar et al (3,4,5), San José et al (6), Povrenovic et al (7), Al-Jabari et al (8), Bi et al (9)), and the spout-fluid beds (Nagarkatti and Chaterjee (10), Sutanto et al (11), Zhao et al (12), Passos and Mujumdar (13), Ye et al (14,15)), which combine the advantages of the spouted bed and of the bubbling fluidized bed, are worth mentioning. Spouted beds with fully conical geometry combine the features of the cylindrical spouted beds (such as the capacity for handling coarse particles, small pressure drop, cyclic movement of the particles and so on) with those inherent to their geometry, such as stable operation in a wide range of gas flow-rates (Olazar et al (3,16), San José et al (6)). This versatility in the gas flow-rate allows handling particles of irregular texture, fine particles and those with a wide size distribution and sticky solids, whose treatment is difficult using other gas-solid contact regimes (Olazar et al (5,17,18), Bilbao et al (19)). Moreover, operation can be carried out with short gas residence times (as low as milliseconds) in the dilute spouted bed (Olazar et al (20,21)).
A crucial parameter that limits scaling up of spouted beds is the ratio between the gas inlet diameter and particle diameter. In fact, the inlet diameter should be smaller than 20-30 times the average particle diameter in order to achieve spouting status. The use of a draft tube is the usual solution to this problem. Nevertheless, solid circulation, particle cycle time, gas distribution and so on, are governed by the space between the bottom of the bed and the draft-tube, Moreover, minimum spouting velocity and operation pressure drop depend also on the type of draft tube used. A study has been carried out in this paper on the hydrodynamics of conical spouted beds with open-sided draft tubes. The main aim is to obtain a correlation for the determination of the minimum spouting velocity when fine particles are used. In a previous paper (Altzibar et al. (22)), correlations have been determined for the design of conical spouted beds provided with non-porous draft tubes, and their performance has been compared with that of an open-sided draft tube. A detailed study is carried out in this paper using open-sided draft tubes of different diameter and aperture ratio in order to establish a reliable correlation for the design of conical spouted beds provided with this type of tube.
EXPERIMENTAL The experimental unit used is described in previous papers and allows for operating with contactors of different geometry (Olazar et al (3,4), San José et al (6), Altzibar et al. (22)). The blower supplies a maximum air flow-rate of 300 m3 h-1 at a pressure of 1500 mm of water column. The flow-rate is measured by means of two mass flowmeters in the ranges 50-300 m3 h-1 and 0-100 m3 h-1, both being controlled by computer. The blower supplies a constant flow-rate and the first mass flow-meter controls the air flow that enters the contactor (in the range 50-300 m3 h-1) by acting on a motor valve that reroutes the remaining air to the outside. When the flow required is lower than 50 m3 h-1, it crosses the first mass flow meter and is regulated by the second one placed in series, which also acts on another motor valve that regulates the desired flow-rate. The accuracy of this control is 0.5% of the measured flow-rate. The measurement of the bed pressure drop is sent to a differential pressure transducer (Siemens Teleperm), which quantifies these measurements within the 0100% range. This transducer sends the 4-20 mA signal to a data logger (Alhborn Almeno 2290-8), which is connected to a computer where the data are registered and processed by means of the software AMR-Control. This software also registers and processes the air velocity data, which allows for the acquisition of continuous curves of pressure drop vs. air velocity. There are three different zones in the conical spouted bed with draft tube, namely, spout, annulus and fountain. Figure 1 shows these different zones. Three conical contactors made of polymethyl methacrylate have been used. Figure 2 shows the geometric factors of these contactors. The dimensions of these contactors are: column diameter, Dc, 0.36 m; contactor angle, γ, 28, 36 and 45º; height of the conical section, Hc, 0.60, 0.45 and 0.36 m; gas inlet diameter, D0, 0.03,
0.04, 0.05 and 0.06 m. The stagnant bed heights used are, H0, 0.14, 0.20, 0.25 and 0.30 m.
Figure 1. Zones in the conical spouted bed with draft tube.
Figure 2. Geometric factors Figure 3. Scheme of the of the conical contactors. open-sided draft tube. Furthermore, three open-sided draft tubes have been used. The scheme and geometric factors of the open-sided draft tube are shown in Figure 3. These tubes are of different aperture ratios with three slots. The widths of the faces on the opensided tubes, ωH, are 0.025, 0.018 and 0.010 m, which mean 57, 65 and 78% of open area (aperture ratio) in the tubes. The diameters of the tubes, DT, are 0.04 and 0.05 m. Moreover, the total length of the open-sided tubes is 0.50 m, which means they stand about 0.20 m above the bed surface. This length has been chosen according to previous experimentation in which lower and denser fountains were observed when the tube end was above the bed surface. In fact, the height above the bed must be at least 2/3 of the stagnant bed height. Runs have been carried out by combining all these contactor and draft tubes variables.
The material used is building sand. Figure 4 shows the particle size distribution obtained by sieving (ISO 3310).
Figure 4. Particle size distribution of the sand. The average particle size (reciprocal mean diameter) has been calculated by means of the expression:
dp =1
[∑ (x
i
d pi
)]
(1)
The average size of the sand obtained using eq.(1) is 0.71 mm and the density of the sand is 2358 kg m-3. In addition, different fractions of this material have also been used in order to establish a more reliable correlation. The mean diameters of these fractions are 0.4 and 0.9 mm, respectively.
RESULTS AND DISCUSSION In order to illustrate the general characteristics of pressure drop evolution in the bed with air velocity, the results for two different systems are shown in Figure 5 as an example. The operating conditions are the same for the two systems and only the value of the width of the faces (ωH) is varied. Figure 5 shows for the two systems that, at first, as air velocity is increased, pressure drop increases to a maximum value. Subsequent to the maximum value, a further increase in air velocity gives way to the fountain and pressure drop decreases. In order to define more precisely the minimum spouting velocity, air velocity is then decreased and the values of operating pressure drop are monitored. A very pronounced hysteresis is noticed, which is due to the fact that peak pressure drop is much higher than operating pressure drop and, furthermore, a much higher velocity than the minimum one is required to break the bed and open the spout. Figure 5 shows that the values of minimum spouting velocity, operating pressure drop and peak pressure drop are highly dependent on the system configuration. As observed, the values of the minimum spouting velocity, operating pressure drop and
peak pressure drop increase as the width of the faces is decreased (or as aperture ratio is increased).
Pressure drop (Pa)
14000 WH=2.5 cm; Increasing air velocity WH=2.5 cm; Decreasing air velocity WH=1 cm; Increasing air velocity WH=1 cm; Decreasing air velocity
ΔPM
12000 10000
ΔPM
8000 6000 4000 2000
ΔPS ΔPS ums
ums
0 0
5
10
15
20
Air velocity (m/s) Figure 5. Evolution of the bed pressure drop with air velocity for different values of the widths of the faces (ωH) when non-porous draft tubes are used. Experimental conditions: γ=36º; D0=0.04 m; H0=0.25 m, DT=0.042 m. These higher values are due to the higher solid-cross flow from the annulus into the spout along the whole length of the spout. Moreover, it is clearly observed that the solid flow rate increases with the aperture ratio. From these plots, the minimum spouting velocity has been determined for a wide range of systems. In order to ascertain the influence of the different factors on the minimum spouting velocity, an analysis of variance (ANOVA) of the data obtained following a design of experiments has been carried out by means of a standard statistical program (SPSS 13.0). The results show that the parameters of greater influence on the minimum spouting velocity, ordered by their significance, are the contactor angle (γ), gas inlet diameter (D0) and width of the faces on the open-sided tubes (ωH), respectively. The quantitative influence of the variables may be observed by plotting the different responses vs. factors. Figure 6 shows the change in minimum spouting velocity caused by the factors of greater influence (contactor angle, a; gas inlet diameter, b; width of the faces, c). Figure 6a shows that the minimum spouting velocity goes through a minimum with contactor angle. Thus, it decreases as contactor angle is increased from 28 to 36 degrees and then increases with this factor. Regarding the gas inlet diameter (Figure 6b), an increase in this factor (D0) gives way to a sharp decrease in the minimum spouting velocity. The same happens when the width of the faces is increased (Figure 6c), but in a much less pronounced way.
Figure 6.
Influence of the contactor angle, the gas inlet diameter and the width of the faces on the minimum spouting velocity.
Based on dimensional and statistical analysis, a hydrodynamic correlation for the calculation of the minimum spouting velocity in open-sided draft tube conical spouted beds has been determined as a function of dimensionless moduli that take into account the geometric factors of the contactor and the draft tube, particle characteristics and operating conditions. The hydrodynamic correlation previously obtained by our research group for conical spouted beds without draft tubes (Olazar et al. (3)) has been taken as a starting point, and a dimensionless modulus related to the aperture ratio of open-sided draft tubes has been introduced. The correlation determined is the following:
(Re0 )ms
1.68
⎛D ⎞ = 0.126 ⋅ Ar ⋅ ⎜⎜ b ⎟⎟ ⎝ D0 ⎠ 0.5
⎡ ⎛ γ ⎞⎤ ⋅ ⎢ tan⎜ ⎟⎥ ⎣ ⎝ 2 ⎠⎦
−0.57
⎛A ⎞ ⋅ ⎜⎜ 0 ⎟⎟ ⎝ AT ⎠
0.32
(2)
This equation is valid for calculating the minimum spouting velocity of stable beds in conical spouted beds with open-sided draft tubes (regression coefficient r2= 0.87, and maximum relative error below 8%) in the range of contactor geometries and operating conditions studied.
CONCLUSIONS The hydrodynamic study of conical spouted beds provided with open-sided draft tubes have been carried out operating with fine particles. The evolution of bed pressure drop with air velocity has been studied in a wide range of conditions. A very pronounced hysteresis, much higher than in conventional conical spouted beds, is obtained in the evolution of pressure drop with air velocity. Hydrodynamics of conical spouted beds with open-sided draft tube is influenced by the geometric factors of the contactor and draft tube, and operating conditions. The parameters of greater influence on the minimum spouting velocity are the contactor angle, the gas inlet diameter and the width of the faces. The value of the minimum spouting velocity increases as the width of the faces of the tube and the gas inlet diameter are decreased.
Based on a wide range of experimental results and taken as a reference the hydrodynamic correlation previously obtained for plain conical spouted beds, a new correlation has been proposed for predicting the minimum spouting velocity in opensided draft tube conical spouted beds.
ACKNOWLEDGMENT This work was carried out with the financial support of the Ministry of Science and Technology of the Spanish Government (Project CTQ2007-61167) and of the Ministry of Industry of the Basque Government (Project IE08-241).
NOTATION A0 [m2] AT [m2] Ar [-] dp [mm] dpi [mm] Dc [m] Do [m] DT [m] g [m s-2] Hc [m] Ho [m] LT [m] (Re0)ms[-] ums γ ΔPS ΔPM μ ωH ρ ρb ρs
[m s-1] [deg] [Pa] [Pa] [kg m-1s-1] [m] [kg m-3] [kg m-3] [kg m-3]
external area of the tube, empty external area of the tube, full Archimedes number, gdp3ρ(ρs-ρ)μ-2 average particle diameter average particle diameter of i fraction column diameter gas inlet diameter draft-tube diameter acceleration of gravity height of the conical section stagnant bed height length of the tube Reynolds number of minimum spouting, per unit area of inlet section, ρumsdpμ-1 minimum spouting velocity at the inlet orifice included angle of the cone operating pressure drop peak pressure drop viscosity of the gas width of the face of the tube density of the gas bed density density of the particle
REFERENCES 1. Freitas, L. A. P.; Dogan, O. M.; Lim, C. J.; Grace, J. R.; Luo, B. Hydrodynamics and Stability of Slot-Rectangular Spouted Beds. Part I: Thin Bed. Chem. Eng. Comm, vol. 181, 243-258, 2000. 2. Dogan, O. M.; Freitas, L. A. P.; Lim, C. J.; Grace, J. R.; Luo, B. Hydrodynamics and Stability of Slot-Rectangular Spouted Beds. Part II: Increasing Bed Thickness. Chem. Eng. Comm, vol. 181, 225-242, 2000. 3. Olazar, M.; San José, M. J.; Aguayo, A. T.; Arandes, J. M.; Bilbao, J. Stable Operation Conditions for Gas-Solid Contact Regimes in Conical Spouted Beds. Ind. Eng. Chem. Res., vol. 31, 1784-1791, 1992.
4. Olazar, M.; San José, M. J.; Aguayo, A. T.; Arandes, J. M.; Bilbao, J. Pressure Drop in Conical Spouted Beds. Chem. Eng. J., vol. 51, 53-60, 1993. 5. Olazar, M.; San José, M. J.; Llamosas, R.; Bilbao, J. Hydrodynamics of Sawdust and Mixtures of Wood Residues in Conical Spouted Beds. Ind. Eng. Chem. Res., vol. 33, 993-1000, 1994. 6. San José, M. J.; Olazar, M.; Aguayo, A. T.; Arandes, J. M.; Bilbao, J. Expansion of Spouted Beds in Conical Contactors. Chem. Eng. J., vol. 51, 45-52, 1993. 7. Povrenovic, D. S.; Hadzismajlovic, Dz. E.; Grbavcic, Z. B.; Vucovic, D. V.; Littman, H. Minimum Fluid Flowrate, Pressure Drop and Stability of a Conical Spouted Bed. Can. J. Chem. Eng., vol. 70, 216-222, 1992. 8. Al-Jabari, M.; Van de Ven, T. G. M.; Weber, M. E. Liquid Spouting of Pulp Fibers in a Conical Vessel. Can. J. Chem. Eng., vol. 74, 867-875, 1996. 9. Bi, H. T.; Macchi, A.; Chaouki, J.; Legros, R. Minimum Spouting Velocity of Conical Spouted Beds. Can. J. Chem. Eng., vol. 75, 460-465, 1997. 10. Nagarkatti, A.; Chaterjee, A. Pressure and Flow Characteristics of a Gas Phase Spout-Fluid Bed and the Minimum Spout-Fluid Condition. Can. J. Chem. Eng., vol. 52, 185-195, 1974. 11. Sutanto, W.; Epstein, N.; Grace, J. R. Hydrodynamics of Spout-Fluid Beds. Powder Technol., vol. 44, 205-212, 1985. 12. Zhao, J.; Lim, C. J.; Grace, J. R. Flow Regimes and Combustion Behaviour in Coal-Burning Spouted and Spout-Fluid Beds. Chem. Eng. Sci., vol. 42, 2865-2875, 1987. 13. Passos, M. L.; Mujumdar, A. S. Spouted and Spout-Fluidized Beds for Grain Drying. Drying Technol., vol. 7, 663-697, 1989. 14. Ye, B.; Lim, C. J.; Grace, J. R. Hydrodynamics of Spouted and Spout-Fluidized Beds at High Temperatures. Can. J. Chem. Eng., vol. 70, 840-847, 1992. 15. Ye, B.; Lim, C. J.; Grace, J. R. Spouted Bed and Spout-Fluid Bed Behaviour in a Column of Diameter 0.91 m. Can. J. Chem. Eng., vol. 70, 848-857, 1992. 16. Olazar, M.; San José, M. J.; Aguado, R.; Gaisán, B.; Bilbao, J. Bed Voidage in Conical Sawdust Beds in the Transition Regime between Spouting and Jet Spouting. Ind. Eng. Chem. Res., vol. 38, 4120-4122, 1999. 17. Olazar, M.; San José, M. J.; Cepeda, E.; Ortiz de Latierro, R.; Bilbao, J. Hydrodynamics of Fine Solids in Conical Spouted Beds. In Fluidization VIII; Large, J. F., Laguerie, C., Eds.; Engineering Foundation: New York; pp. 196-201, 1996. 18. Olazar, M.; San José, M. J.; Peñas, F. J.; Bilbao, J. Segregation in Conical Spouted Beds with Binary and Tertiary Mixtures of Equidensity Spherical Particles. Ind. Eng. Chem. Res., vol. 33, 1838-1844, 1994b. 19. Bilbao, J.; Olazar, M.; Romero, A.; Arandes, J. M. Design and Operation of a Jet Spouted Bed Reactor with Continuous Catalyst Feed in the Benzyl Alcohol Polymerization. Ind. Eng. Chem. Res., vol. 26, 1297-1304, 1987. 20. Olazar, M.; San José, M. J.; Peñas, F. J.; Aguayo, A. T.; Arandes, J. M.; Bilbao, J. A Model for Gas Flow in Jet Spouted Beds. Can. J. Chem. Eng., vol. 71, 189-194, 1993b. 21. Olazar, M.; Arandes, J. M.; Zabala, G.; Aguayo, A. T.; Bilbao, J. Design and Operation of a Catalytic Polymerization Reactor in a Dilute Spouted Bed Regime. Ind. Eng. Chem. Res., vol. 36, 1637-1643, 1997. 22. Altzibar, H.; Lopez, G.; Aguado, R.; Alvarez, S.; San José, M.J.; Olazar, M. Hydrodynamics of Conical Spouted Beds Using Different Types of Internal Devices. Chem. Eng. Technol., vol. 32, 463-469, 2009.
WASTE WOOD GASIFICATION: DISTRIBUTION OF NITROGEN, SULPHUR AND CHLORINE IN A DUAL FLUIDISED BED STEAM GASIFIER V. Wilk 1 , C. Aichernig 3 and H. Hofbauer 2 1 Bioenergy2020+ GmbH, Wienerstraße 49, 7540 Güssing, Austria, [email protected], + 43 1 58801 17313 2 Vienna University of Technology, Institute of Chemical Engineering, Austria 3 Repotec Umwelttechnik GmbH, Austria Abstract Waste wood was gasified in a dual fluidised bed gasifier in order to investigate the behaviour of waste fuels in this technology. The distribution of nitrogen, sulphur and chlorine between the gasifier and combustor of the dual bed system was studied to identify the requirements for gas cleaning devices. The gasification system is suitable for the use of waste wood. A slight adaption of the gas cleaning equipment was necessary compared to gasification of natural woody biomass. INTRODUCTION In order to address the challenge of climate change and global warming the European Union agreed on the binding target to supply 20% of energy from renewable sources by 2020 (1). Solid biomass is an important renewable energy carrier, and it is going to play a major role in the future. Steam gasification converts solid biomass into a high quality producer gas. Electricity and heat can be produced in an efficient way from this producer gas; it is also suitable for chemical synthesis of fuels and chemicals. At the Vienna University of Technology (VUT) the dual fluidised bed steam gasifier has been developed. The process was demonstrated in Guessing (Austria) in an 8 MWth gasification plant, that has operated successfully since 2002. In the meantime further plants are in operation or currently under construction. In all cases wood chips from natural sources are used as the feedstock. The scope of this work is to investigate the suitability of waste wood for the gasification system described above. The use of waste wood in biomass gasifiers can increase the feedstock flexibility of the gasifier and could also offer some economic advantages. Furthermore, gasification is also an interesting approach for thermal waste wood treatment in general.
1
There are different types of waste wood according to precise quality standards. Slabs, logs or chippings, bark, fibreboards and surface treated wood are suitable for biomass combustion plants. Those plants use biomass from forestry as standard fuel. In Austria there are several power plants, where waste wood is combusted, among them the biomass plant in Timelkam and St. Veit an der Glan. The boilers are circulating fluidised beds (2), (3). However, contaminated wood, such as coal tar oil treated wood or wood-polymer-composites containing halogens, is not suitable for those plants and has to be treated in waste incineration plants. Waste wood is already used in gasification processes at an industrial scale. In the autothermal gasifier in Lahti (Finland) waste wood is part of the feedstock mix (4). In the Amer power plant in the Netherlands 150.000 t/a of demolition wood are gasified. In both plants producer gas is combusted in a coal fired power station (5). The gasifiers are circulating fluidised beds with air as the gasification agent. Air gasification yields nitrogen-diluted producer gas with a low calorific value. THE DUAL FLUIDISED BED GASIFIER The dual fluidised bed gasifier is an allothermal gasifier, where gasification and combustion take place in spatially divided reactors. The principle is shown in Figure 1. The gasification reactor is fluidised with steam and a bubbling fluidised bed is formed. The residual ungasified char is transported into the combustion reactor together with the circulating bed material and is combusted with air. Combustion takes place in a highly expanded fast fluidised bed. Heat is delivered back to the gasifier by the bed material to satisfy the endothermic gasification reactions. producer gas (CH4, CO, H2, CO2, H2O)
flue gas
heat gasification
combustion
biomass steam
circulation air (bed material, char)
auxiliary fuel
Figure 1: Principle of the dual fluidised bed gasifier This gasification process has been demonstrated successfully in Guessing (Austria). Due to steam gasification the producer gas is virtually free of nitrogen. It is characterised by a high hydrogen content (>40%) and an average heating value of 12-14 MJ/m³ (stp) of dry gas. Producer gas is converted into electrical power in an internal combustion engine. Heat occurring in the process is fed into the local district heating system (6). This technology is about to be commercialised. Four new plants in the range of 10-20 MW are currently being built or are in the start-up phase. The 100kW pilot plant At the Vienna University of Technology a 100 kW pilot plant was installed. It is a prototype of the Guessing gasifier, and has been used for the design of the Guessing gasifier and is now used to further develop the dual fluidised bed gasifier. In Figure 2 the pilot plant is schematically illustrated.
2
Figure 2: 100 kW dual fluidised bed pilot plant for steam gasification Superheated steam for gasification is produced in an electrically heated steam generator. The gasification reactor and the combustion zone are connected by loop seals. In order to promote transport of solids and to prevent leakage the loop seals are also fluidised with steam. In the combustion reactor heat is generated as residual char is combusted with air. At the bottom of the combustor primary air is injected and a dense fluidised bed is formed in the bottom region. Light fuel oil is used as auxiliary fuel; it is also added there. The temperature in the gasifier is controlled by the amount of oil inserted. Usually the temperature difference between the gasification zone and the combustor is about 70 to 100°C depending mainly on the water content of the fuel and the circulation rate. Secondary air is injected at a higher level to transport particles to the top of the riser. Hot bed material is transported back into the gasification reactor. In this gasification process olivine is used as the bed material. It has been proven that olivine has good mechanical stability and shows moderate tar cracking activity (7). After leaving the gasifier the producer gas is cooled in an oil-cooled heat exchanger to temperatures around 250°C and is sampled for gas analysis. Producer gas and flue gas are mixed and combusted in an afterburning chamber with air. A cyclone removes particles before passing to the stack. Measurement equipment During the experiments, producer gas and flue gas properties are measured. The main producer gas compounds are analysed by a Rosemount NGA2000 device. The range of measurement is 0 to 100% for CH4, CO, H2, and CO2 and 0 to 25% for O2. A gas chromatograph (Syntech Spectras GC 955) is employed for the onlinemeasurement of N2, C2H4, C2H6 and C3H8. An impinger bottle method for tar measurement has been developed at VUT. It is similar to the conventional tar protocol, but has been adapted for producer gas from steam gasification. Toluene is employed as a tar absorbent. Dust, entrained char,
3
water and tar content can be analysed from one sample. Further description of the tar measurement is available in (8). NH3, HCl and H2S are also measured by the impinger bottle method. The ammonia content is determined by dissolution in H2SO4 with a molar concentration of 0.05. Ammonium sulphate is formed and detected by ion chromatography. The concentration of HCl is analysed by dissolution in H2O2 in impinger bottles. Chlorine is measured by ion chromatography. For the measurement of H2S impinger bottles filled with 35%-KOH are used. H2S is detected by potentiometric titration. In the flue gas stream the CO, CO2 and O2 content is measured by a Rosemount NGA2000 device. The range of measurement is 0 to 100% for CO and CO2 and 0 to 25% for O2. For the assessment of NO and SO2 a Rosemount NGA2000 MLT4 device is used, which allows online measurement. The HCl concentration in the flue gas stream is determined by impinger bottles filled with H2O2. Chlorine is measured by ion chromatography. FEEDSTOCK CHARACTERISATION Two different types of waste wood have been gasified in the pilot plant at VUT. Waste wood A has been provided by a manufacturer of windows and doors. It contains pieces of coated chipboards, fibreboards, surface treated wood and cardboard. It is in the form of chips with particle sizes in the range of 10 to 30 mm with a considerably high amount of fine particles. Waste wood B mainly consists of shredded furniture in the form of chips and fibres ranging from 10 to 40 mm. The content of fine particles is higher than in waste wood A. Table 1 gives an overview at the elementary analyses. Table 1: Composition of wood pellets, waste wood A and waste wood B
water content volatile matter ash content carbon hydrogen oxygen nitrogen sulphur chlorine net calorific value
% %, dry %, dry %, dry %, dry %, dry %, dry %, dry %, dry kJ/kg
wood pellets 6.11 86.45 0.29 50.23 6.04 43.67 0.05 0.005 0.003 18 753
A 6.73 81.39 1.56 48.31 5.51 43.64 2.49 0.03 0.02 18 420
B 15.49 74.82 7.90 48.71 4.78 36.41 1.99 0.08 0.13 17 719
Soft wood pellets are the standard feedstock for the pilot plant, as they are standardised fuel with defined water content and heating value. A significant ash content has been measured in waste wood B. Therefore, there is less volatile matter in this sample. The water content is also markedly higher in waste wood B. Soft wood pellets contain virtually no nitrogen, sulphur and chlorine. Noxious gases are formed from these elements. Waste wood A contains 2.49% of nitrogen, which is present in adhesives and coatings. The nitrogen content of waste wood B is in a comparable range. The chlorine and sulphur content in waste wood B exceeds the sample of waste wood A.
4
GASIFICATION EXPERIMENTS During all experiments the main operating conditions of the gasifier are kept constant in order to achieve similar reaction conditions and comparable results. The fuel input is about 100 kW. The characteristic temperature in the gasifier is 850°C in all experiments. The steam-to-carbon ratio, which is determined by fluidisation settings and water content of the feedstock, is in the range of 1.8 to 1.9. For one experiment stable gasification of feedstock lasts an average for 6 to 8 h. During gasification of waste wood B more light fuel oil was required to maintain the temperature in the gasification reactor. Due to the high water content more energy is consumed to evaporate the water in the feedstock, which results in a greater requirement of auxiliary fuel. In Figure 3 the main producer gas compounds are illustrated. All experiments yield a similar gas composition, which is typical for steam gasification of woody biomass. Gasification of waste wood decreases the hydrogen content, but increases CO and C2H4 yield. Because of the higher nitrogen content in waste wood, a significant concentration of ammonia has been detected. H2S and HCl content increases with increase of sulphur and chlorine in the feedstock.
Figure 3: Producer gas composition in vol% referred to dry gas Table 2: Solid impurities and tar in producer gas
dust content char content GC/MS tars gravimetric tars
g/Nm³, db g/Nm³, db g/Nm³, db g/Nm³, db
wood pellets 8.3 37.7 5.5 1.8
A 18.8 31.8 12.4 7.8
B >> >> >> >>
Table 2 gives an overview of the solids and tars in the producer gas. Gravimetric tars are weighed after evaporation of the solvent. A GCMS device is used to measure the content of many different tar species. As lighter hydrocarbons (especially indene and naphthalene) disappear during evaporation and not all higher hydrocarbons are analysed by GC/MS, the measuring range overlaps. The content of tars and inorganic dust increases significantly, when waste wood is gasified. Waste wood B contains high amounts of fine particles and ash, which are found in
5
the producer gas. The particle content of producer gas from waste wood B is so high, that it is not possible to take a sample for tar measurement. Thus, there is no quantitative information on tar, dust and char for the time being. DISTRIBUTION OF POLLUTANTS IN THE DUAL FLUIDISED BED GASIFIER Due to the spatial separation of the gasification and combustion reactors, two gas streams, producer gas and flue gas, are generated. Volume flows of producer gas and flue gas are calculated with IPSEpro, an equation-oriented steady state simulation software. Due to measurements the mass and energy balances form an over-determinated equation system, which is solved by the Method of Least Squares. More details about this procedure can be found in (9). Flow rates of the equilibrated solution represent the basis for balancing trace elements like N, S and Cl. Balance of nitrogen Figure 4 shows the distribution of the main nitrogen compounds in the pilot plant during the gasification of waste wood. There is N2 in air injected into the combustion zone and N2 to flush the hopper. Output streams are NH3 and N2 in the producer gas, and N2 and NO in the flue gas. In the illustration N2 in combustion air, in the hopper flush and flue gas is omitted, because it does not react. The balance shows that the vast majority of nitrogen (ca. 90%) is present in the producer gas in the form of NH3 (waste wood A = 3.4 vol%, waste wood B = 2.7 vol%). Only a small portion of fuel nitrogen is transported to the combustion section with the char. There it is oxidised, NO averages 190 ppm (waste wood A) and 110 ppm (waste wood B). During gasification also other nitrogen compounds such as HCN can be formed; they have not been analysed yet.
Figure 4: Distribution of nitrogen (left: waste wood A, right: waste wood B) Balance of sulphur As sketched in Figure 5, input streams containing sulphur are fuel and light fuel oil.
Figure 5: Distribution of sulphur (left: waste wood A, right: waste wood B)
6
In the producer gas concentrations of 210 ppm (waste wood A) and 570 ppm (waste wood B) were measured. A small amount of sulphur sticks to ash. In the pilot plant, producer gas and flue gas are mixed and combusted, then particles are separated in a cyclone. Thus, producer gas ash and flue gas ash are analysed as a mixture. When waste wood A is gasified, the SO2 concentration in the flue gas is below the detection limit. Flue gas of waste wood B contains on average 5 ppm of SO2. Other sulphur compounds that can be formed during gasification have not been analysed yet. Balance of chlorine Fuel is the only source of chlorine in the gasification process (Figure 6). Minor contents of HCl are measured in the producer gas, 35 ppm for waste wood A and 70 ppm for waste wood B. The majority of chlorine is bound to ash particles (more than 90%). HCl in the flue gas was below the detection limit in all experiments. Other chlorine compounds that might occur have not been determined yet.
Figure 6: Distribution of chlorine (left: waste wood A, right: waste wood B) GAS CLEANING The distribution of pollutants in producer gas and flue gas is an important basis for adaption of the gas cleaning system for use of waste wood. The balance of pollutants shows that nitrogen and sulphur are mainly present in producer gas and chlorine is mainly found in ash. At the demonstration plant in Guessing, producer gas cleaning consists of the following devices: a precoated bag house filter to remove particles and an organic scrubber to precipitate tars. That is sufficient for gasification of wood chips from the forest. If the system has to be adapted to waste wood, some changes will be necessary. The majority of chlorine is bound to particles, which are removed in the bag house filter. It has been shown that the precoat material, which is injected at the Güssing plant prior to the fabric filter, captures chlorine too (10). Thus, no additional effort might be necessary for chlorine. In the scrubber water in the producer gas is condensed. If the scrubber is operated at a low pH value, ammonia will be dissolved in water and will also be precipitated (11). Depending on the concentration of H2S in the producer gas and legal requirements an additional scrubber for sulphur might be necessary. Only traces of pollutants are present in flue gas in the pilot plant. As a first assessment, no adaption of the flue gas cleaning equipment (consisting only of a bag house filter) is necessary.
7
CONCLUSION Gasification experiments with two different types of waste wood have been carried out in the pilot plant at the Vienna University of Technology. From these experiments it can be concluded that the dual fluidised bed system is suitable for the gasification of waste wood. The majority of pollutants is present in the producer gas. Thus gas cleaning equipment in the producer gas line has to be adapted according to the higher concentration of pollutants in the waste fuels. ACKNOWLEDGEMENT We want to thank the team of the Notified Testing Laboratory for Combustion Systems at VUT for their support. Many thanks to EnergieAG Oberösterreich for providing the waste wood samples. Bioenergy2020+ is funded within the Austrian COMET program managed by the Austria Research Promoting Agency FFG. The financial support of the funding association FFG and the company partners Magna, Repotec and BKG is gratefully acknowledged. REFERENCES 1.
Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources 2. M. Bolhàr-Nordenkampf, I. Tschanun, S. Kaiser: Two new biomass fired FBCplants with a high fuel flexibility, Proceedings of Power Generation Industry Conference and Exhibition, Spain (2007). 3. http://www.hsenergie.eu/pdf/Prospekt%20BioCOM_080411_%20englisch_k.pdf downloaded on 7th October 2010, 04:00pm. 4. D. Granatstein: Case study on Lahden Lampovoima Gasification Project Kymijarvi power station, Lahti. IEA Bioenergy Agreement Task 36 (2002). 5. M. Spanjers, W. Willeboer: Biomass Gasification for Power Generation – Essent’s own developments, Proceedings of International Conference on Polygeneration Strategies, Germany (2010). 6. H. Hofbauer, R. Rauch, G. Löffler, S. Kaiser, E. Fercher, H. Tremmel: Six years experience with the FICFB gasification process, Proceedings of the 12th European Biomass Conference, Italy (2002) p982-985. 7. R. Rauch, C. Pfeifer, K. Bosch, H. Hofbauer, D. Swierczynski, C. Courson, A. Kiennemann: Comparison of different olivines for biomass steam gasification. Proceedings of the Conference for Science in Thermal and Chemical Biomass Conversion, Canada, vol. 1, (2004) p799-809. 8. U. Wolfesberger, I. Aigner, H. Hofbauer: Tar content and composition in producer gas of fluidized bed gasification of wood - influence of temperature and pressure, Environmental Progress & Sustainable Energy (2009) p372-379. 9. T. Pröll, H. Hofbauer: H2 rich syngas by selective CO2 removal from biomass gasification in a dual fluidized bed system - Process modelling approach, Fuel Processing Technology, vol. 89/11 (2008) p1207-1217. 10. I. Siefert: Stickstoff-, Chlor- und Schwefelbilanzen über das Biomasse- BlockHeiz- Kraftwerk Güssing, PhD Thesis, Vienna University of Technology (2004). 11. T. Pröll, H. Hofbauer: Removal of NH3 from Biomass Gasification Producer Gas by Water Condensing in an Organic Solvent Scrubber. Industrial & Engineering Chemistry Research, 44 (2005), p1576-1584.
8
COLD MODEL INVESTIGATIONS OF A HIGH TEMPERATURE LOOPING PROCESS IN A DUAL CIRCULATING FLUIDIZED BED SYSTEM Ajay R. Bidwe, Miguel A.M. Dominguez, Craig Hawthorne, Heiko Dieter, Alexander Charitos, Anja Schuster, Günter Scheffknecht Institute of Combustion and Power plant technology (IFK), University of Stuttgart, Pfaffenwaldring 23, 70569, Stuttgart, Germany. Email: [email protected] ABSTRACT The Calcium Looping process is a promising post-combustion CO2 capture technology. A 200 kWth Dual Circulating Fluidized Bed has been built at IFK, University of Stuttgart. Tests were carried out on a hydrodynamically scaled cold model. Operating parameters have been varied, while the suitability of the 200 kWth design has been proven. INTRODUCTION It is now evident that the anthropogenic CO2 emissions are causing serious global warming. Coal and natural gas based power plants are the major source of CO2 emissions and cause nearly 30 % of global CO2 emissions (1). Various pre- and post-combustion CO2 capture options are currently under investigation. One of the attractive options is the Calcium Looping (CaL) process which can be Figure 1- Scheme of Calcium Looping process integrated with the existing power plants (2). The CaL process has the advantage of low efficiency penalties of 4 to 6% (3) and can be economical compared to other suggested methods (3). The basic reaction of the Calcium Looping Process is given in Eq. 1. CaO(s) + CO2 (g) ↔ CaCO3(s)
(1)
A schematic of the CaL process is shown in Fig. 1. This process was first suggested by Shimizu et al. (4). It consists of the carbonator and the regenerator for the CO2 capture and the sorbent regeneration, respectively. The CO2 rich flue gases are fed into carbonator where CO2 reacts with CaO to form CaCO3 at 600-700°C. As a result a CO2 lean gas can is released from the carbonator. The formed CaCO3 is transferred to the regenerator where at >900 °C CO2 is released again. The sorbent (CaO) is regenerated by the reverse reaction of Eq. 1. Since the regeneration step requires energy to heat up and support the endothermic reaction at 900°C; an
oxyfuel combustion is conducted in the regenerator. The exit stream of the regenerator is CO2 rich, and after compression can be stored geologically (2). The most suitable reactors are the dual fluidized bed reactors (DFB) and the process has been successfully demonstrated in various DFB facilities (5)(6). Blamey et al. (2) reviewed the CaL process as matured enough for a stage of pilot scale demonstration. 200 kWth Pilot Plant at IFK, University of Stuttgart At IFK, University of Stuttgart, to realize the application of CaL process at pilot scale a dual circulating fluidized bed (DCFB) with a capacity of 200 kWth has been installed (7). The schematic is shown in Fig. 2, where the carbonator and the regenerator are circulating fluidized beds (CFB) and are connected together by cone valves for the solid looping between the reactors. In previous studies at IFK, CFB reactors as carbonator were found to be kinetically more effective than the bubbling fluidized bed (BFB) (5)(6). In the regenerator the heat is generated by the combustion of fuels. A CFB is commercially well proven for its ability as a combustor (8). Therefore the regenerator is also Figure 2 - Schematic of DCFB pilot plant and cold selected as a CFB. The Cone model for CO2 capture using CaO as sorbent and valve is a mechanical valve which important pressure drops considered in pressure is used also in commercial CFB boilers for transporting material to balance the external heat exchangers (9). Charitos et al. (5) used the cone valve to control the solid looping rate between two fluidized beds effectively. The cone valve offers the control and variation of the looping rate independent of the fluidization velocities in the reactors and thus allows a high flexibility in operation. The twin cone valve coupled DCFB system at IFK is a new process concept. In order to support the design of the pilot plant, a scaled cold flow model has been built and operated under conditions scaled to the real conditions in the pilot plant. The aim of this paper is summarized as follows a) Prove the hydrodynamic feasibility of this novel DCFB concept shown in Fig. 2 b) Testing of the cold model and operational boundary conditions. c) Investigate the effect of important operational parameters on the operation of the DCFB and find out the optimum way to control the solid looping rates. The operational parameters are stated in Table 1. The test will show if the required riser inventories, entrainment rates and solid looping rates can be met in the pilot plant.
DCFB Cold Model Description As shown in Fig. 2 the pilot plant consists of two CFBs namely the carbonator and the regenerator. Table 1 shows the important dimensions of the carbonator and the regenerator. A detailed description of the 200 kWth pilot plant can be found in (7). Each CFB has its own internal circulating system comprising of riser - cyclone – standpipe – loop seal - return leg - riser. Both CFBs reactors are connected by the cone valves which are placed at the bottom of the loop seals on the supply side of the standpipes. The discharge of each cone valve is directed to the other reactor. The cold model, shown in Fig. 2 is geometrically scaled in the ratio of 1:2.5. This ratio is also maintained for other components of the fluidized bed, i.e. standpipes, loop seals, etc. The cold model presented in this study is based on the scaling laws developed by Glicksmann (10). The complete set of equations is given in Eq. 2.
Δp riser u o2 ρ s u o L1 G s , , , , , , φ , PSD gL ρ g u mf L2 ρ s u o ρ s gDriser
(2)
Basic operational values of the cold model derived from the application of the above mentioned scaling laws are listed in Table 1. The particles were chosen in order to match the density ratio of Eq. 1 for the carbonator. The required particle density for these experiments is 5440 kg/m³. Iron oxide (Fe3O4) particles with a particle density of 5170 kg/m³ have been chosen. The particle size distribution (PSD) was 100 to 200 µm with a mean size of 166 µm. This PSD at cold flow scale corresponds to a PSD of about 300-500 µm for the conditions in the pilot plant. Moreover, the gas density in the regenerator is different than in the carbonator due to different gas composition and higher temperatures. The gas density of the regenerator is thus adjusted by using a mixture of CO2 and air, whereas the carbonator is operated on air. The operational parameters considered in this study are the riser velocity, the total solid inventory and cone valve opening. While parameters such as riser pressure drop ( ), riser inventory ( ), entrainment rate ( ) and cone valve flow rate ( ) are obtained as experimental results. The experimental results from the cold model are extrapolated to the pilot plant scale using the ratios shown in Table 1. These ratios are derived from Eq. 2. For example the pressure term in Eq. 2 0.87 is obtained for the hot pilot and cold model can be matched and from geometric ratio and particle densities of the hot plant and cold model. Experimental Procedure Since the regeneration of the sorbent will be conducted with high O2/CO2 ratios in the real process, oxidant staging is necessary in order to avoid temperature hot spots in the hot facility. Moreover oxidant staging will improve the combustion quality. Therefore, the pilot plant regenerator will have different axial velocity profiles. For the cold model experiments, a combined velocity rise due to air staging and gas release is simulated and the scaled velocity is applied with three air stages. The air in both risers is supplied by a blower and the gas flow rates are measured by the rotameters. Pressure drop measurements are recorded in different positions at the cold model using pressure transducers and a data acquisition system. During the experiment the following parameters are varied: carbonator velocity (u0 Ca), regenerator velocity (u0 Re), regenerator air staging ratio, total solid inventory (TSI) and cone valve openings (Acv Ca, Acv Re). Both riser circulation rates are determined
by stopping the loop seal aeration and measuring the time required to achieve a specific increase of the solid level in the standpipe. Cone valve flow rates are measured by diverting the cone valve flow into sampling ports, situated below the cone valves. Flow diversion is done for a specific time and the sample is subsequently weighed. Table 1 – Basic values of cold model and 200 kWth Calcium looping DCFB pilot plant
Parameter
Unit m m °C kg/m³ kg/m³ m/s mbar kg/m²s kg/h kg
Gcv
Pilot plant
Carbonator Cold model
0.23 10 650-700 1800 0.39 4-6 100 5-25 500-1200 30-50
Pilot plant
0.092 4 20 5170 1.18 2.5-4 115 10-45
Regenerator Cold model
0.17 10 850-900 1800 0.44 4-6 60-80 10-40 500-1200
5.52-9.05
0.069 4 20 5170 1.26 2.5-4 69-92 15-70
Ratio
2.5 2.5
1.58 0.87 0.55 5.43
Pressure Balance As seen in Fig. 2 there are two clear distinct pressure balance loops for each CFB, i.e. riser-cyclone-standpipe-loop seal-riser-loop-return leg. The pressure balance for this loop can be described as follows in Eq. 3 and Eq. 4. This is similar to what has found in other works on CFB loop (11)(12). ∆ ∆
∆ ∆
∆ ∆
∆ ∆
(3) (4)
∆ and ∆ are the pressure drops in the riser above the return leg of the loop seal. The pressure drop between the distributor and the return leg entrance and ∆ ) does not take part in the pressure balance. The total (∆ and ∆ . pressure drop in the riser is the sum of ∆ However, in the present DCFB system, there exists another loop linking carbonator and regenerator similar to Eq. 3 and Eq. 4. The pressure balance for this loop can be described as follows in Eq. 5 and Eq. 6. ∆ ∆
∆ ∆
∆ ∆
∆ ∆
(5) (6)
and standpipe pressure drop ∆ of one The absolute pressure at the exit CFB equals the sum of the cone valve pressure drop ∆ , riser top ∆ , cyclone and absolute exit pressure of the other CFB . Eq. 7 is the pressure drop ∆ cone valve characteristic equation deduced from Charitos et al. (12). It shows the relation between flow rate and pressure drop across the valve and its opening. (7) ∆ In order to ensure mass flow through the cone valve, the pressure at the bottom of the standpipe should be sufficiently high to overcome the pressure in the other
reactor. It is very important that both cone valves deliver equal amounts of mass in order to provide a hydrodynamic steady state for the entire solid looping process. RESULTS AND DISCUSSION Hydrodynamics of Single Loop Carbonator Fig. 3 shows the effect of the superficial velocity on the riser inventory. During the tests only the carbonator has been operated and the total solid inventory (TSI) kept constant. The total riser inventory clearly decreases with increasing superficial velocity. This fact is a result of the pressure balance, since a raise in velocity increases the mass in the riser top region of the riser as shown in Fig. 3 and thus taking part in the pressure balance. To balance this increased pressure drop ∆ the standpipe should create enough ∆ according to Eq. 2. This is accomplished by adjusting the required mass from the riser bottom to the standpipe. The decrease of inventory in the riser bottom is a proof of this. The inventory carried by the riser is determined to 8.5 kg at 2 m/s which corresponds to 46.1 kg for the pilot plant extrapolated by scaling ratios (see Table 1). This amount is within the inventory range required in the pilot plant. Moreover Fig. 3 shows an increase of circulation rate (measured after the cyclone). This results from the velocity increase in the carbonator and shows that the circulation rates are Figure 3 - Effect of carbonator velocity on inventory and directly related to the circulation rates. velocity. The circulation rate varies from 6.6 to 26.1 kg/m²s and corresponds to 3.64 to 14.38 kg/m²s or 545 to 2150 kg/h extrapolated to the pilot plant using scaling ratios in Table 1. Furthermore, the circulation rate should be higher than cone valve flow rate. The cone valve flow rates required in the pilot plant are in the range of 500 to 1200 kg/h, thus circulation rates projections from carbonator are satisfactory. Coupled DCFB Behaviour The most important aspect of the cold model investigations was to prove the feasibility of a DCFB system interconnected with two cone valves. The pressure balance as explained earlier shows that reactors, connected with two cone valves are hydrodynamically linked. Therefore, single parameters affecting the pressure balance will influence the hydrodynamics of both reactors. For the operation of a coupled DCFB system, the effect of parameter variations, such as riser velocities, solid inventory, cone valve openings and its effect on the cone valve flow rate as well as riser hydrodynamics were studied. For a given set of riser velocities, the total solid inventory, the cone valve opening, the cone valve flow rate and the riser pressure drops were always adjusted according to the pressure balance
established. The mass flow rates at both cone valves were equal. Variations of the riser velocity or the solid inventory have effect on the cone valve mass flow rates. The inventory transfer between both reactors achieves a stable point with a stable pressure balance and flow conditions. This behaviour can be seen in Fig. 6 and 7. The most reliable way to control the flow rates is to change the opening of the cone valves. In the experiments of Fig. 4 the u0 in both risers and TSI in the system was kept constant and only Acv in both CFB was varied. With increasing cone valve opening the flow rate increased. The cone valves used in this study were geometrically identical and flow rates measured had little deviation as observed in Fig. 4.
Figure 4 - Variation of cone valve flow with opening
Figure 5 - Cone valve characteristics
Cone Valve Characterization In Fig. 5 the variation of cone valve flow rate is shown with the product of area of the cone valve and square root of the pressure drop across the valve. A linear curve with a satisfactory fit could be observed. The final equation of the cone valve mass flow is given in Eq. 8. The deduced empirical Eq. 8 is according to equations found in the literature (13), where the solid flow through mechanical valves is a function of the opening area and square root of the pressure drop across the valve. 0.5 GCV = 0.0261ACV ΔpCV + 26.479
(8)
Regenerator Hydrodynamics at Different Air Staging Ratios In Fig. 6 and Fig. 7 the effect of air staging in the regenerator on the operational conditions in the coupled DCFB system is presented. During these experiments, the conditions in the carbonator, the total solid inventory (TSI) and the cone valve openings (Acv) were kept constant. The total volumetric flow rate (VFR) in the regenerator was kept constant at 50 m³/h therefore final u0 Re is equal to 3.7 m/s. The required VFR in the regenerator is divided into primary air (PA), secondary air (SA) and tertiary air (TA) as shown in Fig. 2. Fig. 6 shows that with increasing PA, the regenerator pressure drop decreases from 73 to 34 mbar and at the same time increases from 61 to 70 mbar giving an indication the carbonator pressure drop that the bed inventory is transferred from the regenerator to the carbonator. The raise in the carbonator pressure drop is lower compared to the regenerator
Figure 6 - Effect of air staging in regenerator on inventory distribution in DCFB system
Figure 7 - Effect of air staging on circulation rates and cone valve flow (Regenerator)
pressure drop because of the larger diameter of the carbonator. From the present results it becomes obvious that the pressure balances in the coupled DCFB system play an important role. More primary air shifts more mass towards the upper region of regenerator. This increases the regenerator standpipe pressure according to Eq. 4 and Eq. 5 increases. An increase of primary air has a major as seen in Fig. 7 and is the main effect on the solid flow rate in the regenerator driving force for the change of circulation rate in the regenerator. The amount of rates as given in Eq. 6, primary air can also increase the cone valve flow rate but due to transfer of inventory and changes of pressure values this increase is limited. CONCLUSIONS The scaled cold model investigations of the proposed DCFB system at IFK, University Stuttgart have been performed. The coupling of two CFB reactors, interconnected with two cone valves is possible. The flow rate between the beds can be controlled by the two cone valves and has a characteristic equation. The pressure balance plays an important role for the inventory distribution and cone valve flow rate. The cold model was investigated within the required design range, corresponding to the planned pilot plant. By means of this investigation, the design of the planned 200 kWth pilot plant was approved. ACKNOWLEDGEMENTS We thank EnBW Kraftwerke AG for the funding of this research project.
NOTATION Acv D
mm2 m
area of cone valve diameter
T u0 i
°C m/s
g
m/s2
gravitational acceleration
umf
m/s
W Δpi
kg mbar
kg/h cone valve flow rate kg/m2s riser circulation rate
temperature superficial velocity of riser i minimum fluidization velocity solid inventory pressure drop in i
L Ø
m [-]
dimension of a component sphericity of particles
ρg ρs
kg/m³ kg/m³
gas density particle density
Abbreviations CFB CV DCFB DFB LS PA
Circulating fluidized bed Cone valve Dual circulating fluidized bed Dual fluidized bed Loop seal Primary air
PSD SA Stp TA TSI VFR
Particle size distribution Secondary air Standpipe Tertiary air Total solid inventory Volumetric flow rate
Subscripts component i Ca Re top bot
carbonator regenerator Part of riser above return leg from loop seal till exit. Part of riser between distributor and loop seal return leg inlet.
REFERENCES 1. Mondol J.B., McIlveen-Wrighta D., Rezvania S., Ye Huanga and Hewitt N., (2009), “Techno-economic evaluation of advanced IGCC lignite coal fuelled power plantsnext term with CO2 capture”, Fuel, 88 (12), 2495-2506. 2. Blamey J., Anthony E.J., Wang J., Fennell P.S., (2010), ''The calcium looping cycle for large-scale CO2 capture'', Progress in Energy and Combustion Science, 36 (2), 260-279. 3. Hawthorne C., Trossmann M., Galindo Cifre P., Schuster A. and Scheffknecht G. (2009), ''Simulation of the carbonate looping power cycle'', Energy Procedia, 1, 1387-1394. 4. Shimizu T., Hirama T., Hosoda H. and Kitano K. A., (1999), ’’Twin fluid-bed reactor for removal of CO2 from combustion processes'', Chemical Engineering Research and Design, 77 (1), 62- 68. 5. Charitos A., Hawthorne C., Bidwe A.R., Sivalingam S., Schuster A., Spliethoff H. and Scheffknecht G., (2010), ''Parametric investigation of the calcium looping process for CO2 capture in a 10 kWth dual fluidized bed.'', Int. J. of greenhouse gas control, 4 (5), 776-784. 6. Rodriguez N., Alonso M., Abanades J.C., Charitos C., Hawthorne C., Scheffknecht G., Lu D.Y. and Anthony E.J., (2010), “Comparison of experimental results from three dual fluidized bed test facilities capturing CO2 with CaO”, In proceeding: GHGT-10 Amsterdam (19-23 Sep 2010). 7. Hawthorne C., Dieter H., Bidwe A., Schuster A., Scheffknecht G., Unterberger S. and Käß M., (2010), “CO2 capture with CaO in a 200 kWth dual fluidized bed pilot plant”, In proceeding: GHGT-10 Amsterdam (19-23 Sep 2010). 8. Basu P., (2006), ''Combustion and Gasification in Fluidized Beds”. Taylor & Francis Group. 9. Wang Q., Luo Z., Fang M., Ni M. and Cen K., (2003), ''Development of a new external heat exchanger for a circulating fluidized bed boiler'', Chemical Engineering and Processing, 42 (4), 327-335. 10. Glicksman L.R., (1993), ''Simplified scaling relationships for fluidized bed'.', Powder Technology, 77 (2), 177-199. 11. Basu P. and Cheng L.,(2000), ''An analysis of loop seal operations in a circulating fluidized bed”. Chemical Engineering Research and Design, 78 (7), 991-998. 12.Charitos A. Hawthorne C., Bidwe A.R., Korovesis L., Schuster A. and Scheffknecht G. (2010), ''Hydrodynamic analysis of a 10 kWth calcium looping dual fluidized bed for postcombustion CO2 capture'', Powder Technology, 200 (3), 117–127. 13. Davidson. J.F and Jones D.R.M. (1965), ''The flow of particles from a fludized bed through an orifice'', Rheologica Acta 4, 180-192.
HIGH SULPHUR LIGNITE FIRED LARGE CFB BOILERS: DESIGN & OPERATING EXPERIENCE M.Lakshminarasimhan, B.Ravikumar, A.Lawrence, M.Muthukrishnan Building 79, Bharat Heavy Electrical Limited, Trichy, Tamil Nadu, India 620014 phone:+91 431 2574049 e-mail: [email protected] INTRODUCTION One of the measures of the prosperity of a nation is per capita consumption of electricity. In developing countries like India the gap between supply and demand is strongly increasing. The demand for all forms of energy is expected to increase substantially in the foreseeable future and is forecasted to double by 2020. Although coal would continue to be a major energy source in India due to its availability, lignite is fast emerging as an alternate source of fuel for electricity generation. In India the total lignite potential is 4177 million tonnes. The varieties found in India (Gujarat & Rajasthan region) have moderate to high sulphur (1 to 15 %wt dry ash free) content. It has become economically necessary to use this lignite for power generation in view of spurt in energy demand while caring for the environment (by controlling the SO2 emission). CFB boilers with their in-furnace SO2 capturing capability perfectly suit these demands and are very attractive while their utilization in comparison with pulverized fuel boilers would require very expensive add-on flue gas conditioning systems. The CFB boiler technology designed by BHEL (see Notation list for acronyms) has been successfully demonstrated for utilities at the 2x125 MWe power project at Surat. Based on the excellent performance of the units at SLPP, BHEL has bagged order for 2x125 MWe CFB power plant for RVUNL at Giral, Rajasthan and 1x75 MWe CFB power plant for GEB, at Kutch, Gujarat. The plant at Giral is now operating after overcoming unique challenges for firing >15%daf sulphur lignite (one of highest sulphur-content fuel used in CFB utility-scale units). This paper provides an overview of the CFB process, its advantages, the development of CFB technology, and the experience gained from these units in particular attention to lignite fired units of 125 MWe capacities. The teething problems experienced during initial operation and their resolution form part of this paper. With the experience gained at Giral, firing high-sulphur lignite, BHEL is uniquely placed among CFB boiler manufacturers to meet market requirement of using such demanding fuels for power generation. The successful operation of the boiler after surmounting the issues is bound to stimulate utility users to adopt CFB technology for their proposed projects for such challenging fuels also. Many other large capacity BHEL CFB boilers (firing range of fuel: from Indonesian coal, lignite with high/medium sulphur to petroleum coke) are under various stages of commissioning and will be in operation in another few months. ROLE OF LIGNITE IN THERMAL POWER PROJECTS IN INDIA With growing energy consumption, India is looking at utilizing its potential energy resource in economically and environmentally sustainable manner. The coal
1
varieties found in India are of high ash content, heating values, and sulphur content. This requires substantial refinement of conventional pulverised fuel combustion technology by BHEL to suit Indian conditions. The focus has now shifted to utilizing low-grade lignite varieties with typically high moisture and ash levels with wide variation in sulphur content. The lignite in India is concentrated geographically to three states - Tamil Nadu, Rajasthan and Gujarat. The total resource estimated is around 39 billion tons with proven resource of 4.2 billion tons Table 1. Lignite resources State Tamil Nadu Rajasthan Gujarat Puduchery Jammu & Kashmir Kerala West Bengal Total
Potential million tons 31327 4485 2663 417 28 10 1 38931
Proven million tons 2831 561 785 0 0 0 0 4177
As per the latest indication, share of lignite based thermal projects is bound to receive an impetus and therefore requires careful selection of combustion technology. The lignites found at the three major states have different composition and their own unique behavior. The level of sulphur, ash and moisture, key parameters for any boiler design, shows remarkable variation among the regions. This view has been further strengthened by the operating experience of the CFB power plants designed by BHEL for these fuels Table 2. Lignite – Key parameters influencing CFB design Parameter Gujarat Rajasthan Tamil Nadu HHV(MJ/kg) 11-13 12-16 10-11 Sulphur (%wt) 1.0-4.0 <6.0 <1.0 Moisture(%wt) <42 <40 <55 Ash (%wt) <19 <20 <14 BHEL’S EXPERIENCE IN CFB BOILER BHEL has developed bubbling fluidized bed technology in response to the national need to utilize low-calorific fuels in an environmentally sustainable way as early as in 1977. As fluidized bed technology was maturing, BHEL established a test facility in 1991 and good progress has been made in basic understanding of the CFB technology. In response to the customer’s requirements and with a view to introduce this technology quickly, BHEL tied up with LURGI LENTJES BABCOCK (LLB) as collaboration partner in the year 1993. In 1995, BHEL was able scale-up to 175 t/hr boiler (scale of 30 in thermal heat input terms). The first CFB utility boiler was introduced in India in the year 1999 after securing an order for 2x125MWe CFB boiler (scaling by a factor of 3), which was the largest CFB boiler in the country till recently. The collaboration agreement period with LLB ended by the year 2003. The successful application of this technology resulted in installation of similar units at Giral & Barsingsar in Rajasthan, designed and supplied by BHEL, and a repeated
2
order at Surat Gujarat. BHEL further scaled up the CFB unit size by securing an order for 250MWe units at Neyveli, Tamilnadu. The first unit of 175t/hr for a paper plant has given a reliability of over 98% till date while firing a variety of fuels ranging from imported Indonesian coal to Indian coals. The CFB unit at Surat has also given excellent operating results with the availability being over 90% and the plant load factor of 85%. BHEL has recently focused its efforts to diversify into the international market and secured the first export order in Indonesia for a 120 t/hr boiler in 2007, which is in successful operation firing low-ash, high-moisture Indonesian coal with unburnt carbon levels in fly ash of less than 2%. A recent order for a 135 MWe CFB power plant of the Xstrata group for their prestigious Koniambo Nickel project at New Caledonia demonstrates the capability of the BHEL CFB boiler in meeting the strict environmental norms required by the project. Figure 1. BHEL’s experience in CFB design.
ADVANTAGE OF BHEL’S CFB BOILER The flue gas flow rate from a unit varies depending on the fuel characteristics, predominantly its moisture and ash contents. A controlled portion of the solids sliding down the return leg to the seal pot is diverted via the cone valve (a mechanical ash-flow control valve) to the FBHEs, while the remaining solids are returned directly to the combustor at the same temperature. By adjusting the cone valve opening, the solids flow to the FBHE can be varied, thereby maintaining the heat absorbed in the combustor constant and consequently its temperature and combustion conditions for different fuels and loads. In the absence of the FBHE this variation of the heat absorption in the primary loop (Combustor, Cyclone and Recycle System) due to variations in fuel quality or load can be achieved only by:
3
(a) (b) (c)
Varying bed density (by altering the primary to secondary air ratio and/or bed inventory), which becomes operationally difficult; Varying combustor temperature; or Varying excess air.
The above variations alter the combustion performance and reduce the boiler efficiency, and therefore BHEL CFB boiler design for lignite utilizes external fluidized bed heat exchangers (FBHE) to maintain combustor and steam temperatures. This provides excellent operational flexibility while maintaining optimum process conditions. FBHE also provides a unique option of achieving the rated reheat temperature down to even 50 % of MCR without any attemperation by only controlling the solids flow through the FBHE containing the reheater coils. This has enabled operators to handle boiler turndown operation smoothly without undue concern regarding combustor temperatures. HIGH SULPHUR LIGNITE CFB BOILER DESIGN The fuel and steam design parameters of the 125 MWe CFBC units are elaborated in Table 3. Table 3. CFB boiler steam parameters Parameters Main Steam Flow Pressure Temperature Reheat Steam Flow Outlet Pressure Outlet Temperature Feed Water Temperature
Units Design kg/s bar °C
112.5 134.6 540
kg/s bar °C
93.3 33.2 540
°C
236.8
Parameters Units Value Proximate Analysis (as fired) Moisture %wt 40.0 Ash %wt 15.0 Volatile matter %wt 20.0 Fixed carbon (by diff) %wt 25.0 High Heating Value MJ/kg 12.56 Ultimate Analysis (dry ash free) Carbon %wt 66.9 Hydrogen %wt 4.9 Sulphur %wt 13.3 Nitrogen %wt 0.9 Oxygen %wt 14.0
Solid System (Fuel, Bed Material, Limestone & Ash System) The pre-crushed lignite is withdrawn from the bunkers by two variable speed extraction drag-link chain conveyors and fed into the seal pot through self-cleaning type of rotary valves and slide gates, which shut off the fuel feed system from the combustor in case of an emergency. The system has two parallel trains both of which need to be operated for optimal fuel combustion. Inert material such as bed ash or sized sand, required for initial start-up, is fed to the combustor directly by gravity through a rotary valve. Sized limestone stored in silos is discharged through variable speed rotary valves at a required rate based on the SO2 content in the flue gas and is fed by gravity to the seal pot. Ash is removed from four different locations in the system. Coarse bed ash from the lower combustor, bed ash from the FBHE, fly ash from the collection hoppers below 4
the convective pass and air heater sections, and fly ash from the electrostatic precipitator. In order to maintain an appropriate solids inventory in the combustor, bed ash is continuously extracted from the lower combustor and FBHE through ash discharge devices cooled in fluidized bed ash coolers. Air and Gas System Combustion air is supplied to the combustor by two main streams. Two fans supply primary air after being heated in a tubular heater. The air is introduced through a wind box and grate assembly located at the bottom of the lower (refractory lined) section of the combustor. Similarly two fans supply secondary air, which after being heated in a tubular air heater is admitted into the lower combustor region by means of multiple ports located on the walls. Fluidizing air for FBHEs, seal pots, ash coolers and purge & seal air also form part of the combustion air. Flue gas leaves the combustor and passes through the cyclones, convective pass, tubular air heaters, and electrostatic precipitators. Two centrifugal-type induced draft fans ensure near atmospheric pressure at the outlet of the cyclones. The convective back pass consists of horizontal superheater, reheater and economizer surfaces with tubular air heaters for additional heat recovery. Start-up System The start-up system consists of two independent start-up burners supplied with air from the secondary air fans arranged on the sidewalls of the combustor. They are used for preheating the combustor system and the ash inventory to the ignition temperature of fuel oil. Fuel oil lances (six units) are then used to further heat up the ash inventory to the ignition temperature of main fuel - lignite. Steam-Water Circuits Feed water enters the in-line horizontal economizer tubes located in the convective back pass. The steam drum receives sub-cooled water from the economizer and feeds the evaporators. The evaporative surfaces of the boiler consist of the combustor water walls, the FBHE water walls and a tube bundle in the FBHE. A system of down-comers, distribution supply pipes and headers and relief tubes ensure adequate flow through the evaporator circuits. Drum internals separate and purify the saturated steam before it feeds the steam-cooled hanger tubes and the enclosure of the convective pass. The steam is further heated in the superheater stage I (a horizontal in-line tube bundle) located above the economizer in the convective pass. After a first stage attemperation the steam flows to the second stage superheater, which is arranged in two parts in the FBHEs. The second stage attemperation is arranged between second stage superheater and the final superheater. The final superheater is the first heat transfer surface in the back pass and is an in-line horizontal tube bundle. Cold reheat steam enters the first stage reheater located in the FBHE. The final reheater stage is located in the convective pass after the final superheater and before the economizer. Reheat steam temperature is primarily controlled by the FBHE cone valve, that controls the ash flow through the FBHE containing the reheater. A spray type attemperator located between two stages of reheater is used as a secondary control. Refer to Fig. 2 for the arrangement of the boiler surfaces.
5
Figure 2. Arrangement of heat transfer surfaces of boiler
3 4 5 6
1 2
8 7
1 Bunker 2 Combustor 3 Back pass 4 Final superheater 5 Final Reheater 6 Low temperature superheater 7 Economiser 8Tubular Air heater 9 Fluidized Ash Cooler
9 ISSUES FACED WHILE FIRING HIGH SULPHUR LIGNITE Standpipe Blockage There were three occurrences of unit stoppage due to ash hold-up in the cyclone at very low loads of about 20 MW and one suspected hold-up at about 70 MW load. Analysis & Improvements Carried Out Ash samples and boiler operating conditions were collected. The chemical compositions of the lignite, limestone and cyclone ash were analyzed. One critical factor noticed early during cyclone choking was low combustor temperature (<750ºC) and excessively high limestone addition due to non functional SO 2 measurement equipment. The phenomenon of recarbonation of calcined limestone not reacted with sulphur dioxide was suspected as a primary cause for the loose bonding of material at the cyclone standpipe leading to blockage of the cyclone. This was also reflected in the analysis by the presence of free lime. The following steps were taken up to relieve the situation: a) Limestone feed size was checked continuously with more sampling; b) Limestone feeder size was optimized by blanking some of its pockets; c) The operation procedure was revised to maintain higher combustor temperatures before start of limestone addition; d) Incorporation of automatic pincing air arrangements at the junction of cyclone and standpipe to disturb the agglomeration. It was found from samples that the limestone size was much finer than recommended. This also resulted in high throughput during low loads because SO 2 measurement was not available to control the volumetric feeder of the limestone. It should be noted that these incidents occurred during boiler commissioning and initial stabilization period.
6
Results After incorporation of the changes in the operation procedure and the pincing air arrangements, the issue stands resolved. The timing of pincing air has been subsequently reduced, as it was found that the temperature regime (stable free lime) is one of key parameter in avoiding the situation of blockage. Figure 3 shows specific recommendations on avoiding the recarbonation-prone regime for limestone addition. The curve denotes the limit of equilibrium of calcium compounds. On the left-hand side of the curve CaCO3 is stable. On the right-hand side CaO is stable. CaO is abundant if excess limestone is added to the furnace. When the temperature is reduced into the critical range indicated in the figure recarbonization may take place, creating a rather sticky carbonate that may cause agglomerates or deposits on tubes. Figure 3. Recarbonation prone zone (reference 4).
Back Pass Tube Fouling Heavy and rapid deposit build-up on the flue gas side of the heat transfer tubes has been experienced in the back pass of the boiler. Deposit buildup seems most severe at the low temperature superheater tube bank. Also growth of ash deposit in finalstage reheater tube bank is observed during the initial period of operation. This deposit increases the gas-side pressure drop and in turn operation of the ID fans with high current, causing boiler trips. Analysis & Improvements carried out The issue of deposits cropped up during loading the boiler after sorting out the cyclone blockage problem when the limestone feed rate was increased to maintain the SO2 emission within limits. The same phenomenon as with the cyclone was suspected as initiator also for this issue. In order to ascertain the root cause, samples were taken by an exposure probe in the back-pass tube location to collect short-term ash deposits, as long term exposure converts calcium carbonate to calcium sulphate. The results of the detailed study clearly points to recarbonation of free lime followed by slow sulphation of the deposit as the primary mechanism of fouling. Improvements in the soot blowing mechanism along with an increase in its frequency have helped in overcoming the fouling issue.
7
Results After implementation of high pressure soot blowers (See Fig. 4) along with a fluidization arrangement for smooth evacuation of the ash falling onto the hoppers, full load operation with limestone addition to ensure sulphur capture of more than 98% (vs. 97% design) was achieved. Figure 4 Before Soot Blowing
After Soot Blowing
CONCLUSION CFB technology has emerged as a reliable and cost-effective process for environmentally friendly power generation. Besides eliminating the need for add-on equipment for control of emission at a huge cost, the process also accepts wide variation in fuel quality, eliminates the need for pulverizer, minimizes oil requirement, and avoids slagging. The issues with regard to utilizing high sulphur fuel have been understood and changes to overcome the same have been presented. The CFB technology has been successfully demonstrated for utilities at the 2x125 MWe power project at Surat. Based on the excellent performance of the units at SLPP, BHEL has bagged order for 2x250MWe CFBC boilers for Neyveli Lignite Corporation, Neyveli, a 1x125MWe CFB Power plant for RRVUNL at GIRAL, Rajasthan, and a 1x75MWe CFB Power plant for GEB, at Kutch, Gujarat. They are under various stages of commissioning. These projects are bound to stimulate utility users to adopt CFB technology for their proposed projects. NOTATIONS CFB - circulating fluidized bed, BHEL -Bharat Heavy Electricals Limited, RRVUNL – Rajasthan Rajya Utpadan Nigam Limited, GEB- Gujarat Electricity Board, SLPP – Surat Lignite Power Plant, FBHE – Fluidized Bed Heat Exchanger, MCR – Maximum continuous rating. REFERENCES 1. 2. 3. 4.
Joint Internal BHEL Analysis report with B. Leckner (2007), Chalmers University, Sweden. M. Hupa, Investigation into backpass fouling, Åbo Akademy, Finland. Investigation for BHEL (2010). E. J. Anthony, R. E. Talbot, L. Jia, and D. L. Granatstein, Agglomeration and fouling in three industrial petroleum coke-fired CFBC boilers due to carbonation and sulfation, Energy & Fuels (2000) 14, 1021–1027. Prabir Basu, Combustion and Gasification in Fluidised beds, CRC eqn 5.7
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CHARACTERISTICS OF THE SOLID VOLUME FRACTION FLUCTUATIONS IN A CFB Sirpa Kallio, Juho Peltola, Veikko Taivassalo VTT Technical Research Centre of Finland P.O.Box 1000, FI-02044 VTT, Finland
ABSTRACT In the paper, the fluctuation characteristics of the solids volume fraction in a CFB are evaluated from measurements and Eulerian-Eulerian CFD simulations. In both cases, similarly large fluctuations are observed in the intermediate voidage range whereas dense and dilute suspension regions are more uniform, as expected. The frequency distributions of solids volume fraction are classified to represent three different suspension density regimes: dilute, dense and intermediate “bimodal” regimes. INTRODUCTION Gas-solid flow in a CFB is governed by strong fluctuations in the local solid volume fraction s. In addition to the hydrodynamics of a CFB process, heat transfer, the distribution of reagents and the chemical reactions are also strongly affected by the distribution of s. Hence accurate information on the solids distribution is vital for modelling any CFB process. Bai & al. (1) and Issangaya et al. (2) analysed temporal variations in s from measurements and Wang (3) from high-resolution simulations. These studies considered Geldart A particles in a narrow geometry. In the present study, the fluctuation characteristics of the solid volume fraction for Geldart B particles in a slightly wider geometry were evaluated from results of Eulerian-Eulerian CFD simulations and from measurements. EXPERIMENTS The solids volume fraction was determined for experiments conducted in a 2D CFB unit at Åbo Akademi University (Guldén, (4)). The height of the riser was 3 m and the width 0.4 m. The distance between the riser walls was 0.015 m which renders the bed pseudo-twodimensional. The effect of wall friction on the solids is largest at the riser bottom. In the upper dilute regions, flow patterns tend to become three-dimensional. The width 0.015 m is thus a compromise selected to reduce these phenomena. The air distributor consisted of 8 equally spaced air nozzles. Solids exit the riser from the top end and they are separated in a simple separation box from which particles fall through the downcomer into a loop seal consisting of two fluidized 0.1 m wide sections. Experiments were carried out with different particle sizes, solids loads and fluidization velocities. In the present paper, a summary of the experimental findings related to the variation in the solids volume fraction is presented. In the experiments, a dense vigorously fluctuating bottom region was observed with the highest particle concentration near the side walls. Above the bottom zone, the suspension travelled mainly upwards in a form of clusters and a more dilute suspension between the clusters. At the side walls, the particles fell down either as clusters or as a thin, more continuous wall layer.
Figure 1 illustrates the flow structure in the bottom section (height 0.03-0.34 m above the air distributor) and in a section higher up (height 1.14-1.45 m). A denser wall region with falling clusters can be recognized at both heights. The images show clusters and strands everywhere in the bed. The widths of the narrowest strands observed were approximately 2 mm. At the higher elevation, the solid concentration inside the clusters was significantly lower than in the clusters of the bottom region.
Figure 1. Images from two experiments, from the left: at the bottom for superficial gas velocity U0=2.75 m/s, at the bottom for U0=3.5 m/s, at 1.14-1.45 m height for U0=2.75 m/s, and at 1.14-1.45 m height for U0=3.5 m/s. Particle diameter is 0.256 mm. The behaviour of the particles in the CFB unit at these two heights was recorded with a digital video camera that has the image resolution of 720x576 pixels. For each case, a 30 s section of the video was analyzed to estimate the average solids volume fractions and the characteristics of the fluctuations in the chosen locations. The local instantaneous solid volume fraction s was estimated based on a comparison between the local instantaneous grey scale values of the video image with the reference values corresponding to an empty bed and to a packed bed. The same method was used e.g. in Grasa & Abanades (5). Before determining local solids volume fractions, the grey scale values were smoothed with a 3x3 pixel Gaussian filter. The used pixel filter corresponds to 1.7 mm scale in the experimental device. In Figure 2, the lateral spatial distribution of the average solids volume fraction is plotted at three heights in the bottom bed region and at 1.14-1.45 m height. The average particle size in this case is 0.256 mm, fluidization velocity 2.75 m/s and the solids loading in the riser 120 kg/m 3. According to Figure 2, a thick wall layer is formed on both side walls at both heights.
Figure 2. Experimental lateral profiles of the average solids volume fraction at three heights in the bottom bed region (left) and at 1.2-1.4 m height. Particle diameter is 0.256 mm and fluidization velocity 2.75 m/s. A more detailed analysis of the volume fraction fluctuations can be done by examining the distribution of the instantaneous solids volume fraction. This analysis can be used to identify
different flow regimes and to explain observed behaviour. The probability distributions of the instantaneous solids volume fraction are presented in Figure 3 for two heights. In the dilute area at 1.24 m height, the local solids volume fraction is always in the dilute range, whereas in the bottom zone it varies more and a bimodal distribution is typical. At the walls in the bottom bed, dense suspension regions dominate.
Figure 3. The histograms of the solids volume fraction at 1.24 m height (top) and at 0.1 m height (below). Lateral location varies from x=0.20 m (middle of the riser) to x =0.38 m (0.02 m from the right wall) (x goes from left to right, cf. Figure 2). Particle diameter is 0.256 mm and fluidization velocity 2.75 m/s. The standard deviation of the volume fraction
(
s
s
)2
(1)
describes the level of time-averaged fluctuation in the solids volume fraction as an easily comparable number. Hence, if the minimum and maximum values of the variable are known, the highest value of the standard deviation it can reach is determined by the mean value. For the solids volume fraction in a granular flow, the theoretical maximum standard deviation is
, max
f(
s
)
s
s ,max
s
,
(2)
where the s , max is the packing limit for the solid particles. The theoretical maximum standard deviation tends to zero at the dense and dilute limits and has a maximum of s , max 2 at s s , max 2 . In Figure 4, the standard deviation of the volume fraction, calculated from the same experiment as illustrated in Figure 2, is plotted at the same three heights in the bottom bed region and at 1.14-1.45 m height. In the bottom bed region, the fluctuations in the solids volume fraction are smallest close to the wall while the highest values are observed at the edge of the wall layer. Higher up in the riser the pattern is completely different. The biggest fluctuations are observed on the walls, while in the centre section the fluctuations are relatively small.
Figure 4. Experimental lateral profiles of standard deviation of the solids volume fraction at three heights in the bottom bed region. Bed mass on the riser side is 1.94 kg, particle diameter 0.256 mm, and fluidization velocity 2.75 m/s. Wang (3) plotted the standard deviation as a function of the local average solids volume fraction. In the dilute and dense regions, the fluctuations were clearly smaller than in the intermediate suspension density range. The shape of the curves was observed to be close to parabolic which can be expected because of the relationship of the mean and the standard deviation definition, Equation (1). A similar analysis of the experimental data is carried out here. Since the lateral position and the distance to the walls could also play a role in the fluctuation characteristics of the suspension, the wall region data (less than 0.02 m from the walls) were considered separately. Due to the experimental arrangement, solids volume fraction could not be measured in points where the distance to the wall was below 24 mm. The effect of the lateral location on the relationship between average and s is shown in Figure 5 for three cases.
a) b) c) Figure 5. The effect of the lateral location on the relationship between average and average s in the bottom section in the experiments. U0 =2.75 m/s. Particle diameter: a) and b) 0.44 mm and c) 0.256 mm. Total solids mass in a) and c) 3260 g, and in b) 1940 g. A high value of the standard deviation indicates extensive clustering and separation into dense and dilute suspension flows. Figure 5 indicates that both the bed mass and the
average particle size affect the clustering of the suspension. A smaller bed mass and a smaller particle size both reduce the maximum value of the standard deviation, indicating that the suspension has become more homogeneous in the experiments. Another reason for the lower measured standard deviations with the smaller particles is the finer cluster structures they produce. These can be partially filtered out by the Gaussian filter used for smoothing the images. CFD SIMULATIONS Simulation models The experiments conducted in the 2D CFB were simulated with the models based on the kinetic theory of granular flow available in the Fluent 6.3.26 CFD software (6). In addition, wider fictitious geometries were simulated for comparison. The continuity and momentum equations used in the transient simulations can be summarized for phase q (gas phase denoted by g and solid phase by s) as follows: q
qm
q
t q
u
qm q , k
=0
xk qm
t
u q ,i
q
qm
u q , k u q ,i
xk
(3)
=
q
M
p xi q
q q ,ik
q
xk qm
gi
( 1)
q ,ik
xk (
qs
1)
K gs u g ,i
pq xi
qs
(4)
u s ,i
where t is time, x is spatial coordinate, volume fraction, density, u velocity, p gas phase pressure, ps solids pressure, g gravitational acceleration, K drag coefficient, qs Kronecker M delta, the laminar stress, and the local scale turbulent stress. The granular temperature was obtained from a partial differential equation using the Syamlal et al. (6) model for the granular conductivity. The solid phase granular viscosity was calculated from the model by Syamlal et al. (7). The solids bulk viscosity and solids pressure were calculated from the formulas by Lun et al. (8) ( s , max =0.63). The k- turbulence model producing the local scale turbulent stress was the version modified for multiphase flows (“dispersed turbulence model”, Fluent (6)). At the walls, the partial slip model of Johnson and Jackson (9) was used for the solids with the specularity coefficient equal to 0.001 and the free slip boundary condition was used for the gas. For the gas-particle interaction, a combination of the Wen & Yu (10) (at the voidage above 0.8) and Ergun (11) equations was used. The frictional solids stresses were calculated from the model of Schaeffer (12). The first-order discretization for time stepping and the second-order spatial discretization were employed. The time step in the simulation was 0.2 ms. Air inflow velocity at the bottom was described by a function that reproduces the orifice locations. Analysis of the fluctuation of solids volume fraction The results were analysed and plotted in the same way as the experimental data in Figure 5. In Figure 6, results from two simulations with different particle sizes are depicted. In addition to the difference in particle size, the simulations differ in terms of the mesh spacings used in the simulations. For the larger 0.385 mm particles, the mesh spacing was 6.25 mm, whereas in the case of the small 0.256 mm particles, the bottom section (< 0.7 m) of the riser was simulated with a finer mesh with a 3.12 mm spacing. As in the experiments, in dense and very dilute flow regions in the simulations, the fluctuations in the solids volume fraction are small whereas in the intermediate suspension density range fluctuations are large due to the strong clustering tendency of gas-solids suspensions.
The results from the two simulations look similar. The highest values in the standard deviation are located very close to the volume fraction of 0.315, predicted by Equation (2), and consistent with the experiments. In both the simulation cases, fluctuations are reduced close to the walls. Similar reduction was also seen in the experiments, but there the observed wall effect was smaller (Figure 5) since no measurement data could be obtained from the near-wall region (less than 2-4 mm from the side walls). The highest value of is smaller in the case of the larger particles. This is opposite to what was observed in the experiments where smaller particles produced a more uniform suspension. In the simulations, the difference in the obtained can also be a result of the differences in the mesh spacings as indicated by the results for the case of a varying mesh spacing in Figure 6. The coarser mesh used in the simulation with larger particles does not resolve the finest clusters and this could produce a more uniform suspension in the simulation. This effect of the mesh on was confirmed by conducting simulations in coarser meshes. In the future, simulations in finer meshes should be conducted to evaluate effects of the particle size.
Figure 6. Average fluctuation in the solids volume fraction as a function of the average solids volume fraction plotted for different regions in simulations of 2D CFB experiments with fluidization velocity 3.15 m/s and particle size 0.385 mm (top) and fluidization velocity 2.75 m/s and particle size 0.256 mm (below). 'Walls' refers to a 0.015 m wide layer at the side walls. 'Centre' refers to the whole domain without the 0.05 m high bottom section, the 0.015 m wide wall sections, and a 0.05 m high section at the top exit.
The same analysis of the temporal distribution of the solids volume fraction carried out with the experimental results (Figure 3) was also repeated with the simulation data. Figures 7 and 8 show the probability distribution of the solids volume fraction and its logarithm in the core region and close to the walls for a few monitoring points. The points represent different suspension regimes.
Figure 7. Probability distributions of solids volume fraction (left) and logarithm of the solids volume fraction (right) in the riser core region at different heights in a CFD simulation. Fluidization velocity 3.15 m/s and particle size 0.385 mm
Figure 8. Probability distributions solids volume fraction (left) and logarithm of the solids volume fraction (right) close to the riser side wall (at x=0) at different heights in a CFD simulation. Fluidization velocity 3.15 m/s and particle size 0.385 mm. Examining probability distributions for a number of points, three distinct types of probability distributions can be defined and used to identify three different suspension density regimes. Firstly, for lower time-averaged volume fractions than 0.01, no volume fraction values close to the packing limit occur and the mode of the volume fraction distributions is very close to zero. In this case, the probability distributions of log( s) resemble the normal distribution. In this “dilute” suspension regime, the volume fraction fluctuations are not affected by the packing limit. Secondly, at average solids volume fractions above 0.03 and below 0.3, there are distributions that have distinct modes at very low volume fractions and right at the packing limit. The distributions of the log( s) consist of a dilute sub-distribution resembling the normal distribution and a sharp peak at the packing limit (Figures 7 and 8). As the average solids volume fraction increases, the dilute mode moves toward the packing limit mode and the packing limit peak grows until the modes merge. In this “bimodal” suspension regime, the formation of densely packed clusters significantly affects the flow behaviour. The standard deviation of the solids volume fraction increases as the mean value increases and reaches its peak values just before the two modes merge.
Thirdly, in the “dense” suspension regime the average solids volume fraction is above 0.35 and there is only one mode in the volume fraction probability distribution: the peak at the packing limit. As the mean solids volume fraction increases, the distribution narrows towards the packing limit reducing the volume fraction fluctuations. CONCLUSIONS In the paper, the variation of the solids volume fraction in measurements and simulations of a 2D CFB was analysed. Both the measured and simulated results showed very similar characteristics. In dense and very dilute flow regions, the fluctuations in the solids volume fraction are small whereas in the intermediate suspension density range fluctuations are large due to the strong clustering tendency in gas-solids suspensions. The probability distribution of the temporal solids volume fraction is characterized by different shapes for the dilute, bimodal and dense suspensions. ACKNOWLEDGMENT The authors gratefully acknowledge the financial support of Tekes, VTT Technical Research Centre of Finland, Fortum Oyj, Foster Wheeler Energia Oy, Neste Oil Oyj and Metso Power Oy. NOTATION g Kgs p t u,u U0
gravitational acceleration [m/s2] momentum exchange coefficient [kg/s2m3] pressure [N/m2] time [s] velocity [m/s] superficial velocity [m/s] volume fraction [-] Kronecker delta material density [kg/m3] standard deviation stress tensor [N/m2]
Subscripts f gas phase q phase index s solid phase x,y,z rectangular coordinates Other symbols and operators gradient operator
x
time average of x
REFERENCES 1. 2. 3. 4.
Bai, D., Issangya, A.S., Grace, J.R., Ind. Eng. Chem. Res. 38 (1999) 803-811. Issangya, A.S., Grace, J.R., Bai, D., Zhu, J., Powder Technology 111 (2000) 104-113. Wang, J., Chemical Engineering Science, 63 (2008) 3341-3347. Guldén, M. Pilotmodell av en circulerande fluidiserad bädd, Masters thesis (in Swedish), Åbo Akademi Univ., Heat Engineering Lab., Turku, Finland, 2008. 5. Grasa G. and Abanades J.C., Powder Technology, Vol. 114 (2001) 125-128. 6. Fluent Inc., Fluent 6.3 Users manual (2006). 7. Syamlal, M., Rogers, W., and O'Brien T. J. MFIX Documentation: Volume 1, Theory Guide. National Technical Information Service, Springfield, VA, DOE/METC-9411004, NTIS/DE9400087 (1993). (Referenced by Fluent (6)). 8. Lun, C. K. K., Savage, S. B., Jeffrey, D. J., Chepurniy, N. J. Fluid Mech., Vol. 140 (1984) 223-256. (Referenced by Fluent (6)). 9. Johnson, P.C., Jackson, R. J. Fluid Mech., Vol. 176 (1987) 67-93. 10. Wen, C.Y., Yu, Y.H., Chemical Engineering Progress Symposium Series, Vol. 66 (1966) No. 62, pp. 100-111. 11. Ergun, S., Chemical Engineering Progress, 48 (1952) 89-94. 12. Schaeffer, D. G. J. Diff. Eq., Vol. 66 (1987) 19-50. (Referenced by Fluent (6)).
COAL AND BIOMASS CO-GASIFICATION IN A CIRCULATING FLUIDIZED BED REACTOR Andrzej Czaplicki, Marek Sciazko Institute for Chemical Processing of Coal, 1 Zamkowa St. 41-803 Zabrze, Poland ABSTRACT Co-gasification tests of subbituminous coal with biomass were performed. A rape straw blended with coal was used in a mass ratios of coal/biomass of 25%, 50% and 75%. The gasification process was conducted in a circulating fluid bed reactor at atmospheric pressure with air and steam addition was applied to the reaction. The addition of biomass to coal resulted in a higher conversion to gas and increased the gas calorific value due to higher content of carbon oxide. INTRODUCTION Co-gasification of biomass with coal can contribute to a reduction of CO2 emissions and may reduce the dependence on fossil fuels [Valero and Uson (1), Kezhong et al (2)]. The high reactivity of biomass and its high volatile matter content suggests that some synergetic effects can be expected in a simultaneous thermo-chemical treatment of biomass and coal [Fermoso (3)]. Co-gasification of coal and biomass has been extensively studied in different scales, reactors and conditions [McKendry (4), Andre et al (5), Aznar et al (6), Pinto et al (7), Kurkela et al (8), de Jong et al (9), Brown et al (10), Chmielniak and Sciazko (11), Kumabe et al (12)]. Gasification of coal and biomass in a circulating fluid bed offers a number of advantages. However, the most important is stabilization of bed inventory due to lower reactivity of the coal char [Collot et al (13)]. As a result of decomposition three products are obtained: gas, coal tar and a solid product – char [Velez et al (14)]. The degree of decomposition of the fuels used and the efficiency of the process depend mainly on the temperature of gasification. Typically the process is carried out in the temperature range of 800 to 1000 °C. The main difference is manifested in the yield of products depending on the coal/biomass ratio.
TEST RIG DESCRIPTION The testing facility used for the study of solid fuels gasification was aimed at obtaining two basic products: char and process gas. The main components of the plant were: a circulating fluidised bed reactor, a system for raw material preparation, char separation from a hot process gas, a process gas cooling and cleaning, and a combustion chamber for process gas utilization. The gasification reactor consists of two sections: the lower, composed of two inverted cones connected by a cylindrical section of 0.38 m I.D., and the upper part – a riser of 0.14 m I.D. The total length of the riser was 4.41 m. The reactor operation depends on a gas phase velocity and on a solid phase concentration. The bottom section operates as a turbulent or a bubbling fluidised bed. These hydrodynamic conditions enable high values of heat/mass transfer coefficients and good mixing of blended fuels in the bed to be achieved. A proper blends of coal and biomass after drying were delivered to the test facility in containers. The plant configuration and instrumentation enabled studying the gasification processes over a wide range of temperatures, using air and steam as gasification agents. The process diagram of the plant is shown in Figure 1.
Figure 1. Test rig for coal - biomass co-gasification The temperature and pressure distributions were measured throughout the system and air, steam and process gas flow rates were recorded for a mass balance evaluation. Coal, biomass and char mass flow rates were determined by averaging measured mass of the solids fed or received over the testing time.
TESTING METHODOLOGY Six tests were performed to gasify coal (Run 1), biomass (Runs 5 and 6) and coal – biomass mixtures (Runs 2 - 4). A hammer mill and a drum dryer-mixer were used to prepare homogenous raw materials and their mixtures. Tests were started once the plant had been checked and the raw material prepared. The reaction system was heated to the desired temperature by burning a butane - propane mixture. The feeding of coal was started when the temperature in the gasification zone reached about 650°C. Having initiated the process of gasification, the burner of the start-up combustion chamber was shut off. Once a stable temperature in the gasification zone was achieved at the level of about 800°C the start-up material was replaced with the proper fuel and the assumed process parameters were achieved. The test was started after stabilisation of process conditions. On average, one test run was carried out over 4 h. PROPERTIES OF FUELS USED The composition of the raw materials gasified is given in Table 1. Table 1. Raw material composition in the gasification tests Raw material composition, w/w % Test No. Wieczorek coal Rape straw Run 1 100 0 Run 2 75 25 Run 3 50 50 Run 4 25 75 Run 5 0 100 Run 6 0 100 Properties of Wieczorek coal and rape straw are given in Table 2. Table 2. Physicochemical properties of Wieczorek coal and of rape straw*) Property Wieczorek coal Rape straw Proximate analysis, w/w % Moisture content 3.9 6.1 Ash content 9.9 4.3 Volatile content 30.6 73.8 Ultimate analysis, w/w % Total sulphur content 0.6 0.1 Total carbon content 72.4 45.1 Hydrogen content 4.2 5.2 Nitrogen content 1.1 0.4 Chlorine content 0.28 0.31 Fluorine content <0.01 <0.01 Net calorific value, MJ/kg 28.4 15.4 )
* air dry basis
The characteristic temperatures of Wieczorek coal and rape straw ash fusibility are given in Table 3.
Table 3. Characteristic temperatures of Wieczorek coal and rape straw ash under oxidative conditions Characteristic temperature, °C Sintering point Softening point Melting point Flow point
Wieczorek coal
Rape straw
1100 1200 1220 1280
900 1380 1530 1540
The partice size analysis of the Wieczorek coal and of rape straw are given in Table 4. Table 4. Particle size analysis of Wieczorek coal and of rape straw Content, % Particle size, mm Wieczorek coal Rape straw >3.15 6.8 3.8 3.15-2.0 14.3 11.0 2.0-1.0 22.9 32.5 1.0-0.5 17.4 29.9 0.5-0.2 15.5 16.7 <0.2 23.1 6.1 Total 100.0 100.0 PROCESS DATA Basic process parameters of gasification tests are presented in Table 5. Table 5. Process parameters of gasification tests Test No.
Run 1
Run 2
Run 3
Run 4
Run 5
Run 6
940
981
910
966
899
812
957
937
990
1094
1021
944
921
865
925
897
912
836
866
825
805
743
793
795
742
705
658
560
655
720
80
63
56
58
60
61
137
152
100
101
149
154
115.2
117.5
90.4
85.6
94.7
92.1
20.5
24.2
20.02
18.9
19.3
18.9
11.0
0.3
0.3
0.8
2.8
0.0
Temperatures o
TR02, C TR03, °C TR04, °C TR05, °C TR06, °C TR10, °C TR11, °C
Gasification zone Outlet from the reactor Riser Expander Downstream the cyclone Air to the reactor Steam
Flow rates 3
FR01, m /h 3
FR02, m /h FR12, kg/h
Air to the reactor Air to the combustion chamber Steam to the reactor
Table 5 (continued) Test No. Process gas downstream 3 FR041, m /h the cyclones battery Pressures Gasification PR02, kPa zone Outlet from PR03, kPa the reactor PR04, kPa Riser
Run 1
Run 2
Run 3
Run 4
Run 5
Run 6
167.0
159.0
133.0
125.0
157.0
163.0
6.1
6.3
6.6
9.3
8.7
9.4
1.8
2.3
1.9
2.1
4.0
5.0
2.0
2.4
2.0
2.2
4.1
5.7
42.9
35.6
44.3
61.7
80,6
83.5
82.7
70.8
97.0
116.0
Output of the screw-conveyor feeder Feeder VR01, % 26.3 revolutions Coal/biomass kg/h
flow
rate,
112,0
ANALYSIS OF RESULTS Mass balances for the gasification tests were made and were found to agree within a relative error not higher than ±3%. The fuel conversion efficiency in a particular test is presented in Fig. 2. It was calculated as feedstock quantity converted to gas divided by the total solid fuels fed to the reactor expressed in w/w %.
Figure 2. Fuel conversion efficiency versus feed composition (TG - gasification temperature) The fuel conversion efficiency increased with increasing rape straw content in the gasified mixture (which ranged from 48% for coal to 81% for rape straw). The highest increase in the fuel conversion efficiency was observed at rape straw contents in the mixture of 75% to 100%. A decrease of straw gasification
temperature by around 90°C resulted in the decrease of its conversion efficiency by about 2%. The output data for process gas and char are shown in Fig. 3. The highest yield of gas (2.4 kg/kg) was obtained for the mixture containing 75% coal and 25% rape straw. The lowest yield (1.71 kg/kg) was for pure rape straw gasified at 812°C. The char yield systematically decreased with increasing rape straw content in the mixture. A decrease in straw gasification temperature by around 90°C resulted in a slight (by 0.2 kg/kg) increase in char yield.
Figure 3. Process gas and char yields
Figure 4. Main combustible component content in the process gas
Hydrogen, carbon oxide, methane and ethane contents in the process gases obtained from the tests are presented in Fig. 4. The content of carbon oxide, methane and ethane show an increasing trend with an increase in the rape straw content in the gasified mixtures. The hydrogen content reached a minimum (4.90%) for the mixture containing 50% of Wieczorek coal and rape straw. The content of the condensable organic matter (tar) in the process gases (presented in Fig. 5) increased with increasing content of the rape straw in the mixtures and ranged from 6 g/Nm3 for pure Wieczorek coal to 12.6 g/Nm3 for pure rape straw. Depending on process parameters the HHV of process gas ranged from 4 to 7 MJ/m3 (Fig. 6).
Figure 5. Yield of condensable organic matter
Figure 6. Process gas high heating value
CONCLUSIONS The mass balances for the gasification tests had relative error smaller than 3%. The fuel conversion efficiency (feedstock quantity converted to gas divided by the total solid fuels fed to the reactor) increased as long as the rape straw content increased in the gasified blend. A drop in gasification temperature with rape straw by 90oC caused the drop in its conversion efficiency by 2%. The addition of biomass to coal resulted in a higher conversion to gas and increased the gas calorific value due to higher content of carbon oxide. REFERENCES 1. A. Valero, S. Uson. Oxy-co-gasification of coal and biomass in an integrated gasification combine cycle (IGCC) power plant. Energy 31 (2006), 1643-1655. 2. l. Kezhong, Z. Rong, B. Jicheng. Experimental study on syngas production by co-gasification of coal and biomass in a fluidized bed. International journal of hydrogen energy 35 (2010) 2722-2726. 3. J. Fermoso, B. Arias, M.V. Gil, M.G. Plaza, C. Pevida, J.J. Pis, F. Rubiera. Co-gasification of different rank coals with biomass and petroleum coke in a high-pressure reactor for H2-rich gas production. Bioresource Technology, 101, (2010), 3230-3235. 4. P. McKendry. Energy production from biomass (part 3): gasification technologies. Bioresource Technology, 83, (2002), 55-63. 5. R.N. Andre, F. Pinto, C. Franco, M. Dias, I. Gulyurtlu, M.A.A. Matos, I. Cabrita. Fluidised bed co-gasification of coal and olive oil industry wastes. Fuel 84 (2005) 1635-1644. 6. M.P. Aznar, M.A. Caballero, J.A. Sancho, E. Frances. Plastic waste elimination by co-gasification with coal and biomass in fluidized bed with air in pilot plant. Fuel Processing Technology 87 (2006) 409-420. 7. F. Pinto, F. Franco, R.N. Andre, M. Miranda, I. Gulyurtlu, I. Cabrita. Cogasification study of biomass mixed with plastic wastes. Fuel 81 (2002) 291-297. 8. E. Kurkela, J. Laatikainen, P. Stahlberg. Clean coal technology programme. Paper C9, vol. III. University of Sttuttgart: 1995. p. 1-20. 9. W. de Jong, J. Andries, K.R.G. Hein. Coal/biomass co-gasification in a pressurised fluidised bed reactor. Renewable Energy 16 (1999) 110-1113. 10. R.C. Brown, Q. Liu, G. Norton. Catalytic effects observed during the cogasification of coal and switchgrass. Biomass Bioenergy 18 (2000) 499-506. 11. T. Chmielniak, M. Sciazko. Co-gasification of biomass and coal for methanol synthesis. Applied Energy 74 (2003) 393-403. 12. K. Kumabe, T. Hanaoka, S. Fujimoto, T. Minowa, K. Sakanishi. Cogasifycation of woody biomass and coal with air and steam. Fuel 86 (2007) 684-689. 13. A.-G. Collot, Y. Zhuo, D.R. Dugwell, R. Kandiyoti. Co-pyrolysis and cogasification of coal and biomass in bench-scale fixed-bed and fluidised bed reactors. Fuel 78 (1999) 667-679. 14. J. F. Velez, F. Chejne, C.F. Valdes, E.J. Emery, C.A. Londono. Cogasification of Colombian coal and biomass in fluidized bed: An experimental study. Fuel 88 (2009) 424-430.
A PYROLYSIS PILOT UNIT INTEGRATED TO A CIRCULATING FLUIDIZED BED BOILER - EXPERIENCES FROM A PILOT PROJECT J. Autioa,*, J. Lehtoa, A. Oasmaad, Y. Solantaustad, P. Jokelab, J. Alinc Metso Power, Kelloportinkatu 1 D, PO Box 109, FI-33101 Tampere, Finland b UPM, Eteläesplanadi 2, PO Box 380, FI-00101 Helsinki, Finland c Fortum, Keilaniementie 1, Espoo, PO Box 100, 00048 Fortum, Finland d VTT, Biologinkuja 5, PO Box 1000, FI-02044 VTT, Finland
a
ABSTRACT A novel integrated pyrolysis pilot plant has been built in Tampere, Finland by Metso, in co-operation with UPM, Fortum and VTT. A 7 tons of bio-oil per day (2 MW fuel input) fast pyrolysis unit has been integrated with Metso’s 4 MW th CFB pilot boiler. Test runs of bio-oil production have been carried out during 2009-2010. More than 80 tons of bio-oil has been produced and utilization tests have been started in district heating burner applications. INTRODUCTION Bio-oils from plant residues are an alternative to fossil fuels. The industrial feedstocks forest residues, forest industry residues, and some agricultural residues such as straw can be used. It has been estimated that these residues offer a considerable, potential source for conversion. The use of bio-oil (bio crude, fast pyrolysis oil, flash pyrolysis oil, pyrolysis liquid) as a fuel oil in industrial kilns, boilers, diesel engines, and gas turbines has been tested. Further use in the production of chemicals and feedstock for synthesis gas production (and the further production of transportation fuels and C1-chemicals) is also being developed. Bio-oil has also been upgraded on a small scale, to transportation fuel fractions. However, at the moment bio-oil is only used commercially in the food flavoring industry (1). Once produced, bio-oils can be shipped, stored and utilized much like conventional liquid fuels, if their specific fuel properties are taken into account. As experienced in test use over the years, bio-oil qualities have varied considerably. However, once larger scale observations are available, the specific properties of bio-oils will be better understood and proper utilization procedures developed. Introducing a new fuel, for example bio-oil, into the markets, will not be easy. Bio-oil is different to conventional liquid fuels and many challenges remain. In view of such difficulties, a stepwise market introduction is proposed: bio-oil would first replace fuel oil in boilers, where its properties would not prove prohibitive. An example of such a chain has been presented earlier (2). Once the entire chain from biomass to fast pyrolysis plants to heat utilization has been proven, other applications may be demonstrated. There is general awareness of the current competition for good quality biomasses for use as fuel. Biomass is especially popular in power production, with market incentives in place in many EU countries to produce green electricity. Power plants will remain large users of wood fuels, since, for example, modern fluidized-bed boilers can use several types of biomass. To compete on the markets, bio-oil
production must provide a higher payoff for the investor than the current alternatives. The bio-oil production potential of the European pulp and paper industry has been analyzed by Sipilä et al (3). Their findings show that the European pulp and paper industry alone has the potential to build up to 50 pyrolyzers integrated with fluidizedbed boilers. In the short-term, the bio-oil market lies in fuel oil and natural gas replacement in lime kilns and boilers. The challenge is to develop and demonstrate a technical and economic concept for these applications. Metso, UPM, and Fortum have agreed on a joint venture forming part of a more extensive development program focusing on the development of pyrolysis technology and its introduction on the market. This joint venture, which targets the promotion and advancement of research on an integrated bio-oil concept, involves expertise in the implementation of this concept across the entire value and production chain. Metso is in charge of delivering the related technology. UPM, a raw material supplier, plant operator and user of the final product, and Fortum, plant operator and user of the final product, complete the value chain. VTT (Technical Research Centre of Finland) is a research partner in the work. INTEGRATED PYROLYSIS PILOT Metso has built the world’s first integrated pyrolysis pilot plant in Finland in cooperation with UPM, and VTT. A 7 tons of bio-oil per day (2 MW fuel) fast pyrolysis unit has been integrated with Metso’s 4 MW th circulating fluidized bed pilot boiler, located at Metso’s R&D Center in Tampere. This project is also partly funded by TEKES, the Finnish Funding Agency for Technology and Innovation. The joint venture was begun in 2007 (Fortum joined in August 2009), the pilot plant was finalized in early 2009, and hot commissioning took place during the spring and summer of 2009. The hot sand of a fluidized bed boiler offers a considerable opportunity to integrate the pyrolysis and combustion processes. A fluidized bed boiler acts as a heat source for pyrolysis, in addition to which it can easily combust the coke and uncondensed gases produced during the pyrolysis process, into electricity and heat. In this way, high efficiency can be achieved also for pyrolyzed fuel. In addition, when integrated with a fluidized bed boiler, pyrolysis represents a cost efficient way of producing biooil, which can be used to replace fossil oils. Considerable savings can be achieved in operating costs and the price of the investment in both new boiler projects and retrofit solutions. Industrial, fluidized bed boilers are very large in size, totaling several hundreds of megawatts at their largest, for which reason even large pyrolyzers can be integrated. The operation of the entire integrated plant can be optimized through efficient and intelligent automation. It may be possible to produce electricity, heat and bio-oil at the same boiler plant in the future. The process is illustrated in Figure 1. Hot solids from the pilot boiler are led into an entrained flow reactor, in which solids (sand and char) are separated from the gas stream and returned to the boiler circulation loop. The formed pyrolysis gases are condensed in a 2-stage scrubber and condenser system. The remaining noncondensable gases are recycled to the reactor as fluidization medium, while surplus
NCG’s are led to the boiler through a lance. Product oil from the condensing stage is led to an intermediate storage tank.
Figure 1. Pyrolysis pilot unit flowsheet First results from bio-oil production have been encouraging. During the first season of test runs, more than 80 tons of bio-oil has been produced. The integrated concept has been verified as a reliable and flexible technology for the production of bio-oil. Compared to stand-alone pyrolysis unit with a non-optimal small boiler for combustion of pyrolysis by-products (char and pyrolysis gases), the integrated concept is easy and smooth to operate. For pyrolysis having a steady and smooth flow of input energy (i.e. boiler sand) to pyrolysis is a considerable advantage from the operator point of view. The integration of pyrolysis and combustion processes has been successful. Pyrolysis process can be started, operated and shut down without compromising boiler process. The controllability of the integrated concept has also been good. One of the critical features of pyrolysis is the ability to maintain constant reactor temperature during operation. An example of the controllability of the integrated concept is shown in Figure 2. Since pyrolysis reactor is integrated directly to the boiler, there is an interest to find out how potential boiler temperature variations effect pyrolysis temperature. Boiler temperature disturbances are seen in Figure 2. It may be seen that pyrolysis temperature control handles rapid changes of boiler sand temperature smoothly.
550
825
525
800
500
775
475
Pyrolysis temperature, °C
Boiler bed temperature, °C
850
Boiler sand Reactor 750
450
5.11.09 11:00 5.11.09 17:00 5.11.09 23:00
6.11.09 5:00
Date & Time Figure 2. An example of pyrolysis reactor temperature control Solids contents in bio-oil have been below 0.2 wt-% using pine as feedstock without any additional solids removal stage. Removal of solids from extractive-rich (forest residues) bio-oils is challenging and needs further development. A continuous centrifuge has been used successfully (Figure 3), other methods will also be developed.
0,2
Solids, wt%
IN
OUT
0,1
0,0 Figure 3. Solids removal by on-line centrifuge, average solids reduction 40 wt% from two measurements QUALITY CONTROL A quality control chain will be developed for the whole chain from biomass selection to oil use. Feedstock drying down to 8 – 10 wt-% moisture content is monitored with on-line moisture analyzer. The ground and dried feedstock is then analyzed for fuel oil properties. The amounts of volatiles and/or ash give indication on pyrolysis organic yields (Figure 4).
75
Organic yield wt%
70 65 60 PDU Pilot pine 1 Pilot pine 2 Pilot pine 3 Pilot FR1 Pilot FR 2
55 50 45 40 35 70
75
80
85
90
Volatiles wt%
Figure 4. Yield of organic liquids in biomass pyrolysis as a function of feedstock volatile matter, wt % based on dry feed
During pyrolysis, water and solids content in the product liquid will be followed online. Due to the novelty of the product medium, pyrolysis oil, analysis was first restricted to laboratory analysis and automatic on-line analyzers, like on-line KarlFischer titration. These provide accurate results from samples, but take time and, in the long run, are expensive due to reactant consumption and labor costs. Advances were made in measurement technology for in-line measurement of bio-oil moisture, one of the key properties that are continuously monitored, when Metso’s microwavebased analyzers (MCA) were successfully tested. Microwave technology is widely used in e.g. pulp & paper industry for pulp consistency measurements. The in-line analyzer provided a fast response time and consistent results (Figure 5) during the tests and is a good candidate for commercialization. In Figure 5 it can be seen that the MCA (green line) followed quite well the laboratory water content analyses (red □) and KF on-line water analyses (blue ◊). First the seed oil (older oil in condensers) was replaced which can be seen as a decrease in water content. Then the water content balanced to about 26 wt-%. Due to change of feedstock batch into a wetter one, the moisture content started to increase. Around 30 – 35 wt-% water content, inhomogeneous oil is produced and water contents vary until they balance again.
40
Moisture, wt%
35 KF 30
OLKF MCA
25
20 16.3.10 12:00
18.3.10 0:00 19.3.10 12:00 Date & Time
21.3.10 0:00
Figure 5. MCA for in-line water content, results from the pilot plant Particle sizing will be carried out using an on-line particle measurement system developed by Pixact Ltd. Measurement will be based on the high-magnification imaging of particles flowing through the measurement cell and an on-line image analysis procedure. Furthermore, a special algorithm will be used to detect particles in the images. In addition to size measurement, other properties such as shape
parameters can also be analyzed. VTT compared the results with a commercial particle counter and a good correlation was obtained. These methods yield reproducible results, but the data is qualitative and intended to register sudden changes in solids concentrations. The results also correlated well with the actual change in solids. BIO-OIL COMBUSTION UPM’s focus is on using bio-oil as a substitute for both light and heavy fuel oil in heating and combined heat and power plants. Fortum is focusing on replacing heavy fuel oil. Their previous combustion tests in 2002 - 2003 (2) showed promising results in relation to combustion on a small scale (below 1 MW). Oilon has developed two model burners for bio-oils. Bio-oil was combusted in Fortum's 1,5 MW district heating plant in Masala, Finland. Existing burner was changed to a new bio-oil burner, which is actually a modified heavy fuel oil burner. Total amount of bio-oil combusted in spring 2010 was about 20 tons. One main focus area was overall functionality of bio-oil receiving, storing and pumping system. Receiving system and oil tank were located outside the building. System worked well despite the outside temperature which varied from -20 °C from +10 °C during test period. Another focus was in burner function. As a result, good reliability and satisfactory turn-down-ratio of 1:3 were gained. Flue gas emissions were close to those of heavy fuel oil - with 4 % O2 CO emissions ranged from 0 to 10 ppm, and NOx from 300 to 400 ppm. Organic compounds were under 5 mg/m3n and particulate emission in range of 150 - 200 mg/m3n. A new test run in Fortum's district heating plant will be carried out in autumn 2010. After demonstrating the replacement of heavy fuel oil use the focus of UPM will be in light fuel oil replacement. Based on the experiences, bio-oil is technically suitable for replacing heavy fuel oil in district heating plants. Emissions (CO, NOx, particulate) were close to heavy fuel oil. No significant odors were released in the neighborhood. SUMMARY A novel integrated pyrolysis pilot plant has been built to Finland by Metso, in cooperation with UPM, Fortum and VTT. A 2 MW fuel (7 t oil/d) fast pyrolysis unit has been integrated with Metso’s 4 MW th circulating fluidized bed boiler, located at Metso’s R&D Center in Tampere, Finland. Test runs of bio-oil production from forest residue chips and sawdust have been carried out during 2009-2010. More than 80 tons of bio-oil has been produced and utilization tests have been started in burner applications. Quality control methods have been developed and hence water can be followed inline. On-line solids measurement is under testing.
The integration of the pyrolysis and combustion processes has been successful, and the controllability of the integrated concept has been excellent. ACKNOWLEDGEMENTS At Metso Heikki Ikonen, Markku Toots, Antti Kaura and Jani Laine are acknowledged. At VTT Sampo Ratinen, Pekka Saarimäki, Jaana Korhonen, Sirpa Lehtinen, Anssi Källi, and Jouko Kukkonen are acknowledged. NOTATION FR = Forest residue KF = Karl-Fischer laboratory titration OLKF = On-line Karl-Fischer analyzer MCA = Metso in-line analyzer REFERENCES (1) (2) (3)
http://www.redarrowusa.com/redarrow.html Gust, S. (1997). Combustion of Pyrolysis Liquids. In: Biomass Gasification and Pyrolysis, State of the Art and Future Prospects, Kaltschmitt, M., Bridgwater, A., (eds), CPL Press, Newbury, UK. Sipilä, E., Vasara, P., Sipilä, K., Solantausta, Y. (2007). Feasibility and Market Potential of Pyrolysis Oils in the European Pulp and Paper Industry. 15th European Biomass Conference & Exhibition. Berlin, Germany, 7 - 11 May, 2007. ETA-WIP
HIGH-FLUX TRIPLE BED CIRCULATING FLUIDIZED BED (TBCFB) GASIFIER FOR EXERGY RECUPERATIVE IGCC/IGFC Chihiro Fushimi1*, Guoqing Guan1,2, Masanori Ishizuka1, Yu Nakamura1, Atsushi Tsutsumi1, Yoshizo Suzuki3, Eldin Wee Chuan Lim4, Yongpan Cheng4, Chi-Hwa Wang4 1
Collaborative Research Center for Energy Engineering, Institute of Industrial
Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 153-8505, Japan 2
NJRISE, Hirosaki University, Japan
3
Clean Gas Group, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan
4
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576 * [email protected], Tel: +81-3-5452-6293 Fax: +81-3-5452-6728
Abstract The flow behavior of silica sand, of average particle size 128 μm, was investigated using a large-scale triple-bed combined circulating fluidized bed (TBCFB) cold model, which was composed of a 0.1 m I.D. ×16.6 m tall riser, a solids distributor, a 0.1m I.D. × 6.5 m long downer, a gas-solids separator, a 0.75 m × 0.27 m × 3.4 m bubbling fluidized bed and a 0.158 m I.D. × 5.0 m tall gas-sealing bed (GSB) with a high solids mass flux. The main focus of this study is to determine effect of riser secondary air injection on solids mass flux (Gs) and solid holdup. Gs slightly increased by secondary air injection when the riser gas velocity (Ugr) was less than 10 m/s. This was caused by the increase in the pressure difference between the GSB and the riser. Secondary air injection had little influence on the solid holdup in the riser. The mixing between silica sand and coal particles was investigated for two different coal feeding arrangements by coupling Computational Fluid Dynamics (CFD) with the Discrete Element Method (DEM). The results show a tangential arrangement provided better mixing than a normal arrangement except near the entrance. INTRODUCTION Coal utilization is one of the major contributors of anthropogenic CO2 and pollutants emission. Clean Coal Technology (CCT) has been under development to find ways
for more efficient utilization of coal. So far, Integrated coal Gasification Combined Cycle (IGCC) and Integrated coal Gasification Fuel-cell Combined cycle (IGFC) have been developed to increase the thermal efficiency of coal-fired power plants. For improvement of the thermal efficiency of coal-fired IGCC/IGFC, an advanced IGCC (A-IGCC) or advanced IGFC (A-IGFC) system with exergy recuperation was proposed (1,2). In this system, the waste heat from a gas turbine or solid oxide fuel cell is recuperated as a heat source for steam gasification of coal to reduce the partial combustion of coal. Because the reaction temperature for gasification is expected to be 700-900 °C, which is not suitable for conventional entrained bed gasifiers, a novel triple-bed combined circulating fluidized bed (TBCFB) gasifier was proposed (3-5). The TBCFB is composed of a downer pyrolyzer, a bubbling fluidized bed (BFB) char gasifier, and a riser partial combustor of unreacted char. The heat generated in the riser partial combustor is supplied to the downer pyrolyzer for the endothermic reaction by using a heat media such as silica sand
(3-5). According to
the mass and energy balance calculation for the system (2,6), the solids mass flux (Gs) of the heat media should be 511 to 684 kg/(m2•s), which is much higher than in a conventional CFB (7-10) to make the A-IGCC/IGFC system feasible. Besides the requirement for a high solids flux of the heat media, the mixing behavior between the heat media and reactant, i.e. silica sand and coal, is also critical. Since the residence time of the solid particles in the downer is usually quite short, the heat carried by the silica sand needs to be effectively transferred to the coal for pyrolysis. In this study, a preliminary investigation was carried out to study the mixing behavior between sand and coal particles by numerical simulation. In our previous study (5), we set up a large-scale TBCFB gasifier cold model and investigated flow behavior of the sand particles. The maximum obtained Gs was over 400 kg/(m2•s) when the riser gas velocity (Ugr) was 12 m/s, and the average solid holdup in the bottom dense region of 0.110 to 0.122 at Gs=377 to 410 kg/(m2•s) was achieved by installing a gas-sealing bed (GSB) between the riser and BFB. However, it was observed that some amount of air passed from the riser bottom to the GSB bottom when the riser gas velocity was high. Thus, in this study, some amount of riser gas was injected in the second nozzle 1.9 m above the riser bottom and the influence of the secondary air on Gs and solids holdup was investigated.
EXPERIMENT AND SIMULATION Experimental Figure 1 shows a schematic image of the large scale TBCFB cold model, which is composed of a riser (0.1 m I.D. × 16.6 m), a solid distributor for downer, a downer (0.1 m I.D. × 6.5 m), a gas-solid separator and a BFB (0.75 × 0.27 × 3.4 m3). Sand particles with a density of 2600 kg/m3 and an arithmetic average particle size of 128 μm (minimum fluidization velocity was 0.0074 m/s) were used as bed material. To increase the driving force to transport a large amount of particles from the BFB
Figure 1 Experimental apparatus (5)
into the riser bottom, a gas-sealing solids bed (0.158 m I.D. × 5.0 m) was installed. The sand particles overflowed from the BFB were transported to the GSB through an inclined tube. The sand particles were fluidized by air in the GSB and transported to the riser bottom. The bed height in the GSB was 4.0 m. At the top of the riser, the solids passed through a smooth elbow into cyclone 1 for gas-solids separation, and some small solids were collected by cyclones 2 and 3, and returned to the dipleg of cyclone 1. At the top of the downer, the solids were redistributed by a solids distributor with 13 vertically positioned brass tubes with a diameter of 19 mm using an air assist. The air was introduced into the downer at the entrance of the downer and the solids and air flowed downwardly. At the end of the downer, the solids were separated from the air by a separator and passed to the BFB. The solids entrained by the gas were collected by a cyclone and returned to the BFB. For the seal between the downer and the BFB, a seal tube (0.15 m I.D. × 1.0 m long) was inserted into the BFB. In this study, the superficial gas velocity of the riser was changed in two ways: i) the air was fed from the bottom of the riser at a volumetric rate to give 6 to 12 m/s in the riser without secondary air injection, ii) the air fed from the bottom of the riser was fixed at volumetric rate to give 6 m/s in the riser and secondary air was fed at the nozzles located 1.9 m above the riser bottom at a volumetric rate to give 0 to 6 m/s in the riser. The superficial gas velocity in the GSB and BFB were fixed at 0.10 and 0.025 m/s, respectively. Static
pressures were measured at 47 pressure taps around the unit using differential pressure sensors (Keyence Corp., AP48). The output signals from the sensors were acquired at a sampling frequency of 100 Hz via a data logger (CONTEC, AIO-163202FX) and a laptop computer. Solids mass flux (Gs) was measured by closing a butterfly valve below the cyclone 1 and measuring the time to accumulate given amounts of particles. This was determined from the mean value after 10 measurements at a steady state. Simulation The commercially available CFD code FLUENT (Ansys, Inc) and a Discrete Element Method (DEM) based code EDEM (DEM-Solutions Ltd) were used to study the dynamics of coal and sand particles in the downer. The air flow was
(a) Normal
(b) Tangential
Figure 2 Arrangements of 4 nozzles
solved by FLUENT using an Eulerian
for coal feeding
approach, and particle motion was computed by EDEM using a Lagrangian approach. At every time step the two methods were coupled such that interactions between gas and solid particles were handled rigorously. Due to CPU and memory limitations, simulations were carried out for sand particles 4 mm in diameter and coal particles 6 mm in diameter in a downer of 2 m in length. The other geometrical dimensions were the same as those in the experimental setup. The basic equations for air flow in the downer are the continuity and momentum equations (11)
u 0 t
(1)
u uu p 2u g S t
(2)
where is the air volume fraction, g is the gravity force vector, S is the momentum sink and the coupling between the gas and solid phases is achieved through the calculation of the momentum sink of the drag force that arises due to the slip velocity between the phases. The momentum sink S is calculated by: S
F
D
V
, where
V is the volume of a CFD mesh cell, and FD is the summation of the drag force exerted on the fluid in the mesh cell. The free stream drag model adopted was
FD 0.5CD A v v
(3)
where the drag coefficient CD depends on the Reynolds number (11) d pvr Re p 24 Re Re p 0.5 p CD 24 1.0 0.15 Re0.687 / Rep p 0.44 Re p 1000
0.5 Re p 1000
(4)
(5)
The sand particles were fed into the downer through 13 tubes in the distributor, and two nozzle arrangements were designed to feed coal into the downer, as shown in Fig.2. The four feeding nozzles were all horizontal. One arrangement was that all the four nozzles were normal to the downer, and the other arrangement was that all four nozzles were tangential to the downer. The uniform inlet velocity for the nozzle was 20 m/s, the standard k ~ turbulent model was adopted. RESULTS AND DISCUSSION Effect of secondary air injection on Gs Figures 3 and 4 show the solids mass flux (Gs) and the pressure difference between the GSB and riser bottom as a function of riser gas velocity (Ugr), respectively. Note that Ugr was defined as the sum of air fed from the riser bottom and the secondary injection nozzle divided by the cross section of
Figure 3 Relationship between riser gas
the riser. When no secondary air was
velocity (Ugr) and solid mass flux (Gs)
injected, Gs monotonically increased with the increase in Ugr. The maximum Gs obtained was 433 kg/(m2•s) at Ugr =12 m/s. When secondary air was injected, Gs peaked at 451 kg/(m2•s) at Ugr =10 m/s (i.e. 6 m/s was fed from the bottom and 4 m/s was fed from the nozzle). Further increases
in Ugr decreased Gs. Compared with the results without secondary air injection, the Gs was slightly larger at Ugr ≤ 10 m/s. However, the influence of secondary air injection was not significant at velocities Ugr ≥ 11 m/s. It can be seen in Figure 4 the pressure difference between the GSB and riser bottom, which is a
Figure 4 Relationship between riser gas
major driving force to transport solids
velocity and pressure difference between
to
GSB bottom and riser bottom
riser,
became
larger
when
secondary air injection was used. Thus, it can be said secondary air injection is an effective way to increase Gs when the total Ugr is not high. Effect of secondary air injection on solids holdup along riser The influence of secondary air injection on riser solids holdup was also studied. Figure 5 shows the apparent solids holdups (εs) along riser calculated by the following equation; ΔP/ΔH=ρpεsg
16
(6)
14
[m/s2] mean pressure difference, the distance
12
between the two sensors, particle density, solids holdup
and
the
gravitational
acceleration,
respectively. The open and closed symbols represent the results with and without secondary air injection, respectively. εs decreased sharply at the bottom part of the riser (Hr < 5 m) and gradually decreased at the middle and top of the riser (Hr ≥ 5 m). The solids holdup decreased in
Height from riser bottom [ m ]
where ΔP [Pa], ΔH [m], ρp [kg/m3], εs [-] and g
2nd 2 m/s (total 8 m/s) 2nd 4 m/s (total 10 m/s) 2nd 6 m/s (total 12 m/s) 2nd 0 m/s (total 6 m/s) 2nd 0 m/s (total 8 m/s)
10
2nd 0 m/s (total 10 m/s) 2nd 0 m/s (total 12 m/s)
8 6 4 2 0 0
0.05 0.1 0.15 Solids holdup [ - ]
0.2
0.25
Figure 5 Solids holdup along riser
the riser as Ugr increased. When Ugr was 10-12 m/s, the εs was almost constant (around 0.02) at Hr ≥ 5 m. The results indicate the formation of dense phase at Hr < 5 m and lean phase at Hr ≥ 5 m. By comparing the results with and without secondary air injection at each Ugr, a slight increase in εs was observed at bottom dense part (Hr < 5 m). However, no significant difference of εs
was observed at middle and top part (Hr ≥ 5 m). This indicated that secondary air injection did not significantly increase solids holdup along riser. Simulation results Figure 6 shows the mixing behavior between sand particles (dark) and coal particles (light). It can be seen that near the entrance, the tangential arrangement resulted in a poorer mixing performance than the normal arrangement. This is because in the normal arrangement,
(a) Normal
strong collisions between the normally
Figure 6 Mixing behaviors for the
injected coal particles and the falling
two types of nozzle arrangements
(b) Tangential
sand particles occurred. In contrast, in the tangential arrangement, the coal particles had a tendency to move spirally along the walls of the downer while most of the sand particles moved downwards along the center of downer. This resulted in less mixing between the coal and sand particles in the latter case. But downstream, the sand and coal particles are distributed more uniformly in the radial direction when the tangential arrangement was used than when the normal arrangement was used, which means the tangential arrangement gave better mixing than the normal arrangement downstream of the feeder. The mixing of coal and sand particles depends on several parameters, such as particle diameters, inlet velocity, downer diameter and solids mass flux. A sensitivity analysis of these parameters on the mixing is still under study. Also, the mixing content will be quantified and a suitable mixing index will be developed. CONCLUSIONS 1) Secondary air injection slightly increases solid mass flux (Gs) at a riser gas velocity ≤ 9 m/s. This is thought to due to the increase in the pressure difference between the GSB and the riser bottom, which is the main driving force to transport solids. 2) The injection of secondary air does not affect solids holdup along riser. 3) The tangential arrangement of nozzles for feeding coal particles into the downer provided better mixing between coal and sand particles except near the entrance.
ACKNOWLEDGEMENT This study was supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan and the Economic Development Board Singapore under grant number R261-501-003-414 (Minerals, Metals and Materials Technology Center, National University of Singapore) REFERENCES 1) A. Tsutsumi, Advanced IGCC/IGFC using exergy recuperation technology, Clean Coal Technol. J. (in Japanese) 11 (2004) 17-22. 2) J.-i. Hayashi, S. Hosokai, N. Sonoyama, Gasification of low-rank solid fuels with thermochemical energy recuperation for hydrogen production, Trans. IChem E: Part B 84, (2006) 409-419. 3) G. Guan, C. Fushimi, M. Ikeda, Y. Nakamura, A. Tsutsumi, T. Suda, M. Ishizuka, H. Hatano, S. Matsuda, Y. Suzuki, Flow behaviors in a high solid flux circulating fluidized bed composed of a riser, a downer and a bubbling fluidized bed, Fluidization XIII 407-414 (2010) 4) G. Guan, C. Fushimi, A. Tsutsumi, Prediction of flow behavior of the riser in a novel high solids flux circulating fluidized beds for steam gasification of coal or biomass, Chem. Eng. J. 164 (2010) 221-229. 5) C. Fushimi, G. Guan, Y. Nakamura, M. Ishizuka, A. Tsutsumi, S. Matsuda, H. Hatano, Y. Suzuki, Hydrodynamic characteristics of a large-scale triple-bed combined circulating fluidized bed, Powder Technol. (accepted) 6) X.T. Bi, X. Liu, High density and high solids flux CFB risers for steam gasification of solids fuels, Fuel Process. Technol. 91 (2010) 915-920. 7) X. Liu, X. Cui, G. Sun, F. Sun, T. Suda, G. Xu, High solid-flux concurrent conveying flow-realized by coupling a moving bed to the bottom section of a riser, Ind. Eng. Chem. Res. 47 (2008) 9703-9708. 8) X. Liu, X. Cui, G. Sun, T. Suda, M. Narukawa, Y. Liu, G. Sun, G. Xu, Buildup of high solids flux conveying flow by coupling a moving bed to the riser bottom, AIChE J. 55 (2009) 2477-2481. 9) X.-B. Qi, J. Zhu, X. Huang, A new correlation for predicting solids concentration in the fully developed zone of circulating fluidized bed risers, Powder Technol. 188 (2008) 64-72. 10) X. Wang, B. Jin, W. Zhong, M. Zhang, Y. Huang, F. Duan, Flow behaviors in a high-flux circulating fluidized bed, Int. J. Chem. Reac. Eng. 6 (2008) A79. 11) User Guide, EDEM-CFD coupling module for FLUENT, (2010).
HYDRODYNAMICS OF CONICAL SPOUTED BEDS WITH HIGH DENSITY PARTICLES Salih Sari1, Aylin Polat2, Dogukan Zaglanmis1, Gorkem Kulah2,*, Murat Koksal3,* 1
Dept. of Nuclear Eng., Hacettepe University, Beytepe, 06800, Ankara, Turkey Dept. of Chemical Eng., Middle East Technical University, 06531, Ankara, Turkey 3 Dept. of Mechanical Eng., Hacettepe University, Beytepe, 06800, Ankara, Turkey * Corresponding authors: [email protected], [email protected]
2
ABSTRACT An extensive experimental investigation of conical spouted beds with high density particles were carried out by measuring bed pressure drop, particle velocity and solids hold-up in a 15 cm ID conical spouted bed at three different cone angles (30°, 45°, 60°) with Yttria-stabilized zirconia (YSZ) particles (ρp = 6050 kg/m3). The results show that the minimum external spouting velocity increases with cone angle, particle diameter and static bed height. The bed is characterized by two regions: upward moving particles with high slip in the spout and slowly downward moving particles at loosely packed conditions in the annulus. INTRODUCTION Due to its unique solids circulation characteristics and excellent gas-particle contact, spouted beds have wide range of applications in many industrial processes like drying, granulation and particle coating. One of the particle coating applications of the spouted beds is the chemical vapor deposition (CVD) coating of uranium dioxide kernels with pyrolytic carbon and silicon carbide to produce spherical fuel elements (known also as TRISO type fuel element) for high temperature gas cooled reactors (HTR) (1). HTR is an advanced reactor technology (still under development) recognized for its inherent safety, high fuel utilization and high efficiency in electricity generation with cogeneration possibilities. Currently, the fuel production for the prototypes of HTR technology is realized in small scale spouted bed coaters with limited capacity. Once the full scale reactors are in operation, there will be a huge need for large scale fuel coaters with mass production capability. To design, scale up and manufacture spouted bed coaters operating with heavy particles, it is of fundamental importance to have a detailed understanding of the hydrodynamics of the system. Although there are a considerable number of hydrodynamic studies published in the literature, a very limited number of these have been conducted in spouted beds operating with heavy particles (ρp > 2500 kg/m3) typically encountered in CVD coating of nuclear fuels. To the authors' knowledge, the most comprehensive study on the hydrodynamic behavior of spouted beds with high density particles is a Ph.D. dissertation from the University of Tennessee (2). In this work, the minimum spouting velocity, timeaveraged and dynamic pressure drops, time-averaged fountain height and gas velocity profiles were measured in a 5 cm ID conical spouted bed at different cone angles (45°,60°,75°) operating with Yttria-stabilized zirconia (YSZ) particles (ZrO2,
also known as zirconia) having a particle density of 6050 kg/m3. Effects of the static bed height and particle size on measurements were investigated and new correlations for minimum spouting velocity, time-average pressure drop and fountain height based on the experimental data were developed. An unpublished final project report by a group of University Tennessee researchers is also available in the open literature (3). In this report, Zhou’s (2) work has been further extended with bed pressure drop, pressure fluctuations and fountain height measurements carried out with alumina particles in a 5 cm ID spouted bed for the aim of developing hydrodynamic scaling relationships. On the computational side, Pannala et al. (4) performed a 2-D Eulerian-Eulerian simulation of a 5 cm ID spouted bed with zirconia particles using MFIX (Multiphase Flow with Interphase Exchanges) code. Centerline axial velocity and Fourier spectra of pressure fluctuations were compared with corresponding experimental data. Their simulations revealed an interesting dynamic behavior - occurrence of regular spontaneous pulsations of gas and particle flow in the spout - which was also observed by Zhou (2). All of the aforementioned studies were performed in a 5 cm ID, conical spouted bed. However, to have a comprehensive understanding of the hydrodynamics of CVD fuel coaters for HTR reactors, further investigations need to be carried in larger inner diameter spouted beds. In addition, complete characterization of the hydrodynamics should also involve the determination of voidage and solids velocity inside the system. Therefore, the objective of this study was to investigate the hydrodynamic characteristics of spouted bed nuclear fuel coaters at cold bed conditions. To achieve this objective, experiments were performed in a 15 cm ID conical spouted bed at different static bed heights and cone angles (30°, 45°, 60°) with zirconia particles (dp = 0.5, 1 mm; ρp = 6050 kg/m3) to simulate the particle properties in hot bed conditions. Bed pressure drop and its fluctuations were measured to determine the minimum external spouting velocity. Local instantaneous particle velocities and solids hold-ups were also measured by an optical fiber probe to better understand the gas-solid dynamics in the spouted bed. EXPERIMENTAL SET-UP The experimental study was carried out in three full circular conical spouted beds made of Polyoxymethylene (also known as Delrin) which is an excellent thermoplastic that can withstand the continuous impact of hard zirconia particles without significant erosion. A schematic diagram of the units is given in Fig. 1 and their geometric parameters and operating conditions are presented in Table 1. Spherical Yttria-stabilized zirconia (YSZ) particles (dp = 0.5, 1 mm; ρp = 6050 kg/m3) were used to simulate the particle properties in hot bed conditions and compressed air at ambient temperature was used as the spouting gas. The total pressure drop across the spouted bed was measured by a differential pressure transducer (Omega PX142-005D5V) connected to the bed internal wall at the base of the conical section. The other line of the transducer was open to atmosphere. The data was fed to a computer by a high speed data acquisition board (National Instruments PCI6280) and processed using LabVIEW version 8. The sampling was performed at a frequency of 1 kHz for 20 seconds. Each measurement was repeated for three times and the corresponding averages are reported. To eliminate the possible effects of the initial packing status on the measurements, following the approach proposed by
Wang (5), the spouted beds were operated for 1 hour prior to each experimental run.
Figure 1. Geometric sketch of conical spouted beds. Table 1. Geometric parameters of the spouted beds and experimental operating conditions. Data Sets 1 2 3 4 5 6 7 8 9 10
γ 60 45
30
d p (mm) 0.5 1 0.5 1 1 0.5 1 1 1 1
Di (mm)
Do (mm)
Dc (mm)
Hc (mm)
25
15
150
108
25
15
150
151
25
15
150
233
Hb (mm) 100 100 100 100 140 100 100 140 180 220
A multi-fiber optical probe, PV-6, developed by the Institute of Process Engineering, Chinese Academy of Sciences, was used to measure simultaneously local instantaneous particle velocities and solids volume concentrations. The probe consists of two bundles of optical fibers. Inside each bundle, there are alternating arrays of light emitting and receiving fibers. The fibers have a uniform diameter of 15 μm. Light was projected into multiphase suspension through the emitting fibers. The backscattered light from the particles were transmitted by the receiving fibers to two photomultipliers, one for each bundle, and was converted to voltage signal. The signals were digitized by the high speed data acquisition board and processed by using LabVIEW version 8. If the flow structure does not change between these two bundles and the particles move in the same direction, the two signals would be identical, but separated by a time delay, τ. This time delay, which was obtained by cross-correlating the two signals, was used to calculate the axial particle velocity in Eq. (1): L (1) Up = e
τ
where Le is the effective distance between the two bundles. In this study, Le was determined to be 2.11 mm through the calibration studies performed with rotating disks with different designs and rotating disks with particles glued. Since the particle
velocity changed significantly in different radial locations of the spouted bed, the sampling frequency and time had to be varied correspondingly. For each measurement, a total of 180,000-500,000 data were collected through each bundle with a sampling frequency of 1-100 kHz. During the particle velocity measurements, particles may reverse directions, or a flow structure travelling non-vertically passing one bundle may not be detected by the second one, causing the cross-correlation coefficients to be low or indeterminate. Such uncorrelatable or poorly correlated data need to be eliminated. In this study, following the approach proposed by Kirbas et al. (6), in order for the results to be acceptable, the correlation coefficients were required to exceed 0.7, and individual calculated velocities were required to differ by no more than 3 standard deviations from the average. The optical fiber probe was capable of simultaneously measuring solids concentration together with particle velocity. The same data used in the calculation of particle velocity was integrated over time and by utilizing a calibration equation, solid hold ups were calculated. Before experiments, the probe was calibrated by using original+black colored zirconium particle mixtures. For this purpose, different concentration mixtures were prepared by combining known masses of original zirconium particles and black color painted zirconium particles. Since the painted zirconium particles were black and therefore absorbed most visible light, it was assumed that they behaved as voids, while only original particles reflected light. The calibration was performed in a system similar to the one described in (7). Using different concentration mixtures, solids hold ups were simulated. A linear relationship was observed between the voltage and solids hold-up. Local instantaneous particle velocities and solids hold ups were measured at several radial positions and three heights in the spouted bed with a cone angle, γ, of 45° operating at 1.25 Ums. RESULTS AND DISCUSSION Bed Pressure Drop Measurements Effects of ascending and descending inlet gas velocity, static bed height, particle diameter and cone angle on bed pressure drop were investigated in this study. Besides, the inlet gas velocity at which the external spouting begins (denoted by Ums) at each case were visually determined and indicated on relevant pressure drop figures with corresponding arrows. The effect of the ascending and descending inlet gas velocity, Uo, on the bed pressure drop in the bed is shown in Fig. 2 (Data set 4 in Table 1). The inlet gas velocity, Uo, is simply the superficial gas velocity based on Do. The descending velocity case does not show a steep peak as in the case of ascending velocity although the average pressure drop after the external spouting begins is the same. Furthermore, the external spouting is observed at the exact same velocity for both cases. The peak observed for the ascending velocity case is attributed to resistance that needs to be overcome as the internal spout first forms at the bottom of the bed. Fig. 3 shows the effect of the cone angle on the bed pressure drop (Data sets 2, 4 and 7 in Table 1). The figure shows that the minimum external spouting velocity increases as the cone angle increases. As the bed wall becomes more horizontal
with increasing cone angle, it becomes more difficult to move particles downward near the wall and in turn a higher velocity is needed to create a spout at the center and establish a circulation pattern typically observed in spouted beds. The same trend is also obtained for the inlet gas velocity at which the pressure peak is observed. Hence, the pressure drop curve shifts to the left as the cone angle increases. Increasing the cone angle also decreases the average bed pressure drop after the external spouting since the weight of the particles is carried more and more by the wall. Fig. 4 shows the effect of particle diameter on the bed pressure drop (Data sets 1 and 2 in Table 1). The minimum external spouting velocity increases with particle diameter. The effect of the static bed height on the bed pressure drop is shown in Fig. 5 (Data sets 7, 8, 9, 10 in Table 1). The bed pressure drop increases with static bed height. Both trends observed in Figs. 4 and 5 are consistent with literature. Fig. 6 shows a complete picture of the effect of the static bed height and cone angle on the minimum external spouting velocity. The minimum external spouting velocity increases linearly with static bed height for all cone angles tested in this work. Radial Particle Velocity and Solids Hold Up Measurements The local particle velocities and solids-hold-ups measured in the spouted bed with a cone angle of 45° operating at Uo = 56.3 m/s (1.25Ums) with a bed pressure drop of 3054 Pa are illustrated in Figure 7 (Data set 5 in Table 1). As depicted in this figure, the spouted bed is made up of two distinct regions: spout and annulus. A high velocity gas (56.3 m/s) enters the bed and the particles are carried up in the spout. It is interesting to note that at z = 42 mm, the local particle velocity at the axis is around 2.5 m/s indicating a large slip between gas and particles. In the annulus, the particles falling down from the fountain move slowly downwards with particle velocities of approximately -0.003 m/s. The particle velocity at any axial location decreases from its maximum value at the axis to zero at the spout-annulus interface. When the evolution of spout diameter is monitored, it is observed that at the two axial locations above the entrance, the spout has a diameter of approximately 17 mm which is close to the value of the inlet diameter of the spouted bed (Do = 15 mm). The spout then starts widening up and its diameter becomes approximately 21 mm at the highest measurement level (z = 120 mm). When the solids hold up profiles are examined it is observed that in the spout solids hold up is much lower compared to the annulus where particles are in close contact with each other and the solids fraction is uniform and almost equal to the loosely packed solids hold up at all levels. The solids hold up increases sharply at the interface between the spout and the annular zones.
5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0
Descending
Bed Pressure Drop (Pa)
Bed Pressure Drop (Pa)
Ascending
Ums,45⁰
0
10
20
30
40
50
60
70
80
γ=60⁰ γ=45⁰ γ=30⁰ Ums,30⁰ Ums,45⁰ Ums,60⁰
5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0
90
0
10
20
Inlet Air Velocity (m/s)
dp=1 mm dp=0.5 mm Ums,1mm Ums,0.5 mm
20
30
40
50
60
50
60
70
80
90
Figure 3. Effect of cone angle on the bed pressure drop.
Bed Pressure Drop (Pa)
Bed Pressure Drop (Pa)
5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 10
40
Inlet Air Velocity (m/s)
Figure 2. Effect of ascending and descending inlet gas velocity on the bed pressure drop.
0
30
70
80
90
18000 16000 14000 12000 10000 8000 6000 4000 2000 0
Hb=100 mm Hb=140 mm Hb=180 mm Hb=220 mm
0
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60
Inlet Air Velocity (m/s)
70
80
90
Inlet Air Velocity (m/s)
Figure 4. Effect of the particle diameter on the bed pressure drop.
Figure 5. Effect of the static bed height on the bed pressure drop.
Min. Ext. Spouting Velocity (m/s)
60
γ=30⁰, dp=1 mm γ=45⁰, dp=1 mm γ=60⁰, dp=1 mm γ=30⁰, dp=0.5 mm γ=45⁰, dp=0.5 mm γ=60⁰, dp=0.5 mm
50 40 30 20 10 0 0
20
40
60
80
100
120
140
160
180
Static Bed Height (mm)
Figure 6. Effect of the static bed height on the minimum external spouting velocity. 3.0
z=42 mm z=82 mm z=120 mm
Particle Velocity (m/s)
2.5 2.0 1.5 1.0 0.5 0.0 -0.5 0 -1.0
10
20
10
20
30
40
50
60
30
40
50
60
Solids Hold-up
0.6 0.5 0.4 0.3 0.2 0.1 0.0 0
r (mm)
Figure 7. Radial profiles of local time-mean particle velocities and solids hold-up at different axial locations. CONCLUSIONS The purpose of this study was to understand the hydrodynamic characteristics of spouted bed nuclear fuel coaters. Experiments were performed in a 15 cm ID conical spouted bed at different static bed heights and cone angles (30°, 45°, 60°) with zirconia particles (dp = 0.5, 1 mm; ρp = 6050 kg/m3) to simulate the particle properties in hot bed conditions. Bed pressure drop, local instantaneous particle velocities and solids hold-ups were measured. The main conclusions are summarized below: • The minimum external spouting velocity increases with cone angle, particle diameter and static bed height.
• The average bed pressure drop decreases with cone angle. • A very large slip is obtained between gas and particle velocities in the spout region. The spout widens with height. At the axis, the particle velocity decreases from 2.5 m/s to 1.6 m/s, whereas the solids hold up increases from 0.08 to 0.20 with height. • The solids hold up increases sharply at the interface between the spout and the annular zones. The annulus is characterized by slowly downward moving particles with velocities of approximately -0.003 m/s at almost loosely packed conditions. ACKNOWLEDGEMENT This work was carried out with the financial support of the Scientific and Technological Research Council of Turkey (Project No: MAG 108M435). NOTATION
γ ρp
Angle of the conical section, degree Particle density, kg/m3
τ
dp
Delay time between two signals from two light receivers, sec Particle diameter, mm
Di ,Do ,Dc
Diameter of the bed bottom, gas inlet, column, respectively, mm
Hc ,Hb
Height of the conical section, static bed, respectively, mm
Le r Ums ,Up ,Uo
Effective distance between two bundles of optical fibers, mm Radial distance from the bed axis, mm Min. external spouting velocity, particle velocity, inlet gas velocity, m/s
z
Axial location, mm
REFERENCES 1. Vahlas, C., Caussat, B., Serp, P., Angelopoulos, G.N., “Principles and applications of CVD powder technology”, Material Science and Engineering, 53, 1-72, (2006). 2. Zhou, J., “Characterizing and modeling the hydrodynamics of shallow spouted beds”, Ph.D. dissertation, University of Tennessee, Knoxville, (2008). 3. Bruns, D., Counce, M. “Ambient laboratory coater for advanced gas reactor fuel development”, Final Technical Report, Office of Scientific and Technical Information, US Department of Energy, retrieved from http://www.osti.gov/bridge/servlets/purl/981929-H1FgNA/981929.pdf, (2010). 4. Pannala, S., Daw, C.S., Finney, C.E.A., Boyalakuntla, D., Syamlal, M., O’Brien, T.J., “Simulating dynamics of spouted-bed nuclear fuel coaters”, Chemical Vapor Deposition, 13, 481-490, (2007). 5. Wang, Z., “Experimental Studies and CFD Simulations of Conical Spouted Bed Hydrodynamics”, Ph.D. Thesis, University of British Columbia, (2006). 6. Kirbas G., Kim S.W., Bi H.T., Lim C.J., Grace J. "Radial distribution of local concentration-weighted particle velocities in high-density circulating fluidized beds", Proc. Fluidization XII, Canada, 71-78 (2007). 7. Kirbas, G., “Solids motion and mixing in high-density circulating fluidized beds”, Ph.D. Thesis, Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, Canada, (2004).
CIRCULATING FLUIDIZED BED COMBUSTION – BUILD-UP AND VALIDATION OF A THREE-DIMENSIONAL MODEL Marko Palonena, David Pallarèsb, Ville Ylä-Outinena, Anton Larssonb, Jukka Lainec, Filip Johnssonb a Metso Power Oy, Lentokentänkatu 11, P.O. Box 109, FI-33101 Tampere, Finland b Chalmers University of Technology, S-41296 Göteborg, Sweden c Tampere University of Technology, P.O. Box 589, FI-33101 Tampere, Finland ABSTRACT This paper presents the validated simulation of a full-scale circulating fluidized bed boiler as obtained via a comprehensive three-dimensional CFB process model. The model is utilized in boiler design and scale-up as well as to study and optimize boiler performance. Feedstock characterization tests, which are also presented, are used to provide data for those parts of the process where up-to-date modeling is not fully reliable, thus enabling the model to provide accurate results. The overall model and its sub-models have been validated against data from numerous experiments ranging from characterization tests at laboratory scale to measurements in a 550 MWth CFB boiler. The paper exemplifies comparisons between model results and experimental data, showing generally good agreement. INTRODUCTION Circulating fluidized bed combustion (CFBC) is a technology where combustion takes place within a gas-solid suspension. CFBC offers many benefits over other combustion technologies, among these fuel flexibility and relatively low emission levels. There is a need for comprehensive modeling of CFBC in order to support design and scale-up of boilers. Despite the availability in the literature of sub-models for specific phenomena in fluidized bed combustion, the need for an overall CFBC model has been addressed by only a few works, the most relevant being the model provided by Hyppänen et al. (1), Hannes (2), and Lücke (3). Metso and Chalmers University of Technology have together developed a comprehensive three-dimensional CFB process model (4, 5), which is an essential part of the knowledge process for Metso CFB combustion technology (see Fig. 1). The CFB process model is utilized in the boiler design process and scale-up as well as to study and optimize boiler performance. Among the concrete applications are evaluation of circulation material behavior and flow rates, assessment of combustion characteristics, dimensioning of heat transfer surfaces, and optimization of the air
and fuel feeds. The model, also widely used in planning and post-processing in experimental research campaigns, is capable of establishing the boundary conditions for other calculation tools, such as water circulation, corrosion, and emission models.
Fig. 1: Knowledge process of the Metso CFB combustion technology
Experimental data in large quantities have been used to verify the sub-models as well as the overall model. The experimental research consisted of: - Laboratory-scale characterization tests - Small-scale FB tube reactors - VTT Jyväskylä 100 kWth CFB
- Metso 4 MWth CFB - Chalmers 12 MWth CFB - Full-scale CFB boilers up to 550 MWth
CFB PROCESS MODEL The CFB process model is a combination of different validated submodels available in literature (6-11) each focusing one key phenomenon in the CFB process. These submodels contain as little empirical content as possible and are linked into an overall model by exchanging data according to the overview shown in Fig. 2. The input data need by the overall model is summarized in geometry of the unit, operational conditions (gas and solids injections, pressure drop over the furnace and waterwall temperature) and solids (including fuel) properties. As seen, some of the submodels use experimental input data from feedstock characterization tests (detailed below). Such tests are used instead of submodels where these would not be reliable enough. The model starts with a transient modeling of the fluid dynamics which accounts for the external actions taken to control the bed inventory (i.e. discharge of bed material and addition of makeup material) and the attrition of the solids, as detailed in (6). It is important to note that the solids inventory is modeled and monitored over the whole circulating loop including also the cyclones, downcomers and particle seals. These elements can contain significant fractions of the total solids inventory in form of finer solids than those found in the furnace. To
account for the solids populating the return leg, a pressure balance over the circulating loop has to be closed, as well explained in (8). From this, the steady-state solids inventory in the unit is determined and can be used as input for the calculation of the steady-state fluid dynamics describing both the gas and solids flows as detailed in (7) and (8) respectively. For this, the solids in the furnace freeboard are divided into superimposing (ballistic) cluster and (core- annulus) disperse phases, each characterized by a decay constant, as detailed in (8). The gas flow is described according to the potential flow theory. The fluctuating nature of its mixing originates from the bubble flow in the dense bed and can be described through the formulation of a series of quasi-steady state pressure balances in the dense bed, as given in detail in (7). It is well known that fuel particles undergo varying mixing behaviors as they convert, due to the gradual decreases of size and density. Thus, a transient representation of the fuel mixing in order to account for the different mixing pattern of fresh fuel and fuel close to burn-out is needed. This is done in combination with a fuel particle Fig. 2: Overall model structure conversion model, as described in (9). The conversion fate of a batch of fresh fuel is modeled on a time-resolved basis, and the solution obtained is then recalculated into a sum of continuously fed batches yielding the solution corresponding to the continuous feeding case. Finally, heat transfer is modeled by separating convection and radiation mechanisms and using individual heat transfer coefficients for convection and radiation, see (10), instead of a lumped coefficient accounting for both mechanisms (as usually is the case in works facing the modeling of the heat transfer in CFB units). This separate treatment of convection and radiation sets a basis for accurate descriptions of each mechanism (e.g. shadowing factors due to gas-solids suspension, internal heat exchanging surfaces such as division walls and wing walls, varying suspension emissivity and local convection peaks in furnace). A very first origin of this overall model is found in an EU-funded project (11). FEEDSTOCK CHARACTERIZATION Conventional laboratory analyses are a starting point for basic balance calculations, but, in addition, more detailed information is needed for modeling purposes. Feed material characteristics, including the fuel fragmentation and attrition characteristics of solids generating the circulation material, must be taken into account because of
their significant influence – in combination with boiler design and the operation parameters – on the CFB process. Attrition of Solids As a result of the strong influence of the particulate phase in the heat and mass transfer of in-furnace processes in CFB boilers, characterization of the solids inventory is a crucial element in modeling. Attrition of different feed solid fractions and their capability to generate circulation material, along with cyclone separation efficiency, have a direct effect on the performance of a CFB boiler. The experimental characterization of the attrition pattern for feed materials is carried out in a small bench-scale setup wherein the solids sample undergoes attrition for a set amount of time. The result for a certain fuel ash is illustrated in Fig. 3, which shows the cumulative mass size distribution of the fuel ash after different attrition times expressed as a fraction of the total test time. The duration of the test is in the range of hours, and the test is developed to provide data used in the modeling of solids inventory as described in Reference 6. After proper processing, the test result data describes which fraction of each size range will be reduced by attrition to finer size grades in a certain time. Production of fine material is found to be faster at the beginning of the test, which is a typical finding for experimental investigations of attrition (12). The evolution of the amount of solids having a particle size smaller than 400 µm and its fitted power law function are presented in Fig. 3.
Fig. 3: Results from attrition testing for fuel ash
Rate of Fuel Conversion The fuel conversion sub-model applied in the CFB process model is based on the formulation originally developed by Palchonok (13) and improved by Thunman (14) and Larsson (15). Fig. 4 schematizes the fluidized bed tube reactor developed to validate the fuel conversion model. Full combustion is ensured by the secondary gas, and fuel conversion is obtained from the time-resolved oxygen concentration in gases exiting the reactor (Fig. 4).
In comparison of the model and measurement results, it becomes obvious that the test method’s contribution to the dynamics of the oxygen measurement must be accounted for. This is done by convolving the model’s output with a convolution model designed for the test reactor used. The convolution model comprises three factors affecting the result: 1) Release and combustion of volatiles 2) The duration of the gas transportation and the mixing effects along the reactor before the gas is detected by the oxygen sensor 3) The contribution of the analyzer response time, which is described by the analyzer time constant analyzer time constant 10
O2 fraction (vol-%)
8
Model + Convolution Measurement
6 4 2 0 0
50
100
150
200
250
time(s)
a) Schematic of the FB tube reactor
b) Dynamic O2 concentration during test Fig. 4: Fuel reactivity test
Fuel Fragmentation Fragmentation is the phenomenon by which fuel particles break into smaller ones during their conversion. There are several types of fuel particle comminution: primary, secondary, percolative fragmentation and attrition, which are involved in different stages of combustion. Primary fragmentation occurs in the first stages of combustion (particle heating, drying, and devolatilization) and is caused by intense thermal shock and local internal overpressures when a fuel particle is fed into a fluidized bed. Comprehensive research has been conducted on the fuel comminution phenomenon from coals and low volatile fuels (16) to waste fuels (17). However, the experimental methods used in these works are too laborious to be adopted as routine procedure in the fuel characterization process. A fragmentation test method based on fuel particle image analysis has been developed. The fuel particle under analysis is placed in a furnace where the radiative heat transfer to the particle is adjusted to be equivalent to the total heat transfer in a fluidized bed environment. From a sequence of tests, the fragmentation probability, time of fragmentation, and number and size of fragments can be determined. This test method characterizes only primary fragmentation. Figure 5 illustrates the particle recognition by digital image analysis of a fragmented fuel particle and exemplifies with test results the change in particle size due to fragmentation.
a) Optical O image analysis of a fragmented f fu uel partticle
b) b Average parrticle size afterr the test vs. in nitial particle p size
Fig. 5: Fuel fragmentation n test results
D TESTS AND A MODEL VALIDAT TION FULL-SCALE CFB FIELD Tests were w carried d out in a 350 3 MWth C CFB boiler with w two cycllones comb busting petrroleum cokke. Both a side s view of the boiler and a the main measure ement locations are show wn in Fig. 6. 6 In total, 32 measurrement portts in the furna ace were us sed for mea asurement o of the main gas components, temp peratures, and a pressu ures. Intensive sampling g of feed materials, circulation material, and ashes was w conductted during th he campaig gn. After se etting of th he input da ata (geome etry, opera ation condition ns, and solids propertie es – including results from f characte erization te ests), the case can n be prop perly modeled d. The calc culation me esh normally consists s of 50,000– –300,000 he exahedral cells c for mo odeling of fullscale CF FB units.
Fig. 6: Boiler unit and main asurement locations mea
Even th hough the model con ntains the least poss sible empirica al content, some sub b-models a are based on experimental corre elations. For example, parame eters g heat tran nsfer, air an nd fuel fee ed penetrattion, affecting dispersio on, and mixing of ga ases and fu uel have been b adjusted d to achiev ve satisfacctory agreement with the measure ed data. These parame eters have been valida ated against several exp periments, with w differe ent boiler siz zes, fuel mixttures, and lo oad levels.
Tem mperature and a main gas g concen ntration profiles were measured simultaneously from m 10 locatio ons with sp pecially desiigned probe es and a multi-channe el gas analy yzer. Measured and d modeled horizontal h te emperature and O2 pro ofiles above e secondary y air leve el are show wn in Fig. 7 (where results r from m wall layerrs are omittted). As se een, agrreement is satisfactoryy for both comparisons c s, although the model overestima ates O2 concentratiion in locattion close to o the wall layers. Thiss is a conse equence off the (no on-accounte ed in the model) m diffusion of volatile matter and charr from the wall laye ers to the co ore region.
8
1000
O2 (vol-%)
Temperature (K)
10
500
6 4 2 0
0
5
5 x, (m)
0 -5
-4 0 -2 4 2 y, (m)
a) Modeled and measured temperature
x, (m)
0 -5
-4 0 -2 4 2 y, (m)
b) Modeled and measured O2 concentration
Fig. 7: Furnace horizontal profiles above secondary air level
The vertical pressure profile in the furnace was measured by differential pressure transmitters and is presented in Fig. 8 together with corresponding modeled data, showing a very good agreement. Fig. 9 presents the modeled and measured cumulative mass size distribution of furnace bottom ash. As observed, the model predicts the presence of a significant mass fraction of particles finer than 100 μm in the bottom ash. The reasons for this disagreement are most likely 1) the perfect vertical mixing in the dense bed assumed in the modeling and 2) not accounting in the model for the size segregation effect in the bottom ash cooler (where a significant part of the finest particles are entrained back to the riser).
Fig. 8: Modeled and measured vertical pressure profile and solids density profile
Fig. 9: Modeled and measured bottom ash particle size distribution
CONCLUSIONS Key features and validation of a new comprehensive model of CFB combustion are presented. The model is formed by a combination of semi-empirical models describing key phenomena in the CFB combustion process. The validated overall model aims at being a tool for boiler design and scale-up as well as to study and optimize boiler performance. Accurate characterization of feed materials plays a critical role in the modeling. The main feedstock characterization tests used in combination with the model are described and examples are given.
NOTATION dp
particle diameter
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
Hyppänen, T., Lee, Y. Y., and Rainio, A. (1991). A three-dimensional model for circulating fluidized bed boilers. Proceedings of the 11th International Conference on FBC. Hannes, J. (1996). Mathematical modelling of circulating fluidized bed combustion. City-Print Verlag GmbH. Lücke, K. (2003). On the influence of mixing on the performance of large-scale atmospheric circulating fluidized bed combustors. PhD thesis, Hamburg-Harburg Technical University. Pallarès, D. (2008). Fluidized bed combustion – modeling and mixing. PhD thesis, Chalmers University of Technology, Gothenburg. Pallarès, D., Johnsson, F., and Palonen, M. (2008). A comprehensive model of CFB combustion. 9th International Conference on Circulating Fluidized Beds. Pallarès, D., Johnsson, F., and Palonen, M. (2010). Modeling of the solids inventory in CFB boilers. 13th International Conference on Fluidization. Pallarès, D. and Johnsson, F. (2009). Dynamical modeling of the gas phase in fluidized bed combustion – accounting for fluctuations. Proceedings of the 20th International Conference on Fluidized Bed Combustion. Pallarès, D. and Johnsson, F. (2006). Macroscopic modelling of fluid dynamics in large-scale circulating fluidized beds. Progress in Energy and Combustion Science, 32 (5–6), pp. 539–569. Pallarès, D. and Johnsson, F. (2008). Modeling of fuel mixing in fluidized bed combustors. Chemical Engineering Science, 63 (23), pp. 5663–5671. Pallarès, D., Johnsson, F., and Palonen, M. (2011). Modeling and validation of the heat transfer in the furnace of large-scale CFB boilers. Proceedings of the 10th International Conference on Circulating Fluidized Beds. Zhang, W. et al. (2000). Final report of EU project JOR3CT980306. Klett, C., Hartge, E.-U., and Werther, J. (2005). Time-dependent behavior of the ash particle size distribution in a circulating fluidized bed system. Proceedings of the Combustion Institute, 30 (2005), pp. 2947–2954. Palchonok, G. (1998). Heat and mass transfer to a single particle in fluidized bed. PhD thesis, Chalmers University of Technology, Gothenburg. Thunman, H. (2001). Principles and models of solid fuel combustion. PhD thesis, Chalmers University of Technology, Gothenburg. Larsson, A., Pallarès, D., Neves, D., Seemann, M., and Thunman, H. (2010). Zero-dimensional modeling of indirect fluidized bed gasification. 13th International Conference on Fluidization. Chirone, R., Massimilla, L., and Salatino, P. (1991). Comminution of carbons in fluidized bed combustion. Progress in Energy and Combustion Science, 17 (4), pp. 297–326. Arena, U., Cammarota, A., and Mastellone, M. (1998). The phenomenology of comminution in the fluidized bed combustion of packaging-derived fuels. Fuel, 77 (11), pp. 1185–1193.
EVALUATION OF A LAGRANGIAN DISCRETE PHASE MODELING APPROACH FOR APPLICATION TO INDUSTRIAL SCALE BUBBLING FLUIDIZED BEDS S. Cloete†, S.T. Johansen†, M. Braun*, B. Popoff*, S. Amini† † Flow
Technology research group, SINTEF Materials and Chemistry, Richard Birkelands Vei 3, 7034 Trondheim, Norway [email protected] *Ansys Germany GmbH, Birkenweg 14a, D-64295 Darmstadt, Germany ABSTRACT Industrial scale bubbling fluidized bed simulations were carried out within the Kinetic Theory of Granular Flows (KTGF). The KTGF was applied within two different modelling frameworks, the traditional Two Fluid Model (TFM) and a new approach in the form of the Dense Discrete Phase Model (DDPM), in order to identify any differences in performance. Only the DDPM was able to attain fully grid independent results for industrial scale 2D simulations. In fact, the performance was sufficiently good to enable the completion of reasonably affordable full 3D simulations. These simulations revealed some differences between 2D and 3D, but the global system behaviour remained relatively similar. Comparisons to experimental pressure drop data for both 2D and 3D simulations were acceptable. INTRODUCTION Grid independence behaviour of fluidized bed simulations depends primarily on the resolution of meso-scale particle structures in the computational domain. In bubbling beds these structures are realized as bubbles, while risers typically display the formation of particle clusters. Within industrial scale fluidized bed systems, the length scales on which these clusters occur generally requires a mesh size which is too small to be realistically simulated with present computational capacities. In order to address this challenge, substantial research effort has been invested into filtered or ‘coarse graining’ approaches (1-5). These methods aim to model the effects of particle structures so that they do not have to be directly resolved on a very fine grid. The filtered approach holds great promise for industrial application and has a solid fundamental basis, but after a decade of study is still said to be in its infancy when reviewed for the highly sensitive Geldart A particle class (6). In order to arrive at a complete predictive model for industrial reactors, these closures will have to be extended to poly-dispersed particle systems and additional closures will have to be formulated for reaction kinetics. It is therefore reasoned that it will be many years before a sufficiently generic and reliable set of sub-grid closures will be developed. The alternative to the filtered approach is fully resolving all the particle structures on a sufficiently fine computational grid. When using this approach, no modelling is needed in addition to the standard Kinetic Theory of Granular Flows (KTGF). Using the traditional Two Fluid Model (TFM), grid independent results cannot be attained for industrial reactors, but an alternative modelling formulation, known as the Dense Discrete Phase Model (DDPM), has been shown to display much improved grid independence behaviour (7). This modelling approach will now be evaluated in an industrial scale bubbling bed reactor without any sub-grid closures incorporated in order to assess the degree to which it can improve grid independence behaviour. 1
SIMULATIONS Model equations Simulations will be carried out both with the TFM and the DDPM in order to compare their grid independence behaviour. A summary of the TFM model equations for this approach can be found in Taghipour et al. (8). The most important closure relations employed was the modelling of the drag and solids viscosity according to Syamlal et al. (9), the frictional viscosity according to Schaeffer (10), the solids pressure according to Lun et al. (11) and the radial distribution function according to Ogawa et al. (12). The granular temperature equation was only solved in its algebraic form, thereby neglecting the contributions of convection and diffusion. Due to its novelty, a more complete description of the DDPM will be provided here. The DDPM is based on the standard Discrete Phase Modelling (DPM) approach where parcels of particles are tracked through the domain in a Lagrangian framework according to Newton’s laws of motion. In its standard form, the DPM does not account for the volume fraction of the discrete phase particles. The DDPM formulation (13) overcomes this limitation by solving a set of conservation equations for multiple phases (generalized form written below for phase p). ∂
(α ∂t
p
∂
(α ∂t
ρ p ) + ∇ (α= ρυ p p p)
p
∑ ( m
n phases
qp
q =1
− m pq )
(1)
ρ pυ p ) + ∇ (α p ρ pυ pυ p ) = −α p ∇p + ∇ ⋅ α p µ p ( ∇υ p + υ pT ) +
αpρpg + F +
∑ ( K (υ
n phases
qp
q =1
q
)
− υ p ) + m qpυ qp − m pqυ pq +
(2)
K DPM (υ DPM − υ p ) + S DPM ,explicit
The conservation equations are not solved for the particulate phase, but the appropriate volume fraction or velocity values are taken directly from the particle field. The particle equation of motion is solved for each particle in the form:
dυ p dt
=
−1
ρp
∇p + FD (υ − υ p ) +
g (ρp − ρ )
ρp
+ F + Finteraction
(3)
The right hand side terms represent the pressure force, drag force, gravitational force, any additional force and the particle-particle interaction force. The drag force is calculated as in equation (4) with the drag coefficient modelled according to Syamlal et al. (9).
FD =
18µ CD Re p
ρ pd
2 p
24
= Finteraction
(4)
2
−1
α pρp
∇p p
(5)
The interaction force is estimated from the solids pressure gradient according to equation (5). This is a simple but fast model for the major physical effect. It does not have the highest possible accuracy but favours efficiency, in particular when compared to DEM like approaches. A major limitation of this formulation is that the particle interaction force does not contain any viscous contribution. The resistance to strain caused by the modelled shear viscosity is therefore not included. In the dense fluidized bed system simulated here, this viscous force could be of significant importance and its negligence is expected to create a more free-flowing bed than might be expected. The granular temperature used in the KTGF is calculated in its algebraic form from the ordinary differential equation below: 3∂
α p ρ p Θ ) = τ p : ∇υ p + γ Θ + φ pq ( 2 ∂t
(6)
Here, the right hand side terms represent the generation of fluctuating energy by the solids stress tensor, the collisional dissipation of fluctuating energy (11) and the energy exchange between the fluctuating particles and any additional phases (14). The solids stress tensor in equation (6) is written as follows: 2 τ p = − p p I + 2 µ p S + λ p − µ p ⋅ ∇υ p I 3
(7)
Here, the solids pressure and the bulk viscosity is calculated according to Lun et al. (11) and the shear viscosity according to Syamlal et al. (9). Within these formulations, the radial distribution function is calculated according to Ogawa et al. (12). Computational Domain Simulations will be compared to pressure drop data collected from an industrial fluidized bed reactor as reported by Gobin et al. (15). The cylindrical reactor was 5 m in diameter and 30 m in height. It was found, however, that only 15 m of height needs to be included in the domain for the flow scenarios investigated in this study. Both 2D and 3D simulations were conducted. The 2D simulations were carried out on a planar domain, 5 m in width and 15 m in height, while the 3D simulations were carried out in a cylindrical domain, 5 m in diameter and 15 m in height. Both domains were meshed with constant sized square (2D) or cubic (3D) structured cells according to the simulation run in question. Material properties The particles used in the experiments were poly-disperse with a mean diameter of 1.3 mm and a density of 850 kg/m3, characterizing them as Geldart D particles (16). The fluidization gas was pressurized hydrocarbons with a density of 20 kg/m3 and a dynamic viscosity of 1.5e-5 Pa.s (15). Boundary Conditions The bottom boundary of the domain was designated as a constant velocity inlet (0.5 m/s) to simulate a perfect plate distributor as the gas inlet. The top boundary was designated as a pressure outlet. Side boundaries were designated as walls with a 3
specularity coefficient of 0.01 to describe a low friction wall in the framework of the Johnson and Jackson (17) boundary condition. Solver settings The commercial CFD package, FLUENT 12.1 was used as the flow solver in this study. The phase-coupled SIMPLE algorithm (18) was selected for pressure-velocity coupling. All remaining equations were discretized using the QUICK scheme (19). 1st order implicit temporal discretization was used. Operation and data extraction Each simulation domain was initialized with a zero value for all flow variables. A region of solids at a volume fraction of 0.35 was subsequently patched into the bottom 8 m of the reactor as specified in Gobin et al. (15). For the DDPM, the particle parcels were injected in the first 0.1 s of the simulation from all of the internal surfaces of the mesh. This was done in such a way that the each cell in the lower 8 m of the reactor would, on average, contain 10 particle parcels. Following the patching and injection, the simulation was run until a quasi-steady state was reached. This was identified by a monitor on the solids velocity. Once the quasi-steady state was attained, the sampling of time statistics was activated in order to get time-averaged axial pressure profiles for each simulation. Time statistics were collected for a minimum of 30 s real time which was tested to be representative of the time-averaged system behaviour. RESULTS AND DISCUSSION As is often the case in industrial reactors, experimental values of pressure drop can only be estimated from the available data (15). Only two pressure measurements were available in the 5 m ID reactor, one at a height of 3.5 m and the other at a height of 6.5 m. The pressure drop between them was experimentally measured to be between 9 and 11 kPa. An average of 10 kPa will be taken. Some more detailed pressure drop measurements were made in a pilot scale unit scaled to one third of the industrial one. These measurements confirmed a virtually linear pressure drop profile along the height of the pilot scale reactor. Under the assumption that the pressure drop profile in the industrial scale reactor is linear as well, a linear pressure drop of 10000/(6.5-3.5)=3333 Pa/m can be deduced. The total pressure drop over the reactor can be estimated from the weight of the solids that has to be fluidized as 23348 Pa. An estimated linear pressure profile can therefore be specified with a gradient of 3333 Pa/m and a y-intercept of 23348. Numerical simulations will be compared against this experimental estimation. The first set of simulations was carried out in 2D on grids spanning from 4 cm to 16 cm. In the domain simulated, this translated to cell counts between 2930 and 46875. The simulation results attained with the TFM and the DDPM are given in Figure 1.
4
Figure 1: Pressure drop profiles for the 2D simulations with the TFM (left) and the DDPM (right) for various mesh sizes from 4 cm to 16 cm. It is clear that both modelling approaches provide adequate fits to the estimated experimental data. The important finding for this study, however, is that the DDPM seems to retain grid independent behaviour throughout with all the grids investigated while the TFM never reaches complete grid independence. Grid independent results for the DDPM with the 16 cm grid implies that reliable results in an industrial reactor can be attained within an industrial reactor with only 2930 cells in 2D. This simulation required about 1 hour of processing time on a single processor, which, in terms of CFD standards, is very fast. In comparison to the TFM, where grid independence might be attained on a 4 cm grid, the DDPM solved on a 16 cm grid would require 16 times less cells in 2D and can be run at a 4 times greater timestep. On a fixed grid, the DDPM is currently about 3 times slower than the TFM, but even with this taken into account, the DDPM can provide grid independent results more than 20 times faster than the TFM. The reason for the good grid independence behaviour displayed by the DDPM is similar to the conclusions drawn in Cloete et al. (7) – the Lagrangian particle tracking provides for a much more accurate representation of the volume fraction field. The volume fraction field tracked by the TFM on coarse grids is subject to substantial numerical diffusion and the large volume fraction gradients cannot be resolved accurately. Instantaneous plots of the volume fraction are displayed in Figure 2 as illustration of this point. Figure 2 shows very clear differences between the volume fraction fields resolved by the TFM and the DDPM. In the DDPM, there is a very clear separation between the bubble and emulsion phases on all the grids investigated, while the TFM does not resolve clear bubbles even on the finest grid investigated. When looking at the DDPM, it is clear that some of the flow detail is lost on the coarser grids, but Figure 1 shows that the global system behaviour is preserved, at least from a hydrodynamic point of view. The degree to which this will be true for reaction kinetic simulations is a subject for future study.
5
Figure 2: Instantaneous volume fraction profiles for the TFM (top) and the DDPM (bottom). The mesh is coarsened from left to right from 4 cm to 16 cm.
Figure 3: Pressure drop profiles for 3D simulations carried out with the DDPM for various mesh sizes from 4 cm to 16 cm.
6
Figure 4: Comparison between the solids volume fraction profiles returned by the DDPM for the 2D and 3D cases with an 8 cm grid.
Following the good grid independence shown by the DDPM on coarse grids, some 3D simulations were also completed for grids of 8 cm and larger. The pressure profiles in Figure 3 show that the 3D simulations also display very satisfactory grid independence behaviour. Comparison to experimental data also shows acceptable agreement, even though a possible under-prediction of bed expansion is observed. In comparison to 2D simulations, the 3D runs also seem closer to reality in that they display a more linear pressure drop trend. Figure 3 seems to indicate that 2D simulations can adequately predict global system behaviour in comparison to 3D at significantly reduced computational costs. When looking at the solids volume fraction profiles (Figure 4), however, significant differences between the 2D and 3D representations are observed. It is clear that the 3D simulations display much smaller bubbles than their 2D counterparts, especially towards the upper regions of the bed. The large voidage at the top of the 2D bed would explain the reduction in the pressure gradient towards the surface. This pronounced difference between particle structure representation in 2D and 3D implies that 2D simulations of 3D industrial beds should be interpreted with caution. The similarity in pressure drop and bed height does suggest that the global system behaviour is preserved even in 2D, but the local transport phenomena in the bed seem to be significantly different. The system seems to be very forgiving towards these differences in terms of global hydrodynamic behaviour, but is likely to be less so when reaction kinetics are eventually incorporated. CONCLUSIONS Industrial scale bubbling fluidized bed simulations were carried out using the traditional Two Fluid Model (TFM) and a new approach known as the Dense Discrete Phase Model (DDPM). The DDPM showed substantially better grid independence behaviour than the TFM. 2D simulations showed that results could be attained at least 20 times faster with the DDPM than with the TFM. Grid independence results with the DDPM were so encouraging that even reasonably affordable 3D simulations could be completed. Comparisons to experimental pressure drop data also proved to be acceptable. It was shown that differences exist between the axial pressure profiles for 2D and 3D cases, but these differences are not as large as might be expected. The local volume fraction distribution through the respective domains did show substantial differences, however, with the 2D simulations showing the formation of much larger bubbles than their 3D counterparts. These differences seem to have only a minor influence on global parameters such as pressure drop and bed height, but should be further investigated in more detailed studies. ACKNOWLEGMENT The authors would like to acknowledge the financial support of the Research Council of Norway under the Flow@CLC grant. Futhermore, the authors acknowledge the use of the supercomputing facilities at the Norwegian University of Science and Technology. NOTATION Regular symbols C Coefficient d Diameter (m)
Greek letters α Volume fraction φ Rate of energy exchange (W/m3) 7
F F g
I K m p Re S t
Force (1/s) Force vector per unit volume (N/m3) Gravity vector (m/s2) Identity tensor Interphase exchange coefficient Mass transfer rate (kg/s/m3) Pressure (Pa) Reynolds number Source term (kg/m2s2) Time (s)
γΘ
Energy dissipation rate (W/m3)
µ
Viscosity (Pa.s)
Θ
Granular temperature (m2/s2) ρ Density (kg/m3) τ Stress-strain tensor υ Velocity vector (m/s) Gradient (1/m) ∇ Subscripts Drag D p Phase p or Particle/Solids q Phase q Transpose T
REFERENCES 1. Agrawal K, Loezos PN, Syamlal M, Sundaresan S. Journal of Fluid Mechanics. 2001;445:151-85. 2. Wang J, Ge W, Li J. Chemical Engineering Science. 2008;63(6):1553-71. 3. Igci Y, Andrews AT, Sundaresan S, Pannala S, O'Brien T. AIChE Journal. 2008;54(6):1431-48. 4. Benyahia S. Industrial and Engineering Chemistry Research. 2010;49:512231. 5. Andrews AT, Loezos PN, Sundaresan S. Industrial and Engineering Chemistry Research. 2005;44(16):6022-37. 6. Wang J. Industrial & Engineering Chemistry Research. 2009;48(12):5567-77. 7. Cloete S, Johansen ST, Braun M, Popoff B, Amini S. 7th International Conference on Multiphase Flow; 2010; Tampa, FL USA; 2010. 8. Taghipour F, Ellis N, Wong C. Chemical Engineering Science. 2005;60(24):6857-67. 9. Syamlal M, Rogers W, O'Brien TJ. Springfield: National Technical Information Service 1993. 10. Schaeffer DG. Journal of Differential Equations. 1987;66:19-50. 11. Lun CKK, Savage SB, Jeffrey DJ, Chepurniy N. Journal of Fluid Mechanics. 1984;140:223-56. 12. Ogawa SU, A.; Oshima, N. Journal of Applied Mathematics and Physics. 1980;31:483. 13. Popoff B, Braun M. A Lagrangian Approach to Dense Particulate Flows. 6th International Conference on Multiphase Flow. Leipzig, Germany 2007. 14. Gidaspow D, Bezburuah R, Ding J. Hydrodynamics of Circulating Fluidized Beds, Kinetic Theory Approach. 7th Engineering Foundation Conference on Fluidization 1992:75-82. 15. Gobin A, Neau H, Simonin O, Llinas J, Reiling V, Selo J. International journal for numerical methods in fluids. 2003;43(10-11):1199. 16. Geldart D. Powder Technology. 1973;7(5):285-92. 17. Johnson PC, Jackson R. Journal of Fluid Mechanics. 1987;176:67-93. 18. Patankar S. Hemisphere Publishing Corporation 1980. 19. Leonard BP, Mokhtari S. NASA TM 1-2568 (ICOMP-90-12); 1990; NASA Lewis Research Center; 1990.
8
HYDRODYNAMICS OF DUAL FLUIDIZED BED SYSTEMS WITH INTERNAL MIXING CHANNELS BETWEEN CIRCULATING AND BUBBLING FLUIDIZED BED REACTORS Uendo Lee*†, Insoo Choi*, Jaehun Song **, Won Yang*, Youngdoo Kim*, and Youngtai Choi* * Korea Institute of Industrial Technology, Cheonan, Korea **SeenTec Co., Ltd., Changwon, Korea †Tel: +82-41-589-8574; Fax: +82-41-589-8323; Email: [email protected] ABSTRACT We have examined a dual fluidized bed (DFB) system having internal mixing channels between the circulating fluidized bed (CFB) and bubbling fluidized bed (BFB) reactors. The bed material of the CFB reactor was supplied to the gasifier both by an external circulation loop, comprising a riser, cyclone, and standpipe, and an internal circulation loop that was implemented by making openings between the CFB and BFB reactors. The hydrodynamics and mixing characteristics of the system were investigated using a cold model test. Simple openings work very well for DFB systems, and internal mixing can be realized while maintaining stable external circulations. INTRODUCTION Gasifiers are systems that are essentially used to convert carbonaceous materials such as coal or petroleum into product gases such as carbon monoxide and hydrogen by reacting the raw materials at high temperatures using controlled amounts of oxygen and/or steam. Three representative types of gasifiers are widely used today—fixed bed, entrained flow, and fluidized bed. Among them a dual fluidized bed (DFB) gasifiers is an advanced type. The DFB produces medium heating value gas because the product gas is inherently separated from the combustion gas that is necessary for providing heat for the endothermic gasification reaction (1-2). In this system, the gasifier is separated from the combustor, and the bed material is used as a heat and mass carrier between the two reactors. The product gases from DFB gasifiers have been used for many applications such as power generation, co-combustion with a conventional fuel, and catalytic synthesis to obtain bio-Fischer-Tropsch (FT) diesel, dimethylester (DME), and synthetic natural gas (SNG), respectively (3-4). After Prof. D. Kunii proposed this system in 1975, various types of DFB gasification systems have been developed; they are summarized in the review paper of Corella et al. (5). Although the system is useful for generating medium heating value gases,
the operation of DFB systems is more difficult than that of the other systems because it has two reactors that are sensitive to the mass and heat balance (6-8). Further, loop seals are typically placed between the two reactors, and their function is to reroute the solids for stable operations and to separate the product and combustion gases; the loop seals complicate the process and makes it difficult to operate. In our study, we have developed a novel DFB gasification system. Instead of having a loop seal between the gasifier and combustor, we have created openings between the two reactors, which enable the movements of the gases and solids. Overall, the solids and gases flow from the gasifier to the combustor, but local internal mixing can occur between the two reactors near the opening; such mixing enhances the heat and mass transfers. In addition, by removing the loop seal, the entire system becomes simpler and easier to operate. COLD FLOW MODEL Figure 1 shows the schematic diagram of the cold flow model as well as the external and internal circulation loops. The external loop comprises a riser, cyclone, and standpipe, while the internal loop was implemented by adding openings between the reactors that are closely connected to each other. The hydrodynamics of the system and the mixing characteristics of the system were expected to be significantly affected by the openings. Under some conditions, internal mixing occurs near the opening, and it helps to heat up the gasification zone along with the hot bed material of the external circulation.
Fig. 1 Schematic diagram of novel dual fluidized bed (DFB) gasification system In this system, the synthetic gas (syngas) from the gasifier flows directly to the CFB combustor. When the syngas is injected into the hot combustion gas, the tar in the syngas is thermally cracked and steam reforming occurs automatically with the
steam of the combustion gas. Moreover, the heating value of the product gas is low because of mixing with the combustion gas. Some syngas loss occurs due to combustion but additional syngas production from the tar cracking can be expected to compensate the loss. Moreover, to decrease the syngas loss, the injection point of the syngas should be kept far from the main combustion zone; however, such a positioning is not considered here. In this paper, we have focused on the function of the openings between the CFB and BFB reactors. As a result of the reduction in the number of loop seals, this design minimizes any need for controlling the system operations and even helps to maintain more uniform gasifier temperatures by realizing simultaneous heating both by external and internal circulations of the hot bed material. EXPERIMENTAL CONDITIONS In this study, the hydrodynamics and pressure balances of the system were investigated in terms of the opening size and location and the number of openings. The experimental conditions are summarized in Table 1. We changed the opening sizes and locations by using partition plates. Three different cases have been tested, as shown in Table 1. The opening height H0 is the length from the bottom of the reactor to the starting position of the opening. Table 1 Experimental conditions Item CASE 1 CASE 2 CASE 3
Ugr (m/s)
Ug (Umf)
0.833
1.2
0.833
1.2
0.833
1.2
Opening size (m × m) 0.05×0.05
0.10×0.05
Opening height HO (m)
0.16×0.05
0.16 × 0.05 Dual openings Bottom Upper 0.16×0.05 0.16×0.05
0.345 0.245
0.295
0.345
HO(m) Bottom Upper 0.245 0.445
EFFECT OF OPENING SIZE In order to determine the effects of the opening size, experiments were conducted with three different opening sizes (L × H: 5 cm × 5 cm, 10 cm × 5 cm, and 16 cm × 5 cm); all other conditions were identical for each case. In this experiment, the opening is located above the free surface of the bed. Figure 2 shows the solid circulation rate (Gs) for each case. Gs is dependent on the amount of solid from the gasifier and the riser velocity. For identical riser velocities, Gs of the smallest opening is larger than that of the other cases. In all cases, Gs increased as the gasifier velocity increased, and the effect of gas velocity became more significant as the opening size decreased.
18
HO = 0.345 m Ugr = 0.833 m/s
16
Opening size (0.05 m x 0.05 m) Opening size (0.10 m x 0.05 m) Opening size (0.16 m x 0.05 m)
2
Gs(kg/m Xsec)
14
12
10
8
6 0.075
0.080
0.085
0.090
0.095
Ug(m/sec)
Fig. 2 Solid circulation rates with different opening sizes and gasifier velocities (single opening) 0.7
0.7
Opening size is fixed (0.05mX0.05m) HO is fixed (0.345m)
R-10 0.6
0.5
R-7
R-7 R-6
R-6 G-35
R-5
Opening
R-4 R-3
0.3
Ugr(0.833m/sec) Ug(1.2Umf)
R-8
H (m )
H (m )
0.5
R-9
Ug(1.2Umf)
R-8
0.4
0.6
Ugr(0.833m/sec)
R-9
Opening size is fixed(0.16mX0.05m) HO is fixed(0.345m)
R-10
0.4
R-5
G-35
Opening
R-4
0.3
R-3
G-20 R-2
0.2
G-20
R-2
0.2
R-1
R-1 0.1
0.1
G-5
G-5 0.0
0.0 0.0
0.5
1.0
1.5
2.0
∆ p (kPa)
2.5
3.0
3.5
4.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
∆ p(kPa)
Fig. 3 Effects of different opening sizes: (a) 0.05 m × 0.05 m (b) 0.16 m × 0.05 m)
Figure 3 shows the pressure drop profile of the riser and gasifier for different opening sizes. When the opening size is small (Fig. 3(a)), the gasifier pressure is always greater than the riser pressure; this implies that the solid and gas flow in one direction from the gasifier to the riser. In the case of such small openings, Gs is higher than in the case of large openings if the riser velocity is sufficient to transport the solid in the upward direction. In the case of large openings (Fig. 3(b)), the pressures of the gasifier and riser become identical, and this implies that a mutual exchange of the solid and gas occurs through the openings. This large opening leads to internal mixing between the two reactors and leads to a decrease in the amount of solid that flows into the external loop in the riser.
EFFECT OF OPENING LOCATION In order to investigate the effects of the opening location, openings were created at three different positions: H0 = 0.245, 0.295, and 0.345 m. Figure 4 shows the solid circulation rate as a function of the riser velocity for different opening locations. When the opening location was lower, the solid circulation rate was higher because the gasifier pressure increases when the opening location becomes closer to the reactor bottom. EFFECT OF NUMBER OF OPENINGS In order to investigate the influence of the number of openings, two openings were installed between the gasifier and riser. The lower opening was located under the bed (H0 = 0.245 m) while the upper opening was installed above the bed (H0 = 0.445 m). The opening size was fixed as 0.16 m × 0.05 m. In the case of such a dual opening, internal mixing between the two reactors was observed. In the lower opening, the solid flows from the gasifier to the riser, while in the upper opening, the solid flows from the riser to the gasifier. Figure 5 shows the pressures of the riser and gasifier near the two openings. The pressure of the gasifier is larger than that of the riser in the lower opening, while the pressures for the two reactors are identical near the upper opening. In the case of the dual opening, a vigorous internal circulation occurred through the upper opening while the external circulation was maintained. Obviously, this simultaneous solid circulation assists in heating the bed material more uniformly in the gasifier.
12
Opening size (0.16m x 0.05m) is fixed. Ug = 1.2Umf 10
HO = 0.245 m
2
Gs(kg/m Xsec)
8
HO = 0.295 m HO = 0.345 m
6
4
2
0 0.65
0.70
0.75
0.80
0.85
Ugr(m/sec)
Fig. 4 Solid circulation rate with different opening locations
0.7 Ugr(0.833m/s) and Ug(1.2Umf) are fixed HO(0.245m,0.445m) is fixed
R-10 0.6
Bottom opening: 0.16mX0.05m Upper opening: 0.16mX0.05m
R-9 R-8
H(m)
0.5
R-7
Upper opening
R-6 0.4
G-35
R-5
R-4 0.3
R-3
Lower opening R-2
0.2
G-20 R-1
0.1
G-5
Riser Gasifier
0.0 0
1
2
3
4
∆ p(kPa)
Fig. 5 Pressure of the gasifier and riser with dual openings
CONCLUSIONS The hydrodynamics of a DFB system having openings between the CFB and BFB reactors were investigated. These openings function as a replacement for the loop seal and have identical functions. The effects of the sizes, numbers, and locations of the openings were examined, and the characteristics of the solid and gas movements through the openings were observed. For single openings, internal mixing occurs when the opening size is large and is located over the bed. For dual openings, vivid internal circulations were observed. In this manner, we have demonstrated that simple openings work very well for the DFB system, and internal mixing can be realized using the openings while simultaneously maintaining stable external circulations of the bed material.
ACKNOWLEDGEMENT The authors are grateful to MKE for providing funding via ” Development of Syngas Production/Utilization System Through Large-scale (CFB)Circulating Fluidized Bed Biomass Gasifier” project. The authors also would like to acknowledge the extensive contributions of Prof. Bo G Leckner.
NOTATION
GS: solid circulation rate [kg/m2s] H: height [m] L: length [m] W: width [m]
△P: pressure drop [kPa]
Uo : Superficial gas velocity [m/s] Ugr: gas velocity in riser [m/s] Ug: gas velocity in gasifier [m/s] Umf: minimum fluidization velocity [m/s] HO : Opening height from the distributor [m]
REFERENCES
1. Boerrigter, H., Den Uil, H., Calis, H., “Green Diesel from Biomass via Fischer-Tropsch synthesis: New Insights in Gas Cleaning and Process Design”, Pyrolysis and Gasification of Biomass and Waste, Strasbourg (2002) 2. Sung, YK, Song, JH, Bang, BR, Yu, TU, Lee, UD, "A hydrodynamic characteristic of a dual fluidized bed gasification" 20th International Conference on Fluidized Bed Combustion. Xian, China (May , 2009) 3. Tijmensen, M., Faaij, A., Hamelinck C. and Hardeveld, M., “Exploration of the possibilities for production of Fischer Tropsch liquids and power via biomass gasification”, Biomass and Bioenergy 23, pp.129-152, (2002) 4. Song, JH, Sung, YK, YU, TU, Choi, YT., Lee, UD, “Optimization of Biomass Gasification Process for F-T Biodiesel Synthesis,“ 20th International Conference on Fluidized Bed Combustion. Xian, China (May, 2009) 5. Corella, J, Toledo, J. M. Molina. G, "A Review on Dual Fluidized Bed Biomass Gasifiers, Ind. Eng. Chem, 46, pp.6831-6839, (2007) 6. Guanwen Xu, Takhiro Murakmi, Yoshiaki Matsuzawa, and Hidehisa Tani, “The Superior Technical Choice for Dual Fluidized Bed Gasification”, Industrial & Engineering Chemistry Research, 45, pp.2281-2286 (2006) 7. Song, JH, Sung, YK, Choi, IH, Jeong HJ, Yu, TU, Lee, UD, "Steam gasifiaction of woody biomass in a fluidized bed for F-T Synthesis", 34th Clear Water Conference, Clearwater, Florida, USA (June, 2009) 8. Jansawang, W., Klimanek, A., and Gupta, A., “Enhanced yield of hydrogen from wastes using high temperature steam gasification”, Journal of Energy Resources Technology, Transactions of the ASME 128 (3), pp. 179-185 (2006)
BIO-GASOLINE FROM JATROPHA OIL: NEW APPLICATIONS FOR THE FCC-PROCESS by Alexander Weinert 1, 2 , Alexander Reichhold 1 , Peter Bielansky 1 , Christoph Schönberger 1 , Bettina Schumi 1 1
Vienna University of Technology, Institute of Chemical Engineering Getreidemarkt 9/166, 1060 Vienna, Austria 2
[email protected]
ABSTRACT Jatropha curcas L. is a very drought-resistant plant, and jatropha oil can be extracted from its seeds. Whilst not suitable for human consumption, we found that it is a promising feedstock for producing (bio)-gasoline. The oil was cracked in an internally circulating FCC-reactor using a Grace Davison Ultima® catalyst. The total conversion was around 65%, with ca. 40% gasoline and ca. 25% crack gas (exact numbers varied with reactor temperature). The gasoline has a RON > 95 and oxygen < 0.3% m. The crack-gas consisted of ca. 35% propylene, ca. 13% 1-butene and ca. 6% ethylene. THE FEEDSTOCK Jatropha curcas L. (JCL) belongs to the family Euphorbiaceae. Jatropha originated in Mexico, Central America and northern South America, but today it grows in many tropical and subtropical countries. In 2008, the worldwide area under cultivation was 900,000 hectares, with Asia (85%), Africa (13%) and South America (2%) being the main producing areas. Estimations suggest that the area of cultivation will increase to a total of 13,000,000 hectares by 2015 (1). The Plant Jatropha curcas grows as small tree or bush, with a maximum height of 5-7 m. Its life expectancy is 50 years and the crop yield increases with age. Under normal conditions jatropha seeds are only harvested once a year, but when watered sufficiently or when grown in humid regions, the fruits ripen throughout the entire year (2). As JCL is a tropical to semi-arid climate well-adapted succulent, it can survive three consecutive years of drought by dropping its leaves (3). Jatropha plants grown from seed develop a strong tap root (up to 5 m long) that can help stabilize the soil and prevent landslides. If propagated vegetatively by cuttings, JCL forms a dense root carpet that can prevent erosion and accumulate humus (4). Jatropha curcas grows on a wide variety of soils, except clay-rich soil. Accordingly, well-drained sandy soils with good aeration are preferred (5). Jatropha has very low nutrient requirements, but for optimal crop yield fertilization is necessary (6).
Because its leaves are inedible to animals, jatropha is often used as a hedge plant to protect fields from animals. Because of a greater interest in non-fossil sources for fuel, there is an increase in large-scale plantations (1). The Oil The most valuable product of JCL is jatropha oil. The quantity of seeds harvested, the oil content in the seeds and thus the total oil yield vary greatly with the climatic region and cultivation conditions. With optimal watering conditions (ca. 1200 mm precipitation per year) and a high soil fertility of around 6000 kg/ha/a, seed can be harvested (7). There are two main ways to obtain the oil: mechanical and solvent extraction. Up to 60% of the total oil can be obtained by using a manually operated oil press. If the press is motorized, jatropha seeds can yield up to 75% oil (8). A more efficient way of obtaining the oil is solvent extraction: when using organic solvents (hexane is most frequently used) practically 100% can be extracted, whereas with water-based solvents these values vary from 65% to 100% (depending on temperature and pHvalue, amongst others). Since solvent extraction is energetically more demanding, it is only economically practical on a larger scale (4). Jatropha oil mainly consists of fatty acids. We determined the exact composition by transesterification followed by GC-analysis (according to EN ISO 5509:2000). The oil was found to contain high amounts of unsaturated fatty acids (76.8%) and 23.2% saturated fatty acids. The details are shown in Figure 1.
Palmitic Acid 14,2% 9,0%
40,4%
36,4%
Stearic Acid Oleic Acid
0%
20%
40%
60%
80%
100%
Linolic Acid
Figure 1: Fatty acid composition of the jatropha oil analyzed
Jatropha oil is unsuitable for human consumption as it contains relatively high amounts of phorbolic esters and lecithins (2). Therefore, unlike conventional biofuel feedstocks (i.e. corn or palm oil), it is not part of the food vs. fuel dilemma. EXPERIMENTAL SETUP The Pilot Plant A continuously working fluid catalytic cracking (FCC)-pilot plant (see Figure 2) was used for the experiments. The different areas in the plant consist of the feed preheater, the riser (where the catalyst comes into contact with the feed and where the cracking takes place), the regenerator (where the spent catalyst is regenerated) and the product gas condensation area. All sections are kept under an inert gas atmosphere (nitrogen), except for the regenerator (this needs oxygen to burn off the coke) (9).
The plant has an internal circulating design, thus the riser is inside the regenerator. This is a major difference from most other FCC-plants, which have external circulation. The main advantages of internal vs. external circulation are: compact design simplified architecture (i.e. no slide valve for catalyst recirculation or cyclones needed) desired heat coupling regenerator (exothermic combustion of coke) – riser (endothermic crack-reaction) The feed pre-heater is an electrically heated tubular oven. The oil is heated to 300°C. The length of the oven (9 m) helps to level out any peaks in flow rate and thus assures a constant transport of the feed into the reactor. Inside the reactor, the pre-heated oil comes into contact with the hot catalyst. This leads to vaporization of the oil. The gaseous feed-molecules can then react with the catalyst particles. The cracking reactions produce many gaseous molecules, which in turn are responsible for the increase in volume. This effect causes an upward movement inside the riser. The flow created conveys the catalyst upwards to the particle separator that diverts the catalyst-flow to the regenerator. The product gas, on the other hand, leaves the reactor and is transported to the product gas condensation area, from where samples are taken for analysis.
Figure 2: Schematic of the used FCC pilot plant
The spent catalyst circulates internally to the regenerator. Since this area of the reactor is fluidized with air, a siphon (fluidized with nitrogen) is used to strip the catalyst and prevent the air from entering the product gas side. The siphon also enables us to measure the catalyst circulation rate: when the siphon fluidization is turned off, no further spent catalyst can enter the regenerator. As some of the catalyst is still being transported through the riser, the level of catalyst in the regenerator drops. This is measured as a drop in pressure, which is converted into the catalyst circulation rate.
The regenerator is a stationary fluidized bed. Air is used as a fluidizing agent in order to burn off the coke that becomes deposited on the catalyst during the cracking reaction. The average residence time in the regenerator is around 10 minutes.
The bottom section is also built with a fluidization system. Like the siphon, this is used to strip the catalyst coming in from the regenerator and to prevent the catalyst flow from stopping. The Catalyst A standard zeolite FCC-catalyst was used for cracking of the oil. The catalyst was an equilibrium catalyst (e-cat) by Grace Davison (E-Ultima®). For more details, please see Table 1 below. Table 1: Specifications of the catalyst used
Name of catalyst Type of catalyst Particle size range Mean particle size
Grace Davison E-Ultima® shape-selective zeolite 20 – 200 µm 75 µm
ANALYSIS There were three main components that needed to be analyzed: the flue gas, the gaseous product and the liquid product. Figure 3 shows a schematic of the sampling and analytics.
Figure 3: Schematic of the sampling and analytics and assignment to the product lumps
A side-stream of the product gas was sucked away for 15 minutes. The hot gas was run through coolers (first and second stage at 4°C and a third cooler at -20°C) and partially condensed. The still gaseous fraction was collected in a gas-sampling bulb and analyzed immediately. The liquid fraction was stored in a glass bottle for subsequent phase separation. Gaseous Phase The gaseous phase mainly contained crack gas: olefins (propylene, 1-butene and ethylene) and paraffins (linear and iso-paraffins). Another part contained uncondensed paraffins (mainly C5 and C6). This group was added to the gasoline lump (see Figure 3). Other components in the gas were CO and CO2, both of which are only formed in the presence of oxygen contained in the biomass.
Liquid Phase The liquid phase was separated into an aqueous and an organic phase. The water formed contained most of the oxygen from the biomass. The rest was found as CO or CO2 (see the section above). This left the organic phase (gasoline, LCO and residue) practically free from oxygen compounds (total oxygen < 0.3% m.). The resulting boiling range of the organic phase was measured by a SimDist so that the contents of the gasoline in the product could be determined. Flue Gas The flue gas provides information about the coke formed during cracking. It was analyzed online with two Rosemount® NDIR-gas analyzers. The detected components were CO, CO2 and O2. The amount and composition of the coke formed was determined from this data. RESULTS First, experiments with varying riser temperatures were conducted. Each data point is an averaged from three single values in order to improve statistical significance. Figure 4 shows the influence of varying riser temperature (averaged along the height of the riser) on the valuable products of gasoline, crack gas and CO. The total fuel yield (TFY) shown was defined as shown in (1) below: (1) With increasing temperature in the reactor, there was a tendency for smaller molecules to form in the cracking process; this led to an increased production of gasoline (+3% / 100 K) and gas (+9% / 100 K). This was mainly at the expense of light cycle oil (LCO) and residue (-11% / 100 K), and coke, to a lesser extent (-2% / 100 K) (see Figure 5). The formation of the oxygen-containing products CO (in Figure 4), CO2 and H2O (both in Figure 5) was barely influenced by the increasing average riser temperature. There was no change in CO2 yield or a redistribution between CO (-1% / 100 K) and water (+1% / 100 K). Further experiments were carried out in order to compare jatropha oil with vacuum gas oil (VGO, the regular feed for FCC units). These were performed at a mean riser temperature of 550°C, which corresponds to the average FCC operating conditions in refineries.
Total Fuel Yield Gasoline Crack Gas CO
80% 70%
25% Percentage feedbased [% m.]
Percentage feedbased [% m.]
90%
60% 50% 40% 30% 20% 10% 0%
LCO & Residue Water Coke CO2
20% 15% 10% 5% 0%
500 550 600 Average riser temperature [°C] Figure 4: Influence of riser temperature on the formation of gasoline, crack gas and CO
500 550 600 Average riser temperature [°C] Figure 5: Influence of riser temperature on the formation of LCO & residue, water, coke and CO2
60% 50% 40% 30% 20% 10% 0%
VGO Jatropha oil
50% 41%
14%
Percentage feedbased [% m.]
Percentage feedbased [% m.]
Figure 6 shows the products of jatropha oil compared to VGO when used as feedstock. Jatropha oil yielded less gasoline (-9%) and crack gas (-6%) per kilogram feed than vacuum gas oil. This was mainly due to the formation of water (+11%) and CO2 (+1%). The oxygen contained in the triglyceride converted into these components.
12%
12% 10%
32% 26% 14%15%
11% 6% 4% 0% 0%1%
Figure 6: Products lumps of jatropha oil as feed compared to VGO (at 550°C riser temperature)
VGO Jatropha oil
9%
8% 6% 4% 2%
5% 2% 1%
3%
4% 3%
0%
Figure 7: Olefins contained in the crack gas of jatropha oil as feed compared to VGO (at 550°C)
Figure 7 depicts the olefin contents of the crack gas. Compared to VGO, the jatropha oil showed a similar distribution of the olefins. The composition of crack gas was about the same for both feeds. The decrease shown above was due to the aforementioned formation of water (and the subsequent reduction in gas yields).
Figure 8 shows the results of a PIONA analysis: paraffins, iso-paraffins, olefins, naphthenes and aromatics. It was performed using two-dimensional GC-analysis (GC × GC).
Percentage of gasoline [% m.]
60%
51% 45%
VGO Jatropha Oil
50% 40%
26% 26%
30% 20% 10%
13%15% 8% 2%
Jatropha oil produced fewer aromatics (-6%) and iso-paraffins (-7%). The increase in paraffins (+11%) and naphthens (+2%) compensated for this. Table 2 compares the gasoline properties with the current legal specifications. The research octane number (RON) and the motor octane number (MON) were comparable to regular FCC gasoline (from VGO). This was mainly due to the very high amounts of aromatics and i-paraffins still present.
6% 8%
0%
The gasoline from the jatropha oil contained virtually no lead. The sulfur Figure 8: PIONA results of gasoline produced contents was also considerably lower than with regular FCC gasoline from VGO. As mentioned before, almost all of the oxygen contained in the biomass was removed: only 0.3% was found in the liquid product. Table 2: Comparison of gasoline properties with current legal specifications
Property
Unit
RON MON Density Pb-contents S-contents O-contents
kg/m³ mg/L mg/kg % m.
Legal min 95 85 720.0
Legal max 775.0 5 10 2.7
ACKNOWLEDGEMENT This work was funded by OMV Holding.
Jatropha gasoline 95 81 801.1 < 0.1 2 0.3
Common FCC-gasoline 91 – 96 78 – 84 100 – 2000
NOTATION CO FCC GC JCL LCO MON PIONA RON TFY VGO
Carbon monoxide Fluid catalytic cracking Gas chromatography Jatropha curcas L., botanical name of the jatropha plant Light cycle oil Motor octane number Acronym for paraffins, iso-paraffins, olefins, naphthenes, aromatics Research octane number Total fuel yield Vacuum gas oil
REFERENCES 1. 2. 3. 4. 5.
6. 7. 8.
9.
The Global Exchange for Social Investment LLP. (GEXSI LLP) (2008). Global Market Study on Jatropha. London/Berlin. Achten W.M.J., Verchot L., Franken Y.J., Mathijs E., Singh V.P., Aerts R., Muys B. (2008). Jatropha bio-diesel production and use. Biomass and Bioenergy, 32(12), 1063-1084. Macintyre, B. (2007). Poison plant could help to cure the planet. The Times, UK (28.07.2007). Achten, W.M.J., Mathijs E., Verchot L., Singh V.P., Aerts R., Muys B. (2007). Jatropha biodiesel fueling sustainability? Biofuels, Bioproducts and Biorefining, 1(4), 283-291. Heller, J. (1996). Jatropha curcas L. Promoting the conservation and use of underutilized and neglected crops. PhD thesis, Institute of Plant Genetic and Crop Plant Research, Gatersleben, Germany, and International Plant Genetic Resource Institute, Rome, Italy. Foidl, N., Foidl G., Sanchez M., Mittelbach M., Hackel S. (1996). Jatropha curcas L. as a source for the production of biofuel in Nicaragua. Bioresource Technology, 58(1), 77-82. FACT Foundation (2010). The Jatropha Handbook – From Cultivation to Application. Fact Foundation, Eindhoven, Netherlands. Jongschaap, R.E.E., Corré, W.J., Bindraban, P.S., Brandenburg, W.A. (2007). Claims and Facts on Jatropha curcas L. Global Jatropha curcas evaluation, breeding and propagation programme. Plant Research International B.V., Wageningen, Netherlands, 158. Reichhold, Al., Hofbauer, H. (1996). Internally Circulating Fluidized Bed as a Reaction/ Regeneration System for Catalytic Cracking. in: 5th International Conference on Circulating Fluidized Beds, Proceedings, Beijing, China.
STUDY OF RECARBONATION IN CIRCULATING FLUIDIZED BED COMBUSTION Irina Hyytiäinen, Anna Mahlamäki, Marko Palonen, and Mikko Varonen Metso Power Oy, Lentokentänkatu 11, P. O. Box 109, 33101 Tampere, Finland Helge Lemmetyinen Tampere University of Technology, Korkeakoulukatu 10, P. O. Box 541, 33101 Tampere, Finland ABSTRACT Oxy-fuel circulating fluidized bed combustion (CFBC) can use calcium based sorbents, primarily limestone, for the in-situ capture of much of the sulfur dioxide in the fuel. Under oxy-fuel CFBC conditions, the CO2 content is usually high, and at high combustor temperatures sulfur capture can occur in two steps, calcination and then sulfation. The typical Ca utilization ratio in oxy-fuel CFBCs is less than half. When temperature is below the calcination temperature while remaining exposed to a high CO2 environment, recarbonation of unused CaO may occur. This reaction between calcium oxide and carbon dioxide has the potential to create serious operational problems and boiler maintenance issues. The main purposes of this study were to design a test method to study recarbonation of limestone under oxy-firing fluidized bed conditions using a test reactor and to carry out test runs using this method. The test runs were carried out in a test reactor at Metso Power Research and Development Center in Tampere, Finland. The results show that the recarbonation and calcination reactions are limited by thermodynamic equilibrium, while the partial pressure of CO2 and the temperature also play important roles. The effects of N2 and O2 are not significant during the fast reaction period, but become more pronounced during the slow, diffusion-controlled reaction stage. Repeatability of the test method was good. INTRODUCTION Carbon-dioxide capture and storage (CCS) offers the potential for major reduction in carbon dioxide emissions from fossil fuel based power generation. Oxygen enriched combustion has been identified as one of the main CCS technology options. The main design parameter in oxy-firing is to burn fossil fuel in a mixture of oxygen instead of air and recycled flue gas, which contain mostly CO2. (Eriksson et al. (1), Zhao et al. (2)) Oxy-fuel circulating fluidized bed combustion (CFBC) can use solid sorbents, primarily limestone, for in-situ capture of much of the sulfur dioxide in the fuel. Under oxy-fuel CFB combustion conditions, the CO2 content is higher than 70%, and at high combustor temperatures sulfur capture can occur in two steps. First, the limestone is expected to decompose to CaO, and calcination (I) and subsequent reaction between CaO and SO2 will lead to the formation of solid CaSO4, sulfation (II). Calcination:
CaCO3(s) + heat
CaO(s) + CO2(g)
Sulfation:
CaO(s) + SO2(g) + 1/2O2(g)
Recarbonation:
CaO(s) + CO2(g)
CaSO4(s) + heat
CaCO3(s) + heat
(I) (II) (III)
Calcination will proceed only if the partial pressure of the CO2 in the gas above the limestone is less than the equilibrium partial pressure of carbon dioxide, at the calcination temperature. For each partial pressure of CO2 there is a corresponding temperature, known as the equilibrium calcination temperature. Figure 1 plots five of the expressions for dissociation pressures and corresponding equilibrium calcination temperatures listed in the literature.
Figure 1. Dissociation pressures and corresponding equilibrium calcination temperatures. (Basu et al. (3), Baker (4), FACT-database (5), CRC Handbook (6), Nskala et al.(7))
The typical Ca utilization ratio during sulfation in CFBCs is about 30–45%. Therefore, the CaO content in CFBC fly ash and bed ash is generally high. When CFBC ash cools while remaining exposed to a high CO2 environment, recarbonation (III) of unused CaO deposited on cool surfaces may occur. (Wang et al. (8)) Recarbonation is the reverse reaction to calcination, and it occurs below calcination temperatures. This phenomenon has the potential to create serious operational problems and boiler maintenance issues. There are relatively few findings on oxy-fuel fluidized bed combustion published in the open literature, especially studies on recarbonation. However, there are a few studies in which the recarbonation was carried out mainly in TGA. The main purposes of this study were to design a test method to study recarbonation of limestone under oxy-firing fluidized bed conditions using a test reactor and to carry out test runs using this method. EXPERIMENTAL SET-UP AND PROCEDURE The experimental tests were carried out in a vertical tube reactor with an internal diameter of 4.9 cm. The test reactor simulates a fluidized bed furnace where a limestone sample is fluidized in a sand bed using inlet gas. In this experimental study, the reactor was supplied with CO2 and O2 in an N2 balance. The pure gases are mixed before entering the reactor inlet. The gases flow through a perforated grid at the bottom of the reactor up to the top of the reactor. The fine particles in the flue gas are separated and captured by a cyclone. The sand and limestone can be fed in batches from the top of the reactor. The hot bed material can be removed by an ejector from above the tube reactor. Also the cyclone can be emptied. The flue gas flows through gas analyzers to the stack. The test equipment used in this study is illustrated in Figure 2.
Figure 2. Diagram of testing equipment.
The test reactor is fully automated and computer-controlled. Its temperature is controlled by electric heaters with a combined heat input of 10.4kW. Electrical heaters encircle the reactor. The heaters can maintain the bed region at a maximum temperature of 850°C. The temperature at many different points along the tube reactor (at different heights from the grid) can be measured with thermocouples. The walls of the reactor are insulated with thermo wool to avoid heat loss. The pressure differences can be controlled by pressure control valves, and pressure differences in the reactor tube and at the grid plate are measured. The pressure difference between the reactor tube and atmospheric pressure is also measured. Mass flow meters are utilized to obtain the desired inlet volume flow rates (ln/min). The concentrations of CO2 and O2 in the gas leaving the bed were measured continuously, using gas analyzers. The evolution of gas concentrations in the outlet gas and the temperature and pressure differences were continuously monitored and recorded at intervals of one second. Table 1. Chemical compositions of Piney Creek limestone.
Element wt-% Element wt-% CaO 51.6 K2O 0.13 SiO2 3.1 Na2O 0.02 TiO2 0.04 MnO 0.053 Al2O3 0.69 P2O5 0.067 Fe2O3 1.4 S-Eltra 0.1 MgO 0.55 H.h. 950°C 41.7
Table 2. Experimental conditions.
Temperature (°C) 650, 750, 850 XCO2 (vol-%) 10,40,60,90 XO2 (vol-%) 4, 10 XN2 (vol-%) 0,6,36,56,86
The limestone used in this study was Piney Creek (PC) limestone from the USA; its chemical composition is shown in Table 1. The limestone samples were prepared before the experiments. All experiments followed the same procedure. First, the limestone was sieved to a size fraction of 500–710µm, which was considered to be appropriate for the fluidized-bed system. Another reason for sieving was to remove all the small particles and to obtain a coarse enough batch, because if the particles are too small they may escape with the flue gas. After sieving, 20 g of limestone was calcined in a furnace at 900°C (30 minutes) under air. The tube reactor was operated in oxy-fuel combustion mode under atmospheric conditions. In each run, 130 g of sand at a size of 0.1–0.5mm was loaded from the top of the reactor to form a fluidized bed. A constant fluidization velocity of 0.3m/s was maintained throughout the tests. Each test run was initiated by loading a calcined and weighed batch of limestone onto the hot sand bed, which had been heated to the required temperature and was simultaneously fluidized by a gas mixture containing the desired content of CO2 and O2 in an N2 balance. The CaO conversion was calculated according to the amount of CO2 which had reacted during the test run. The calcination was studied by using 20 g of fresh and unprepared Piney Creek limestone as the sample instead of the calcined sample. Table 2 shows the experimental conditions during the series of experiments. RESULTS AND DISCUSSION To estimate the composition of the samples (calcined limestones) it is necessary to determine the free lime (CaO) contents. After calcination and before each recarbonation test, the calcined sample was weighed to measure mass loss during the calcination process, and thereby the extent of the calcination. The total weight loss of the sample after calcination should be due to the weight of the CO2 lost through the CaCO3 decomposition. The total molar content of CaO in the calcined sample was calculated from this weight loss. Respectively, the conversion of CaO to CaCO3 during the recarbonation test in the tube reactor was calculated according to the amount of CO2 reacted during the test run, while conversion of calcium oxide was calculated on a molar basis. The first 320 seconds of reaction were taken into consideration, as this period was ascertained to be sufficient to achieve the maximum reaction rate and therefore bring the reaction into the slower (diffusion-limited) reaction regime. Effect of CO2 content Conversions of CaO vs. time for different CO2 contents at three different temperatures are presented in Figures 3 (a)–(c). There were four different CO2 contents and 4% of O2 in N2 atmosphere. As can be seen in Figure 3 (a), high CO2 plays a particularly important role in the recarbonation of CaO. The figure shows that the conversions of CaO are close to each other except for conversion in high (90%) CO2 content. The higher content of CO2 corresponds to the increased conversion of CaO. CO2 and CaO are both reactants and the higher content of CO2 leads to the adding shift of the reaction toward the product side (CaCO3), which compensates for the imposed change in the conditions.
(a) 650°C
(b) 750°C
(c) 850°C
Figure 3. Conversion of CaO at temperature of (a) 650°C, (b) 750°C, and (c) 850°C. 10–90% CO2, 4% O2 in N2 atmosphere.
Figure 3 (b) shows that in the recarbonation results obtained at a temperature of 750°C, the conversion at 90% CO2 is highest while the difference in the CaO conversion between 60% CO2 and 40% CO2 is not significant. Several observations can be made for conversion at 10% CO2. First, the low concentration of CO2 leads to the low conversion of CaO. Second, as can be seen in Figure 1, this test point overlaps the equilibrium curve, and such a test point position causes a drop in the conversion of CaO. The recarbonation of CaO at 850°C is shown in Figure 3 (c). The conversion of CaO at 90% CO2 is over 1. This is impossible and must represent a measuring error. The conversion of CaO at 40% CO2 is close to zero, because at this point the pressure of CO2 is close to the dissociation pressure. At 10% CO2, conversion of CaO is negative because the partial pressure of CO2 is below the dissociation pressure at this point. Under these conditions, the reverse reaction to recarbonation, i.e calcination, takes place. Effect of temperature Figures 4 (a)–(d) show the recarbonation results obtained for a CO2 content of 10–90%, 4% O2 in an N2 balance. The effect of temperature on recarbonation was studied and was found to have a strong effect on the conversion of CaO. Recarbonation is exothermic, which means that the forward reaction is favored by lower temperatures. Nevertheless, experiments show that higher temperatures lead to higher conversions of CaO.
(a)
90% CO2
(b) 60% CO2
(c)
40% CO2
(d) 10% CO2
Figure 4. Conversion of CaO at (a) 90% CO2, (b) 60% CO2, (c) 40% CO2, and (d) 10% CO2.
The conversion of CaO decreases rapidly as the temperature approaches the equilibrium temperature, as shown in Figure 4 (b) at 850°C, (c) at 850°C and (d) at 750°C. When the temperature is above the equilibrium temperature, the conversion of CaO is negative, as shown in Figure 4 (c) at 850°C. Effect of N2 and O2 The effect of temperature on the reaction between CaO and CO2 in a mixture of 90% CO2 and 10% O2 without N2 is shown in Figure 5, curves (b), (d), and (f). It seems that the higher temperature corresponds to the increased conversion of CaO. This is the same observation as that made for the tests at 90% CO2, 4% O2 in an N2 balance, and three different temperatures, as presented in Figure 4 (a).
Figure 5. Conversion of CaO at a mixture of 90% CO2 and 10 % O2 at (b) 850°C, (d) 750°C, and (f) 650°C. Conversion of CaO at a mixture of 90% CO2, 4% O2 and 6% N2 at (a) 850°C, (c) 750°C, and (e) 650°C.
Figure 5 presents in parallel the curves of CaO conversion with and without N2 and with different O2 content. Figure 5, curves (c) and (d) at 750°C and curves (e) and (f) at 650°C show that the conversions of CaO at the same temperature (650°C and 750°C) with and without N2 (with 4% and 10% O2) during the fast reaction period are similar. The slight differences between the runs become more prominent during the slow, diffusion-controlled reaction stage, when conversion of CaO at 90% CO2, 4% of O2 in an N2 balance becomes greater than the conversion of CaO at 90% CO2 and 10% O2. As can be seen in Figure 5, curves (a) and (b) at 850°C, there is difference between the conversions of CaO, probably due to an error in measurement of curve (a). Repeatability To verify the oxy-fuel firing results, repeat tests were conducted with calcined samples. Repeatability is assessed through the variation in measurements taken under the same conditions. The test conditions were identical in both tests, but they were carried out on different days. It seems that the consumption rates of CaO were close to each other for the tests and repeat tests. In summary, it can be said that the repeatability of this test method is good. CaCO3 feeding – calcination The conversion of CaCO3 at 850°C is presented in Figure 6. Here, the conversion of CaCO3 is the molar ratio of the reacted CaCO3 in relation to the initial content of CaCO3 in the sample.
Figure 6. Conversion of CaCO3 at temperature of 850°C. 10–90% CO2, 4% O2 in an N2 balance.
As can be seen in Figure 6, the highest conversion of CaCO3 is at 10% CO2. The conversion of CaCO3 at 40% CO2 is low, and at 60% CO2 near to zero. This is because the partial pressures of CO2 at these test points are close to the dissociation pressure of CO2. The conversion of CaCO3 at 90% CO2 is negative, because the partial pressure of CO2 at this point is above the dissociation pressure of CO2. This means that the recarbonation takes place at these conditions and the equilibrium is on the reactant (CaCO3) side. CONCLUSIONS The experimental tests on recarbonation were carried out in a test reactor, which simulates oxy-firing fluidized bed conditions. The extent of recarbonation was determined using the differences in the concentrations of CO2 in the flue gas during the tests.
The conversion of CaO showed that recarbonation occurred easily at conditions, where the values for the CO2 concentration was noticeable higher than the equilibrium CO2 concentration at the reference bed temperature under certain conditions. Observations during the experiments show that the conversion of CaO is also sensitive to temperature, the conversion of CaO being significantly higher at high temperatures than at low temperatures, when the temperature is above the equilibrium temperature. The experiments show that the conversion of CaO decreases rapidly as the temperature approaches the equilibrium temperature. When the temperature is below the equilibrium temperature, the conversion of CaO is negative, and under these conditions the reverse reaction to recarbonation (calcination) takes place. Calcination was examined by using fresh limestone as the sample instead of the calcined sample. The highest conversion of CaCO3 was when the CO2 content was low, whereas when the CO2 content was high, recarbonation took place instead of calcination. The conversions of CaO with and without N2 (with 4% and 10% O2) during the fast reaction period are similar. The small differences between the runs are pronounced during the slow, diffusion-controlled reaction stage. The experimental data show that repeatability for this test method is good. The study of recarbonation will continue in a 4 MW oxy-fuel CFB pilot-scale unit during several weeks in the fall of 2010. The bigger test equipment makes it possible to study the effects of many important parameters, e.g. moisture, temperature and Ca/S ratio, on recarbonation. REFERENCES 1. Eriksson, T., Sippu, O., Hotta, A., Fan, Z., Myöhänen, K., Hyppänen, T., Pikkarainen, T. 2007. Oxyfuel CFB boilers as a route to near zero CO2 emissions coal firing. Power Gen, Europe. 2. Zhao, C.S., Duan, L.B., Chen, X.P., Liang, C. 2009. Latest evolution of oxy-fuel combustion technology in circulating fluidized bed. 20th International Conference on Fluidized Bed Combustion. Xian City, China. 3. Basu, P. 2006. Combustion and gasification in fluidized beds. CRC Press. Taylor & Francis Group. pp. 143. 4. Baker, E.H. 1962. The calcium oxide-carbon dioxide system in the pressure range 1–300 atmospheres. Journal of the Chemistry Society 70, pp. 464–470. 5. FACT-database. Bale, C.W., Pelton, A.D., Thompson, W.T. CRCT École Polytechnique de Montréal, Quebec, Canada. 6. CRC Handbook of Chemistry and Physics. 70th edition. 1989–1990. Editor-in-Chief: Weast, R.C., Editor: Lide, D.R., Associate Editors: Astle, M.J. and Beyer, W.H. CRC Press, Inc. Boca Raton, Florida. F-72. 7. Nskala, N., Liljedahl, G.N., Turek, D.G. 2007. Commercialization development of oxygen fired CFB for greenhouse gas control. Volume I Pilot scale testing and design study of an existing CFB retrofit to oxygen firing and CO2 capture. Final report submitted by Alstom Power Inc. 8. Wang, C., Jia, L., Tan, Y., Anthony, E.J. 2008. Carbonation of fly ash in oxy-fuel CFB combustion. Fuel 87, pp. 1108–1114.
EXPERIMENTS AND MODELLING OF MICRO-JET ASSISTED FLUIDIZATION OF NANOPOWDER J. Ruud van Ommen1, David M. King2, Samantha Johnson2, Niels Looije1, Alan W. Weimer2, Robert Pfeffer3, Berend G.M. van Wachem4 1
Department of Chemical Engineering, Delft University of Technology Julianalaan 136, 2628 BL Delft, The Netherlands T: +3115 278 2133; F: +3115 278 5006; E: [email protected]
2
Department of Chemical and Biological Engineering, University of Colorado Boulder, CO 80309-0424, USA; [email protected] 3
Department of Chemical Engineering, Arizona State University Tempe, AZ 85287, USA; [email protected]
4
Department of Mechanical Engineering, Imperial College London London SW7 2AZ, United Kingdom; [email protected] ABSTRACT The fluidization of nanoparticle agglomerates can be largely improved by using downward pointing micronozzles, creating a high-velocity jet, as experimentally shown. By discrete particle simulations – treating the agglomerates as single particles – we show that the microjet strongly reduces the amount of gas in voids. INTRODUCTION In recent years, the fluidization behaviour of nanoparticles has been receiving increased attention. It poses challenging scientific questions, but also has practical applications. For example, through atomic layer deposition it is possible to provide individual nanoparticles with an ultrathin coating. Weimer and co-workers demonstrated this technique for a wide variety of materials (1,2,3); recently van Ommen and co-workers showed that it is possible to carry out the process at atmospheric pressure (4). Although it sounds counterintuitive that nanoparticles could be fluidized, it is possible since they form agglomerates. Primary particles with sizes ranges from 7 to 500 nm typically form agglomerates from about 100 to 700 μm (5). These agglomerates are so dilute they are often assumed to have a fractal nature, thus making the coating of individual nanoparticles possible. Moreover, they are dynamic in nature, meaning that each agglomerate continually sheds a significant fraction of its composition, while simultaneously adding material from other agglomerates. Over the past decade, several researchers have made efforts to model the formation and fluidization of nanoparticle agglomerates (see, e.g., 6,7,8,9). Because of the large cohesive forces, fluidization aids are often needed to
obtain proper fluidization of nanoparticles. Several ways have been proposed, such as vibration, sound wave pulsation, and the use of AC electric fields (10). Recently, Quevedo et al. (11) proposed the use of microjets as an alternative. They showed that the fluidization behaviour of nanoparticle agglomerates is greatly enhanced by adding a secondary flow in the form of a high-velocity jet produced by one or more micronozzles pointing vertically downward toward the distributor. The micronozzles produced a jet with high velocity (up to near sonic velocities), breaking up large nanoagglomerates, preventing channelling, curtailing bubbling, and promoting liquidlike fluidization. In addition, they claimed that microjet-assisted nanofluidization was also found to improve solids motion and prevent powder packing in an internal, is easily scaled-up, and can mix and blend different species of nanoparticles on the nanoscale. They proposed that microjets improve the fluidization by increasing the turbulence and inducing high shear forces, which lead to agglomerate breakage. In this paper, we aim at achieving a further elucidation of the mechanisms through which a microjet enhances nanoparticle fluidization using experiments and modelling. APPROACH Experimental Experiments are carried out in a glass column with a diameter of 26 mm, equipped with a porous stainless steel distributor plate and a conical freeboard section to minimize particle elutriation. A HEPA filter and water bubbler at the outlet of the freeboard ensured that no nanoparticles were released to the environment. The entire system was kept inside of a fume hood to protect operators. The bed material consists of microfine TiO2 (Evonik Aeroxide P-25) with a primary particle diameter 25 nm, which tend to form soft agglomerates. In all experiments except those explicitly mentioned, the powders were sieved to have a diameter between 70 and 180 µm. This enabled comparison between the jetted and unjetted systems, as unsieved powders in the unjetted bed would segregate by size and only a portion of the bed would expand. However, some experiments were completed with unsieved powders to show the magnitude of the effect of the microjet. The bed is fluidized at atmospheric pressure and room temperature with nitrogen at superficial gas velocities ranging from 0 to 0.12 m/s. The bed was fluidized with a downward pointing tube (2mm diameter) inserted at the axis of the column; at the end of the tube a micro-nozzle has been attached with an internal diameter of 254 μm. Through the nozzle, we apply a nitrogen flow that is 30% of the base flow through the distributor. Modelling For the modeling of particles and fluids, different approaches and models exist, depending on the scale and region of interest. In this research, the interaction between the fluid and the particle agglomerates is of interest. Therefore, a CFDDEM (Eulerian-Lagrangian) model was chosen. In this model, the fluid is represented as a continuous medium. Since agglomerates typically consist of billions of nanoparticles, it is not possible to model each individual nanoparticle. Instead, we model the agglomerates as spheres with a typical density and diameter
that has been found experimentally in previous studies (7,9). For simplicity, we assumed all agglomerates to have the same size, and we did not include the breakage of agglomerates. Although we realize that this is a rough approximation, we think this approach is a good first step to obtain insight in the forces that are exerted on the nanoparticle agglomerates. We intend to extend the model to include agglomerate breakage in the near future. The program that was used is MultiFlow (12). Gas-agglomerate interactions (drag force) are calculated by the Wen and Yu correlation (13). Agglomerate-agglomerate interactions are calculated using the softsphere approach. This type of modeling enables multiple collisions, which occur frequently in a dense fluidized bed. When agglomerates collide, they will have a reversible deformation, leading to a repulsive force between the agglomerates. The elastic deformation is approximated by allowing a small overlap, and a repulsive force model is based upon the magnitude of the overlap. The model is based upon the pioneering work of Mindlin and Deresiewicz (14) and Tsuji et al. (15). Model details and implementation can de found in Hemph et al. (16). To properly model cohesive particles, the interparticle forces are calculated by van der Waals forces according to Hamaker (17). This force is inversely proportional to the square of the interparticle distance and is characterized by the Hamaker constant which has typical values of 10-19 J. The agglomerate motion is calculated by integrating Newton's law of motion and the fluid is modeled by approximating the Navier-Stokes equations in a finite volume discretized framework. Table 1: Agglomerate parameters
Table 2: System settings
Property
Value
Property
Value
Model type
Lagrangian
Steps per collision
36
Diameter
260 μm
Time step hydrodynamics
1·10-4 s
Density
30 kg/m3
Gravitation constant
10 m/s2
Youngs modulus
1.0 GPa
X-dimension
30·10-3 m
Coef. of restitution
0.90
Y-dimension
4.0·10-3 m
Poisson ratio
0.25
Z-dimension
100·10-3 m
Coef. Of Friction
0.35
Superficial gas velocity
2.0·10-2 m/s
Number of agglom.
260,000
The most important properties of the agglomerates are shown in Table 1. A value of 1.0 GPa is used for the Young modulus. The minimum fluidization velocity for these agglomerates was calculated to be 0.6 mm/s, using the Wen and Yu correlation (13). Note, however, that the Wen and Yu correlation has not been validated for particles (agglomerates) with such a low density. The properties of the walls with respect to collision are equal to the agglomerates' properties. The fluid is air at ambient conditions with a temperature of 298.15 K and a pressure of 1·105 Pa. The density of air is 1.21 kg/m3 and the viscosity 1.52·10-5 Pa· s. The other simulation settings are given in Table 2. The time steps for the particle phase in the model are determined by the collisions. Each collision is calculated in 36 steps and depending on the collision properties, such as velocities and masses, a time step is calculated. The jet tip is positioned in the centre of the horizontal cross-section at 100·10-3 m above the distributor; the jet is pointing downward. The
mesh is refined around the microjet. We carried out two different simulations: a base case with a superficial gas velocity of 2.0·10-2 m/s and the jet turned off, and a second simulation with a superficial gas velocity of 1.4·10-2 m/s through the distributor and a gas velocity of 18 m/s through the jet. The horizontal cross-section of the jet is 200 μm x 200 μm. The total amount of gas provided to the bed is equal for the two cases.
RESULTS AND DISCUSSION Experiments Before carrying out the micro-jet experiments, we have investigated the effect of isopropanol as a fluidization aid on the fluidization of nanopowders and heating and sieving as pre-processing techniques. Isopropanol is hypothesized to suppress the electrostatic forces. It also is useful in hydrating the system for de-aeration tests, so it was important to see the effect. This was completed by a bubbler system, where nitrogen, the fluidization gas, would flow through isopropanol, loading the nitrogen with isopropanol solely through the vapour pressure of the isopropanol. Subsequently, the bed heights of beds fluidized would be recorded with and without isopropanol. Bed collapse experiments were also carried out to determine if the isopropanol affected the distribution of gasses between the dense and bubble phases. However, the effect of adding isopropanol is apparent from bed collapse tests (not shown here): isopropanol leads to a slower de-aeration of the bed. Isopropanol is hypothesized to suppress the electrostatic forces between particles, but not to contribute to liquid bridging as long as it is in the vapour form. This effect would allow almost all of the nanoagglomerates to participate in the fluidization (rather than sticking to the wall or distributor), and lead to smaller bubbles, since powders would not aggregate due to electrostatics.
Normalized Height [-]
3 Sieved & heated, IPA
2.5
Sieved, IPA
2 1.5
Unsieved, IPA
1
Unsieved, no IPA
0.5 0 0
0.02
0.04
0.06
0.08
0.1
Superficial Velocity [m/s]
Figure 1. The effect of sieving, heating, and adding isopropanol (IPA) on the normalized bed height for the case without a microjet.
Figure 2. The powder in the bottom of the bed before and after processing with the microjet. Figure 2 shows the stark contrast in the unjetted and jetted beds, as indicated by bed height. The jet (internal diameter 254 µm) was operated at 30% of the base flow. For the case without the jet we found a minimum fluidization velocity of 3.2 cm/s; the jet reduced the minimum fluidization velocity to 2.0 cm/s. This implies that less gas is required to fluidize the bed with a higher void fraction. Another important aspect is the amount of gas that is in the bubble and dense phases. The goal is to minimize the amount of gas in the bubble phase, especially if the fluidization gas is a reactant. Measurements of the percentages in each phase can be done using bed collapse test. In case of the jetted bed (both with sieved and unsieved material), we found an even slower bed collapse that for normal fluidization with isopropanol added. The bed collapse data indicates that the fraction of gas in the dense phase increases from 0.55 (no jet, no isopropanol) and 0.75 (no jet, with isopropanol) to
(a)
(b)
Figure 3. The powder in the bottom of the bed (a) before and (b) after processing with the microjet. While in (a) agglomerates are clearly visible, no agglomerates can be seen in (b).
0.85-0.90 with the jet turned on and isopropanol added. This is likely due to the fact that the jet breaks up the agglomerates and diminishes the stratification. This can be seen in Figure 3. In the left-hand picture, the visible, millimetre-sized aggregates can be seen congregating in the bottom of the reactor. This formation hinders fluidization by encouraging channelling. The right-hand picture was taken after the bed was fluidized for ten minutes with the microjet turned on. This picture shows a much more homogeneous bed with no visible aggregates. Our results indicate that with the microjet no prior sieving of the bed material is needed, making it an industrially advantageous technique. Simulations With the simulations, it is not possible to mimic the experimental set-up completely: the amounts of nanoparticles agglomerates would become too large (>>106) to keep the computational times within reasonable limits. Therefore, we decided to study a pseudo 2D geometry. The depth is limited (4 mm), but large enough in term of agglomerate diameters (>10 times the agglomerate diameter). For the simulations, the ratio of the microjet cross-section (200 μm x 200 μm) compared to the bed cross-section (30 mm x 4 mm) is much larger than in the experimental setup (3.3 x 10-4 versus 1.6 x 10-5). In order to keep the volumetric flow rate through the jet in the optimum range (10-30% of the total volumetric flow rate (11)), we used a much lower jet velocity in the simulations (18 m/s). In spite of these differences between the experimental setup and the simulations, we still expect to obtain qualitative insight in the mechanisms in which the jet enhances the fluidization of nanoparticle agglomerates. The simulations have been run for a period of 1 s of real time. Although this is a very short time, it gives us a first impression of the hydrodynamics. Longer simulations are currently being carried out.
(a)
(b)
Figure 4. Snapshots of the voidage as a function of the Hamaker constant, respectively A = 0, 10-21 and 10-19 J, in the bed for (a) the jet turned off and (b) the jet turned on. The snapshots are taken 1 s after the start of the simulation.
We show the results in colour contour plots of a vertical cross section through the middle of the fluidized bed. Figure 4 shows the voidage distribution over the bed, with the jet turned off (a) and on (b). The figure clearly shows that there is a strong bubble formation in the case with the jet turned off, while the functioning jet leads to a much more homogeneous bed. Figure 4(b) does not show a large bed expansion. This means that the high jet velocity itself does not cause the large bed expansion, but rather causes the agglomerate breakage due to the action of the jet, and its impact on agglomerate-agglomerate collision frequency and force. Since these simulations assume a constant agglomerate size (i.e. agglomerate breakage is not considered), no bed height increase is observed. These results are well in line with results we reported earlier (18). We also varied the interparticle forces during the simulations. The left-hand-side contour plots in Figs. 4(a) and (b) are the voidage distributions without interparticle forces. The middle and right-hand-side plots in Figs. 4(a) and 4(b) show the voidage for non-zero Hamaker constants, yielding low (A=10-23 J) and normal (A=10-21 J) magnitudes of interparticle forces. The value of the Hamaker constant seems to have little influence; even for the absence of interparticle forces (A=0 J) a very similar voidage profile is observed. The results show that the inclusion of the microjet leads to a more even distribution of the particles over the bed (i.e., absence of large voids), irrespective of the presence and magnitude of interparticle forces. Preliminary results from both experiments and simulations (not shown in this paper) indicate that the jet just penetrates a few cm in to the bed. This means that in deeper beds than currently investigated, it is best to position the jet relatively close to the bottom, as most large agglomerates will be present in the bottom zone. CONCLUSIONS Experiments have shown that the fluidization behaviour of nanoparticle agglomerates is greatly enhanced by adding a secondary flow in the form of a highvelocity jet produced by a micronozzle pointing vertically downward toward the distributor. We found that the microjet increases the bed expansion and the amount of gas in the dense phase, which can be explained by a reduction in agglomerate size. Discrete particle simulations were performed using a pseudo 2D geometry, in which the agglomerates were mimicked by single particles and agglomerate breakage was not taken into account. These simulations showed that the microjet lead to a reduction of the amount of gas in voids, and a more homogeneous nature of the bed. This agrees well with the experimental findings. NOTATION A
Hamaker constant [J]
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17. 18.
Hakim, L. F., Blackson, J., George, S. M., Weimer, A. W. (2005), ‘Nanocoating individual silica nanoparticles by atomic layer deposition in a fluidized bed reactor’, Chem. Vap. Deposition 11, 420-425. King, D.M., Johnson, S.I., Li, J., Du, X., Liang, X., Weimer, A.W. (2009), ‘Atomic layer deposition of quantum-confined ZnO nanostructures’, Nanotechnology 20, 195401. Liang, X., King, D.M., Li, P., George, S.M., Weimer, A.W. (2009), ‘Nanocoating hybrid polymer films on large quantities of cohesive nanoparticles by molecular layer deposition ’, AIChE J. 55, 1030-1039. Beetstra, R., Lafont, U., Nijenhuis, J., Kelder, E.M., van Ommen, J.R. (2009), ‘Particle coating by Atomic Layer Deposition: protection of cathode material for Li-ion batteries against degradation’, Chem. Vap. Deposition 15, 227 - 233. Zhu, D., Yu, Q., Dave, R.N., Pfeffer, R. (2005), ‘Gas Fluidization Characteristics of Nanoparticle Agglomerates’, AIChE J. 51, 426-439. Chaouki, J., Chavarie, C. Klvana, D., Pajonk, G. (1985), ‘Effect of interparticle forces on the hydrodynamic behaviour of fluidized aerogels’, Powder Technol. 43, 117-125. Yao, W., Guangsheng, G., Fei, W., Wu, J. (2002), ‘Fluidization and agglomerate structure of SiO2 nanoparticles’, Powder Technol. 124, 152–159. Jung, J. and D. Gidaspow (2002), ‘Fluidization of nano-size particles’, J. Nanopart. Res. 4, 483-497. Hakim, L.F., Portman, J.L., Casper, M.D., Weimer, A.W. (2005), ‘Aggregation behavior of nanoparticles in fluidized beds’, Powder Technol. 160, 149-160. Lepek, D., Valverde, J.M., Pfeffer, R., Dave, R.J. (2010), ‘Enhanced nanofluidization by alternating electric fields’, AIChE J. 56, 54-65. Quevedo, J.A., Omosebi, A., Pfeffer, R. (2010), ‘Fluidization enhancement of agglomerates of metal oxide nanopowders by microjets’, AIChE J. 56, 14561468. van Wachem, B.G.M. (2008) MultiFlow - A fully coupled solver for multiphase flows, www.multiflow.org Wen, C., Yu, Y. (1966), ‘Mechanics of fluidization’, Chemical engineering programming symposium 62, 100-110. Mindlin, R., Deresiewicz, H. (1953), ‘Elastic spheres in contact under varying oblique forces’, J. Appl. Mech. 20, 327-344 Tsuji, Y., Kawaguchi, T., Tanaka, T. (1993), ‘Discrete particle simulation of two-dimensional fluidized bed’, Powder Technol. 77, 79-87 Hemph, R., van Wachem, B., and Almstedt, A.-E. (2006), ‘Distinct element modeling of hopper flows comparison and validation of models and parameters’, In: Proc. Fifth World Congress on Particle Technology, Orlando, Florida. Hamaker, H.C. (1937), ‘The London-van der Waals attraction between spherical particles’, Phyisica IV, 10, 1-15 van Ommen, J.R., King, D.M., Weimer, A.W., Pfeffer, R., van Wachem (2010), ‘Experiments and Modeling of Micro-jet Assisted Fluidization of Nanoparticles’, in: Kim, S.D., Kang, Y., Lee, J.K., Seo, Y.C. (Eds), Proceedings of the 13th International Conference on Fluidization, pp. 479-486, Engineering Conferences International, New York.
EFFECTS OF SECONDARY AIR INJECTION UPON THE FLUIDIZATION CHARACTERISTICS OF THE LOWER STAGE IN A TWO-STAGE, VARIABLE-AREA FLUIDIZED BED RISER Eric K. Johnson and Steven L. Rowan West Virginia University, Morgantown, WV ABSTRACT A transparent scale model of a two-stage fluidized bed coal dryer with a small diameter lower stage and a large diameter upper stage, separated by a conical transition zone with secondary air injection ports, has been constructed to study the effects of secondary air injection upon the fluidization characteristics of the lower riser stage. The superficial velocity of the lower stage of the riser was held constant within the turbulent fluidization regime while the superficial gas velocity in the upper riser stage was varied by changing the volumetric flow rates of air introduced between the upper and lower riser stages. Through examination of time series pressure data via standard deviation, autocorrelation, spectral density plots and visual observation of dense bed height, it becomes apparent that secondary air injection has a dominant effect upon the fluidization characteristics below the injection location, leading to a transition from a dense to a dilute bed. INTRODUCTION While not commonly used for commercial drying of fine coal particles, many other industries have utilized fluidized beds for the drying of granular materials such as grains, fertilizers and chemicals [1,2,3,4]. Fluidized beds possess many advantages over more conventional drying techniques, among these advantages are: better temperature control, more uniform temperature distribution, higher thermal efficiency and intensity of drying, better gas-particle contact and less degradation of the particles. Unfortunately, there are also disadvantages associated with fluidized bed drying. These disadvantages include high pressure drops, non-uniform moisture content in the product (when operated in continuous mode) and the inability to adapt to counter-current operations [5,6,7,8]. While not commonly utilized in commercial coal drying applications, there has been some research conducted to study aspects of fluidized bed drying of coal. Diamond
[7] concluded in a study to determine the effects of temperature and particle size on the fluidized bed drying of northern Ireland lignite coal that drying rates increased as air temperatures increases, as well as when particle sizes decreased. Calban et al [5] obtained similar results while studying the drying characteristics of Turkish lignite in a batch bubbling fluidized bed. In addition to temperature and particle size considerations, Calban et al [5] determined that the velocity of the drying air had no significant effect on drying rates. In another study, Calban [6] investigated the effects of bed height and initial moisture concentration on drying rates of Turkish lignite. Rowan [9] examined the performance of a two-stage, variable area fluidized bed dryer with secondary air injection. Commonly used in pulverized coal boilers, secondary air injection in fluidized beds typically consists of splitting the fluidizing gas supply and introducing it into the fluidized bed riser at multiple bed height locations. Ersoy et al [10] examined the hydrodynamics of a circulating fluidized bed with secondary air injection, finding that secondary air injection led to an increase in solids holdup in the zone below the injection height for both radial and tangential injection modes. Above the secondary air injection location, it was found that only tangential injection led to an increase in solids holdup. It was also found that using a higher ratio of secondary air to primary air (into the bottom distributor) also led to higher solids holdup values. Ersoy et al [10] also looked at the effects of secondary air injection on the axial particle velocities and noted a decrease in the primary zone below the injection location and an increase in the secondary zone above. Chen et al [11] examined the effects of secondary air injection on the distribution of solids concentration and proposed a correlation between the secondary air penetration distance and the velocity of secondary air. In each of the previous studies, the fluidized beds used were of constant crosssectional area, and the given superficial gas velocities were calculated by combining the flow rates of air into the bottom bed distributor as well as the secondary air. The current study utilizes a novel two-stage, variable area geometry, as described in the following section. In addition, the objective of the current study was to maintain a constant superficial gas velocity in the lower riser stage, UL, while increasing the superficial gas velocity in the upper riser stage, UU, by increasing the amounts of secondary air injection. In this way, it was possible to characterize the effects of secondary air injection on the fluidization characteristics below the secondary injection location. EXPERIMENTAL SETUP The fluidized bed system shown in Figure 1 is a scale model of a larger system designed to be a warm air dryer of fine coal particles. The model riser is constructed primarily of transparent acrylic sections to allow for visual observation during operation. The lower stage has an inside diameter of 2.29 inches and a height of approximately 55.625 inches, the upper riser stage has an inside diameter of 4.0 inches and a height of 36.75 inches. The system also contains an air distributor at the bottom of the lower stage, and a second conical distributor located between the lower and upper riser stages. Both distributor plates are designed so that air is injected radially into the riser. In addition, the lower distributor has another air inlet that forms a vertical jet along the centerline of the riser. The air supply for both of the
distributors and the bottom jet is supplied by a compressor and are individually controlled via rotameters. Sand with a specific gravity of 2.65 and particle sizes ranging between 150 and 500 microns are supplied at a constant feed rate into a pneumatic transport line and enter the bottom of the fluidized bed riser through the bottom air jet. Particle-laden air exits the riser through an exit port in the upper riser stage and enters a cyclone, which separates the sand from the exhaust gases. The sand is then collected in a solids collection bin attached to the bottom cyclone exit and the air leaving the cyclone is passed through a water filtration drum to trap any sand not separated out by the cyclone. Omega Engineering PX35k1- series pressure transducers are located along the height of the riser. Three are located within the lower riser stage; they are located at the bottom, middle and top, respectively. Another is located at the bottom of the upper riser stage, and the final is located just below the exit port at the top of the upper riser stage. Each of the pressure transducers measure absolute pressures in units of psia at a sample rate of 100 samples per second and the resulting signal was recorded for later analysis via an Omega Engineering OMB-DAQ-300 usb data acquisition system.
Figure 1: System diagram
RESULTS AND DISCUSSION For this series of testing, the superficial gas velocity in the lower stage of the fluidized bed riser (UL) was maintained at a constant 2.36 m/s, which was determined to be within the turbulent fluidization regime by prior fluidization regime mapping. The superficial gas velocity within the upper stage of the riser (UU) was
then varied from 1.42 m/s to 3.35 m/s by the addition of secondary air injection at the transition between the two stages. Table 1 shows all of the test conditions. Table 1: Test point upper and lower riser stage superficial velocities
Test # UL (m/s) UU (m/s)
1 2.36 1.42
2 2.36 1.74
3 2.36 2.06
4 2.36 2.38
5 2.36 2.70
6 2.36 3.02
7 2.36 3.35
Figures 2 and 3 show the effects of increasing levels of secondary air injection upon the standard deviations of pressures in the lower and upper riser stages. Similar trends can be seen in both figures. Each exhibits an initial increase in the magnitude of standard deviation as the pressure fluctuations increase. This increase is then followed by a subsequent decrease and then leveling out in the values of standard deviation. The peak at UL = 2.36 m/s and UU = 1.74 suggests that both riser stages are undergoing transition to turbulent fluidization at those operating conditions. The curves in both figures, with the exception of the Lbottom location (Figure 1a), exhibit a transition to fast fluidization when the amount of secondary air injection leads to approximate superficial velocity matching between the two riser stages (i.e. UL = 2.36 m/s and UU = 2.38 m/s). In addition, as can be seen in Figure 2, the relative magnitudes of the standard deviation decreases with increasing height within the lower riser stage, and the Lbottom location exhibits a more gradual transition to core-annular flow than seen at the Lmid and Ltop locations. This suggests that the effects of secondary air injection occur first close to the area of injection and propagate progressively lower down the riser with increasing amounts of secondary air injected, as denoted by increasing values of upper riser stage superficial velocity, UU.
Figure 2: Lower riser stage standard deviation of pressure (a) Lbottom (b) Lmid (c) Ltop pressure transducer locations.
Figure 3: Upper riser stage standard deviation of pressure (a) Ubottom (b) Utop pressure transducer locations.
The effects of increases levels of secondary injection upon the fluidization characteristics within the lower stage of the two-stage fluidized bed can also be seen when examining the autocorrelation and spectral density plots shown in Figures 4 and 5. Due to space considerations, only data for the Lbottom pressure transducer location are presented here. Figure 4 shows the effects of increasing amounts of secondary air injection upon the autocorrelation function of the Lbottom pressure transducer data. The autocorrelation curves in lots a-c show evidence of very little periodicity in the fluctuations of pressure. Plots d-g exhibit a higher frequency of extremely low magnitude oscillations about zero and are assumed to primarily represent signal noise in the instrumentation system due to a lack of bubble formation within the riser as it transitions from a dense bed to a dilute fluidization regime.
Figure 4: Autocorrelation of Lbottom pressure: UL = 2.36 m/s, UU = (a) 1.42 m/s (b) 1.74 m/s (c) 2.06 m/s (d) 2.38 m/s (e) 2.70 m/s (f) 3.02 m/s (g) 3.35 m/s.
Perhaps the most obvious indicator of the effects of secondary air injection upon the fluidization characteristics of the lower riser stage of the two-stage fluidized bed is the spectral density plots, as shown in Figure 5. For levels of secondary air injection resulting in upper riser superficial velocities UU ≤ UL=2.36 m/s, the frequency of pressure fluctuations are concentrated within a range of frequencies between 0 and 2.5 Hz. In addition, the magnitude of power associated within these frequencies increases as UU approaches the value of UL. As the magnitude of UU increases beyond UL, there is a resulting widening of the range over which the pressure fluctuation frequencies are spread. In addition, there is a general shift towards higher frequency ranges and the amount of power associated with each frequency becomes several orders of magnitude less than seen with lower amounts of secondary air injection.
Figure 5: Spectral density of Lbottom pressure: UL = 2.36 m/s, UU = (a) 1.42 m/s (b) 1.74 m/s (c) 2.06 m/s (d) 2.38 m/s (e) 2.70 m/s (f) 3.02 m/s (g) 3.35 m/s.
Figure 6 shows the effects of increasing levels of secondary air injection (reflected by increasing upper riser stage superficial velocity UU) upon the height of the visually observed interface between the dense turbulent bed and dilute fast fluidization in the lower riser stage. In the figure, the area above the curve exhibited visual indications of fast fluidization (or core annular flow), while the region below the curve appeared to be in a state of turbulent fluidization. As UU increased, the height of the dense turbulent bed within the lower riser stage was observed to decrease. This supports the conclusion from examining Figure 2 that increasing levels of secondary air
injection results in transition to fast fluidization progressively lower with the lower riser stage.
Figure 6: Effects of upper riser stage superficial gas velocity on normalized height of interface between dense bed and dilute regions of lower riser stage.
CONCLUSIONS A transparent scale model of a two-stage, variable area fluidized bed was constructed in order to study the effects of secondary air injection when introduced above a lower riser stage operating within the turbulent fluidization regime. It was discovered that increasing levels of secondary air injection eventually leads to a transition from a dense bed turbulent regime to a dilute bed fast fluidization regime below the level of secondary air injection. It is assumed that this occurs because the solids carrying capacity above the secondary air injection point increases with increasing levels of secondary air, limiting the recirculation of solids particles from the upper riser stage back into the lower riser stage. This assumption is supported by visual observation of greater amounts of solid material being pneumatically conveyed out of the upper riser stage while operating at higher upper riser stage superficial velocities. ACKNOWLEDGEMENT The authors would like to thank the National Research Center for Coal & Energy (NRCCE), as well as the Center for Advanced Separation Technologies (CAST) for their support and funding of this research effort. NOTATION UL UU HI HLRH Lbottom Lmid Ltop
lower riser stage superficial gas velocity upper riser Stage superficial gas velocity height of turbulent/fast fluidization regime interface height of lower riser stage Pressure transducer, located 2 inches above bottom air distributor Pressure transducer, located 27.5 inches above bottom air distributor Pressure transducer, located 53 inches above bottom air distributor
Ubottom Utop
Pressure transducer, located 62 inches above bottom air distributor Pressure transducer, located 92 inches above bottom air distributor
REFERENCES
1. Balasubramanian, N., Srinivasakannan, C. Drying of granular materials in circulating fluidized beds. Advanced Powder Technology, 18 (2007), 135142. 2. Jumah, R.Y., Mujumdar, A.S., Raghavan, G.S.V. Batch drying kinetics of corn in a novel rotating jet spouted bed. The Canadian Journal of Chemical Engineering, 74 (1996), 479-486. 3. Kannan, C.S., and Subramanian, N.B. Some drying aspects of multistage fluidized beds. Chemical Engineering Technology, 21 (1998), 961-966. 4. Tatemoto, Y., Mawatari, Y., Sugita, K., Noda, K., Komatsu, N. Drying characteristics of porous materials in a fluidized bed under reduced pressure. Drying Technology, 23 (2005), 1257-1272. 5. Calban, T. and Ersahan, H. Drying of a Turkish lignite in a batch fluidized bed. Energy Sources, 25 (2003), 1129-1135. 6. Calban, T. The effects of bed height and initial moisture concentration on drying lignite in a batch fluidized bed. Energy Sources, 28 (2006), 479485. 7. Diamond, N.C., Magee, T.R.A., McKay, G. The effect of temperature and particle size on the fluid bed drying of northern Ireland lignite. Fuel, 69 (1990), 189-193. 8. Nonhebel, M.A., Moss, A.A.H. Drying of solids in the Chemical Industry. Butterworth & Co. Publishers, 1971. 9. Rowan, S. Analysis and Scaling of a Two-Stage Fluidized Bed for Drying of Fine Coal Particles Using Shannon Entropy, Thermodynamic Exergy and Statistical Methods. Doctoral Dissertation, West Virginia University, Morgantown, WV, 2010. 10. Ersoy, L.E., Golriz, M.R., Koksal, M., Hamdullahpur, F. Circulating fluidized bed hydrodynamics with air staging: an experimental study. Powder Technology, 145 (2004), 25-33. 11. Chen, J., Lu, X., Liu, H. and Liu, J. The effect of solid concentration on the secondary air-jetting penetration in a bubbling fluidized bed. Poweder Technology, 185 (2008), 164-169.
EXPERIMENTAL STUDY ON REFORMING ACTIVITY AND OXYGEN TRANSFER OF FE-OLIVINE IN A DUAL CIRCULATING FLUIDISED BED SYSTEM Stefan Koppatz, Tobias Proell, Christoph Pfeifer, Hermann Hofbauer Institute of Chemical Engineering, Vienna University of Technology, A-1060 Vienna, Austria e-mail: [email protected]
ABSTRACT Fe-olivine was investigated in a dual circulating fluidised bed reactor system with focus on hydrocarbon reforming activity and effects of oxygen transfer. H2, CO2, CH4 and 1-methylnaphthalene were fed as a surrogate gas mixture to the reforming part. Oxygen transport was developed by solids circulation. Tar decomposition was marginally affected by partial oxidation. The overall degree of tar decomposition was found to be in the range of 70 to 80%. INTRODUCTION Biomass gasification is a suitable option for syngas generation and is the only renewable carbon source for partial substitution of fossil fuels. Fluidised bed processing is a common technology for the conversion of solid fuels into product gas and therefore is the pre-process for direct use (heat and power supply) or for further conversion processes into synthetic natural gas, liquid fuels or other chemicals. The catalytic approach to biomass gasification, related to the reforming of hydrocarbons (tars), is widely considered and under investigation by several groups (i.e. Kiennemann et al., Corella et al. or Simell et al.) and is specified within this section. Generally, catalysts can be grouped into natural or synthetic materials. Comprehensive reviews on catalysts in biomass gasification are given by Dayton (1) and Yung et al. (2).Natural olivine is often applied as bed material in fluidised bed gasification. Several investigations have been performed focusing the catalytic properties of olivine in terms of reforming of hydrocarbons or the gasification of biomass. Comprehensive investigations have been published by Devi et al. (3), Abu el Rub et al. (4), Kuhn et al. (5), Constantinou et al. (6), Rapagna et al. (7) or Rauch et al. (8). Generally, the studies outline that olivine acts as a catalyst. It is assumed that the catalytic property is associated with pre-treatment and iron content of the particle surface. Thus, it is supposed that iron is responsible for the catalytic activity. Pecho et al. (9) studied olivine and emphasised the oxygen transfer capability and reforming activity by means of toluene. Beyond these applications, iron is usually applied as a catalyst in the chemical industry (Fischer-Tropsch synthesis, ammonia synthesis). However, the latter studies were carried out at lab-scale and
non-application oriented facilities. Thus, predictive or general statements with regard to industrial application are rather limited. The present study focused on the investigation of iron-supported olivine (Fe-olivine) to elucidate the effect of iron in terms of tar reforming. Further, it is aimed to apply Fe-olivine as an in-bed catalyst, for fluidised bed biomass steam gasification, as the iron material is considered to be active for tar reforming. The investigations were carried out at a dual circulating fluidised bed reactor system with 120 kW fuel power to consider application-oriented aspects. A previous experimental study with natural olivine in terms of hydrocarbon reforming activity and oxygen transfer was carried out by Koppatz et al. (10). EXPERIMENTALS Natural olivine (silicate tetrahedra with iron and magnesium) was used as the carrier material for synthesis of the iron catalyst. The natural olivine (Mg1-x,Fex)2SiO4 (x ~ 8 wt.-%) was provided by Magnolithe GmbH (Austria). Thermal pre-treatment of the raw material was carried out by the manufacturer in order to dry the water of crystallization. Through the pre-treatment process, the bulk material passes through a rotary kiln for approximately 4 h at 1600°C. Thus, sintering of the material was further caused by the high temperature treatment. The Fe-olivine was developed by the University of Strasbourg (Laboratoire des Matériaux, Surfaces et Procedés pour la Catalyse). The material was synthesised with 10 wt.% at a large scale by wet impregnation with iron nitrate solution. Drying of water excess (at 100°C) and calcination (at 1000°C) of the material were carried out. Thus, the final global iron content was at about 20 wt.% considering that the major part of the iron was available at the particle surface. Preparation and characterisation of Fe-olivine has been detailed by Virginie et al. (11). Moreover, Virginie et al. (12) studied Fe-olivine as a catalyst for toluene steam reforming (at a micro-reactor scale) and found high toluene conversion (95%). The study assumed that metallic iron was responsible for the high conversion rates, since catalyst reduction with H2 was carried out as the pre-treatment step.
Fig. 1. Particle size distribution
Fig. 2. Sketch of the dual circulating fluidised bed reactor system
Further, natural olivine is a non-porous material with a BET surface area of < 0.5 m²g-1 is reported Devi et al. (3), but has due to its hardness of 6.5 – 7.0 on the Mohs scale a high attrition resistance. Besides mechanical stability, high availability at low cost makes the olivine advantageous for fluidised bed applications. The cumulative size distribution of the applied Fe-olivine particles is depicted in Fig. 1. According to the Geldart particle classification, the applied Fe-olivine is classified as Geldart group B. The DCFB pilot rig consists of two interconnected circulating fluidised bed reactors, named the fuel reactor (FR) and the air reactor (AR), allowing continuous circulating of solid material between the reactors. A sketch of the reactor system with dimensional information is displayed in Fig. 2. The pilot rig (nominal fuel power of 120 kW) was developed for chemical looping combustion or chemical looping reforming processes for gaseous fuels with a focus on scalability to large scale application, whereas the circulating solids act as an oxygen carrier or reforming catalyst. Further, sensitive heat transfer between the reactors is enabled by the circulating bed material. The AR is designed as a fast fluidisation regime and the FR for a turbulent fluidisation regime. Mixing of the gas phases is avoided by steam or nitrogen fluidised loop seals. Downstream of each reactor, gas and solids are separated in cyclone separators. After solid separation, the gas streams pass a common post combustion chamber equipped with a support burner for complete combustion. The exhaust gas stream is cooled down, cleaned in a bag filter and sent to the chimney. The pilot rig and further auxiliary units are detailed by Kolbitsch et al. (13) or Pröll et al. (14). For determination of fuel reactor exhaust gas concentrations, a Rosemount NGA 2000 is used and, additionally, an online gas chromatograph (Syntech Spectras GC 955) allows for cross-checking of carbon species and determination of the N2 content for evaluation of possible gas leakages from the air reactor to the fuel reactor. The air reactor exhaust stream is analysed using a Rosemount NGA 2000. Tar measurement was carried out to determine the tar concentration and tar conversion, respectively. The tar measurement method is based on the tar protocol given by Neeft et al. (15). Further details are given by Pfeifer (16). The combustion of propane in the AR serves as the heat source to cover the heat demand in the FR whose global reaction is endothermic. Generally, the gaseous species in the FR undergo CO-shift and reforming reactions, Reactions (a) – (c). Cat . ΔHR,850°C = -33.6 kJ/mol + H2 CO2 ←⎯ ⎯→ Cat . xCO + (x+y/2)H2 ΔHR,850°C > 0 CxHy + xH2O ⎯⎯→ ⎯ Cat . ΔHR,850°C > 0 2xCO + (y/2)H2 CxHy + CO2 ⎯⎯→ ⎯ H2O + Fe2O3 + 2FeO H2 ⎯ ⎯→ CO2 + 2FeO CO + Fe2O3 ⎯ ⎯→ xCO2 + Fe2O3 + ½y H2O + (4x+y) 2FeO CxHy ⎯ ⎯→ Fe2O3 2FeO + ½ O2 ← ⎯→
CO
+ H2O
(a) (b) (c) (d) (e) (f) (g)
In parallel to the gas-gas reactions, CO, H2 or CxHy may be oxidised through partial oxygen input by the oxygen carrier, Reactions (d) – (f). The oxidation-reduction chemistry of iron is lumped into the formal expression FeO (FeII) and Fe2O3 (FeIII), Reaction (g). The air reactor was fuelled with propane (C3H8) and air. A surrogate gas mixture of H2 (52 vol.%), CO2 (40 vol.%) and CH4 (8 vol.%) was fed into the FR. The surrogate gas mixture is related to product gas derived by steam gasification of biomass, assuming that CO and H2O are formed in the FR, Reaction (a).
1-Methylnaphthalene (1-MN) was added as a tar model compound to the FR gas feed. Thus, a strong reducing atmosphere developed in the FR. The AR was alternately operated at stoichiometric air/fuel ratios (λAR) > 1 and < 1, resulting in O2 or CO excess in the AR exhaust gas. Fig. 3 displays the experimental matrix. RESULTS AND DISCUSSION
Fig. 3. Experimental matrix
Fluid Dynamic Characteristics Fig. 4 displays the pressure profile of the reactor system, which was about the same for all the operation points. Due to the application of coarse particles compared to the conventional particle size for circulating fluidised risers and the design particle size of the DCFB reactor system, the exponential decay of pressure in the air reactor was less developed. The pressure profiles (AR and FR) indicate the decrease of solid concentration and the increase of voidage with reactor height, respectively. A low solid circulation developed in the AR. For the FR, a high solid concentration was found in the bottom region and the upper part was lean in solids. Fig. 5 highlights the mapping of the fluidisation regime according to the map suggested by Bi and Grace (17). The AR was operated slightly above the terminal velocity with superficial gas velocity of ~ 4.5 m/s. A bubbling fluidised bed developed in the FR with a U/Umf ratio (superficial velocity/minimum fluidisation velocity) of 7 – 8 at a superficial gas velocity of ~ 3.5 m/s.
Fig. 4. Pressure profile in the reactor system
Fig. 5. Fluidisation regime of FR and AR according to Bi and Grace (17)
Reaction and Conversion Behaviour The mean residence time in the FR amounted to ~ 3 s. Further, the FR operated at a mean gas hourly space velocity (GHSV) of ~ 1350 h-1. The FR exhaust gas composition for OP1 to OP4 is highlighted in Fig. 6.
Fig. 6. FR exhaust gas composition, mean values and standard deviation of measurement
Fig. 7. Mapping of oxygen transport and gas conversion (logarithmic deviation from CO-shift equilibrium)
Due to nitrogen fluidisation of the loop seals and argon flushing of the pressure measurements, the FR exhaust gas stream was diluted by about 25 vol.%db. OP1 was taken as the benchmark point and was characterised by highly developed oxygen transfer. The restricted oxygen transport which developed in OP2 (AR operation with CO excess) resulted in a considerably different exhaust gas composition compared to OP1, see Fig. 6. CO and H2 were partially oxidised towards CO2 and H2O. Compared to OP1, the CO content increased to about 45% and the H2 content increased to about 41% during OP2. The shift in CO, CO2 and H2 indicates partial oxygen transfer with selectivity towards the oxidation of CO and H2. The content in CH4 was marginally influenced. Tar feed (13.3 g/Nm³) was performed within OP3 at λAR > 1. The feed of the tar compound (C11H10) caused a shift in gas composition, since the total input in carbon and hydrogen increased. Based on OP1, the content of H2, CO and CH4 increased. The content in CO2 decreased, since the relative amount of oxygen was lowered by the hydrocarbon feed. However, the gas composition was further affected by the overall reaction behaviour and the solids. Limited oxygen transfer developed in OP4 (AR operation with CO excess). Hence, the partial oxidation of CO and H2 was disabled. Thus, the relative contents in CO2, CO and H2 shifted. Evaluation of process data using a data reconciliation method for fulfilment of mass and energy balances was carried out, Bolhàr-Nordenkampf et al. (18). Thus, the oxygen transfer in the reactor system is specified. Further, the logarithmic deviation from CO-shift equilibrium was determined to rate the gas conversion, Eq. (1). ⎡ ∏ pi ⎤ ⎥ pδ eq ( p i , T ) = log 10 ⎢⎢ i K P (T ) ⎥ ⎥⎦ ⎢⎣
Eq. (1)
K P ,CO −shift = −1.6164 + 1.7632 ⋅
1000 T [K]
Eq. (2)
The actually measured gas phase partial pressure of the species i is expressed as pi. Kp is the equilibrium constant calculated from pure substance thermodynamic data as a function of temperature, according to Eq. (2), derived by (19). Oxygen transfer and deviation from CO-shift equilibrium are highlighted in Fig. 7. In each case (OP3 and OP4), three tar samples were taken. Thus, in contrast to OP1 and OP2, three values
for oxygen transfer and pδeq are given for OP3 and OP4. Thus, the values correspond to the tar sampling time. Generally, the oxygen transfer was lowered in the case of CO excess in the AR. However, the oxygen transfer was not completely inhibited. The total solid inventory ha a certain quantity of oxygen available, which was present in different iron oxidation states and was released with advancing operation. Despite the substoichiometric oxygen supply (CO excess in the AR exhaust gas), oxidation of iron might occur, but at lower quantities. Furthermore, the oxygen transfer capacity decreased, since particle attrition minimised the total solid inventory. It is generally found that (Fig. 7):
oxygen transfer (during OP2) was reduced to about 30% compared to OP1 similar reduction comparing OP3 and OP4, but at lower quantities
Fig. 8. Tar feed and tar measurement FR
The CO-shift was close to equilibrium for OP1 and OP2 (Fig. 7), but showed minor values above zero. Hence, the actual state of the exhaust gas composition was slightly on the side of the products, Reaction (a). Lower oxygen transport (OP2) resulted in a shift towards the equilibrium state. Thus, the oxidation caused by oxygen transfer dominated the product side of the CO-shift reaction (CO2 content increased). Considerable deviation in CO-equilibrium was found for the operation points with tar feed. The shift from the equilibrium state proceeded towards the product side (CO2 and H2), (pδeq.~ 0.25, deviation of about 75%). The results of tar measurement (three measurements per operation point) are summarised in Fig. 8. The graph depicts the amount (logarithmic scale) of tar compound feed (1-MN), the residual tar content in the FR exhaust gas (according to GC/MS and gravimetric method) and the content of 1-MN (unconverted part) in the FR exhaust gas. The tar feed ranged from 13.3 to 13.9 g/Nm³, due to fluctuation of the pumping system. 1-MN was converted to about 99%, since residual contents of 1-MN below 0.1 g/Nm³db were measured. Certain hydrocarbons were subject to re-combination. Thus, heavy tars (GC/MS undetectable) were formed. The mean gravimetric tar content for OP3 was about 1.6 g/Nm³db. A lower content of about 1.3 g/Nm³db was found for OP4 with
a decreasing tendency during the operation point. The amount of GC/MS detectable tars ranged from 3.5 to 4.0 g/Nm³db. Hence, the conversion amounted to roughly 73%. The major part (~94 wt.%) of the GC/MS detectable tars was naphthalene (C8H10). The minor part (~6 wt.%) consisted of intermediate products of decomposition (1H-indene, 1-benzothiophene, acenaphthylene and 2-methylnaphthalene). CONCLUSION The investigation focused on the reforming activity (tar conversion) and influence of partial oxygen transfer. A surrogate gas mixture with 1-Methylnaphthalene as a model tar compound (related to the product gas derived by biomass steam gasification) was exposed to the catalytically active material Fe-olivine. Thus, the performance was studied under ash, chlorine and sulphur-free conditions. It was found that the catalyst acted as an oxygen carrier. It was assumed that FeIII (hematite) was reduced to FeII (wustite) in the FR. However, Fe3O4 (magnetite) might occur as part of the reduction-oxidation chemistry. The oxygen transfer to the fuel reactor was influenced by modification of the air reactor operation (O2 or CO excess in the air reactor exhaust gas). Complete prevention of oxygen transfer was not achieved, since the total solid inventory possessed a certain quantity of oxygen which was bound as iron oxides and was not entirely reduced during the operation time. Further, iron oxidation occurred unavoidably to a certain quantity, due to contact of the solids with the oxygen-containing fluidisation agent (propane and air). However, it was found that CO and H2 were oxidised towards CO2 and H2O, respectively. The oxidation selectivity was highly developed towards CO and H2, since it was found that tar reduction was marginally influenced by partial oxidation. The 1-MN was decomposed to 99 wt.%. However, full hydrocarbon decomposition was not achieved, since further hydrocarbons were generated by recombination. The tar conversion based on GC/MS measurement was approximately 73%, containing mostly naphthalene. Naphthalene is a representative of aromatic ring hydrocarbons and is therefore difficult to open due to the bonds of the aromatic rings. Hence, the catalyst activity was classified in the medium to high range. It is suggested that the iron did not act as a CH4 reforming catalyst, but CH4 was a product of tar conversion. The overall hydrocarbon reforming capacity of Fe-olivine might be influenced by the occurring iron state at the particle, since it has been reported by Nordgreen et al. (20) and Virginie et al. (12) that metallic iron is obviously active in reforming and cracking. Thus, a further activity upgrade is might be achieved (i.e. long term behaviour). Moreover, the Fe-olivine catalyst featured the CO-shift equilibrium. This aspect is corroborated by the findings of Rhodes et al. (21), who reported that iron oxide is a catalyst for CO-shift. ACKNOWLEDGMENTS The authors gratefully acknowledge the financial support granted by the European Commission, Contract No. 211517 (www.uniqueproject.eu). We sincerely thank the Notified Testing Laboratory at Vienna University of Technology for their support. Christoph Varga and Klemens Marx are gratefully acknowledged for their support. NOTATION AR FR
air reactor fuel reactor
pδeq. λ
logarithmic deviation from equilibrium stoichiometric air/fuel ratio
1-MN U Umf Ut
1-Methylnaphthalene superficial velocity, [m/s] minimum fluidisation velocity, [m/s] terminal velocity, [m/s]
Kp pi Use Uc
equilibrium constant partial pressure of gas species i superficial velocity at border between turbulent and fast fluidisation, [m/s] superficial velocity at border between bubbling and turbulent regime, [m/s]
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
Dayton, D. A. Report NREL/TP-510-32815; National Renewable Energy Laboratory (NREL) Technical Report: Golden, CO, (2002). Yung, M. M.; Jablonski, W. S.; Magrini-Bair, K. A. Energy & Fuels (2009), 23, pp. 1874–1887. Devi, L., PhD Thesis, Technical University of Eindhoven, Netherlands, (2005). Abu El-Rub, Z.; Bramer, E. A.; Brem, G. Ind. Eng. Chem. Res. (2004), 43, pp. 6911–6919. Kuhn, J. N.; Zhao, Z.; Felix, L. G.; et al. Applied Catalysis B: Environmental, (2008), 81, pp. 14 – 26. Constantinou, D. A.; Fierro, J. L. G.; Efstathiou, A. M. Applied Catalysis B: Environmental, (2010), 95, pp. 255–269. Rapagna, S.; Jand, N.; Kiennemann, A.; Foscolo, P. U. Biomass and Bioenergy, (2000), 19, pp. 187-197. Rauch, R.; Pfeifer, C.; Bosch, K.; Hofbauer, H.; Świerczyński, D.; Courson, C.; Kiennemann, A. Science in Thermal and Chemical Biomass Conversion Conference, Victoria, BC, Aug 3rd−Sept 2nd, (2004). Pecho, J.; Schildhauer, T.; Sturzenegger, M.; et al. Chemical Engineering Science, (2008), 63, pp. 2465–2476. Koppatz, S., Proell, T., Pfeifer, C., Hofbauer, H. Proceedings of the Fluidization XIII Conference, May 16-21, (2010), Gyeong-ju, Korea, ISBN: 978-0-918902-57-3, pp. 901-908. Virginie, M.; Courson, C.; Niznansky, D.; Chaoui, N.; Kiennemann, A. Applied Catalysis B: Environmental, (2010), 101, pp. 90–100. Virginie, M.; Courson, C.; Kiennemann, A. Comptes Rendus Chimie, (2010), 13, pp. 1319 – 1325. Kolbitsch, P.; Pröll, T.; Bolhàr-Nordenkampf, J.; Hofbauer, H. Chem. Eng. Technol. (2009), 32, pp. 398-403. Pröll, T.; Kolbitsch, P.; Bolhàr-Nordenkampf J.; Hofbauer, H. AIChE Journal, (2009) 55 (12), pp. 3255-3266. Neeft, J. P. A.; Knoef, H. A. M.; Zielke, U. et al. “Guideline for Sampling an Analysis of Tar and Particles in Biomass Producer Gas, Version 3.1; Energy project EEN5-1999-00507 (Tar protocol)” (1999). Pfeifer, C., PhD-Thesis, Vienna University of Technology (2004). Bi, H. T.; Grace, J. R. Int. Journal of Multiphase Flow, (1995, 21, 1229-1236. Bolhàr-Nordenkampf, J., Pröll, T., Kolbitsch, P., Hofbauer, H., Chem Eng Technol. (2009), 32, pp. 410–417. HSC Chemistry 6.1. Chemical reaction and eq. software, thermochem. database and simulation module; oututec research Oy, Pori, Finland, 2008. Nordgreen, T.; Liliedahl, T.; Sjöström, K. Energy & Fuels, American Chemical Society, (2006), 20, pp. 890-895. Rhodes, C.; Hutchings, G. J.; Ward, A. M. Catalysis Today, (1995), 23, pp. 43–58.
FAST PYROLYSIS PROCESS INTENSIFICATION: STUDY OF THE GAS PHASE RESIDENCE TIME DISTRIBUTION AND BACKMIXING IN A DOWNER REACTOR Martin Huard, Franco Berruti and Cedric Briens Institute for Chemicals and Fuels from Alternative Resources (ICFAR) The University of Western Ontario 22312 Wonderland Road North, N0M 2A0, Ilderton, Ontario, Canada ABSTRACT A downer reactor was developed at ICFAR to maximize the liquid yield of pyrolysis in the conversion of biomass and heavy oil feedstock. An experimental study using hot wire anemometers was performed to measure the residence time distribution of the gas phase in the reactor and around the gas-solids separator. Some gas backmixing was observed around the gas-solids separator, which decreased with increasing distance between the separator and the gas outlet. INTRODUCTION The Institute for Chemicals and Fuels from Alternative Resources (ICFAR) is developing and testing reactor technology for the conversion of biomass and heavy oil feedstocks to useful bio-oil, bio-char, syngas, and other valuable chemical products via pyrolysis. Among the various biomass conversion processes developed at ICFAR, a downer reactor was designed and manufactured for the pyrolysis of biomass and heavy oil feedstocks to maximize the liquid yield. The downer configuration was selected over other reactor types for careful control of thermal cracking reactions and gas-solid contact times. To achieve these goals, a novel gassolids separator was developed and tested (1,2) specifically for the downer to quickly and efficiently separate heat-bearing sand particles from product vapors. The purpose of this study is to ensure that the separator does not introduce excessive gas backmixing, which would degrade the downer performance. Several gas phase RTD studies have previously been performed in circulating fluidized bed (CFB) riser reactors (3,4,5,6). Most authors reported significant gas backmixing in the riser and increased backmixing with increasing solids flux. However, only Brust and Wirth (7) measured gas backmixing in a downer reactor and found that backmixing was reduced at high gas velocities. Hence, in the present study, the gas residence time distribution (RTD) and backmixing in the downer reactor and around the gas-solids separator were investigated in the absence of solids. A simple hot wire anemometry technique was developed to measure the concentration of helium tracer in the model downer and around the gas-solids separator. Some initial results of the study are presented to demonstrate the applicability of hot wires in measuring the gas phase RTD. BACKGROUND Knowledge of the RTD in a reactor may be obtained by measuring the concentration of a tracer in the reactor as time proceeds. Thin filament hot wire anemometers were used in this study to detect the presence of helium tracer in air. These sensors detect
differences in the heat transfer characteristics of gas flow over the thin electrified resistive film or filament. Heat generated by the resistive element must be dissipated by the flowing gas, which results in decreased detector resistance or voltage. For fluid flow past a resistive element, the heat balance for the hot wire is given by ⁄ = ℎ − ,
(1)
where Vw is the voltage applied to the detector, Rw is the resistance of the detector, hw is the heat transfer coefficient between the detector and the surrounding fluid, Aws is the heat transfer surface area of the detector, Tw is the temperature at the surface of the detector, and Tg is the gas temperature. Variation in gas composition as the gas flows over the hot wire is detected in the constant-voltage mode as a change in the probe resistance Rw. For most hot wire filaments, the resistance is linearly related to the probe temperature in the form = ∆ + ,
(2)
where ∆T = Tw - Tg and m and b are empirical coefficients of resistivity specific to the filament material. Substituting Equation (2) in (1) and solving for ∆T gives
.
(3)
Changes to ∆T in Equation (3) are due only to changes in hw and possibly to Vw if not operated in constant-voltage mode. The hot wire may be treated as a smooth cylinder, so that hw can be determined by the Churchill-Bernstein correlation (8) for external cross-flow over a cylinder. For known gas properties, extreme values of Rw may be calculated for pure carrier gas flow and for pure tracer. Measured values of Rw between these extreme values represent mixtures of carrier and tracer, and the concentration of tracer CHe may be inferred by interpolating in a theoretical curve of Rw for different gas mixture properties. The time-dependent input and output tracer concentration signals cin(t) and cout(t) are related to the RTD function e(t) by the convolution integral: !
!
= ∗ = "! − = "! − ,
(4)
where t refers to the time domain and t1 is a shift of one of the functions relative to the other in the time domain. In the present study, cin and cout were measured and e(t) was unknown; hence, deconvolution was used to obtain the RTD function between any two given concentration signals. A fast Fourier transform algorithm was used to convert measured concentration signals to the frequency domain, where deconvolution is simplest. After obtaining the RTD in the frequency domain, the inverse Fourier transform was used to convert the RTD e(t) back to the time domain.
MATERIALS & METHOD A model downer similar to the one used by Huard et al. (1) and Huard (2) constructed of transparent acrylic was used to perform cold flow gas phase RTD experiments as shown in Figure 1. The downer had an internal diameter of 6.99 cm and a total height of 133.5 cm. The cone deflector was attached to an adjustable rod to change the distance LS between the gas outlet and the cone rim from -1.40 cm to 6.99 cm. Values of LS < 0 indicated that the cone rim was below the gas outlet.
Figure 1 – Sketch of Experimental Model Downer Compressed air was used as the carrier gas in the downer. Three calibrated sharpedged orifice nozzles with diameters of 0.20 cm, 0.22 cm, and 0.31 cm were used to control the mass flowrate of air in the downer. The range of superficial gas velocities Ug in the downer was 0.30 m/s to 1.27 m/s for the present study. A tracer injection line was installed upstream of the downer as depicted in Figure 2. As shown in Figure 2, air was permitted to flow into the downer inlet either through the tracer gas injection line or the air bypass line. Before injection, solenoid valves on the injection line were opened and the bypass line was closed. Helium was then permitted to flow through the open injection line and into the downer for about one minute to flush the injection line. Both solenoid valves were then closed to trap tracer in the line. Next, the air bypass line was opened with a slight restriction in the line for strong preferential flow to the injection line during injection. Data logging was then initiated on the data acquisition system. Finally, the tracer injection was performed by opening sequentially the solenoid valves at the inlet and outlet of the injection line, respectively. The inlet valve was first opened to equilibrate the pressure between the flowing air and the trapped helium, while the outlet valve was opened about two seconds later to flush the injection line with air and inject the helium pulse into the downer.
Figure 2 – Schematic Diagram of Experimental Apparatus Three hot wire anemometers (Probes 1 to 3) were used to measure the concentration of helium, whose approximate locations in the downer are shown in Figure 2. Probe 1 was installed 5.1 cm below the downer inlet to measure the incoming helium concentration signal. Probe 2 was located 16.5 cm above the gas outlet just upstream of the gas-solids separator, and Probe 3 was located 47.0 cm downstream of the entrance to the gas outlet. These detection points allowed measurement of the RTD through the entire downer and across the gas-solids separator. A 6.0 VDC regulated power supply was used to power each probe circuit, which were operated in a mode similar to constant-voltage mode. This allowed for a very simple electronic setup but slightly sacrificed probe sensitivity compared to other standard operation modes. Each detector was connected in a Wheatstone bridge circuit to increase the sensitivity of the detectors. RESULTS & DISCUSSION Effect of Gas Velocity Gas phase RTD measurements were performed in the absence of solids to gain insight into potential gas backmixing in the reactor and around the gas-solids separator. The gas velocity Ug and separation length LS/D were adjusted to understand their respective effects on the RTD in the downer. The effect of gas velocity was investigated for the range of gas velocities Ug = 0.30 m/s to 1.27 m/s. This range corresponded to average residence times of 3.9 s to 0.92 s between the downer inlet and the gas outlet, and to residence times of 5.0 s to 1.2 s when also
including the reactor volume below the gas outlet. Hence, the gas outlet was an open boundary through which the flow could penetrate. Figures 3(a) and 3(b) show typical normalized helium concentration CHe curves measured at Probes 1, 2, and 3 and the corresponding RTD curves for Ug = 1.05 m/s and LS/D = 0. Concentration measurements were acquired at 500 Hz. Huard (2) showed that maximum particle removal efficiency in the separator occurred at LS/D = 0 for the operating conditions expected in the actual pyrolysis process in the downer reactor. Hence, this condition was of particular focus in the present study.
Figure 3 – Measured downer concentration signals and corresponding RTD curves for Ug = 1.05 m/s and LS/D = 0 The RTDs shown in Figure 3(b), obtained by deconvolution of the concentration signals, indicated some spreading and potential bypassing of the initial helium pulse in the downer upstream of the gas-solids separator. This was indicated by a bimodal RTD curve e1-2 (i.e. RTD between Probes 1 and 2) in Figure 3(b). A small secondary peak was also observed in curve e2-3 as shown in Figure 3(b), which indicated some backmixing around the gas-solids separator. Although future introduction of solids would likely increase backmixing in the reactor, these initial results confirmed the possibility to operate the downer close to the plug flow regime. The severity of backmixing was gauged by analyzing the average residence time and variance of the measured RTDs. Figure 4 shows the actual average residence time and variance of the measured RTD curves for the range of tested gas velocities. As shown in Figure 4(a), the measured residence times in the downer before the separator were reasonably close to the nominal values calculated by dividing the reactor volume by the volume flowrate, which confirmed near plug flow and use of the deconvolution technique. Based on the RTD variance curves shown in Figure 4(b), the gas-solids separator was the major contributor to RTD spreading for all tested gas velocities. This result indicated very little RTD spreading and backmixing in the downer before the separator. Another important indicator of reactor performance was the effective gas penetration length LP. This indicator represented how far the gas “effectively” flowed past the gas outlet based on the measured residence time compared to the nominal residence time between the inlet and the gas outlet. In other words, measured residence times longer than the nominal values gave LP > 0, indicating gas flow below the outlet. Measured residence times shorter than the nominal values gave LP < 0, indicating
either no gas flow past the outlet or bypassing. The effect of gas velocity on LP is shown in Figure 4(c). The results show that gas did not penetrate past the gas outlet for velocities Ug ≤ 0.79 m/s, which corresponded to short durations in the gas-solids separator. Long penetration lengths past the outlet were observed for gas velocities Ug ≥ 1.05 m/s. Penetration past the gas outlet did not necessarily correspond to backmixing, but the relative duration in the separator was much longer for LP > 0. This result also indicated that future use of stripping gas below the gas outlet would likely decrease the penetration length and separation time.
Figure 4 – Effect of Gas Velocity on Average Residence Time, Variance, and Effective Penetration Length Effect of Separation Length The effect of separation length on the residence time, variance, and penetration length in the downer was investigated over the range of LS/D = -0.2 to LS/D = 1.0. The gas velocity was set at Ug = 1.27 m/s to represent the actual pyrolysis process operating conditions. As shown in Figure 5(a), a modest increase in the residence time with decreasing separation length was observed for 0 ≤ LS/D ≤ 1.0. Considering also the gas-particle separation efficiency, which increased with decreasing separation length to a maximum at LS/D = 0 for similar operating conditions (2), the present results suggest that there may be a trade-off between separation efficiency, residence time, and backmixing for optimum process performance in the downer. However, for LS/D = -0.2, an enormous increase in the residence time, variance, and penetration length was observed. This indicated very significant backmixing below the gas outlet and gas flow reaching the solids outlet. Hence, this operating condition must be avoided to achieve plug flow in the reactor and to avoid pyrolysis product vapor degradation.
Figure 5 – Effect of Separation Length on Residence Time, Variance, and Penetration Length CONCLUSION A simple hot wire anemometry technique was developed and used successfully to measure the gas phase RTD in a downer reactor. The measured RTD curves show near plug flow behavior in the downer for most tested operating conditions. Some gas backmixing was observed around the gas-solids separator at high gas velocity and for very short separation lengths. The most severe backmixing occurred for LS/D = -0.2. The best operating conditions for near plug flow in the reactor were Ug = 1.27 m/s and for separation lengths LS/D ≥ 0. NOTATION Aws
Hot wire heat transfer surface area [m2]
b
Empirical coefficient of electrical resistivity [Ω]
CHe
Normalized concentration of helium [-]
cin(t)
Input tracer concentration [kg/m3]
cout(t)
Output tracer concentration [kg/m3]
D
Downer internal diameter [cm]
Dw
Hot wire diameter [mm]
E(s)
Residence time distribution function in the frequency domain [-]
e(t)
Residence time distribution function in the time domain [-]
hw
Heat transfer coefficient between the hot wire and the surrounding fluid [W/m2K]
LS
Vertical distance between gas outlet and cone deflector rim [cm]
m
Empirical coefficient of electrical resistivity [Ω/K]
Rw
Hot wire probe resistance [Ω]
s
Frequency domain [s-1]
Tw
Hot wire surface temperature [K]
Tg
Ambient gas temperature [K]
t
Time [s]
Ug
Superficial gas velocity [m/s]
Vw
Voltage applied to hot wire [V]
σ2
Variance [s2]
τavg
Average residence time [s]
REFERENCES 1. 2.
3.
4.
5.
6. 7. 8.
Huard, M., Briens, C., Berruti, F. (2010) Experimental study of a novel fast gassolid separator for pyrolysis reactors. Int. J. Chem. Reactor Eng. 8, A134. Huard, M. (2009) An investigation of a novel gas-solid separator for downer reactors. M.E.Sc. Thesis. The University of Western Ontario, London, Ontario, Canada. Gauthier, T., Andreux, R., Verstraete, J., Roux, R., & Ross, J. (2005) Industrial development and operation of an efficient riser separation system for FCC units. Int. J. Chem. Reactor Eng. 3. Smolders, K., & Baeyens, J. (2000) Residence time distribution of the gas phase in dilute phase circulating fluidized beds. Powder Hand. Proc. 12(3), 265-269. Dry, R. J., & White, C. C. (1989) Gas residence-time characteristics in a highvelocity circulating fluidised bed of FCC catalyst. Powder Technol. 58(1), 1723. Gauthier, T. (1991) Étude de la separation rapide dans un cyclone a cocourant. Thèse de Doctorat. L’Université de Paris VI, Paris, France. Brust, H., Wirth, K.-E. (2004) Residence time behavior of gas in a downer reactor. . Ind. Eng. Chem. Res. 43(18), 5796-5801. Churchill, S.W., Bernstein, M. (1977) A correlation for forced convection from gases and liquids to a circular cylinder in cross flow. J. Heat Transfer, Trans. ASME. 94, 300-306.
DEM-CFD MODELING OF A TOP-SPRAY FLUIDIZED BED GRANULATOR AND A WURSTER-COATER Lennart Fries1, Sergiy Antonyuk1, Stefan Heinrich1 and Stefan Palzer² 1
Institute of Solids Process Engineering and Particle Technology, Hamburg University of Technology Denickestr. 15, 21073 Hamburg, Germany T:+49-40-42878 2143; F:+49-40-42878-2678; E: [email protected] ² Nestlé Product Technology Centre York, Haxby Road, York YO91 1XY, United Kingdom ABSTRACT Coupled DEM-CFD simulations were performed to study the fluid and particle dynamics of a fluidized bed granulator on the micro-scale. In a first study, wetting of the particles is estimated based on the residence time distribution inside a conical spray zone. The effect of the geometry of the apparatus on the homogeneity of wetting is analyzed in order to understand the performance and specificity of different granulator configurations. For a small simulation system, heat and mass transfer laws were resolved to calculate the moisture content of the individual particles An effective modelling tool for design of a fluidized bed spray granulator is obtained. INTRODUCTION Fluidized bed spray granulation plays an important role in the manufacturing of powder granules in the food and pharmaceutical industries as it allows producing dust-free and free-flowing particles. Liquid binder is sprayed into a bed of solids to achieve granule growth. A homogeneous distribution of the spray liquid is a prerequisite for uniform growth, whereas local overwetting leads to the formation of particle clusters. The moisture distribution in the apparatus is a key parameter affecting both particle size and structure of the product [1]. The fluid bed granulation process is controlled by many parameters which can influence size and structure of the product. Since so far the choice of operating conditions and the design of the equipment geometry have been made empirically, the actual influence of the fundamental mechanisms in the process is not well understood. To describe the process in detail on the scale of individual particles, the DiscreteElement-Method (DEM) offers large potential. As each particle is tracked individually, the method allows a complete representation of the particle-particle and particle-wall interactions and their influence on the process dynamics.
MATHEMATICAL MODEL The motion of each particle i in the system is calculated using Newton's second law:
mi
Vi β g − p dv i (u g − v i ) + mi g + Fc,i + FA,i = −Vi ∇p + dt 1− ε
(1)
The forces on the right hand side of Eq. 1 are respectively due to pressure gradient, drag, gravity and contact forces (i.e. due to collisions). The interphase momentum transfer coefficient βg-p is modeled by combining the Ergun equation [2] for dense regimes (gas volume fraction ε < 0.8) and the correlation by Wen&Yu [3] for dilute regimes (ε ≥ 0.8). The gas is considered as continuous phase. The geometry of the apparatus is discretized in mesh cells and the flow profile is calculated using volumeaveraged Navier-Stokes equations.
∂ (ερ g ) + ∇(ερ g u g ) = 0 ∂t ∂ (ερ g u g ) + ∇(ερ g u g u g ) = −ε ∇p g − ∇(ε τ g ) − S p + ερ g g ∂t
(2) (3)
Due to momentum exchange and the reduced free gas volume the particles influence the velocity profile of the gas phase. This effect is accounted for by adding a sink term Sp to the momentum balance, which closes the two-way-coupling. Forces between the gas and particle phase are of opposite direction and equal magnitude. The flow around particles is not fully resolved, as the size of the fluid mesh cells is 2-10 times larger than the particle diameter. Contact forces between particles are calculated according to a contact model (Eq. 4) based on the theory developed by Hertz [4] for the normal impact. A no-slip approximation of the model by Mindlin and Deresiewicz [5] is applied for the tangential component of the contact force, as proposed by Tsuji et al. [6]. According to the Hertzian theory [4], the relation between the elastic deformation and the displacement is nonlinear, due to the elliptical pressure distribution in the circular contact area Fc ,n = −k n ⋅ δ n ⋅ n ab − η n v ab,n (4) 3
2
For the tangential component of the contact force Ft an equation analogue to Eq. 4 is used [6]. Energy dissipation is included in the model using coefficient of restitution, which is defined as the ratio of the square root of the elastic strain energy Ekin,r released during the restitution to the impact energy, i.e the initial kinetic energy Ekin [7]:
en =
E kin ,r E kin
=
vr v
(5)
The coefficient of restitution can be determined experimentally with a free-fall tester. Model of the spray zone The spray injection is represented in the form of a conical zone starting from the nozzle tip, where the suspension is injected. All particles inside the spray zone are wetted. As a first approximation, the wetting intensity is supposed to be homogeneous in the entire
spray zone. By integrating over the particle trajectories, their residence time inside the spray zone is obtained, which allows estimating their moisture content. In a second step, the model of the spray is refined by taking into account the distance between particle and nozzle tip and the local solids volume fraction. Particles in the spray zone are assigned to discrete levels (see Fig. 1).
Fig. 1. Discretization scheme of the spray Fig. 2. Heat and enthalpy streams on the zone. particle surface.
Starting from the nozzle tip at level j = 1, the suspension is deposited on the particles according to the ratio of the projected area of the particle to the total area of level j, according to Eq. 6. Suspension that is not deposited on level j is passed to level j+1. A ,i j M& susp ,i = M& susp ⋅ pp j Alevel
(6)
∑ App ,i j +1 j (7) M& susp = M& susp ⋅ 1 − j Alevel Suspension, which has not been deposited until the last level of the nozzle is supposed to leave the system as overspray.
Modeling of heat and mass transfer By applying heat and mass transfer laws, the moisture content and temperature of each individual particle can be calculated. It is assumed that suspension deposited on a particle spreads on its surface and forms a film with constant thickness Lfilm of 0.1 mm. Penetration into pores and wetting of the particle core are neglected. Deposition and evaporation of liquid influence the size of the wetted area Afilm. If the entire particle surface is wet, it will not absorb further suspension. The enthalpy and heat streams considered in this model are shown schematically in Fig. 2. The liquid film interacts with the particle and the gas phase, which is expressed through the enthalpy balance:
dH film dt
= H& susp + Q& pf − H& vap + Q& af
(8)
Evaporation of liquid will change the mass of the film. The evaporation rate depends on the mass transfer coefficient β, which is a function of the relative velocity between particle and fluid. The vapor mass flow from a particle surface is calculated: M& vap = β ⋅ A film ⋅ (Ysat − Ya ) (9)
Heat transfer between the gas phase and the liquid film is described with the help of & = α ⋅ A ⋅ (ϑ − ϑ ) Q af af film a film (10) For dry zones on the particle surface the heat exchange between the particle and gas phase is described using & = α ⋅ (A − A ) ⋅ (ϑ − ϑ ) Q ap ap p film a p (11) The current contribution does not include particle growth or agglomeration. SIMULATION CONDITIONS In order to describe the mechanical behavior of the material (spherical γ-Al2O3 granules loaded with solid) the input parameters for the contact model were determined experimentally. By image analysis, the impact and rebound velocity as well as the rebound angle and the rotational speed of the particle were determined. According to results by Antonyuk et al. [7], a constant coefficient of restitution of 0.8 for γ-Al2O3 granules was used in the simulations. See Table 1 for all parameters of the model. Table 1: Overview on simulation parameters.
Parameter Fluidization vel. annulus Fluidization vel. Wurster Fluidization vel. top-spray Nozzle injection rate Fluidization air flow rate Wurster gap distance Normal rest. coefficient Shear modulus
Value 4 m/s = 3.7·umf 11 m/s = 10·umf 5.5 m/s = 5.1·umf 7 m³/h 600 m³/h 15 mm = 5·dp 0.8 1·108 Pa
Parameter Poisson ratio Static friction coeff. DEM time step CFD time step Air temperature Particle heat capacity Suspension spray rate Liquid film thickness
Value 0.25 0.1 2·10-6 s 2·10-4 s 75 °C 900 J /kgK 5·10-5 kg/s 0.1 mm
For two different granulator configurations the residence time distribution of the particles inside the spray zone was analyzed. As shown in Fig. 4a, the injection nozzle can be positioned above the bubbling fluidized bed (top spray). Another common configuration is the Wurster-coater [8], where a cylindrical draft tube is inserted vertically into the granulator, as shown in Fig. 4b. Combined with a bottom-spray injection nozzle, the Wurster geometry induces a circulating movement of the particles. The draft tube physically separates the wetting zone at the center from the drying zone in the annulus. Simulations were performed with 45,000 spherical particles and a particle diameter of 3 mm, which corresponds to a batch size of 0.95 kg at a particle density of 1500 kg/m³. The geometry of the GF5-insert used in the simulations was supplied by Glatt Ingenieurtechnik GmbH, Germany. The outer dimensions of the top-spray granulator are identical to those of the Wurster-coater. The same fluidization air flow rate and nozzle injection rate is used in both configurations. While the top-spray granulator has a homogeneous distributor plate with constant porosity, the bottom plate of the Wurster coater consists of 2 segments with different porosity. With the help of pressure drop correlations the flow rate in both segments of a Glatt GF3 insert was approximated. The nozzle injection rate in the simulations was set according to the used experimental device (Schlick type 970).
a)
b)
Fig. 4: Configurations of a fluidized bed granulator: (a) top-spray, (b) Wurster-coater.
For a precise investigation of particle wetting and drying in a top-spray granulator, the calculation of heat and mass transfer was coupled with the DEM-CFD simulation. Due to the higher numerical effort, a small system with 10,000 particles was analyzed, where the particle properties were identical to those given in the previous section. The simulation allows monitoring the moisture content and temperature of each individual particle. Based on the gas flow profile obtained from the CFD model, local heat and mass transfer coefficients are calculated. SIMULATION RESULTS The particle and fluid dynamics of both granulator configurations have been studied. In the Wurster-coater the particles move at high velocity inside the draft tube. A relatively low injection depth of the spray nozzle of approximately 45 mm was found. Obviously, the momentum of the jet is quickly transferred to the particles. In the top-spray granulator the average gas velocity is lower than in the Wurster configuration. A stagnant zone can be seen above the counter current injection nozzle. Residence time distribution For the Wurster-coater configuration, the residence time of the particles inside the spray zone is primarily determined by the upflow velocity of gas and particles in the draft tube. Once a stable circulating regime is established, the particles are transported through the spray zone at constantly high velocity (driven by the spout), which corresponds to a well-defined residence time and homogeneous wetting of the particles. For the given process conditions the residence time distribution obtained after 5 s simulation time is shown in Fig. 5. A clear peak at 22 ms for particles which have passed once through the spray zone and smaller peaks at multiples of this period are found. If the simulation is continued, the distribution will continuously shift to the right with time. For the top spray configuration a different picture is obtained (see Fig. 6). Since there is no directed flow profile as in the Wurster-coater, the particle trajectories are randomly
influenced by the high number of individual collisions in the fluidized bed. This leads to a wide residence time distribution of the particles in the spray zone, which corresponds to inhomogeneous wetting. Some particles spent up to 5 % of the simulation time inside the spray zone, whereas 30 % were not wetted at all. a) b) simulation time t = 2.5 s simulation time t = 5.0 s
3000
2000
1000
0 0.00
simulation time t = 2.5 s 4000 number of particles N (-)
number of particles N (-)
4000
simulation time t = 5.0 s
3000
2000
1000
0
0.04
0.08
0.12
0.16
0.20
0.00
0.04
residence time (s)
0.08
0.12
0.16
0.20
residence time (s)
Fig. 5: Residence time distribution in the Fig. 6: Residence time distribution in the spray zone for the Wurster-coater. spray zone for the top-spray granulator.
Simulations including heat and mass transfer Temperature and moisture content of the individual particles as well as temperature and humidity profiles of the gas phase in the apparatus can be calculated. Initially all 10,000 particles are dry. Following the continuous liquid injection, more and more particles are wetted. Accordingly, the peak in Fig. 7 is shifted to the right with time. Due to inhomogeneous wetting, the particle moisture distribution gets broader. After 30 s simulation time the wetting/drying equilibrium is reached. gwater / kg dry air 10.2
10.1
10.0 30 20 Time [s]
Fig. 7: Particle moisture distribution.
10 0 0
5
10 15 height [cm]
Fig. 8: Humidity profile of the gas phase.
20
In Fig. 8, the humidity of the gas phase is presented as a function of the height of the granulator and time. Due to evaporation, the humidity increases steeply with the height of the apparatus and slowly with time. EXPERIMENTAL RESULTS
density distribution q3 (a) d / 1/µm
Experimentally the effect of different granulator configurations on the agglomeration behavior of spray dried glucose syrup in a fluidized bed (Glatt GCPG 3.1) was studied. The geometry of the fluidization chamber is the same in both cases, only the position of the nozzle was varied and the draft tube was inserted for the bottom spray case. Pure water was injected as binder and all operating conditions were kept constant. While a narrow particle size distribution of the agglomerates is obtained with the Wurster-coater, the size distribution of the product from the top spray granulator is much broader. In the top spray granulator more oversize agglomerates are built. The experimental results of the particle size distribution (Fig. 9) correspond very well with the residence time distributions of the particles in the spray zone found in the numerical simulations (Figs. 5 and 6). 0,0035 The simulations showed that the top spray configuration had 0,0030 an inhomogeneous residence 0,0025 time of the particles in the 20% water (wb), liquid flow rate spray zone indicating 30g/min; bottom spray + Wurster 0,0020 tube unfavorable wetting. This is reflected by the broad 0,0015 size distribution of the 0,0010 produced agglomerates. The 20% water (wb), liquid flow rate Wurster-coater had a narrow 30g/min; top spray 0,0005 residence time distribution, which matches well with the 0,0000 0 500 1000 1500 2000 shape of the particle size particle diameter d a / µm distribution of the agglomeFig. 9. Comparison of the agglomerate size distribution rates. obtained in two different granulator configurations.
CONCLUSIONS For two different granulator configurations the homogeneity of the liquid distribution among the particle phase was investigated numerically on the scale of individual particles with the help of coupled DEM-CFD simulations. The residence time distribution of the particles in a conical spray zone at the tip of the injection nozzle allows estimating their moisture content. The results show that the Wurster-granulator is characterized by a narrow residence time distribution, whereas the top spray configuration leads to a wide residence time distribution. The positive effect of a Wurster tube on the distribution of the spray liquid seen in the DEM-CFD simulations was approved by agglomeration experiments. Coupled with heat and mass transfer relations, the DEM-CFD model
allows the direct calculation of the temperature and moisture content of the individual particles and the gas phase in a fluid bed granulator. ACKNOWLEDGEMENT We would like to thank Nestec S.A. for the financial support and Dr.-Ing. Michael Jacob from Glatt Ingenieurtechnik GmbH, Weimar for the supply of geometry data. NOTATION A e Ekin F g H k m Ṁ
area coefficient of restitution kinetic energy force gravitation enthalpy stiffness mass mass flow
m² J N m s-2 J N m-1 kg kg s-1
n p Q Sp t u v V Y
normal unit vector pressure heat stream momentum sink term time gas velocity particle velocity volume gas moisture content
Pa W N m -³ s m s-1 m s-1 m³ -
α β βg-p δ
heat transfer coeff. mass transfer coeff. momentum transfer coeff. displacement (overlap)
Wm-2K-1 m s-1 kgm-3s-1 m
ε η ρ τ
porosity damping coefficient density gas phase stress tensor
Nsm-1 kg m-3 Pa
REFERENCES [1] Palzer, S.: Influence of material properties on the agglomeration of water-soluble amorphous particles. Powder Technology 189 (2), 2009, 318-326. [2] Ergun, S.: Fluid flow through packed columns. Chemical Engineering Progress 48, 1952, 89–94. [3] Wen, Y.C. Yu, Y.H.: Mechanics of Fluidization. Chemical Engineering Progress Symposium Series, 62, 100-111.[4] Hertz, H. Über die Berührung fester elastischer Körper. Journal für die Reine und Angewandte Mathematik 92, 1882, pp. 156-171. [5] Mindlin, R.D., Deresiewicz, H.: Elastic spheres in contact under varying oblique forces. Transactions of ASME, Ser. E. J. of applied Mechanics 20, 1953, pp. 327-344. [6] Tsuji, Y., Tanaka, T., Ishida, T.: Lagrangian numerical simulation of plug flow of cohesionless particles in a horizontal pipe. Powder Technology 71, 1992, pp. 239-250. [7] Antonyuk, S., Heinrich, S., Tomas, J., Deen, N.G., van Buijtenen, M.S. and J.A.M. Kuipers: Energy absorption during compression and impact of dry elastic-plastic spherical granules. Granular Matter 12 (1), 2010, pp. 15-47. [8] Wurster, D. E.: Particle Coating Process. U.S. Pat. No. 3253944, May 31, 1966.
DESIGN REQUIREMENTS FOR PRESSURIZED CHEMICAL LOOPING REFORMING K. Marx, T. Pröll and H. Hofbauer Vienna University of Technology, Institute of Chemical Engineering, A-1060 Vienna, Austria ABSTRACT A key issue in chemical looping reforming is to operate the process under pressurized conditions. Applicability of dual fluidized bed systems, currently used in atmospheric chemical looping processes, is affected by pressure. Critical design issues were studied and experimentally verified by cold flow model experiments. It turns out that it is important to achieve sufficient global solids circulation and to keep the pressure difference between the reactors low enough for proper operation of the loop seals. INTRODUCTION Hydrogen is an important raw material for production of basic chemicals, in oil refining, and many other industrial applications. Although naturally occurring, most of the hydrogen used is produced from fossil raw materials (1). Catalytic steam reforming of hydrocarbons is currently the cheapest way to produce hydrogen and accounts for more than 90% of the world’s hydrogen production. In such systems heat transfer is a key issue to improve the process performance (2). H2, CO,
(CO2, H2O) Chemical looping combustion (CLC) recently attracted N2, (O2) interest as a carbon capture technology. A variant of the process is chemical looping reforming (CLR) MeOa where less oxygen than required for complete Fuel Air combustion is supplied. In chemical looping two Reactor Reactor separate reaction zones, an air reactor (AR) and a fuel (FR) (AR) MeOa-1 reactor (FR) are available. A metallic solid is kept in circulation between the reactors, usually called oxygen carrier (OC). The solids are used to transport air/fuel ratio < 1 oxygen and heat and, especially in CLR, also act as a Air Fuel reforming catalyst. While in CLC full conversion of the Fig. 1 Chemical looping fuel is intended, a syngas is produced in CLR. reforming principle
The CLC process has been intensively studied over the recent years (3-4). Many different oxygen carrier materials have been tested (3-5) and the technology has been demonstrated for more than 1000h (6) and at scales of up to 150kW (7). Dual fluidized bed (DFB) systems have been applied to the process claiming to fit the requirements of chemical looping the best (8-9). Nickel based oxygen carriers are beneficial in CLR because of their high catalytic activity towards methane steam reforming. Promising results have been obtained with such oxygen carriers at atmospheric conditions (10-11). Thermodynamic
equilibrium has been reached and no coke formation has been observed even at very low steam to carbon ratios in the FR feed. Pröll et.al. (11) addressed the main advantages of CLR compared to catalytic steam reforming to be: • • •
Heat required in the reactions is supplied inherently, no external heating is needed, thus no heat transfer limitations are expected. Less steam is required. Fewer concerns with respect to sulfur contaminants (12).
A key issue in CLR is to operate the process at pressurized conditions (PCLR). Dual fluidized bed systems have never been applied to pressurized systems. Considerable operational limitations occurring from reactor pressure difference and solids throughput are expected. In this study limitations occurring from pressure were addressed and critical design issues in PCLR were identified. Cold flow model tests at conditions corresponding to pressurized conditions were carried out in an atmospheric dual circulating fluidized bed system. CHEMICAL REACTIONS The main reaction occurring in the air reactor is the oxidation of the oxygen carrier which is in case of a nickel based oxygen carrier: 1 ( reac. 1) ∆𝐻𝑅900°𝐶 = −234.6 𝑘𝐽⁄𝑚𝑜𝑙 𝑁𝑖 + 𝑂2 ⇌ 𝑁𝑖𝑂 2 The situation inside the fuel reactor is governed by many reactions taking place in parallel or consecutively. The most relevant catalytic activated gas-phase reactions are the steam reforming and CO-shift reactions. Which, for methane will be: ( reac. 2) 𝐶𝐻4 + 𝐻2 𝑂 ⇌ 𝐶𝑂 + 3𝐻2 ∆𝐻𝑅900°𝐶 = 225.6 𝑘𝐽⁄𝑚𝑜𝑙 900°𝐶 ( reac. 3) 𝐶𝑂 + 𝐻2 𝑂 ⇌ 𝐶𝑂2 + 𝐻2 ∆𝐻𝑅 = −33.1 𝑘𝐽⁄𝑚𝑜𝑙 The heat needed is supplied either by oxidation of reforming products or fuel with the metal oxide, or by the circulating solids transporting heat from the AR to the FR. ( reac. 4) 𝐶𝐻4 + 𝑁𝑖𝑂 ⇌ 𝐶𝑂 + 2𝐻2 + 𝑁𝑖 ∆𝐻𝑅900°𝐶 = 211.6 𝑘𝐽⁄𝑚𝑜𝑙 900°𝐶 ( reac. 5) 𝐶𝑂 + 𝑁𝑖𝑂 ⇌ 𝐶𝑂2 + 𝑁𝑖 ∆𝐻𝑅 = −47.2 𝑘𝐽⁄𝑚𝑜𝑙 ( reac. 6) 𝐻2 + 𝑁𝑖𝑂 ⇌ 𝐻2 𝑂 + 𝑁𝑖 ∆𝐻𝑅900°𝐶 = −14.0 𝑘𝐽⁄𝑚𝑜𝑙 Practically, in CLR just enough air will be supplied to the AR to keep the desired operating temperature. The oxygen transport can be varied without influencing the global heat balance if a fraction of the fuel is fed to the AR. In this way the oxygen transport can be theoretically reduced to zero. From the principle of Le Chatelier it is evident that in equilibrium the methane content decreases with increasing temperature and higher steam ratios, and the methane content increases with pressure. Thus to improve the methane conversion high temperatures, high excess steam and low pressure are favorable. Compared to conventional catalytic methane steam reforming methane breakthrough at increased pressure can be avoided by increasing the FR temperature in PCLR. CHALLENGES AT INCREASED PRESSURE With increasing pressure, the gas density increases while the solids properties remain unchanged. This means that, for a certain gas mass flow rate, either the riser cross section or the superficial gas velocity will decrease with increasing pressure, both resulting in lower solids entrainment rates. The main challenges with respect to
pressurized chemical looping reforming therefore are: • • • •
The higher gas-solids reaction intensity. The fluidizing velocities must be kept within reasonable limits. The increased solids flux. Proper sealing between the reactors by the loop seals.
AR off-gas recycling is necessary to product gas limit the solids throughput per cross section area in the AR as depicted in MeOa Fig. 2. The power demand of the recycle blower is expected to be low AR MeO FR a-1 because of the low compression pressure ratio. Anyhow, the recycle htx. gas stream has to be kept low to fuel at reduce the energy penalty from gas reblower pressure heating. This requires optimization of the ratio of solids circulation rate relative to gas velocity in the AR riser. to heat rcovery The loop seals have to be designed ambient air considering dynamic backpressure Fig. 2 Proposed PCLR arrangement. changes from the two exhaust lines. Therefore, deep loop seals which can handle significant level changes are required. In addition an active control setup of the backpressure is necessary for proper operation of the system. EXPERIMENTAL An existing dual circulating fluidized bed cold flow model (CFM) erected to study the dual circulating fluidized bed (DCFB) concept for chemical looping at atmospheric conditions (13) was modified and operated at conditions simulating pressurized conditions. A schematic drawing of the cold flow model is shown in Fig. 3. The system includes two risers, the air reactor and the fuel reactor, and three loop seals, the upper-, lower-, and internal loop seal. The CFM is a model of the existing hot 120 kW chemical looping pilot unit at Vienna University of Technology at a scale of 1:3. It is built of transparent acrylic glass allowing visual observation of the fluid dynamic pattern. 23 pressure probes were placed and connected to a personal-computer assisted measurement equipment. Solids circulation rates are measured by stopping the loop seal fluidization and measuring the rate of solids accumulation inside Fig. 3 the appropriate downcomer. More specific details about the cold flow model can be found elsewhere (13).
Sketch of the DCFB CFM with pressure measurement ports indicated by dots.
The Glicksman criteria (14) Table 1 Characteristic design parameters of the are a set of dimensionless system Hot unit CFM numbers which are used to Unit ARH FRH ARC FRC maintain hydrodynamic air syngas air air similarity in the cold flow Type of gas Reactor inner model and the mm 50 51 50 54 corresponding hot unit. diameter The necessity of gas-solids Superficial gas m/s 6 3 5.75 9 density ratio similarity velocity requires very light weight Operation 10 10 1.013 1.013 bar(a) particles to simulate pressure pressurized conditions in Operation °C 1000 900 25 25 the atmospheric CFM temperature Ni/NiO fluidized with air. For good Particle 40wt% NiO Polystyrene agreement with the definition 60wt% NiAl2O4 Glicksman criterion Particle mean μm polystyrene particles were 120 110 diameter used with a density of Particle kg/m³ 3250 1050 1050 kg/m³ and a Sauter density mean diameter of 110 µm. Spericity 0.99 0.99 To avoid buildup of electrostatic forces ATMER 163, an anti-static agent, was added. The dimensions of the hot unit are subjected to detailed mass and energy balance investigations of a 150 kW methane input pilot PCLR unit operated at 10 bar(a) pressure where equilibrium of the reactions is reached theoretically. The dimensions and important parameters of the hot unit as well as the corresponding cold flow model are summarized in Table 1. For the given CFM geometry only the air reactor agrees well with similarity rules, shown in Table 2. Considering that the FR flow regime has a minor effect on the global system loop (13) the cold flow model can be used for investigating the behavior of the global solids loop with little error. Table 2 Comparison of dimensionless groups Parameter Rep Ar Fr density ratio diameter ratio spericity
Definition 𝑑𝑝 ∙ 𝑈0 ∙ 𝜌𝑔 ∙ 𝜓 𝜂𝑔 𝜌𝑔 ∙ �𝜌𝑝 − 𝜌𝑔 � ∙ 𝑑𝑝 3 ∙ 𝑔 𝜂𝑔 2 𝑈0 2 𝑔 ∙ 𝑑𝑝 𝜌𝑝 𝜌𝑔 𝐷 𝑑𝑝 𝜓
Hot unit ARH FRH
Cold unit ARC FRC
Hot/Cold AR FR
39.3
15.5
40.4
63.2
0.97
0.24
65.3
7.6
48.1
48.1
1.35
0.16
3.0∙10
4
7.5∙10
4
3.0∙10
4
7.5∙10
4
1
1
1265.8
2355.3
903.9
903.8
1.4
2.6
416.7
425
454.5
490.9
0.92
0.87
0.99
0.99
0.99
0.99
1
1
RESULTS AND DISCUSSION Observed pressure profiles The pressure profile of fluidized beds can be used to identify design problems and to determine the solids distribution within the system. A typical pressure profile of the
Height in mm
CFM in operation is shown in Fig. 4. The 1600 low density of the particles used caused AR cylone that the observed overall pressures are 1400 relatively low in the range of 15 mbar. AR The solids distribution curve of a typical 1200 FR cyclone circulating fluidized bed has a lower dense region and a lean upper section. 1000 AR downcomer Derived from momentum balance it is evident that in steady-state conditions the 800 decay of the pressure profile indicates the ULS solids inside the volume. Thus, typically a 600 FR downcomer pressure profile in a CFB unit has a shape of high pressure gradients at the 400 ILS lower and low gradients at the upper LLS 200 section. On the other hand in dilute phase FR or pneumatic transport regime the solids 0 are equally distributed over the riser height indicated by a nearly constant 0 5 10 15 pressure decay along height. The Pressure in mbar(g) pressure profile of the FR shows the typical shape of a CFB while the profile in Fig. 4 Typical pressure profile of the CFM with 0.5 kg total inventory the AR had a shape similar to the one of and at following fluidization rates a pneumatic conveyor. This occurs in Nm³/h: AR 25/FR 5/LLS 0.5 because of the relatively low gas velocity ULS 0.6/ILS 0.2 in the FR and the high gas velocity and low solids inventory in the AR. In the DCFB concept three loop seals are needed. Proper operating loop seals show a pressure drop in solids flow direction indicating movement of solids and appropriate gas sealing. Operation stability of loop seals towards pressure fluctuations between the loop seal inlet and outlet can be obtained by increasing the pressure at the bottom of the loop seal. In the DCFB concept the lower and upper loop seal are directly exposed to fluctuations and differences of pressure between the two fluidized beds. For that reason deeper loop seals will improve the operation stability of the system. Impact of gas velocity To study the effect of the AR fluidization the gas volumetric flow to the AR was varied at constant FR and loop seal fluidization. Pressure profiles are shown in Fig. 5. With increasing gas flow rate to the AR it was observed that the overall pressure profiles were shifted towards a higher pressure which was caused by increased back pressure from the filter bag and cyclone. It was also observed that although the pressure profile of the AR was affected by gas velocity in the AR the FR profile itself remained nearly unchanged. Inaccurate pressure probe placement was detected at the bottom of the AR showing a discrepancy from the expected pressure profile. Rearrangement of the probe has to be considered in further investigations. Results of solids circulation rate measurement are depicted in Fig. 6. At low flow rates of 20 to 30 Nm3/h in the AR an increasing solids flux was observed while at high fluidization velocities the solids flux decreased. This is in contrast to the expected behavior that the solids circulation rate increases with fluidization rate.
Impact of reactor outlet pressure difference
1600
AR20/FR5 AR30/FR5 AR40/FR5
1400 1200
Height in mm
One reason for this seems to be that the increasing back pressure from the filter bag with AR fluidization rate is affecting the actual solids inventory in the AR. It might also be that due to the fact that the fluidization nozzles of the AR are inclined downwards (Fig. 3) the dynamic pressure of the gas flow increases the back pressure at the outlet side of the lower loop seal, thus inhibiting solids flow. Fig. 6 also shows that the AR fluidization rate has only a minor effect on the FR internal solids circulation rate which is in agreement with the observed pressure profiles and previous investigations (13).
1000 800 600 400 200 0 0
5
10
15
20
25
Pressure in mbar(g)
15 10 AR FR
5 0 20
30
40
AR fludization rate in Nm3/h
Fig. 6 Influence of the AR fluidization on the solids circulation relative to reactor cross-section at a total inventory of 0.5kg and the following fluidization rates in Nm³/h: FR 5/ULS 0.5/LLS 0.5/ ILS 0.2
Solids entrainment flux in kg/m²s
Solids enrtainment flux in kg/m²s
To study the effect of reactor outlet pressure difference the FR Fig. 5 Influence of AR fluidization on the pressure profile at a total solids backpressure was changed by closing inventory of 0.5 kg and following or opening a valve placed after the fluidization rates in Nm³/h: FR 5/ dip tube of the FR cyclone. The CFM ULS 0.5/LLS 0.5/ILS 0.2 results are shown in Fig. 7. It was found that with increasing pressure difference the AR solids entrainment rate increased nearly linearly with the pressure difference while the FR internal 20 15 AR FR
10 5 0 0
1
2
3
4
Pressure difference in mbar(g)
Fig. 7 Influence of the riser outlet pressure difference on the solids circulation relative to riser cross-section at a total inventory of 0.5kg and the following fluidization rates in Nm³/h: AR 25/FR 5/ULS 0.5 / LLS 0.5/ILS 0.2
circulation rate remained unaffected. This can be explained by the increased driving force for particle movement through the lower loop seal which is governed in the DCFB concept by the solids inside the riser of the fuel reactor and the back pressure from the fuel reactor exhaust line. It is important to note that when the pressure difference between the risers is increased too far, solids accumulation in the downcomer of the AR cyclone can lead to loop seal blockage by occurrence of a slugging fluidized bed regime in the downcomer. On the other hand also emptying of the upper loop seal can occur at inversed pressure differences. Generally, in pressurized conditions, small relative backpressure changes can cause significant changes in solids circulation and possibly lead to failure of loop seal operation. Deep loop seals better resisting pressure difference fluctuations between loop seal inlet and outlet can be part of a solution. In addition it seems that an automatic backpressure control setup is inevitable for pressurized operation of a DCFB. CONCLUSIONS The chemical looping reforming process for autothermal steam reforming has shown great potential at atmospheric conditions. To minimize the compression work needed a key issue is to operate the process under pressurized conditions. Pressurization influences the process from the chemical-, as well as from the hydrodynamic point of view. Because of increased gas density the reactor cross section area decreases resulting in increased gas-solids reaction intensity. To avoid occurrence of critical solids flux values in the AR riser recycling of parts of the AR off-gas is needed. This recycling gas stream should be kept low to decrease the energy penalty from re-heating the recycle-gas. This has to be considered when aiming for high process temperatures to improve the methane conversion. Possible problems occurring at pressurized operation were indentified in a dual circulating fluidized bed cold flow model. Proper operation of the loop seals placed between the two risers requires controlling the pressure difference between the reactors. High solids throughput and high pressure differences might lead to loop seal feeding tube blockage or emptying of the loop seal. A pressurized system will therefore be characterized by loop seals larger in both cross section and depth. It was also found that increasing the back pressure of the FR increases the global solids circulation rate which might be used to control the solids circulation rate between the air reactor and the fuel reactor. NOTATION AR CFM
Air reactor Cold flow model
CFB CLC
CLR DCFB dp
Chemical looping reforming Dual circulating fluidized bed Particle mean Sauter diameter, µm Gravitation constant, m/s² Internal loop seal Metal oxide Pressurized chemical looping reforming
D DFB FR
Circulating fluidized bed Chemical looping combustion Bed diameter, mm Dual fluidized bed Fuel reactor
htx LLS OC U0
Heat exchanger Lower loop seal Oxygen carrier Superficial gas velocity, m/s
g ILS MeO PCLR
ULS
Upper loop seal
ΔHR900°C
ηg ρp
Gas viscosity, Pa·s Particle density, kg/m³
ρg Ψ
Reaction enthalpy at 900°C, kJ/mol Gas density, kg/m³ Sphericity, -
REFERENCES 1.
Bellona Report 6:02. Bellona Foundation 2002.
2.
Dybkjaer I.: Fuel Processing Technology 42 (April 1995) 85-107.
3.
Lyngfelt A., Johansson M., Mattisson T., CFB Technology IX. Hamburg, Germany: TuTech; 2008.
4.
Hossain M.M., de Lasa HI., Chem. Eng. Sci. 63 (2008) 4433-4451.
5.
Johansson M., Mattisson T., Rydén M., Lyngfelt A., International Seminar on Carbon Sequestration and Climate Change, Rio de Janeiro, October 2006.
6.
Linderholm C., Mattisson T., Lyngfelt A., Fuel 88 (2009) 2083-2096.
7.
Kolbitsch P., Pröll T., Bolhàr-Nordenkampf J., Hofbauer H., Proceedings of the 9th International Conference on Greenhouse Gas Control Technologies (GHGT-9), Washington DC., November 2008.
8.
Lynfelt A., Leckner B., Mattisson T., Chem. Eng. Sci. 56 (2001) 3101-3113.
9.
Kolbitsch P., Bolhàr-Nordenkampf J., Pröll T., Hofbauer H., Chem. Eng. Sci 32 (3) (2009) 398-403.
10. Rydén M., Lyngfelt A., Mattisson T., Energy & Fuels 22 (2008) 2585-2597. 11. Pröll T., Bolhàr-Nordenkampf J., Kolbitsch P., Marx K., Hofbauer H., Proceedings of the 2009 AIChE Spring National Meeting and 5th Global Congress on Process Safety, Tampa, FL, April 26-30, 2009. 12. de Diego LF., Gayán P., Adánez J., Abad A., Dueso C., Ind. Eng. Chem. Res. 48 (2009) 2499-508. 13. Pröll T., Rupanovits K., Kolbitsch P., Bolhàr-Nordenkampf J., Hofbauer H., Chem. Eng. Technol. 32 (2009) 418-424. 14. L. R. Glicksman, Chem. Eng. Sci. 39 (1984) 1373-1379.
FLUID DYNAMIC EFFECTS OF RING-TYPE INTERNALS IN A DUAL CIRCULATING FLUIDIZED BED SYSTEM D. C. Guío-Pérez, K. Marx, T. Pröll and H. Hofbauer Vienna University of Technology, Institute of Chemical Engineering, Getreidemarkt 9 / 166, A-1060 Vienna, Austria ABSTRACT Intensified gas-solids contact in the fuel reactor of chemical looping combustion systems can be a key issue for achieving the necessary gas-phase conversion rates. Wedge-shaped rings were designed and installed in the fuel reactor of a cold flow model for fluid dynamic testing. These internals are meant to reduce the typical radial and axial solids concentration nonuniformity. It is shown, that more solids are present in the upper regions of the riser when internals are used. Additionally, increase of the solids elutriation rate from the riser was found. INTRODUCTION Dual Circulating Fluidized Bed System The general idea of dual fluidized bed (DFB) reactor systems is to expose two different gas streams to a circulating stream of solids transporting chemical species and often also heat. The use of loop seals avoids direct contact between the two main gas streams. Chemical looping processes in particular require high gas–solid interaction and sufficient contact time in both reactors. The unit (Figure 1) has been designed as dual circulating fluidized bed system with the solids elutriated from a primary circulating fluidized bed reactor (air reactor, AR) separated in a cyclone and fed to a secondary CFB reactor (fuel reactor, FR) passing first through a loop seal (upper loop seal, ULS). The particles from the fuel reactor are fed back into the Figure 1: Sketch of the 120kW DCFB pilot unit. fuel reactor itself after separation in a second cyclone provided with a loop seal too (internal loop seal, ILS). The global solids circuit is closed via a loop seal in the lower part of the reactors (lower loop seal, LLS) that constitutes a hydraulic communication of the reactor inventories. In this design, only the air reactor entrainment is responsible for the global solids circulation between the two reactors, while the fuel reactor can be optimized in order to reach maximum fuel conversion. An excellent gas-solids contact is especially required in the fuel reactor to prevent undesirable presence of unconverted fuel in flue gas (1) (2).
Application of Internals Introduction of internals has been studied for decades in slow velocity fluidized beds, several types of internals are reported in literature already; rings, perforated plates, tubes, etcetera, an initial classification of internals and their effects was done by Harrison and Grace (3). Whereas, for circulating fluidized beds (CFB) some pioneering efforts have been completed (summaries can be found in Lim et al. 1995 (4) and Zhu et al. 1997 (5)). The principal objective of internals installation is the improvement of gas-solids contact; it has been indicated that walls with baffles reduce the typical radial solids concentration nonuniformities by removing the particles flowing down near the wall (annulus region) and bringing them to the core, where reaction by contact with the gas is possible. They can likely also minimize solids back-mixing along the riser while preserving overall solids inventory. Even a series of baffles might divide a fluidized column in a multilayer fast fluidized bed that alternates dense and dilute zones. Above all, wall baffles in the form of flat rings have been of special interest when it comes to internals in CFB and their effects on the gas-solids flow characteristics were well described by Bu and Zhu in (6). They also concluded that elimination of sharp edges in the baffles would be practical to reduce erosion. Likewise, the observed accumulation of particles in the corner formed between flat rings and the wall, particularly important at low velocities and small opening percentage of the ring, can be avoided by using wedged profile rings. Optimal sizing of the baffles has been found to be decisive since they can affect the pressure drop, on one hand, by scraping the down-flowing solids at the wall and deflecting them into the core region where they can be quickly entrained upwards, and on the other hand, by creating extra resistance to the gas-solids up-flowing stream. “When the rings are only in the annulus region, the first mechanism would dominate, but when the rings extend into the core region, the second mechanism would become more and more significant” (6). For this particular dual circulating fluidized bed system, given that, the global circulation rate is governed by the primary reactor fluidization and that the secondary reactor (where the internals need to be installed) is a counter-current reactor, effect of the rings introduction must be seen from an additional point of view. It is, the internals not only intensify the contact between the phases and promote a denser core, but also prolong the time needed for the particles to reach the bottom of the reactor and hence increase their availability for reaction in this section of the system. Additionally, it is important to notice that the investigated unit represents a system of freely circulating CFBs where the total inventory is constant and the solids fluxes in the risers result as a consequence of inner fluid dynamics. Rings Design The rings were designed with a wedge shape section and an aperture ratio of 60% (Figure 2). The wedge form of the rings can likely be applied in refractory-lined hot systems and have the advantages of reducing the impact of erosion and of eliminating any dead area above the internal. This shape is expected to influence the flow structure of particles and gas by both, promoting the change of direction in particles flow and by creating a narrowed length for the flow of gas. Figure 2: Rings (measures in mm).
The present study was carried out in a cold flow model previously designed and built for the fluid dynamic analysis of a 120 kW chemical looping pilot rig for gaseous fuels. The model is a 3:1 scale of the hot unit and was dimensioned based on data of the hot facility and according to Glicksman criteria (7); these principles allow the transformation of the results from the model to the pilot plant conditions. A basic fluid dynamic description of the system was previously carried out, in which influence of main parameters, namely fluidization velocities and inventory, were characterized (8).
EXPERIMENTAL Cold Model P13 P14
P2 P3 P12 P4 P5
P6
P19 /18
P1
P7
P15
P8 P9
Figure 3 presents the configuration of the experimental rig. The most important parameters are listed for both, hot and cold units in Table 1. Compressed air was used as the fluidization agent for all fluidization points. The air flows were regulated by means of several rotameters and fed to the different fluidization points, namely, primary and secondary air injection of both, air and fuel reactors, as well as upper, lower and internal loop seals. Bronze powder, with a mean diameter of 64 μm was selected as the bed material. Elutriated particles from each reactor are separated by independent cyclones and each stream passes through a filter before leaving the unit. Parameter
Dimension
ηG ρG
Pa s
U
P22
.
-3
kg m
.
FRH -5
.
ARC -5
4.7 10
4.1 10
.
FRC -5
1.79 10
.
0.316
0.288
1.22
1.22
. -1
7.32
2.08
4.25
1.21
.
ms
ρP
kg m
3200
3200
8730
8730
µm
161
161
64
64
P11
dP Φ D
P23/24
-3
-5
1.79 10
P10 P16 P17
.
ARH
0.99 0.99 1.00 1.00 mm 150 159 50 54 Table 1: Main fluid dynamic parameters in hot (H) and cold (C) units.
P21
Three identical rings (Figure 2) were inserted in the secondary reactor of the cold flow model at the positions indicated in Figure 3: Sketch of the cold flow model Figure 3. Different operating conditions were tested in order including rings. to determine the effect of the internals on pressure profiles of the system as well as on global and internal solids circulation rates. Pressure was recorded at 9 positions along the height of the secondary reactor. Circulation rates were calculated based on the accumulation velocities of particles measured in each of the downcomers after interruption of fluidization gas flow in the corresponding loop seal; ULS for global circulation rate and ILS for internal circulation rate. RESULTS AND DISCUSSION Inherent Pressure Drop of the Unit with Rings Pressure along the empty unit was measured under several aeration velocities. Pressure pro
1.2
Fuel Reactor
1 0.8 Height [m]
files are presented in Figure 4. The pressure drop expected to appear in the places were rings were installed, was very slight or even nonexistent. This supports the statement that for the type of rings and the aeration velocities used here, the influence of the rings on the pressure profiles due to extra resistance to the up-flowing gas stream might be low.
Internals location
0.6 0.4
Effect of Internals on Pressure Profiles
5 10 15 20 25
0.2
Figure 5 presents a comparison of pressure 0 profiles for the unit with and without internals at standard operating conditions, 30Nm³/h in AR 0 10 20 30 Pressure [mbar] and 10Nm³/h in FR. This profile is especially interesting since it exhibits a comparable total Figure 4: Pressure profile of FR with no bed material 3 pressure drop in FR with and without rings. for variation of fluidization between 5 and 25Nm /h. Here it can be seen that pressure profiles of AR are hardly altered after installation of rings. In the FR, a notable increment in pressure is seen for the profiles with rings, particularly in the lower part of the column; while in the top, pressures appear to be very similar for both profiles. It can be said then, that for a similar total pressure drop along the FR (which would also mean, similar total particles hold up), pressure drop, and hence particles, are redistributed upwards in the column. A relatively larger pressure drop is observed across ring positions than along the length between them, the effect is more important as lower is the ring located, it is, as more concentrated is the bed. Since, no important pressure drop was found to be caused by the presence of the rings themselves (Figure 4), this pressure drop would be then due to accumulation of particles visually observed in the regions right above the rings. This tendency has been also found by Jiang et al. 1991 (9) and Zhu et al. 1997 (5). The observation that the pressure drop occurs across the ring while solids density is high above it, could be explained by the obvious 1.6
1.6
1.4
1.4 1.2
Air Reactor
1
Height [m]
Height [m]
1.2
0.8
-10
0.8 0.6
0.4
0.4
0.2
0.2
0 10 20 30 Pressure relative to pressure at AR top [mbar]
Internals location
1
0.6
0
Fuel Reactor
0 40 3
-10
0 10 20 30 Pressure relative to pressure at FR top [mbar] 3
40
Figure 5: Pressure profiles for operation at 10Nm /h in FR and 30Nm /h in AR; with (solid line) and without (dotted 3 3 3 line) rings. 4kg inventory, 1.0 Nm /h fluidization in LLS, 1.0Nm /h fluidization ULS and 0.6Nm /h fluidization in ILS.
acceleration of the solids in the area of the ring. The acceleration phenomena cause a deviation of the pressure profile and solids concentration in the vicinity of the internals. Zhu et al 1997 (5) described a detailed flow structure around the internals as follows: “particles have the tendency to form a relatively dense region above the rings under high solids circulation rates, the hold-up of this relatively dense region increases with the solid circulation rate and decreases with the distance from the ring, this hold-up region is besides, denser at lower gas velocities. The tendency to form a dense region depends also on the open area, a wider ring with relative smaller open area provides, more “protected” space for the solids to form a dense region”. Experiments in the modified model were performed varying systematically AR and FR fluidization gas flow rates. Figure 6 shows pressure profiles of the unit with and without rings for a fluidization gas rate of 30Nm³/h in AR and variation of fluidization rate in FR. In general, some effects of variation of FR fluidization gas flow rate on the pressure of the unit are preserved whether the rings are installed or not; namely: − Pressure profile of FR is erected as the fluidization gas flow is increased, since higher fluidization velocities carry higher amounts of particles to upper sections of the reactor. − Neither the pressure in AR nor in LLS undergo important modifications due changes in FR fluidization gas flow as long as the latter is kept in lower values. Strong fluidization in FR (see profile for 15Nm³/h) leads to accumulation of particles in ILS-downcomer and consequent decrease of pressure in LLS as well as in both reactors. The effect is intensified if the AR fluidization velocity in reduced (Figure 7). 1.6
1.6
1.4
1.4 1.2
Air Reactor
1
Height [m]
0.8 7 10 15 7 10 15
0.6 0.4 0.2
1
Internals location
0.8 7 10 15 7 10 15
0.6 0.4 0.2
0
0
Pressure [mbar]
0 10 20 30 Pressure relative to pressure at AR top [mbar] 80 40 Pressure [mbar]
-10
Fuel Reactor
60 40 20 0
Lower Loop Seal P11
P21
P22
40
-10
30
0 10 20 30 Pressure relative to pressure at FR top [mbar] 80 Internal Loop Seal 60 Pressure [mbar]
Height [m]
1.2
20 10 Upper Loop Seal
0 P17
P12 3
P19
P18
40
40 20 0
P6
P10
P24
P23
P1 3
Figure 6: Pressure profiles for operation at 30Nm /h constant in AR and variation between 7 and 15Nm /h in FR. 4kg 3 3 3 inventory, 1.0Nm /h in LLS,1.0Nm /h in ULS and 0.6Nm /h in ILS. With rings: solid lines, without rings: dotted lines.
−
Pressures of ULS and ILS are increased with the increment of FR fluidization gas flow, in the first case, as a consequence of the increased pressure in the discharge branch, and in the second due to increment of elutriation from FR.
However, the increase of pressure in FR due to the presence of rings has an interesting influence on the ILS, since a higher pressure difference needs to be overcome. Especially at low FR fluidization gas low rates (with very low circulation rates), ILS does not reach a desirable operation state, and for high FR fluidization gas flow rates, where enough circulation occurs, pressures in ILS are higher for the unit with rings. Pressure profiles prove as well, the increment of total pressure difference in the FR for every set of conditions, caused by the installation of rings; which indicates a general increment of hold up in this reactor. Figure 7 presents a comparison of pressure profiles for the unit with and without rings at 20Nm³/h in AR and variation of FR fluidization velocity. Comparing with Figure 6, where the AR fluidization gas flow and thus the solids circulation between AR and FR was higher; it can be seen that even preserving the tendencies valid for the unit without rings with changes in AR fluidization velocity (it is, redistribution of pressure drop in the AR riser and increment of pressure in ULS), the presence of the rings makes the FR more sensitive to changes in the AR fluidization gas rate. In Figure 8, pressure profiles for different inventories are shown for the unit with internals, the expected increments in pressure drop due to increased inventory are observed in both reactors. 1.6
1.6
1.4
1.4 1.2
Air Reactor
1
Height [m]
Height [m]
1.2
0.8 0.6
0.2
1 0.8
0.2 0 40
-10
0 10 20 30 Pressure relative to pressure at FR top [mbar] 60 Upper Loop Seal Internal Loop Seal 50 Pressure [mbar]
30
60
20
40
10
20 0
Pressure [mbar]
0 10 20 30 Pressure relative to pressure at AR top [mbar] 40 80 Pressure [mbar]
7 10 15 7 10 15
0.4
0 -10
Internals location
0.6
7 10 15 7 10 15
0.4
Fuel Reactor
Lower Loop Seal P11
P21
P22
0
40
40 30 20 10 0
P17
P12 3
P19
P18
P6
P10
P24
P23
P1 3
Figure 7: Pressure profiles for operation at 20Nm /h constant in AR and variation between 7 and 15Nm /h in FR. 4kg 3 3 3 inventory, 1.0 Nm /h in LLS, 1.0Nm /h in ULS and 0.6Nm /h in ILS. With rings: solid lines, without rings: dotted lines.
1.6
1.4
1.4
1.2
1.2
Air Reactor
1
Height [m]
Height [m]
1.6
0.8 0.6
0.6
0.4
0.4
0.2
0.2 0
60 40 20 Lower Loop Seal
0
P11
P21
P22
40
-10
0 10 20 30 Pressure relative to pressure at FR top [mbar] 60 Upper Loop Seal Internal Loop Seal 50 Pressure [mbar]
Pressure [mbar]
0 10 20 30 Pressure relative to pressure at FR top [mbar] 80 40 Pressure [mbar]
Internals location
0.8
0 -10
Fuel Reactor
1
30 20 10 0
40
40 30 20 10 0
P17
P12
P19
P18
P6
P10 P24 P23
3
P1
3
Figure 8: Pressure profiles operation (with rings) at 10Nm /h in FR and 30Nm /h in AR for variation of inventory 3 3 3 between 3kg (dotted line) and 4kg (solid line). 1.0 Nm /h in LLS, 1.0Nm /h in ULS and 0.6Nm /h in ILS.
Effect of Internals on Circulation Rates Given the increased pressures in both ULS and ILS after installation of rings, global and internal circulation rates are expected to be raised. This can be confirmed in Figure 9, the increments are proportional to the fluidization rates, it is, larger differences appear for higher fluidization gas flow rates. However, at 15Nm3/h of fluidization gas flow in FR, the increase of the internal circulation rate and the accumulation of material in FR-downcomer reduce dramatically the concentration of particles in the rest of the system (as already seen in the profiles) and
100
100
100
80 60
AR 10Nm³/h
40 20
Circulation rate [kg/m²s]
120
Circulation rate [kg/m²s]
120
Circulation rate [kg/m²s]
120
80 60
AR 20Nm³/h
40 20 0
0 0
5
10 15 FR Nm3/h
20
80 60
AR 30Nm³/h
40 20 0
0
5
10 15 FR Nm3/h
20
0
5
10 15 FR Nm3/h
20
Figure 9: Global circ. rate (triangles ∆) and internal circ. rate (circles Ο) with variation of FR fluidization for different values of AR fluidization. Comparison between operation with (solid markers) and without (empty markers) rings in FR. 4kg total inventory.
hence the global circulation rate. The balance between global and internal circulation rates should be thus better considered in the unit with internals. It must be additionally indicated that at stronger fluidization rate in FR, high elutriation was observed. CONCLUSION The designed rings with wedge-shaped section installed in the secondary reactor of a DCFB cold flow model were found to be effective on increasing the inventory in this section and improving the contact between solid and gas phases by reducing the typical radial solids concentration non-uniformities. After comparison of pressure profiles and circulation rates between the unit with and without rings it can be affirmed that the pressure in the FR has been increased and redistributed after installation of rings; total pressure drop is also increased, meaning an increment in total hold up in this reactor. Particularly high pressure drops are observed across the sections were rings were installed, which can be attributed to the reacceleration of particles in these segments after the bed material is scrapped from the wall and directed to the core zone by effect of the rings. Slightly higher internal circulation rate as well as the corresponding pressure increase in ILS was observed in the unit with rings. Additionally, the influence of the aperture ratio of rings will be studied in further work. ACKNOWLEDGEMENT Financial support from the Austrian Government’s climate and energy research program, organized by the Austrian Research Promotion Agency (FFG), contract No.8219511 Gvolution, is gratefully acknowledged. NOTATION AR Ar D dP DCFB FR
Air Reactor Archimedes Number, [-] inner riser diameter, [m] mean particle diameter, [m] dual circulating fluidized bed Fuel Reactor
Fr g ILS LLS ReP U
Froude Number, [-] . -2 grav. acceleration, [m s ] Internal Loop Seal Lower Loop Seal particle Reynolds N., [-] . -1 superficial gas velocity,[m s ]
ULS
Upper Loop Seal
V
normal vol. flow, [Nm s ] mean sphericity of part., [-] . dynamic viscosity, [Pa s] . -3 density, [kg m ]
Φ η ρ
3. -1
REFERENCES 1. Pröll T., Kolbitsch, P., Bolhàr-Nordenkampf, J., Hofbauer, H. A novel DCFB system for chemical looping processes. AIChE J. Vol.55, N.12, 3255-3266. 2009. 2. Kolbitsch, P., Pröll T., Bolhàr-Nordenkampf, J., Hofbauer, H. Design of a Chemical Looping Combustor using a DCFB Reactor System. Chem. Eng. Technol. 32, No.3, 398–403. 2009. 3. Harrison, D. and Grace, J.R. Fluidized bed with internal baffles. In: J.F. Davidson and D. Harrison, Editors, Fluidization, Academic Press, London. pp. 599–626. 1971. 4. Lim, K.S. Zhu, J.X., Grace, J.R. Hidrodynamics of gas-solid fluidization. Int.J. Multiphase Flow. Vol.21, Suppl., 141-193. 1995. 5. Zhu, J-X. Salah, M. Zhou, Y. Radial and axial voidage distributions in CFB with ring-type internals. J. Chem. Eng. Jpn. Vol.30, N.5, 928-937. 1997. 6. Bu J., Zhu J-X. Influence of ring type internals on axial pressure distribution in CFB. Can. J. Chem. Eng., Vol. 77, 26-34. 1999. 7. Glicksman, L. Scaling relationships for fluidized beds. Chem. Eng. Sci. 39, 1373-79. 1984. 8. Pröll T., Rupanovits K., Kolbitsch P., Bolhàr-Nordenkamf J., Hofbauer H. Cold flow model study on a DCFB system for chemical looping processes. Chem. Eng. Technol., 32, No.3, 418-424. 2009. 9. Jiang, PJ., Bi, H.T., Jean, R.H. and Fan, L.S. Baffle effects on performance of catalytic CFB reactor. AIChE J. 37, 1392-1400. 1991.
OPERATING EXPERIENCE AND LATEST DEVELOPMENTS OF ALSTOM POWER‘S 300 MWe CLASS CFB BOILERS. Bruce Wilhelm1, Pierre Gauvillé2, Iqbal Abdulally1, Christian Enault2 1
Alstom Power R&D Execution, 175 Addison Road, P.O. Box 500, Windsor, CT 06095, USA
2
Alstom Power CFB Product Line, 12 Rue Jean Bart, 91300 Massy, FRANCE
ABSTRACT Fuel flexibility combined with low emissions, particularly with high sulphur fuels, are the key drivers for installing a Circulating Fluidised Bed (CFB) Boiler. Taking into account the global drive for more efficient and clean combustion, and the development of CFB combustion and emission reduction technologies accordingly, worldwide applications for CFB Boilers are increasing. Accordingly, Alstom Power continues the evolutionary development of its CFB Boiler portfolio, based on the experience gained with operating units. This paper focuses on Alstom Power’s 300 MWe Class CFB Boiler products. East Kentucky Power Cooperative's (EKPC) Spurlock unit 4 is one of the large Alstom’s 300 MWe CFB boilers in operation since 2009. This unit is a duplicate of EKPC’s Gilbert #3 CFB boiler unit, which was also supplied by Alstom. The lessons learned from the Gilbert #3 unit were applied to the Spurlock #4 unit, which has resulted in high availability and smooth operation to date. The performance and operation of the Flash Dryer Absorber system and SNCR for NOx removal, which are an integral part of the CFB unit where stringent emission control is a requirement, are also discussed. Low NOx emissions were achieved by the low temperature combustion and by the use of the selective non-catalytic reduction (SNCR) system developed and tested by Alstom at the EKPC Spurlock station, leading to 20% reduction of the ammonia consumption. A 98% SO2 removal rate was achieved with limestone injection into the furnace. The proprietary Flash Dryer Absorber (NID™) system developed and tested by Alstom has helped to improve overall O&M by reducing the limestone consumption by around 25%. Finally, the paper introduces the latest 300 MWe Class CFB Boiler product, built on the well proven features of the operating units, which has been developed to further enhance fuel flexibility and clean combustion on a cost-effective basis.
INTRODUCTION The latest 268 MWe (net) boiler, named Spurlock #4, is the fourth generating unit at East Kentucky Power Cooperative’s (EKPC) Spurlock Station near Maysville, Kentucky (KY), USA. It is located alongside Gilbert #3, as well as two pulverized
coal units, 300 MWe and 500 MWe, that were built over 25 years ago. It burns a variety of coals primarily supplied by Kentucky mines and reached commercial operation in 2009. Low SO2 emissions is achieved by sulfation of limestone sorbent in the CFB and by additional sulfation of unreacted sorbent in the NID™, a proprietary flash dryer absorber (FDA) system located downstream of the CFB. This permits low SO2 emissions (245 mg/Nm3, 98% removal). Very low NOx emissions ( 85 mg/Nm3) are enabled by the low combustion temperatures of the CFB and by the use of proprietary selective non-catalytic reduction (SNCR). The latter employs the addition of anhydrous ammonia and extended residence times at low temperature to further reduce NOx within the boiler.
CFB BOILER DESCRIPTION The CFB is a natural circulation boiler consisting of a single grate furnace; three recycle cyclones and two Fluidized Bed Heat Exchanges (FBHEs) in the hot loop. Some superheater and evaporator panels are provided in the furnace to attain the total duty required in the hot loop. The finishing superheater and reheater are located in the FBHEs. A portion of the solids from the recycle loop are passed through the FBHEs in a controlled manner in order to maintain the duty and, therefore, the final reheat and superheat steam temperatures. With this approach, the plant heat rate is minimized by not using spray water to control the final reheat temperature and only a minimum amount of spray to control the final steam temperature.
A Just-In-Time (JIT™) limestone system is provided for sorbent injection into the CFB for 90+% reduction of SO2 within the furnace. The JIT™ system utilizes two 100% capacity Raymond™ Roller Mills operating with a pressurized Primary Air system to directly transport the limestone to the furnace. A Selective Non Catalytic Reduction (SNCR) system is provided that optimally discharges ammonia reagent into the inlet and outlet of the cyclone in order to control the NOx emission to the permitted levels.
Coal Silos Cyclones
Furnace Limestone Silo
Backpass
NIDTM/Fabric Filter FBHE Airheater
FBAC JIT System
Figure 1: Model of the Gilbert 3 and Spurlock #4 Boiler Island
The flue gas leaving the cyclones is passed through a convection backpass consisting of the primary reheater and superheater and economizer surface. Final heat recovery is done by Alstom’s tri-sector Air Preheater. The flue gas leaving the
Air Preheater is directed to a NIDTM and baghouse for final SO2 and particulate control. The solids inventory is controlled by removing bottom ash via two Fluidized Bed Ash Coolers (FBACs) consisting of water cooled surface.
FUEL FLEXIBILITY Coal Firing: Being situated on the Ohio River and adjacent to CSX rail lines, EKPC is able to choose fuels from a number of sources. The use of a CFB boiler enhances fuel flexibility by being able to fire a wide range of fuels. Table 1 shows the range of fuels used in the design of the Gilbert #3 and Spurlock #4 units. The use of a CFB boiler also allows EKPC to burn this wide fuel range while maintaining low emissions rates as called for in the permits. The coals are High Volatile Bituminous coals as per ASTM classification which can produce up to 12000 mg/Nm3 uncontrolled SO2. The permitted emissions for the Spurlock #4 Unit are well below the New Source Performance Standards (Table 2)
Table 1: Coal Fuels Ultimate Analysis Fuel analysis (%) on weight basis Carbon
Design fuel
Pine Branch
Pittsburgh 8
53.48
61.67
70.84
Range for all Fuel Fired 47.79 – 71.48
Hydrogen Oxygen Nitrogen Sulfur Ash Moisture
4.40 7.21 0.90 4.50 20.00 9.51
2.40 5.03 1.33 1.50 20.00 8.07
3.87 3.95 1.41 4.00 8.00 7.85
2.40 – 4.56 3.79 – 10.11 0.72 – 1.41 1.21 – 5.56 6.30 – 30.00 7.24 – 17.46
Higher Heating Value Btu/lb (Kcal/kg)
10,400 (5800)
10,215 (5600)
12,502 (7000)
9,154 – 12,710 (5100 – 7000)
Table 2: Comparative Emissions Levels Spurlock #4 Emissions Lb/mmBtu ( mg/Nm3, 6% o2 dry gas) NOx SO2 Particulate PM10
Spurlock #4 Permitted Emissions 0.07 (85) 0.2 (245)or 98+% reduction 0.015 (20)
New Source Performance Standards 0.60 (740) 1.2 (1500) 0.03 (35)
Biomass and Opportunity Fuel firing: In addition to the coals specified, the unit is also designed to burn biomass and approximately five million tires per year. Having the widest possible fuel choices allows EKPC to continuously supply power to their customers at the lowest possible rates. EKPC CFB units to date have co-fired biomass up to 45 MWth and Tire Derived Fuel (TDF) up to 135 MWth (20% of firing capacity). Table 3 shows the analysis of these alternative fuels.
Table 3: Alternative Fuels Ultimate Analysis Fuel analysis (%) on weight basis Carbon
Switchgrass
TDF
Wood chips
46.38
78.35
24.5
Hydrogen Oxygen Nitrogen Sulfur Ash Moisture
5.56 40.20 0.24 0.05 2.53 5.04
6.62 1.22 0.22 1.15 11.74 0.7
3.00 22.00 0.5 50.0
Chlorine
0.049
-
-
Higher Heating Value Btu/lb (Kcal/kg)
7,764 (2300)
14,950 (4350)
4,730 (2600)
Biomass and opportunity fuels are fired into the unit as and when they are available. No impact on operation, performance and emissions were observed at these rates of firing. These fuels were fired without additional equipment. The raw limestone storage and conveying system to the JITTM system was used to add the biomass. When feeding the biomass, the conveyor system leaving the limestone storage discharges the biomass onto the conveyor belt carrying prepared coal to the common tripper. The biomass is introduced at a predetermined ratio via the limestone system while the prepared coal is loaded onto the coal bunkers. So, the biomass is blended with the coal and introduced to the CFB furnace via the normal feed system. If the raw limestone make-up is required the control system switch to the usual mode so as to feed separately the raw limestone bins.
EMISSIONS CONTROL With a CFB boiler, combustion takes usually place in the range 850 – 900°C, which results in a lower NOx emission compared to the higher combustion temperature associated with PC boilers. SO2 emission can also be reduced by injecting limestone into the furnace. The limestone is calcined in the boiler to become lime that subsequently reacts with SO2 released in the combustion process to form gypsum, a very stable inert compound. This is then removed from the CFB unit either with the bottom or the fly ash.
To control emissions further, the Gilbert #3 and Spurlock #4 boilers include additional methods of SNCR technology for NOx control and the recently developed Alstom NID™ system for advanced SO2 emissions control. In the SNCR process ammonia gas is injected into the flue gas stream where it thermally reduces the NOx in the flue gas to form nitrogen (N2) and water vapor. The anhydrous ammonia is injected in the CFB’s gas ducting via a novel proprietary grid, providing excellent mixing and dispersion of the reagent. The NID™ system uses the fly ash produced in the boiler that contains unused lime as the reagent. The fly ash collected in the baghouse is hydrated slightly and then re-entrained in the flue gas stream. The hydrated lime then reacts with any SO2 present in the flue gas. In order to maintain the permitted SO2 emission, the overall removal rate for the Spurlock #4 CFB with the NID™ system is greater in operation than 95% over the complete range of fuels.
NOx REDUCTION FROM GILBERT #3 TO SPURLOCK #4 The operation of Gilbert #3 unit has highlighted a difference of bed temperature between the right side and the left side of the furnace. The deviation in the bed temperature led to a hotter area resulting in higher NOx formation in comparison with the opposite side. On Spurlock #4 unit additional evaporative panel was added to the left side for keeping a bed temperature uniformly low. After over a year in commercial operation, EKPC has reported that the addition of the evaporative panel has in effect lowered the bed temperatures on the left side of the unit by an average of 10°C and has met its intended performance of reducing the formation of NOx. The second effort was focused on the SNCR’s efficiency by improving the quality of ammonia injection into the flue gas. Alstom’s original SNCR employed three to four wall lances in the cyclone outlet and inlet duct (Figure 2). Through testing it was determined that the standard CFB industry approach of injecting ammonia reagent to the gas stream was ineffective because of the following reasons:
SNCR Lances
Cyclone Outlet Hood Side and Plan View
Figure 2: Original SNCR Lance Configuration
1-The gas distribution was uneven over the duct section 2-The NOx concentration was uneven over the duct section 3-The lances were not as effective in distributing the ammonia as required to address the non-uniformity as indicated in 1 and 2 above.
This leads to the innovation of the new SNCR grid system. The new grid system has multiple lances that spans across the duct from side-to-side (Figure 3). Each half of these lances receive reagent with a supply line that can be controlled independently. Each lance has evenly distributed nozzles to ensure even distribution of reagent from each section of the lance.
Location of Improved SNCR System
Cyclone Outlet Hood Side and Plan View
Figure 3: New Alstom SNCR Grid System
This arrangement gives the ability to distribute the reagent between cyclone and with each cyclone duct to match the NOx concentration in the flue gas streams entering and leaving each of the cyclones thereby optimizing the SNCR system resulting in lower NOx emission, ammonia consumption and slip. Both EKPC and Alstom have been actively improving NOx control with the objective of reducing both ammonia consumptions and NOx emission. To this end, several optimization tests and modification of the SNCR were done on both Gilbert #3 and Spurlock #4 as follows: 1) 2005 – SNCR optimization with original system on Gilbert #3 unit 2) 2006 – Modification of SNCR system to change original lance system to a ammonia grid distributions system 3) 2007 – Optimization testing on Gilbert # 3 unit 4) 2008 – Addition of evaporative panel on Spurlock unit #4 5) 2009 – SNCR optimization test on Spurlock #4 6) 2009 – Emissions optimization test on Spurlock #4 unit 7) 2010 – Emissions optimization test on Spurlock #4 unit During the optimization test all emission gas constituents (SO2, NOx, CO and O2) were measured. The measurements were completed either at the outlet duct of the economizer or the outlet duct of the each of the three cyclones. On the cyclone outlet duct the gas was sampled at three points before and after the new SNCR grid system . The cyclone outlet measurements gave a better indication of the gas composition leaving each of the three furnace sections associated with a given cyclone. The tests showed that there was an uneven distribution of emission during the various test campaigns. The main changes were as follows: 1) Side-to-side biasing of fuel 2) Side-to-side biasing of Secondary air 3) Deeper staging of combustion air. That is, decrease in primary/secondary air ratio.
These changes, together with the use of the new SNCR distribution grid resulted in the lowering in-furnace and final NOx, ammonia consumption by greater than 20% while maintaining ammonia slip to less than 2 ppm.
SO2 REMOVAL RATE The NIDTM system provided on this unit was of the first generation for a large-scale unit. There were several upgrades made to eliminate problems encountered during initial operation. However, the NIDTM system has helped to improve overall O&M cost by reducing the overall limestone consumption by as much as 27%. This reduction in limestone rate has secondary benefits as follows: o o o o
0.8% increase in boiler efficiency 2.6% decrease in CO2 emission 16% decrease in ash flow A few percent decrease in ammonia consumption.
PATH FORWARD Based on overall experience on large scale CFBs, Alstom is developing a common product platform of standardized equipment based on various popular size units such as 100, 150, 300 MWe. The 300 MW-class CFB product has been developed and is currently offered by Alstom. The 300 MW-class CFB is a natural circulation boiler consisting of a single grate furnace with evaporative, final reheat and superheat panels and three steam cooled recycle cyclones to attain the total duty required in the hot loop. By locating the finishing superheater and reheater in the furnace, a proper blend of radiant and convection heat duty is maintained at all loads. This ensures that a steady and high final steam and reheat steam temperature are maintained through out the control load range.
Limestone bin
Coal silos
JITTM
Rotary ash Figure 4: 300 MW-Class CFB Standard Boiler Arrangement
A reheat steam bypass system is provided around the convection surface in the backpass in order to control the final reheat temperature without the use of spray
water. With this approach, the plant heat rate is minimized by not using spray water to control the final reheat temperature and only a minimum amount of spray to control the final steam temperature. Alstom’s Just-In-Time (JIT™) limestone system is provided for sorbent injection into the CFB for 90+% reduction of SO2. The system provides sorbent to the front and back of the furnace. Alstom’s proprietary Selective Non Catalytic Reduction (SNCR) system consisting of injection grids is provided that is capable of discharging aqueous or anhydrous ammonia reagent evenly to the inlet and outlet of the cyclone in order to control the NOx emission with high reduction levels to the permitted levels while maintaining acceptably low ammonia slip. The flue gas leaving the cyclones is passed through a conventional convection backpass consisting of the primary reheater and superheater and economizer surface. Final heat recovery is done by a tri-sector Air Preheater. The flue gas leaving the Air Preheater is directed to a NIDTM and baghouse for final SO2 and particulate control. The solids inventory is controlled by removing bottom ash Rotary Ash Coolers (RACs). This eliminates the issues encountered when firing fuels with large quantities of ash as rocks or agglomerates that may form in the furnace.
CONCLUSION EKPC Spurlock #4 300MWe Power Plant is one of the cleanest burning coal fired power plants and demonstrated the flexibility of the CFB technology by burning different kinds of biomass as well as tires. The development of the SNCR distribution grid resulted in the reduction of ammonia consumption around 20% while keeping the ammonia slip below 2 ppm. The reactivation of free lime included in the fly ash by using the Flash Dryer Absorber ( NIDTM) enabled the EKPC power plant to reduce the limestone consumption by around 25%. The experience learned from the Gilbert #3 contract execution, commissioning and operation was applied to the Spurlock #4 unit, which has resulted in 94% availability after the first year of commercial operation. Finally, an excellent relationship with EPKC enabled Alstom to develop and improve the integrated CFB solution with FDA and SNCR as an available clean coal technology to make coal generation a vital and environmentally responsible means of power generation in the United States. Going forward, all design enhancements derived from this cooperation have been implemented into the current large-scale designs such as the 300 MW-class CFB standard boiler offered by Alstom.
OBSERVATION OF FLOW REGIME TRANSITION IN A CFB RISER USING AN LDV Paul C. Yue, Joseph S. Mei, Lawrence J. Shadle National Energy Technology Laboratory U. S. Department of Energy 3610 Collins Ferry Road Morgantown, West Virginia 26507-0880 Abstract The solids flow in a circulating fluidized bed (CFB) riser is often described to have a core-annular structure. For a given superficial gas velocity, at the initial introduction of solids into a riser a flow structure of dilute upflow regime exists. Continuing to increase the solids flow in the riser transitions the flow structure to the core-annular flow regime. However, with further increase of solids flow a condition is reached, depending on the superficial gas velocity, where all the solids across the riser cross section flow upwards, even those at the wall. When the solids flux, solids fraction and gas velocity are relatively high, such a condition is described as the dense phase suspense upflow (DSU) regime. In this paper we report our observations of these flow regime transitions by using a laser Doppler velocimeter (LDV) to monitor the upward and downward particle flow velocities at and near the riser wall of the National Energy Technology Laboratory’s 30.4 centimeters diameter CFB cold flow model. The particles were high density polyethylene (PPE) spheres with a Sauter mean diameter of 861 micron and a density of 800 kg/m3. Three superficial gas velocities of 6.55 m/s, 10.67 m/s and 13.72 m/s were used in this study. For the case of superficial gas velocity 6.55 m/s, the experimental data show that the transition from dilute upflow to core-annular flow occurred when the solids flux was about 7 kg/m2-s and the transition from coreannular flow to dense suspension upflow was about 147 kg/m2-s. As the superficial gas velocity was increased to 10.67 m/s the corresponding flow regime transitions were at 34 kg/m2-s and 205 kg/m2-s, respectively. For the case of superficial gas velocity of 13.72 m/s the data showed no distinct transition of flow regimes. The particles were all upflow for the range of solids fluxes from 10 kg/m2-s to 286 kg/m2s.
INTRODUCTION The fluidization condition in a circulating fluidized bed (CFB) is classified as leanphase fluidization. Within this lean-phase fluidization, two flow regimes exist, the dilute phase upflow regime and the fast fluidization regime (1). The dilute phase upflow regime is characterized by a homogeneous flow structure. The fast fluidization regime is characterized by a heterogeneous flow structure and has higher slip velocity between the gas and solids than that in the dilute phase transport
regime. In a typical fast fluidized bed, there often exists particle agglomerates or clusters moving up and down especially along the riser wall. This leads to a coreannulus flow structure. The formation of clusters is an important and necessary feature, but by itself is not an adequate condition for a fast fluidized bed (2). In a core-annulus flow structure, the center or core region consists of a mostly dilute phase of upward-flowing gas and particles surrounded by an annular region of descending particles (3). This flow structure is the basis of various CFB models [1]. However, there exists a third flow regime, the dense suspension upflow (DSU), first pointed out by Grace (4). The solids hold-up in his flow regime is typically in the 10% to 20% range and the superficial gas velocities on the order of 6-10 m/s with very high solids net circulation rates, 300-1000 kg/m2-s. Another feature in DSU is the relative lack of particle downflow. Hence, measuring particle velocities at the wall over different solids fluxes should provide the necessary information to identify and discriminate the transitions between these three flow regimes. We have observed that during the introduction of solids into the riser at the initial stage of operation of a CFB, the solids flow at the upper section of the riser is homogeneous, i.e. in the dilute upflow regime. Upon increasing the solids flux, clusters begin to appear and the flow structure eventually transitions into the fast fluidization regime. Monazam and Shadle (5) have developed a transient method to identify the transition velocities to identify when riser operations are in slugging, fast fluidized or core-annular regimes making the case that many of the riser’s dynamic responses are dependent only upon the gas velocities and solid properties, but independent of the solids flux. Note that this analysis does not distinguish between risers operating with different solids holdups, such as being above and below the saturated carrying capacity (6). Thus, it is of interest to explore the behavior of particles near the Figure 1. Schematic of National Energy Technology wall at an axial location that is Laboratory’s cold flow circulating fluidized bed. normally in the fully developed region of the riser. Basu (2) has stated that there is a lack of a clear picture of the transition to and from fast fluidization. This is interpreted to include transitions due to both the gas velocity and solids flux.
We conducted particle velocity measurements with a laser Doppler velocimeter (LDV) system. By using the axial down-flow velocity component (one can also use the upflow component), we were able to identify the flow regime transitions from the velocity counts. The solids hold up in these flow transitions were compared with the apparent solids fraction profiles based on the differential pressure measurements along the vertical axis of the riser. FLOW REGIME TRANSITION MEASUREMENTS The measurements were conducted at the National Energy Technology Laboratory’s cold flow CFB model illustrated in Figure 1. The riser has a height of 15 meters and a diameter of 30.4 centimeters. It has been described in detail elsewhere (5-8). The particles were high density polyethylene (PPE) spheres with a Sauter mean diameter of 861 micron and a density of 800 kg/m3. Three superficial gas velocities of 6.55 m/s, 10.67 m/s and 13.72 m/s were used in this study. The classical or lower transport velocity was 4.3 m/s for this material while the upper transport velocity was found to be 6.3 m/s (5). Thus, all of these tests were conducted in dilute, core annular, or dense suspension upflow conditions. The LDV was used to monitor the particle flow in the axial direction. The probe volume of the LDV was positioned at 9.3 meters above the centerline of the solids entry port. This location is the upper part of the middle region of the riser (9). The riser wall in this section is of optical quality acrylic material. The approach was to obtain the upflow and downflow velocity counts rather than velocities in the space close to the inside wall of the riser. We set the transceiver of the LDV on a manually operated translation platform. A digital meter with a resolution of 0.01 mm is attached to the driving screw. In order to ensure the LDV probe volume is at the riser Figure 2. LDV in CFB riser wall velocity readings with wall, we initially positioned it only air circulating taken at the wall, P0. inside the acrylic wall of the riser. With the CFB running on air only, the vibrations induced on the structure by the air flow was sufficient to generate detectible velocity signals on the LDV, shown in Figure 2. Monitoring these signals while advancing the probe volume of the LDV through the wall, the "at wall" position was determined accurately when these noise velocity signals disappeared. Figure 3. LDV at CFB riser wall velocity readings Figure 3 is an example when with only air circulating within the riser at P0 + 6.8 such a condition was reached. The few particles detected over
the 30 second period were probably particles entrained from the CFB wall. RESULTS AND DISCUSSIONS Since the condition of a flow regime is defined by the axial component of the particle velocity we used the downflow velocity counts in our analysis. It would be equally valid to use the upflow velocity counts.
y
Velocity Count Ch. 1
80
g
Flow Regimes Transition at Superficial Gas Velocity Ug = 6.55 m/s
60
40
20
0
-21
-14
-7
0
7
Velocity Ch. 1 (m/sec) Figure 4. An example of LDV particle velocity histogram taking measurement at CFB riser wall, Ug=6.58 m/s. Ms=2,665 kg/hr.
This superficial gas velocity was chosen to be consistent with previous CFB operation conditions to provide experimental data for model validation study. Figure 4 shows that a typical velocity distribution containing broadly two to three velocity groups. The flow direction convention used by the instrument is such that positive velocities indicate downflow direction and negative for upflow direction. Hence, in Figure 4 the negative velocities are particle upflow velocities in the CFB riser and the positive velocities are downflow particle velocities.
With reference to the velocity histogram, notice that the high velocity group has velocities more than twice the superficial gas velocity used. It has been reported that some particles in a riser may have velocities much higher than the gas velocity [10]. However, because of their relatively low count, most of the data sets showed that they Figure 5. Percentage of downflow velocity counts comprised less than 10% of the at height 9.36 m versus solids flux for Ug=6.55 m/s. total velocity counts; we have not included them in our analyses. We expressed the down flow velocity counts, respectively, as a percentage of the total velocity count, excluding the high velocity group. In Figure 5 we plotted the downflow velocity count in percentage of the total velocity count (both upflow and downflow) against particle flux derived from mass flow rate measured by a spiral device (6). However, for particle
flux greater than 100 kg/m2-s we observed that the downflow velocity count percentage decreased as the particle mass flux increased. This indicates a shift in the particle flow direction towards the dense suspension upflow condition.
Figure 6. Solid fraction profiles along the axis of CFB riser at Ug=6.55 m/s for a low and a high solids flux conditions corresponding to the dilute and the dense suspensions upflow regime.
Figure 7. Percentage of downflow velocity counts at height 9.36 m versus solids flux for Ug=10.67 m/s.
Figure 8. Solids fraction profiles along the axis of CFB riser at Ug=10.67 m/s and a low and a high solids flux conditions.
We compared this observation based on the velocity count analyses with the solids fraction profiles for two flow conditions, one in the dilute upflow and one in dense suspension upflow as shown in Figure 6. For low solids flux condition (the red curve) the apparent solids fraction at the 9 to 10 meter level shows a value of much less than 10% indicating the bed is in the dilute transport regime which is also indicated in Figure 5, showing low downflow velocity count percentage. On the other hand, for the high flux case the solids fraction profile (the blue curve in Figure 6) indicates a solids fraction of higher than 10% exists at the 9 to 10 meters level agreeing with that indicated in Figure 5. Flow Regime Transition at superficial velocities of 10.67 m/s and 13.72 m/s Similar measurements were made at higher superficial gas velocities. The purpose is to see how, if any, the superficial gas velocities would affect the flow regimes transition. It is obvious that at higher superficial gas velocity, because of the dilution effect, one would expect the transition would require higher solids flux. For the case of superficial gas velocity of 10.67 m/s the downflow velocity counts are plotted in Figure 7 as in Figure 5. However, it seems that the transition from dilute transport regime to coreannular flow regime was not clearly observed. The overall downflow velocity count
percentage is low throughout this solids flux range indicating that high percentage of the particles were being carried upwards by the higher carrier gas velocity. The decrease in downflow as solids flux is increased suggests that the flow structure is approaching the dense suspension upflow (DSU) regime. The data show that at high gas velocities (for both low and high solids fluxes) there can be no net downflow at the wall. These data also suggest that dilute phase flow may transition to DSU regime without going through the core annular flow regime. However, more experimental data will be needed to verify this trend. Figure 8 show the apparent solids fraction profiles. As the data suggested, at riser gas velocity of 10.67 m/s and at solids flux of 205 kg/m2-s, the average solids fraction was still less than 10%.
Figure 9. Peercentage of downflow velocity counts at height 9.36 m versus solids flux for Ug=13.72 m/s.
The corresponding downflow velocity count percentage plot and the apparent solids fraction profile plot for the superficial gas velocity of 13.72 m/s are shown in Figures 9 and 10, respectively. Figure 9 shows that there is no apparent flow regime transition detected. The apparent solids fraction profile, Figure 10, also indicates low solids hold-up in the riser at the sample location showing the diluting effect. Indeed, at these high gas velocities it may require much higher solids flux to the riser to achieve dense suspension upflow. CONCLUSIONS
We have shown that it Herman Yue is possible by monitoring either the downflow or upflow velocity counts measured by an LDV system at the riser wall to demarcate the transitions of flow regimes. These transition Figure 10. Solids fraction profiles along the axis of measurements were supported CFB riser at Ug=13.72 m/s and a low and a high by apparent solids fractions solids flux conditions. obtained from differential pressure measurements at the corresponding sampling region of the riser. At higher riser gas velocity, the flow regime transition from dilute phase flow may directly transit to DSU flow regime without going through the core annular flow regime. This solids flow behavior may be attributed to the higher percentage of particles are being transported out of the riser through the high gas velocity. However, more experimental data will be needed to verify this trend.
NOTATION Ms Ug
Mass circulation rate, kg/hr superficial gas velocity, m/s
REFERENCES 1. Fan, L-S and Zhu, C., Principles of Gas-Solid flows, Cambridge University Press, 1998, p.448. 2. Basu, P., Combustion and Gasification in Fluidized Beds, Taylor & Francis Group, 2006, p. 36. 3. Lints, M. C., and Glicksman, I. R., The Structure of Particle Clusters Near the Wall of a Circulating Fluidized Bed, in Fluid-Particle Processes. Fundamentals and Applications, Weimer, A. W., Chen, J. C., Fan, Liang-Shih, and Yang, W-C (Eds), AIChE Symposium Series No 296, Vol. 89, 1993. 4. Grace, J.R., Reflections on Turbulent Fluidization and dense suspension upflow, Powder Technology, 113 (2000), 242-248. 5. Monazam, E.R. and Shadle, L.J., Method and Prediction to Determine Transitional Velocities in a CFB’s Riser, Ind. Eng. Chem. Res., 2011, 50 (4), pp 1921–1927. 6. Ludlow, C., Lawson L. O. and Shadle, L. J., Proc. Cir. Fluid. Bed Tech. VII (Grace, J.R., J., and de Lasa, H. (Eds.)), Can., Soc, Chem. Eng. Ottawa, 2002, pp, 513-520. 7. Monazam, E., Shadle, L. J., and Lawson, L., A Transient Method for Determination of Saturation Carrying Capacity. Powder Technology, 121 (2005), 205-212. 8. Mei, J. S., Shadle, L J., Yue, P. C., and Monazam,E. R., 12th International Fluidization, Vancouver, Canada, 2007, p. 63. 9. Mei, J S., Lee, G. T. Seachman,S. M., Ludlow, J. C., and Shadle, J. L., Proceedings of the 9th International Conference on Circulating Fluidized Beds, Hamburg, Germany, 2008, p. 177. 10. Bierl, T. W., Gaido, L. J., McIver, A. E. and McGovern, Jr., J. J., Studies in Support of Recirculating Bed Reactors for the Process of Coal, Final Report, CarnegieMellon University, Department of Energy Contract No. EX-C-76-01- 2449,1980.
THE DEVELOPMENT OF A NOVEL Cu-Mn OXYGEN CARRIER FOR THE CHEMICAL LOOPING GASIFICATION OF BIOMASS Milad Aghabararnejad, Jamal Chaouki*, Gregory S. Patience Department of Chemical Engineering, Ecole Polytechnique de Montreal, C.P. 6078, succ. Centre-Ville, Montreal, Que. H3C 3A7, Canada *
Corresponding author: Tel.: +1-514-340-4711 X 4034; fax: +1-514-340-4159. E-mail address: [email protected]
ABSTRACT Circulating fluidized bed technology applied to combustion processes in which oxygen and fuel are fed into separate reactors is referred to as Chemical Looping Combustion. Typically, oxygen reacts with a reduced metal (-oxide), then it is transferred to a second vessel where the metal oxide is reduced by a hydrocarbon. In chemical looping gasification, a fuel is contacted indirectly by oxygen and/or steam again with a metal oxide shuttling between two vessels reducing the contact between fuel and air. In this case, a concentrated stream of syngas exits the fuel reactor undiluted by nitrogen. The objective of this study is to develop a solids substrate capable of releasing oxygen in the fuel reactor. A bimetallic Cu-Mn oxygen carrier was synthesized by incipient wetness impregnation at ambient conditions over Al2O3. Copper-based oxygen carriers have superior oxygen transfer capacity and environmental and economical characteristics compared to nickel, iron and cobalt, but the operating temperatures are limited due to the low melting point of the metallic copper. Adding manganese to copper minimizes the formation of copper aluminate. Moreover, it inhibits copper agglomeration and carbon deposition. The developed oxygen carriers were characterized by BET, XRD and SEM analyzers. Also, oxygen transfer capacities of particles were tested using thermo gravimetric analysis (TGA). Results indicate that Cu-Mn is a superior carrier, which is suitable for the separation of oxygen in a chemical looping process. Also, adding manganese to copper allows working at high temperatures and improves the reactivity of copper. INTRODUCTION Energy-efficient practices are becoming more and more necessary as energy prices continue to rise and the impact of climate change emerges. Institutions that adopt measures to reduce energy and its associated greenhouse gas emissions will obtain financial, environmental, and social benefits. New opportunities for biomass energy are developing as environmental and economic concerns encourage governments, industries, and consumers to explore alternatives to fossil fuels. Combustion and gasification are two processes, which convert hydrocarbons to energy. Gasification has some advantages over combustion, such as higher efficiency, treating with a lower volume of gas and low emission of toxic gases. Gasification also makes it possible to have synthesis fuels, which can be used for transportation. However, an
1
oxygen separation unit is needed before each gasification process, which can raise the capital and variable costs of the process. A chemical looping process is a relatively new technology for combustion and gasification processes. Figure 1 shows a schematic of a chemical looping combustion process.
Figure 1: Chemical looping combustion
Chemical looping combustion consists of two reactors. On the right side is the fuel reactor, where oxygen from a metal oxide (MyOx) reacts with fuel according to the following reaction: (2n+m)MyOx + CnH2m → (2n+m) MyOx-1 + mH2O + nCO2 On the left side is the air reactor, where an oxidation reaction occurs between reduced metal oxide (MyOx-1) and air: MyOx-1 + 1/2O2 → MyOx Consequently, CO2 is separated from water by condensing the product stream. Both reactors should operate as circulating fluidized beds in order to supply the circulation of oxygen carrier between the two beds. It has been reported that chemical looping combustion is the most economical way to capture CO2 compared to post capture and oxyfuel processes (1). Besides combustion, chemical looping is a suitable technology for gasification. It can provide the oxygen needed for gasification reactions. In this way a pure syngas can be obtained. Mattison and Lyngfelt found that some oxide systems of the transition metals, Fe, Cu, Co, Ni and Mn could be used as oxygen carriers (2). Some researchers (3-5) reported that nickel is the best oxygen carrier in terms of reactivity, although there are thermodynamic restrictions in the presence of CO and H2 (6). Hossain et al. (7-9) observed that carbon deposition is a major problem in dealing with nickel carriers. In addition to nickel, copper deserves serious consideration due to its high reactivity and low cost. De Diego et al. (10) developed Cu-based oxygen carriers by mechanical
2
mixing, co-precipitation, and impregnation methods. They analysed the behaviour of CuO in a thermogravimetric analyser. Their results showed that carriers prepared by impregnation exhibited excellent chemical stability without sustainable decay of the mechanical strength in multicycle testing. Copper suffers from agglomeration due to its low melting point. Researchers have tried to improve the properties of copper by using promoters. Adanez et al. (6) prepared a nickel-copper oxygen carrier using the dry impregnation method. They showed that CuO is used for the reduction reaction before NiO. In addition, it was observed that NiO stabilized the CuO and allowed working at 950οC. Pure manganese oxide is expected to release oxygen at 900οC, but there are thermodynamic limitations for the reoxidation of these materials at low oxygen concentration levels in the air reactor at elevated temperatures (11). Recently, Moghtaderi (12) studied the separation of oxygen from air in a chemical looping process and demonstrated that a 1:1 mixture of Mn/Co metal oxide systems has more favourable redox characteristics than its parent materials. Considering the different properties of individual Mn- and Cu-based oxygen carriers, the objective of this work was to use manganese as a promoter for the copper oxygen carrier. It is claimed that manganese can enhance the thermal stability of copper as well as its reactivity. EXPERIMENTS Oxygen Carrier Preparation The incipient wetness impregnation method was used for catalyst preparation. In the following order a specific amount of nitrate hydrate salt of the desired metal was mixed with distilled water. All salt should be dissolved in water. Then, activated alumina was added gradually as a binder to the nitrate solution until it makes a paste. The entire amount of water solution should be adsorbed by the pores of the binder. Drying, which is the next step, was performed at 140οC for 12 hours. Finally, prepared powders were calcined at 850οC for 5 hours in air atmosphere. In each impregnation step a specific amount of active phase settles on the support. Impregnation steps should be repeated in order to reach the desired concentration of the active phase. Stirring can be done at higher temperatures in order to increase the solubility of salt in water and, consequently, increase the amount of settling of the active phase on the binder during each impregnation step. Copper, manganese, nickel, and cobalt have been used as the active phase and activated alumina as the binder. Also, in the case of the bimetallic oxygen carrier 50-50% of each metal has been used. Table 1 shows the amount of loading of the active phase versus impregnation steps:
3
Table 1. Loading of active phase versus impregnation step %Metal loading ௪௧ ௧ Impregnation Step (௪௧ ௧ା௪௧ ௦௨௧ ∗ 100) 1
10.8
2
18.6
3
24.6
CHARACTERIZATION Several techniques have been used to characterize the prepared oxygen carriers. A laser diffraction apparatus has been used to determine the particle size distribution of powders. The surface area of the oxygen carrier was measured by BET. The identification of the crystalline phases of the oxygen carrier was carried out using powder X-ray diffraction (XRD) analysis in an X-ray diffractometer using Cu-Kα radiation. Changes in morphology were determined in a scanning electron microscope (SEM). Table 2 shows the physical properties and the crystalline phases of the oxygen carrier. Table 2. Properties of prepared oxygen carriers Particle Surface Density XRD phases (fresh) Sample 2 size(µm) area (m /g) (kg/m3) Cu
109
Cu-Co
2346
CuO, Cu2O, CuAl2O4, Al2O3
127
67.49 45.92
2383
CuO, CoO, CuAl2O4, Al2O3
Cu-Ni
140
61.25
2437
CuO, Cu2O, NiO, CuAl2O4, NiAl2O4,
Cu-Mn
102
58.99
2135
Al2O3 CuO, Cu2O, MnO, Mn3O4, MnAl2O4, Al2O3
The reactivity of the oxygen carrier was determined using a thermogravimetric analyzer. For the reactivity test 20 mg of sample were exposed to alternating reduction and oxidation cycles. The particles were well spread out in the platinum basket (5 mm in diameter and 1 mm in height) forming a single layer, thus avoiding the inter-particle mass transfer resistance. Air (20 ml/min STP) was used as the oxidizing agent and nitrogen (20 ml/min STP) was used as carrying gas during the reduction step. The experiments were carried out at 800οC and atmospheric pressure. Usually, the reactivity of the oxygen carrier will reach a constant value after the second cycle. However, the reactivity data related to the fifth cycle has been chosen for comparison purposes.
4
RESULTS AND DISCUSSION The purpose of this study was to separate oxygen from air using a chemical looping process. The main step for commercializing the process is to develop an oxygen carrier with superior reactivity and stability properties. Besides having high reactivity during oxidation and reduction reactions, an oxygen carrier should have sufficient thermal and mechanical stability and be environmentally friendly and inexpensive. Copper is one of the candidates for the oxygen carrier in the chemical looping process. Its high reactivity, low cost, and friendly environmental properties make it one of the best materials, like metal oxide. However, copper suffers from agglomeration due to its low melting point (1083οC). In chemical looping processes, the oxygen carrier must pass around one hundred thousand cycles through circulating fluidized beds. As a result, agglomeration can be a huge problem. For this reason, it is suggested to use a bimetallic Cu-Mn oxygen carrier. It has been shown that besides increasing the agglomeration resistance, manganese can enhance the oxygen transport capacity of copper oxide. During calcination, which is one of the catalyst preparation steps, a solidsolid reaction takes place between copper oxide and alumina as the binder. During this reaction CuAl2O4 forms, which has a higher oxidation temperature compared to CuO. Consequently, it is trying to avoid the reaction between copper and alumina. During XRD experiments, it was investigated whether adding manganese can decrease the amount of copper aluminate, because between copper and manganese, the latter has a higher tendency to react with alumina. Thus, CuO remains as the active phase. The oxidation and reduction reactions are: Cu+1/2O2→CuO CuO→Cu+1/2O2 Figure 1 shows SEM micrographs for Cu and Cu-Mn oxygen carriers before and after 20 oxidation-reduction cycles. It is evident that copper particles stick together at 800ο C because of agglomeration. By adding manganese, copper particles can withstand higher temperatures without agglomerating.
5
Figure 2. SEM micrograph of fresh and after 20 cycles tests in TGA of samples Cu, Cu-Mn
Particle diameter will increase because of agglomeration and it can cause defluidization of the bed. Figure 3 compares the reactivity of various oxygen carriers, which were prepared in this study.
Figure 3: Oxidation and reduction activity of different carriers
Cu-Mn has a high oxygen transport capacity as well as high oxidation and reduction rates compared to other carriers. Figure 4 shows the effect of a number of oxidationreduction cycles on the reactivity of the Cu and Cu-Mn oxygen carrier.
Figure 4. Reactivity versus number of cycles (left) Cu (right) Cu-Mn
6
In the case of pure copper, its reactivity will decrease as it is cycled and especially from the first to second cycle due to agglomeration and the formation of copper aluminate. Manganese can stabilize the reactivity of copper as can be seen in Figure 4(b). CONCLUSION Biomass as a renewable source of energy has been the focus of much interest in recent years. Gasification technology is very effective in terms of conversion of biomass, environmental aspects, efficiency, etc. However, the gasification process has some challenges, such as oxygen separation. In the present study, a chemical looping process was proposed for the separation of oxygen from air using a novel oxygen carrier. To achieve this, a Cu-Mn oxygen carrier was prepared via the incipient wetness impregnation method. The performance of the oxygen carrier to separate oxygen from air was tested in a thermogravimetric analyzer. Results showed that manganese can enhance the properties of copper. By increasing the agglomeration temperature of copper, it can withstand high temperatures. Also, adding manganese decreases the possibility of forming copper aluminate during the calcination process resulting in improved reactivity. REFERENCES 1. Moghtaderi, B., Wall, T., An overview of chemical looping combustion as a potential clean coal technology, in Coal 21 conference. 2007: Crowne Plaza Hunter Valley. 2. Mattisson, T., Lyngfelt, A., Capture of CO2 using chemical-looping combustion, in First Biennial Meeting of the Scandinavian-Nordic Section of the Combustion Institute. 2001: Göteborg, Sweden. p. 163-168. 3. Gayán, P., Dueso, C., Abad, A., Adanez, J., F. de Diego, L., García-Labiano, F., NiO/Al2O3 oxygen carriers for chemical-looping combustion prepared by impregnation and deposition-precipitation methods. Fuel, 2009. 88(6): p. 1016-1023. 4. Mattisson, T., A. Järdnäs, and A. Lyngfelt, Reactivity of Some Metal Oxides Supported on Alumina with Alternating Methane and OxygenApplication for Chemical-Looping Combustion. Energy & Fuels, 2003. 17(3): p. 643-651. 5. Mattisson, T., M. Johansson, and A. Lyngfelt, The use of NiO as an oxygen carrier in chemical-looping combustion. Fuel, 2006. 85(5-6): p. 736-747. 6. Adanez, J., García-Labiano, F., F. de Diego, L., Gayán, P., Celaya, J., Abad, A., Nickel-copper oxygen carriers to reach zero CO and H-2 emissions in chemicallooping combustion. Industrial & Engineering Chemistry Research, 2006. 45(8): p. 2617-2625. 7. Hossain, M., Fluidized bed chemical-looping combustion: Development of a bimetallic oxygen carrier and kinetic modeling. 2007, The University of Western Ontario (Canada): Canada. p. 178. 8. Hossain, M.M. and H.I. De Lasa. Reduction kinetics of CoO-NiO/AI2O3 oxygen carrier for chemical-looping combustion. in 2006 AIChE Annual Meeting, November 12, 2006 - November 17, 2006. 2006. San Francisco, CA, United states: American Institute of Chemical Engineers. 9. Hossain, M.M. and H.I. de Lasa, Reactivity and stability of Co-Ni/Al2O3 oxygen carrier in multicycle CLC. AIChE Journal, 2007. 53(7): p. 1817-1829.
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10. de Diego, L. F., Garcı́a-Labiano, F., Adánez , J., Gayán, P., Abad, A., M. , Corbella, B., Marıa ́ Palacios J., Development of Cu-based oxygen carriers for chemical-looping combustion. Fuel, 2004. 83(13): p. 1749-1757. 11. Shulman, A., Cleverstam, E., Mattisson, T., Lyngfelt, A., Manganese/Iron, Manganese/Nickel, and Manganese/Silicon Oxides Used in Chemical-Looping With Oxygen Uncoupling (CLOU) for Combustion of Methane. Energy & Fuels, 2009. 23(10): p. 5269-5275. 12. Moghtaderi, B., Application of Chemical Looping Concept for Air Separation at High Temperatures. Energy & Fuels, 2009. 24(1): p. 190-198.
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FLOW FIELD IN A NOVEL SHORT RESIDENCE TIME GAS-SOLID SEPARATOR Mengxi Liu, Chan Zhou, Chunxi Lu, Zhuan Wang State Key Laboratory of Heavy Oil Processing Faculty of Chemical Engineering, China University of Petroleum, Beijing, Changping, Beijing, P.R.China, 102249 T: 0-8610-89733803; F: 0-8610-89733803; E: [email protected] ABSTRACT The gas flow field in a short residence time separator was investigated. The tangential velocity in the separator housing increases with increasing angle to the positive x axis, and decreases with increasing radial position. A swirl of opposite direction to the main current in the separator housing occurs in the gas outlet. INTRODUCTION Gas-particle separators play a key role in a gas-solid fluidized bed by effectively collecting and recycling particles. Over the years, collection efficiency and pressure drop have been considered as the most important characteristics of a gas-particle separator (1-3). However, more concerns should be made when a separator is employed in a specific industrial process in which the reaction time is elaborately controlled, such as in the fluid catalytic cracking (FCC) process. Preferably, catalytic cracking reactions only occur in the riser reactor, which usually takes 1 to 5 s (4). Further contact of the highly active cracking catalysts and the cracked hydrocarbon vapor in the separator and the dilute phase is harmful, causing unwanted side reactions and byproducts (5-6). The catalysts are separated from the vapor at the end of the riser reactor, and the post riser reactor residence time is critically restricted. This is defined as the time the cracked hydrocarbon product remains in the separators and the dilute phase. A conventional scheme consists of multiple stage separators, usually a riser termination device and a cyclone. It provides high efficiency gas-solid separation, but allows a long and uncontrolled hydrocarbon post-riser residence time of 15-45 s in the separators and the reactor dilute phase, which significantly exceeds the time required by catalytic cracking reactions (4). Work associated with reducing post-riser residence time has been rare, mainly focused on the reduction in residence time in the separators and in the reactor dilute phase. Gas-solid separators can be roughly categorized into centrifugal separators and inertial separators. A common and traditional centrifugal separator is the cyclone, which provides high separation efficiency, but allows relatively long contact time of gas and particles. Typical inertial separators are T-type ballistic riser terminators, which offer rough separation efficiency of approximately 60%. Recently, some novel short residence time separators (SRTS) have been proposed. Donsí et al (7)
presented a horizontally placed inertial riser terminator with a U-bend channel. Pictures and video show a particle-rich layer occurring within the first 60° of the separator circumference, indicating a rough separation of the gas-particle suspension. With increasing inlet gas velocity, the collection efficiency approximately remains constant (nearly 100%) for the high mass flow rate ranging from 260 to 340 kg/m2·s, but significantly decreases for the low mass flow rate less than 90 kg/m2·s. The retention time of the separation is only 4 ms. Andreus et al (4) presented a horizontal short contact time separator. In the separator, the gas-particle is centrifuged in a half-turn elbow, with particles moving downwards through the dipleg and gas exiting from a gas outlet horizontally located on the center of the separator head. Results show that the particle collection efficiency increases with increasing inlet solid loading and reaches an asymptotic value close to 95%. Variation of the back pressure exerted on the outlet of the dipleg has a slight influence on the particle collection efficiency, but a significant effect on the gas collection efficiency. A modified cyclone model was employed for prediction of the particle collection efficiency. Letzsch et al (5) and Joseph et al (6) proposed a novel separator called a rams horn, which combines centrifugal with inertial separations. The ram horn separator contains an upward flowing suspension inlet, a downward flowing solids outlet, a horizontally flowing gas outlet and a semi-circular separator area. The gas outlet extends through the separator housing and contains a horizontally disposed gas opening. Gas-solid suspension enters the SRTS vertically upwards and is then centrifuged in a half-turn elbow. Particles segregate at the concave wall of the separator housing and move vertically downward from the solid outlet, while the gas is withdrawn through gas openings on the gas outlet. The ram horn separator provides a number of significant potential advantages: small size, simple configuration, high collection efficiency, low pressure drop and short gas residence time. This work evaluates a novel ram horn-type short residence time separator with multiple gas openings uniformly disposed on the gas outlet. MODEL DESCRIPTION Mathematic Models
The flow field in a SRTS is characterized by a strongly swirling flow and anisotropic turbulence. The k-ε model, algebraic stress model (ASM) and Reynolds stress model (RSM) have been commonly employed in flow field simulation, but the RSM provides a more precise estimation on the strongly swirling flow and anisotropic turbulence (1). The governing equations of the continuous phase can be written as ∂ ∂ ∂ ∂φ (ρφ ) + (ρu j φ ) = (Γ φ ) + Sφ (1) ∂t ∂x j ∂x j ∂x j where φ is a universal variable, Гφ is the transport coefficient, Sφ is the source item. The expression of φ, Гφ and Sφ in different equations is listed in Table 1, where σk=0.82 and σε=1. The inlet boundary was set as the uniform velocity inlet, and the turbulence quantities were also uniformly imposed on the inlet by using the correlations: kin=3/2(uinI)2, and εin=Cμ0.75k1.5/l, where I = 0.05, Cμ=0.09, l =0.07 DH, and DH = 0.108 m. The boundary condition at the gas outlet was based on the fully developed flow assumption where the gradients of all variables in the flowing direction were taken to be zero. No-slip conditions were assumed at the wall.
Table 1 Governing equations of the CFD model Equations
φ
Γφ
Continuty equation
1
0
Momentum equation
ui
μ
Turbulence dissipation equation
ε
μ + μt / σε
ui' u 'j
μ+ μ /σ t k
Reynolds stress equation
Sφ
0
∂p − + ∂xi
∂(
μ∂u j ∂xi ∂x j
) + ρg i −
∂( ρui' u 'j ) ∂x j
ε(cε1Pii / 2 − cε 2 ρε ) / k
Pij + εij + φij
Geometric Model The inlet gas velocity is set as 16.12 m/s. The schematic diagram and structured mesh of the SRTS is shown in Fig. 1. It is seen that the SRTS contains a semi-circular separator housing which is connected with an inlet of upward flowing gas-solid suspension and a downward flowing solid outlet or dipleg. A 156 mm ID gas outlet is horizontally and centrally located, extending through the separator housing and paralleling the base of the separator and the inner concave surface of the separator housing. Several horizontal gas openings are disposed uniformly around the gas outlet. The width of the inlet and outlet is 68 mm and 73 mm, respectively. The radius of the concave surface and the width of the separator housing is 157.5 mm and 268 mm. Coordinate directions of x, y, z are illuminated in Fig.1.
(a)
(b) Fig.1 Schematic diagram and structured mesh of the SRTS
Model Validation In order to validate the CFD simulation method described above, the predicted results were compared with available experimental data obtained in a cold model SRTS having the same size. Fig.2 shows that data from both methods are close, indicating validation of the established CFD models. 40 35
ut(m/s)
30 25 20 15 10
experiment simulation
5 0
0.60
0.65
0.70
0.75
0.80
0.85
r/R
Fig. 2 Comparison of results from experiment and simulation RESULTS AND DISCUSSION Flow Field in Separator Housing Fig.3 gives the velocity vectors on the vertical plane of z =100 mm of SRTS. In the range of 0º< A < 180º, the gas velocity is always higher than the inlet gas velocity, indicating a greater gas flow rate in the SRTS. As shown in Fig. 1, the gas flow in the SRTS can be categorized into two types: gas flow entering the gas outlet through the gas openings and gas flow circulating in the separator housing. The latter significantly increases the gas flow rate in the separator housing, leading to a greater gas velocity than in the inlet. Moreover, the gas velocity in the bottom region, the space between the gas outlet and the base, is large, mainly arising from the decrease of the cross-sectional area.
Fig.3 Velocity vectors on the plane of z =100 mm
The tangential gas velocity governs the centrifugal force and the particle collection. Fig. 4 shows the variation of tangential gas velocity as a function of dimensionless radial position and A, the angle to the positive x axial as shown in Fig. 1. It is seen that the tangential gas velocity decreases with increasing radial position, with the maximum near the outer surface of the gas outlet and the minimum at the vicinity of the inner concave surface of the separator housing. The tangential gas velocity also varies along the circumference. As shown in Fig. 4, the tangential velocity decreases as A increases, arising from the decrease of the gas flow rate and leading to decreasing centrifugal force. 36
A = 0° A = 45° A = 90° A = 135° A = 180°
34 32 ut, m/s
30 28 26 24 22 20 0.5
0.6
0.7
0.8
r/R
0.9
1.0
Fig. 4 Variation of tangential gas velocity as a function of radial position and A 8
A = 0° A = 45° A = 90° A = 135° A = 180°
8
ur, m/s
6 4 2
4 2 0 -2 -4
A = 0° A = 45° A = 90° A = 135° A = 180°
-6
0
-8
-2 0.5
6
uz, m/s
10
-10
0.6
0.7
r/R
0.8
0.9
1.0
-12 0.5
0.6
0.7
r/R
0.8
0.9
1.0
(b) axial velocity component (a) radial gas velocity Fig. 5 Variation of radial and axial velocity components as a function of radial position and A Fig. 5 presents the variation of radial and axial velocity components as a function of radial position and A. In this work, the radial velocity in the direction opposite to the centrifugal force is defined to be positive. It is seen that the radial velocity decreases with increasing A within the first 45º of the separator circumference, while it is approximately zero when A is greater than 45º. The axial velocity does not show a regular variation, except that it is negative at the vicinity of the gas outlet (r/R=0.495), probably influenced by the gas flow in the gas outlet which also has a negative axial velocity. Flow Field in the Bottom Region As discussed before, gas not only enters the gas outlet through the gas openings, but also circulates in the separator housing. The variation of the cross-sectional area
of the bottom region governs the circulation gas flow rate and influences the pressure drop of the SRTS, while the former significantly increases the gas flow rate in the SRTS and affects the particle collection efficiency. Fig. 6 shows the tangential gas velocity in the bottom region. When gas passes through the bottom region, the tangential velocity increases first, reaches the maximum when x is close to 0, and then decreases. This is dominated by the variation of the cross-sectional area of the bottom region, with a contraction for x<0 and an enlargement for x>0 (Fig. 1). Fig. 6 shows that the tangential velocity also changes with the distance to the gas outlet. The closer to the gas outlet, the higher the tangential velocity. This is similar to the variation of the tangential velocity for 0º
ut, m/s
30 25 20
y = -92mm y = -102mm y = -112mm y = -122mm
15 10 -0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
x, m Fig. 6 Variation of the tangential velocity in the bottom region
Fig.7 (a) presents the profile of the radial velocity in the bottom region. It is seen that the radial velocity increases with increasing x, with consistently negative values for x<0, which is probably caused by inertial force, and positive velocity for x > 0. Moreover, the radial velocity increases with height. The axial velocity seems to be almost constant along the x axis, Fig.7(b). 6
y = -92mm y = -102mm y = -112mm y = -122mm
4 2 0 uz , m/s
ur, m/s
6 4 2 0 -2 -4 -6 -8 -10 -0.06
-2 -4 -6 -8
-0.04 -0.02 0.00 0.02 0.04 0.06 x, m
(a)
y = -92mm y = -102mm y = -112mm y = -122mm
-10 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 x, m
(b) Fig. 7 Variation of the radial and axial velocities in the bottom region Flow Field in the Gas Outlet Fig. 8 presents the tangential velocity variation in the gas outlet. It is seen that the tangential velocity is consistently negative, indicating a clockwise swirling flow occurring in the gas outlet. Gas openings were initially made with slanting edges.
These slanting edges lead to a sharp turn when the gas-particle suspension enters the gas openings. In this way, particles are separated by inertial force. The tangential velocities are close to each other for different A, signifying that every gas opening on the gas outlet gets approximately the same gas flow rate. Moreover, the tangential velocity is very small for r/R close to zero, mainly caused by the eddy at the center of the gas outlet. The axial gas velocity at different cross sections of the gas outlet is shown in Fig. 9. It is seen that the axial velocity near the wall of the gas outlet is greater than that at the center. There is a big difference in the axial velocity between the cross sections of z=100 mm and 200 mm, signifying a spiral motion in the gas outlet. Moreover, the evolution of the axial velocity along the radial direction at the cross-section of z=600 mm is more fluent than that of z=100 mm and 200 mm, signifying a fully developed flow near the exit of the gas outlet. 0 A = 45° A = 135° A = 225° A = 315°
ut , m/s
-5 -10 -15 -20 -25 0.0
0.2
0.4
0.6
0.8
1.0
r/R
Fig. 8 Tangential velocity in the gas outlet 5
uz, m/s
0
z = -100 mm z = -200 mm z = -600 mm
-5 -10 -15 -20
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 r/R
Fig. 9 Axial velocity at different cross-sections of gas outlet CONCLUSION The flow field in a novel ram horn-type short residence time separator (SRTS) was investigated by CFD simulation. The gas flow rate in the SRTS is significantly greater than the feed flow rate. The tangential velocity in the separator housing increases
with the increase of the angle to the positive x axis, and decreases with increasing radial position. The radial velocity is positive at the vicinity of A=0º, but approximately equals zero in the remaining regions. Influenced by the gas flow in the gas outlet, the axial velocity is negative at the vicinity of the gas outlet (r/R=0.495). In the bottom region, the tangential velocity is governed by the cross-sectional area of the region. The radial velocity increases with increasing x, being negative for x<0 and positive for x>0. Because of the slanting edges of the gas openings, the gas passing through the gas openings in the reverse direction, and a swirl in the opposite swirling direction to the main flow in the separator housing occurs in the gas outlet. NOTATION angle of the gas opening to positive x axis, º A DH hydrodynamic diameter, m turbulent kinetic energy k turbulence intensity I Sφ source item of equation (1) radial velocity, m/s ur tangential velocity, m/s ut axial velocity, m/s uz x axis as shown in Fig.1 x Greek letters universal variable, φ Гφ transport coefficient,density, kg/m3 ρ viscosity, Pa.s μ σ k, Prandtl number of turbulent kinetic energy k σε, Prandtl number of turbulent dissipation rate ε turbulent dissipation rate, m2/s3 ε REFERENCES 1. 2. 3. 4. 5. 6. 7.
G. Wan, G. Sun, X. Xue, M. Shi. Solids concentration simulation of different size particles in a cyclone separator. Powder Technol., 183 (2008), 94-104. L. Shi, D.J. Bayless. Comparison of boundary conditions for predicting the collection efficiency of cyclones. Powder Technol., 173 (2007), 29-37. F. Kaya, I. Karagoz. Numerical investigation of performance characteristics of a cyclone prolonged with a dipleg. Chem. Eng. J. 151 (2009), 39-45. R. Andreux, G. Ferschneider, M. Hémati, O. Simonin. Experimental study of a fast gas-particle separator. Chem. Eng. Res. and Des. 85(6) (2007), 808-814. W. S. Letzsch, G. Earl. Short residence time cracking apparatus and process. U.S. Patent 5, 662, 868, Sep. 2, 1997 J.L. Ross, Jr., C.S. Caty, C.J. Horecky. Apparatus for separating fluidized cracking catalysts from hydrocabon vapor. U.S. Patent 5, 259, 855. Nov. 9, 1993. G. Donsì, L.S. Osséo, M. Schenato. Experimental characterization of a short retention time gas-solid separator. Powder Technol., 85 (1995), 11-17.
CO-GASIFICATION OF BIOMASS AND COAL IN AN 8 MW DUAL FLUIDIZED BED STEAM GASIFIER Christoph Pfeifer, Isabella Aigner, Hermann Hofbauer Vienna University of Technology, Institute of Chemical Engineering Getreidemarkt 9/166, A-1060 Vienna, Austria ABSTRACT Gasification of biomass is an attractive technology for combined heat and power production. Co-gasification of biomass and coal was tested in an 8 MW dual fluidized bed steam gasifier with coal ratios up to 22% on an energy basis. Hydrogen levels in the producer gas increased with the addition of coal as well as ammonia, hydrogen sulfide and tars. Addition of coal to the system stabilized the process and improved gas quality. INTRODUCTION Coal as a substitute for oil is nothing new. During the oil crises in the 1970s and up until the middle of the 1980s, coal was already being used as a substitute for oil (1). But in these days, not only was an oil substitute required but also a way to minimize the carbon dioxide emissions, which many countries agreed to in the Kyoto protocol. Since carbon dioxide emissions from biomass are perceived as being neutral (1), and since coal is a fuel with a high availability (especially in politically stable countries) and is less expensive than oil, the gasification of mixtures of these two oil alternatives is a natural consequence. The availability of biomass fluctuates mostly with the season (2), hence the idea of gasifying miscellaneous mixtures in one plant looks economically advantageous. Generally, co-firing is the use of different fuels at the same time for combustion or gasification. For example, biomass is co-fired in existing coal plants with fuels that cannot be burned alone because of the low energy content (such as sewage sludge), and it could be burned together with natural gas to give a good performance. In industrial coal-fired power plants co-firing is often used to add a green fingerprint without any loss in efficiency and with only minor changes to plant settings. Therefore, only low percentages of the other fuels are usually used. Co-combustion of biomass with coal is a matter of intensive research for different applications and several comprehensive studies exist on this topic (3-11). Different types of reactors can be used such as fixed bed, fluidized bed, dual fluidized bed and entrained flow reactors. Co-firing can be accomplished via three different modifications, which are direct, indirect and parallel co-firing. The first two are favorable since for indirect co-firing a separate gasification unit is required. Direct co-firing uses blends of both fuels, and for parallel co-firing the fuels are fed into separate boilers to produce steam. Different kinds of fuels have been investigated, such as agricultural residues together with coal (12). Moreover, the use of energy plants such as Cynara (13) and sewage sludge (14) are options for the cofiring of biomass feedstocks together with coal. Recently, the co-gasification of biomass and coal has attracted more interest due to the environmental benefits such as reduced sulfur and nitrogen emissions when adding biomass to the fuel for systems designed for coal. Moreover, CO2 emissions can also be reduced. Several reports about co-gasification are available (15-19). However, only a small amount of literature is available for tests with different ratios of biomass and coal (2,20-21).
Within this work the suitability of coal for the dual fluidized bed gasification process was tested in a commercially operated 8 MW plant in Güssing, Austria. In order to guarantee a positive test run at the plant, test runs at the 100 kW scale were previously undertaken (22-23). These tests showed that blend ratios of 0 to 100% were possible. Thus, the goal of the tests described in this paper was to verify the findings from the pilot scale to the large scale. Due to the limitations (see the Results section) of existing plants, the maximum possible coal ratio was 22% in terms of energy. However, the general aim was to demonstrate the fuel flexibility of the dual fluidized bed process. EXPERIMENTAL Producer gas
Figure 1 shows the principles behind the dual Heat fluidized bed steam gasification process and Figure 3 shows a schematic Gasification Combustion (~ 850 °C) (~ 920 °C) of the process. The biomass Additional Biomass fuel enters a bubbling fluidized bed gasifier where drying, Circulation (bed material, thermal degasification, and char coal) Steam Air partially heterogeneous char gasification take place at bed temperatures of about Figure 1: Principles behind the dual fluidized bed gasifier 850-900 °C. The residual biomass char leaves the gasifier together with the bed material through an inclined, steam fluidized chute, towards the combustion reactor. The combustion reactor is used to heat up the bed material and is designed as a highly expanded fluidized bed (riser). Air is used as a fluidization agent in the riser. After particle separation from the flue gas in a cyclone, the hot bed material flows back to the gasifier via a loop seal. Both connections, between the loop seal and the chute, are fluidized with steam, which effectively prevents gas leakage between the gasification and combustion zones and also allows high solid throughput. The temperature difference between the combustion and the gasification reactors is determined by the energy needed for gasification as well as the circulation rate of the bed material. The system is inherently auto-stabilizing since a decrease in the gasification temperature leads to higher amounts of residual char, which results in more fuel for the combustion reactor. This, in turn, transports more energy into the gasification zone and thereby stabilizes the temperature. In practical operations, the gasification temperature can be influenced by the addition of fuel (e.g. recycled producer gas, sawdust, etc.) into the combustion reactor. The pressure in both gasification and combustion reactors is close to atmospheric conditions. (CH4, CO, H2, CO2, H2O)
Flue gas
The process yields two separate gas streams at high temperatures: a high quality producer gas and a conventional flue gas. The producer gas is generally characterized by a relatively low content of condensable higher hydrocarbons (210 g/m³ of so called tars, heavier than toluene), low N2 (< 1 vol%db), and a high H2 content of 35-40 vol%db. For practical use, Olivine, a natural mineral, has been proven to be a suitable bed material with enough resistance to attrition and moderate tar cracking activity.
A combined heat and power plant in Güssing, Austria The demonstration plant in Güssing was developed based on the results gathered from a 100 kWFuel input pilot plant at Vienna University of Technology. The fuel power of the demonstration plant is 8 MWFuel input, the electrical output is 2 MW and the thermal output 4.5 MW. A simplified flow sheet is shown in Figure 2. More than 42,000 hours of combined heat and power (CHP) operation has been achieved since it was commissioned in 2002. Wood chips from the forestry are used as fuel. The producer gas is cooled and cleaned by a two-stage cleaning system. A water cooled heat exchanger reduces the temperature from 850-900 °C to about 160180 °C. The first stage of the cleaning system is a fabric filter to separate the particles and some of the tar from the product gas. These particles are returned to the combustion zone of the gasifier. In the second stage, the gas is liberated from the tar by a scrubber. At the same time, the product gas is cooled down to about 40 °C, which is necessary for the gas engine. The spent scrubber liquid saturated with tar and condensate is vaporized and fed into the combustion zone of the gasifier for thermal disposal. If the gas engine is not in operation the entire amount of producer gas can be burned in the boiler to produce heat. The sensible heat of the engine´s flue gas is used to produce district heat, and the flue gas from the combustion zone is used for preheating air and superheating steam, as well for delivering heat to the district heating grid.
Figure 2: Simplified flow sheet of the combined heat and power plant in Güssing, Austria
Analysis The components CH4, H2, CO, and CO2, as well as O2, were measured by a Rosemount NGA 2000. The components N2, C2H4, C2H6, and C3H8 were measured via an online gas chromatograph. Minor contaminants (H2S, NH3) and tar were measured between the producer gas filter and the scrubber. The tars were sampled isokinetically with washing bottles with toluene as the absorption liquid and gravimetric as well as GC/MS tars were determined. For the ammonia measurement, gas was sampled in a similar way to the tar measurements, using washing bottles. The solvent used in this procedure was diluted sulfuric acid at a temperature of about 2 °C. Hydrogen sulfide was again sampled first using washing bottles filled with an aqueous potassium hydroxide solution at a temperature of about 2 °C. Subsequently, the H2S values were determined by potentiometry. The flue gas was measured with a Rosemount NGA 2000 (CO, CO2, O2, and NO).
Sulfure dioxide (SO2) was measured by potentiometry after sampling with washing bottles with an aqueous potassium hydroxide solution as the absorption liquid at a temperature of about 2 °C. Details about the measurement methods can be found elsewhere (22). RESULTS Throughout the whole test campaign all of the main parameters were kept constant; where this was not possible it will be mentioned in the following discussion. The bed pressure in the gasification section was adjusted to 120 mbar, the steam-to-fuel ratio to 0.67 kgsteam/kgdry fuel (which corresponded to 1.35 kgsteam/kgcarbon) and the gasification temperature was 870 °C by default. A schematic of the gasification reactor and a regime map of the gas/solid contact are given in Figure 3 and Figure 4, respectively. A comprehensive description of the gas/solid contact in a fluidized bed reactor was previously given (24-25).
Figure 3: Process schematic of the dual fluidized bed gasifier in Güssing, Austria
Figure 4: Regime map of the dual fluidized bed gasifier in Güssing, Austria (25)
It can be seen that the regime in the gasification section of the reactor is a bubbling bed, whereas the combustion section lies in the fast fluidization section. The operational area is determined by the fact that producer gas is produced in the bed over the height and in the combustion section air is introduced at three different levels. The superficial gas velocity in the riser after the secondary inlet ranges from 10 to 13 m/s, whereas superficial gas velocities of 1.8 to 3 m/s occur in the gasification zone. The minimum fluidization velocity Umf for both reactors (product gas at 850 °C and flue gas at 920 °C) varied from 0.11 to 0.13 m/s, and the terminal velocity Ut was in the range of 4.6 to 5.3 m/s.
Untreated wood chips from the forestry were used (mainly hard wood) as a standard fuel for the plant. Polish hard coal was added at ratios of 12, 18, and 22% on an energy basis. The proximate and ultimate analyses of the fuels are listed in Table 1. Each operation point was applied for at least one day in order to obtain reliable figures. It should be mentioned that due to an accumulation of coal in the system the gasification temperature slightly increased at the operation point with 22% of coal. However, at the pilot scale it was possible to operate the gasifier even with 100% of coal, whereas the load had to be reduced due to the low reaction rate of coal at these temperatures (23). Polish hard coal Water content Ash content C H N O S Volatile matter Fixed carbon LHV
[mass %]
[mass %] [mass%] [MJ/kg]
Dry basis 2.89 82.17 4.57 1.66 8.08 0.14 34.68 65.32 31.6
As received 6.11 2.76 78.43 4.36 1.58 12.27 0.13 33.1 62.35 30.1
Wood chips Dry basis 1.0 48.82 5.87 0.15 44.16 0.005 84.02 15.98 18.2
As received 27.7 0.94 46.06 5.54 0.14 47.32 0.003 79.27 15.08 17.0
Table 1: Proximate and ultimate analyses of the fuels
Figure 5 shows the main producer gas components and Figure 6 shows the higher hydrocarbons as well as the lower heating value LHV vs. the coal ratio. Hydrogen, carbon monoxide and methane slightly increased whereas carbon dioxide decreased. Surprisingly, the opposite trend was found for carbon monoxide in the pilot plant (fluidization 2010). Ethane (C2H6) and propane (C3H8), showed no significant trend, whereas ethene (C2H4) seemed to decrease slightly, which corresponds to the findings at the pilot scale (100 kWFuel input) (22). Moreover, the amount of char transported with the bed material to the combustion zone increased due to the lower reaction rate of coal in comparison to the biomass. Hence, more thermal energy bound in char was available in the combustion part of the facility and less additional fuel needed to be burned in the combustor. This is in fact the limitation for the coal ratio at the existing plant since the gas burners are cooled by the gas and the volume flow cannot be reduced under a certain limit. Generally, the dual fluidized bed gasifier can also handle 100% coal as fuel (22-23), whereas due to the lower reaction rate of coal in comparison to biomass the load had to be removed. Another option would be to increase the residence time of the char fraction in the gasification section to increase the conversion rate by changing the geometry and/or the bed material circulation rate. In summary, the process was stabilized since pressure fluctuations due to the devolatilization of the biomass were reduced. As described above, two different tar measurements were taken via GC/MS as well as gravimetrically. Three samples were taken daily between the producer gas filter and the scrubber (see Figure 2). With an increase in the coal ratio the gravimetric tar and the GC/MS tar increased significantly, as shown in Figure 7. The same trends, although less distinctive, were found at the pilot scale. Figure 8 shows the nitrogen and sulfur mass flows into the gasifier via the fuel versus the applied coal ratio. The mass flows of nitrogen released as NH3 and sulfur released as H2S with the producer gas are displayed. Linear trends were measured for both
impurities. Nearly all sulfur ended up in the producer gas as H2S, whereas only about 50% of the nitrogen inlet flow was transferred to ammonia. This correlation was previously found for the dual fluidized bed steam gasification technology (26). 5
50
30 CO CO2
20
10
CH4
10 3 C2H4 2 5 1 C3H8 C2H6
0
0 0
5
10
15
20
0
25
5
15
20
0
25
Coal ratio [energy %]
Coal ratio [energy %]
Figure 5: Main producer gas components vs. coal ratio
5
10
Lower heating value [MJ/Nm³]
4 Gas composition [vol %dry]
Gas composition [vol %dry]
LHV
H2
40
15
Figure 6: Higher hydrocarbons and lower heating value vs. coal ratio
12
Tar GC/MS
1)
Nin
released as NH3
2)
10
Mass flow [kg/h]
4 Tar content [g/Nm³ dry]
released as H2S
Tar grav
3
2
1
8 6
Nout1)
4 Sin
2
Sout2) 0
0 0
5
10
15
20
25
Coal ratio [energy %]
Figure 7: Gravimetric and GC/MS tars in the producer gas vs. coal ratio
0
5
10
15
20
25
Coal ratio [energy %]
Figure 8: Nitrogen and sulfur mass flows in/out vs. coal ratio
CONCLUSIONS The experiments showed that coal can be added to the biomass as fuel for the dual fluidized bed steam gasification process. The gas composition shifted to higher hydrogen and carbon monoxide contents, which increased the lower heating value of the gas. Thus, the H2/CO ratio can be adjusted to the needs of the applied utilization route (e.g. synthetic natural gas synthesis). The addition of up to 22% coal on an energy basis could be applied without major operational problems. During cogasification the process was stabilized due to the lower reaction rate of coal as well
as to the reduced level of devolatilization. Tar levels in the producer gas slightly increased. Ammonia and hydrogen sulfide linearly increased with the addition of coal due to the higher nitrogen and sulfur contents in coal in comparison to biomass. Generally, the dual fluidized bed system offers excellent fuel flexibility for use in advanced power cycles as well as in future liquid/gaseous fuel production systems. ACKNOWLEDGEMENTS The authors want to acknowledge the financial support of the European Commission since this work was carried out under the EU Project Flexgas (CONTRACT N° RFCR-CT-2007-00005) and under the EU Project Fecundus (CONTRACT N° RFCR-CT-2010-00009). Moreover, the authors would like to express their thanks to the team members of the ´Testing Laboratory for Combustion Systems´ at Vienna University of Technology for the measurements as well as their support in analytical concerns. NOTATION Umf Ut
minimum fluidization velocity, m/s terminal velocity, m/s
REFERENCES 1. Prins, M.J., Ptasinski, K.J., Janssen, F.J.J.G. From Coal to biomass gasification: Comparison of thermodynamic efficiency. Energy, 2007, 1248-1259 2. André, R.N., Pinto, F., Franco, C., Dias, M., Gulyurtlu, M., Matos, M.A.A., Cabrita, I. Fluidised bed co-gasification of coal and olive oil industry wastes. Fuel, 2005, 84(12-13):1635-1644 3. Baxter, L. Biomass-coal co-combustion: opportunity for affordable renewable energy. Fuel, 2005, 84(10), 1295-1302 4. Dai, J., Sokhansanj, S., Grace, J.R., Bi, X., Lim, C.J., Melin, S. Overview and some issues related to co-firing biomass and coal. Canadian Journal of Chemical Engineering, 2008, 86(3), 367-386 5. Gajewski, W., Kijo-Kleczkowska, A. Co-combustion of coal and biomass in the fluidized bed. Archives of Thermodynamics, 2007, 28(4), 63-68 6. Huang, Y., D. McIlveen-Wright, S., Rezvania, Y.D., Wang, N.H., Williams, B.C. Biomass Co-Firing in a Pressurized Fluidized Bed Combustion (PFBC) Combined Cycle Power Plant: A Techno-Environmental Assessment Based on Computational Simulations, Fuel Processing Technology 87, 2006, 927-934 7. Hupa, M. Interaction of fuels in co-firing in FBC. Fuel, 2005, 84(10), 1312-1319 8. Leckner, B. Co-firing of wastes and biofuels with coal. Proceedings of the International Symposium on Coal Combustion, 5th, Nanjing, China, Nov. 23-26, 2003 9. Nevalainen, H., Jegoroff, M., Saastamoinen, J., Tourunen, A., Jantti, T., Kettunen, A., Johnsson, F., Niklasson, F. Firing of Coal and Biomass and Their Mixtures in 50 kW and 12 MW Circulating Fluidized Beds-Phenomenon Study and Comparison of Scales. Fuel, 2007, 86, 2043-2051 10. Sami, M., Annamalai, K., Wooldridge, M. Co-firing of coal and biomass fuel blends. Progress in Energy and Combustion Science, 2001, 27(2), 171-214 11. Zulfiqar, M., Moghtaderi, B., Wall, T.F. Flow Properties of Biomass and Coal Blends. Fuel Process. Technol., 2006, 87, 281-288
12. Ghani, W.A.W.A.K., Alias, A.B., Savory, R.M., Cliffe, K.R. Co-combustion of agricultural residues with coal in a fluidised bed combustor. Waste Management, 2009, 29(2), 767-773 13. Aho, M., Gil, A., Taipale, R., Vainikka, P., Vesala. H., A pilot-scale reside deposit study of co-ring cynara with two coals in a fluidised bed. Fuel, 2008, 87(1), 58-69 14. Leckner, B., Amand, L. E., Lüucke, K., Werther, J.. Gaseous emissions from cocombustion of sewage sludge and coal/wood in a fluidized bed. Fuel, 2004, 83(4-5), 477-486 15. Beenackers, A.A.C.M., Maniatis, K. Gasification technologies for heat and power from biomass. Biomass Gasification and Pyrolysis: State of the Art and Future Prospects, [Conference], Stuttgart, Apr. 9-11, (1997), 24-52 16. Chmielniak, T., Sciazko, M. Co-Gasification of Biomass and Coal for Methanol Synthesis. Applied Energy, 2003, 74, 393-403 17. Hernandez, J.J., Aranda-Almansa, G., Serrano, C. Co-Gasification of Biomass Wastes and Coal-Coke Blends in an Entrained Flow Gasifier: An Experimental Study. Energy & Fuels, 2009, 24(4), 2479-2488 18. Kurkela, E. Recent results and plans concerning co-gasification of biomass and coal - an overview. Biomass for Energy and the Environment, Proceedings of the European Bioenergy Conference, 9th, Copenhagen, June 24-27, (1996), 164-169 19. McLendon, T.R., Lui, A.P., Pineault, R.L., Beer, S.K., Richardson, S. W. HighPressure Co-Gasification of Coal and Biomass in a Fluidized Bed. Biomass and Bioenergy, 2004, 26, 377-388 20. Kumabe, K., Hanaoka, T., Fujimoto, S., Minowa, T., Sakanishi, K. Cogasification of woody biomass and coal with air and steam. Fuel, 2007, 86, 684689 21. Li, K., Zhang, R., Bi, J. Experimental study on syngas production by cogasification of coal and biomass in a fluidized bed. International Journal of Hydrogen Energy, 2010, 35(7), 2722-2726 22. Aigner, I., Pfeifer, C., Hofbauer, H. Co-Gasification of Coal and Wood in a Dual Fluidized Bed Gasifier, submitted to Fuel, April 2010 23. Aigner, I., Pfeifer, C., Hofbauer, H. Co-Gasification of Coal and Wood in a Dual Fluidized Bed Gasifier: Variation of Fluidization Conditions and Load Ratio. Proceedings of the Fluidization XIII Conference, May 16-21, 2010, Gyeong-ju, Korea, ISBN: 978-0-918902-57-3, pp. 527-534 24. Grace J.R. Contacting Modes and Behaviour Classification of Gas-Solid and Other Two-Phase Suspensions. The Canadian Journal of Chemical Engineering, 1986, 353-363 25. Kunii D., Levenspiel, O. Circulating fluidized-bed reactors, Chemical Engineering Science, 1997, 52(15), 2471-2482 26. Siefert, I.G. Stickstoff-, Chlor- und Schwefelbilanzen über das Biomasse - Block - Heiz - Kraftwerk Güssing. Vienna: PhD thesis, Vienna University of Technology, 2004
GAS TRACER STUDY IN A NON MECHANICAL L-VALVE Mohammad-Mahdi Yazdanpanah1, Ali Hoteit1, Ann Forret1*,Thierry Gauthier1, Arnaud Delebarre2 1 IFP Énergies nouvelles, Rond-point échangeur de Solaize- 69360 Solaize, France *T: + 33 (0)4 37 70 20 00; F: +33 (0)4 37 70 20 08; E: [email protected] 2 ESSTIN, Université Henri Poincaré, Nancy, France ABSTRACT A gas tracer (Helium) was used to study solid and gas flow in an L-valve. Effect of solid flow rate and pressure drop variation on the quantity of gas in the vertical section of the L-valve is presented. Results were then used to calculate the voidage of the moving solid bed in the L-valve vertical section. INTRODUCTION The control of solid flow rate is an important element in many different solid circulating fluidized bed processes. The solid circulation control could be best achieved through use of the mechanical valves as in some mature catalytic process like FCC (1). However, use of the mechanical valves is more complex and limited in high temperature processes (>800°C) due to the material selection and resistance problem. Chemical Looping Combustion (CLC) is an example of this kind. CLC is a promising novel combustion technology involving inherent separation of the CO2 (2). An oxygen carrier, mostly a metal oxide, transports oxygen from the air reactor to the fuel reactor while circulating between them. The solid transport rate controls the extent of reaction conversion in each reactor while controlling the energy balance in the system. The control of particles circulation rate is hence one of the most important factors in the CLC system. The non-mechanical valves are a category of solid flow control devices employing no mechanically moving part (3). Accordingly, they can be easily adapted to high temperature conditions such as CLC. An external gas injection is used to control solid flow rate in these valves. L-valve is one of the possible choices for CLC process among different existing non-mechanical valves. It is simple in design, easy to operate, effective in solid flow control and requires minimum maintenance (4;5). Various studies on the L-valve behavior have been published to date (3;5-9). These studies mostly illustrate the effect of operating and geometrical parameters on the solid flow rate actuated in the L-valve and the corresponding pressure drop (3;6-8). The flow in the horizontal section of the L-valve is an important subject in these studies. Three flow patterns have been distinguished in horizontal flow based on the aeration rate including: fast moving stream flow, dune-ripple flow, and dune-slug flow (5;7;9). The solid flow in vertical section of the L-valve (standpipe) is in downwards cocurrent or counter current solid and gas flow. The particles flow in the dense
phase mode due to the gravity force. The pressure drop in this section is developed through the relative movement of gas and solid (3). As solids are not fluidized, change in the relative solid–gas velocity can change the pressure drop across the solid bed. Ergun equation (10) modified for slip velocities is used to calculate the pressure drop in the standpipe of the L-valve (3;7;11). Gas-solid flow in a standpipe can be largely divided into non-fluidized and fluidized flows. The non-fluidized bed flows are divided into the packed bed and the transitional packed bed flows by slip velocity (12). The slip velocity (vsl) is defined as the interstitial gas velocity (vg) minus the solid interstitial velocity (vs) with downwards solid velocity as positive direction. Packed bed flow occurs when the interstitial slip velocity is positive and gas pressure is higher at the top of the standpipe than at the bottom. The bed porosity is believed to remain constant regardless of vsl value in this case (12). The transitional packed bed flow occurs when vsl is negative, and the gas pressure at the bottom of the standpipe is higher than that at the top (13). Different correlations have been developed to estimate the voidage of the solid bed in a standpipes. Some of the existing correlations in the literature are listed in Table 1. Table 1: Correlations of solid bed voidage in a standpipe/ downcomer ( sp). Correlation Condition Reference (ε mf − ε s ) linear relation between Knowlton T. M. ε SP = ε s + v sl v mf gas velocity and voidage and Hirsan I. (3) ε SP = 1 −
1−εs R
R = 3 . 1( d p ρ s )
0 .5
for
U Ut
R = 23.94( d p ρ s ) 0.3
ε sp = 0 . 6953
Re s =
ε sp D
0.4
d pρg
µg
= 2.25
Cd C ds
±
Uo U < 0 .0168 Ut Ut
Qsp A
Cd Cds
for − 0 . 054
+
− 0.6
U U > 0.0168 o Ut Ut
−0.6
Cd Ar = Cds 18 Re s + 2.7 Re s 1.687 ,
ε sp νs 1 − ε sp
−0.2
Cd Ar = Cds 18 Re s + 2.7 Re s 1.687 ,
Sand particles dp (503, 232, 90) µm 3 s(2818,2730,2365)kg/m Geldart group A Alumina/Hydrated alumina/ FCC catalyst dp (34.11,46.66, 54) µm s (2770.4, 2037.4, 1760.9) kg/m3 Sand particles dp (95) µm, s (2260) kg/m3
Yagi (14)
Li et al. (15)
Chan C. W. et al. (16)
EXPERIMENTAL SETUP IFP EN and TOTAL are collaborating on an R&D project on CLC. A novel CLC design based on the interconnected bubbling fluidized beds was developed at IFP EN to build a 10 kW th pilot plant. The main concern was to insure an independent solid flow control, and to achieve minimum gas leakages between the air reactor and the fuel reactor. A cold prototype was constructed with identical geometrical dimensions as the hot 10 kW th CLC prototype. The system is schematically illustrated in Figure 1. Detailed explanation of the system and function of each section can be find elsewhere (17). Helium was used to trace the gas flow in different L-valve sections. Helium flow rate
was controlled by means of a rotameters at fixed pressures fed from a bottle with 99.99% Helium purity. A ProtecTM Helium Sniffer Leak Detector was used as the Helium measurement device. It is capable of detecting He sucked in the device though a sniffer line by means of a mass spectrometer. The Helium injection and detection points are illustrated in Figure 1. Silica sand was used as the solid in the current study. The properties of particles are listed in Table 2. Table 2: Properties of sand particles used in this study. Property Value 3 2650 s (kg/m ) ds (µm) 321 d50 (µm) 334 Umf (m/s) 0.068 0.514 mf (free settled) 0.46 s 0.40 st (tapped) (sphericity) 0.76
He detection
He
Injection
Section I
Section II
DR1 HR1 Dlv Hlv LH HL
0.1 m 1.0 m 0.017 m 1.27m 0.14 m 2.15 m
Figure 1: The scheme of the CLC cold flow prototype. Different Helium injection and detection positions are indicated in the figure.
GAS TRACING IN THE L-VALVE The gas and solid flow in two main sections of L-valve were experimentally investigated. The first section is the vertical part above the external gas injection point. Second section is the vertical section below external gas injection point together with elbow and horizontal pipe of the L-valve, (Figure 1). Tracer gas (Helium) was injected into the fluidization gas of the reactor with low concentration of 0.33 vol. %. Low inlet concentration of tracer gas was used to ensure that the tracer gas does not modify significantly properties of the fluidization gas (air). Helium was then detected in the cyclone gas exit (Figure 1). Pressure drop in the standpipe ( Pv) is a variable parameter that adjusts to match the pressure balance across the solid circulation loop. Change of Pv will in turn change the quantity of gas passing through this section. Series of tests were conducted where the gas flow rate in the vertical section of the L-valve was measured for different pressure drops across the element. A unique feature of the current CLC installation is the possibility to adjust Pv through variation of the pressure drop across the reactor ( PR) by change of the solid height in the reactor. This permits control of pressure drop across the standpipe independently from the L-valve external aeration. The measurements were conducted during the steady state solid circulation for
different bed height in the reactor with constant external aeration rate in the L-valve (Qlv). The superficial velocity of the gas and pressure drop in the vertical section of the L-valve are illustrated in Figure 2. Increase in PR causes the Pv to reduce from +60 mbar to –30 mbar. The gas flow changes based on the pressure drop across the standpipe. Two zones can be distinguished for gas flow in the standpipe (Uv). In the case of high pressure drops in the standpipe, gas flows upwards in the standpipe and no Helium was detected in the gas exit of the cyclone (Cyc1 in Figure 1). As the pressure drop reduces below a critical value, gas starts flowing downwards in the standpipe and progressively increases as the pressure drop reduces further in the standpipe. The variation of the interstitial velocities in the standpipe is illustrated in Figure 3 for the same tests series above. In the case of negative pressure drop, the slip velocity is positive and the gas velocity is higher than the solid velocity. As the pressure drop in the reactor increases, slip velocity reduces progressively to zero (the solid and the gas have the same velocity). At higher reactor pressure drops, the slip velocity becomes negative. In this case, the particles flow faster than the gas. Finally, the relative velocity increases more than the solid velocity in value, which means the gas flows upward in the standpipe in the opposite direction of the solids flow. 80
0.03
Vsl Vs Vg
0.06
40
0.02
20 0
0.01
V (m/s)
Pv (mbar)
60
0.08
Zone II
Uv (m/s)
Zone I
0.04 0.02 0.00 -0.02
-20 -40 0
20
40 60 DPR (mbar)
80
0.00 100
Figure 2: The effect of the reactor pressure drop on the pressure drop and the gas flow in the vertical section of the L-valve with Ulv = 0.2 m/s.
-0.04 -40
-20
0
20
40
60
DPv (mbar)
Figure 3: The change of the interstitial velocities in the standpipe due to the change of the standpipe pressure drop with Ulv = 0.2 m/s and Gs = 35 - 64 kg/m².s (equivalent to variation of Vs).
The variation of the gas flow through the vertical section of the L-valve changes the total gas flow around the bend of the L-valve. The solid flow rate in the L-valve is controlled by the gas flow rate through the bend of the L-valve (QH) (3). Therefore, the solid flow actuated in the L-valve will be affected due to the variation of the gas flow in the L-valve(UV). This phenomenon is shown in Figure 3 where increase of Pv from -30 to 33 mbar reduces solid velocity from 0.042 to 0.026 m/s equivalent to solid flux variation of 64 to 43 kg/m².s. The internal and the external L-valve air flow rates versus the solid flux in the Lvalve are presented in Figure 4 for two test series. In the first series, the solid flow rate was controlled by the variation of the external L-valve aeration (Ulv). As expected, increase in Ulv increases solid flux. Moreover, the gas flow rate in the standpipe increases as solids entrain more gases downwards in the standpipe. Gas
flow rate in the horizontal section of the L-valve which is sum of Ulv and Uv increases in the same order. 0.10
U (m/s)
U (m/s)
HR=cte: UH Ulv UH' The external L-valve aeration (Ulv'), 0.4 Qlv=cte: Ulv' Uv Uv' 0.08 was kept constant in the second 0.3 series of experiments presented in 0.06 the Figure 4. The standpipe gas 0.2 0.04 flow rate was then varied by adjusting the pressure drop across 0.1 0.02 the standpipe through changes in the reactor pressure drop. 0.0 0.00 Accordingly, for a constant external 0 50 100 150 200 Gs (kg/m².s) aeration, horizontal gas flow rate in the L-valve increases as Uv' Figure 4: The variation of the solid flux versus increases. This demonstrate that the measured gas flow velocity in the L-valve for a constant external aeration of for two experimental series: the change in the L-valve, the solid flow rate can external L-valve aeration with the fixed solid be varied through variation of the height (♦, , ) and the variation of the Uv imposed by change of pressure pressure drop in the standpipe with fixed drop across the standpipe. external aeration ( , , ). Therefore, the solid flow rate actuated in the L-valve is not only a function of the external aeration but also pressure drop across the L-valve. Knowlton (3;4;18) has explained this phenomenon being due to the fact that the solid flow rate in the L-valve is controlled by the quantity of the gas flowing around the bed of the L-valve (QH). This theory is well illustrated in the Figure 4 where the QH curve lays on the same line for both of the experimental series. Standpipe gas flow variation is steeper in case of pressure drop change compared to the external aeration change for unit change of the solid flux. This is due to the fact that Qv must compensate required QH variation for a unit change of solid flux while external aeration is constant.
These results illustrate that a desired solid flow rate in the L-valve can be achieved by different configurations of the external aeration and the gas flow in the standpipe. This is of particular interest in case of CLC process where the gas flow out of the reactor is considered as a leakage and is desired to be minimized. Therefore, a proper control of pressure drop across the system together with appropriate external aeration helps to optimize this gas leakage out of the reactor. VOIDAGE OF THE MOVING SOLID BED The experimentally measured pressure drop, gas flow rate and solid flow rate values were used to calculate the average voidage of the moving solid bed ( sp) based on the Ergun equation. As discussed before, the current installation permits to control the pressure drop across the standpipe independently from Qlv. Using this feature, the voidage of the solid bed was measured for two non-fluidized solid flows regimes (Figure 5). The packed bed solid flow regime was reached when the pressure drop across the standpipe was negative, corresponding to positive slip velocities (see Figure 3). This corresponds to Zone II illustrated in Figure 5. The slip velocity is positive in this regime, as gas downward flow is faster than solids velocity (see Figure 3). The resulting moving bed voidage in this case is constant and corresponds roughly to the tapped bed voidage. In other words, gas flow pushes to
(%)
pack moving solids tighter together in this regime. Transition packed bed solid flow attained by imposing positive pressure 60% drop in the standpipe. Slip velocities was Zone II Zone I (Vsl <0) negative in this region (see Figure 3). In (Vsl >0) mf this case, reactor pressure drop was 50% kept constant and solid flow and pressure drop variation was adjusted s 40% through changes of Qlv. Resulting variation of average voidage is illustrated vmf in Figure 5, Zone I. Slip velocity is 30% negative in this region, meaning that -0.15 -0.10 -0.05 0.00 0.05 solid particles have higher velocity than Vsl (m/s) gas. As slip velocity increases in value, Figure 5: The experimental results of voidage of solid bed expends from the variation of the average voidage of tapped bed voidage toward bed voidage moving solid bed in the standpipe of the at the minimum fluidization condition. L-valve. Yagi
80% 60% (%)
The correlations listed in the Table 1 were then compared with experimental results in Figure 6. Linear correlation proposed by Knowlton and Hirsan (3) results in closest prediction. Correlation of Li et al. (15) and Chan et al. (16) give a good estimate of the voidage variation trend, they however over/under estimates the voidage values. The significant difference observed in some cases is mostly due to different operating conditions in this test and developed correlations.
Li et al. Knowlton
40% 20% 0% 0.00
Chan et al.
0.04
-Vsl (m/s)
0.08
0.12
Figure 6: The experimental results of variation of the voidage of the moving solid bed in the standpipe of the L-valve compared with the literature L-VALVE LIMITING OPERATION correlations. The L-valve operation is reported to be limited due to different limiting conditions including: fluidization of vertical arm (3), limit of solid flow rate into the L-valve (8), or unstable operation of the inclined section of the L-valve (19) (in case of hybrid standpipe as in the current configuration). However, these limits depend on the pressure drop across the standpipe of the L-valve which is a dependent variable parameter. Accordingly, the upper operation limit of the L-valve can be adjusted depending upon the operating conditions. A test was carried out to investigate continuous operation of the L-valve in the limiting condition. External gas flow rate was set to a high value of 0.416 m/s. The resulting pressure drop variation and gas flow in the standpipe is illustrated in Figure 7. An oscillatory behavior of the pressure drop and gas the flow was observed in the system. Moreover, height of the moving bed in the standpipe was also behaving in an oscillatory mode. Once the L-valve was opened, solid height started to gradually decrease while pressure drop was increasing across the standpipe. The solid height finally got to a minimum level where it started to raise again. The solid height then increased till it reached the top of the L-valve where the solid height started to decrease again. This unsteady phenomenon repeated itself in an oscillatory mode
as illustrated in Figure 7.
80
PR PH
Pv Uv
PL
0.08
Uv (m/s)
DP (mbar)
When a very high gas flow rate is 60 0.06 injected into the L-valve, a very high solid flow rate is actuated in 40 0.04 the L-valve exit (W s) higher than solid flow into the L-valve (W in). 20 0.02 The solid height in the standpipe (Hs) then reduces, resulting in 0 0 increase of the pressure drop per 0 100 200 300 400 500 unit height of the standpipe t (s) ( P/Hs). Consequently, the downward gas flow rate in the Figure 7: Oscillatory operation of L-valve in standpipe (Uv) reduces gradually, limiting condition of Ulv = 0.416 m/s. get to zero and finally gas flows upwards in the standpipe. Accordingly, UH reduces, resulting in reduction of solid flow rate actuated in the L-valve. The solid inventory in the reactor then increases. Consequently, pressure drop in the reactor increases, resulting in reduction of the total pressure drop in the standpipe. As the solid flow rate reduces, W in exceeds W out and standpipe starts to fill. As the solid height in the standpipe increases, P/Hs reduces. Therefore, more gas passes downwards, and the solid flow rate increases gradually. This continues until solid height gets its maximum once standpipe is filled with the solid and again W out exceeds Win. Once more, the same cycle starts to repeat again. At the same time, the second L-valve (lv2 in Figure 1) was operating normally with an average solid flow rate equal to that of the first L-valve (lv1) which was operating in an unstable manner. The stable operation of the second L-valve was due to proper adjustment of the external aeration and the pressure drop along the standpipe. The external air flow velocity was set to 0.381 m/s (compared to 0.416 m/s in the lv1) and the pressure drop in the reactor was slightly higher in the reactor R2 compared to R1. This illustrates importance of proper selection of the operating conditions in high solid flow rate operations to avoid possible instable instabilities. This is of particular interest in the CLC process where a high solid circulation rate is desired between two reactors to transfer the required oxygen for combustion. CONCLUSION The results of experimental study of an L-valve in a chemical looping combustion cold prototype was presented in this work. Helium was used as gas tracer to experimentally measure quantity of gas flow in the different sections of the L-valve. Gas flow rate in the vertical section of the L-valve increased by increasing solid flow rate or by decreasing pressure drop across the vertical section of the L-valve. Experimental results were then used to calculate the average voidage of the moving solid bed in the vertical arm of the L-valve. Voidage variation in two regimes of packed bed flow and transitional packed bed flow were presented. Finally, oscillatory operation of L-valve in the limiting condition was presented in terms of gas and solid flow and pressure drop. NOTATION Gs: solid flux, kg/m².s. Hs: Height of solid bed in the standpipe, m.
Vg: interstitial gas flow rate, m/s. Vs: interstitial solid velocity, m/s.
Qlv: The external L-valve aeration, Nm3/h. PR: pressure drop in the reactor, Ulv: external aeration flow velocity calculated mbar. based on the L-valve area, m/s. Pv: pressure drop in he vertical arm UH: gas flow velocity in the horizontal section of the L-valve, mbar. of the L-valve, m/s. s: voidage of free settled solid bed. Uv: gas flow velocity in the vertical section of sp: voidage of solid bed in the the L-valve, m/s. standpipe. Vsl: gas-solid slip velocity, m/s. st: voidage of tapped solid bed. REFERENCES 1. Gauthier T., Bayle J., Leroy P. FCC: Fluidization Phenomena and Technologies. Oil & Gas Science and Technology - Rev IFP 2000;55(2):187. 2. Lyngfelt A., Leckner Bo, Mattisson T. A Fuidized-bed combustion process with inherent CO2 separation; application of chemical-looping combustion. Chemical Engineering Science 2001;56:3101. 3. Solids Flow Control using a Nonmechanical L-valve. Ninth Synthetic Pipeline Gas Symposium; Chicago, illinois. 1997. 4. Knowlton T.M. Feeding and Discharge of Solids Using Nonmechanical Valves. Institute of Gas Technology . 1988. Chicago, Illionois, Institute of Gas Technology. 5. Yang Wen-Ching, Knowlton Ted M. L-valve equations. Powder Technology 1993;77:49. 6. Chan CW, Seville J, Fan X, Baeyens J. Particle motion in L-valve as observed by positron emission particle tracking. Powder Technology 2009;193(2):137. 7. Geldart D., Jones P. The behaviour of L-valve with granular solids. Powder Technology 1991;67:163. 8. Smolders K, Baeyens J. The Operation of L-valve to Control Standpipe Flow. Advaced Powder technology 1995;6(03):163. 9. Yang TY, Leu LP. Multi-resolution analysis of wavelet transform on pressure fluctuations in an L-valve. International Journal of Multiphase Flow 2008;34(6):567. 10. Ergun S. Fluid flow through packed columns. Chemical Engineering Process 1952;48(2):89. 11. Smolders K, Baeyens J. The Operation of L-valve to Control Standpipe Flow. Advaced Powder technology 1995;6(03):163. 12. Kojabashian C. Properties of dense-phase fluidized solids in vertical downflow Massachusetts Inst. of Technology; 1958. 13. Zhang J.Y., Rudolph V. Transitional Packed Bed Flow in Standpipes. Can J of Chem Eng , 1991;69:1242. 14. Yagi S. Chemical Machinery, Japon 1952;16:307. 15. Li Y., Lu Y., Wang F., Han K., Mi W., Chen X. et al. Behavior of gas-solid flow in the downcomer of a circulating fluidized bed with a V-valve. Powder Technology 1997;91:11. 16. Chan C.W., Seville J., Fan X., Baeyens J. Solid particle motion in a standpipe as observed by Positron Emission Particle Tracking. Powder Technology 2009;194(12):58. 17. Yazdanpanah M.M., Hoteit A., Forret A., Delebarre A., Gauthier T. Experimental Investigations on a Novel Chemical Looping Combustion Configuration. OGST Revue d'IFP Energies nouvelles 2010. 18. Wet and Dry Limestone Feeding Using an L-valve. Sixth International Conference on Fluidized Bed Combustion; Atlanta, Georgia. 1980. 19. Knowlton T.M. Standpipes and Nonmechanical Valves. In: Yang W-C, editor. Handbook of Fluidization and Fluid-Particle Systems. NEW YORK: MARCEL DEKKER, INC; 2003. p. 576.
3D CFD SIMULATION OF COMBUSTION IN A 150 MWe CIRCULATING FLUIDIZED BED BOILER
Nan Zhang, Bona Lu, Wei Wang, Jinghai Li State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
ABSTRACT Eulerian granular multiphase model with meso-scale modeling of drag coefficient and mass transfer coefficient, based on the energy minimization multi-scale (EMMS) model, was presented to simulate a 150 MWe CFB boiler. The three-dimensional (3D), full-loop, time-dependent simulation results were presented in terms of the profiles of pressure, solids volume fraction and solids vertical velocity, the distributions of carbon and oxygen, as well as the temperature. The EMMS-based sub-grid modeling allows using coarse grid with proven accuracy, and hence it is suitable for simulation of such large-scale industrial reactors.
INTRODUCTION The gas-solid flow, heat /mass transfer and chemical reactions in Circulating Fluidized Bed (CFB) reactors are inherently coupled with spatio-temporal multi-scale structures, and featured with non-linear non-equilibrium behavior (1). For such a multi-phase complex system, it is inadequate to reproduce the real physical process by refining the grid in the conventional Two-Fluid Model (TFM), thus calling for establishing meso-scale models (2). The present study firstly presents a 3D full-loop simulation for a 150 MWe CFB boiler, showing the powerful capability of the sub-grid EMMS model in predicting the hydrodynamics in a CFB. Then, a reduced EMMS/mass model is incorporated into the mass conservation equations for combustion species, and a 3D combustion simulation is realized for the furnace part.
MODEL DESCRIPTION AND SIMULATION SETTING Governing equations The Eulerian granular model in Fluent®6.3.26 was used to study the flow behavior in the full-loop of the boiler, in which the stress of the solid phase was described with the kinetic theory of granular flow; the drag coefficient correlation was corrected with consideration of particle clusters, which was detailed in (3) and would not be stressed here. By neglecting of energy due to viscous dissipation, compression or expansion and interfacial flow, the internal energy balances and the conservation equations of chemical species in gas and solid phase were used to study the combustion in the furnace. As a first step to combustion simulation, only the main reaction of carbon react with oxygen to carbon dioxide was considered here. The volatile combustion and moisture release was considered in the heat balance through a UDF which filled the gap between the low heating value of the coal and the reaction heat as carbon was combusted completely, and the value was added averagely on the solids phase in the lower section (h <= 6m) of the furnace. The volatile and moisture was neglected in the mass balance equation, as they are less than 2% compared with the total air input, detailed in (4). Geometry and mesh The 150 MWe CFB boiler was designed by Harbin Boiler Co. Ltd. and installed in Guangdong, China. The main cross section of the furnace is a rectangle of 15.32 × 7.22 m2; the chamber height is 36.5 m; detailed information can be found in (3). The geometry and surface mesh of the whole loop of the solids material was shown in Fig. 1. As can be seen from the figure, most of the boiler was meshed with hexahedron, others meshed with polyhedron, all with size scale of 0.1 m. For convenience, the primary air and the loop-seal aeration were assumed in plug flow from their bottoms. To balance the solid inventory in the boiler, the solids exiting from the cyclone outlets, which was about 6%(mass fraction) of the circulating solids during the simulation, were returned via the coal-feed inlets. The origin point is set at the center of the primary inlet at the bottom of the furnace; the x-axis is along the front-to-back wall direction (width direction); the y-axis is along the side-to-side wall direction (depth direction), and the z-axis is against the gravity direction. Simulation settings
For full-loop hydrodynamic simulation, the boiler was considered operated at the design temperature of 917℃ and atmospheric pressure, thus the physical properties of the gas was considered as a const, while for combustion simulation in the furnace, the mixing laws of the gas mixture were summarized in Table 1. The boundary and initial conditions of the hydrodynamic simulation and the combustion simulation are listed in Table 2 and Table 3 separately.
Fig. 1. Geometry and surface mesh of the whole loop of the 150 MWe CFB boiler.
Table 1. Mixing laws of the gas mixture Property
Unit
Density
3
(kg/m )
Mixing law
Equation
Incompressible ideal gas
ρ = pop RT ∑ (Yi MWi )
law Specific
Heat
(J/kg·k)
Mixing law
Capacity Thermal
c p = ∑ Yi c p ,i i
(w/m·k)
mass weighted mixing law
Conductivity Viscosity
i
k = ∑ Yi ki i
(kg/m·s)
mass weighted mixing law
μ = ∑ Yi μi i
Mass Diffusion
(m2/s)
Dilute Approximation
Di ,m
Table 2. Boundary and initial conditions of hydrodynamic simulation in full-loop of the boiler Boundary and Initial Conditions
Gas Phase Flow rate
Solid Phase
Area
Velocity
2
(kg/s)
(m )
(m/s)
Primary air inlet
94.16
50.88
6.32
0
Secondary air Inlet
53.21
0.92
198.01
0
Slag-cooler inlet
8.00
0.75
36.32
0
Loop-seal inlet
2.32
8.02
0.99
0
Coal-feed inlet
12.48
1.16
36.70
UDF
Initial solid packing height
2.5 m
Cyclone Outlet
101325 Pa
Wall
No-slip
Partial slip
Table 3. Boundary and initial conditions of combustion simulation in the furnace Boundary and Initial Conditions
Gas Phase
Solid Phase
Temperature
Velocity
Temperature
Velocity
(K)
(m/s)
(K)
(m/s)
Primary air inlet
500.15
2.662
1190
0
Secondary air Inlet
500.15
83.397
1190
0
Slag-cooler inlet
293.15
9.018
1190
0
Loop-seal inlet
293.15
0.245
1190
0
Coal feed inlet
293.15
15.458
1190
0.013+returned
Initial solid packing height
1.5 m
Cyclone Outlet Wall
101325 Pa No-slip
Partial slip
HYDRODYNAMIC FLOW IN THE FULL-LOOP Fig. 2(a) shows the simulated pressure balance in the boiler: pressure gradient is large at the bottom and comparatively small at the top in the furnace, while the largest gradient occurs at the return legs. Fig. 2(b) shows that the general trends of the simulated data were comparable with experimental data. Fig. 3 and Fig. 4 show profiles of solids volume fraction and solids vertical velocity at different heights, respectively. Fig. 3(a) shows the so-called core-annulus structure, i.e. a denser solids concentration near the wall than in the center. Fig. 3(b) shows that solids concentration profiles along the depth direction are flatter than those along the width direction. Fig. 4(a) shows falling clusters near the walls while rising particles in the center. Fig. 4(b) shows two maximum rising velocities not far from the walls, which may be affected by the secondary air inlets at the side walls.
(a)
(b)
Fig. 2. Pressure distribution: (a): in the whole loop (b): compared with experimental data in the furnace (simulation data were taken from the center line of the furnace).
Fig. 3. Solids volume fraction at different heights in the boiler.
Fig. 4. Solids vertical velocity at different heights in the boiler.
COMBUSTION IN THE FURNACE PART Fig. 5 shows the simulated temperature in the furnace, which is low at the bottom because of the injected cold air and is high at the top because of combustion energy released. Fig. 6 and Fig. 7 show snapshots of simulated carbon concentration and oxygen concentration with several slices in different directions, respectively. Fig. 6(a) and Fig. 6 (b) Show that carbon concentration is large at the bottom and small at the top, Fig. 6(b) and Fig. 6 (c) show that carbon concentration reaches local maximum
near the solids return ports and causes non-uniform in the bottom of the furnace, and this local non-uniform caused by local ports will decrease along the height because of combustion and dispersion. Fig. 7(a) shows that oxygen concentration is large at the bottom and is small at the top because of combustion. Fig. 7(b) shows that the injected secondary air causes local maximum of oxygen concentration, while the maximum oxygen is not at the center line of the furnace but there are two maximum near the center line, which is caused by the fact that the injection does not reach the center of the furnace, this phenomenon was also reported by Myöhänen et al. (5). Fig. 7(c) shows that the injected secondary air leads to non-uniform oxygen concentration at the bottom and this non-uniformity decreases along the height but still exists at the top of the furnace.
Fig. 5. Temperature profile in the furnace.
(a)
(b)
(c)
Fig. 6. Simulated carbon concentration distributions with vertical slices (a): side-to-side slice ，(b): front-to-back slices and (c): horizontal slices.
(a)
(b)
(c)
Fig. 7. Simulated oxygen concentration distributions with vertical slices (a): side-to-side slice ，(b): front-to-back slices and (c): horizontal slices.
CONCLUSIONS Simulation results show the capability of the current model, with emphasis on the EMMS-corrected drag coefficient, in predicting the two-phase flow behavior. The reduced multi-scale mass transfer model and the drag coefficient correction based on the EMMS model were coupled to realize combustion simulation in the furnace. It is an extension to our experience on virtual experimentation to investigate the combustion within an industrial reactor.
ACKNOWLEDGMENT The authors are grateful to Professor Junfu Lu of Tsinghua University for providing the blueprint of the boiler. The financial supports from NSFC under Grant No. 20821092, MOST under Nos. 2007AA050302-03 and 2008BAF33B01, and CAS under No. KGCX2-YW-222 are also gratefully acknowledged.
NOTATION cp
specific heat capacity, J/kg·k
D
mass diffusion coefficient, m2/s
k
thermal conductivity, w/m·k
p
pressure, Pa
R
gas constant, 8.314 J / mol·K
MW
molecular weight, kg/mol
T
temperature, K or ℃
V
velocity, m/s
x
coordinate, m
y
coordinate, m
Y
mass fraction, dimensionless
z
coordinate, m
Greek letters
ε μ ρ
volume fraction, dimensionless viscosity, kg/(m·s) density, kg/m3
Subscripts s
solid phase
i
species
REFERENCES 1. J. Li, M. Kwauk, Particle-fluid two-phase flow: The energy-minimization multi-scale method. Beijing: Metallurgical Industry Press, 1994. 2. B. Lu, W. Wang, J. Li, Searching for a mesh-independent sub-grid model for CFD simulation of gas-solid riser flows. Chemical Engineering Science 2009, 64 (15), 3437-3447. 3. N. Zhang, B. Lu, W. Wang, J. Li, 3D CFD simulation of hydrodynamics of a 150 MWe circulating fluidized bed boiler. Chemical Engineering Journal 2010, 162 (2), 821-828. 4. N. Zhang, B. Lu, W. Wang, J. Li, 3D CFD simulation of combustion in a 150 MWe circulating fluidized bed boiler. To be submitted. 5. K. Myöhänen, T. Hyppänen, M. Loschkin, Converting Measurement Data to Process Knowledge by Using Three-Dimensional CFB Furnace Model, in: K. Cen (Eds.), Circulating Fluidized Bed Technology VIII - Proceedings of the Eighth International Conference on Circulating Fluidized Beds, International Academic Publishers, World Publishing Corp., Hangzhou, 2005, pp. 306-312.
HYDRODYNAMICS OF A LOOP-SEAL OPERATED IN A CIRCULATING FLUIDIZED BED: INFLUENCE OF THE OPERATING CONDITIONS ON GAS AND SOLID FLOW PATTERNS Roberto Solimene1, Riccardo Chirone1, Piero Bareschino3, Piero Salatino2 1 Istituto di Ricerche sulla Combustione – CNR 2 Dipartimento di Ingegneria Chimica - Università degli studi di Napoli Federico II Piazzale Tecchio, 80 80125 Napoli Italy 3 Dipartimento di Ingegneria – Università degli Studi del Sannio Piazza Roma, 21 82100 Benevento Italy T: +390817682237; F: +390815936936; E: [email protected] ABSTRACT Hydrodynamic features of a loop-seal operated as solids re-injection device in a labscale cold CFB apparatus are studied. Gas flow patterns are characterized by means of gas tracing experiments with continuous injection of CO2 in the loop-seal chambers. Solids flow patterns are characterized by impulsive injection of dyecoloured particles into the supply chamber, followed by particle tracking. INTRODUCTION The dual fluidized bed technology has recently risen to renewed interest for process applications whenever distinct reactive environments (e.g. oxidative/reducing, temperature and/or pressure looping) need to be established while sharing a solid stream which acts as reactant or thermal carrier. Exploitation of the dual fluidized bed concept is currently being explored in fields like: i) CO2 capture in coal-fired generating stations and cement kilns (1)); ii) chemical-looping combustion (2-3); iii) biomass pyrolysis and gasification (4). The dual loop system presents, however, some criticalities that are mostly related to the effective control of solids recirculation between the beds and to the establishment of leak-tight operation of the beds with respect to the gaseous streams. These criticalities involve both design and operational variables of the plant which determine the hydrodynamic behaviour of different plant components (5-6). Several types of non-mechanical valves (L-valve, J-valve, seal pot and loop-seal) are commonly used in CFBs and DIFBs to control the mass flow rate of solids or to seal gaseous streams. Despite empirical or semiempirical criteria for correct loop-seal design have been laid (7-15), thorough characterization of loop-seal hydrodynamics, especially under large solid throughput conditions, is still lacking. In the present study the hydrodynamics of a loop-seal is investigated. The seal is operated as solids re-injection device between a 40 mm ID downcomer and a 102 mm ID riser in a lab-scale cold CFB apparatus. The experimental procedure includes time-resolved measurements of gas pressure at several locations along the CFB loop and in the loop-seal and of solids mass flux along the riser. The gas flow patterns are characterized according to tracing methods based on continuous CO2 injection at the bottom of the loop-seal chambers. Solids flow patterns are characterized by impulsive injection of dye-
coloured particles into the supply chamber, followed by particle tracking by means of a video camera interfaced to a computer. The characterization of the gas and solids flow patterns is carried out under a broad range of operating conditions by varying: i) the gas superficial velocity in the riser; ii) the fluidization velocity in the loop seal; iii) the throughput of circulating bed solids in the loop seal. Results are presented and discussed with an emphasis on the establishment of gas cross-flow between the loop seal chambers and its relevance to effective solids recirculation and gas “leakage”. EXPERIMENTAL Apparatus The lab-scale cold model CFB (Figure 1) consisted of a riser, a cyclone, a solids reservoir, a standpipe and a loop seal. The Plexiglas riser, 0.102 m ID and 5.6 m high, was equipped with a gas distributor consisting of several stainless steel nets layered one on the other up to 2 mm thickness, characterized by low-pressure drop. An high-efficiency cyclone, 0.09 m and 0.32 m high, was installed at riser outlet. The solids return line consisted of the solids reservoir, 180 mm ID 0.40 m high, a 40 mm standpipe and a loop seal. The supply and recycle chambers of the loop seal have a square cross section (50mm side), height of 300 and 250 mm, respectively, and separate wind-boxes. The gap at the bottom of the two chambers is about 50 mm. The CFB loop was equipped with 21 pressure taps at different locations. The overall solids mass flux was determined by closing for a pre-set time a butterfly valve located along the standpipe and measuring the solids mass accumulated upstream the valve. Diagnostics High-precision piezo-resistive gas pressure transducers have been adopted to measure the gas pressure along the CFB loop. An on-line gas sampling carried out by means of a probe connected to a CO2 gas analyzer (ABB advance optima Uras 14) was optimized to investigate the gas flow in the loop seal. All signals coming from the instruments were logged on a data acquisition unit consisting of a PC equipped with a data acquisition board. A high-speed, highly light-sensitive megapixel CMOS camera interfaced to a computer and controlled with an image processing software has been adopted for particle tracking in the loop seal chambers. Materials and Operating Conditions Table 1 reports the properties of quartz adopted for the tests. Fluidizing gas was technical air at room temperature. Gaseous CO2 from cylinders and black glass (500 μm) beads were used as gas and solids tracer, respectively. Solids inventory was 3.5 kg. Gas superficial velocity in the riser Ug,r ranged between 2.1 and 4.4 m/s. The minimum value of Ug,r was always larger than the terminal velocity (Ut) of the granular solid. Air was injected at the bottom of the loop-seal so that gas superficial velocity in loop-seal chambers (Ug,ls) results to be 1, 1.5 Fig. 1: and 2 times Umf. Solids mass circulation fluxes in the riser Gs ranged Experimental apparatus.
between 2 and 11 kg/(s m2). Voidage (ε) along the riser was calculated by processing the pressure drops, ΔP, measured at successive pressure taps located at distance Δz according to the relationship ΔP=ρsg(1-ε)Δz, where g is the acceleration due to gravity.
Table 1 - Properties of the granular solids Material Quartz 253 Sauter mean diameter (dp), μm 100-400 Size range, μm 2300 Particle density (ρs), kg/m3 Incipient fluidization velocity (Umf)(1), m/s 0.047 Terminal velocity (Ut)(1), m/s 1.77 (1) calculated
Procedure Bed Hydrodynamics: Steady state operation of the CFB loop has been investigated at a pre-set fluidization velocity in the riser and in the loop seal. The steady state condition has been recognized by continuous monitoring of the gas pressure along the loop and by step-wise measurement of the mass flow rate. Once the steady state condition was reached, the gas pressure signal was acquired for about 5 minutes. The experiments have been characterized in terms of fluidization velocity both in the riser and in the loop seal. Gas and Solids Tracing in the Loop Seal: Some experiments have been carried out to investigate the gas and solids flow patterns in the loop seal at Ug,r= 2.85m/s. Once the steady state operation of the CFB loop was reached at a pre-set Ug,ls, a batch of solids tracer (50g) was injected along the standpipe. The tracing particles fell down in the loop seal and their motion has been recorded by the video camera at 125 fps for 30s. The experiments has been repeated for three times and they have characterized in terms of Ug,ls. An ad-hoc procedure of image analysis has been developed to follow the trajectories of the tracing particles. In the same operating conditions, gas tracing has been carried out by continuous feeding of a gas fluidizing stream with a pre-set CO2 concentration (in the range of 10-20%v) in the supply and in the recycle chamber, alternatively. The CO2 concentration has been measured at the bottom and the top of beds of the loop seal. The obtained data have been analyzed to achieve the gas flow distribution in the loop seal. RESULTS AND DISCUSSION Hydrodynamics of the Riser and of the Loop Seal: Figure 2 reports relative pressures measured at different pressure taps located around the CFB loop during runs operated at Ug,r=2.85m/s. The gas superficial velocity in the loop seal Ug,ls was varied in the range Umf≤Ug,ls≤2Umf. Analysis of the pressure loop indicates that the pressure gradient at the bottom of the riser (i.e. at levels above the distributor up to 0.1m) increases as Ug,ls is increased. The pressure gradient in the recycle chamber of the loop seal remains constant at a value consistent with the bulk density of the bed material under bubbling fluidization conditions at any Ug,ls. The pressure gradient in the supply chamber increases from a minimum at Ug,ls=Umf to approach values corresponding to the bulk density of a fully fluidized bubbling bed as Ug,ls is increased. Figure 3 reports the solids mass flux in the riser as well as the pressure drop and the height of the bed of solids, respectively, in the recycle and in the supply chamber of the loop-seal. Data points correspond to the three experimental conditions reported in Figure 2. As Ug,ls is increased the following features can be recognized: i) the solid mass flux remains substantially constant; ii) the pressure drop in the recycle chamber slightly decreases; ii) the pressure drop in the supply chamber undergoes a pronounced increase whereas the bed height decreases as
Ug,ls increases from Umf to 1.5Umf, both remaining nearly constant at values larger than Umf. Altogether, the fluidization velocity in the loop seal exerts a moderate influence on riser hydrodynamics, whereas loop seal hydrodynamics is affected to a larger extent. As Ug,ls was increased from Umf to 1.5Umf, the bed material present in the supply chamber becomes fully fluidized, consistently with the change of pressure drop and gradient reported in Figures 2 and 3.
Ug,ls=Umf; GS=4.75 kg/(s m2) 2
Ug,ls=2Umf; GS=4.15 kg/(s m ) Ug,ls=1.5Umf; GS=3.47 kg/(s m2) 500
400
Height, cm
When the supply chamber is fluidized, a fraction of the solids present in this chamber is transferred to the riser, and the loading of solids at the bottom of the
600
300
200
10
A
Ug,r = 2.85 m/s
GS, kg/(s m2)
8
6
100
4
2 0
0 30 28
0
10
20
30
40
50
60
Pressure, mbar
B
Fig. 2: Pressure loop for different Ug,ls/Umf ratio;Ug,r = 2.85 m/s.
26
22
H, cm
ΔPls, mbar
riser is increased. This feature is reflected by the change of bed height in the supply 20 chamber and of the pressure gradient at the bottom of the riser. Figure 4 reports the 18 Recycle chamber solids mass flux in the riser and the bulk 16 Supply chamber density (ρ) of the bed at the bottom of the 14 55 riser as a function of the fluidization velocity C Recycle chamber (Ug,r) for different values of the ratio Ug,ls/Umf 50 Supply chamber in the loop seal. The following features can 45 be recognized: i) Gs increases almost 40 linearly with Ug,r; ii) Gs is barely influenced 35 by Ug,ls/Umf, except for Ug,ls=Umf and for high values of Ug,r; iii) ρ is also barely influenced 30 by Ug,ls/Umf except for Ug,ls=Umf, when the 25 values of ρ recorded are smaller regardless 20 of Ug,r; iv) ρ remains substantially constant 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 U /U at a value of about 140kg/m3 for Ug,r>3m/s, Fig. 3: Solids mass flux (A) pressure drops (B) it increases as the fluidization velocity in the and bed height (C) in the loop-seal chambers riser is decreased at Ug,r<3m/s; v) for values 24
g,ls
mf
as a function of Ug,ls/Umf ratio. Ug,r=2.85m/s.
of Ug,r<3m/s, as the terminal velocity of bed particles is approached, a dense bed is established at the bottom of the riser with values of ρ in the order of 800-1000kg/m3. It can concluded that loop seal operation in the CFB loop becomes fully effective when the supply chamber, and not only the recycle chamber, becomes fully fluidized. This condition requires the gas superficial velocity in the loop seal to exceed Umf.
12
GS, kg/(s m2)
10
Ug,ls/Umf = 1.0 Ug,ls/Umf = 1.5 Ug,ls/Umf = 2.0
A
8 6 4 2
0 1200 U
/U
= 1.0
ρ, kg/m
3
g,ls mf 1000 B Ug,ls/Umf = 1.5 Analysis of Solids Flow Patterns in Ug,ls/Umf = 2.0 the Loop Seal: Loop seal 800 hydrodynamics has been also 600 investigated by inspection of the gas and solids flow patterns in the loop 400 seal chambers as Ug,ls has been 200 changed. Figure 5 shows the trajectory and the time-resolved components (VX, 0 VY) of the instantaneous velocity of 1.5 2.0 2.5 3.0 3.5 4.0 4.5 three tracer particles. Trajectories were Ug, m/s reconstructed by frame-by-frame analysis of videorecordings taken Fig. 4: Solids mass flux (A) and bottom bulk density (B) as a function of Ug,r for different Ug,ls/Umf ratio. during system operation at Ug,ls equal to Umf and 1.5Umf, with a riser gas superficial velocity set at Ug,r=2.85m/s. Images acquired for Ug,ls=2Umf could not be analyzed as the stochastic motion of the particles typical of a bubbling fluidized bed prevented to follow a single particle along the test. Trajectories are mapped in Figure 5 assuming as origin of the reference axes the bottom-left corner of the supply chamber, taking x positive along the direction pointing toward the riser and y positive along the direction opposed to gravity. The tracer particles were selected as they were at the top of the supply chamber: one at the center and the other two near the external wall and the separation wall (vertical solid line) with the recycle chamber, respectively. The tracer particles were then followed frame by frame during their motion toward and beyond the gap between the loop seal chambers. At Ug,ls=Umf, the following features can be recognized: i) the streamlines followed by the tracer particles in the supply chamber converge toward the top of the gap between the chambers; ii) a significant part of the supply chamber bottom appears stagnant (below the dashed line); iii) a limited part of the gap between the two loop seal chambers is characterized by flow of bed solids; iv) the velocity component along the x axis (VX) is substantially similar for all the tracer particles and constant with time (about 1.5 mm/s), but it increases significantly as they approach the gap between the two chambers; v) the velocity component along the y axis (VY) is similar for all the tracer particles and decreases as they approach the gap between the two chambers; vi) the tracer particle initially close to the supply chamber axis presents the largest value of VY; vii) VY is negative and positive in the supply and recycle chamber, respectively. Increasing Ug,ls (Ug,ls=1.5Umf), it can be recognized that: i) convergence of the trajectories followed by the tracer particles in the supply chamber takes place at a later stage close to the
Y, a.u.
Y, a.u.
upper part of the gap Ug,ls=1Umf Ug,ls=1.5Umf 900 between the two 900 left left center chambers; ii) the stagnant 800 center right 800 right region at supply chamber bottom is less extended 700 700 than that observed at Ug,ls=Umf; iii) the fractional 600 600 area of the gap between 500 the two chambers where 500 solids flow takes place to 400 a significant extent, is 400 expanded; iv) VX is initially 300 negligible for all the 300 tracing particles except 200 200 when they approach the gap; v) VY is similar for all 100 100 the tracer particles and constant with time (about 0 0 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 12.5 mm/s) in the supply X, a.u. X, a.u. chamber; it becomes left V left V center V center V positive and increases right V right V with time in the recycle chamber; vi) the tracer particle initially located close to the external wall impinges the stagnant left V left V region and stops there. In center V center V the range Umf
80
120
X
60
X
80
VX, mm/s
X
VX, mm/s
X
100
X
40
20
X
60 40 20
0
-20
0
-40
-20
-60
30
200
Y
20
Y
150
Y
Y
100
Y
VY, mm/s
VY, mm/s
10
0
-10
Y
50
0
-20
-50
-30
-100
-150
-40
0
2
4
6
0
1
2
3
4
5
6
Analysis of gas flow patterns in the loop seal from gas tracing experiments: Figure 6 shows a schematic sketch of the gas flow pattern in the loop seal during gas tracing experiments. Two separate gas streams of equal flow rates are fed to the bottom of the supply (QSCin) and recycle (QRCin) chambers of the loop seal, characterized by different CO2 concentrations xSCin and xRCin, respectively. Experiments have been carried out at a pre-set inlet concentration both in the supply and recycle chamber and in the same operating conditions of bed solids tracing (Ug,r=2.85m/s, Ug,ls=Umf, 1.5Umf, 2Umf). The gas cross-flow between the two chambers has been represented by two streams: one (QRCbypass) directed from the recycle chamber toward the supply chamber, the other (QSCbypass) in the opposite direction. It has been assumed that the by-pass stream CO2 concentration is that of the inlet gas flow rate at the corresponding chamber. From the known values of
QSCin, QRCin, xSCin, xRCin and the measured values of the CO2 concentration (xSCout, xRCout) at the outlet, it is possible to calculate all the other gas flow rates (QRCbypass, QSCbypass, QSCout and QRCout) on the basis of global material balance and on a balance on CO2. Figure 7 shows xSCin, xRCin, xSCout and xRCout as a function of Ug,ls during gas tracing experiments in both loop seal chambers. It can be recognized that: i) xSCout is nearby to xSCin both when a pre-set CO2 concentration is fed to the supply chamber (supply chamber gas tracing) and when only air is used as fluidizing gas (recycle chamber gas tracing); ii) xRCout shows departures from xRCin: it is smaller than the inlet pre-set CO2 concentration when gas tracing occurs in the recycle chamber and it is larger than air CO2 concentration when gas tracer is fed to the supply chamber. On the basis of the data reported and on the material balances, the calculated QRCbypass is negligible (in order of 0.01m3/h), whereas QSCbypass does not significantly depend on Ug,ls and it is about 0.2m3/h. Taking into account that the minimum fluidization gas flow rate is about 0.45m3/h for a single chamber, QSCbypass represents a significant fraction of the effective gas superficial velocity in the supply chamber. The fluidization condition in the supply chamber can be approximately based on the following simplified relationship:
U geff,ls ≥ U mf −
Gs Ar ε mf
(1)
Als ρ s (1 − ε mf )
out
out
QSC, xSC
out
out
QRC, xRC
P17
P20
bypass
QRC P18
in
P19
bypass
QSC
in
QSC, xSC
in
in
QRC, xRC
Fig. 6: Schematic sketch of the gas flow pattern in the loop seal.
where Ug,lseff, ρs, εmf, Ar, Als are the effective gas superficial velocity, bed solids density, bed voidage at incipient fluidization, cross sections of the riser and of the supply chambers, respectively. Equation (1) expresses the net gas superficial velocity established in the emulsion phase of the bed in the supply chamber when the downflow of bed solids is taken into account. This value could be negative for low values of Umf or large values of Gs. It is worth noting that a negative value of Ug,lseff is not per se undesirable from the standpoint of an effective recirculation of solids, but it is extremely important when “leakage” of gas from the supply chamber to the recycle chamber must be 0.25 required by process constraint. inlet CO concentration air CO concentration This is the case, for instance, supply chamber gas tracing: x 0.20 when the loop seal is part of a supply chamber gas tracing: x recycle chamber gas tracing: x dual interconnected fluidized recycle chamber gas tracing : x bed-system with distinct reactive 0.15 environments. The relationship (1) is satisfied in all the 0.10 experimental conditions tested, except for those carried out at 0.05 Ug,ls=Umf. Under this condition, the supply chamber of the loop 0.00 seal is largely unfluidized, 1.0 1.2 1.4 1.6 1.8 2.0 consistently with the pressure U/Umf drop data and with the analysis of Fig. 7: Inlet and outlet CO2 concentrations in the loop seal bed solids tracing. 2
CO2 molar fraction, -
2
out RC out SC out RC out SC
chambers as a function of Ug,ls during gas tracing experiments.
CONCLUSIONS The hydrodynamics of a loop-seal operated as solids re-injection device in a labscale cold CFB apparatus was characterized by a combination of methods, including solids and gas tracing. For a given riser gas superficial velocity, the loop seal fluidization velocity exerts a limited influence on riser hydrodynamics, but significantly modifies the gas and solids flow patterns in the loop-seal. As the loop seal is kept at gas superficial velocities just beyond incipient fluidization, the supply chamber is only partly fluidized and an extended stagnant zone is observed at its bottom. As fluidization becomes more vigorous, stagnant zone extension is reduced and more solids are transferred to the riser. Analysis of solids and gas tracing tests highlights the role of solids downflow in the supply chamber and of gas cross-flow from the supply to the recycle chamber on the fluidization patterns. An approximate criterion is given for the onset of gas “leakage” between the two loop seal sections, relevant to loop seal applications in dual interconnected fluidized beds. NOTATION Alp Ar dp g Gs P bypass QRC in QRC out QRC bypass QSC in QSC out QSC Ug,ls eff Ug,ls [m/s] Ug,r Umf Ut
2
loop seal cross section [m ] 2 riser cross section [m ] particle size [μm] 2 acceleration due to gravity [m/s ] 2 solids mass flux [kg/m s] gas pressure [Pa] 3 recycle chamber by-pass flow rate [m /s] 3 recycle chamber inlet flow rate [m /s] 3 recycle chamber outlet flow rate [m /s] 3 supply chamber by-pass flow rate [m /s] 3 supply chamber inlet flow rate [m /s] 3 supply chamber outlet flow rate [m /s] loop seal gas superficial velocity [m/s] effective loop seal gas superficial velocity riser gas superficial velocity [m/s] minimum fluidization velocity [m/s] particle terminal velocity [m/s]
horizontal velocity of tracing particle [m/s] VX vertical velocity of tracing particle [m/s] VY in recycle chamber inlet CO2 molar fraction xRC [gmol/gmol] out recycle chamber outlet CO2 molar fraction xRC [gmol/gmol] in supply chamber inlet CO2 molar fraction xSC [gmol/gmol] out supply chamber outlet CO2 molar fraction xSC [gmol/gmol] X horizontal coordinate of tracing particle [a.u.] Y vertical coordinate of tracing particle, [a.u.] z riser axial coordinate [m] ε gas voidage [-] minimum fluidization gas voidage [-] εmf 3 ρ bulk density at riser bottom [kg/m ] 3 particle density [kg/m ] ρs
REFERENCES 1. Hughes, R. W., Lu, D. Y., Anthony, E. J., Macchi, A. (2005) Fuel Process. Technol., 86, 1523. 2. Kronberger, B., Lyngfelt, A., Löffler, G., Hofbauer, H. (2005) Ind. Eng. Chem. Res., 44, 546. 3. Hossain, M. M., de Lasa, H. I. (2008). Chem. Eng. Sci., 63, 4433. 4. Pfeifer, C., Proll, T., Punchner, B., Hofbauer, H. (2007) Fluidisation XII (Bi, X., Berruti, F., Pugsley, T., (Eds.)), Engineering Foundation New York, pp. 889. 5. Kaiser, S., Löffler, G., Bosch, K., Hofbauer, H. (2003). Chem. Eng. Sci., 58, 4215. 6. Bai, D., Issangya, A. S., Zhu, J.-X., Grace, J. R. (1997) Ind. Eng. Chem. Res., 36, 3898. 7. Cheng, L., Basu, P., Cen, K. (1998). Trans. Inst. Chem. Eng., 76, 761. 8. Cheng, L., Basu, P., (2000). Powder Tech., 103, 203. 9. Basu, P., Cheng, L., (2000). Trans. Inst. Chem. Eng., 78, 991. 10. Botsio, E., Basu, P. (2005) Can. J. Chem. Eng., 83, 554. 11. Kim, S. W., Namkung, W., Kim, S. D. (1999). Korean J. Chem. Eng., 16(1), 82. 12. Kim, S. W., Namkung, W., Kim, S. D. (1999). Chem. Eng. Technol., 24, 843. 13. Kim, S. W., Kim, S. D., Lee, D. H. (2002). Ind. Eng. Chem. Res., 41, 4949. 14. Kim, S. W., Kim, S. D. (2002). Powder Tech., 124, 76. 15. Basu, P., Buttler, J. (2009). Applied Energy, 86, 1723.
SULPHUR UPTAKE BY LIMESTONE-BASED SORBENT PARTICLES IN CFBC: THE INFLUENCE OF ATTRITION/FRAGMENTATION ON SORBENT INVENTORY AND PARTICLE SIZE DISTRIBUTION Fabio Montagnaro#, Piero Salatino*, Fabrizio Scala§, Massimo Urciuolo§ #
Dipartimento di Chimica, Università degli Studi di Napoli Federico II, Complesso Universitario del Monte di Sant’Angelo, 80126 Naples (Italy) *
Dipartimento di Ingegneria Chimica, Università degli Studi di Napoli Federico II, § Istituto di Ricerche sulla Combustione, Consiglio Nazionale delle Ricerche, Piazzale Vincenzo Tecchio 80, 80125 Naples (Italy)
ABSTRACT This paper presents a population balance model aiming at the prediction of sorbent inventory and particle size distribution establishing at steady state in the bed of an air-blown CFBC fuelled with a sulphur-bearing solid fuel. The core of the model is represented by population balance equations on sorbent particles which embody terms expressing the extent/rate of sorbent attrition/fragmentation. The effect of the progress of sulphation on attrition and fragmentation is taken into account by selection of appropriate constitutive equations. Model results are presented and discussed with the aim of clarifying the influence of particle attrition/fragmentation on sorbent inventory and particle size distribution, partitioning of sorbent between fly and bottom ash, sulphur capture efficiency. A sensitivity analysis is carried out with reference to relevant operational parameters of the combustor. INTRODUCTION Substantial changes in the particle size distribution of limestone-based SO2 sorbents can be brought about by particle attrition/fragmentation in fluidized bed combustors. The mutual interference between chemical reactions (calcination and dehydration, sulphation) and attrition/fragmentation of limestone has been largely disclosed (116). It has been shown that primary fragmentation occurs immediately after the injection of sorbent particles in the hot bed, as a consequence of thermal stresses and internal overpressures due to release of gas (CO2 following calcination of raw sorbent, steam following dehydration of spent/reactivated sorbent). Primary fragmentation takes place in the dense bed/splashing region of FBC reactors, resulting in the generation of coarse and fine fragments. Further breakage occurs as a consequence of mechanical stresses experienced by the particles during their lifetime in the reactor. Attrition by abrasion is related to the occurrence of surface wear as the emulsion phase of the FB is sheared by the passage of bubbles, and generates fine fragments that are quickly elutriated. Secondary fragmentation
gives rise to coarse and fine fragments, and may onset as a result of high-velocity impacts of sorbent particles against targets (bed material, reactor walls/internals); these impacts are likely experienced by the particles in the jetting region of FBC. The exit region of the riser and the cyclone are other potential locations of impact damage of sorbent particles in a CFBC reactor. The main features of attrition/fragmentation mechanisms are summarized in Table 1 and a conceptual framework for analyzing the effects of particle sulphation/attrition/fragmentation on the fate of sorbents is represented in Figure 1a, where the simplification of lumping sorbent particles into coarse and fine and the population of sorbent particles (of different age and sulphation degree) into lime and sulphated limestone components has been adopted. In this work a population balance model is presented, which aims at predicting the sorbent inventory and particle size distribution, the partitioning of sorbent between fly and bottom ash and the sulphur capture efficiency during steady operation of an air-blown CFBC. The influence of attrition/fragmentation on the main output parameters is assessed. Table 1 Main features of attrition/fragmentation mechanisms. Mechanism Primary fragmentation (decrepitation) Attrition by abrasion (surface wear) Secondary fragmentation (impact fragmentation)
Caused by… thermal/ mechanical stresses rubbing of bed solids collisions against targets
Location: dense bed/ splashing zone dense bed
Generation of… coarse/fine fragments fine fragments coarse/fine fragments
jetting region + riser exit/cyclone
(b)
ei
cyclone
&i m
standpipe EHE
riser
gi
bottom bed Wi bi
Fig. 1. (a) Particle sulphation/attrition/fragmentation network for the assessment of the fate of sorbents; (b) Schematic representation of CFBC loop with the indication of relevant sorbent fluxes. MODEL A schematic representation of the CFBC loop with the indication of relevant sorbent fluxes is reported in Figure 1b. Population balance equations can be written on the sorbent present at steady state in the combustor. Calcination and primary fragmentation are assumed to occur almost instantaneously so that the inventory of the raw limestone can be neglected. Each particle in the population is characterized by two variables: the particle size (d) and the sulphation degree (XS). A simplified
version of the population balance is hereby developed based on the assumption that the sorbent can be lumped into two classes as far as XS is concerned: the unconverted lime (L) and the sulphated limestone (SL). Accordingly, the population balance in the d-XS domain simplifies yielding two 1-D equations in the d-domain. Upon discretization of the domain and referring to the i-th particle size bin, equations concerning the L and SL components read, respectively (cf. Notation): & P0 (di )∆d + m
kCSO2 Wi,L MWL =
∆d
n
W j,L
j =i +1
dj
+ ∑ k a,LU
[(1 − X S,i )MWL + X S,iMWSL ] +
Ri,SL Wi,SL ∆d
Ri +1,L Wi +1,L
Pa,L (di )∆d =
Ri+1,SL Wi +1,SL ∆d
Ri,L Wi,L ∆d
+ ei,L + ai,L + bi,L + kCSO 2 Wi,L
n
Wj,SL
j=i +1
dj
+ ∑ k a,SLU
(1)
Pa,SL (di )∆d =
(2)
+ ei,SL + ai,SL + bi,SL
In Eq. (1), at LHS three inlet terms can be found, namely i) a term related to the feeding; ii) a term related to continuous particle shrinkage, where: R = k aU / 3 (3) and iii) a term under summation, which takes into account particles formerly belonging to coarser particle size bins that fall into the i-th size bin due to attrition/fragmentation. At RHS, five outlet terms can be found, namely: i) a term related to continuous particle shrinkage; ii) a term related to sorbent loss by elutriation at the cyclone: ei,L = [1 − η(di )]gi,L (4) where gi,L is computed as a function of Wi,L according to (17) and the cyclone collection efficiency is expressed as (cf. (18)): η(di ) = 1/[1 + (dcut / di )c ] for di<120 µm, otherwise η=1 (5) iii) a term related to attrition/fragmentation, where: ai,L = k a,LUWi,L / di (6)
iv) a term related to the bed drain where, according to the hypothesis of perfect mixing of bed material in the bottom bed, it is: bi,L / b = W i,L / W (7) v) a term related to the transfer from the L to the SL phase, driven by the sulphation kinetics. Eq. (2) can be written taking into account that, for particles sulphated according to a core-shell pattern, it is: shell 3 X S,i = X S [di − (di − 2δ)3 ] / di3 (8) shell for di>2δ, otherwise X S,i = X S . In particular, it has been discussed (19,20) that sorbent sulphation in FBC takes place in two subsequent stages (Figure 2): stage I, associated with the initial build-up of a sulphate-rich particle shell, and stage II, associated with attrition-enhanced sulphation promoted by continuous attrition of sulphated material. Accordingly, the SO2 capture efficiency can be calculated as: n
cap inlet & /(MWLrCa / S )] ηdes = FSO / FSO = [kCSO 2 ∑ ( Wi,L X S,i ) / MWL ] /[m 2 2 i =1
(9)
where the contribution to SO2 capture given by stage II is assumed negligible, based on preliminary model computations. Finally, the SO2 concentration is calculated as: C SO 2 = Cinlet (1 − ηdes ) (10) SO 2
Fig. 2. Outline of sulphation regimes. Stage I: build-up of the sulphated layer of thickness δ up to a maximum local sulphation degree in the sulphated shell; Stage II: attrition-enhanced sulphation regime.
RESULTS AND DISCUSSION Evaluation of Model Parameters The model was solved in MATLABTM environment, assuming typical values for the input parameters as reported in Table 2. In particular, the total bed inventory in the riser per unit cross-sectional area has been set at 800 kg m-2, a typical figure based on admissible pressure drops across the riser in practical operation of CFBC. Based on realistic sorbent feeding rates and fuel ash content, it is further assumed that the total bed inventory consists of W=250 kg m-2 of sorbent (either L or SL) and 550 kg m-2 of fuel ash. Furthermore, P0 was expressed as a log-normal distribution extending to the particle size range 5-2000 µm, with a mean particle size of 110 µm, about 28% of particles finer than 100 µm and about 4% of particles coarser than 1000 µm. The size distribution of attrited fines Pa was expressed as an exponential function for both L and SL: Pa,L (di ) = Pa,SL (di ) = (1/ d) exp( −di / d) (11)
with d =35 µm. Attrition/fragmentation was considered to be active for particles coarser than 50 µm, while for finer particles its contribution was supposed to be negligible (ka,L=ka,SL=0). Moreover, the values of ka,L and ka,SL take into account both attrition by surface wear (3) and impact fragmentation (10). This latter contribution was calculated by estimating the fractional mass of fragments formed upon multiple particle impacts in the jetting region of a typical full-scale CFBC. The entrainment rate of sorbent particles into the jets, required to calculate the frequency and kinetic energy of impacts, has been estimated according to Massimilla (21). Finally, attrition in the cyclone has been neglected.
Table 2 Main input parameters of the model. & 0.013 kg m-2 s-1 m ka,L 5×10-9 ka,SL 10-9
c k shell XS
δ rCa/S
2.5 m s-1 250 kg m-2 10 µm
U W dcut
4 10-6 ppm-1 s-1 0.55 50 µm 2.5
Cinlet SO
2
2000 ppm
Model Results The influence of attrition on the combustor’s performance has been assessed by comparing results obtained from computations performed neglecting attrition (i.e., ka=0 for both L and SL) with those in which attrition was considered. Results of computations for the no-attrition case are reported in Figure 3 and Table 3. Figure 3a shows the probability density functions (PDF) of particle sizes, whereas the distribution is reported as cumulative undersize in Figure 3b. A noticeable shift toward coarser particles can be observed for material reporting to bottom ashes as compared with the lime feeding. The PDF peak is located at 120 µm, particles finer than 50 µm are hardly found and only 7% of particles mass is finer than 100 µm. On the other hand, the PDF peak is located at 65 µm in the population reporting to the fly ash: particles coarser than 120 µm are not present, 95% of particles mass is finer than 100 µm and the mean Sauter diameter is 48 µm. These values are critically dependent on cyclone efficiency characteristics. Values reported in Table 3 show that sorbent reporting to fly ashes accounts for 21% of the total sorbent ashes even without attrition, due to the presence of elutriable fines in the sorbent feeding. The inventory of lime (W L) is 22 kg m-2, corresponding to 8.8% of the total sorbent inventory. Sulphur capture corresponds to ηdes=0.78 when attrition is neglected.
lime feed bottom ashes (b-stream) fly ashes (e-stream)
0.010
0.005
0.000
cumulative PSD, mass basis [− −]
1.1
(a)
-1
PDF of particle sizes [µ µm ]
0.015
(b)
lime feed bottom ashes (b-stream) fly ashes (e-stream)
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
1
10
100 particle diameter [µ µm]
1000
10000
1
10
100
1000
10000
particle diameter [µ µm]
Fig. 3. Model results without attrition: (a) Probability density functions (PDF) of particle sizes for lime feed, bottom and fly ashes; (b) Cumulative particle size distributions (PSD) for lime feed, bottom and fly ashes.
Table 3 Selected output parameters of the model. Without attrition With attrition With attrition (ka,L*3) With attrition (ka,L*5) With attrition (ka,L*8) With attrition (ka,L*10)
f [-] 0.21 0.38 0.43 0.47 0.51 0.53
WL -2 [kg m ] 22 18 13 11 9 8
WSL -2 [kg m ] 228 232 237 239 241 242
dS for fly ashes [µm] 48 34 31 30 28 28
ηdes [-] 0.78 0.74 0.68 0.63 0.57 0.54
Model results considering the effect of attrition are reported in Figure 4 and Table 3. Plots in Figure 4 display the same general features of those corresponding to the no-attrition case. However it can be recognized that attrition/fragmentation induce a pronounced shift of the curves toward finer particle sizes. The PDF peaks are located at 115 µm and 55 µm for bottom and fly ashes, respectively. The fractional mass of sorbent finer than 100 µm is 8% and 97% for bottom and fly ashes, respectively. The mean Sauter diameter of sorbent reporting to fly ashes is dS=34 µm. As expected, consideration of attrition brings about an increase of the fractional mass of sorbent reporting to the fly ash (f raises to 0.38) and a decrease of W L (18 kg m-2, that is 7.2% of the total sorbent inventory). Correspondingly, W SL increases due to the fact that attrition of L particles determines finer L-particle sizes and, in turn, a better Ca exploitation for SO2 capture (cf. Eq. (8)). It is noteworthy that this feature does not imply an improvement in ηdes, which decreases from 78% to 74%. This finding should be related to the competing effects of the better Ca exploitation in fine particles, on the one hand, and of the larger amount of material which is lost at the cyclone, on the other, when attrition/fragmentation are at work. Under the operating conditions selected in this work, the negative effect associated with fine sorbent loss at the cyclone overweighs the positive effect of a more extensive degree of calcium exploitation, resulting into a worse SO2 abatement efficiency.
lime feed bottom ashes (b-stream) fly ashes (e-stream)
0.010
0.005
0.000
cumulative PSD, mass basis [− −]
1.1
(a)
-1
PDF of particle sizes [µ µm ]
0.015
(b)
lime feed bottom ashes (b-stream) fly ashes (e-stream)
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
1
10
100 particle diameter [µ µm]
1000
10000
1
10
100
1000
10000
particle diameter [µ µm]
Fig. 4. Model results with attrition: (a) Probability density functions (PDF) of particle sizes for lime feed, bottom and fly ashes; (b) Cumulative particle size distributions (PSD) for lime feed, bottom and fly ashes. A sensitivity analysis has been carried out with reference to the attrition/fragmentation constant for L particles (ka,L). In fact, while the value assumed for ka,SL (cf. Table 2) is to be considered as an upper limit for typical sorbents and operating conditions, the higher comminution susceptibility of L particles makes it
possible to consider ka,L-values also greater than the one reported in Table 2. Thus, Table 3 lists the main results obtained when, holding all the other operating conditions/parameters, ka,L was increased by a factor of 3, 5, 8 or 10. The model sensitivity was appreciable: as expected, enhanced attrition gives rise to increased f-values (up to 53%), decreased W L-values (down to 8 kg m-2) and decreased dSvalues for fly ashes (down to 28 µm). The overall detrimental influence that attrition/fragmentation do have on ηdes is again highlighted: ηdes is as low as 54% when ka,L was 10-times increased.
CONCLUSIVE REMARKS The influence of particle attrition/fragmentation on sorbent inventory and particle size distribution in air-blown circulating fluidized bed combustors was carefully investigated by solving a population balance model able to take into account attrition and fragmentation phenomena, generation of fly and bottom ashes, kinetic aspects related to the SO2 capture by lime-based sorbent particles. With reference to a base-case in which attrition was deliberately neglected, it was clearly shown that the presence of attrition ends up into increased fly ash amounts having finer mean particle sizes and decreased lime inventories in the bottom bed. Interestingly, the desulphurization ability of the system decreased since the effect related to the greater amount of material lost at the cyclone seemed to overweigh the effect related to a better calcium exploitation for SO2 capture when finer particles are considered. The sensitivity of the model with reference to attrition/fragmentation constants appeared to be noticeable. Altogether, model computations confirmed the relevance of attrition and fragmentation to the performance of circulating fluidized bed combustors. NOTATION -2
-1
a b c C SO2
attrition rate [kg m s ] -2 -1 overall mass rate of sorbent bed drain [kg m s ] exponent appearing in Eq. (5) [-] SO2 concentration [ppm]
Cinlet SO
SO2 inlet concentration [ppm]
∆d δ d
particle diameter interval [m] thickness of the sulphated shell [m] particle diameter [m]
d dcut dS η ηdes e f
mean diameter of attrited fragments [m] cyclone cut diameter [m] mean Sauter diameter [m] cyclone collection efficiency [-] SO2 abatement efficiency [-] -2 -1 overall mass rate of sorbent lost by elutriation [kg m s ] ratio e/(e+b) [-]
2
cap FSO
molar rate of SO2 captured by the sorbent [kgmol m s ]
inlet FSO
molar rate of SO2 at the inlet [kgmol m s ]
g k ka & m MW
overall net mass rate along the riser of sorbent material [kg m s ] -1 -1 sulphation kinetic constant [ppm s ] attrition/fragmentation constant [-] -2 -1 overall mass feed rate of lime particles [kg m s ] -1 molecular weight [kg kgmol ]
2
2
-2
-2
-1
-1
-2
-1
-1
P0 Pa R rCa/S U W XS
particle size distribution of lime after primary fragmentation [m ] -1 particle size distribution of attrited fragments [m ] -1 rate of particle shrinkage due to attrition/fragmentation [m s ] calcium to sulphur inlet molar ratio [-] -1 gas superficial velocity in the primary region of the riser [m s ] -2 overall mass of sorbent in the bottom bed [kg m ] sulphation degree [-]
shell XS
sulphation degree in the sulphated shell [-]
Subscripts: i, j, n are referred to particles in the i-th, j-th, last (coarsest) size bin; L is referred to lime particles; SL is referred to sulphated limestone particles.
REFERENCES [1]. Chandran RR, Duqum JN. Attrition characteristics relevant for fluidized bed combustion. In: Grace JR, Shemilt LW, Bergougnou MA, editors. Fluidization VI, New York: Engineering Foundation; 1989, pp. 571-80. [2]. Couturier MF, Karidio I, Steward FR. Study on the rate of breakage of various Canadian limestones in a circulating transport reactor. In: Avidan AA, editor. Circulating Fluidized Bed Technology IV, New York: American Institution of Chemical Engineers; 1993, pp. 672-8. [3]. Scala F, Cammarota A, Chirone R, Salatino P. Comminution of limestone during batch fluidizedbed calcination and sulfation. AIChE J 1997;43:363-73. [4]. Di Benedetto A, Salatino P. Modelling attrition of limestone during calcination and sulfation in a fluidized bed reactor. Powder Technol 1998;95:119-28. [5]. Scala F, Salatino P, Boerefijn R, Ghadiri M. Attrition of sorbents during fluidized bed calcination and sulphation. Powder Technol 2000;107:153-67. [6]. Montagnaro F, Salatino P, Scala F. The influence of sorbent properties and reaction temperature on sorbent attrition, sulfur uptake, and particle sulfation pattern during fluidized-bed desulfurization. Combust Sci Technol 2002;174:151-69. [7]. Werther J, Reppenhagen J. Attrition. In: Yang WC, editor. Handbook of Fluidization and FluidParticle Systems, New York: Dekker; 2003, pp. 201-37. [8]. Chen Z, Lim J, Grace JR. Study of limestone particle impact attrition. Chem Eng Sci 2007;62:867-77. [9]. Saastamoinen JJ, Shimizu T. Attrition-enhanced sulfur capture by limestone particles in fluidized beds. Ind Eng Chem Res 2007;46:1079-90. [10]. Scala F, Montagnaro F, Salatino P. Attrition of limestone by impact loading in fluidized beds. Energy Fuels 2007;21:2566-72. [11]. Montagnaro F, Salatino P, Scala F, Chirone R. An assessment of water and steam reactivation of a fluidized bed spent sorbent for enhanced SO2 capture. Powder Technol 2008;180:129-34. [12]. Scala F, Montagnaro F, Salatino P. Sulphation of limestones in a fluidized bed combustor: the relationship between particle attrition and microstructure. Can J Chem Eng 2008;86:347-55. [13]. Shimizu T, Saastamoinen J. Optimization of limestone feed size of a pressurized fluidized bed th combustor. In: Yue G, Zhang H, Zhao C, Luo Z, editors. Proceedings of the 20 International Conference on Fluidized Bed Combustion, Xi’an; 2009, pp. 1028-34. [14]. Montagnaro F, Salatino P, Scala F. The influence of temperature on limestone sulfation and attrition under fluidized bed combustion conditions. Exp Therm Fluid Sci 2010;34:352-8. [15]. Montagnaro F, Salatino P, Santoro L, Scala F. The influence of reactivation by hydration of spent SO2 sorbents on their impact fragmentation in fluidized bed combustors. Chem Eng J 2010;162:1067-74. [16]. Yao X, Zhang H, Yang H, Liu Q, Wang J, Yue G. An experimental study on the primary fragmentation and attrition of limestones in a fluidized bed. Fuel Process Technol 2010;91:1119-24. [17]. Wirth KE. Fluid mechanics of circulating fluidized beds. Chem Eng Technol 1991;14:29-38. [18]. Redemann K, Hartge EU, Werther J. A particle population balancing model for a circulating fluidized bed combustion system. Powder Technol 2009;191:78-90. [19]. Shimizu T, Peglow M, Sakuno S, Misawa N, Suzuki N, Ueda H, et al. Effect of attrition on SO2 capture by limestone under pressurized fluidized bed combustion conditions-Comparison between a mathematical model of SO2 capture by single limestone particle under attrition condition and SO2 capture in a large-scale PFBC. Chem Eng Sci 2001;56:6719-28. [20]. Scala F, Salatino P. Limestone attrition under simulated oxyfiring fluidized-bed combustion conditions. Chem Eng Technol 2009;32:380-5. [21]. Massimilla L. Gas jets in fluidized beds. In: Davidson JF, Clift R, Harrison D, editors. Fluidization II, London: Academic Press; 1985, pp. 133-72.
CaO LOOPING CYCLE FOR CO2 SEPARATION Tadaaki Shimizu*1, Takayuki Takahashi2, Hiroko Narisawa1, Yasuyuki Murakami2, Liuyun Li 1, and Heejoon Kim1 1
Department of Chemistry and Chemical Engineering, Niigata University Graduate School of Science and Technology, Niigata University *: corresponding author 2-8050 Ikarashi, Niigata, 950-2181, Japan, [email protected] 2
ABSTRACT A dual fluidized bed process using CaO-based solid sorbent is considered to be a promising technology to separate CO2 from flue gas with low energy penalty. As reactor for CaO-looping cycle, both bubbling fluidized bed and “fast” fluidized bed are available, thus four possible combinations, (bubbling or fast absorber)x(bubbling or fast regenerator), are conceivable for this process. In this work, the authors discuss favorable combination of reactor type from viewpoints of heat removal from carbonation reactor and on energy penalty associated with dilution of pure oxygen by CO2 in the regenerator. As conclusion, suitable combination was found to be bubbling bed absorber and fast regenerator. Design of bench-scale experimental apparatus of the present system was also carried out. Bubbling bed absorber was designed to achieve 86 % CO2 removal efficiency from flue gas. Preliminary operating results of solid circulation at room temperature are also presented. INTRODUCTION It is well known that the concentration of CO2 in the atmosphere has been increasing and the greenhouse effect (global warming) is anticipated. Fossil fuel combustion such as coal-fired power plant is one of the major sources of CO2. An approach to reduce CO2 emission into the atmosphere is the separation of CO2 from flue gas of conventional air-blown combustion system followed by CO2 storage in the ground. Attempts have been made to separate CO2 from flue gas using absorption by amine solution (1) and adsorption by solids such as zeolite (2). Another way is to burn fuel using pure oxygen, which is separated from air in advance, to make the flue gas pure CO2 (1). For combustion using O2, pure O2 is usually diluted with recycled CO2 to control the temperature in the combustion chamber. CaO-looping cycle has been developed as a CO2 separation process with low energy penalty (3); CaO particles capture CO2 from flue gas in one reactor (absorber) and produced CaCO3 is decomposed to CaO in another reactor (regenerator) by supplying heat through fuel combustion in O2/CO2 atmosphere,. Since CaO-looping process needs continuous solid transportation between absorber and regenerator, fluidized bed solid circulation system is considered to be suitable for reactor system. There are two types of fluidized beds which can be used as reactor,
bubbling fluidized bed and “fast” fluidized bed. In the fast fluidized bed, all of the particles are entrained to the top of the reactor in high-velocity gas stream. In literature, different combinations of reactor type have been employed not only for experiments but also for simulation of reactors; bubbling bed absorber (3, 4, 5), fast bed absorber (6, 7, 8, 9), bubbling regenerator (9), and fast bed regenerator (4, 5, 7, 8). Both bubbling bed and fast bed can be used to capture CO2 in the absorber if sufficient gas-to-solid contact time is to be given. However, for put this concept into practice, one must take account of heat removal from the exothermic reaction in the absorber to maintain bed temperature. In general, reactor size is determined not only by reaction rate but also installation of heat transfer surface. Thus the design of heat transfer surface is necessary. The objective of the present work is to discuss suitable combination of reactor type for the CaO-looping process. Design of heat transfer surface was conducted for both fast fluidized bed absorber and bubbling bed absorber. Then design of a bench-scale experimental apparatus was then carried out based on the selection of reactor type. Also preliminary experiments were carried out to measure solid circulation rate in the system under a room temperature condition. REACTOR DESIGN AND SELECTION OF REACTOR TYPE Absorber Design First, material and heat balances of CaO-looping process were calculated. Fig.1 shows an example of material balance and heat balance of this process of total gross electricity output of 350 MW. In order to avoid hot-spot formation within the regenerator, in which coal particles are burned in high O2 concentration atmosphere, fed O2 (pure) was assumed to be diluted by CO2 at a ratio of CO2/O2=1. From the material balance, feed rates of gases were calculated to design horizontal cross sectional area of absorber and regenerator. Fig.2 shows enthalpy-temperature diagram of heat source and heat sink for secondary steam cycle denoted as “Steam2” in Fig.1-b. The heat from the absorber and sensible heat of CO2 stream from regenerator is assumed to produce subcritical steam as a separated steam cycle from existing steam cycle of air-blown combustor (denoted as “Steam1”). From the heat removal requirement and temperature difference between steam and heat source, the required heat transfer surface area was calculated. Heat transfer design in a fast bed absorber was carried. Heat transfer coefficient of reactor wall (h [W/m2K]) is empirically given as a function of suspension density (susp [kg/m3]) as (10, 11): h = asusp0.5 (a = 30 - 40) (1) h = 58susp0.36. (2) Both equations give similar results. When susp is assumed to be 55 kg/m3, h is estimated to be 260 (eq.1) – 245 (eq.2) W/m2K. At this suspension density, total pressure drop across a fast bed riser of a height of 50 m is 27 kPa, which is nearly the same as that of bubbling bed absorber, thus an advantage of fast bed, lower pressure drop, is lost under the present suspension density condition. Also it should be mentioned that this suspension density of 55 kg/m3 is far higher than solid loading in the gas at the exit of the absorber: the solid loading in the gas at the exit should be 1.2 kg/m3 to attain solid/gas material flow ratio shown in Fig.1. A measure to enhance
internal solid circulation in the riser is thus required. Therefore, this suspension density of 55 kg/m3 is considered to be maximum conceivable value. CO2 O2 N2 H2O
0.17 0.23 5.16 0.46
CO2 O2 N2 H2O
2.85 0.06 0.03 0.41
CaO 7.60 Absorber CaCO3 0.84 Regenerator (T=1223K) (T=873K) CaO 8.44 CO2 O2 N2 H2O
1.02 0.23 5.16 0.46 Main combustor (air-blown)
Flue gas 39 Steam1 89 Steam2 286 ΔHR 144
Absorber (T=873K)
CO2 32 Steam2 134 CaO CaCO3 273 CaO 393
HR 144
Regenerator (T=1223K)
Loss 7
Steam1 62
Coal 13.1 O2 1.09 CO2 1.09
Steam1 263
Unit: Coal: [kg/s] Others: [kmol/s]
Loss 7
Fuel 436 Gas 0 Main combustor (air-blown)
Unit: [MW] Steam1: Primary cycle Steam2: Secondary cycle ΔHR: Heat of reaction
Fuel 483 Air 0
Coal 14.5 Air 6.53
(a)Material flow (b) Heat flow Fig.1 An example of material and heat balance of a CaO-looping cycle of total gross electricity output of 350 MW with dilution of pure O2 by CO2 at CO2/O2=1. Temperature [K].
1400
Heat transfer surface design in a fast bed was conducted assuming a bed-to-wall heat transfer coefficient of h = 250 W/m2K. The overall heat transfer resistance is the sum of heat transfer resistances, i.e., tube-to-steam/water heat transfer resistance, conductive resistance in tube wall, and bed-to-wall heat transfer resistance. Thus the overall heat transfer coefficient was calculated separately in water preheater, evaporator, reheater, and superheater regioms (Table 1). The detail of calculation is available elsewhere (3). In order to avoid erosion of heat transfer surface, flat panel heat transfer exchangers are employed in fast beds when heat transfer surfaces are to be installed in addition to reactor wall.
A
1200
B B1
B2
WH
Ev
C B3
B4
1000 800 600 400
SH RH SH
200 0
100 200 300 Enthalpy rate [MW]
400
Fig.2 Enthalpy-temperature diagram of heat source and secondary steam cycle denoted as “Steam2” in Fig.1-b. (Dash line: Heat source; Solid line: Heat sink; A, C: sensible heat of CO2; B: heat recovery in absorber; WH: water preheat; EV: evaporation; SH: superheat; RH: reheat).
Fig.3 shows an example of estimated plant size and heat transfer panel requirement for the case of fast bed absorber combined with fast bed regenerator. The cross sectional area of absorber is determined by gas flow rate and superficial gas velocity. In this work, a superficial gas velocity of 6 m/s was assumed. Cross sectional area of absorber was 2.25 times of that of regenerator. This proportion is similar to the design by Ströhle (8) which gave an absorber/regenerator ratio of 2.6. To install heat transfer panels, rectangular horizontal cross section of 11 m x 7 m was assumed.
The reactor wall is used as water prehater. To attain required evaporation, reactor height of 50 m was found to be required. For evaporator, reheater, and superheater, flat panel heat transfer surfaces of 40 m in height and 7 m in width are assumed. Since both sides of flat panel can be used for heat transfer, a heat transfer surface area of 560 m2 is available for one panel. Total 13 panels were calculated to be required, in which two panels are for evaporator, seven for reheater, and four for supereheater, respectively. Installing such large number of heat transfer panels in a fast bed is considered to be not easy. Therefore, the idea of employing fast bed for absorber is abandoned in this work. Cy clon es
Cyc lon e
HT p an els 2 E vap. 7 R eheat. 4 S u per h .
Cyclone
φ 6.6 m
50 m M em b r an e wall f or w at er pr eh eatin g
φ 6 .6 m
26 m
13 m
Ab sor ber
Ab sor ber Reg en erator
Regenerator 11m
7m
Side view Abs or ber Side v iew
Abs or ber F ron t view
Fig.3 Estimated plant size and heat transfer panel requirement for the case of fast bed absorber combined with fast bed regenerator for total gross electricity output of 350 MW.
Fron t view
Fig.4 Estimated plant size and heat transfer panel requirement for the case of bubbling fluidized bed absorber combined with fast bed regenerator for total gross electricity output of 350 MW.
Table 1 Estimation of required heat transfer surface area for heat removal from fast bed absorber assuming bed-to-wall heat transfer coefficient of 250 W/m2K Heat recovery Overall heat transfer Heat transfer rate [MW] coefficient [W/m2K] area [m2] Water preheater 111 183 1753 Evaporator 72 218 1369 Reheater 68 101 3874 Superheater 36 85 2302 Bubbling fluidized bed absorber has already been designed in the authors’ previous work (3). The cross sectional area was given by the volume flow rate and superficial gas velocity (assumed to be 1 m/s to suppress elutriation of solids). The bed height to install heat transfer tubes had been calculated to be 2.4 m and this height was higher than the bed height to capture CO2. As shown in Fig. 4, considerably large cross sectional area is required because of low gas velocity. Stacked bed design may be required to reduce the plant size. Nevertheless, bubbling fluidized bed is considered to be suitable for absorber because the density of heat transfer surface can be higher than that of fast bed absorber. Regenerator Design Regenerator should be basically adiabatic to suppress heat loss, thus one important operating parameter is dilution of fed oxygen by recycled CO2 in order to prevent hot-spot formation. As shown in Fig.5, dilution by CO2 decreases net efficiency
through increase power consumption of air separation unit (ASU) because CO2 carries sensible heat away from the reactor and more fuel combustion is necessary with increasing CO2 recycle. To reduce the risk of hot-spot formation at low recycle ratio of CO2, fast bed is considered to be advantageous because of vigorous solid mixing near the reactor inlet. 35
0.1
30 0.08
25
ASU power / gross output [-].
Net efficiency [%] (HHV).
Design parameters and reactor size
0.12
40
BENCH SCALE REACTOR DESIGN
As discussed above, one advantageous 0.06 20 combination of reactor type for 15 CaO-looping cycle is bubbling bed 0.04 (a) 10 absorber and fast bed regenerator. A Net efficiency 0.02 bench scale experimental apparatus is 5 ASU power consumption designed based on this combination. 0 0 Material flow was calculated assuming 0 0.5 1 1.5 2 that oxygen is diluted with the same CO2 recycle/O2 feed [mol/mol] amount of CO2. Table 2 shows the design parameters. For the absorber, a Fig.5 Estimation of the change in net bubbling bed of 93 mm in I.D. is efficiency and ASU power consumption adopted. For the regenerator, a fast bed with dilution of O2 by recycled CO2 at of 22 mm in I.D. is adopted. The design ambient temperature. size of sorbent particle is 0.3 mm, whose minimum fluidizing velocity at 873 K and terminal velocity at 1273 K are 0.026 m/s and 1.48 m/s in each atmosphere, respectively. Thus fluidization in absorber and transportation of particles in regenerator are expected to be attained. The design parameters such as the ratio of the cross sectional area of two reactors and gas velocities are different from that of large scale plant (Fig. 4) because of the height limitation of bench-scale unit. Nevertheless this reactor is expected to attain necessary CO2 recovery and solid circulation as discussed later. The material flow rates are determined by scaling down the material flow shown Fig.1-a. Table 2 Design parameters of CaO-looping bench scale plant Absorber ID Gas feed rate Gas velocity Inlet gas composition [%] [mm] [mol/s] [m/s] CO2 H2O O2 N2 93 0.0213 0.226 14.8 6.7 3.3 75.2 Regenerator ID Gas feed rate O2 CO2 [mm] [mol/s] [mol/s] 22 0.0034 0.0034 Solid circulation CaO/Captured CO2 [mol/mol] 10
Gas velocity Bottom Top [m/s] [m/s] 1.79 2.75
CaO circulation rate [g/s] 1.47
Coal feed rate [g/s] 0.041
Temperature [K] 873
Temperature [K] 1273
Solid circulation/Gas flow rate in riser [kg/kg] 3.2
Estimation of CO2 capture efficiency in absorber
Averaged CO2 conc. [%]
In order to design the bed height of absorber, CO2 capture efficiency was estimated using Kunii-Levenspiel model. The detail of the model is described elsewhere (3). The reaction of CaO with CO2 is characterized by maximum utilization of CaO (D) and rate constant (kR); change in CaO conversion (X) with time is given as follows: 3 -1 -1 dX/dt = kR(D - X). (3) kR=25[m kmol s ] 16 The maximum utilization of CaO is known to 14 D=0.17 decrease after repeating carbonation – 12 calcination cycles (3, 12). After a number of 10 carbonation – calcination cycles, maximum 8 conversion finally decreased to 0.17 (12). Thus 6 D=0.17 was assumed. Reaction rate constant 4 of kR =25 m3/kmol.s was assumed based on the 2 author’s previous work (3). The gas velocity is 0 given in Table 2. The calculation result is shown 0 0.1 0.2 0.3 in Fig.6. A CO2 removal efficiency of 86% was Bed height [m] calculated to be attained when a bed height of Fig.6 Estimated CO2 capture in 0.30 m and bubble diameter of 4 cm was bubbling fluidized bed operated assumed. Thus the present experimental under a condition shown in apparatus is expected to be suitable for CO2 Table 2 assuming a bubble capture experiments. diameter of 4 cm. PRELIMINARY OPERATION RESULTS OF BENCH SCALE REACTOR A bench-scale reactor of CaO-looping cycle was constructed and operated at room temperature. The objective of the cold-experiments was to confirm whether required solid circulation rate could be attained. Schematic diagram of the experimental apparatus is shown in Fig.7. The experimental apparatus consisted of riser reactor (22 mm in I.D. and 1.93 m in height) and bubbling fluidized bed reactor (93 mm in I.D.), both of which were made of stainless steel. The gas exit at the top of the riser is connected to a cyclone in which particles were separated from the gas then the captured particles were transported to the bubbling fluidized bed by gravity. Particles in the bubbling fluidized bed were drained from an overflow tube. The fluidizing bed height (i.e. height of overflow tube) was 0.30 m above gas distributor. The particles flowed through a standpipe whose bottom is connected to a loopseal. The particles from the loopseal were fed into the bottom of the riser. The bed material was quartz sand of average size of 0.15 mm. Under the present cold model experiments, gas velocities were 1.75 m/s, 0.050 m/s, and 0.067 m/s in the riser, in the bubbling bed, and in the loopseal, respectively. In order to measure the solid circulation rate, the standpipe was equipped with three thermocouples at an interval of 10 cm and with a tracer injection system from the top. As tracer, heated bed material was employed. A batch of tracer was injected from the top of the standpipe. The change in temperature with time after tracer injection was continuously measured. When the tracer particles passed the location of the thermocouple, a peak of the temperature was observed (Fig.8). From the time lag (tD) of the peaks and the distance between two thermocouples (L), the descending velocity (UD) of solids in the standpipe is given as: UD=L/tD. (4)
By assuming that the solids in the standpipe forms moving bed without bubbles and the solid density in the standpipe is identical to the bulk density of the solids (b), the solid circulation rate (Gs) is given as a product of descending velocity, bulk density, and cross sectional are of loopseal (As) as follows: Gs As bU D . (5) Fig.9 shows the solid circulation rate that attained at a pressure drop across the riser of about 3 kPa. Solid circulation rate under the present condition was 3 – 4 g/s, which is considered to sufficient for CO2 capture experiments in the present test apparatus (Table 2). The present measurement method can be applied for hot experiments. For hot experiments, cold limestone particles can be used as tracer because circulating particles from the bubbling bed absorber are sufficiently hot (about 873 K) and the temperature decrease due to tracer can be easily detected. Cyclone
Sand
Top - 10cm below top 10 - 20 cm below top Estimated from riser pressure drop
Heater
Absorber (BFB)
Solid circulation rate [g/s]
5
ΔP Regenerator (Fast FB)
Air T
T
Stand pipe T
4 3 2 1 0
Loopseal
0
Air
40
o
Temperature [ C]
Pressure drop across riser [kPa]
Fig.7 Bench-scale fluidized bed solid circulation system for cold experiments. tD tD
35 30 25 Top Top - 20cm
Top - 10cm
20
4
Fig.9 Comparison between solid circulation rate measured by tracer injection into the standpipe and estimation from riser pressure drop.
Air
45
1 2 3 Pressure drop across riser [kPa]
10
1
0.1 0
20
40
60
Time after tracer injection [s]
Fig.8 Typical temperature change in standpipe at different vertical position after injecting a batch of hot sand (tracer) into the standpipe.
0
20 40 60 80 Time after stopping solid circulation [s]
Fig.10 Decrease in pressure drop across riser after stopping solid circulation.
Another approach to measure the solid circulation rate is to measure the pressure drop across the riser ( PR ) after stopping solid circulation by stopping gas feed into
the loopseal. As shown in Fig.10, the pressure drop across the riser decreased with time (t) according to the first-order equation after stopping solid circulation as: ln PR ln PR ,0 kt (6) where k is first-order decay constant. This relationship indicates that the decreasing rate of pressure drop across the riser is proportional to the pressure drop itself. By neglecting pressure drop due to gas flow, the pressure drop across the riser is given by total amount of solid in the riser (Ws), cross sectional area of riser (AR) and acceleration of gravity (g). Thus the entrainment rate of solids is given as:
dPR g dWs kPR . dt AR dt
(7)
Under constant pressure drop condition, solid circulating rate is thus given as:
Gs
dWs kPR AR kWs . dt g
(8)
As shown in Fig.9, solid circulation rate measured by tracer injection into the standpipe agreed well with that estimated by eq.8. Therefore, the solid circulation rate can be monitored by measuring the pressure drop across the riser. CONCLUSION Conceptual design of a CaO-looping cycle was carried out. To recover heat from absorber, bubbling fluidized bed is considered to be more suitable than fast bed. Experimental apparatus is designed and CO2 capture efficiency in the absorber was estimated. Solid circulation rate was measured under a cold model condition and required solid circulation rate was attained. REFERENCES (1) Noguchi, Y., Nakayama, S., Kiga, T., Utada, O., Makino, H., Karyoku-Genshiryoku-Hatsuden (The Thermal and Nuclear Power, in Japanese), Vol.44, pp.412 – 419, 1993 (2) Naka, H., Shibagaki, T., Takatsuka, T., Kajiyama, R., Hirohama, S., KaryokuGenshiryoku- Hatsuden (The Thermal and Nuclear Power, in Japanese), Vol.44, pp.816 – 821, 1993 (3) Shimizu, T., Hirama, T., Hosoda, H., Kitano, K., Inagaki, M., Tejima, K., Trans IChemE Part A (Chem. Eng. Res. Des.), Vol.77, pp.62-68, 1999 (4) Romeo, M., Energy Procedia, Vol.1, pp.1099 - 1106, 2009 (5) Hughes, R.W., Lu, D.Y., Anthony, E.J., Macchi, A., Fuel Processing Technol., Vol.86, pp.1523-1531, 2005 (6) Ströhle, J., Lasheras, A., Galloy, A., Epple, B., Chem. Eng. Technol., Vol.32, No.3, pp.435 – 442, 2009 (7) Abanades, J.C., Alonso, M., Rodriguez, N., Gonzalez, B., Grasa, G., Mrillo, R., Energy Procedia, Vol.1, pp.1147- 1154, 2009 (8) Ströhle, J., Galloy, A., Epple, B., Energy Procedia, Vol.1, pp.1313-1320, 2009 (9) Charitos, A., Hawthorne, C., Bidwe, A.R., Korovesis, L., Schuster, A., Scheffknecht, G., Powder Technol., Vol.200, pp.117 – 127, 2010 (10) Andersson, B.A., B.Leckner, Int. J. Heat Mass Transf., Vol.35, pp.3353-3362, 1992 (11) Andersson, B.A., Powder Technol., Vol.87, pp.239-248, 1996 (12) Abanades, J.C., Chem. Eng. J., Vol.90, pp.303-306, 2002
ECVT IMAGING OF 3-D FLOW STRUCTURES AND SOLIDS CONCENTRATION DISTRIBUTIONS IN A RISER AND A BEND OF A GAS-SOLID CIRCULATING FLUIDIZED BED Fei Wang, Qussai Marashdeh and Liang-Shih Fan* William G. Lowrie Department of Chemical and Biomolecular Engineering The Ohio State University 140 West 19th Avenue, Columbus, Ohio 43210, USA *To whom correspondence should be addressed ([email protected]) ABSTRACT Experimental studies using electrical capacitance volume tomography (ECVT) are conducted to examine gas-solid flows in a riser and a bend of a 0.05 m (2 in) ID gassolid circulating fluidized bed (CFB) system. The quantitative measurements using ECVT are made that illustrate a three-dimensional symmetric core-annulus structure in the riser and a non-centro-symmetric core-annulus structure in the bend. Results on the volume solids holdup distributions in the riser and in the bend at various operating conditions are also obtained. INTRUDUCTION Gas-solid flows have been employed extensively in industrial operations (Kunii and Levenspiel (1), Fan and Zhu (2)). Bent vessels are commonly used in solids handling systems such as an exit of a riser in a gas-solid circulating fluidize bed (CFB) and elbows to change the solids transport direction in solids pneumatic conveying. The details of the gas-solid flow behaviors in such bends as well as in straight vessels are of great importance for the design of the CFB reactors and pneumatic conveying systems. Due to the lack of advanced imaging technologies in the past, visualizations of on-line three-dimensional gas-solid flow patterns and measurements of volumetric solids holdup in straight vessels and bends were rarely reported. Currently, there are two main methods of measurement, intrusive and non-intrusive techniques, applied to gas-solid flows. Classification of these methods is predicated by the mechanism by which measurement signals are acquired. For intrusive techniques, the measurement sensor, such as a capacitance probe (Lanneau (3), Geldart and Kelsey (4)), optical fiber probe (Yasui and Johanson (5), Cui and Chaouki (6)), endoscopic probe (Peters et al. (7), Du et al. (8)) or pressure transducer probes (Kang et al. (9), Geldart and Xie (10)), requires direct contact with the flow media to record a measurement. This intrusive nature may potentially disturb the physical flow behavior. Conversely, non-intrusive techniques are based on remote acquisition of the measurement signals from sensors mounted away from the flow and avoiding interference with the internal of a multiphase flow system. Nonintrusive techniques are widely applied for the measurements of gas-solid flows. In this regard, electrical capacitance volume tomography (ECVT) has emerged as a practical technology for realistic measurements without interfering with the flow
(Warsito et al. (11), Marashdeh et al. (12), Wang et al. (13, 14, 15)). ECVT has provided the means for imaging gas-solid flows in complex geometries due to the flexibility of its sensors (Marashdeh et al. (12), Wang et al. (13, 15)). In this study, an advanced ECVT sensor system is designed for imaging gas-solid flows in complex geometries. Two developed sensors are used for imaging real-time three-dimensional gas-solid flows in a riser and a 90o bend at the exit region of a CFB. The flow structures in the riser and the bend are analyzed based on quantitative ECVT images. The volumetric solids holdup in the riser and the bend in the CFB are obtained for various superficial gas velocities and solids fluxes. EXPERIMENTAL SETUP Figure 1 is a schematic diagram of the gas-solid circulating fluidized bed. The CFB unit, made of Plexiglas, consists of a 0.05 m ID riser with a height of 2.6 m, a porous distributor, a 90o bend, a cyclone system, a standpipe/downer and an L-shape nonmechanical valve. The FCC particles (Geldart group A) with a mean diameter of 60 µm and a particle density of 1400 kg/m3 and air are used as the fluidized particles and fluidizing gas. A porous plate with a pore size of 20 µm and a fractional free area of 60% is used as the distributor of the CFB riser. The gas flow rate in the riser is controlled by a flowmeter in the main gas line. Another flowmeter is used to control the aeration gas flow rate at the bottom of the downer to provide different solids flux in the CFB. A bend sensor and a cylindrical sensor are mounted at the exit region and in riser in the CFB, respectively. Figure 2 is a photo of the real gas-solid circulating fluidized bed mounted with the ECVT bend sensor and the cylindrical sensor. Gas outlet
Cyclone
ECVT sensor II
Riser Downer
ECVT sensor I Gas
Distributor Gas
Figure 1. Schematic diagram of the gas-solid circulating fluidized bed mounted with ECVT sensors.
Figure 2. Photo of the real gas-solid circulating fluidized bed mounted with ECVT bend sensors at the Ohio State University. In an ECVT, a volume image of different phases in the test domain is reconstructed based on utilizing nonlinear distributions of electric field lines. An ECVT system consists of three basic components: (1) a capacitance sensor, (2) a data acquisition system, and (3) a computer system for reconstruction and viewing. Figure 3 is a schematic diagram of the ECVT system incorporating the three components. The capacitance sensor is made of a number of capacitance electrodes, ne , distributed around the peripheral of the domain of interest. Additionally, there are ne ( ne − 1) / 2 combinations of independent capacitance measurements between all pairs of electrode. The ECVT image reconstruction employed here is based on an optimization reconstruction technique called the neural network multi-criterion optimization image reconstruction technique (NN-MOIRT) (Warsito and Fan (16)). The technique has also been extended to reconstruct volume images from 3D capacitance sensors (Warsito et al. (11)). This extension increased the accuracy of reconstructed images. Recent developments have focused on the ECVT sensor design with 3D features for detecting capacitance variations due to permittivity perturbations in the imaging volume. For imaging complex geometries using ECVT, sensor design is the main element of the imaging system to define the volume under interrogation. In this work, two ECVT sensors are designed to image flows in the riser and the transition region in the right-angle bend. The design of the cylindrical sensor is aimed at establishing an electrically varied field around the riser by arranging 12 electrodes in three layers as depicted in Figure 4 (a). The capacitance measurements are obtained between the plates at each layer and between plates in different layers of the cylindrical sensor to image the flow in the riser. The design of the bend sensor is also aimed at establishing an electrically varied field at and around the corner of the bend by arranging 12 electrodes in two layers perpendicular to each other as depicted in Figure 4 (b). While the plates at each layer image the flow entering and exiting the bend, it is the interaction between plates in both layers that most reveals the flow dynamics in the region where the flow changes direction. The ECVT sensor design is developed intuitively and confirmed by computer simulation (Marashdeh et al., 2008). Simulations in this case confirmed the sensitivity distribution is focused at and around the bend corner. The acquisition frequency is 80 Hz and the reconstruction resolution is 20 × 20 × 20 for the three-dimensional reconstructed images of all tests.
Volume fraction as measured by ECVT is in the range of 2-5% of total volume. The accuracy varies within this range depending on the flow structure and particles used in flow. The assessment for the accuracy of ECVT images of a flow in a bend is based on previous assessments of ECVT imaging accuracy in straight sections.
Figure 3. Schematic diagram of the ECVT system.
(a) (b) Figure 4. Configuration of the ECVT sensors: (a) cylindrical sensor; (b) bend sensor. RESULTS AND DISCUSSION Solids Holdup Distribution in the Riser The cylindrical sensor is used for imaging real-time three-dimensional gas-solid flows in the riser of the CFB. The volumetric solids holdup distribution in the sensor section of the riser at varying superficial gas velocity and solids flux is obtained. The instantaneous three-dimensional dynamic gas-solid flow structures in the riser are analyzed based on quantitative ECVT images. Figure 5 shows the time-averaged solids holdup distribution in the test region of the riser with superficial gas velocity, Ug, from 0.97 m/s to 1.94 m/s and a solids flux, Gs, of 21.6 kg/m2s within 10 seconds. The sub-image on the left in each image is the solids concentration distribution in the X-Z (X, Y: two horizontal directions; Z: direction of the axis of the riser) whereas the right sub-image represents the solids concentration distribution at the top, middle, and bottom cross-sectional planes of the test region of the riser. Figure 6 shows the time-averaged volume solids holdup in the riser at Gs=21.6 kg/m2s. The experimental results indicate that the averaged volume solids holdup decreases with Ug. A symmetric core-annulus structure with a low solids holdup in the core area and a high solids holdup in the annulus area in the riser is observed. The thickness of the annulus and solids holdup in the annulus near the wall decrease with Ug.
Figure 5. Time-averaged solids holdup distribution in the riser at Gs=21.6 kg/m2s: (a) Ug=0.97 m/s; (b) Ug=1.16 m/s; (c) Ug=1.36 m/s; (d) Ug=1.55 m/s; (e) Ug=1.75 m/s; (f) Ug=1.94 m/s.
Solids Holdup
0.3 0.2 0.1 0 0.5
1
1.5
2
2.5
U g (m/s)
Figure 6. Time-averaged solids volume holdup in the riser at Gs=21.6 kg/m2s. Solids Holdup Distribution in the Bend The bend-sensor is used for imaging real-time three-dimensional gas-solid flows at the exit region of the CFB riser. The volumetric solids holdup distribution in the exit region of the CFB riser at varying superficial gas velocity and solids flux is probed. The three-dimensional solids holdup distributions in the bend of the riser are illustrated by slices in the volume image cut through the bend vertically and horizontally. The configurations of the vertical and horizontal slices are given in Figure 7. Figure 8 shows the solids holdup distribution in the bend of the riser with Ug of 1.36 m/s and Gs of 21.2 kg/m2s. The images indicate that a core-annulus flow structure is formed both in the vertical and horizontal parts of the bend. The solids holdup in the core region is relatively low compared to that in the annulus region. The annulus structure is non-centro-symmetric in the horizontal part of the bend (Grace, et al. (17)). The asymmetry of the solids volume fraction in the bend is due to the exit at one side and the tortuosity of the flow path at entrance of the cyclone. The solids holdup in the annulus near the top wall area in the bend is higher than that in other
locations of the annulus. The asymmetry is due to the following reasons: (1) back mixing and reflection of solids from the upper wall of the horizontal duct; (2) solids in the bend are difficult to entrain with the gas flow due to an abrupt turn of the gas stream in the bend; (3) a zone with low gas velocities at the upper corner of the bend is formed. The images also indicate that a solids “dune” is formed, at the bottom of the horizontal section of the bend. The sedimentation of solids in the horizontal duct is due to: (1) the velocity of the main gas stream is not high enough to carry all the solids horizontally to the cyclone, and thus the sedimentation of solids occurs; (2) after an abrupt turn in the bend, the gas moves towards the top of the horizontal duct in the bend, which forms a zone with relatively low gas flow rate at the bottom of the horizontal duct. Figure 9 shows the solids holdup distribution in the bend of the riser with Ug of 1.16 m/s and Gs of 21.2 kg/m2s. The comparison between Figures 8 and 9 indicates that the solids holdup near the top wall area in the bend increases with the superficial gas velocity. More solids move to the outside wall area from the main stream in the bend due to high solids velocity at high superficial gas velocity. Figure 10 shows the time-averaged volume solids holdup in the bend at a superficial gas velocity of 1.16 m/s. The experimental results indicate that the time-averaged volume solids holdup near the top wall area increases with solids flux. More solids are separated to the outside of the bend from the main stream of the gas-solid flow at high solids flux in the CFB.
(a) (b) Figure 7. Configuration of the slices for the plots of the tomographic images in the bend: (a) vertical slices; (b) horizontal slices.
(a) (b) Figure 8. Solids holdup distribution in the bend of the CFB riser at Ug=1.36 m/s and Gs=21.2 kg/m2s: (a) vertical slices; (b) horizontal slices.
(a) (b) Figure 9. Solids holdup distribution in the bend of the CFB riser at Ug=1.16 m/s and Gs=21.2 kg/m2s: (a) vertical slices; (b) horizontal slices.
εs
0.08 0.07 0.06 0.05 18
20
22 2
G s (kg/m s) Figure 10. Time-averaged volume solids holdup at the top wall region at the start of the horizontal duct of the bend at Ug=1.16 m/s. CONCLUSIONS An advanced ECVT sensor system is designed for real-time, three-dimensional imaging of gas-solid flows in a riser and a 90o bend at the exit region of a CFB. The instantaneous 3-D dynamic gas-solid flow structures in the riser and the bend are analyzed based on quantitative ECVT images. A symmetric core-annulus structure in the riser is observed. It is found that the thickness of the annulus and solids holdup in the annulus near the wall of the riser decrease with Ug. A core-annulus flow structure is formed both in the vertical and horizontal parts of the bend. The annulus structure is non-centro-symmetric in the horizontal part of the bend. The solids holdup in the annulus near the top wall area in the bend is higher than that in other locations of the annulus. A solids “dune” is observed by ECVT at the bottom of the horizontal duct of the bend. The solids holdup at the top wall region in the bend increases with the superficial gas velocity. The time-averaged volume solids holdup near the top wall area increases with the solids flux. ACKNOWLEDGEMENTS The support of the US Department of Energy DOE/NETL under Grant # DENT0005654 and Tech4Imaging is greatly appreciated. The CFB design adopted in this work is similar to the one employed by Professor Lynn Gladden’s research group at University of Cambridge, with whom collaborative research efforts on tomography techniques are in progress. The assistance of Mr. Paul Green and Mr. Leigh Evrard in the fabrication of the circulating fluidized bed, Mr. Mustafa Mergaye in the fabrication of the sensor, and Mr. Samuel Bayham and Mr. Aining Wang in the operation of the circulating fluidized bed is gratefully acknowledged. NOTATION Gs ne Ug
solids flux (kg/m2s) number of capacitance electrodes superficial gas velocity (m/s)
Greek letters εs solids holdup
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
14. 15. 16.
17.
Kunii, D. & Levenspiel, O. Fluidization engineering, second ed. ButterworthHeinemann, Boston (1991) Fan, L.-S. & Zhu, C. Principles of Gas-Solid Flows. Cambridge University Press, Cambridge (1998) Lanneau, K. P. Gas-solids contacting in fluidized beds. Transactions of the Institution of Chemical Engineers, 38(3), 125-43 (1960) Geldart, D. & Kelsey, J. R. The use of capacitance probes in gas fluidised beds. Powder Technol., 6(1), 45 – 50 (1972) Yasui, G. & Johanson, L. N. Characteristics of gas pockets in fluidized beds. Am. Inst. Chem. Engrs J., 4, 445 – 452 (1958) Cui, H. & Chaouki, J. Effects of temperature on local two-phase flow structure in bubbling and turbulent fluidized beds of FCC particles. Chem. Eng. Sci., 59(16), 3413-3422 (2004) Peters, M. H.; Fan, L.-S.; and Sweeney, T. L. Study of particle ejections in the freeboard region of a fluidized bed with an image carrying probe. Chem. Eng. Sci., 38(3), 481-485 (1983) Du, B.; Warsito, W.; and Fan, L.-S. ECT Studies of the Choking Phenomenon in a Gas–Solid Circulating Fluidized Bed. AIChE Journal, 50(7) 1386 – 1406 (2004) Kang, W. K.; Sutherland, J. P.; and Osberg, G. L. Pressure fluctuations in a fluidized bed with and without screen cylindrical packings. Ind. & Eng. Chem. Fund., 6(4), 499-504 (1967) Geldart, D. & Xie, H. Y. The use of pressure probes in fluidized beds of group A powders. Fluid. VII, Proc. Eng. Found. Conf. Fluid. 749-756 (1992) Warsito, W.; Marashdeh, Q.; Fan, L.-S. Electrical Capacitance Volume Tomography (ECVT). IEEE Sens. J., 7, 525-535 (2007) Marashdeh, Q.; Fan, L.-S.; Du, B.; Warsito, W. Electrical Capacitance Tomography-A Perspective. Ind. Eng. Chem. Res., 47, 3708-3719 (2008) Wang, F.; Marashdeh, Q.; Fan, L.-S.; Williams, R. A. Electrical capacitance, electrical resistance, and position emission tomography techniques and their applications in multi-phase flow systems, in: Li, J. H.(Ed.), Advances in Chemical Engineering Vol. 37: Characterization of flow, particles, and interfaces. Academic Press, Amsterdam, 179-222 (2009) Wang, F.; Yu, Z.; Marashdeh, Q.; Fan, L. -S. Horizontal Gas and Gas/Solid Jet Penetration in a Gas-Solid Fluidized Bed, Chemical Engineering Science, In Press (2010), doi:10.1016/j.ces.2010.02.036 Wang, F.; Marashdeh, Q.; Fan, L.-S.; Warsito, W., Electrical Capacitance Volume Tomography: Design and Applications. Sensors, 10, 1890-1917 (2010) Warsito, W. & Fan, L.-S. Neural network based multi-criterion optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography. Meas. Sci. Technol., 12, 2198–2210 (2001) Grace, J. R.; Bi, X. T.; Golriz, M. Circulating Fluidized Beds in: Yang, W.-C. (Ed.), Handbook of Fluidization and Fluid-Particle Systems. Marcel Dekker, Inc. New York, NY, 485-544 (2003)
THE RELATIONSHIP BETWEEN FLUIDIZATION VELOCITY AND SEGREGATION IN TWO-COMPONENT FLUIDIZED BEDS: A PRELIMINARY ANALYSIS Brunello Formisani, Rossella Girimonte and Vincenzino Vivacqua Dipartimento di Ingegneria Chimica e dei Materiali, Università della Calabria, I 87030 Arcavacata di Rende (Cosenza), Italy
ABSTRACT Experiments are presented that provide the initial and final fluidization velocity of binary mixtures at varying composition. Introducing just one parameter to account for their segregation level, the theoretical equations of the two characteristic velocities are derived. These equations are then employed to predict the concentration profile obtained by slowly defluidizing the bed down to the fixed state.
INTRODUCTION In many industrial processes in which fluidized beds are employed (combustion and gasification of coal and biomass, polymerization, etc.), solids of various kinds are simultaneously subjected to fluidization. During the process, gas-solid and solid-solid interaction determines the tendency of system components to segregate or mix up so as to give place to a characteristic axial concentration profile. Segregation phenomena may lead, in some cases, to strong inhomogeneities or even to partial defluidization of the bed with detrimental effects on process performance. In spite of the huge number of experimental studies published in the last three decades, even for mixtures of only two components the relationship between fluidization regime and level of segregation is still poorly understood, so that no reliable quantitative theory of segregating fluidization is, to date, available. Attempts made to refer the state of mixing of a binary bed to the characteristics of its fluidization regime have led to a few relationships, mostly empirical. To this regard, a well-known equation is that proposed by Nienow et al. (1), valid for two-density mixtures and flotsam-rich systems, which expresses the variation with the fluidization velocity of a component mixing index defined by the authors as the ratio between the jetsam concentration in the upper region of the bed and its overall value. Modified versions of this equation, meant to extend its validity to two-size mixtures and to improve its accuracy, were proposed by Rice and Brainovich (2), Peeler and Huang (3) and Wu and Baeyens (4). All these works make use of the “minimum fluidization velocity of the mixture” umf and correlate the degree of bed mixing to the “excess gas velocity” u−u mf. However, the concept of umf, taken from the theory of monosolid fluidization, has no meaning when applied to the suspension process of a binary bed, because of the gradual nature of the phenomenon (5,6).
This paper, instead, develops a novel approach, based on definition of the “initial” and “final fluidization velocity” of the binary mixture. As shown by previous studies (5-8), in a ∆p versus u diagram uif is determined where the pressure drop first deviates from the fixed bed curve, uff where it attains its final value. Introduction of these two characteristic velocities is suggested by the actual phenomenology of the fluidization process, analysed in detail elsewhere (5,6). The mechanism of binary fluidization is here summarized in Fig.1.
Fig.1 - Fluidization mechanism of a homogeneous bed of two solids.
THEORY Any bed of two solids homogeneously mixed (Fig.1a) achieves suspension gradually, while the axial distribution of its components changes along with the increase of the fluidizing gas velocity. The process begins at uif, whose calculation can be accomplished by a fully predictive equation devised in a previous stage of the research (5,6):
180 µ g u if (1 − ε mf ,m )
2
d av2
3 ε mf ,m
[
]
= (ρ f − ρ g )x f 0 + (ρ j − ρ g )(1 − x f 0 ) (1 − ε mf ,m )g
(1)
In it, dav is the Sauter mean diameter, calculated from
x f 0 1− x f 0 1 = + d av df dj
(2)
while the value of bed voidage is drawn from the experimental curve of εmf,m versus xf. At any operating velocity intermediate to uif and uff the axial distribution of mixture components is approximately that sketched in Fig.1b. The bed consists of a top flotsam layer, a middle jetsam stratum and a residual homogeneous portion whose heights are indicated as hf ,hj and hm, respectively. As the flotsam layer results fluidized since the beginning of its buildup at uif, a force balance between the total drag force and the buoyant weight in the remaining two layers provides the following expression relating uff (Fig.1c) to the properties of the two solids, the mixture voidage and the level of segregation corresponding to the height hm of the static mixed layer:
u ff =
[(ρ
f
]
− ρ g )x f 0 hm + (ρ j − ρ g )(1 − x f 0 )h0 g
(1 − ε mf ,m ) (1 − ε mf , j ) ( ) ( ) 180 µ g 3 h h h 1 x + − − 0 m f 0 m 2 ε mf3 , j d 2j ε mf ,m d av
(
)
(
(3)
)
In order to make eqn (3) deterministic, it is necessary to fix the value of the parameter hm. In the absence of a fundamental theory, the following correlation has been devised for hm,ff, that is at the final fluidization velocity, capable to give accurate predictions (9):
hm , ff h0
(1 − ε ) ε =k (1 − ε ) ε mf , m
3 mf , j
mf , j
3 mf , m
x f 0 (1 − x f 0 )
(4)
Eqn (4) relates the height of the residual homogeneous layer to mixture composition and to the gain in drag force effectiveness provided by the void condition typical of the mixed state. In it, k is a best fit parameter which does not depend on composition. For a two-density mixture, assuming the invariance of voidage with composition, uff calculated from eqn (3) turns out to be equal to the velocity obtained by substituting in eqn (1) the value of the flotsam fraction in the system made of the jetsam and the mixed layer, of height hj and hm respectively:
x fb =
x f 0 (hm , ff h0 )
x f 0 (hm , ff h0 ) + 1 − x f 0
(5)
The correspondence between the value of uif evaluated at xfb and that of uff calculated at the overall concentration xf0, exact for density segregating beds, is well approximated also for two-size systems. Given that uff does not depend on the initial state of mixing of the two solids (5), xfb may be also considered as the concentration of the first thin layer that settles down during the defluidizing procedure; eqn (5) can hence be regarded as a phase equilibrium relationship, where h0/hm,ff=α plays a role analogous to that of the relative volatility in gas-liquid equilibrium, in a way that:
xf0 =
α ⋅ x fb
(6)
α ⋅ x fb + (1 − x fb )
When a packed portion of the bed is still present and the defluidization front has reached the height z, the average flotsam fraction in the overlying fluidized region is:
∫ (1 − ε )x
H
mf
yf =
f
dz (7)
z
∫ (1 − ε )dz
H
mf
z
ӯ f can now be linked to xf, i.e. the flotsam fraction of the thin layer which settles
down by slightly decreasing the gas flow rate; the way to relate these two variables is suggested by eqn (6): xf0 is substituted with ӯ and xf with xfb, so that
yf =
α ⋅ xf
α ⋅ x f + (1 − x f )
(8)
Under the assumption of constant voidage (H=h0), a simplification that introduces a negligible error:
[
]
x f dz = −d (h0 − z ) ⋅ y f = −(h0 − z )d y f + y f dz (9) Introducing the dimensionless height Z=z/h0 and considering that Fig. 2 - Sketch of the defluidization process.
d yf dZ
=
dx f dy f dZ dx f
(10)
eqn (9) gives rise to the first order differential equation:
dx f dZ
=
yf − xf 1 (1 − Z ) ∂ y f ∂x f
=
1 (x f + α )(1 − x f (1 − Z ) α 2 −1
(11)
From eqn (7) it is then obtained:
dx f dZ
[
]
)
2
(12)
Once integrated with the condition Z=0, xf=xfb, eqn (12) allows calculating the axial profile of xf as:
1 − x f Z = 1 − x f
α
1 − x fb α −1 αx f + 1 − x f x fb 1 − x f
αx fb + 1 − x fb 1− x fb
(13)
EXPERIMENTAL All the experiments of this study were carried out in a transparent fluidization column of 10 cm ID, equipped with a plastic porous distributor 4 mm thick. The pressure drop across the column was measured by means of a U-tube water manometer connected to a tap located 1 mm above the distributor plane. Bed heights were evaluated by averaging the values read on three graduated scales put at 120°C around the column wall, and then used for determining bed void fractions. The concentration profiles were obtained by gently drawing the solids from the top of the column by means of a vacuum device, in horizontal layers of particles generally 2 cm thick (or 1 cm thick, when a higher resolution was needed). Each of these layers was then sieved to measure the mass fraction of either solid component by weighing. Concentration values were then referred to the average height of the relevant layers and used to trace the respective profiles in function of height. Measurements involved mixtures of various spherical solids, closely sieved. The properties of each cut are reported in Table 1, together with those of the mixtures investigated. In all the experiments the aspect ratio h0/D of the fixed bed was 1.7. Tab.1 – Properties of the experimental solids and mixtures Density Sieve size Sauter mean diameter [g/cm3] [µm] [µm] 500-710 593 Glass ballotini (GB) 2.48 450-600 499 150-180 172 Ceramics 3.76 500-710 605 Solid
ρj/ρf [-] Density-segregating CE605-GB593 1.52 Size-segregating GB499-GB172 1 Type
Mixture
dj/df k εmf [-] [-] [-] 1.02 0.405 0.39 2.90 See Fig.1 0.18
VALIDATION As a major difference with the case of two-density mixtures, the binary beds in which a significant difference of component diameters is present exhibit a voidage which varies with their composition (6,7). Thus, for the system CE605-GB593 εmf,m can be assumed as practically constant with xf, and an average values of 0.405 was used.
As regards instead the mixture GB499-GB172, εmf,m varies with xf according to the experimental curve of Fig.2.
εmf,m [ - ]
0.5
0.4
0.3 0.0
0.2
0.4 0.6 xf , [ - ]
0.8
1.0
Fig.2 - Voidage of the homogeneous mixtures GB499-GB172 at varying composition. In Fig. 3 are shown the velocity diagrams of the two systems investigated. Model curves for uif are generated by the theoretical eqn (1), wheras uff is predicted from eqns (3) and (4), using the values of k reported in Table 1. The errors do not exceed 10% for both the density- and the size-segregating mixture.
40
60 50
GB499-GB172
u if , u ff , [ cm/s ]
u if , u ff , [ cm/s ]
30 40 30 CE605-GB593
20
uif uff
uif uff
20
10
10
0
0 0
0.2
0.4 0.6 xf , [ - ]
0.8
1
0
0.2
0.4 0.6 xf , [ - ]
0.8
1
Fig.3 - Fluidization velocity diagrams of the homogeneous mixtures CE605-GB593 (density-segregating) and GB499-GB172 (size-segregating).
CE605-GB593
GB499-GB172
1
1
xf0=0.1
0.8
xf0=0.2
0.8
xf0=0.4
0.6
xf0=0.5
Z, [-]
Z, [-]
0.6
0.4
0.4
xf0=0.9
0.2
0.2
0
xf0=0.8
0
0
0.2
0.4
xf, [-]
0.6
0.8
1
0
0.2
0.4
xf, [-]
0.6
0.8
1
Fig.4 - Concentration profile of the fixed bed obtained by a slow defluidizing procedure at varying flotsam total concentration for the mixtures CE605-GB593 and GB499-GB172. By introducing into eqn (4) the values of k used to correlate the velocity data, it is possible to calculate hm,ff at a given overall concentration and, thanks to eqn (6), the corresponding value of xfb. Finally, the concentration profile of the fixed bed obtained after slow defluidization of the mixture is found by eqn (12). Model curves and experimental points are compared in Figs 4 for the two different types of mixtures under examination, over the whole field of system composition. Although the agreement is not always satisfactory from the quantitative point of view, the model seems capable to reproduce the general trend of the component composition profiles. While confirming the potentiality of the approach followed, these preliminary results encourage the effort of addressing segregating phenomena occurring in multisolid systems in the light of the fundamental theory of fluidization.
CONCLUSIONS Results relevant to mixtures subjected to segregation by difference of either density or size between their components show that a unique theory based on fundamental analysis proves capable to relate the progress of fluidization to the extent of segregation. The model equations proposed in this paper successfully reproduce the effects of segregation by means of a parameter whose determination can be carried out with minimal experimental effort, by a single fluidization experiment. In the prediction of the final fluidization velocity of a two-solid mixture, an important role is played by the fact that the force balance is applied to a realistic picture, even when some evident simplifications are introduced. The difference uff-uif, i.e. the width
of the fluidization velocity interval of the binary bed, seems related to the concentration profile obtained from its slow defluidization down to the fixed state.
NOTATION A D d dav g H h0 k uif, uff umf xf xf0
ӯ
column cross section [cm2] column bed diameter [cm] particle diameter [µm] Sauter mean diameter (eqn.4) [µm] gravity acceleration [cm/s2] total bed height [cm] height of the fixed bed [cm] best-fit parameter (eqn. 17) [-] initial, final fluidization velocity [cm/s] minimum fluidization velocity [cm/s] solid fraction of flotsam [-] overall solid fraction of flotsam [-] average flotsam fraction of the fluidized portion of the bed [-]
z Z
axial coordinate [cm] dimensionless height z/h0 [-]
εmf ε0 µg ρ ρg
minimum fluidization voidage [-] fixed bed voidage [-] gas viscosity [g/cm s] solid density [g/cm3] gas density [g/cm3]
Subscripts f,j m ff
of the flotsam, jetsam component (or layer) of the homogeneous mixture at the final fluidization conditions.
REFERENCES 1) Nienow, A.W., Rowe, P.N. and Cheung, L.Y.L. (1978). A quantitative analysis of the mixing of two segregating powders of different density in a gas-fluidized bed, Powder Technol., 20, 89–97. 2) Rice, R.W. and Brainnovich, Jr J.F. (1986). Mixing/segregation in two- and three-dimensional fluidized beds: binary systems of equidensity spherical particles, A.I.Ch.E J., 32, 7–16. 3) Peeler, J.P.K. and Huang, J.R. (1989). Segregation of wide size range particle mixtures in fluidized beds, Chem. Eng. Sci., 44, 1113–1119. 4) Wu, S. Y. and Baeyens, J. (1998). Segregation by size difference in gas fluidized beds, Powder Technol., 98, 139-150. 5) Formisani, B., Girimonte, R. and Longo, T. (2009). The fluidization process of binary mixtures of solids: Development of the approach based on the fluidization velocity interval, Powder Technol., 185, 97-108. 6) Formisani, B., Girimonte, R. and Longo, T. (2008). The fluidization pattern of density-segregating binary mixtures. Chem. Eng. Res. Des., 86, 344-348. 7) Chen, J. L.-P. and Keairns, D. L. (1975). Particle segregation in a fluidized bed, Can. J. Chem. Eng., 53, 395-402. 8) Vaid, R. P. and Sen Gupta P. (1978). Minimum fluidization velocities in beds of mixed solids, Can. J. Chem. Eng., 56, 292-296. 9) Formisani, B., Girimonte, R. and Vivacqua, V. (2010). Fluidization of mixtures of two solids differing in density or size, AIChE J., (in press, DOI: 10.1002/aic.12450).
THE VARIATION OF THE BUBBLE PHASE PROPERTIES OF A FCC CATALYST FLUIDIZED BED AT HIGH TEMPERATURE Rossella Girimonte and Brunello Formisani Dipartimento di Ingegneria Chimica e dei Materiali, Università della Calabria, I 87036 Arcavacata di Rende (Cosenza), Italy T: +39-0984-496689; F: +39-0984-496655; E: [email protected] ABSTRACT Some important characteristics of the bubbling regime, relevant to a cut of FCC catalyst with an average size of 110 µm were investigated at 100, 400 and 700 °C. This was achieved by imploying data of bed collapse tests as well as images of the bubble eruption at the free surface of the bed by varying fluidization velocities and bed aspect ratios. The results show that a smoother regime of fluidization is observed at superambient temperature. INTRODUCTION Industrial fluidized bed units are often operated in the bubbling regime at high temperatures, like in the case of catalytic reactors, combustors or gasifiers. In spite of that, current descriptions of their hydrodynamics are still widely based on the results of investigations carried out at room conditions. Very little is known, therefore, on the specific relationship that links operating temperature and general properties of the bubbling regime of a fluidized bed, a lack of information also connected to the difficulty of producing experimental data by affordable diagnostic techniques. A noticeable uncertainty exists, for instance, on the nature and the behaviour of the bubble phase at elevated temperatures, as bubbles flow across a particulate phase whose properties are definitely influenced by temperature, T. Literature studies have reported so far some contradictory results, possibly due to the frequent use of intrusive techniques or to the adoption of indirect measurements. With beds of Geldart B particles in the temperature range 20-300°C, Geldart and Kapoor (1) observed that the diameter of bubbles, measured at their eruption, decreased by 15-25%. Sitthiphong et al. (2) reported opposite results for beds of larger particles. Stubington et al. (3), who used a three-dimensional resistivity probe, determined bubble sizes in a coal bed at temperatures ranging from 20 to 1000°C: the equivalent sphere diameter exhibited a 5-15% reduction as T was raised from ambient level to about 300°C. Sishtla et al. (4 ) used pressure probes to determine bubble frequency, velocity and size in fluidized beds of solids of various particle size (100-400 µm) and density (1.25-2.56 g/cm3) at temperatures up to 980°C. They
reported that the average pierced length of bubbles, as well as their frequency and velocity, did not change with T. Hatate et al. (5), who analysed the behaviour of cuts of sand in the range 75-521 µm, found that the effect of temperature on the properties of the bubble phase was significant only for fine particles. With coarser solids, instead, the dependence of the equivalent bubble diameter on variables such as bed height and excess gas velocity did not differ from that observed at ambient conditions. Llop et al. (6) investigated the influence of temperature on bubble hold-up in beds of particles of Geldart’s groups B and D. With the former type of solids, the bubble fraction was found to diminish as T increased, whereas an opposite trend was obtained with D particles. Contradictory results were obtained by Cui and Chaouki (7), whose measurements were carried out by an optical fiber probe immersed in the fluidized bed. They reported that both the frequency of the cycle and the ratio of the dilute/dense phase duration were enhanced by high temperature, with the consequent increase of bubble frequency and size. On this basis, their questionable conclusion was that at elevated temperatures the fluidization behaviour of a solid like FCC catalyst, which belongs to Geldart's group A, becomes that typical of B particles. Given the relative inconsistency of the few studies that have tried to characterize the bubble regime at high temperature, the main aim of the present study is that of providing some clear result by non-invasive measurements based on image analysis.
EXPERIMENTAL The present investigation addresses the influence of temperature on the overall properties of the bubble phase as well as on some characteristic of the individual bubbles erupting at the free surface of the bed. Bed collapse tests and video-recording of bubble eruption at the free surface of the bed were carried out in a transparent column (ID=90mm). In either case, image analysis procedures were used. Both techniques were applied at different values of fluidization velocity, bed aspect ratio and temperature. Each collapse test provides a diagram of bed height versus time capable of describing the bed deaeration process that follows the instantaneous interruption of gas feed. From it, several parameters are determined: the dense phase voidage εd, the average velocity ud of the gas flowing through it, the volumetric bubble fraction δ (otherwise termed "bubble hold-up"), and the average gas velocity in the bubble phase, ub. At the same operating conditions, images of the upflowing bubbles as they reach the free surface of the fluidized bed, were recorded and processed to determine the bubble frequency fb, and the distribution of their diameters at eruption; from these data, the average bubble diameter db,av was also evaluated. The present investigation was focused on a specific system, i.e. a cut of FCC catalyst fluidized by air. Solid density was 1600 kg/m3; the average particle diameter
was 110 µm and fines were practically absent (F22=0 and F45=0.02). The estimated value of particle sphericity, equal to 0.99, was assumed as that capable to give the best fit of data of minimum fluidization velocity at ambient conditions. This assumption was confirmed by SEM analysis, that showed that the catalyst particles were substantially spherical. The experiments were carried out at 100, 400 and 700 °C. The fluidization velocity was set equal to 1.6, 2.1, 2.7, 3.2, 4.2 and 5.3 cm/s, with corresponding values of the excess velocity equal to 1.1, 1.6, 2.1, 2.7, 3.7 and 4.8 cm/s respectively. Given that the minimum fluidization velocity is nearly independent of temperature, the same is true for u-umf in the field 100-700°C. At each experimental temperature, the bed aspect ratio H0/D of the particle bed was fixed at values of 3, 3.8 and 4.6.
500
H , [mm]
400
H0/D=4.6
H0/D=4.6
H0/D=4.6
H0/D=3.8
H0/D=3.8
H0/D=3.8
H0/D=3
300
FCC 110 µm T=100°C u-u mf=4.8 cm/s
200 0
1
2 t , [s]
3
4
0
H0/D=3
H0/D=3
FCC 110 µm T=400°C u-u mf=4.8 cm/s
FCC 110 µm T=700°C u-u mf=4.8 cm/s
1
2 t , [s]
3
4
0
1
2 t , [s]
3
4
Figure 1. FCC catalyst collapse curves at varying H0/D and T; u-umf=4.8 cm/s.
RESULTS Bed collapse experiments show that the expansion of the dense phase of the fluidized bed past incipient fluidization is independent of bed height (see Fig. 1) and that at relatively high gas velocity (above 4 cm/s) the dense phase properties become insensitive to further increases of the fluidization velocity (see Figs 2 and 3).
0.7
0.6
ε , εd ,[-]
FCC 110 µm T=400°C
FCC 110 µm T=100°C
umb,e
umb,e
0.4
εd umf u mb,s
0.3 0
2
4 u , [cm/s]
6
FCC 110 µm T=700°C
ε
ε
ε
0.5
umb,e=umb,s
εd
εd umf
8 0
umb,s
2
umf
4 u , [cm/s]
6
8 0
2
4 u , [cm/s]
Figure 2. FCC catalyst fluidization maps at varying temperature.
6
8
As illustrated by the fluidization maps in Fig. 2, at T=100 and 400°C the transition to the bubbling regime occurs with a contraction of the dense phase voidage whereas at 700°C the high level of voidage attained by the dense phase of the bed becomes stable. After the commencement of bubbling the properties of the particulate phase apparently depend only on operating temperature and their trends of variation are different from those of the corresponding minimum fluidization parameters (see Fig. 3): the dense phase voidage εd is constantly higher than εmf and the interstitial gas flows at a velocity ud higher than umf.
0.6
2.5
u-umf=2.7 cm/s u-umf=3.7 cm/s u-umf=4.8 cm/s
u-umf=2.7 cm/s FCC 110 µm u-umf=3.7 cm/s u-umf=4.8 cm/s
2
εd , [-]
u d , [cm/s]
0.5
0.4
1.5 1 0.5
εmf
FCC 110 µm
umf
0
0.3 0
200
400 T , [°C ]
600
800
0
200
400 T , [°C ]
600
800
Figure 3. Dense phase properties at varying temperature. These results confirm that with solids of Geldart’s Group A, application of the two-phase theory to the behaviour of the fluidized bed should account for the fact that the actual excess gas velocity u-ud is significantly different from u-umf. Thus, running experiments at constant u-umf corresponds to working with u-ud that increases with T. The dense phase porosity is constantly higher than that of the bed at incipient fluidization, but the properties of the particle emulsion noticeably change over 400°C, a thermal level past which any further growth of εd is accompanied by the progressive diminution of the gas velocity through the emulsion phase. As this should cause a remarkable increase of the excess gas flowing in the form of bubbles, it is expected that the bubble phase characteristics vary accordingly. To this regard, Fig. 4 illustrates the variation of the hold-up of bubbles as function of the excess velocity at different values of temperature and of the bed aspect ratio H0/D. The volume fraction of bubbles strongly increases over 400°C. Over this temperature thermally induced interparticle forces are likely to be much stronger, as shown by the corresponding fluidization map of Fig. 2 where εd is much higher than at lower T. The increase of interparticle cohesion may thus be thought to stabilize the dense phase structure, so that the same excess gas is converted into a more abundant bubbly flow. As for the effect of changing the bed aspect ratio, its increase is associated to a reduction of the volumetric extension of the bubble phase. As this occurs without the excess flow rate being varied, increasing the bed height appears to give place to a different partition of the total gas flow rate into the two phases.
0.1
0.04
0.06 0.04
0.02
0.02
0
0 2
4 6 (u-u d ) , [cm/s]
8
3.0 3.8 4.6
0.08
0.06
0
FCC 110 µm T=400°C
H0/D
δ, [-]
δ, [-]
0.08
0.1
FCC 110 µm H0/D=4.6
100 °C 400 °C 700 °C
10
0
2
4 6 (u-u d ) , [cm/s]
8
10
Figure 4. Bubble hold-up versus excess gas velocity at varying T and H0/D.
Measurements of the average velocity of the bubble phase confirm that temperature has an influence on this parameter: the velocity reduction observed at high T (see Fig. 5) indicates that the flow section of bubbles becomes larger (consistently with the correspondent growth of δ). Similarly, the increase of ub at large values of H0/D is coherent with the parallel decrease of bubble hold-up already shown in Fig. 4. 300
FCC 110 µm H0/D=4.6
200
u b , [cm/s]
u b , [cm/s]
300
100
u-umf=1.6 cm/s u-umf=2.7 cm/s u-umf=3.7 cm/s u-umf=4.8 cm/s
0 0
200
400 T , [°C ]
FCC 110 µm u-umf=4.8 cm/s
200
100
H0/D 3.0 3.8 4.6
0 600
800
0
200
400 T , [°C ]
600
800
Figure 5. Average bubble phase velocity in function of temperature. In order to confirm this hypothesis, the images of bubbles erupting at the free surface of the bed were recorded. It has to be mentioned that the analysis could not be conducted at all the operating velocities employed for running the collapse experiments, due to the excessive turbulence encountered at high velocities. Observations are thus limited to the lower bound of the velocity field, namely to values of u=1.6 and 2.1 cm/s (i.e. u-umf=1.1 and 1.6 cm/s, respectively). The eruption diameters were calculated by converting the surface limited by bubble perimeter into that of an equivalent circle and the diagrams of their distribution reconstructed at varying operating conditions.
100 FCC 110 µm H0/D=3.0 u-umf=1.6 cm/s
Nb , [%]
80
FCC 110 µm H0/D=3.8 u-umf=1.6 cm/s
T °C 100 400 700
60
FCC 110 µm H0/D=4.6 u-umf=1.6 cm/s T °C 100 400 700
T °C 100 400 700
40 20 0 0
1
2
3 4 d b , [cm]
5
6
7 0
1
2
3 4 d b , [cm]
5
6
7 0
1
2
3 4 d b , [cm]
5
6
7
Figure 6. Distribution of bubble eruption diameters at varying H0/D and T. This procedure allows stating that the distribution of bubble diameters depends on the bed aspect ratio as well as on temperature (see Fig. 6), a result consistent with those previously provided by the bed collapse experiments. The two series relevant to different superficial velocity appears quite similar, then only data for u-umf=1.6 cm/s are shown. At any temperature level increasing the bed height has no significant influence on the amplitude of bubble size distribution but causes the increase of the average diameter of the bubbles, possibly due to the enhanced role played by coalescence. At any given bed height, on the other hand, raising the process temperature lowers both limits of the bubble size distribution while also their average diameter is reduced. Such effects constitute an indirect confirmation of the fact that high temperature changes the nature of the dense phase of the bed, making bubbles flow through a medium characterized by an increasingly cohesive behaviour. 6
8
FCC 110 µm u-umf=1.6 cm/s
FCC 110 µm u-umf=1.6 cm/s
fb , [1/s]
db,av , [cm]
6 4
4
2 H0/D 3.0 3.8 4.6
0 0
200
H0/D 3.0 3.8 4.6
2
0 400 T , [°C ]
600
800
0
200
400 T , [°C ]
600
800
Figure 7. Average bubble diameter and bubble frequency in function of T. The two plots of Fig. 7 show the variation with T of the properties of the bubbles that flow across the bed, at a fixed value of the excess velocity. It is observed that their average diameter reduces as much as the operating temperature is raised while
their frequency of eruption accordingly increases; these seem two clear findings in a field where literature results are few and rather controversial. Altogether the results presented so far clearly indicate that temperature plays a key role in determining the characteristics of the bubbling regime in that it changes the properties of the two phases of the fluidized system. CONCLUSIONS Measurements conducted in a fluidized bed of FCC catalyst operating at high temperature show that temperature influences the quality of bubbling. The results provided by the bed collapse technique indicate that bubble hold-up increases in response to the temperature increase as a consequence of the decrease in the gas velocity through the dense phase. Connected to that is the fact that the average gas velocity in the bubble phase decreases with T, while bubble diameters, as determined by image analysis at their eruption, become smaller. Such phenomena, more easily recognizable over 400°C and influenced also by bed height, are associated to the tendency of the dense phase of the bed to become increasingly cohesive due to thermally induced interparticles forces (7, 8). On the whole, the different quality of the bubbling regime at high temperature, with a larger number of smaller bubbles travelling across a more permeable particulate phase, makes clear that substantial corrections have to be introduced in modelling the performance of fluidized bed reactors characterized by significant thermal effects.
ACKNOWLEDGEMENT This work was financially supported by the University of Calabria. NOTATION db db,av fb F22 F45 H H0 D H0/D Nb t T u ub ud umb,e umb,s
Bubble eruption diameter, cm Average bubble eruption diameter, cm Bubble frequency, Fine fraction under 22 µm, Fine fraction under 45 µm, Fluidized bed height, mm Static bed height, mm Column diameter, mm Bed aspect ratio, Bubble fraction, % Time, s Temperature, °C Superficial velocity, cm/s Bubble phase velocity [=(u-ud)/δ], cm/s Emulsion phase velocity, cm/s Maximum velocity of homogeneous expansion, cm/s Minimum velocity of a stable bubbling regime, cm/s
umf δ ε εd εmf
Minimum fluidization velocity, cm/s Bubble hold-up, Bed voidage, Emulsion phase voidage,Minimum fluidization voidage, -
REFERENCES (1) Geldart, D. and Kapoor, D.S., 1976, Bubble sizes in a fluidized bed at elevated temperatures, Chem. Eng. Sci. 31, 842-843. (2) Sitthiphong, N., George, A.H. and Bushnell, D., 1981, Bubble eruption diameter in a fluidized bed of large particles at elevated temperatures, Chem. Eng. Sci. 36, 1259-1260. (3) Stubington, J.F., Barrett, D. and Lowry, G., 1984, Bubble size measurements and correlation in a fluidised bed at high temperatures, Chem. Eng. Res. Dev. 62, 173-178. (4) Sishtla, C., Chan, I. and Knowlton, T., 1986, The effect of temperature on bubble parameters in gas-fluidized beds, Fluidization V, Eds. K. Ostergaard and A. Sorensen, 127-134. (5) Hatate, Y., Ijichi, K., Uemura, Y., Migita, M. and King, D.F., 1990, Effect of bed temperature on bubble size and bubble rising velocity in a semi-cylindrical slugging fluidized bed, J. Chem. Eng. Japan 23, 765-767. (6) Llop, M.F., Casal, J. and Arnaldos, J., 2000, Expansion of gas-solid fluidized beds at pressure and high temperature, Powder Technol. 107, 212-225. (7) Cui, H. and Chaouki, J., 2004, Effects of temperature on local two-phase flow structure in bubbling and turbulent fluidized beds of FCC particles, Chem. Eng. Sci. 59, 3413-3422. (8) Formisani, B., Girimonte, R. and Pataro, G., 2002, The influence of operating temperature on the dense phase properties of bubbling fluidized beds of solids, Powder Technol. 125, 28-38.
CHARACTERIZATION OF THE FLUIDIZATION AND MIXING OF BINARY MIXTURES CONTAINING BIOMASS AT LOW GAS VELOCITIES Farzam Fotovat1,Jaber Shabanian1,Jamal Chaouki1,and Jeffrey Bergthorson2 1 Department of Chemical Engineering, École Polytechnique, Montréal, QC, Canada 2 Department of Mechanical Engineering, McGill University, Montréal, QC, Canada ABSTRACT To judge qualitatively the effect of biomass properties on its fluidizability and mixing tendency with sand particles at low fluidization velocities, local pressure gradients in the top and bottom of the bed were compared for all investigated systems. It was found that changing the mass fraction and the size of biomass impacted the onset of bubbling and the size of bubbles across the bed, which, in turn, affected the mixing/segregation of the bed content. INTRODUCTION Biomass, living and recently dead biological material, is known as one of the highly potential renewable sources of energy. Biomass materials stand as the third energy resource after oil and coal due to their abundance and rapid replenishment. Producing energy from biomass results in the mitigation of greenhouse gas emissions making biomass an outstanding substitution for fossil fuels. Nowadays, a variety of thermo-chemical processes, e.g., combustion, gasification, and pyrolysis, are being developed worldwide to convert biomass to energy and added-value fuels (1). Due to the unique advantages of fluidized beds, they are widely used as the heart of nearly all of the above-mentioned processes. Nevertheless, fluidization of biomass particles alone is a problematic or even impossible task due to their peculiar sizes, densities, and shapes. The most widespread solution for this problem is adding inert materials, such as sand particles commonly used in regular fluidized beds. Highly different properties of biomass and bed material particles, in terms of hydrodynamics, result in some shortcomings, e.g., segregation, which negatively affects fluidization performance. In spite of the noticeably different characteristics of mixing and segregation in the biomass-sand mixture from that of a common binary mixture, a few studies have been devoted to examine the mixing dynamics of systems, including biomass particles (2-4). On the other hand, the scarcity of phenomenological studies on the mixing/segregation behavior of biomass particles hinders the industry from applying general solutions for biomass fluidization.
1
In general, the difference between the density, size, and/or shape of components of fluidizing solids causes segregation and, as a consequence, particles of each component tend to accumulate at the top (flotsam) or bottom (jetsam) of the bed. Early investigations addressed mainly segregation patterns in binary mixtures of particles of equal size but with different densities. More recent studies have been carried out on the equal density and different size systems. Only a few works have considered mixing and segregation phenomena in systems dealing with particles differing in both size and density; however, such systems have a vast application not only in biomass processing, but also in pharmaceutical and chemical industries (5). To understand the phenomena underlying segregation in mixtures dealing with biomass, the main objective of the present work is devoted to scrutinizing the binary fluidization behavior of relatively light biomass and dense sand particles at comparatively low superficial gas velocities, namely, around the limit required for complete fluidization. In particular, the analysis of the mixing/segregation trend of binary mixtures consisting of biomass particles with two different sizes and/or mass fractions has been attempted on the basis of mechanisms governing the mixing/segregation in binary mixtures of granular solids. EXPERIMENTAL Apparatus The experiments were conducted in a cold conventional fluidized bed consisting of a Plexiglas fluidization column 1.5 m high and 0.152 m in diameter. A perforated plate containing holes 1 mm in diameter and arranged in a triangular pitch was used as a distributor. Three graduated scales spaced at 120◦ around the column wall were used to determine the bed height. The dynamic pressure fluctuations were monitored along the bed via a couple of pressure transducers mounted flush with the wall of the bed. One of the pressure transducers was used to register the pressure fluctuations of the whole bed (PT1). The local pressure signals corresponding to the low and high levels of the bed were obtained by two pairs of differential pressure transducers, namely PT2 and PT3, respectively. The position and configuration of all pressure transducers has been shown in Figure 1. The pressure data were acquired at a sampling frequency of 400 Hz for 180 seconds through a 16 bit A/D data acquisition board with the help of the Labview 9.0.1® program. The pressure signals were lowpass filtered at 100 Hz to remove the signal noises. This frequency is sufficiently higher than the frequency of typical phenomena taking place at low velocities (1-10 Hz), thus it seems unlikely to affect the signal fluctuations. Materials Biomass particles were provided from birch cylindrical rods with two different diameters cut into identical lengths of tiny particles. The bed material used had a continuous normal size distribution ranging from 100 to 1000 µm. Table 1 reports more details of all materials used in this investigation. 2
Figure 1. Schematic diagram of experimental setup.
Material Sand Biomass 1 Biomass 2
Table 1. Properties of materials used dp(mm) Shape hp(mm) ρp(kg/m3) Spherical 0.381 2865 Cylindrical 3.175 6.350 670 Cylindrical 6.350 6.350 670
ρb(kg/m3) 1632 331.5 332
ε (-) 0.43 0.50 0.50
Procedure To investigate the effect of the cross section size and mass fraction of biomass particles four different mixtures of bed material and biomass, whose properties are mentioned in Table 2, were studied. In each experiment, the height of the static bed was set at 225 mm (H/D=1.5). To gain insight into the fluidization behavior of the bed material used in the mixtures, the sand alone was fluidized first under the same conditions as the other experiments. In an attempt to make the he mixing state of the systems uniform before starting the experiments experiments, the binary mixture was vigorously fluidized for about 15 min. Experiments were conducted at ambient pressure and temperature. Starting from the fixed bed state, the superficial gas velocity was quasi quasisteadily increased until it reached the desired value. Then, the bed was slowly defluidized until it returned to a fixed state.
System System 1 System 2 System 3 System 4
Table 2. P Properties of binary mixtures investigated Biomass Sand Biomass Wt.% of type mass (kg) mass (kg) biomass Biomass 1 5.363 0.282 5 Biomass 2 5.364 0.282 5 Biomass 1 4.365 0.485 10 Biomass 2 4.367 0.484 10 3
Vol.% of biomass 20.58 20.55 35.36 35.33
Analysis Methods Processing the time-series signals reflecting the pressure fluctuations in the different vertical positions of the bed was the main method used to characterize the fluidization and mixing of particles. In this regard, the time-averaged value of all 72000 data collected during each run was considered as the static pressure of the relevant section. Moreover, signals were analyzed dynamically in time and frequency domains. In terms of statistical analysis in the time domain, the mean amplitude or standard deviation (σ) of the fluctuation signals over the bed has an intense interrelation with mean bubble size. The standard deviation of pressure data is calculated as follows (6):
=
−
(1)
where N is the number of data points at the intended time interval and is the average of recorded Pi s. RESULTS AND DISCUSSION The most common method for identifying the fluidization status at different velocities is studying the whole bed pressure drop as a function of superficial gas velocity. This method is not, however, sufficient to distinguish the phenomenological differences between the fluidization behavior of mixture components and, despite the diversities existing between the systems investigated in the present work, their overall pressure drops versus gas velocity show more or less a similar trend during fluidization and defluidization, correspondingly.
As observed, the bed pressure drop increases initially in the fixed bed mode. It is well known that for the ideally mono-sized and homogenous particle systems transition from fixed to fluidized state takes place at a specific gas velocity known as minimum fluidization velocity (Umf). Realistically speaking, Umf is determined by finding the superficial gas velocity, which corresponds to the intersection of the pressure drop line of the fixed bed with that for the fluidization state. Unlike the monodispersed systems, for the binary solids mixtures the transition from fixed to fully fluidized occurs gradually at a gas velocity interval beginning with initial fluidization velocity (Uif) and ending at complete fluidization velocity (Ucf). Uif is located at the point where ∆P first deviates from the fixed bed curve. In relation to Umf of sand alone, the values of Uif of investigated binary systems were slightly lower. This is due to the difference between the packing extent and bulk densities of sand alone and sand mixtures containing biomass. For the systems investigated, particles might stay in a bubble-free fluidization regime during the transition from initial to complete fluidization mode. In other words, deviation from the fixed bed state did not necessarily result in the immediate formation of bubbles. The occurrence of such phenomenon is the first departure of the fluidization behavior of binary mixtures whose components differ in density from that of uniform particles (7).
4
A further increase in the gas velocity brought about the appearance of bubbles in the bed. It should be noted that before bubbles became large enough to move upward along the entire height of the bed, bubbling might take place just occasionally and locally at the bed depending on the mixture properties. The velocity corresponding to the onset of bubbling across the entire bed is defined as the initial bubbling velocity (Uib). In the present work, Uib was considered as the velocity at which a sudden jump was observed in the dominant frequency of the signal representing the pressure fluctuations of the whole bed (PT1 signal). It is noteworthy that Uib is always located between Uif and Ucf. Ucf is defined as the velocity beyond which no considerable change in the total bed pressure drop can be observed. Indeed, the essence of the Ucf is the same as that of Umf defined for the fluidization of homogeneous particles; however determination of Ucf is not as straightforward as the aforementioned method used for finding the Umf of monodispersed particle systems. In spite of the similar features of whole bed pressure drop for systems containing different sizes and/or amounts of biomass particles, pressure drop profiles of low (PT2) and high (PT3) levels are meaningfully different for the investigated systems. As shown in Figure 2, the discrepancy between up and down pressure drops of the bed in systems 1 and 2 is lower than those of systems 3 and 4, signifying the inferior segregation propensity of sand and biomass particles in systems having less biomass mass fraction. Notably, it seems that biomass particles with a small cross section (system 3) are subject to an intensified segregation at a very low superficial gas velocity compared to biomass particles having a larger cross section (system 4). Comparing the local pressure drops of systems composed of the same biomass particle but differing in the mass fraction of components (systems 1 & 3 or 2 & 4) reveals that the change of the biomass mass fraction considerably changes the behavior of the systems at low gas velocities. For example, increasing the biomass content of the mixture could result in a delay in the formation of bubbles in the bed. As a result, the bed content stayed in a bubble-free mode at a range of very low gas velocities. Local segregation was extremely severe in this bubble-free fluidization regime due to the percolation effect. Upon the emergence of bubbles in the bed, sand particles moving with the bubble wake can penetrate to the top flotsam-rich layer and partially offset the segregation. Accordingly, as seen in the curves denoting system 3, the difference between up and down pressure drops decreases sharply. A progressive increase of the gas velocity brings about the formation of bubbles at lower layers of the bed content. Thus, one can consider a downwardly moving front for the bubbling fluidization regime whose movement is significantly slower for system 3 in comparison with system 1. On the other hand, comparing the standard deviations of systems 1 & 3 or 2 &4 at corresponding velocities (Figure 3) verifies that the increase of biomass concentration in the mixture leads to a decrease of pressure fluctuation amplitude (standard deviation, σ, Eq. (1)). Since the standard deviation of pressure fluctuations as a function of gas velocity correlates with the bubble diameter (6), it can be concluded that the size of bubbles decreases when the portion of biomass particles increases in the bed. Therefore, it is not surprising that the ‘well mixing’ of the bed inventory caused chiefly by strong and energetic bubbles is achieved at the higher gas velocities. 5
Pressure gradient, kPa/m
16
System 1
12 8 4
Top Bottom
0 0
0.05
0.1
0.15
0.2 U, m/s
0.25
0.3
Pressure gradient, kPa/m
16
0.35
0.4
System 2
12 8 4
Top Bottom
0 0
0.05
0.1
0.15
0.2 U, m/s
0.25
0.3
Pressure gradient, kPa/m
16
0.35
0.4
System 3
12 8 4
Top Bottom
0 0
0.05
0.1
0.15
0.2 U, m/s
0.25
0.3
Pressure gradient, kPa/m
16
0.35
0.4
System 4
12 8 4
Top Bottom
0 0
0.05
0.1
0.15
0.2 U, m/s
0.25
0.3
0.35
0.4
Figure 2. Bottom and top pressure drop gradients of systems investigated at increasing values of the superficial gas velocity (Top and bottom curves obtained via time-averaging the pressure fluctuations recorded by PT2 and PT3, respectively.)
6
0.6 σ (PT1), kPa
σ (PT1), kPa
0.6 0.4 0.2 System 1 System3
0 0.15 0.2 0.25 0.3 0.35 0.4
0.4 0.2 System 2 System 4
0 0.15 0.2 0.25 0.3 0.35 0.4 U, m/s
U, m/s
Figure 3. Comparison of standard deviation of signals representing the global pressure fluctuation in systems differing in mass fraction of biomass (σ PT1)
Pressure gradient, kPa/m
The analysis of pressure signals shows that the bubble-free transient condition persists only at a short range of low gas velocities for system 4. Since the number of biomass particles in this case is about ¼ of those in system 3 (because both of these systems contain 10% wt. biomass but the cross section of biomass particles is four times bigger in system 4), the particle packing is less loose. Therefore, in relation to large biomass particles, remarkable multiplicity of small biomass significantly intensifies segregation in the early stages of fluidization; however, this condition can improve easily in favor of mixing by the bubbles passing through the bed. The effect of a higher number of biomass particles can be seen during the defluidization of systems. For example, as seen in Figure 4, the larger discrepancy between top and bottom pressure drop profiles in system 3 with respect to those of system 4 (especially at the transition zone from fluidized to fixed state) expresses the higher sensitivity of system 3 to a gradual decrease of gas velocity. In other words, for system 3 biomass particles possess greater potential to be segregated from sand during the defluidization. Consequently, the system falls into the fixed bed state earlier.
16 12 8 4 0
System 3 Top Bottom
Pressure gradient, kPa/m
0
0.1
0.2 U, m/s
0.3
16 12 8 4 0
0.4
System 4 Top Bottom
0
0.1
0.2 U, m/s
0.3
0.4
Figure 4. Bottom and top pressure drop gradients of systems 3 and 4 at decreasing values of the superficial gas velocity 7
CONCLUSION To investigate the effect of biomass properties on its fluidization behavior in the presence of bed material, four mixtures of woody biomass and sand particles differing in biomass size and mass fraction were investigated. The applied characterization method was measuring the global and local pressure drops of the bed by means of pressure transducers mounted along the bed. The intensity of segregation in different systems was analyzed by comparing the time-averaged amplitude of signals representing pressure fluctuations at the top and bottom of the bed. Through comparing the profiles of local pressure drops for the mixtures investigated, it can be concluded that since varying the mass fraction of biomass particles changes the distribution of inlet gas between the bubble and emulsion phases, it can considerably affect the mixing/segregation extent, particularly, at low gas velocities. Besides, it seems that for a given mass fraction of biomass, the number of biomass particles can influence the sensitivity of mixtures towards mixing/segregation at least for the range of gas velocities studied. REFRENCES 1. Khan, A., De Jong, W., Jansens, P.J. and Spliethoff, H., 2009. Biomass combustion in fluidized bed boilers: Potential problems and remedies. Fuel Processing Technology 90 (1), 21-50. 2. Zhang, Y., Jin, B., Zhong, W., 2008. Fluidization, mixing and segregation of a biomass-sand mixture in a fluidized bed. International Journal of Chemical Reactor Engineering 6, A88. 3. Zhang, Y., Jin, B., Zhong, W., 2009. Characterization of fluidization andsegregation of biomass particles by combining image processing and pressurefluctuations analysis. International Journal of Chemical Reactor Engineering 7,A81. 4. Zhang, Y., Jin, B., Zhong, W., 2009. Experimental investigation on mixing and segregation behavior of biomass particle in fluidized bed. Chemical Engineering and Processing 48, 745-754. 5. Joseph, G. G., Leboreiro, J., Hrenya, C. M., Stevens, A. R., 2007. Experimental segregation profiles in bubbling gas-fluidized beds. AIChE Journal 53 (11), 2804-2813. 6. Johnson, F., Zijerveld, R. C., Schouten, J. C., Van Den Bleek, C. M., Leckner, B., 2000. Characterization of fluidization regime by time series analysis of pressure fluctuation. Int. J. Multiphase Flow 26, 663–715. 7. Olivieri, G., Marzocchella, A., and Salatino, P., 2004. Segregation of fluidized binary mixtures of granular solids. AIChE Journal 50 (12), 3095-3106.
8
STUDIES ON PROPANE DEHYDROGENATION TO PROPYLENE IN A GAS-SOLID-SOLID FLUIDIZED BED REACTOR Yue Chu, Tongwei Wu, Yunxin Li, Zeeshan Nawaz, Yao Wang, and Fei Wei Beijing Key Labrotary of Green Chemical Reaction Engineering & Technology (FLOTU), Department of Chemical Engineering, Tsinghua University, Beijing 100084, China ABSTRACT Platinum and tin deposited on mixed support of SAPO-34 and alumina oxide at certain proportion constitute a new catalyst of good catalytic performance. The catalyst was tested in a Gas-Solid-Solid fluidized bed reactor. Cold model experiment was carried on to obtain fluidization curves and characteristic velocities. Reaction results in the GSS-FBR showed that propylene yield was improved by 5 % compared with that in micro fixed bed reactor. INTRODUCTION Light alkenes such as propylene are indispensable raw material in numerous (petro)chemical applications. To comply with the development of downstream industries, propylene demand has been growing quickly (1). On-purpose propylene production technologies such as direct propane dehydrogenation (PDH) have been focused on as one of major process to make up the shortfall of propylene supply left by catalytic and steam cracking of naphtha in which propylene is called a by-product (2, 3, 4). Nowadays, chromia-alumina catalysts and platinum based catalysts are used in commercial dehydrogenation plants. In the late 1980s, Catofin technology applying chromia-alumina catalyst was commercialized by ABB Lummus (6). Then during the 1990s, UOP (Universal Oil Products, USA) developed Oleflex process. In Oleflex process, Pt-Sn/Al2O3 catalyst was used (7). The effect of support has been discussed by many researchers. Traditional catalysts with Al 2O3 support had problems in application especially of stability and selectivity. As a result a variety of catalysts in which Pt-Sn was supported on various supports like SiO2, Y-zeolite, Beta, SBA-15, MgAl(O), ZSM-5 were studied in an effort to find an optimum catalyst (5, 8, 9, 10). In our work, a kind of silicoaluminophosphate zeolite called SAPO-34 is chosen as catalyst support which is a microporous sieve with chabasite-like structure. This
zeolite has good thermal stability and is inherently resistant toward hydrothermal treatment (5), making it possible for support of the propane direct dehydrogenation catalyst. As a highly endothermic reaction, direct dehydrogenation process is suitable to be operated in a fluid bed reactor which offers a lot of advantages such as high rate of mass and heat transfer and solids mobility. The mobility of catalyst particles gives the deactivate catalyst a chance to be regenerated. In PDH process, because of the high temperature and olefins product, coke deposited rate is high resulting in deactivation of the catalyst. In Circulating Fluidized Bed, catalysts can move into the regenerator continuously making sure of the continuous operation. While the particle attrition rate in a fluidized bed is much faster than fixed bed reactor, to save the noble metal Pt of the PDH catalysts, an idea of binary particles fluidized bed reactor (Gas-Solid-Solid fluidization, GSS) is proposed (11). In our previous work, mechanical attrition behavior in binary fluidization was examined. The negligible attrition of large particles in the experiment indicated that GSS fluidized reactor was applicable for platinum based catalytic process (12). This paper presents some experimental results from a study of Pt-Sn catalysts supported on SAPO-34 and specially pelletized supports making up of SAPO-34 zeolite and alumina oxide binder. Effect of the improvement in catalyst supports on catalytic activity is tested in a micro fixed bed reactor. And then in a cold model, fluidization characteristics of pelletized catalysts are studied. Finally, the process is operated in a lab-scale Gas-Solid-Solid fluidized bed reactor. EXPERIMENTAL SECTION Catalyst Preparation Three kinds of supports were used in this article to compare their activity in propane direct dehydrogenation. Besides pure SAPO-34 zeolite, γ-Al2O3 and their mixture were also used. The specially pelletized support made up of SAPO-34 and Al2O3 at certain proportion was produced by a manufactory named Hui er green chemical technology corporation, Beijing.The Pt-Sn based catalyst was prepared by sequential impregnation method (5). For the three kinds of catalysts made with different supports, metallic composition was the same by 0.5, 1.0 wt % of Pt, Sn. Catalytic Tests in a Micro Fixed Bed Reactor The catalytic tests of different catalysts were performed using a micro fixed-bed plug
flow reactor working at atmospheric pressure. The reactor was a 8 mm i.d. and 240 mm long quartz tube placed inside an electrical furnace. Mass flow controllers were used to adjust the amount of inlet gas. The product analysis was accomplished by an online gas chromatograph. The deposited coke content in the catalyst sample was analyzed by thermal gravimetric analysis (TGA) using Netzsch STA 409. Catalytic Tests in a Gas-Solid-Solid Fluidized Bed Reactor Cold model fluidization experiment To be used in the Gas-Solid-Solid fluidized bed reactor, prepared catalysts were pelletized to coarse particles with diameter of 590~840μm (20~30 mesh). SiO2 particles of average diameter 87.76μm that had similar physical properties with FCC catalyst were used as small particles. The fluidization characteristics of that system were studied in a cold model Perspex equipment with dimensions of diameter 5 cm and height 100 cm. Pure nitrogen was used as fluidization medium. Reactive fluidization experiment Figure 1 shows a scheme of the Gas-Solid-Solid fluidized bed reactor used in this work. The fluidized bed reactor was a steel tube with inner diameter 50 mm and height 600 mm. Inside the tube several fins were added in order to enlarge the heat transfer area and improve the fluidization state.
P 1
P
7
2 5 50 10
600
Typically 50 g pelletized Pt-Sn based catalyst and 100 g small particles were charged in the fluidized bed reactor. Preheated propane and hydrogen, sometimes including inert fluidization medium nitrogen, were let into the reactor from the bottom. The flow rate of hydrogen changed as to keep the reactant ratio H2/C3H8 0.25.
4
6
T6 T5 T4 T3 T2
1 3
T1
8 9
1- Fluidized bed 2- Disengager 3- Gas distributor 4- Condenser 5- Sampling 6- Filter 7- Catalyst inlet 8- Reactant inlet 9- Discharge catalyst 10- Fin T1-T6: Thermocouples P1: Manometer
Figure 1 Scheme of the reactive fluidized bed reactor
RESULT AND DISCUSSION Influence of Supports on Catalyst Performance
Figure 2 shows the experimental result of different catalysts in a micro fixed-bed reactor. It can be drawn that using SAPO-34 as support can largely improve propylene selectivity as has been mentioned in our previous work (5). By adding Al2O3 into SAPO-34 at a certain proportion as binder, the specially pelletized catalyst made a great improvement in both propane conversion and propylene selectivity. The coke deposited catalysts were analyzed by TGA to measure the amount of coke produced during five hours’ reaction. The calculated data was listed in Table 1. By comparing the coke selectivity of Pt-Sn/SAPO-34 and Pt-Sn/mixed supports, it’s clear that coke selectivity decreased significantly through specially pelletization of the support. The low rate of coke deposition is one of the reasons why catalysts’ activity and stability improved by using the specially pelletized support. Figure 3 shows the different curves of TGA results of coke deposited catalysts after five hours’ reaction. The position of peaks which was in accordance with literatures (5, 13) showed that coke deposited on the catalyst was of different forms. The peak at 450oC shows coke deposit on Al2O3 support while the peak at 630oC represents coke deposit on SAPO-34 support. In the mixed carriers, two kinds of coke existed simultaneously. 100
35
Propylene Selectivity (%)
Propane Conversion (%)
40
30 25
Pt-Sn/SAPO-34 Pt-Sn/mixed supporter Pt-Sn/Al2O3
20 15 10
0
1
2
3
4
95
90
80
5
Time (hr)
Pt-Sn/SAPO-34 Pt-Sn/mixed supporter Pt-Sn/Al2O3
85
0
1
2
3
4
5
Time (hr)
Figure 2 Evolution of catalyst performance with time in micro fixed bed reactor. T=863K; WHSV=2.8 h-1; mcatalyst=200mg; QC3H8=5.5 ml/min; QH2=0.25 QC3H8 . Table 1 The amount of coke formed after five hours’ on-stream and selectivity for coke of the three kinds of catalyst calculated from TGA results Catalyst Pt-Sn / SAPO-34 Pt-Sn / mixed support Pt-Sn / Al2O3 Pt-Sn / mixed support*
C, wt% 7.68 10.73 5.3 8.25
S coke, % 4.11 2.98 2.90 1.06
* This catalyst was tested in the GSS-FBR. Fluidization Properties of Binary Mixtures in the GSS-FBR To determine the proper range of operating parameters, experiments in a cold model of same diameter to the hot model reactor were done using 50 g pelletized Pt-Sn based catalyst and 50 g SiO 2 particles. Figure 4 shows the fluidization curve and the calculated minimum fluidization velocity is 0.035 m/s. Fluidization of binary particles with significhant difference had been studied decades of years and several formulas to calculate U mf had been described. In this work a semi-empirical formula presented by Noda was chosen to calculate the minimum fluidization velocity (14). Calculative process was as follows: Calculation of the average density and diameter of the mixture:
1
m
f p ; f p
1 dm m
f p d f f dpp
Calculation of Umf with the following equation: Ar A Re 2mf B Re mf
In which, Ar
d m3 ( m ) g
2
dp f A 36.2 d f p
Re mf
;
0.196
;
d umf
dp f B 1397 d f p
0.296
, if d p / d f 3
By using formula listed above, the Umf of our fluidization system can be calculated and its value was 0.041 m/s. There were some difference between the experimental result and the calculated result. This had something to do with the wide diameter distribution of the large particles as well as the limitation of applicable range of the formula.
350
0.000
300
dm (g)
-0.002 -0.004 -0.006 -0.008 -0.010
Pt-Sn/SAPO-34 Pt-Sn/mixed supporter Pt-Sn/Al2O3
-0.012 -0.014 100
200
300
400
500
600
700
800
Temperature (oC)
900
Pressure drop (Pa)
0.002
250 200 150 100 50 0
umf
0.00
0.02
0.04
0.06
Superficial velocity (m/s)
Figure 3 TGA results of coke deposited Figure 4 Cold model fluidization result of catalysts the binary mixture
Reactive Fluidization Experiment Figure 5 shows the evolution of propane conversion and propylene selectivity with time in the micro fixed bed reactor and in the Gas-solid-solid fluidized bed reactor. The experiments used the same specially pelletized catalysts and were run under the same temperature, weight hourly space velocity of propane and H2/C3H8 ratio that had been optimized in micro fixed bed reactor previously (15). It can be seen that propane conversion was more stable in the fluidized bed reactor compared to the fixed bed reactor. After six hours’ reaction, the remaining conversion was about 60 % of the initial in fixed bed reactor while in fluidized bed reactor that percentage was about 95 %. For selectivity of propylene, in both reactors, trend of two curves was identical that in the initial period a significant increase existed and finally a stable state of higher than 96 % can be achieved. In the GSS-FBR, though the growth speed of selectivity was slower, high selectivity of 97 % remained steady in the later 4 hours. Propylene yield was improved by 5 % in the GSS-FBR than the fixed bed reactor. This improvement in propylene yield profits from the high value of heat transfer coefficient in the fluidized bed reactor. Due to uniform bed temperature in the reactor, the selectivity of coke deposition and byproduct like methane and ethylene resulting form propane cracking would decrease. The value of coke deposited on catalyst after five hours’ reaction in the GSS-FBR was measured by TGA and listed in Table 1. Coke selectivity was found to be much lower than that of the same catalyst tested in fixed bed reactor.
100 Micro fixed bed reactor Fluidized bed reactor
35
Propylene Selectivity (%)
Propane Conversion (%)
40
30 25 20
0
1
2
3
4
5
6
7
8
Time (hr)
90 80 70 Micro fixed bed reactor Fluidized bed reactor
60 0
1
2
3
4
5
6
7
8
9
Time (hr)
Figure 5 Comparison of catalytic performance in fixed bed reactor and fluidized bed reactor.T=863K; WHSV=5.6 h-1; In micro fixed bed reactor: mcatalyst=100mg ; QC3H8=4.75 ml/min; QH2=0.25 QC3H8. In fluidized bed reactor: m catalyst = 50 g; QC3H8 = 2.38 L /min; QH2 = 0.25 QC3H8. CONCLUSION Pt-Sn based catalysts were tested in a micro fixed bed reactor and the specially pelletized catalyst of higher conversion, better stability and lower coke selectivity than others was chosen to be tried in the Gas-solid-solid fluidized bed reactor. Cold model experiment was run to study the fluidization characteristics of binary particles mixture with significant size difference obtaining fluidization curves and minimum fluidization velocity. Finally, using the chosen catalyst as big particle and inert substance SiO2 as small particle, propane dehydrogenation reaction was tried in a fluidized bed reactor. Stable propane conversion and high propylene selectivity were achieved. The reaction result in the GSS-FBR indicated that this design of fluidized bed reactor was practicable for PDH process. NOTATION GSS-FBR: Gas-solid-solid fluidized bed reactor TGA: Thermal gravimetric analysis WHSV: Weight hourly space velocity, h-1 Umf: Minimum fluidization velocity, m/s ρm: Density of mixed particles, kg/m3 dm: Diameter of mixed particles, m ωf: Mass fraction of fine particle, ωp: Mass fraction of large particle ρf: Density of fine particle, kg/m3 ρp: Density of large particle, kg/m3 df: Diameter of fine particle, m
dp: Diameter of large particle, m ρ’: Density of gas, kg/m3 μ: Viscosity of gas, Pa•S g: Acceleration due to gravity, m/s2 REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
Wang G, Xu CM, Gao JS. FUEL PROCESSING TECHNOLOGY. 2008, 89(9), 864-873 L. Bednarova, C.E. Lyman, E. Rytter, A. Holmen. Journal of Catalysis. 2002, 211(2), 335-346 Odd A. Bariås, Anders Holmen, Edd A. Blekkan. Journal of Catalysis. 1996, 158(1), 1-12 Toshio Waku, Joseph A. Biscardi, Enrique Iglesia. Journal of Catalysis. 2004, 222(2), 481-492 Zeeshan Nawaz, Xiaoping Tang, Qiang Zhang, Dezheng Wang, Wei Fei. Catalysis Communications. 2009, 10(14),1925-1930 Won, Wangyun, Lee, Kwang Soon, Lee, Seokho, Jung, Chansul. COMPUTERS & CHEMICAL ENGINEERING. 2010, 34(4), 508-517 M. M. Bhasin, J. H. McCain, B. V. Vora, T. Imai, P. R. Pujadó.Applied Catalysis A: General. 2001, 221(1-2), 397-419 M. Santhosh Kumar, Anders Holmen, De Chen. Microporous and Mesoporous Materials. 2009, 126(1-2), 152-158 Linyang Bai, Yuming Zhou, Yiwei Zhang, Hui Liu, Xiaoli Sheng, Yongzheng Duan. Catalysis Communications. 2009, 10(15), 2013-2017 D. Akporiaye, S. F. Jensen, U. Olsbye, F. Rohr, E. Rytter, M. Rønnekleiv, A. I. Spjelkavik. Ind. Eng. Chem. Res. 2001, 40(22), 4741-4748 Shuiyuan Huang, Zhanwen Wang, Yong Jin. Chemical Engineering Science. 1999, 54(13-14), 2067-2075 Zeeshan Nawaz, Yue Chu, Wei Yang, Xiaoping Tang, Yao Wang, Fei Wei. Ind. Eng. Chem. Res. 2010, 49(10), 4614-4619 Júlio C. Afonso, Donato A. G. Aranda, Martin Schmal, Roger Frety. Fuel Processing Technology. 1997, 50(1), 35-48 K. Noda, S. Uchida, T. Makino, H. Kamo. Powder Technology. 1986, 46(2-3), 149-154 Zeeshan Nawaz, Xiaoping Tang, Yao Wang, Fei Wei. Ind. Eng. Chem. Res. 2010, 49(3), 1274-1280
DESIGN AND OPERATION OF BIOMASS CIRCULATING FLUIDIZED BED BOILERS WITH HIGH STEAM PARAMETERS Shiyuan Li, Shaolin Bao, Qinggang Lu, Dongyu Wang, Haipeng Teng Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, 100190, China E-mail: [email protected] Yicheng Peng, Zhibin Liu, Bo Hong Changsha Boiler Plant Co., Ltd., Changsha, 410114, China ABSTRACT Two circulating fluidized bed(CFB) boilers with capacity of 12 MWe and 25 MWe, respectively, with biomass as fuel, adopting the basic technology independently developed by Institute of Engineering Thermophysics (IET), Chinese Academy of Sciences, have been in commercial operation since March 2010 in China. This paper focuses on the design principles, the design specifications and operating results of the two CFB boilers. INTRODUCTION Power demand has been mostly met by consuming fossil fuels for a long time in China. However, fossil fuels are limited and results in severe air pollution. Due to the increasing environmental concerns, especially on greenhouse gas emissions related to the use of fossil fuels, new solutions to limit the greenhouse gas are continuously sought. The available alternative energy sources are hydro, solar and wind etc. In order to mitigate greenhouse emissions, biomass is the only carbon-based sustainable option, which is potentially CO2-neutral and a renewable energy source. At present, biomass has been converted into heat and electricity on a large scale most often by combustion. Over the past five years, more than 20 commercial biomass boilers have been put into operation in China. These boilers are all for 100% biomass combustion because the government has a policy of providing a subsidy for this kind of combustion. Most are grate boilers with electrical capacity from 6 MWe to 25 MWe. Recently, since the CFB combustion technology develops quickly in China, circulating fluidized beds which can be used for a broad variety of biomass inputs have been gradually accepted by the Chinese market. Furthermore, the cost of fluidized beds is also its strength compared with other combustion technologies for biomass fuels. In recent years, some biomass CFB boilers have been put into operation for power generation with the capacities from 6 MWe to 25 MWe and at the highest steam parameters of 9.8 MPa and 540 oC. However, to some extent the technology of biomass CFB boilers is still immature and in the demonstration phase in China.
1
This paper reports two CFB boilers with capacity of 12 MWe and 25 MWe, respectively, for 100% biomass as fuel using basic technology independently developed by Institute of Engineering Thermophysics (IET), Chinese Academy of Sciences. The paper focuses on the design principles applied to the two boilers and presents commercial operation results. DESIGN PRINCIPLES OF BIOMASS CFB BOILERS Generally, compared with coal, biomass has more oxygen, more chlorine and potassium, less carbon, aluminum, iron, titanium and sulfur, and sometimes more calcium. Some special elements in biomass should deserve special attention because the existence of them will be the reason for designing different type boilers from those for coal. The most important element in biomass is potassium, which may form a low melting point ash during combustion in a CFB boiler. The low melting point ash constituents can induce the formation of agglomerates in a CFB boiler, in addition to deposition and corrosion. Accumulation of the agglomerates composed of sand and ash particles bound by fused and glassy materials may lead to defluidization and unscheduled shutdown of a plant (1, 2). The other important element is chlorine with regard to its behavior for related problems in different combustors. Chlorine found in high quantities in certain kinds of biomass, such as straw, may affect a boiler’s operation since it can cause serious corrosion. The high chlorine and alkali content of some biomass fuels can lead to severe damage of combustion units. The greatest concern is high temperature corrosion of the superheater by chlorine on the tube surface. Therefore, agglomeration, deposition and corrosion should all be concerned in the biomass CFB boilers design. To biomass CFB boilers, the IET’s design has the features as the follow: z
Relatively low temperatures at the combustion chambers, the cyclones and the loop seals, approximately 780 oC to 880 oC, which will be different to different kinds of biomass;
z
To adopt the final stage superheater panels at the tops of the combustion chambers in order to reduce corrosion. There is no deposition on these tubes due to the movement of bed material particles;
z
Kaolin or other minerals are used as bed material or added to the bed to avoid agglomeration of bed material;
z
Relatively low gas velocities in the back passes to reduce deposition.
The bulk density of the most biomass fuels, especially agricultural origin, is much lower than that of coals (3). Many disadvantages of biomass fuels, for example,
2
relatively low heating values per unit volume, difficulty of feeding control, requirement of huge storage vessels and expensive transportation, are due to the very low bulk densities The energy density resulting from the bulk density and the net calorific values influences the process control of the fuel supply system. Densification is a good process to overcome these disadvantages. However, it is expensive and some project owners would not like to accept the extra cost to the fuel pretreatment. Nevertheless, herbaceous biomass should be densified before being fed into a biomass CFB boiler. For woody biomass, suitable pretreatment for sizing is necessary to meet the CFB facilities requirements. Biomass fuels show higher combustion reactivity due to their high volatile content and highly reactive char, but have much lower carbon and high oxygen contents which are responsible for their lower heating values. The moisture content of biomass can vary in a wide range from 10% to 60% in different seasons in a year. High moisture contents would cause ignition and agglomeration issues and reduce the combustion temperature, which in turn hinder the combustion of the reaction products and consequently affects the quality of combustion. Therefore, larger dimensions are required to guarantee the normal operation of the boiler for biomass fuel with high moisture content which generates more flue gas. Most biomass fuels have low inherent ash contents. However, some materials are produced from the process because soil is incorporated into the fuel. Although dirt increases the ash content, it is still difficult to form good bed material circulation without adding external bed material during the operation of a CFB boiler. Therefore, adding kaolin or other minerals during operation is necessary for a biomass CFB boiler, which also can control the agglomeration of bed material at the same time. 12 MWe BIOMASS CFB BOILER Design Specification of the 12 MWe Biomass CFB Boiler Biomass fuels for the boiler are mainly sawdust, wood chips and bamboo. The ultimate and proximate analyses of the fuel for the boiler design are listed in Table 1, the biomass boiler arrangement is shown in Figure 1, the main design parameters are given in Table 2.
3
Table 1 Composition of biomass For the 12 MWe Boiler sawdust bamboo wood Proximate analysis Moisture (wt% as received) 48.57 Ash (wt% as dry) 3.67 Fixed carbon (wt% as dry) 17.85 Volatile (wt% as dry) 78.48 Ultimate analysis (wt% as dry) C 49.41 H 5.76 N 0.63 O 40.43 S 0.08 Cl 0.07 Heating value (MJ/kg, as received) LHV 9.48
For the 25 MWe Boiler corn stalk cotton stalk
30.4 4.02 — 53.36
42.1 0.74 — 47.55
9.0 22.28 15.60 62.12
10.5 11.79 18.74 69.47
48.19 5.01 0.66 42.03 0.05 —
51.19 6.10 0.16 41.73 0.02 —
38.47 4.70 1.11 33.27 0.16 0.515
44.03 5.23 0.94 37.88 0.14 0.226
11.70
10.03
14.10
16.43
Figure 1 The 12 MWe biomass CFB boiler arrangement
In Figure 1, there are three sets of horizontal double screw feeders conveying the biomass to the bottom of the furnace. Two adiabatic cyclone separators with a
4
diameter of 3.0 m with water-cooled diplegs are placed between the rear wall of the furnace and the back pass. Two non-mechanical loop seals are located below the two separators. There are two water wall panels and three final-stage superheater panels close to the front wall in the furnace. In the back pass, a middle-stage superheater, a primary superheater, an economizer and an air preheater are arranged along the flow direction of flue gas. Only one stage of spray desuperheater is arranged at the outlet of the middle-stage superheater in the superheater system. Operation Results The boiler is at Changguang electricity generation plant in Changxing county of Zhejiang province, China. Commercial operation of the biomass CFB boiler started in March 2010. After a successful 72-hours trial run, the plant was handed over to the owner. The performance test of the boiler was conducted in August 2010. The design of the biomass CFB boiler was confirmed during commissioning and subsequent commercial operation. The main commercial operating results of the biomass CFB boiler are shown in Table 2. All gas emission values are far below the guaranteed limits. Table 2 Main design values and operating results of the 12 MWe biomass CFB boiler Item
Unit
Design Values
Operating results
Nominal steam capacity
t/h
75
75.18
Steam temperature
o
C
485
481.3
MPa
Steam pressure
5.3
5.3
Feed water temperature
o
150
149
Flue gas exit temperature
o
138
142
Preheated air temperature
o
C
180
180
Guaranteed boiler efficiency
%
89.37
90.75
Furnace temperature
o
810
800
Outlet of furnace temperature
o
NOx emission (O2=6%) SO2 emission (O2=6%)
C C
C
—
730
3
200
66
3
200
4
C
mg/Nm
mg/Nm
Unburnt carbon contents of recirculation ash, fly ash and bottom ash were 0.49%, 3.47% and 1.35%, respectively. The size distributions of recirculation ash and fly ash are shown in Figure 2 and Figure 3. The size of the recirculation ash is widely distributed from 10 μm to 450 μm with a 50% cut size of 135 μm, and the size of the fly ash ranges from 0.5 μm to 120 μm with a 50% cut size of 40 μm.
5
Figure 2 Size distribution of recirculation ash
Figure 3 Size distribution of fly ash
However, the moisture content of the biomass fuel often reached 40%~45% in summer. The high moisture content and the non-uniformity feeding of biomass fuel affected the boiler load greatly. In addition, some problems occurred in the biomass fuel feeding system, which resulted in the break age of the fuel supply. The fuel feeding system has now been improved. 25 MWe BIOMASS CFB BOILER Design Specification of the 25 MWe Biomass CFB Boiler The Main design parameters of the 25 MWe (130 t/h) boiler are given in Table 3. The biomass fuel for this project were mainly corn stalk and cotton stalk pellets, the ultimate and proximate analyses for boiler design are listed in Table 1, the boiler arrangement is shown in Figure 4. Table 3 Main design parameters of the 25 MW CFB Biomass Boiler Item
Unit
Value
Nominal steam capacity
t/h
130
Steam temperature
o
C
540
MPa
9.81
Steam pressure Feed water temperature
o
215
Flue gas exit temperature
o
150
Preheated air temperature
o
C
220
Guaranteed boiler efficiency
%
89
Average furnace temperature
o
C C
C
NOx emission limit SO2 emission limit
6
818
mg/Nm
3
200
mg/Nm
3
200
Figure 4 The 25 MWe biomass CFB boiler arrangement
In Figure 4, there are four sets of horizontal double screw feeders conveying the biomass pellets to the bottom of the furnace. Two adiabatic cyclone separators with a diameter of 4.0 m with water-cooled diplegs are placed between the rear wall of the furnace and the back pass. Two non-mechanical loop seals are located below the two separators. There are four water wall panels, three final-stage superheater panels and three middle-stage superheater panels close to the front wall in the furnace. In the back pass, a primary superheater, an economizer and an air preheater are arranged along the flow direction of flue gas. Two stages of spray desuperheater are arranged at the outlet of the primary and middle-stage superheater separately in the superheater system. Operation Results The 25 MWe demonstration biomass plant is located in Guantao county of Hebei province, China. Commissioning of the plant started in October 2010. In January 2011, the plant met all guaranteed targets in the performance test. CONCLUSIONS The 100% of IET CFB biomass boiler technology is capable of combusting a broad
7
range of different biomass fuels, including wood biomass and other agriculture biomass. Furthermore, the technology ensures high steam parameters and enables biomass fired power plants to obtain high efficiencies with emissions far below the limits. So far, the 25 MWe demonstration power plant has been the biggest 100% biomass fired CFB power plant in China, which is an important milestone. REFERENCES 1. 2. 3.
W. G. Lin, K. D. Johansen, F. Frandsen. Agglomeration in bio-fuel fluidized bed combustors. Chemical Engineering Journal. 2003, 96: 171-185. S. Y. Li, L.L. Shang, H. P. Teng, Q. G. Lu. A model for Agglomeration in Bio-fuel Fired Fluidized Bed. Journal of Thermal Science. 2010, 19(5): 451-458. A. Khan, W. de Jong, P. J. Jansens, H. Spliethoff. Biomass combustion in fluidized bed boiler: Potential problems and remedies. Fuel Processing Technology. 2009, 90 (1): 21-50.
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DYNAMICS OF GAS-SOLID FLUIDIZED BEDS THROUGH PRESSURE FLUCTUATIONS: A BRIEF EXAMINATION OF METHODS OF ANALYSIS Srdjan Sasic1, Marc-Olivier Coppens2, John van der Schaaf3, Stefan Gheorghiu4, Filip Johnsson5, J. Ruud van Ommen6 1
Chalmers Univ. of Technology, Dept. of Applied Mechanics, Göteborg, Sweden Rensselaer Polytechnic Inst., Isermann Dept. of Chem. and Biol. Eng., Troy, NY, USA 3 Eindhoven Univ. of Technology, Lab. of Chem. React. Eng. the Netherlands 4 Center for Complexity Studies, Bucharest 061942, Romania 5 Chalmers Univ. of Technology, Dept. of Energy and Environ. Göteborg, Sweden 6 Delft Univ. of Technology, Dept. of Chem. Eng., 2628 BL Delft, the Netherlands 2
ABSTRACT This paper revisits and critically examines a number of methods used for analysis of in-bed pressure signals recorded in gas-solid fluidized beds. The goal is to obtain information on the time scales of dominant phenomena present in the pressure time series of four fluidization regimes. It is demonstrated that the average cycle time represents an effective alternative to spectral analysis. In addition, we give evidence that the average cycle time yields equivalent information as some of the advanced methods of non-linear analysis (e.g. the Kolmogorov entropy). Finally, by using wavelets and wavelet packets, we show how to obtain an accurate time localization of the different frequency components present in the pressure signal. INTRODUCTION The dynamics of gas-solid fluidized beds are often characterized by investigating pressure fluctuations. A pressure measurement system is robust, cheap and nonintrusive, thus avoiding distortion of the flow around the point of measurement. In addition, pressure is easily measured, even in industrial conditions. The in-bed pressure fluctuations are predominantly related to bubble motion within the bed, but a more comprehensive explanation on the origin of the fluctuation has already been debated for a long time (e.g. Kage et al., (1); van der Schaaf et al., (2); Bi (3)). The pressure signal has an intrinsically non-local nature, and due to this fact, the interpretation of pressure measurements is far more complicated than of a more local measurement, such as local solids concentration measurements using optical probes. An important aspect of any interpretation is to evaluate available techniques of signal analysis, related to their ability to describe the dynamics of the bed. In general, the techniques can be grouped into three categories: (1) time domain methods, (2) frequency domain methods, and (3) state space methods. It is not feasible to analyse in this work all methods regularly used in the literature for the analysis of fluidized-bed pressure signals; a broader review has recently been
published (4). In the current paper, our aim is to demonstrate how to most conveniently gain fundamental information on the dynamics of fluidized beds (e.g., the main time scales) by using some of the commonly employed methods of signal analysis. Furthermore, we will critically evaluate these techniques nowadays frequently used and show that often some very advanced methods do not give more insight into the system behaviour than do some considerably simpler ones. We will carry out the analysis by looking into data sets for four fluidization regimes investigated by Johnsson et al. (5). In summary, our goal is, by calculating the main time scales present in the signals, to provide important recommendations on the suitability of the use of the methods examined. EXPERIMENTS The data sets applied here are the same as those used in Johnsson et al. (5). In brief, the experiments were carried out in a CFB unit operated under ambient conditions. The riser has a cross-section of 0.12 × 0.7 m and a total height of 8.5 m. The bed material was silica sand with an average particle size of 0.32 mm and a particle density of 2600 kg/m3, i.e., Group B particles. In the riser, pressure fluctuations were measured at 0.2 m above the air distributor through a 50 mm long and 4 mm ID steel tube with a fine mesh net at the side facing the fluidized bed; these probe dimensions in combination with the transducer minimize the distortion of the pressure signal (van Ommen et al., 6). The pressure is measured “single ended”: the fluctuations are recorded and the signals were low-pass filtered at the Nyquist frequency. The sampling frequency was 400 Hz in all cases, with 33 minutes of total sampling time. The four fluidization regimes identified are: the multiple bubble regime, the single bubble regime, the exploding bubble regime and the transport regime. To obtain the multiple bubble regime, a distributor with a higher pressure drop was used (Johnsson et al., (5)). Note that, although the names of the identified regimes are not standard in the fluidization community, we have nevertheless used them in this work, in accordance with (5). The main conditions are presented in Table 1. Table 1. Operating conditions for the four pressure time-series used in this paper Regime condition Multiple Single Exploding Transport bubble bubble bubble conditions gas velocity [m/s] 0.6 0.6 2.2 4.1 solids mass flux [kg m-2 s-1] 0 0 ~1 25 bottom bed height [m] 0.40 0.37 0.30 bottom bed voidage [-] 0.51 0.50 0.58 0.80* bottom bed pressure drop 4 960 4 730 3 310 1 120* [Pa] distributor pressure drop 4 200 660 3 090 13 700 [Pa] *No bottom bed present, values given over the lower 20 cm of the columns THEORY As indicated above, this paper is not a full review on all the methods employed in the literature when analyzing pressure signals in fluidized beds. Alternatively, we
have chosen here to discuss only the techniques that are either a most straightforward choice when looking at time scales of the governing phenomena existing in a signal, or are at present extensively used (perhaps sometimes without justification, as we will show here). The most common way to look at the time scales of a signal is to analyze the power spectrum (frequency domain analysis) and a brief explanation of the procedure is given here. Since the conclusions obtained by the spectral analysis may not be so clear in the case of non-periodic or non-smooth signals, an alternative in the form of the average cycle time or wavelets may be a suitable option. Finally, if we assume that a pressure signal from fluidized beds is non-linear in nature, it is of interest to characterize its unpredictability (i.e. the loss of information per unit of time). Accordingly, a concise description of those methods is given in this section. Spectral Analysis Fourier spectral analysis often aims at obtaining the dominant frequencies present in time series and assigning them to various physical phenomena (1). In the present paper we will use the Welch’s method (7), where the variance is reduced by estimating the power spectra as an average of several sub-spectra. The number of sub-spectra is chosen to obtain a satisfactory trade-off between frequency resolution and variance. Therefore, the signal treated is divided into time segments and an estimate of the power spectrum of each segment is obtained. An important feature of the spectral analysis is that the energy of the signal is conserved in the frequency domain. Hence, summation of the power spectra over the range of interest yields the total energy of the signal in a given frequency range. Average Cycle Time A suitable alternative to spectral analysis is to look at the average cycle time of the signal. The method belongs to the time domain analysis. It is calculated as two times the pressure signal duration divided by the number of times the pressure signal crosses its average value (e.g. 8). The technique can be sensitive to the presence of noise in the data, but when a low-pass filtering of the signal is applied, the average cycle time yields useful information. A change in the trend of the average cycle time typically indicates a regime change. Wavelets Wavelets allow for the representation of a signal simultaneously in time and in frequency. In fluidization, wavelets are used to characterize the heterogeneous nature of fluidization, and for the study of short-time or transient phenomena. Since fluidization is a multiscale phenomenon, signals measured in fluidized beds typically contain components on at least three frequency scales: the high-frequency scale associated to particle motion, the medium-frequency scale related to particle clusters, and low-frequency scale related to voids. We use here the discrete version of the wavelet transform, which is based on a pair of digital filters. The latter decompose the signal into a low frequency component A1 called the “approximation”, and a high frequency component D1 called the “detail”. The operation is then repeated using the approximation A1 as the input signal. By doing
this operation recursively up to a desired level N, one obtains a hierarchical multiresolution representation of a signal f (Mallat, 9), such that each detail Dk contains frequency information in a range around fs/2k, where fs is the sampling frequency, and k is an integer. The inverse wavelet transform allows for reconstruction of a signal without loss of information. Entropy Fluid dynamics in fluidized beds are governed equations of motion with a non-linear nature. It is then not surprising that numerous results have appeared so far in the literature from applying non-linear analysis to describe various aspects of performance of fluidized beds, such as behaviour of bubbles and information on flow regimes. The methods applied are based on the construction of an attractor representing the dynamic evolution of the system in the state space, defined as a multi-dimensional space containing all the variables governing the system. An attractor is a clearly identified structure in the state-space domain, and probably the most commonly applied method for its characterization is the Kolmogorov entropy (also called correlation entropy or just entropy). The latter is a measure of predictability of a system: it expresses the sensitivity to small changes in the initial conditions. Linear systems have an entropy of zero and are predictable at infinitum, whereas random systems have an infinite entropy and are thus unpredictable. RESULTS AND DISCUSSION
PSD(Pa2/Hz)
Analysis in the frequency domain most often aims at characterizing fluidization regimes by finding the dominant frequency at which bubbles pass through the bed. Fig. 1 shows power 10 10 spectra of pressure fluctuations for the four 8 10 regimes as obtained by the Welch method. The 6 10 low frequency region is dominated by large 4 10 structures (bubble flow), 2 and, at higher frequencies, 10 single bubble multiple bubble finer structures are exploding bubble 0 represented. The relation transport conditions 10 between the two regions is -2 still not clear, but it is often 10 -2 -1 0 1 2 3 10 10 10 10 10 10 argued (5) that the fine Frequency (Hz) structures are not primarily Figure 1: Power spectra of the four regimes treated in governed by the bubble this work. flow. As for the analysis of the time scales of the signals, it is obvious that valuable information can be obtained from spectral analysis (e.g. the existence of the dominant frequency in the regimes studied). However, applying power spectral analysis to strongly non-periodic or nonsmooth signals, such as those recorded in fluidized beds, may not always turn beneficial. In such a case, it is useful to look at alternatives in the time domain. As mentioned above, an easy-to-calculate characteristic is the average cycle time.
Aveage cycle time [-]
Figure 2 shows the average cycle time as the function of the gas velocity. It can be shown that, at least within the non-circulating fluidization regimes, the average cycle time is in effect independent of gas 0.8 velocity, solid particles Circ. conditions Transport 0.7 inventory and particle size. with dense conditions 0.6 bottom bed For the time series applied here, this would imply that 0.5 the regime change, most 0.4 likely from bubbling to 0.3 turbulent fluidization, takes place at a gas velocity 0.2 Non-circulating around 0.88 m/s. conditions 0.1
If we want to obtain a more detailed picture, we can plot the cycle time Figure 2: The average cycle times as a function of the distribution instead of just fluidization velocity. The error bars give the standard calculating the average deviation. The dashed vertical lines give the cycle time. Since fluidizedboundaries between the different regimes. The large bed pressure signals are squares indicate, from left to right, the values for the typically non-periodic selected data sets for the single bubble, exploding signals, and in the same bubble and transport conditions, respectively. time contain information at multiple time scales, it may be a good idea to use wavelets in the analysis. In this work, we have decomposed the signals up to the 9th level, using the discrete version of the Meyer wavelet, implemented in the Matlab Wavelet Toolbox. For every level k of the decomposition, a reconstruction has been computed using only the detail coefficients DK. The variance of the reconstruction 0 10 is then proportional to the power of the signal in that particular frequency window. The resulting spectrum (Fig. -2 3) shows that the peaks at 10 low frequencies, as well as power-law tails at higher frequencies, are nicely recovered. -4 0
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Figure 3: Representation of the wavelet power spectrum of the four signals. The x-axis corresponds to frequency, increasing from left to right.
However, with wavelets it can be difficult to interpret the results when the studied phenomenon does not reside exactly into one of the frequency bands of the wavelet decomposition. In such a case, wavelet packets may be used. With the latter, instead of decomposing only
pressure [Pa]
Frequency [Hz]
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the approximation Ai at stage i, both Ai and Di are passed through the low- and highpass filters, thus producing four components: an approximation of the approximation, a detail of the approximation, an approximation of the detail and a detail of the detail. As an example, Fig.4 shows the results for the exploding bubble regime, with the logarithm of the coefficients plotted. Even with this representation, it is not straightforward 6000 to see a clear separation in frequency between 0 different components of the signal. The -4000 1 2 3 4 5 frequencies seem to be time [s] mixed together, and we 153 may even conclude that 103 the broader bands of an 52.6 ordinary wavelet 1.4 1 2 3 4 5 analysis do a better job separating them. Figure 4: Logarithm of the wavelet packets coefficients of Alternatively, if we for the exploding bubble regime. choose to present the reconstructions of the signals from the coefficients plotted, we are in a position to recover the total time resolution. Fig. 5 exemplifies the result of the latter procedure, again for the exploding bubble regime. 6000
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Figure 5: Reconstruction of the original signal from the wavelet packets coefficients for the exploding bubble regime.
The procedure may be summarized as follows: we set all but one of the coefficients of the terminal nodes to zero. Then, the signal is reconstructed from just the coefficients of that terminal node. It is now feasible to recognize the various components as shadows in the figure.
Finally, we will assess non-linear analysis (also called chaos analysis or state space analysis) in relation to the results obtained so far. State space analysis of pressure data in fluidized beds has been extensively used since the second half on the 1990s. In that period, the Kolmogorov entropy has been often used to characterize fluidized bed-hydrodynamics. For example, Schouten et al. (10) have suggested that the Kolmogorov entropy is proportional to the number of bubbles per unit of time, and to a bubble impact factor, defined in (10) as the ratio of the diameter of a bubble and that of a fluidized bed. This conclusion is more valid if the signal is recorded in the upper part of a riser, since these fluctuations reflect the local bubble behaviour more that if the signal is measured in the bottom of the bed. However, there is a potential problem when obtaining the Kolmogorov entropy. Namely, the entropy should be independent of the length scale at which it is calculated, if the latter is
Kolmogorov entropy [bits/s]
Average cycle frequency [s]
chosen small enough. Such a scaling region is very difficult, if possible, to find. This statement then implies that the entropy analysis does not prove that fluidized beds indeed exhibit low-dimensional chaotic behaviour. Furthermore, we will show on the data sets used in the present paper that there is a strong correlation between the Kolmogorov entropy calculated at a specific length scale and the average cycle frequency (the inverse of the 30 6 average cycle time), see Fig. 5.5 25 6. Note that similar 5 conclusions have already 4.5 20 been suggested by Johnsson 4 et al. (5) and van der Schaaf 3.5 15 et al. (11). If we plot the 3 10 entropy versus the average 2.5 cycle frequency (Fig. 7), we 2 5 see that the two are in fact 1.5 linearly proportional. It can be 1 0 0 1 2 3 4 5 6 demonstrated that the Superficial gas velocity [m/s] proportionality constant is Figure 6: The maximum likelihood entropy and the directly related to the shape average cycle frequency as functions of the gas of the power spectrum. Since velocity. The squares indicate the four data sets; the average cycle frequency the other markers represent the additional and the power spectrum are more easily correlated to measurements at intermediate gas velocities. physical phenomena, these characteristics should be preferred over the Kolmogorov entropy (or any similar feature from the state space analysis, such as the correlation dimension). The latter conclusion is further supported by the fact that the average frequency is not dependent on calculation parameters, whereas the Kolmogorov entropy clearly is. Since the application of Figure 7: The maximum likelihood entropy versus non-linear analysis is typically complicated, we the average cycle frequency for the four regimes more recommend its use only if it investigated in the paper. yields information that is not obtainable by linear analysis, such as an early detection of non-stationarities in fluidized bed behaviour (12). CONCLUSIONS When pressure is recorded in a gas-solid fluidized bed, the obtained signal can yield significant information on the bed dynamics. The interpretation of signals is, however, not always straightforward. In this paper, we have revisited some of the
most commonly used methods of analysis of the pressure time series. The work is not meant as a complete review paper. Instead, we have chosen to go through the techniques frequently used to obtain information on main time scales of the dominant phenomena present in the bed. We have shown that the cycle time and its distribution provide useful information on the dynamics of the bed. As such, they represent an easy-to-calculate alternative to frequency analysis. The latter, in general, provides essential information, but may be problematic when non-periodic and non-smooth signals are investigated. To provide information on time localization of particular frequency components in a signal, we have carried out the analysis using wavelets and wavelet packets. We have seen that the main features of the spectral analysis are adequately reproduced by wavelet analysis. We have used wavelet packets to obtain an unambiguous separation in frequency between different components of the signals. Finally, we have shown that the information given by the Kolmogorov entropy is entirely equivalent to that of the average cycle frequency, obtained by linear methods of analysis. REFERENCES 1. Kage, H., Iwasaki, N., Yamaguchi, H., Matsuno, Y., 1991. Frequency analysis of pressure fluctuation in fluidized bed plenum. J. Chem. Eng. Jpn. 24, 76-81. 2. van der Schaaf, J., Schouten, J.C., van den Bleek, C.M., 1998. Origin, propagation and attenuation of pressure waves in gas-solid fluidized beds. Powder Technol. 95, 220-233. 3. Bi, H.T., 2007. A critical review of the complex pressure fluctuation phenomenon in gassolids fluidized beds. Chem. Eng. Sci. 62, 3473-3493. 4. van Ommen, J.R., Sasic, S., van der Schaaf, J., Gheorghiu, S., Johnsson, F., Coppens, M.O., 2010, Time series analysis of pressure fluctuations in gas-solid fluidized beds – a review, in press, Int. J. of Multiphase Flow, dx.doi.org/10.1016/j.ijmultiphaseflow.2010.12.007 5. Johnsson, F., Zijerveld, R.C., Schouten, J.C., van den Bleek, C.M., Leckner, B., 2000. Characterization of fluidization regimes by time-series analysis of pressure fluctuations. Int. J. Multiphase Flow 26, 663-715. 6. van Ommen, J.R., Schouten, J.C., vander Stappen, M.L.M., van den Bleek, C.M., 1999. Response characteristics of probe-transducer systems for pressure measurements in gassolid fluidized beds: How to prevent pitfalls in dynamic pressure measurements. Powder Technol. 106, 199-218. 7. Welch, P.D., 1967. The use of fast Fourier transform for the estimation of power spectra: a method based on the averaging over short, modified periodograms. IEEE Transactions Audio Electroacoustics AU-15, 70-73. 8. Briens, L.A., Briens, C.L., 2002. Cycle detection and characterization in chemical engineering. AIChE J. 48, 970-980. 9. Mallat, S., A wavelet tour of signal processing. Academic Press, London, (1999) ISBN 012-466605-1. 10. Schouten, J.C., vander Stappen, MLM, van den Bleek, CM., 2000, Scale-up of fluidized bed hydrodynamics. Chem. Eng. Science 51, 1991-2000. 11. van der Schaaf, J.,van Ommen, J.R., Takens, F., Schouten, J.C., van den Bleek, C.M., 2004. Similarity between chaos analysis and frequency analysis of pressure fluctuations in fluidized beds. Chem. Eng. science 59, 1829-1840. 12. van Ommen, J.R., Coppens, M. O., van den Bleek, C.M., Schouten, J.C., 2000. Early warning of agglomeration in fluidized beds by attractor comparison, AIChE J., 46, 2183 2197.
A STUDY OF SOLID AND GAS MIXING IN A PARTITIONED FLUIDIZED BED Jong-Ho Moon, Young-Ju Seo, Solim Kang, Seung-Yong Lee, Young-Cheol Park, Ho-Jung Ryu, and Gyoung-Tae Jin Korea Institute of Energy Research, Greenhouse Gas Research Center 71-2 Jang-dong, Yuseong-gu, Daejon 305-343, Korea T: +82-42-860-3676. F: +82-42-860-3134. E: [email protected] ABSTRACT A partitioned fluidized bed gasifier has been developed for improving coal gasification performance. The basic concept is to divide a fluidized bed into two parts, a gasifier and a combustor, by a partition. Char is burnt in the combustor and generated heat is supplied to the gasifier by solid mixing. Therefore, solid mixing should be maximized whereas gas mixing between syngas and the combusted gas should be minimized. In this study, gas and solid mixing behaviors were verified in cold model acrylic beds. For monitoring solid mixing behavior, transient temperature trends in the beds were analyzed. A heat source and a heat sink were installed in each bed. Dozens of thermocouples were used to monitor temperature distribution. INTRODUCTION Since the world recoverable oil resources will be depleted within the next few decades, a search for alternatives to oil is an urgent task. In the post-oil era, coal technologies are of great interest as a practical alternative to oil, due to their cost, availability and versatility. However coal causes 40% of global CO2 emissions. CO2 is considered as a representative green-house gas and increased levels of CO 2 with other green-house gases in the atmosphere contribute to global warming. To sum up, mankind will have to use coal, but at the same time restrain CO 2 emissions. Therefore, clean coal technologies should be developed. The solid residence time depends on solids feed rate in a fluidized bed. In a dual bed system, the solid residence time depends on the sum of the solid feed rates and solid circulation rates. If the heat requirement is increased, it is necessary to increase the solid circulation rates. This shortens the residence time of solids and reduces the solid conversion. To overcome this, a “partitioned fluidized bed” concept for low rank coal gasification has been developed (figure 1). The upper part of the bed is blocked by a partition, but the lower part of the partition is open to connect the two beds. Endothermic reactions (coal gasification) and exothermic reactions (coal combustion) occur simultaneously in a gasifier and combustor. For coal gasification, a high temperature of over 900oC is needed. Most of the heat is generated by coal combustion. Therefore, the generated heat should be supplied from a combustor to a gasifier using the bed materials. In this study, cold model experiments in a “partitioned fluidized bed (transparent acrylic bed)” were executed at atmospheric conditions to understand solid and gas mixing behavior. N2, CO2 and compressed air were introduced as fluidizing gases into each partitioned fluidized bed. For the gas mixing experiments, glass beads with an average diameter of 150 microns and particle density of 2.5 g/cm3 were used. As
can be seen in figure 2(a), CO2 and N2 were introduced into each distributor in the bed. Outlet gas flow rates and concentrations for each bed were analyzed by gas flow meters and FT-IR respectively. The gas exchange ratios between the reactors were then calculated. For the solid mixing experiments, 1000-micron polypropylene particles with a density of 0.883 g/cm3 were continuously fed into the reactor. As a fluidizing gas, compressed air was introduced into each bed. For improving solid mixing, newly developed distributors and plenums were installed. The objectives of this study were to understand gas and solid mixing characteristics and to quantify the solid flux between the reactors under various operating conditions.
Figure 1. Schematic diagrams of coal gasifiers: conventional, dual, and partitioned fluidized bed gasifiers EXPERIMENTAL Experimental Method (1): Gas Mixing For gas mixing experiments, CO2 and N2 were introduced into each distributor. Experimental procedure was (Figure 3): step 1. Initially, introduce N2 into each partitioned bed (L, C, R) to fluidize the solids, step 2. Switch N2 to CO2 in the center bed by using a 3-way valve, step 3. Measure flow rates and concentrations. Outlet gas flow rates and concentrations of each fluidized bed were analyzed by gas flow meters and IR respectively. Then, the gas exchange rates between the reactors were calculated.
Mixed Gases Center Right
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Figure 2. Schematic diagram of a partitioned fluidized bed: (a) Cold model configuration, (b) Diagram, and (c) Apparatus for cold mode run
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Figure 3. Experimental procedure for gas mixing evaluation At first, CO2 and N2 compositions and flow rates at the inlet and the outlet of each bed were measured and/or calculated respectively. Then, they were substituted into the following equations;
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Where, XRC (Eq. 1) is the gas exchange ratio from the right side bed to the center bed and the XCR (Eq. 2) is the gas exchange in the opposite direction. F and V are inlet and outlet flow rates respectively. Small x and y are inlet and outlet mole fractions respectively. Subscripts R and C mean left and center beds. Experimental Method (2): Solid Mixing For the solid mixing experiments, the 1000-micron polypropylene particles with a density of 0.883 g/cm3 were continuously fed into the left bed (L). Mixed solid particles were withdrawn from the center bed (C) and the right bed (R) (figure 4). As a fluidizing gas, compressed air was introduced into each partitioned fluidized bed by controlling input flow rates. Outlet gas flow rates of each fluidized bed were measured by gas flow meters. Withdrawn mixed solid particles (GB/PP) were separated by sieves. The solid particle distributions were measured by weighing the separated particles.
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Figure 4. Experimental procedure of solid mixing at a three partitioned fluidized bed gasifier.
Center and right side solid discharge ratio could be controlled by the fluidizing gas velocity in each side bed. The discharge percentage of center was mainly affected by the ratio of UC (input fluidizing velocity at center bed) to UR (right bed). As can be seen in figure 5, solid mixing in a three partitioned fluidized bed can be considered the same as in a CSTR.
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Figure 5. Analogy between a CSTR and a Partitioned fluidized bed. RESULTS AND DISCUSSION Gas Exchange Experiments The effect of gas velocity on gas mixing is shown in figures 6 and 7. In this experiment, the gas velocity of each reactor was same. 100
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Figure 6. Transient gas mixing behavior (UL=UC=UR=2Umf): (a) Center Bed and (b) Right bed The left side, center and right side gas velocities were 2, 3, and 4 times the minimum fluidizing velocity (Umf). Figure 6 shows transient gas mixing behavior for the center and right beds. It reached a steady state within 80 sec. After reaching steady state, CO2 mol% in the center bed was 93.5% (XCR=0.032) and N2 mol% in the right bed was 97.0% (XRC=0.0.032). This means that the effect of gas mixing in the partitioned fluidized bed was very small. Increasing the gas velocity from 2.0 to
4.0 Umf, caused the gas exchange rates from the center to left(XCL) and right(XCR) to decrease while the gas flows from the left and right to center (X LC and XRC) increased. At the same gas velocity in each bed (figure. 7 (a)), the gas exchange ratios, from center to right and from right to center, had similar values. At higher flow rates, gas mixing was slightly enhanced. At a low gas velocity condition in the center (figure. 7(b)), XCR was far higher than XRC. The exchanged CO2 amount from center to right decreased, whereas the exchanged N2 amount from right to center increased. XRC was not affected by the center flow rate. At low flow rates in right and left beds (figure 7 (c)), the exchanged CO2 amount from the C to R bed linearly increased, while the exchanged N2 amount from the right to center bed decreased. That means the flow from the side to the center became dominant. XCR was not affected by flow rate in the center bed.
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Figure 7. The effect of flow rate on gas mixing (exchange ratio): (a) case1 (UL = UC = UR), (b) case2 (UL = UR = 4Umf), and (c) case3 (UL = UR = 2Umf)
Solid Mixing Experiments Figure 8 shows analogies between a CSTR and a partitioned fluidized bed. CSTR behaviors are based on perfect mixing. That is, as soon as reactants are introduced into a reactor, mixing and reaction occurs immediately. For no reaction, a CSTR is a perfect mixer. Similarly, if solids are well mixed in a three-partitioned fluidized bed, the solid concentration in each partitioned bed will be the same. Figure 8(a) shows a comparison between calculated CSTR behavior and experimental data from a partitioned fluidized bed. The calculated line is CSTR behavior and the squares are experimental data from a partitioned fluidized bed, where the x-axis is time and the y-axis is PP (polypropylene) concentration in the mixtures.
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The second figure indicates PP concentration dispersion between the center and right beds. The x-axis is PP concentration in the center bed and the y-axis is that in the right bed. The diagonal line shows the case when the PP concentration in the center and right beds are exactly same, i.e. CSTR behavior. The squares are the experimental data.
0.6 0.4 Center Column Right Column CSTR Behavior
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Figure 8. Experimental vs. theoretical results of solid mixing behavior (UL = UC = UR = 2Umf): (a) Comparison between CSTR and partitioned fluidized bed, and (b) Particle distribution at the partitioned bed fluidized bed Temperature Trend As can be seen from the above results, the solid mixing behavior in a three partitioned bed is analogous to liquid mixing behavior in a CSTR. These results were be confirmed by analyzing temperature distribution. Dozens of thermocouples were added to the beds for monitoring temperature distribution. (See Figure. 9) For improving solid mixing, newly-developed distributor systems were installed. For monitoring solid mixing behavior, transient temperature trends in the beds were analyzed. A heat source (right bed) and a heat sink (left bed) were installed in each bed at a height of 55 cm. Dozens of thermocouples were equipped for monitoring temperature distribution. Figure 10 shows the transient temperature trend at the heat source (combustor) and the heat sink (gasifier). Initially, fluidizing air was not introduced into the beds. Therefore, the temperatures at the heat source and the
heat sink could be kept constant at 24℃ and 19℃, respectively. After introducing fluidizing air (UL = UR = 2Umf) into the beds, generated heat was transferred from the right bed (heat source) to the left bed (heat sink). Temperatures at all positions reached steady state within 50 seconds. This means that the solids (or temperature) can be dispersed well in spite of a partition in the bed.
Figure 9. Cold mode partitioned bed for analyzing temperature distribution.
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Figure 10. Transient temperature trend in a partitioned fluidized bed (UL = UR = 2Umf); temperature at heat source (white circles) and heat sink (black diamonds) CONCLUSIONS Gas and solid mixing experiments in partitioned fluidized beds were conducted. Gas mixing between the partitioned fluidized beds can be minimized by controlling the gas velocity in the beds.
The center and right side solid discharge ratio can be controlled by the fluidizing gas velocity in each side bed. The discharge percentage in the center bed is mainly affected by the ratio of UC to UR. Solid mixing in partitioned fluidized beds can be considered well mixed as in a CSTR. ACKNOWLEDGMENT This work was supported by Energy Efficiency and Resources R&D program (2009T100100675) under SK Innovation Co., Ltd. and the Ministry of Knowledge Economy, Republic of Korea NOTATIONS F r t U V XCR x y
inlet mass flow rates respectively (g s-1) rate of reaction (g cm-3 s-1) time (s) gas velocity (cm s-1) outlet mass flow rates (g s-1) gas exchange ratio from the center to the right side (-) inlet mole fraction (-) outlet mole fraction (-)
Subscripts L, C, R left, center and right respectively mf minimum fluidization conditions REFERENCES 1. Murakami, T., Xu, G. Suda, T. and Matsuzawa, Y. Tani, H., Fujimori, T. (2007). “Some process fundamentals of biomass gasification in dual fluidized bed”, Fuel, 86, 244–255 2. Du, B., Fan, L-S., Wei, F. and Warsito, W. (2002). “Gas and solid mixing in a turbulent fluidized bed“, AIChE J., 48(9), 1896-1910 3. G.T. Jin et al., ISOPE2010, Beijing, China (2010) 4. G.T. Jin et al., FLUIDIZATION XIII, Gyeong-ju, Korea (2010)
EXPERIMENTAL STUDY ON THE EFFECTS OF GAS PERMEATION THROUGH FLAT MEMBRANES ON THE HYDRODYNAMICS IN FLUIDIZED BEDS J.F. de Jong, M. van Sint Annaland, J.A.M. Kuipers Multiphase Reactors Group, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, the Netherlands. E-mail: [email protected], URL: http://www.chem.tue.nl/smr, T: +31(0)402472241, F: +31(0)402475833 ABSTRACT In this work, the effects of gas permeation through flat membranes on the hydrodynamics in a pseudo-2D membrane-assisted gas-solid fluidized bed have been investigated experimentally. A combination of the non-invasive Particle Image Velocimetry (PIV) and Digital Image Analysis (DIA) was employed to simultaneously investigate emulsion phase and bubble phase properties in great detail. Counterintuitively, addition of secondary gas via the membranes, that constituted the confining walls of a gas-solid suspension at conditions close to incipient fluidization, did not result in a larger, but in a smaller bubble diameter, while gas extraction on the other hand, resulted in a larger equivalent bubble diameter, although in this case the effect was less pronounced. This could be explained by changes in the larger scale particle circulation patterns due to gas extraction and addition via the membranes: gas extraction leads to densely packed zones near the membranes, forcing bubbles through the center of the bed, where they become elongated and increase in size. Gas addition, on the other hand, totally inverts the particle circulation compared to a fluidized bed without membranes, splitting up bubbles in the center and forcing them towards the membranes, thus decreasing the bubble size. INTRODUCTION Fluidized bed membrane reactors combine the excellent separation properties of membranes with the advantages of fluidized beds. Moreover, the utilization of membranes enables to overcome reaction equilibrium limitations, thus resulting in higher reactant conversions and product yields. These clear advantages have led to an increasing number of applications of fluidized bed membrane reactors being proposed, for both product removal (e.g. hydrogen with palladium membranes) and reactant dosing (mostly oxygen) via membranes (Adris et al. (1); Mleczko et al. (2); Grace et al. (3); Gallucci et al. (4)). Despite the current developments, however, detailed understanding of the effect of the presence and permeation of gas through membranes immersed in a fluidized bed is lacking. The majority of current research relies on experimentally acquired data in experimental setups designed to provide a proof-of-concept and on phenomenological models, which often make use of ad-hoc empirical correlations that neglect the influence of internals.
Al-Sherehy et al. (5) investigated distributed feed and concluded that oxygen distribution is beneficial in expanding the range of reactant feed compositions beyond those normally allowed by safety constraints, while the selectivity was increased. Deshmukh et al. (6) confirmed these findings and, moreover, made great advances with respect to the effect of the presence of – and permeation through – the membranes on the extent of gas back mixing and the bubble-to-emulsion phase mass transfer rate. With ultrasound gas tracer experiments, they showed that due to the presence of membranes, but particularly due to gas permeation through the membranes, the macro-scale solids circulation was strongly reduced, resulting in a near plug-flow behavior for the gas phase. They also found smaller average bubble diameters for higher permeation ratios relative to the total gas flow. Christensen et al. (7) confirmed that such systems indeed lead to a decrease in bubble size and bubble hold-up, and therefore to an increase in the total number of bubbles. This paper aims to advance the fundamental understanding by investigating experimentally the effect of a change in gas flow rate inside a fluidized bed membrane reactor, where gas is added or extracted through the side-walls of the fluidized bed. Therefore, we focus specifically on bubble formation/annihilation close to the membranes, bubble size distribution and particle mixing as a function of the gas permeation ratio, i.e. the ratio of gas added/extracted relative to the total feed. After a description of the experimental setup and the procedures used for data postprocessing, we discuss and compare the PIV/DIA results for cases of gas extraction and gas addition. EXPERIMENTAL SETUP A pseudo-2D setup 30cm in width, 1.5cm in depth and 1m in height has been constructed. For the front of the bed a glass plate is used, for the back an anodized aluminum plate to provide good contrast between emulsion phase and background. The distributor is a porous plate with a mean pore size of 40 μm. At both sides of the fluidized bed, up to a height of 30cm above the distributor, gas can be added to or extracted from the fluidized bed through a 10 μm porous plate. For all experiments, glass beads with a particle size distribution of 400-600 μm and a density of 2500 kg/m3 (Geldart B) have been used. Air has been used as a fluidization agent. The
Figure 1: Process flow diagram of the experimental setup
process flow diagram is given in Figure 1. The minimum fluidization velocity Umf was determined to be 0.25 m/s by slowly decreasing the fluidization velocity. All experiments reported here have been performed with a total gas feed corresponding to 2.6.U/Umf (see Table 1). A lower velocity would significantly de-fluidize the bed during gas extraction experiments, while a much higher gas velocity is not possible with the current setup. The number of images has been determined to be sufficient for obtaining reliable time-averaged results; the error in the vector plots presented in this paper is below 6%. The error in the equivalent bubble diameter depends on the number of bubbles detected at a certain height in the bed, and ranges from <1% at a height of 2.5 cm to 3% at a height of 45 cm. PIV/DIA Procedure PIV is a non-intrusive optical technique based on the comparison of two images recorded with a very small time delay (here 0.82-1.98 ms) with a high speed CCD camera. It divides every image into interrogation zones (here 32x32 pixels were used), and uses a special cross-correlation on two consecutive images to obtain an average displacement of the particles in that interrogation zone. These PIV image pairs were post-processed using the commercial software package DaVis. DIA is an image post-processing algorithm, that discriminates bubble and emulsion phase based on the pixel intensity. Prior to the actual bubble detection, the algorithm corrects for the camera lens effect, inhomogeneous lighting and shadow effects near the walls. For every measurement series at least 10 random images were inspected visually, to ensure that the script is functioning correctly. Only by using the combination of PIV and DIA, it is possible to determine the time-averaged emulsion phase velocity profiles from the obtained instantaneous particle velocity profiles and correct for particle raining through the bubbles to avoid under-estimation of the particle fluxes in the centre of the bed (Laverman et al. (8)). Table 1: Measurement series Measurement name [-] Reference 100% + 20% 100% + 40% 100% - 20% 100% - 40%
Background gas flow / velocity [%] [m/s] 100 0.65 100 0.65 100 0.65 100 0.65 100 0.65
Total membrane flow / velocity [%] [m/s] 0 0 +20 + 0.065 +40 + 0.130 -20 - 0.065 -40 - 0.130
Number of images For DIA [-] For PIV [-] 2700 2160 2700 2160 2700 2160 1350 2160 1350 2160
RESULTS & DISCUSSION The discussion on the experimental results on the effect of gas permeation on the hydrodynamics of the fluidized bed is started by first focusing on the solids circulation patterns, followed by the bubble properties. Emulsion Phase Circulation Patterns The time averaged solids circulation pattern and the time-averaged lateral profile of the axial emulsion phase velocity at different heights in the bed is shown in Figure 2. In the reference series without secondary gas extraction or addition, the characteristic pattern for fluidized beds with an upwards directed solids flow through
Figure 2: Time-averaged particle movement and time-averaged lateral profile of the axial emulsion phase velocity for different heights in the fluidized bed with (a) 40% gas extraction, (b) 20% gas extraction, (c) the reference series with no gas addition/extraction, (d) 20% gas addition and (e) 40% gas addition. the core and a downward solids flow along the walls of the fluidized bed can be clearly discerned. This well-known solids circulation pattern is also clearly visible in the lateral profiles of the axial emulsion phase velocity at different heights: a broad region in which particles move upwards in the center of the fluidized bed, and near the walls a small region where the particles move downwards. It is interesting to notice the local minimum in the axial emulsion phase velocity in the centre at 10 cm above the bottom distributor plate, corresponding to the well-known average bubble trajectories from the walls towards the centre in the lower sections of the fluidized bed (see also Laverman et al. (8)). When comparing the cases with gas extraction to the reference case, a striking difference is the stagnant regions near the membranes in case of gas extraction. It is already appearing in the 100%-20% case, but becomes even more pronounced when 40% of the background fluidization gas is extracted. These stagnant zones have two consequences: the first consequence is that the bed height is reduced, implying a smaller number of bubbles or smaller bubbles inside the fluidized bed. Secondly, the velocity plot shows that the peak of upward moving solids has become steeper, while the downward directed ‘peak’ for the downward moving solids has become less pronounced and has shifted somewhat towards the center of the bed. The reason for these phenomena is that the stagnant zones near the
membranes leave less space for bubbles to rise and for particles to re-circulate to the bottom of the bed, resulting in narrower vortices in the solids circulation. In contrast to gas extraction, gas addition via the membranes has an even more distinctive effect on the particle circulation pattern: gas addition inverts the circulation pattern. This phenomenon shows that there is a competition between the background gas velocity and the additional gas entering via the membranes to drag the particles along. Already in the 100%+20% series this phenomenon starts to become apparent, but is even more pronounced for the 100%+40% series. Usually particles would move downwards near the walls. However, due to the gas addition, particles near the wall (in the first 30 cm) are dragged upwards instead, causing the particles to move downwards in the center of the bed. This phenomenon is also illustrated by the lowest three lateral profiles of the axial emulsion phase velocity profiles; the upwards directed peak is now near the wall, while the velocity in the center of the bed is slightly negative. Above the membrane (above 30 cm), the particles are pushed towards the center, and continue their way as usual: upwards via the center and downwards via the sides. This division in a part with membrane and a part without membrane results in four vortices inside the fluidized bed, each one rotating differently than its neighbor. The findings described above can be schematically summarized as depicted in Figure 3. In all cases, the magnitude of the effects depends on the background fluidization velocity and amount of gas extraction or gas addition. It can be expected that the change in particle behavior has a pronounced effect on the bubble properties and bubble size distribution, which is discussed next.
Figure 3: Illustration of the particle circulation patterns for (a) gas extraction, (b) the reference and (c) gas addition. Bubble Properties Firstly, the obtained experimental results were validated by comparison with literature; both the equivalent bubble diameter, as well as the bubble rise velocity compared well to the corresponding literature correlations (not shown here). Subsequently, the equivalent bubble diameter as a function of the axial position in the fluidized bed is shown in Figure 4.a. In the lower part of the fluidized bed, the bubbles remain approximately the same size, irrespective of the amount of gas extraction or addition. Only from a height of approximately 20 cm, a difference becomes apparent. However, unlike what would be expected intuitively, extracting gas leads to larger bubbles, while adding gas results in smaller bubbles.
Figure 4: Effect of gas extraction and addition on (a) equivalent bubble diameter as a function of the bed height, (b) average number of bubbles per frame as a function of the bed height, (c) average bubble diameter as a function of the lateral position and (d) bubble hold-up as function of the bed height. In particular the experimental series in which gas is added via the membranes deviate substantially from the reference case above a height of 30 cm. Note that the largest bubbles for the cases of gas extraction appear at 40 cm height, the ones for the reference case at about 46 cm, and the bubbles for the cases with gas addition appear even at 52 cm height, reflecting the difference in fluidized bed height. Figure 4.b shows a slight difference in the average number of bubbles present in every frame. For gas addition, it can be concluded that there are more bubbles (Figure 4.b) with a smaller diameter (see Figure 4.a). For gas extraction, the number of bubbles is decreased, but they have a larger equivalent diameter. However, the difference in the number of bubbles is less important in comparison with the difference in bubble diameter. The bubble rise velocity as a function of the equivalent bubble diameter (not shown here) is quite similar for all cases. The graphs of the lateral profile of the equivalent bubble diameter and the axial profile of the bubble hold-up (Figure 4.c and 4.d) provide more insight into the bubble behavior. There is a significant difference in the average lateral position of the bubbles. The reference case shows an almost parabolic distribution, as expected, because bubbles are formed over the entire width of the fluidized bed and move towards the center due to bubble coalescence. The 100%-20% and 100%-40% series show a similar distribution, although bubbles are situated more in the center (which is in line with the conclusions drawn from Figure 2). The 100%+20% and 100%+40% series reveal a very different bubble distribution: in these cases the large bubbles are situated much closer to the walls. In the center, a significant decrease in bubble diameter is visible, indicating that the movement of the bubbles
is reversed, i.e. while bubbles are rising and growing, they are moving away from the center and towards the membranes. This is in line with the particle movement seen in Figure 3. Not only the location, but also the bubble volume is different or these cases. The series with gas extraction show a slightly larger bubble volume, although this difference is very small. However, the series with gas addition reveal that – in particular in the top section of the bed – the bubble volume is much smaller compared to the reference case. Now the question remains why for the 100%+20 and 100%+40 series, both the average bubble diameter as well as the average bubble volume are lower than the reference case. This phenomenon is caused by a combination of particle movement and bubble detection: large gas voids near the walls are likely to be part of the freeboard of the fluidized bed, and are therefore no longer defined as bubbles. This is caused by particles near the freeboard that are – in contrast to the reference case – moving away from the wall toward the center of the fluidized bed, and as a consequence, there are much fewer large bubbles surrounded by emulsion phase. The effect of gas extraction and gas addition on the bubble behavior is schematically depicted in Figure 5.
Figure 5: Illustration of the bubble size distribution and movement for (a) gas extraction, (b) the reference and (c) gas addition. CONCLUSIONS A pseudo-2D experimental fluidized bed setup with membranes (porous plates) at both the left and right side has been constructed to investigate the effect of gas extraction or gas addition on the emulsion and bubble phase behavior in detail. A combination of Particle Image Velocimetry (PIV) and Digital Image Analysis (DIA) was employed. The experimental results revealed that gas addition via the membranes counterintuitively leads to significantly smaller bubbles, whereas gas extraction slightly increases the bubble size. During gas addition, the bubble size in the top of the bed decreased to 60% of the original bubble size. During gas extraction, a small increase in bubble size was found (an increase in bubble size of 10% and 20% was observed relative to the reference case for 20% and 40% gas extraction respectively). The explanation was found in the lateral bubble distribution and particle circulation patterns. During gas addition, the bubbles are split up and distributed towards both membranes. The particle circulation therefore inverts, and particles move upwards with the bubbles via the sides, and downward through the
center of the bed. During gas extraction, on the other hand, stagnant zones near the membranes emerge. These zones force upwards moving bubbles and particles, as well as downwards moving particles towards the center of the fluidized bed, which results in bubbles that are vertically stretched and therefore slightly larger than in the reference case. The experimental findings have shown a large effect of gas extraction or addition on the fluidized bed hydrodynamics, which should be properly taken into account in the optimization and design of membrane-assisted fluidized bed reactors. It would be interesting to validate the conclusions from this work in 3D systems, but these systems require different measuring techniques. In the near future, we will compare the obtained results to numerical simulations with a Euler-Euler model. Furthermore, the hydrodynamics in a fluidized bed with a membrane configuration consisting of horizontal membrane tubes instead of vertical membranes will be investigated both numerically as well as experimentally in order to derive improved design rules for future fluidized bed membrane reactors. ACKNOWLEDGEMENT We gratefully acknowledge SenterNovem, the agency for sustainability and innovation within the Dutch Ministry of Economic Affairs, for its financial support of this project. NOTATION U Umf
gas velocity [m/s] minimum fluidization velocity [m/s]
REFERENCES 1. Adris, A.M., Elnashaie, S.S.E.H., and Hughes, R., 1991. A Fluidized-Bed Membrane Reactor for the Steam Reforming of Methane. Canadian Journal of Chemical Engineering 69, 1061-1070 2. Mleczko, L., Ostrowski, T., and Wurzel, T., 1996. A fluidised-bed membrane reactor for the catalytic partial oxidation of methane to synthesis gas. Chemical Engineering Science 51, 3187-3192 3. Grace, J., Elnashaie, S.S.E.H., and Lim, C.J., 2005. Hydrogen production in fluidized beds with in-situ membranes. International Journal of Chemical Reactor Engineering 3, 4. Gallucci, F., Annaland, M.V., and Kuipers, J.A.M., 2009. Autothermal reforming of methane with integrated CO2 capture in novel fluidized bed membrane reactors. AsiaPacific Journal of Chemical Engineering 4, 334-344 5. Al-Sherehy, F., Grace, J.R., and Adris, A.E.M., 2005. The influence of distributed reactant injection along the height of a fluidized bed reactor. Chemical Engineering Science 60, 7121-7130 6. Deshmukh, S.A.R.K., Annaland, M.V., and Kuipers, J.A.M., 2007. Gas back-mixing studies in membrane assisted bubbling fluidized beds. Chemical Engineering Science 62, 4095-4111 7. Christensen, D., Vervloet, D., Nijenhuis, J., van Wachem, B.G.M., van Ommen, J.R., and Coppens, M.O., 2008. Insights in distributed secondary gas injection in a bubbling fluidized bed via discrete particle simulations. Powder Technology 183, 454-466 8. Laverman, J.A., Roghair, I., Annaland, M.V., and Kuipers, H., 2008. Investigation into the hydrodynamics of gas-solid fluidized beds using particle image velocimetry coupled with digital image analysis. Canadian Journal of Chemical Engineering 86, 523-535
UOP FCC INNOVATIONS DEVELOPED USING SOPHISTICATED ENGINEERING TOOLS Lisa M. Wolschlag and Keith A. Couch UOP LLC, a Honeywell Company Des Plaines, Illinois, USA
INTRODUCTION Fluid Catalytic Cracking (FCC) technology has been a part of the petroleum industry since the 1940’s. Yet, despite being a very mature technology, continued development is vital, especially as many refiners move their FCC operations from fuels production to higher value products. Advanced diagnostic and design tools are accelerating process developments and have resulted in several innovations. Through the development and commercialization of world record scale FCC units, technical discoveries have emerged, which have provided opportunities for improvements across all units, independent of size. Through the development and use of sophisticated engineering tools such as Computational Fluid Dynamic (CFD) modeling, combined with radioactive tracer and tomography, physical inspection reports and commercial yield analysis, new technological innovations can be delivered more confidently, faster and with reduced risk. This paper will highlight advancements in regenerator technology for higher capacity through existing assets, emissions reduction and feed distribution systems for large diameter risers. It will showcase how UOP is using and validating innovative tools to both improve existing FCC designs and move an aged technology towards true growth opportunities. DUAL RADIUS FEED DISTRIBUTORS As refiners look to capitalize on economies of scale, design throughputs of FCC units have reached record levels. At this scale, opportunities have emerged from the background noise of the data to improve FCC technology. Through pushing multiple constraints to design limits, yields and conversion deviated from benchmark performance with gasoline selectivity lower, conversion lower and dry gas higher than benchmark performance. To get more out of the existing asset, an intensive program was undertaken to achieve benchmark performance. The riser for a particular FCC Unit has an inner diameter (ID) of 2 meters (6.6 feet) at the point of feed injection, which expands to 2.7 meters (9 feet) immediately above. The feed is injected into the riser through a set of circumferentially positioned distributors. The combination of low
1
conversion and high dry gas yield seems counter-intuitive given traditional FCC operations. A hypothesis was raised that the large riser diameter might be preventing the feed from adequately distributing across the full cross-sectional area of the riser. To help test this hypothesis, a CFD model of the riser was created to analyze the fluid dynamics of the system. The results of the model supported the theory that raw oil feed would only penetrate the riser a finite distance thus creating a vapor annulus, and that much of the catalyst flowing up the riser would form a high density core. Based on CFD results, a tomographic analysis (gamma scan) of the riser was completed. The scan results confirmed the CFD model prediction, as illustrated in Fig. 1. Figure 1. CFD Prediction and Gamma Scan of 2 meter (6.6 foot) ID Riser
Actual Riser Gamma Scan
CFD Prediction
0.9 0.8 0.7 0.6 0.5 0.4 0.3
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Radioactive tracer work was also completed on the 2 meter (6.6 foot) ID riser. Irradiated Krypton-79 gas was injected into the riser base. Detectors were positioned along the riser length and reactor to measure the tracer as it moved through the system. The results indicated that the time of flight of the krypton gas from one detector to another did not provide a sharp response peak. Rather, there was an early peak followed by a secondary peak and a high degree of skewness (Fig. 2). A mathematical evaluation was performed to determine what type of continuous stirred tank reactor (CSTR) response would be needed to emulate the measured data. To accurately reproduce the field data plot, required a composite plot combining the 100, 40 and 15 CSTR responses (Fig. 2). Unit performance, CFD modeling, tracer and tomography tests, and mathematical analysis all indicated the same pathology: that the feed was not adequately accessing the full crosssectional area of the riser leading to the presence of a high density core of catalyst and a low
2
Figure 2. Mathematical Composite
14 Mar 2007 Gas Injection 1000
Response
800
Centroid 77.18s t-res = 1.32s Velocity 9.3 m/s
Early Peak Feed Plug Flow Core?
Riser Ex Top – Avg 40 – CSTR Response Composite 100 – CSTR Response 15 – CSTR Response
600 400
Late Peak Wall Holdup?
200 0 0.0
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Seconds density annulus which caused low conversion and high dry gas and coke make. One solution to this problem would be to install two, smaller diameter risers to match more conventional FCC sizes. However, installing dual risers, even with a new construction is substantially more expensive. For an FCC Unit of 200,000 barrels per stream day (BPSD), the estimated cost difference between a single, large-radius riser and a pair of smaller risers was estimated to cost $60 million, in 1998 US Dollars. UOP has developed a substantially lower cost solution with implementation of dual radius feed distributors (Fig. 3). This design helps ensure optimal feed distribution across the entire riser, while avoiding adjacent spray impact that could cause undesirable spray interference. Another CFD model that now incorporated the dual radius feed distributors was created. Fig. 4 shows catalyst density profiles of an axial slice of the riser, both with and without dual radius feed distributors. The riser on the left side without the dual radius feed distributors show the high density core of catalyst, the CFD model with the dual radius feed distributors indicates that the dense core of catalyst is effectively eliminated. The dual radius feed distributors were installed on an FCC Unit designed with a 2.4 meter (8 foot) diameter riser. The unit was commissioned in May 2009. Results indicate that dry gas yield, conversion and gasoline selectivity are all within expectations. The riser’s gamma scans
3
Figure 3. Dual Radius Feed Distributors Schematic
indicate that the high density core of catalyst was effectively eliminated. The catalyst density profile of the riser at approximately 1 pipe diameter above the point of dual radius feed injection indicates that core annular flow has been achieved with a very evenly distributed catalyst density profile (Fig. 5). Additional tomography scans were completed at varying feed ratios, to optimize the distribution of oil and steam across the riser. SPENT CATALYST DISTRIBUTOR PROBLEM The Engineering tools and associated skills used to solve the previous problems on very large FCC Units can be used on FCC Units of all sizes and types, to support operating and reliability needs of individual refiners. In one example, an 80,000 BPSD FCC Unit with a bubbling bed regenerator exhibited a regenerator cyclone outlet temperature differential of 56°C (100°F) from one side of the regenerator to the other. This afterburn differential resulted in a localized hot spot that limited the throughput of the unit against a main air blower constraint. The regenerator was an older design that employed a gull wing spent catalyst distributor design. Catalyst maldistribution in the regenerator causes fuel rich areas in the dense phase with localized hot spots directly above in the dilute phase. Hot spots can be completely invisible within a unit depending on where the TI instrumentation is placed in relation to the spent catalyst inlet.
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Figure 4. CFD Models of the Riser Catalyst Profiles with and without Dual Radius Feed Distributors
Dual Radius Feed Distribution Eliminates Dense Core
lb/ft3
Figure 5. Gamma Scan of 2.4 meter (8 Foot) ID Riser with Dual Radius Feed Distributors
Design Ratio
5
To validate the temperature data, catalyst tracer work was completed on the regenerator to evaluate the flow distribution in the unit. With ideal distribution, a radar plot of the detector signals would show perfect symmetry. The actual unit data showed that the catalyst was heavily skewed to one side, which was not a surprise (Fig. 6). Figure 6. Catalyst Tracer Results for Bubbling Bed Regenerator with Gull Wing Design Cyclone 10
14%
Cyclone 1 12%
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10% 8%
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Cyclone 7 Cyclone 4 Cyclone 6 Cyclone 5
SOLUTION The typical spent catalyst distributor installed in a bubbling bed regenerator of this vintage was the gull wing design with an external lift riser. A schematic of this distributor is shown in Fig. 7. Air maldistribution in this type of regenerator design results from two sources. First, the external riser lift air discharges vertically out of the disengager, resulting in an oxygen rich environment in the dilute phase. Second, the high localized catalyst density and resultant hydraulic head causes preferential flow of combustion air to the opposite side of the regenerator. To achieve a more even catalyst density and uniform coke distribution, the piped spent catalyst distributor was developed (Fig. 7). The piped distributor was designed to radially distribute both the lift air and spent catalyst across the regenerator bed through a set of side arms. The size and orientation of the distributor arms were designed in an iterative process with CFD modeling to ensure as even catalyst and air distribution as possible within the back pressure limitations of the existing lift air blower. CFD models of the gull wing distributor and the piped spent catalyst distributor were created to predict the catalyst distribution, gas flow paths and bed density profiles in the bubbling bed regenerator. With the gull wing distributor, the catalyst was concentrated in the center of the
6
bed. With the piped spent catalyst distributor, the catalyst distribution was much more uniform throughout the bed (Fig. 8). Figure 7. Gull Wing and Piped Spent Catalyst Distributors
Gull Wing Distributor
Piped Spent Catalyst Distributor
RESULTS The piped spent catalyst distributor was commissioned in December 2006. Post-revamp tracer tests were conducted on the regenerator to evaluate the results of the design. The actual catalyst distribution is very close to the ideal distribution (Fig. 9). Operational data also indicates a significant improvement in the regenerator performance. The dilute phase temperature differential was reduced from 56°C (100°F) pre-revamp to about 8°C (15°F) following the implementation of the pipe spent catalyst distributor. As a result, the refiner was able to lower the excess oxygen level in the flue gas from a pre-revamp minimum of 2 mol% to a post-revamp 1 mol%, enabling a higher capacity through existing assets and saving on utility consumption.
7
Figure 8. CFD Model of the Catalyst Densities in a Regenerator with Gull Wing and Piped Spent Catalyst Distributors
Piped Distributor
Gull Wing
Figure 9. Catalyst Tracer Results for Bubbling Bed Regenerator with Piped Spent Catalyst Distributor 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00%
NOTATION t-res
residence time
8
REMOVAL OF NITRATE FROM WATER USING A FLUIDIZED BED ION EXCHANGE COLUMN Ammar Arab Beddai and V.V.Basava Rao Universaity College of Technology, Osmania University Hyderabad-500 007 Email: [email protected] Email: [email protected]
ABSTRACT Experimental and theoretical studies were carried out to investigate the performance of a fluidized bed ion exchange system to remove nitrate. The exchange of nitrate on strong anion exchange resin (TULSION A-27) was studied in the flow rate range of 2 to 7 L/h. Nitrate removal was done at five conditions of the expanded bed height (Z). The results showed that the experimental data can be fitted to Richardson and Zaki equation, and the comparison between the experimental and calculated terminal velocities showed low relative error. INTRODUCTION Increased levels of nitrate in ground water have made many wells unsuitable as sources for drinking water. Nitrate is so toxic, especially to pregnant women and infants, that the USEPA (United States Environmental Protection Agency) standard of 10 mg NO3—N/L or less in drinking water was established for human health [Taekyung et al (1); Kavita et al (2); Lucija et al (3)]. Ion exchange is a chemical treatment process used to remove unwanted ionic species from wastewater [M. Matosic et al (4); Robert Kunin (5); Ammar Arab (6)]. Solid-liquid fluidized beds (SLFB) are used in industry for hydrometallurgical operations, catalytic cracking, chromatographic separation, ion exchange, adsorption, crystallization and sedimentation, etc [Prakash and Jyeshtharaj (7); Srikuma et al (8)]. However, the packed bed ion exchange process has some disadvantages such as high pressure drop and bed clogging. These disadvantages can be eliminated if the packed bed is replaced by a fluidized bed [Shyh-Jye and Wen-Jang (9); Hideaki et al (10); Seung-Jai et al (11)]. The purpose of this study was to investigate nitrate removal in a liquid-solid fluidized bed ion-exchange system. The effects of operating parameters including liquid flow rate and height of the bed on the removal rate of nitrate were studied. The experimental data of voidage versus superficial velocity were successfully correlated using the Richardson-Zaki Equation.
1
THEORY OF FLUIDIZED BED Minimum fluidization velocity The minimum fluidization velocity can be obtained using the Ergun Equation by sitting the pressure drop equal to the pressure required to suspend the bed or the weight of the bed divided by the cross sectional area [McCabe et al (12); Vassilis et al (13)]:
150m u F d 2 s
fm 2 p
1- e
e
3 fm
fm
+
1.75r u 2fm 1 = g (r p - r ) F s d p e 3fm
(1)
Re fm =
and
u
fm
rd
p
m
(2)
Estimation of bed voidage (ε) Bed voidage was obtained by substitution of data into the following equation [Mohsen et al (14)]:
ù Vp ù é mp é Vpù é e = ê1 - ú = ê1 ú ú = ê1 ë V L û ë A T .H û ëê r p . A T .H úû
(3)
Bed expansion characteristics Before processing with feedstock, determination of the bed expansion characteristics was necessary. It was decided to use the well-known Richardson–Zaki Equation [Mohsen et al (14)]:
U = e U t
(4 )
n
Z f =Z
The expanded bed height is [Inglezakis and Poulopoulos (15)]:
1- e 1- e f
(5)
The average bed voidage can be estimated from Eq. (3) or the following equation:
e f = 1 - (1 - e )
Z Zf
(6)
Where ε0=0.41 is the packed-bed voidage. Values of the Richardson–Zaki coefficients n, and the apparent terminal velocities of the particles Ut were calculated from linear regression of plots of ln(U) versus ln(ε). For the particles which had a mean diameter of 750 μm, the apparent terminal velocities of the particles Ut were calculated from Allen’s equation, Eq. (7). For adsorbents with an approximate mean particle size of 200 μm one can calculate Ut using Stokes’ law. Ut Stokes is given by Eq. (8) [Mohsen et al (14)].
U t = 0.27
d p (r p - r )g
r
Re t0.4
(7 )
U
2
t - stokes
=
g (r
p
- r )d
18m
2 p
(8)
Where (n) is the index which depends on the particle size and sedimentation velocity. The expansion index (n) was calculated by following equations [Yong-Hong et al (16)]:
n = 4.65
n = 4.4 Re
Re t < 0.2
0.2 < Re t < 1
- 0 .03 t
(9)
1 < Re t < 500
n = 4.4 Re t- 0.1
500 < Re t
n = 2. 4
The expansion behavior can also be estimated from empirical considerations. Hartmann obtained a good fit of their experimental data for n using Eq. (10) [Mohsen et al (14)].
n =
5.09 + 0.2309 Re t0.877 1 + 0.104 Re t0.877
(10)
The model of Shiller and Naumann is commonly used for the prediction of terminal velocity of a spherical particle [M.H.Shahavi et al (17)]:
G a = 18 Re t + 2.7 Re1t .687
(11)
3.6 < G a < 10 5
Where the Grashof number is given by the following equation:
Ga =
r (r p - r )gd m
3 p
(12)
2
The fluidization behavior of particles is based on the Gallileo number [Vassilis et al (13)]:
Ga =
r p g (r p - r )d m
3 p
(13)
2
From Ga the terminal velocity Reynolds number can be calculated for different column and particle diameters: -1 dpù 0. 6 ù é é 23 + Re t = ê 1 + 2.35 ú 0 .5 ú ê dc û ë Ga Ga û ë
-1
(14)
The expansion index was estimated from Ga:
5 .1 - n = 0.016Ga n - 2 .4
(15)
0.67
For the relatively large particles (of several millimeters) in water, the equation proposed by Lewis, Gilliland, and Bauer can be used to calculate bed [voidage Inglezakis and Poulopoulos (15)]:
e
f
éu ù =ê s ú ëê u fm úû
1/ m
e
(16 )
3
The exponent m is a function of the particle Reynolds number based on the minimum fluidization velocity. It can be estimated by the following correlation:
(17 )
m = 4.21 Re -fm0.0804
The value of m is between 4.2 and 4.5 for 0.1
e
f
æ 18 Re p + 0.3 Re 2p =ç ç Ar è
ö ÷ ÷ ø
0.21
(18)
Where
Ar =
d 3p r ( r
m
- r )g
p 2
(19)
MATERIALS AND METHODS Resin Material The ion-exchange resin employed was the OH-1 type TULSION A-27 which was a strong base anion exchange resin. TULSION A-27 (OH-1) was obtained from the Thermax Company. The particles are in the shape of almost perfect spheres with an average diameter 0.7 mm (700 µm) and a wet density of 1.08 g/mL. The capacity of the resin was measured from the breakthrough curve of the OH-/NO-3 exchange experiments. The total exchange capacity was about 1.2 meq/ml of resin. Fluidized Bed System The overall experimental apparatus is depicted in Figure 1. The column was filled with resin and washed with distilled water. Experiments were carried out in a glass column having 1 cm diameter and 100 cm high. To obtain the hydroxide, the resins were regenerated in downflow with four bed volumes of 4% NaOH solution and washed with distilled water. The temperature was maintained at 31 ± 1 oC. As the fluidized bed showed a quiescent behavior, the height of the bed could be determined visually. 8 6
1 2 3
5
7
4
Figure 1. Experimental system: (1) NaNO3,(2) H2O,(3) NaOH ,(4) pump, (5) rotameter, (6)column, (7)resin, and (8)effluent.
4
CHEMICAL ANALYSIS Nitrates were measured by a UV-Vis spectrophotometer. The absorbance was measured at 220 nm and a second reading was taken at 275 nm. This allowed correction for the interference due to dissolved organic matter. The difference between the two absorbance measurements was then calculated by the formula [Andre (18)]: Abs220 - 2*(Abs275)
(20)
RESULTS AND DISCUSSION The effect of Reynolds number on fluidization voidage is presented in Figure 2. Values of the Richardson-Zaki coefficients n, and the apparent terminal velocity of the particle were calculated from linear regression of the plots of log (U) versus log(ε), and the results are shown in Figure (3). The parameters correlated are listed in Table 1. An experimental value of n=3.0007 was obtained. The theoretical values of n were calculated by Equations 9, 10, and 15. A comparison of results showed a good agreement between the experimental and theoretical values of n and Ut. This comparison showed the operation of bed agreed with theory. The diffusion of nitrate ion with a resin particle at height 2 cm was slower than that at 3, 6, and 10 cm in the bed.
εf
1 0.8 0.6 0.4 0.2 0 0
Rep 10 5 Figure 2. Effect of Rep on fluidized bed voidage (εf)
0.2
y = 3.0007x + 0.3349 R2 = 0.9759 -0.2
-0.15
-0.1
15
0.1
-0.05
0 log εf -0.1 0 -0.2
log u Figure 3. Richardson-zaki correlation between flow velocity and bed voidage for determination of n and Ut
5
Table 1. Experimental and theoretical values of Ut and n. Ut (cm/s) n Eq. (7) Experimental Eq. (9) Eq. (10) Experimental 2.162 2.16 3.0007 3.261 3.4
Eq. (15) 2.68
Figure (4) shows a comparison of the voidage estimated by various models at different particle Reynolds numbers. Figure (5) shows the experimental breakthrough curves at various bed heights.
1 EXP.
0.8
Eq. (16) Eq.(18)
0.6
εf
Eq. (10) Eq. (15)
0.4
Eq. (9)
0.2 0 0
5
10
Rep Figure 4. Comparison of overall voidage estimation models with experimental data
15
1.2
C/C0
10 cm
1
2 cm 0.8
3 cm 6 cm
0.6 0.4 0.2 0 0
20
40
60
80
100
Time (min) Figure 5. Breakthrough curves of NO3 ions in the fluidized bed at different bed heights Z and a constant flow rate of 2 L/h
6
CONCLUSIONS An experimental study of NO3 removal from water by using a fluidized bed was carried out, and the following conclusion were made: 1- The Richardson and Zaki model showed a good fit with the experimental data, and can be use for voidage estimation. 2- Results of a fluidized bed operation using an ion exchange resin indicate that nitrate removal was improved significantly by increasing the bed height. ACKNOWLEDGEMENT The authors wish to acknowledge a research grant-in-aid from the Osmania University, Hyderabad, India. NOTATION Abs Ar AT C Co dp g Ga Ga H mp Refm Rep Ret ufm us U Ut VL Vp Z Zf ε εf εfm µ ρ ρp Фs
absorbance measurements Archimedes number area of the cross-section of the column (m2) concentration (mol/m3) initial concentration (mol/m3) particle diameter (m) gravity (m2/s) Gallileo number Grashof number bed height (m) mass of particles (kg) minimum fluidization Reynolds number particle Reynolds number terminal velocity Reynolds number minimum fluidization velocity (m/s) superficial fluid velocity (m/s) fluid velocity (m/s) terminal fluid velocity (m/s) liquid volume (m3) particle volume (m3) bed height (m) bed height at fluidization (m) bed voidage voidage at fluidization. voidage at minimum fluidization dynamic viscosity of the fluid (Kg/m.s) density of the fluid (kg/m3) density of the particles (Kg/m3) the spherity of the particle
7
REFRENCES 1-Taekyung Yoon, Zang HoShon, Gangchoon Lee, Byunghyun Moon, Byeongil Noh and Nakchang Sung, Korean J. Chem. Eng., 18(2), 170-177 (2001). 2-Kavita Batheja, A.K. Sinha and Gita Seth, Asian J. Exp. Sci., Vol. 23, No. 1, 2009; 6166. 3-Lucija Foglar, Laszlo Sipos, Nenad Bolf, World J Microbiol Biotechnol (2007) 23:1595-1603. 4-M. Matosic, Mijatovic, and E. Hodzic, Chem. Biochem. Eng. Q. 14 (4) 141-146 (2000). 5-Robert Kunin, “ Ion Exchange Resins”, Rohm and hass company, Philadelphia, Pennsylvania, 1958. 6-Ammar Arab Beddai, “Water treatment of Cooling Towers Blowdown from Dissolved salts”, M.Sc. Thesis, Baghdad University, 2002. 7-Prakash V. Chavan and Jyeshtharaj B. Joshi, Ind. Eng. Chem. Res., Vol. 47, No. 21, 8458-8470, 2008. 8-Srikuma V. Murli, Prakash V. Chavan and Jyeshtharaj B. Joshi, Ind. Eng. Chem. Res., Vol. 46, No. 6, 1836-1842, 2007. 9-Shyh-Jye Hwang and Wen-Jang Lu, Ind. Eng. Chem. Res., Vol. 34, No. 4, 14341439, 2008. 10-Hideaki Tokuyama, Susumu Nii, Fumio Kawaizumi, and Katsuroku Takahashi, Ind. Eng. Chem. Res., Vol. 41, 3447-3453, 2002. 11-Seung-Jai Kim, Kyung-Ran Hwang,Sung-YongCho and Hee Moon, Korean J. chem.. Eng., 16(5), 664-669, (1999). 12-McCabe, Smith, and Harriot, Unit Operation of Chemical Engineering, 1993. 13-Vassilis J. Inglezakis, Marinos Stylianou, Maria Loizidou, International Journal of Chemical Reactor Engineering, Vol. 8, 1-17, 2010. 14-Mohsen Jahanshahi, Ghasem Najafpour, Melika Ebrahimpour, Solmaz Hajizadeh, and Mohammad H. Shahavi, Phys. Status Solidi C, 1-8 (2009). 15-V.J.Inglezakis and S.G. Poulopoulos, Adsorption, Ion Exchange and Catalysis Design of Operations and Environmental Applications, 2006. 16-Yong-Hong Liu, Yan-Ling He, Shu-Cheng Yang, and Chun-Jiang An, Water SA, Vol. 32, No. 4, 555-560, October 2006. 17-M.H.Shahavi, G.D. Najafpour, and M.Jahanshahi, African Journal of Biotechnology, Vol. 7(23), pp. 4336-4344, 3 December 2008. 18-Andre D. Eaton, Leonore S. Clesceri, Eugene W. Rice and Arnold E. Greenberg,” Standared Methods for the Examination of Water and Wastewater”, American Public Health Association (APHA), American Water Works Association (AWWA) & Water Environment Federation (WEF), 2005.
8
PARTICLE-FLUID FLOW SIMULATION OF AN FCC REGENERATOR Sam Clark CPFD Software, LLC 10899 Montgomery Blvd. NE, Suite A Albuquerque, NM 87111 [email protected] ABSTRACT A particle-fluid flow simulation of a commercial-scale fluidized catalytic cracking unit has been conducted. The simulation was full-scale, three-dimensional, and with complex internal geometries. The focus of the computational model was to predict wear on internal structures. The geometry of the particle feed pipe was found to cause asymmetric flow of high-speed gas which led to significant wear. INTRODUCTION A particle-fluid flow simulation of a commercial-scale fluidized catalytic cracking (FCC) unit has been conducted using CPFD's Barracuda software. Barracuda is based on a multi-phase particle-in-cell (MP-PIC) implementation of computational particle fluid dynamics (CPFD), which uses an Eulerian scheme for the fluid field and a Lagrangian scheme for the particles. For the sake of brevity, the current paper does not go into the numerical details of the CPFD method, and the reader interested in such details is directed instead to Andrews and O’Rourke (1) and Snider (2). The regenerator simulation was full-scale, three-dimensional, and with complex internal geometries. The geometry was generic, i.e. the simulation was not of any actual operating FCC unit. Though chemistry can be included in Barracuda models, as shown by Snider and Banerjee (3) and Snider, Clark, and O’Rourke (4), the current simulation was isothermal, and did not include chemical reaction calculations. The hydrodynamic behavior was of primary interest, so thermal and chemical effects were neglected. The simulation was run to quasi-steady state operating conditions, and both transient and time-average data were collected. The results of the simulation included prediction of fluidization characteristics, wear on internal structures due to particle impact, and entrainment of solids into cyclones. It was found that an asymmetric feed pipe geometry was the reason for the extreme wear observed on specific internal structures.
GEOMETRY AND OPERATING CONDITIONS The physical configuration and operating conditions of the FCC regenerator in the current simulation were chosen to be generically representative of what might be found in industry. Fig. 1 shows the geometry of the regenerator, which was a cylindrical vessel with domed bottom and top, a diameter of 15.2 m (50 ft), and an overall height of 29.0 m (95 ft). The bottom portion of the vessel was equipped with three gas distribution rings to provide fluidizing gas. Twelve primary-secondary cyclone pairs were positioned in the top portion of the vessel, and each cyclone had a dipleg extending down to return entrained particles to the fluidized bed. The regenerator was also equipped with a single standpipe, which in an operating system would allow particles to be discharged back to the FCC reactor.
Figure 1: Geometry and boundary conditions for the FCC regenerator The geometry of the FCC catalyst particle feed pipe was of particular interest in the current simulation. The horizontal pipe led to an upwardly curved “J-bend”, which transitioned to a vertical riser section. This type of “J-bend” configuration is necessary for reactor-regenerator configurations where the units are side-by-side, and the FCC catalyst particles must be transported horizontally from the reactor to the regenerator. In other reactor system designs, the reactor might be positioned directly above the regenerator, and a “J-bend” would not be necessary.
Particles exiting the vertical riser section of the feed pipe impacted a flat plate positioned above the pipe outlet. This plate was used as a crude termination device for the purposes of the current simulation. In operating FCC regenerators, more sophisticated termination devices are used. These more complex designs attempt to distribute particles more evenly into the fluidized bed, which leads to better regeneration of the catalyst. For the purposes of the current generic geometry, it was not necessary to use a more sophisticated termination device. As approximations of typical operating conditions, the values shown in Table 1 were used in the current simulation. Table 1: FCC operating conditions Parameter
Value
Freeboard pressure
203 kPa (2 atm)
Temperature (isothermal)
994 K (1330 °F)
FCC catalyst particle density
1,425 kg/m3 (89 lb/ft3)
FCC catalyst d50 particle diameter
78 microns
Initial bed catalyst particle mass
70,000 kg (77 tons)
Superficial velocity in fluidized bed
0.42 m/s (1.4 ft/s)
SIMULATION SETUP Computational Grid and Particles The first step of the simulation process was the definition of a computational grid. The computational cells defined by the grid are used to solve the fluid transport equations, the results of which are strongly coupled with particle momentum equations. A finer grid gives higher fidelity in the computational solution, but requires a longer calculation time. Barracuda uses a regular rectangular grid. The grid used for the current simulation had computational cells with side-lengths ranging from 15 to 46 cm (6 to 18 inches) throughout most of the domain. The computational grid contained 270,000 real cells. In a large commercial vessel such as the regenerator currently under consideration, there could be on the order of 1015 individual particles in the system. With current computers, it is not feasible to model the detailed motion of this many individual particles in a simulation. Barracuda uses so-called computational particles, which allows for useful engineering results in reasonable solution times. Each computational particle represents a group of real particles that share physical properties such as material, radius, and density. The number of computational particles used the simulation affects the accuracy of the results. As with the grid, using more computational particles gives more accuracy, but also requires longer run-times. In the current simulation, about 1.8 million computational particles were used.
Initial and Boundary Conditions Barracuda solves for the motion of particles and fluid by solving conservation equations for all computational cells and particles in the simulation (2). The initial conditions provide a starting point for all calculations, while the boundary conditions (BCs) specify where fluids and particles are entering or leaving the system. Fig. 1 shows the regenerator geometry with BC definitions. The initial condition for the simulation was specified such that the regenerator was filled to a target level with FCC catalyst particles at rest. The fluid, air, was also at rest. The pressure and temperature of the system were set to match the desired operating conditions. Flow BCs that brought in only gas were defined for the fluidizing air rings. Flow BCs that brought in both gas and particles were defined at the bottoms of the primary cyclone diplegs and at the FCC catalyst particle feed at the end of the feed pipe. Pressure BCs were defined at the inlet horns of the primary cyclones and at the bottom of the standpipe. Particles were allowed to exit at all pressure BCs, and the overall system mass was maintained at a constant value by using a particle feed controller, which adjusted the flow rate of particles at the cyclone diplegs as needed to maintain a constant system mass. Wear Due to Particle Impact Estimating the expected wear on internal structures was an important goal of the simulation, so the Barracuda wear model was used. The wear model calculates the cumulative wear due to impact of particles on all wall surfaces in the simulation. Research has shown that the damage to a surface from particle impacts is dependent on factors such as particle mass, velocity, and angle of incidence (5). In the Barracuda wear model, when a particle hits a wall, the wear due to this impact is calculated based on the particle mass, velocity, and angle of incidence. The user has control over the dependence on each of these factors, and the appropriate values for each are material-specific. For example, FCC catalyst particles impacting bare metal would have different values for the calculation parameters than FCC catalyst particles impacting refractory. For the current simulation, the particle mass, m, and velocity, u, terms contributed to the calculated wear as m1.5 and u3.5. RESULTS The simulation was set up to collect various types of data, including transient pressure at specific locations, time-averaged gas and particle mass fluxes, and particle residence times for FCC catalyst particles entering through the feed pipe. Many aspects of the system could be examined based on the available data, but this study was focused specifically on two related behaviors shown by the simulation: flow of particles and gas around the “J-bend” in the feed pipe, and high wear on internal cyclone diplegs in one half of the regenerator. Transporting FCC catalyst particles horizontally is necessary in situations where the reactor and regenerator vessels sit side-by-side. In the current simulation, the FCC catalyst particles being fed to the regenerator traveled through the “J-bend” and then vertically through a riser section before discharging into the regenerator. Ideally, particles should be dispersed uniformly into the fluidized bed. However, the “J-bend” geometry causes the particles to pack against the far wall of the bend, which in turn favors high gas flow rates on the inside portion of the curved section. Figs. 2 and 3
show thin-slice views of particles and gas traveling through the feed pipe. The particles are colored by speed, with faster moving particles having darker colors. The gas vectors have their lengths scaled based on gas velocity and are colored with darker vectors representing higher gas velocity. The particles which pack up on the far wall of the “J-bend” tend to be low-speed, and gas prefers to avoid the higher solids concentration region, leading to a high-speed condition along the inner portion of the curved pipe. The high-velocity gas continues up the vertical riser section, and exits the feed pipe on the inner-curve side.
Figure 2: Particles colored by speed in the feed pipe
Figure 3: Vectors of gas velocity in the feed pipe
Fig. 4 shows isovolumes of regions where the time-averaged gas velocity is greater than 5 m/s. The tendency of the high-speed gas to stay on the inner-side of the curved pipe is shown by the large isovolume structure emanating from the same side of the feed pipe outlet. Other regions of high fluid velocity include the standpipe area and the inlet horn areas for the primary cyclones. All of these locations are high flux regions, and the relatively small open areas compared with the overall crosssectional area of the regenerator lead to high gas velocities.
Figure 4: Isovolmues of regions with gas velocity higher than 5 m/s The high gas velocity on the inner side of the inlet pipe propagates through the fluidized bed, and the effect is significant on the wear calculated for the cyclone diplegs on that side of the regenerator. Fig. 5 shows regions where more and stronger particle impacts occurred. These regions are the most likely to incur damage from wear due to particles hitting the internal structures of the regenerator.
CONCLUSIONS The FCC regenerator simulation provided insight into the flow behavior of gas and solids through the “J-bend” in the feeding pipe. The asymmetric flow of high-speed gas into the fluidized bed penetrated into the freeboard, and was found to be responsible for high wear on cyclone diplegs on one particular side of the regenerator. If this were an operating unit, it would be difficult to identify the asymmetric flow behavior through typical pressure taps or thermocouples. If the cyclone diplegs were damaged sufficiently, an emergency shutdown might be required, which would be very costly. This simulation shows that CPFD modeling can be applied to large commercial fluid-particle systems to solve performance issues.
Figure 5: Isovolumes of regions with high predicted wear due to particle impact
REFERENCES 1. Andrews, M. J., and O'Rourke, P. J., 1996. The multiphase particle-in-cell (MPPIC) method for dense particle flow. Int. J. Multiphase Flow 22, 379-402. 2. Snider, D. M., 2001, An Incompressible three dimensional multiphase particle-incell model for dense particle flows. Journal of Computational Physics 170, 523549. 3. Snider, D. M., Banerjee, S., 2009, Heterogeneous gas chemistry in the CPFD Eulerian–Lagrangian numerical scheme (ozone decomposition). Powder Technology, http://dx.doi.org/10.1016/j.powtec.2009.04.023 4. Snider, D. M., Clark, S. M., and O’Rourke, P.J., 2011, Eulerian-Lagrangian method for three dimensional thermal reacting flow with application to coal gasifiers. Chemical Engineering Science, http://dx.doi.org/10.1016/j.ces.2010.12.042 5. Mills, D., and Mason, J.S., 1979. Evaluating the conveying capacity and service life of pipe bends in pneumatic conveying systems. Journal of Powder & Bulk Solids Technology, 3 (1979) 2; 13-20.
PARTICLE TO GAS HEAT TRANSFER IN A CIRCULATING FLUIDIZED BED (CFB) RISER Yassir T. Makkawi European Bioenergy Research Institute (EBRI), School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, United Kingdom T: +44 (0)121 204 3398; E: [email protected] ABSTRACT The main objective of this study was to measure the heat transfer from particle to gas in a dilute CFB riser ( 0.8 ) and to derive a predictive equation for the local particle-gas heat transfer coefficient. This coefficient was found to be a strong function of the particle velocity, concentration and length of the heat transfer section. Accordingly, a new correlation in terms of these parameters is proposed. INTRODUCTION Studies on heat transfer in a Circulating Fluidized Bed (CFB) have been mainly focused on two characteristic heat exchange mechanisms (i) exchange between the wall to gas or particle (ii) exchange between the gas to particle. While considerable amount of research has been conducted on the first mechanism, which is relevant to fluidized bed boilers, limited attention has been paid to the second mechanism. Particle to gas heat transfer is of particular interest in CFB thermo-chemical processes, such as biomass thermal conversion and the new emerging technology of chemical looping. In biomass gasification/pyrolysis, circulating hot particles, such as sand and char, are used to drive the thermochemical conversion of fresh biomass feed via particle-gas heat exchange. While in chemical looping, hot metal particles are circulated between two reactors as a mean for carrying oxygen. This process is also relevant to drying and catalytic reactions. MOTIVATION OF THIS STUDY Review of the published literature on heat transfer in dilute-phase suspension, such as in a CFB riser, indicate considerable uncertainties with respect to heat transfer coefficient. Table 1 shows two of the most widely used correlations and two recently developed ones. The literature contain a fairly large number of studies, using the correlations of Ranz and Marshall (1) and Gunn (2), in the simulation of particle-gas heat transfer in a CFB, e.g. Xie et al. (3) and Watanabe and Otaka (4). We recently carried out a numerical simulation, Fluent code (5), to investigate the particle-gas heat transfer in dense suspension (bubbling bed) and dilute suspension (CFB riser). Using Gunn’s correlation, the latter case has shown poor agreement with experimental measurements, while excellent agreement has been noticed for the case of dense suspension as shown in Fig. 1. The same was observed when applying Ranz
1
and Marshall correlation (1). It should be noted that, in both cases, the hydrodynamics were in good agreement with the experimental measurement (not shown here). Table 1. Particle-gas heat transfer correlations Correlation (a) (b)
(c) (d)
Nu 2 0.6 Re Pr 0.5 p
Author
0.33
Range
Ranz & Marshall (1)
Re <104 Pr 0.7
10<
Nu 7 10 5 2 1 0.7 Re 0p.2 Pr 0.33 1.33 2.4 1.2 2 Re 0p.7 Pr 0.33 0.3< <1.0 Gunn (2) 7
'' 5.3365
Nu 8.295 10 Re p 4
'' 2.7624
Nu 1.336 10 Re p
Fm1.3863 Fe 5.0530
Rajan et al. (6)
dilute phase
Fm0.6792 Fe 1.8344
Rajan et al. (6)
dense phase
Figure 1. Simulation and experiment results of suspension temperature in (a) bubbling bed with initial cold bed fluidized by hot air and (b) CFB with hot particles inlet and cold fluidizing air.
Rajan et al. (6) recently developed a new correlations for particle-gas heat transfer coefficient in a vertical pneumatic conveyer operating at low particle to gas flow ratio in the range of Fm 1.0 . The reported correlations, given in Eq. (c) and Eq. (d) in Table 1, were derived from data collected at the inlet and exit of the conveyers, thus representing the overall average performance across the whole conveyer height. The coefficients were categorized to dilute and dense flow regimes, however the authors did not give clear cut boundaries between what they call “dense” and “dilute”. In this study, the main objective was to carry out localized measurements in a CFB riser operated at low suspension density within the range of 0.8 , this corresponds to a particle-gas flow ratios of 0.5 Fm 10 . The data was then used to derive a simple correlation for particle-gas heat transfer coefficient.
2
EXPERIMENTAL Set-up and Procedure Fig. 2 shows a schematic diagram of the circulating fluidized bed, which mainly comprises a 163 cm height and 5.2 cm diameter riser, connected to a primary and secondary cyclones and a particle feeding/receiving tank. The fluidizing air flow rate was measured by two rotameter, giving a maximum flow rate of 1300 lit/min. The average suspension temperature and pressure along the riser were measured by eight thermocouples and pressure transducers. Two electric heating elements, wrapped around the particle feeding tank, were used for heating the particles up to a maximum temperature of 100 ºC. The riser, downer and particle tank were all thermally insulated to minimize heat losses. Glass beads and sand in the range of 235 m to 700m were used as the bed material. The experiment starts by loading the feeding tank with around 10 kgs of particles. The particles are then heated to the desired temperature using the electric heater at a selected set point. At fluidization, the pressure and temperature of the suspension along the riser Figure 2. Circulating fluidized bed were logged at the rate of 1 Hz. The particle mass flow rate was determined by relating the particle mass to collecting time. To ensure comprehensive data, a total of 48 different combinations of operating conditions were considered. Data Analysis Method If we assume uniform cross-sectional particle and gas distribution and negligible downfall of particles at the wall, then local heat transfer coefficients along the riser can be estimated by dividing the riser to a number of axial segments. Fig. 3 shows a schematic representation of this method. The heat transfer in each section was calculated using the measured gas/particle flow rate and temperatures at the end of each section such that:
q mg C p , g (Tg ,i 1 Tg ,i ) ms C p , s (Ts ,i 1 Ts ,i )
(1)
The average void fraction ( ) for each bed section of length ( z ) was determined from pressure drop measurement ( P ) such that,
1
P s g gz
(2)
3
With the available information on temperature and pressure variations along various sections of the riser, the local particle-gas heat transfer coefficient was determined by relating the localized heat transfer ( q ) to the particle surface area ( As ) and the temperature difference between the two phases ( T ) as follows:
h
q As T
Tg (out) Ts (out)
(3)
The particles surface area was related to the estimated void fraction, such that,
6(1 ) As Ac z ds
Ts (i+1)
(4)
where Ac is the riser cross-sectional area and d s is the particle diameter. The heat transfer driving force ( T ) was given in terms of the log mean temperature as follows:
T
Tg (i+1)
(Tg ,i Ts ,i ) (Tg ,i 1 Ts ,i 1 )
log[(Tg ,i Ts ,i ) (Tg ,i 1 Ts ,i 1 )]
Section (i)
Tg (i)
z
Ts (i) Ts (in) ms
(5)
Tg (in), mg In obtaining the particle temperature, we start by assuming that the measured temperature along the riser represents Figure 3. Data analysis method
the suspension temperature or bed temperature (gas and particle mixture). Hence, this can be expressed as volume-weighted average temperature:
Tmix (1 )Ts Tg
(6)
The exit air temperature in each section is obtained from Eq. 6 as follows:
Tg ,i 1
Tmix ,i 1 (1 i 1 )Ts ,i 1 (1 i 1 )
(7)
Equating both sides of Eq. 1, the exit particle temperature in each section is given by:
Ts ,i 1 Ts ,i Taking
mg C p ,s ms C p , g
(Tg ,i 1 Tg ,i )
mg C p , g mg C p , g
into Eq. 8, gives
(8)
, and substituting Eq. 7
1 Ts ,i 1 Ts ,i i (Tmix ,i 1 Tg ,i i ) i (1 i )
(9)
Figure 4. Gas and particle temperature along the CFB riser. Inlet particle temp=70 oC, inlet gas temp=18 oC
4
RESULTS AND DISCUSSION Variation of Temperature, Pressure and Particle Concentration Examples of the variations in the temperature, pressure and particle concentration along the CFB riser are shown in Figs. 4 and 5. These figures typically demonstrate the great complexity and vast variations in the hydrodynamics and heat transfer in such system. The temperature profiles shown in Fig. 4 indicate steep variations at the bottom part of the riser, within the range of ~40 cm above the particle entrance (thermal entrance length). It is interesting to note that both phases are at thermal equilibrium at the height of ~140 cm, just few centimeters below the riser exist. However, for higher particle temperature and particle loading, thermal equilibrium at exit was never reached. This is expected for such a relatively short riser. The pressure and particle volume fraction along the riser are shown in Fig. 5. Similarly, the variations are steep at the dense bottom zone. It is also interesting to note the significant entrance effect on particle concentration near the particle feeding point (10 cm from bottom). This is of significant important since most of the heat transfer takes place in this region.
Figure 5. Pressure and particle concentration variations along the riser for glass beads of 700 m diameter at two different operating conditions.
Particle-Gas Heat Transfer Coefficient Particle-gas heat transfer coefficient in a dilute CFB is believed to be dominated by particle/gas axial convection. This behavior is rather different than what is observed in CFB boilers and combustors, where the process is controlled by radial convection and conduction between the bed and wall. Therefore, in terms of operating conditions, we are interested in establishing relations between the local heat transfer coefficient and particle/gas axial velocities and particle concentration.
5
For the range of operating conditions considered in this study the particle-gas heat transfer coefficient was found to fall within the range of 0.1-350. Fig. 6 shows an example of the variations in heat transfer along the riser. As expected, rapid heat transfer takes place at the bottom region, which then decreases towards the top. The thermal entrance length increases with increasing the particle to gas flow ratio. Fig. 7 show the local heat transfer coefficient as function of the particle concentration and axial velocity for data collected from different sections of the riser and at all range of operating conditions considered. The cross-sectional local average particle concentration was obtained from pressure drop measurements (Eq.2) and the average local Figure 6. particle-gas heat transfer as particle axial velocity was obtained from function of riser height. Inlet particle temp=70 oC, inlet gas temp= 18 oC particle mass flux measurement such that:
us
Gs (1 ) s
(10)
In Fig. 7a, it is observed that the heat transfer coefficient decreases with increasing particle concentration. This is exactly opposite to CFB wall-bed heat transfer, where the heat transfer coefficient is directly proportional to particle concentration, mainly due to the dominated radial conduction and convection through the dense wall layer. In dilute CFB, the heat transfer mechanism is rather different, dominated by axial convections. As the particle concentration decreases, gas-particle convection diminishes, but at the
Figure 7. Local heat transfer coefficient as function of particle concentration and velocity
6
same time the gas convection component becomes important. This is consistent with the observation reported by Rajan et al. (6) and Bandrowski et al. (7), where it is was shown that the heat transfer coefficient decreases with increasing particle concentration within the limit of 0.95 . In Fig. 7b the local heat transfer coefficient is observed to consistently increase with increasing the axial particle velocity. PROPOSED CORRELATION FOR HEAT TRANSFER COEFFICIENT The analysis shown in section 4.2 suggests that the local particle-gas heat transfer coefficient in the CFB riser can be best correlated with particle velocity, concentration and length of the heat transfer section. Hence, dimensional analysis leads to the following function:
(1 ) z h f Re 'p , ds
(11)
' where the Reynolds number, Re p , is
expressed in terms of the particle velocity (i.e. g u s d s g ). Using regression analysis, the following proposed correlation for heat transfer coefficient is obtained:
h 8.4(Re )
' 0.871 p
(1 ) z ds
0.924
(12) Figure 8. Correlation for heat transfer coefficient
This relation is valid for 0.8 and 0.1 Re 'p 200 . Fig. 8 shows the measured heat transfer coefficient for all range of operating conditions considered in the study against the values obtained from Eq. 12. Comparison of the measured heat transfer coefficient and values obtained from literature correlations given in Table 1 is shown in Fig. 9. Generally, the data is scattered; however, Rajan correlation for dense phase (Eq. d) appear to give the closest match with our experimental measurements. The other two
Figure 9. Proposed heat transfer coefficient
7
correlations, of Gunn (Eq. b) and Rajan for dilute phase (Eq. c), considerably deviates from our measurements. Results from Eq. (a) are omitted as it gives almost the same values as that from Gunn correlation. CONCLUSION Particle-gas heat transfer coefficient in a dilute CFB riser ( 0.8 ) has been found to be a strong function of the particle axial velocity, concentration and length of the heat transfer section. A new correlation equation, in terms of these three parameters has been developed. The present experimental data for particle-gas heat transfer coefficient has also been compared with selected correlations from the literature. Work is in progress to extend this correlation to higher temperatures and to incorporate it in Fluent CFD code for the simulation of a CFB gasifier. ACKNOWLEDGEMENT The author acknowledges the help of Mr. Joseph Eke in the experimental runs. NOTATION Ac As Cp ds Fe
cross-sectional area of the riser (m2) surface are of particles (m2) specific heat capacity (J kg-1 K-1) particle diameter (m) Fedorov number,=ds[4gg2(s/g-1)/3g2]0.3 (-)
Fm Gs h m Nu P Pr Q
particle to gas flow ratio (-) Particle flux (kg m-3 s) particle-gas heat transfer coefficient ( W m-2 K) mass flow (kg/s) Nusselt number, =hds/kg (-) pressure (N m-2) Parndtl number, =Cp/kg (-) heat transfer (W)
Re’p Re’’p T u U
Reynolds number, =gusds/g Reynolds number, =gUds/g temperature (oC) velocity (m s-1) superficial gas velocity (m/s)
z
length of heat transfer section (m) Greek symbols density (kg m-3) void fraction (-)
g s
Subscripts gas particle
REFERENCES 1. Ranz, W. E. Marshall, W. R. (1952). Evaporation from drops, Chem. Eng. Prog. 48 (Part I) (1952) 141–146. 2. Gunn, D. J. (1978). Transfer of Heat or Mass to Particles in Fixed and Fluidised Beds. Int. J. Heat Mass Transfer, 21, 467-476. 3. Xie, D., Bowen, B. D., Grace, J. R., Lim, C. J. (2004). Two-dimensional model of heat transfer in circulating fluidized beds. Part I: Model development and validation, Int. J. of Heat and Mass Transfer 46 (2003) 2179–2191 4. Watanabe, H. Otaka, M. (2006). Numerical simulation of coal gasification in entrained flow coal gasifier, Fuel 85, 1935–1943 5. Fluent 6.3, 1996. Fluent 6.3 Users Guide. Lebanon, NH, 1996, Ansys Inc. 6. Rajan, K. S., Dhasandhan, K., Srivastava, S.N., Pitchumani, B. (2008). Studies on gas–solid heat transfer during pneumatic conveying, Int. J. of Heat and Mass Transfer 51, 2801–2813. 7. Bandrowski, J., Kaczmarzyk, G. (1976). Gas-to-particle heat transfer in vertical pneumatic conveying of granular materials, Chem. Eng. Sci., 1303–1310.
8
DEM STUDY OF FLUIDIZED BED DYNAMICS DURING PARTICLE COATING IN A SPOUTED BED APPARATUS Sergiy Antonyuk, Stefan Heinrich, Anastasia Ershova Institute of Solids Process Engineering and Particle Technology, Hamburg University of Technology, Denickestrasse 15, 21073 Hamburg, Germany T: +49-40-42878-2748; F: +49-40-42878-2678; E: [email protected] ABSTRACT A novel process for coating of spherical aerogel particles in a spouted bed is suggested. Using Discrete Element Method the influence of the density and restitution coefficient of experimentally coated aerogels on the fluidized bed dynamics in the developed apparatus was described. INTRODUCTION The aerogels are nanoporous materials which show extremely low density, high surface area and excellent insulation properties. However, the limitation of aerogels in a number of applications is their open-pore structure, allowing the penetration of liquids therein. Their structure might be destroyed by contact with water because of capillary forces which are higher than the strength (Antonyuk et al. (1)). This drawback could be overcome by coating of aerogels with a polymeric protection material. In this work the aerogel particles were coated in a spouted bed apparatus. This technology offers the fluidization of small and light or very large particles, which can be non-spherical or sticky with a broad size distribution, Mörl et al. (2). One objective of this work was to produce the coated aerogel particles with good structural properties. In order to optimize the coating process parameters the particle and fluid dynamics of spouted bed apparatus was investigated by Discrete Element Method (DEM) which is coupled with CFD. EXPERIMENTAL Experimental Slit-shaped Spouted Fluidized Bed 8
1 3
process chamber 2
7 4 2 Nozzle air
Process air
6 5
h
Coating fluid
Fig. 1 Experimental spouted bed apparatus for the coating of aerogels (left) and its fluidization chamber (middle and right).
To perform the coating of the aerogels an experimental spouted bed apparatus was built, Fig. 1, which allows an operation under batch conditions. The pilot plant has a cylindrical chamber, Item 1, which is connected through a conic part with a prismatic fluidization chamber, Item 2, with two horizontal gas inlets (slots) for adjustable gas supply. The velocity of the inlet air can be varied changing the height of these slots h. The air flow produced from blower, Item 3, can be heated up to 100 °C in a 500 W heater, Item 4. The solution or melt prepared in the vessel, Item 5, was transported in a coated hose using a peristaltic pump, Item 6, and injected in the fluidized bed using a two-component nozzle 7. The nozzle was heated up to temperature of injecting fluid. A fabric filter, Item 8, separates small particles from the exhaust gas. The air temperatures before, after and inside the bed (chamber 2), the temperatures of the liquid and nozzle and pressure drop were measured. Materials for the Coating Experiments Fig. 2 shows nearly spherical silica aerogels produced by supercritical extraction of a gel-oil emulsion (Alnaief&Smirnova (3)). The particles with the size ranging from 100 µm to few millimetres and the mean density of 190 kg/m³ were coated with Eudragit® 200 μm (solution with 25.8 vol-% of the solid material) which Fig. 2 SEM of produced provides a pH sensitive release of the drugs. silica aerogel particles. Parameter and Results of the Coating Experiments During the first experiments the high shrinking of aerogels was observed. The droplets of Eudragit solution destroyed the particle surface of aerogel and their size decreased (Antonyuk et al. (1)). The Eudragit layer showed many cracks (Fig. 3). Hence, to avert the breaking of the particles the coating with two materials was carried out. Firstly the PEG 2000 was sprayed in the apparatus forming a protection layer on the aerogel. Thereafter the Eudragit® was injected (Fig. 4). The different process parameters were varied during coating experiments and their optimal values were obtained, which are summarised in Table 1. Table 1. Process parameters of the coating. Parameter process air mass flow in m3/h bed temperature in °C mass flow of the coating fluid in g/min temperature of the coating fluid in °C flow rate of the nozzle air in l/min temperature of the nozzle in °C
PEG 2000 25-40 45 20 95 15-20 80
Eudragit® L 25-40 21 10 25 15-20 30
Mechanical Properties of the Aerogels The mechanical properties characterize the product quality. They are also important parameters in the calculated here DEM model (Antonyuk et al. (4)). The stiffness and breakage properties of aerogel were measured by compression tests (Antonyuk et al. (1)) and presented in Table 2 for the different sizes (uncoated particles with d50 = 0.7 mm and two fractions of coated particles with d50 = 0.8 mm and 3.71 mm with layer thickness of approximately 50 µm).
Eudragit-Aerogel layer
Aerogel microstructure 100 µm
10 µm
100 nm
Fig. 3 Aerogel particle coated in spouted bed apparatus with Eudragit® L solutions (left) and the cross section area of the layer (middle and right). Eudragit® layer protection layer
10 µm
50 µm
Fig. 4 Cross section area of a silica aerogel particle coated with two layers. Table 2 Mechanical characteristics of aerogels. Diameter in mm
Breakage force in N
Strength in kPa
Stiffness in N/mm
0.70 ± 0.14
0.37 ± 0.2
970 ± 0.50
10.3 ± 2.0
0.80 ± 0.12
0.33 ± 0.1
650 ± 460
10.0 ± 3.0
3.71 ± 0.40
0.61 ± 0.2
60 ± 30
3.0 ± 0.6
The aerogels with Eudragit shell show smaller strength in comparison with the uncoated particles. The decreasing of the strength occurs due to damaging of the particle surface by contact with Eudragit®. No significant influence of the shell on the particle stiffness was obtained. With the increasing of the particle size (from 0.8 to 3.7 mm) the strength and stiffness decrease. The impact and rebound behaviour of aerogels before (d50 = 1.5 mm) and after coating (d50 = 1.7 mm) was analyzed with the help of a free fall set-up (Antonyuk et al. (8)). The normal coefficients of restitution (euncoated = 0.6±0.13 and ecoated = 0.4±0.16) were measured at the impact velocity range of 1 m/s. They are input parameters for the DEM model, which will be described in the next paragraph. MODELLING OF PARTICLE AND FLUID DYNAMICS DPM Model A discrete particle model (DPM) was employed to study the particle and fluid dynamics of spout bed apparatus used for the coating of aerogel. The DPM is a coupling of the discrete element method (DEM), which describes the motion of every individual element, and the computational fluid dynamics (CFD), which calculates the gas phase (van Buijtenen et al. (5), Fries et al. (6)). The motion of each particle i can completely be described using Newton's and Euler's laws:
r dv i r m r r Vi β g − p r r mi u v m = −Vi ∇p + − + g ( g i ) i + ∑ Fc, j ,i , dt 1− ε j =1
(1)
Ii
r d ωi = dt
l
r
∑M
i, j
.
(2)
j =1
The force balance on the right side of Eq. (1) consists of the force due to pressure gradient, drag, gravity and contact forces by a particle-particle and particle-wall collision, respectively. Ii and ωi in Eq. (2) are the moment of inertia and angular velocity for particle i. Mi,j are the moments generated from tangential contact forces acting on the particle i. The interphase momentum transfer coefficient βg-p is modelled by combining the Ergun equation (1952) for dense regimes (ε ≤ 0.8) and the correlation proposed by Wen&Yu (1966) for more dilute regimes (ε > 0.8). Contact forces between particles are calculated according to a viscoelastic contact model based on the Kelvin-Voigt law with a constant restitution coefficient (Cundall and Strack (7)). The normal and tangential contact forces are defined as follows: r r Fc(,ijn ) = (kc ,ij ,n ⋅ sij ,n + ηij ,n ⋅ s&ij ,n ) ⋅ nij , (3)
r (kc ,ij , s ⋅ sija, s + ηij , s ⋅ s&ij , s ) ⋅ tij r ( ij ) Fc , s = min , r ( μij ⋅ Fc(,ijn ) ) ⋅ tij
(4)
where kc,ij,n and kc,ij,s are the contact stiffness in normal and shear direction, μij is the dynamic friction coefficient. The overlap in normal and shear direction is sij,n and sij,s. ηi,j,n and ηi,j,s are the normal and shear damping given as:
⎡
⎛ π ⎞⎤ ⎟⎥ . ⎝ ln e ⎠ ⎦
η = 4m* ⋅ kc / ⎢1 + ⎜ ⎣
(5)
m* is equivalent mass of contact partners. kc is the contact stiffness. The coefficient of restitution, e, describes the energy dissipation during impact and can be found as a ratio of rebound velocity to impact velocity of the particle, Antonyuk et al. (8). The hydrodynamics of the gas phase considered as continuum is calculated using volume-averaged Navier-Stokes equations (6)-(7).
∂ (ερ g ) + ∇ (ερ g ug ) = 0 , ∂t ∂ (ερ g u g ) + ∇ (ερ g u g u g ) = −ε ∇pg − ∇ (ε τ g ) − S p + ερ g g . ∂t
(6) (7)
Simulations were performed using the commercial simulators EDEM and Fluent. Simulation Parameters The geometry of fluidization chamber (2 in Fig. 1) is discretized in mesh cells (Fig. 5). The mesh consists of 76.725 tet/hybrid cells with an interval size of 0.008 and minimum volume of 4 mm3. The air with the temperature of 25 °C, density of 1.18 kg/m3 and kinematic viscosity of 15.7·10-6 m2/s was calculated. To describe the effects of turbulent fluctuations of velocities on the pressure drop of the empty apparatus a k-ε model (with the turbulence intensity of 5%) was applied for the calculation which showed good results for the spout beds in CFD simulations of Gryczka et al. (9). The parameters of the DEM model are given in Table 3.
Table 3 Properties of the particles in DEM model. Parameter uncoated coated diameter in µm
800
820
density in kg/m
190
300
stiffness in N/mm
10
10
shear modulus in MPa
6.25
6.25
restitution coefficient
0.6
0.4
friction coefficient
0.8
0.8
rolling friction coefficient
0.01
0.01
number of the particles
150.000
150.000
3
Gas inlet
Fig. 5 Mesh of CFD simulation.
The DPM simulations were performed for the dry uncoated aerogel particles and compared with the case of coated particles. The coated particles are significantly heavier than dry uncoated aerogel. Moreover, with the wetting the energy adsorption during impact increases that results in the decreasing the coefficient of restitution (Antonyuk et al. (10)). SIMULATION RESULTS First simulations were performed for the empty apparatus without the solid particles. The inlet velocity of the gas was varied in the range of 1-2 m/s. The calculated pressure drop increases with increasing inlet velocity (Fig. 6). The calculated pressure drop predicted well with the experimental measurements that were carried out for the full spout bed apparatus included its cylindrical chamber (Fig. 7). Therefore the predicted values of the pressure drop are smaller than experimental obtained pressure drops. The gas velocity reaches its maximum in the narrow vertical inlet splits (Fig. 8). This velocity in these zones increases linearly with increasing the inlet velocity (Fig. 6). 15
150
10 100 5 50
0
0 0
1
2
inlet velocity in m/s
3
200
pressure drop [Pa]
pressure velocity
max. velcity [m/s]
pressure drop [ Pa]
200
Experiment 150
CFD 100 50 0 0
20
60
40 3
volume flow [m /h]
Fig. 6 Calculated pressure drop and Fig. 7 Comparison of the calculated maximum gas velocity in the empty pressure drop depending on the apparatus versus the inlet gas velocity. volume flow of the inlet gas. Fig. 8 shows the time-averaged flow profiles. The flow starts with a relatively high velocity at the bottom and becomes wider and slower with the height. Due to turbulence, two vortices are arisen that generate the secondary flow moving from the top to down in the near-wall region.
On top of the T-shaped bottom a stagnant air region takes place. The movement and drying of the particles will be reduced in this area. The particles can sink on the bottom, as shown in Fig. 9 (DPM simulation, after real fluidization time of 1.1 s). During coating experiments that leads to sticking of the particles in this region. To overcome this problem, the nozzle can be placed above this zone and the T-shaped element must be produced as knee-shaped. Fig. 10 shows the instantaneous particle positions in the apparatus. As expected the maximum particle velocity is reached in the spout region. Here the inlet gas accelerates the particles and picks up nearly vertically according to primary flow. The gas velocity is decreased gradual over the apparatus height and leads to decreasing the particle velocity. The particles deviate from vertical air flow and moves downward along the walls. The maximum bed height depends on the gas velocity, particle mass and restitution coefficient. With increasing of the particle mass, the necessary bed height decreases. The Fig. 11 compares the calculated bed heights.
particles in sagnant zone
stagnant zone
Fig. 8 The plot of time-averaged fluid Fig. 9 The particle deposition on the velocities in the empty apparatus at the inlet middle profile in the stagnant zone velocity of 2 m/s. of the gas (see Fig. 8). Maximum bed height in mm .
300
3
ρ = 190 kg/m
200
y
100
x z
3
ρ = 300 kg/m 0 0.2
0.6
1
1.4
Time t in s
Fig. 10 Instantaneous particle positions and velocity distributions inside the spout bed apparatus (particle density = 190 kg/m3, inlet gas velocity = 1.3 m/s).
Fig. 11 Influence the particle density on the maximum bed height in the spout bed apparatus (inlet gas velocity = 1.3 m/s).
The wet coated aerogel particles showed a lower translation and rotation velocities in comparison with dry and light aerogels (Table 4). Therefore, during the coating process, the inlet gas flow and the velocity must be increased in order to keep constant particle dynamics and to avoid sticking and agglomeration. The obtained distributions of the average particle velocity and impact velocity in spouted bed can be described with a lognormal distribution function, as it shown in Fig. 12. The average particle-particle impact velocity (Fig. 12 right) is about 7 times smaller than the absolute particle velocity at the same simulation time. The mean impact velocity by particle-wall impact is at the average 15 % higher than that by impact of particles. Table 4 Time-averaged motion parameters. parameter
uncoated particles
coated particles
maximum bed height in mm
165
130
mean/maximum particle velocity in m/s
0.36/1.62
0.28/1.20
average particle rotation in 1/s
275
226
P(v)
30
2
0.6
p(v) 0.4
1
P(v imp) [-]
0.8
p (v) [1/(m/s)]
0.8
P (v) [-]
1
20
impact velostiy:
0.6 0.4
P(vp-p) _V
P(v p-w p-w)
p(vp-p) _V
p(v p-wp-w) 10
0.2
0.2 0
0
0 0
0.5
1
0 0
1.5
p(v imp) [1/(m/s)]
3
1
0.1
0.2
0.3
0.4
impact velocity v imp [m/s]
velocity [m/s]
Fig. 12 Distribution function P and its density p for: (left) average absolute particle velocity and (right) relative impact velocity in the spout bed apparatus. (particle density of 300 kg/m3; p-p - particle-particle impacts, p-w - particle-wall impacts). 6
1500
ρ = 300 kg/m
Impact force in mN
Collision rate [1/s].
2000
3
1000
500 ρ = 190 kg/m
3
0 0.3
0.6
0.9
Time t in s
1.2
1.5
Fc,n,breakage = 310 ± 100 mN
4
Fc,n,max
2
Fc,t,max
0 0.2
0.6
1
1.4
Time t in s
Fig. 13 (left) Collision rate of the particles during the fluidization time, (right) maximal values of the normal and tangential impact forces (particle density of 300 kg/m3).
Fig. 13 shows the calculated particle-particle collision rates. The fluidized bed of wetted particles shows smaller height and porosity and so higher collision rates than for dry aerogels. The particle collides with another particle almost ten times more frequently than with a wall. The calculated forces acting on the particles during collisions in the bed (Fig. 13 right) are significantly smaller than breakage range of the aerogels (Table 2). This confirms the experimental fact that no breakage occurs during fluidization of aerogels. The small magnitude of the force can be explained by relative small particle impact velocities in the presented apparatus (Fig. 12). CONCLUSIONS The process of coating silica aerogels with pH sensitive polymers was performed successfully in the experimental spouted bed apparatus. To produce a closed Eudragit® layer and to avert the shrinking and breakage of the aerogel the particles can be coated with PEG 2000 as protection material. The DPM simulations showed a high gas velocity in the bottom part of the apparatus and its gradual decrease over the apparatus. The increasing mass and energy dissipation at the contact during the coating decreases the bed height, particle velocities and increases the collisions rate. The average impact velocity in spouted bed can be described with a lognormal distribution function. No breakage of the aerogels was obtained because the impact forces acting in the fluidized bed are significantly smaller than the measured breakage force of aerogel particles. REFERENCES 1. Antonyuk, S., Heinrich,S., Alnaief, M. and I. Smirnova: Application of a novel spouted bed process for the drying and coating of silica aerogel microspheres for advanced drug delivery, 17th International Drying Symposium, Magdeburg, 2010. 2. Mörl, L., Heinrich, S., Peglow, M., The Macro Scale I: Processing for Granulation, in Handbook of Powder Technology, Vol. 11, Elsevier Science, (2005), 21-188. 3. Alnaief, M., Smirnova I.: In situ production of spherical aerogel microparticles, J. of Supercritical Fluids 55 (2010) 3, 1118-1123. 4. Antonyuk, S., Palis, S., Heinrich, S.: Breakage behaviour of agglomerates and crystals by static loading and impact, Powder Technology 206 (2011) 88-98. 5. van Buijtenen, M.S., Deen, N.G., Heinrich, S., Antonyuk, S. and J.A.M. Kuipers: A discrete element study of wet particle-particle interaction during granulation in a spout fluidized bed, Can. J. Chem. Eng., (2009), Vol. 9999, 1-10. 6. Fries L., Antonyuk, S., Heinrich, S., Palzer, S.: DEM-CFD modelling of a fluidized bed spray granulator, Chemical Engineering Science (2011), in Press. 7. Cundall, P.A., Strack, O.D.L., A discrete numerical model for granular assemblies. Geotechnique 29 (1979), 47-65. 8. Antonyuk, S., Heinrich, S., Tomas, J., Deen, N.G., van Buijtenen, M.S. and J.A.M. Kuipers: Energy absorption during compression and impact of dry elastic-plastic spherical granules, Granular Matter 12 (2010) (1), 15-47. 9. Gryczka, O., Heinrich, S., Deen, N. van Sint Annaland, M., Kuipers, J.A.M. and L. Mörl: CFD-modeling of a prismatic spouted bed with two adjustable gas inlets, Can. J. Chem. Eng. 87 ( 2009), 318-328. 10. Antonyuk, S., Heinrich, S., Deen, N.G. and J.A.M. Kuipers: Influence of liquid layers on energy absorption during particle impact, Particuology 7 (2009), 245-259.
FLUIDIZED BED GASIFICATION OF MIXED PLASTIC WASTES: A MATERIAL AND A SUBSTANCE FLOW ANALYSIS Maria Laura Mastellone*,** and Umberto Arena*,** * Department of Environmental Sciences - Second University of Naples, Via A. Vivaldi, 43 - 81100 Caserta, ITALY ** AMRA s.c. a r.l., Via Nuova Agnano, 11 - 80125 Napoli, ITALY ABSTRACT Gasification as a reliable and convenient waste-to-energy process for the economic analysis of mixed-plastic waste (MPW) was investigated. To this end a pilot scale bubbling fluidized bed air gasifier was fired with two commercially available MPWs to obtain syngas composition and characterization of the bed material, cyclone collected fines and purge material from the scrubber. These results were then processed by means of Material and Substance Flow Analyses to evaluate the main process performance parameters for the two MPWs tested. INTRODUCTION Pervasive use of plastics as packaging materials makes this the most important fraction of municipal solid waste to be considered to reach a gradually larger intensity of separate collection (6). The sorting process of this fraction after a household separate collection generally produces a high percentage of residues, together with some completely recyclable streams and a not negligible fraction of a non homogeneous plastic scrap, called mixed plastic waste (MPW). This latter stream contains several types of plastic polymers that often are together with a not negligible amount of ferrous and non-ferrous metals. Due to its heterogeneity MPW can be utilized to substitute virgin materials only for a limited number of goods. On the other hand, its high calorific value makes thermal treatment an environmental sustainable and economic attractive alternative (9,1). The study investigates the possibility to utilize the gasification as a reliable and convenient waste-to-energy process for the economic valorisation of mixedplastic waste. To this end a pilot scale bubbling fluidized bed air gasifier, having a thermal capacity of 500kJ/s, was fired with two commercially available MPWs. The results have been combined with an environmental assessment tool, the Material Flow Analysis, which is named Substance Flow Analysis when it is referred to a specific chemical. MFA/SFA is a systematic assessment of the flows and stocks of materials and elements within a system defined in space and time. It connects the sources, the pathways, and the intermediate and final sinks of each species in a specific process (7). In this study MFA/SFA was applied to a system boundary that includes the BFB gasifier and the cleaning system for ash separation (cyclone and wet scrubber). The BFB gasifier was further divided into two sections: the first corresponds to the dense bed and splashing zone; the second to the freeboard. THE PILOT SCALE FLUIDIZED BED GASIFIER The utilized pilot scale BFB gasifier has the characteristics schematically listed in Table 1. An olivine - a magnesium-iron silicate, (Mg,Fe2)SiO4 - was selected as material for the fluidized bed on the basis of results of previous investigations carried out on the same pilot-scale BFBG [Arena et al., 2010a] that indicated olivine as an interesting candidate to act as a bed catalyst for the tar cracking
reactions in waste-derived fuel gasification, even taking into account its low cost and excellent resistance to attrition in the fluidized bed reactor. The main characteristics of the utilized olivine are reported in Table 2. Table 1. Main design and operating features of the pilot scale bubbling fluidized bed gasifier. ID: 0.381m; total height: 5.90m; Geometrical parameters reactive zone height: 4.64m 100 kg/h Feedstock capacity 145 kg Typical bed amount over-bed air-cooled screw Feeding system feeder 700-950°C Bed temperatures 0.3 –1m/s Fluidizing velocities cyclone, scrubber, flare Flue gas treatments Table 2. Characteristics of the olivine particles utilized as bed material in the pilot scale bubbling fluidized bed gasifier. Mg-Fe silicate Mineral Chemical composition, % 39-42 SiO2 48-50 MgO 8-10.5 Fe2O3 <0.4 CaO K2O TiO2 Al2O3 0.8 Cr2O3 Mg3O4 0.20 LOI (loss of ignition) 200 ÷ 400 Size range, μm 298 Sauter mean diameter, μm 2900 Particle density, kg/m3 In the reported experiments, air was used as reducing agent and always injected at the bed bottom while the fuel was fed by means of an over-bed feeding system. The fluidizing air stream was heated up to 450°C by a two electric heaters before entering the reactor. The fuel and blast flow rates were mutually adjusted so that, at the fixed fluidizing velocity, the desired equivalence ratio ER was obtained (where ER is defined as the ratio between the oxygen content of air supply and that required for the stoichiometric complete combustion of the fuel effectively fed to the reactor). The cylindrical BFB reactor was heated up to the reaction temperature by the sensible heat of pre-heated blast gases and by a set of three external electrical furnaces. The gas generated in the reactor was sent to a high efficiency cyclone and then to a wet scrubber (for the removal of tars, residual fly ash and acid gases) and finally incinerated by a safety flare. An accurate description of the plant and of experimental procedures is provided elsewhere (3). Here it is sufficient to highlight that gas composition was on-line measured upstream and downstream of the syngas conditioning section as well as at the reactor height corresponding to the end of splash zone. The diagnostic apparatus utilizes IR analyzers for the main syngas components (carbon monoxide and dioxide, hydrogen, methane) and two micro-gas-chromatographs equipped with different columns for the detection of light and heavy hydrocarbons as well as of carbon monoxide and dioxide, hydrogen, nitrogen and water.
EXPERIMENTAL RESULTS The plant was fed with one of the two mixed plastic wastes taken in consideration, both obtained as by-products of the sorting process of end-of-use plastic packaging from separate collection (Table 3). The first, named GS3, is a mixture of recycled polyolefins obtained from plastic packaging for food and beverages by means of sorting and washing treatments. The second, named SRA, is a mixture of several plastic wastes obtained from separate collection of packagings made of plastic as well as ferrous and non-ferrous metals, as resulting after an intensive treatment aimed to produce a fuel that can meet even high quality standards, as those of metallurgical industry. Table 3. Chemical characterization of the two MPWs utilized in the study. GS3 SRA Mixed Plastic Wastes Ultimate analysis, % wb C (min-max) 84.4 (84.3-84.8) 79.5 (75.9-83.1) H (min-max) 14.0 (13.5-14.2) 13.1 (12.8-13.4) N (min-max) 0 0.2 (0.15-0.25) S (min-max) 0 0.1 (0.08-0.12) Moisture (min-max) 0.3 0.7 (0.6 – 0.8) Ash (min-max) 1.3 1.9 (1.4 – 2.4) O (by difference) 0 4.5 46.0 43.4 (41.8-45.0) HHVa, MJ/kgfuel,db b 42.9 40.2 (38.6-41.8) LHV , MJ/kgfuel,ar 7, 1 irregular Size (diameter and height), mm 3 460 310 Bulk density, kg/m a) evaluated by means of relationship proposed by Channiwala and Parikh (2002).b) evaluated by the HHV on dry basis by taking into account the latent heats of vaporization of fuel moisture and water obtained as product of hydrogen combustion;wb= weight basis; db=dry basis; ar=as received.
The pilot scale BFBG was operated with the mixed plastic wastes in a bed of olivine particles fluidized at a velocity of 0.7m/s, at a bed temperature of about 850°C, preheated air of about 450°C and an equivalence ratio of 0.27. The performances of the BFBG were measured and recorded only when the chemical composition of the produced syngas and the temperature profile along the reactor reached steady-state conditions. The experimental activity provided the complete chemical composition of gas stream at two levels of BFB gasifier (2m above the air distributor and at the reactor exit) together with those of streams leaving the cyclone and the wet scrubber system. These latter data, (Table 4), have been elaborated and used for the substance flow analysis of carbon, hydrogen, iron, magnesium and other elements and for the feedstock energy flow analysis (5). THE MFA/SFA ANALYSIS Figures 1-3 report the quantified flow diagrams resulting by the MFA/SFA applied to the above cited sections of the bubbling bed gasifier (dense bed + splashing zone and freeboard zone) and of the cleaning system (cyclone for ash separation +wet scrubber for gas cleaning) of the pilot scale gasification system, when operated with the two MPWs.
Figure 1. Layers of total mass balances (kg/h) throughout the pilot scale gasifier, for test with GS3 (left) and SRA (right)mixed plastic waste fuels. The layer of total mass flow rate is reported in Figure 1. The input flows to the BFBG unit are the stream of plastic fuel and that of air used as oxidizing agent and fluidizing gas. The output flow stream from the dense bed and splashing zone is that of the obtained syngas, which still contains heavy hydrocarbons and entrained fines. This stream is visualized in Figs. 1-3 as two different arrows, one indicating the syngas, i.e. the mixture of N2, CO, H2 and CnHm with n<6, the other indicating the contaminants, mainly heavy hydrocarbons and entrained fine particles. The output from this first section moves throughout the freeboard and then to the cyclone for dust abatement and to the wet scrubber for removal of tars and inorganic compounds. Along these paths both chemical and physical reactions occurred so that the mass flow and composition of each stream were modified (Figs. 1-3). By looking at Figure 1 it is evident the different process performances obtained with the mixture of polyolefin plastic waste (GS3) and with the other mixed-plastic waste (SRA). The analysis of the results of the test with GS3 in a bed of olivine indicates a great performance, with the almost complete absence of tar (Fig. 1), a consequently high value of the specific syngas conversion efficiency (122.2/(28.5+97.6)=0.97) and a high concentrations of molecular hydrogen and carbon monoxide in the syngas (Table 4). Specific studies about the role of olivine as a tar removal catalyst during the gasification of polyolefin plastic wastes [Arena et al., 2009; 2010a] indicated that magnesium and iron, both largely present in the olivine particles, activate the endothermic decomposition reactions of hydrocarbon fragments that are the first precursors of tar formation. The very low tar concentrations are always coupled with low concentrations of methane (less than 3%), ethane, ethylene, acetylene, propylene or, in other words, with high extension of the cracking and dehydrogenation reactions (10).
Table 4. Operating conditions and performance parameters of the pilot scale BFBG operated with the SRA fuel under two values of equivalence ratio. Mixed Plastic Wastes GS3 SRA Operating Conditions ER (equivalence ratio), 0.27 0.27 AF (air/fuel ratio), kgair/kgfuel 3.95 3.59 Output Process Data Temperature of fluidized bed,°C 830 890 Qsyngas,m3N/kgfuel 5.82 3.75 LHVsyngas, kJ/m3N 6850 6430 Specific energy, kWh/kgfuel 11.1 6.7 CGE (cold gas efficiency), 0.83 0.77 Syngas composition (downstream of cyclone and scrubber) N2, % 46.25 65.11 CO2, % 1.50 9.80 CO, % 21.07 5.34 H2, % 28.18 8.58 CH4, % 2.31 7.30 C2H4 + C2H6+ C2H2 + C3H6, % 0.55 3.76 The analysis of the results for the SRA fuel indicates that syngas has very low concentrations of H2 and CO and larger concentrations of CH4 and C3Hm together with a higher content of tar (11.4/(25.1+97.6)=0.093) and a correspondingly lower specific syngas yield (111.2/(25.1+97.6)=0.91): this suggests that the catalytic action of olivine is not present. Moreover, the role of the freeboard section appears negligible since the decreasing of heavy hydrocarbons and elutriated fines fraction between the exit of dense bed and splashing zone and the reactor exit is very low (3.5%). Figure 2 reports the results of the mass balances applied to the carbon element, i.e. the carbon layer of SFA, for both MPWs. It provides the carbon conversion efficiency CCE, defined as the ratio between the mass flow rate of the carbon present in the syngas as CO, CO2, CH4 and light hydrocarbons (until C5Hm) and the mass flow rate of the carbon that enters the reactor with the fuel. In the case of the GS3, the CCE increases from the value of 0.77 at the exit of the splashing zone to 0.81 and 0.83 related to the freeboard and cleaning system exit, respectively. These values confirm that the largest part of fuel conversion into syngas occurs in the dense bed and splashing zone, which is characterized by an intense turbulence of gas phase and by the effect of heterogeneous and/or catalytic reactions (dehydrogenation and carbonization). Analogous calculations can be made for the SRA test (right side of Fig. 2). In this case, the CCE does not vary between the three measurement points and remains almost equal to 0.76. This different behaviour is confirmed by the value of carbon accumulated in the bed (that is almost zero) as well as by that of carbon fines elutriation rate (that is negligible). The absence of carbon fines along the freeboard can affect the type of reactions occurring in this region: oxygen is absent and heterogeneous reactions cannot occur, so that the only expected reactions are those of recombination of reactive molecules that can lead to an increasing of heavy hydrocarbons (tar).
Figure 2. Layers of total carbon balances (kg/h) throughout the pilot scale gasifier, for test with GS3 (left) and SRA (right)mixed plastic waste fuels. The graph on the left side highlights the completely different behaviour of the GS3 waste. The carbonization was so present, and strong, that an accumulation of carbon on the bed particles surface was present: the stock of 145kg of bed particles was progressively incremented (3.7kg/h) as a result of opposite effects of elutriation losses and carbon accumulation. The fines collected at the cyclone in the run with GS3 were mainly produced by the attrition between the bed particles and the carbon layer deposited on their surface. They contained quantity of iron larger than that entering the reactor with the fuel (4): this means that part of the elemental iron of olivine, linked with the carbon by coordination complexes, was then detached from the particle by mechanical attrition and entrained out of the reactor in the syngas. This behaviour allowed that, in the test with GS3, less than 6% of the fuel carbon was used to produce tar precursors while, in that with SRA where this mechanism was inactive, the 24% of the fuel carbon was transformed into heavy compounds in the dense bed+splashing zone. These considerations can be repeated and further supported by analyzing the hydrogen layer (Fig. 3). In the test with GS3 the hydrogen conversion into syngas moved from 0.82 to 0.89 and to 1, i.e. the dehydrogenation of the fuel was completed. All the fuel hydrogen was transferred into syngas as H2 and light hydrocarbons. No hydrogen was present as tar compounds and as carbon fines. This result was due to the completion of carbonization/dehydrogenation reactions that largely occurred in the dense bed and partly occurred in the freeboard and in the cyclone, thank to the contact between the carbon fines (that contained metal active particles absorbed over and inside its surface) and heavy hydrocarbons not yet converted into small molecules in the dense bed+splashing zone. The dehydrogenation was
Figure 3. Layers of total hydrogen balances (kg/h) throughout the pilot scale gasifier, for test with GS3 (left) and SRA (right)mixed plastic waste fuels. due to the increasing of aromatization until to the complete hydrogen abstraction from heavy hydrocarbons and PAHs. The hydrogen flow analysis of SRA test shows, again, a different behaviour (Fig. 3). In this case, the increasing of hydrogen conversion into syngas components moved from 0.74 to 0.75 and to 0.77. The values are, as with CCE, very close to each other, and the final value was very far from the total conversion obtained with GS3. This result was due to the absence of any heterogeneous reactions in the dense bed as well as in the other zones of gasifier: the carbon accumulation rate was almost zero and, as a consequence, the carbon elutriation rate too. CONCLUSIONS The industrial application of plastics-to-energy gasifiers was investigated. The process performances of two mixed-plastic wastes, gasified in a pilot scale bubbling fluidized reactor having a bed of olivine have been evaluated. Experimental measurements taken at different points inside and downstream of the gasifier, combined with mass balances and material and substance flow analyses, indicated the MPW that offers the higher performance and reliability. In particular, SRA, a MPW obtained from a separate multi-material collection (plastics+ferrous+non-ferrous packaging) processed by an intense treatment, presently designed to be utilized in the metallurgic industry, appeared convenient for a gasification-based, plastics-to-energy plant only if a downstream recovery and valorization of tar stream is provided. On the contrary, GS3, a more homogeneous MPW, mainly made of polyolefin plastics with a substantial absence of ferrous or non-ferrous packaging, and just processed by means of a shredding and washing treatment gave the best performance.
During the gasification of SRA, the catalytic effect of olivine particles appeared absent or limited: carbonization was practically absent and the produced tar was captured by the wet scrubber so determining a not negligible environmental burden and a remarkable energetic loss and, then, a relevant economic cost. It is likely that the catalytic support to the cracking and isomerization was always active (the heavier fragments are broken and a number of unsatured hydrocarbons with two or three carbon atoms are formed) but the catalytic enhancement of the dehydrogenation and carbonization determined by active sites of iron was absent (the hydrogen content remains low and the tar formation was not inhibited). During the gasification of GS3, the catalytic effect of olivine particles was so strong that no tars were detected downstream of the cleaning section and the endothermic reactions of carbonization clearly reduces the bed temperature. The negative aspect of this mechanism is that an amount of carbon continuously accumulates in the bed, indicating the necessity of an overflow of exhausted olivine particles and a corresponding make-up of fresh particles. Exhausted olivine could be regenerated by burning the carbon layer covering the external surface (2). ACKNOWLEDGEMENTS The study was carried out with the financial support of CONAI (Italian National Consortium for Packaging). Authors are indebted to to Mr. Donato Santoro for all the chemical analyses related to the experimental activity. REFERENCES 1. Arena U. and M.L. Mastellone (2006) Fluidized Bed Pyrolysis of Plastic Wastes. Chapt. 16 in Feedstock Recycling and Pyrolysis of Waste Plastics, J. Scheirs and W. Kaminsky (Eds). J. Wiley&Sons Ltd, ISBN: 0-470-02152-7, pp. 435-474 2. Arena U, L. Zaccariello, M.L. Mastellone (2008) Gasification of a plastic waste in a fluidized bed of olivine. Proc. of CFB9 - 9th Int. Conf. on Circulating Fluidized Beds. J. Werther, W. Nowak, K.-E. Wirth and E.-U. Hartge /eds.), ISBN 978-3930400-57-7, p. 691-696 3. Arena U., L. Zaccariello, M.L. Mastellone (2009) Tar Removal During the Fluidized Bed Gasification of Plastic Waste. Waste Management, 29:783-791 4. Arena U., L. Zaccariello, M.L. Mastellone (2010a). Fluidized Bed Gasification of Waste-Derived Fuels. Waste Management, 30:1212-1219 5. Arena U., F. Di Gregorio, L. Zaccariello and M.L. Mastellone (2010b) Fluidized bed gasification of biomass: a substance flow analysis, in Fluidization XIII, S.Done Kim, Y. Kang, J.Keun Lee and Y. ChilSeo (Eds), Engineering Conferences International, ISBN 978-0-918902-57-3, pp. 805-812 6. Brandrup J., M. Bittner, W. Michaeli and G. Menges (Eds.) (1996). Recycling and recovery of plastics, New York, NY: Hanser Publ 7. Brunner P.H. and V. Rechberger (2004) Practical Handbook of Material Flow Analysis. CRC Press LLC, Boca Raton (FL) 8. Channiwala S.A and P.P. Parikh (2002). A unified correlation for estimating HHV of solid, liquid and gaseous fuels, Fuel, 81:1051-1063 9. Hermann C., F.J. Schwager, K.J. Whiting (2001).Pyrolysis & Gasification of Waste.A Worldwide Technology & Business Review. 2nd Edition. Juniper Consultancy Services Ltd 10. Mastellone M.L. and U. Arena (2008). Olivine as a Tar Removal Catalyst During Fluidized Bed Gasification of Plastic Waste. AIChE Journal, 54/6: 16561667
EFFECT OF GAS BYPASSING IN DEEP BEDS ON CYCLONE DIPLEG OPERATION A. S. Issangya, S. B. Reddy Karri, Ted M. Knowlton and Ray Cocco Particulate Solid Research, Inc., 4201 W 36th Street, Suite 200, Chicago, USA ABSTRACT Cyclone diplegs play a major role in the functioning of fluidized beds. Previous studies have shown that at certain operating conditions there can be severe gas bypassing (also referred to as jet streaming) of gas in deep beds of Geldart Group A materials which leaves significant portions of the fluid bed defluidized. If cyclone diplegs are immersed in these defluidized regions, solids discharge from the dipleg may be hindered, which can lead to the flooding of the dipleg and the cyclone. This could result in high solids losses from the fluidized bed. Tests were conducted to demonstrate that cyclone diplegs can flood when discharging into a bed with gas bypassing. Tests were also conducted to determine how gas bypassing affects the operation of cyclone diplegs that have a splash plate or a trickle valve. These tests were conducted in a 1.52-m-diameter semicircular column equipped with a Plexiglas faceplate to allow visual observation. INTRODUCTION Cyclone diplegs are pipes attached to the conical bottom of cyclones that return the collected solids back into the system. Cyclones are widely used in catalytic processing units where diplegs are used to return collected catalyst particles back to fluidized beds. Diplegs can discharge solids above a fluid bed (suspended diplegs) or directly into the fluid bed (submerged diplegs). During start-up, the fluidizing gas preferentially flows up the dipleg of a second-stage cyclone until a solids level in the dipleg is established to seal it. First-stage cyclone diplegs generally have enough solids flow through them to allow a seal to be established in spite of the initial upward gas flow in the dipleg, and sealing devices at the dipleg exit of first stage cyclones are typically not necessary. Splash plates, placed a short distance below the dipleg, are often been used with first stage diplegs to try to prevent gas from entering the dipleg. The solids flow rate in second stage cyclones is generally too small to establish a seal unless a device such as a trickle valve is attached to the dipleg end to prevent gas from flowing up the dipleg. Despite the wide use of cyclones and diplegs in fluidized beds, there are only a few dipleg studies in the open literature. Bristow and Shingles (1) identified four operating modes of trickle valves in diplegs: trickling, dumping, trickling/dumping, and flooding. Flooding occurs if the solids flow into the dipleg is greater than the solids being discharged from the dipleg causing the dipleg to fill with solids and back-up into the cyclone. A blowing mode can also occur in some cyclone diplegs
(2) where the pressure inside the cyclone can be greater than at the end of the dipleg. Geldart et al. (3) found that a considerable amount of the cyclone inlet gas can be dragged downwards by the solids if the dipleg is operating in streaming flow. Dries and Bouma (4) proposed five modes of flow in a cyclone dipleg; (a) stick-slip flow when the solids in the dipleg were in packed bed condition, (b) transition flow mode in which the dipleg had a dense fluidized region on top of a packed bed region, (c) unstable flow at low solids fluxes, and (d & e) at higher solids fluxes, a wholly dilute phase flow or a dipleg flow with a dilute region on top of a dense fluidized region. Wang et al (5, 6) measured axial pressure and radial solids volume fractions and particle velocity profiles in cyclone diplegs and the amount of gas flowing down the dipleg. The formation of a dense phase in a dipleg was found to significantly decrease the amount of gas downflow. Karri and Knowlton (2) found for FCC catalyst that aeration substantially increased the amount of solids flow through both immersed and nonimmersed trickle valves and that the solids flux through the dipleg was not a function of the flapper weight when aeration was used. The best aeration locations for a trickle valve were found to be just above the mitered bend and at the midpoint of the mitered section. It was also found that cyclone gas was dragged down the dipleg into the bed by the fast moving solids when the solids flux exceeded about 100 kg/ s-m2. One factor that is missing in submerged dipleg studies is how solids discharge from a dipleg is affected by the quality of fluidization at the point of discharge. Studies (Wells, 7; Knowlton, 8; Karri et al., 9; Issangya et al., 10, 11; Karimipour and Pugsley, 12) have shown that deep beds of Geldart Group A materials can have defluidized regions caused by gas bypassing. Most of the fluidizing gas was observed to preferentially flow up near the column wall in a single or a number of high velocity streams of bubbles. Gas bypassing was attributed to decreased voidage and permeability of the emulsion phase due to the compression of gas in the emulsion phase caused by the pressure head developed in deep beds. Bolthrunis (13) found that severe gas maldistribution occurring in a large fluid bed reactor could be prevented by installing baffles. If cyclone diplegs are located in the poorly fluidized region of a gas bypassing bed, solids discharge from the dipleg will likely be hindered, which can lead to the flooding of the dipleg and the cyclone. Defluidization can also result from poorly designed or defective gas distributors. This paper discusses tests conducted to determine if cyclone diplegs with and with no exit attachments will flood because of gas bypassing in the fluid bed. EXPERIMENTAL Tests were conducted in a 1.52-m-diameter, 5.2 m tall semicircular fluidized bed that had a Plexiglas faceplate (Figure 1). The unit had three, 41-cm-diameter, tangential inlet internal first stage cyclones (Figure 2). The left and the right hand side cyclones had 15-cm-diameter fully round diplegs that discharged solids into the bed 25 cm away from the faceplate. The middle cyclone had the test dipleg which was transparent and semicircular and was attached to the flat faceplate to enable visual observation of solids flow through it. Air exiting the three primary cyclones entered a
51-cm-diameter second stage cyclone and then passed into a third stage cyclone of the same diameter before entering the exhaust header. The third stage cyclone dipleg returned solids to the second-stage cyclone dipleg via an automatic L-valve and the combined flow was returned to the column by another automatic L-valve. The operation of the middle cyclone dipleg was studied for various solids fluxes through the dipleg, and with and without dipleg aeration. In order to have a wide range of solids fluxes through the test dipleg, diplegs of two sizes, 7.6 and 20 cm diameters, were tested. The solids flux was also varied by changing the bed superficial gas velocity. The solids flux through each dipleg was calculated from the measured fluid bed entrainment rate at a given superficial gas velocity by assuming that the loading was split equally among the three cyclones. At superficial bed velocity of 0.9 m/s, the solids fluxes in the 15-cm-diameter round dipleg and the 20-cm-diameter semicircular dipleg were 85 and 96 kg/s-m2, respectively. These values are within the high solids flux range of commercial second stage diplegs which are normally operated at fluxes much less than 100 kg/s-m2. The solids flux in the 7.6-cm-dia. semicircular dipleg was 684 kg/s-m2 at the same gas velocity in the bed. This was within the range of the solids fluxes in commercial first stage cyclone diplegs which are typically 350 to 750 kg/s-m2. The Plexiglas faceplate allowed visual observation of the quality of fluidization in the bed and of the flow of solids and bubbles in the diplegs. Digital videos of the dipleg flow were made at selected operating conditions. Differential pressure (∆P) fluctuations were measured across bed axial lengths of 60 cm at five locations around the circumference of the unit. The ∆P fluctuation data offered another way of assessing if there was gas bypassing in the bed. Locations near the gas bypass stream have been found (Issangya et al. (3)) to have significantly higher ∆P fluctuations than the rest of the bed. The middle cyclone dipleg was tested having (a) no exit terminations (b) a trickle valve and (c) a splash plate (Figure 3). The two 15-cm-diameter interior cyclone diplegs had no exit attachments. The splash plate tests were conducted with the 7.6cm-diameter dipleg where high solids fluxes, typical of those in primary cyclone diplegs, could be achieved. An 8.9-cm-diameter semicircular steel splash plate was placed 8.9 cm below the dipleg end. The distance of the splash plate from the dipleg end was calculated such that the solids discharge area was twice the area of the open end of the dipleg, a criterion often used in industry. The trickle valve tests were conducted with the 20-cm-diameter dipleg. The trickle valve was made from a 15-cm-diameter pipe whose opening was inclined 4 degrees from the vertical. The trickle valve flapper plate was attached to the pipe by loose hanger rings. The cyclone diplegs were operated both without and with dipleg aeration. The dipleg aeration was equivalent to a gas velocity of 0.03 m/s in the dipleg. The aeration for the diplegs with no exit terminations and the one with a splash plate were located 2.54 cm above the open end. Aeration was supplied to the dipleg with a trickle valve 15.2 cm above the bend and at the midpoint of the underside of the inclined part of the trickle valve as recommended by Karri and Knowlton (4).
Tests were conducted with two static bed heights: 1.52 m, referenced to the air distributor, to obtain strong gas bypassing in the bed, and 1.07 m to obtain a uniformly fluidized bed. The test material was a 2.5% fines (<44 μm) FCC catalyst with a median particle diameter (dp50) of 85 microns and particle density of 1488 kg/m3. The particle size distribution is given in Figure 4. RESULTS AND DISCUSSION Gas bypassing in the semicircular unit occurred in the form of two gas bypass streams located near the corners where the flat faceplate met the semicircular column. The two streams occasionally moved toward the center of the faceplate or moved inward and around the unit along the wall. Because the shell of the column was made of steel, the motion of the gas bypass stream was only detected from the ∆P fluctuation signals. It was possible to visually observe solids and bubble flow in and around the test cyclone located at the center of the faceplate. Results presented in this study are visual observations of the bed fluidization behavior, and of whether flooding was occurring in the test cyclone dipleg. Effect on Straight Diplegs With no Exit Attachments Table 1 shows the results for the 20-cm-diameter semicircular cyclone dipleg with no exit attachment. The fluid bed unit was operated at superficial gas velocities of 0.15 to 0.9 m/s and the static bed height was 1.52 m. The solids flux in the dipleg ranged from 0.24 kg/s-m2 at a bed velocity of 0.15 m/s to 96 kg/s-m2 at a bed velocity of 0.9 m/s. With or without dipleg aeration, the dipleg operated well without flooding at all gas velocities. Gas bypassing was present for all gas velocities except at 0.9 m/s where it was significantly reduced because of the high superficial gas velocity. Occasionally, a relatively stagnant dense (but not packed) region formed around the dipleg exit region. This dense region was frequently broken by bubbles rising directly up from the air distributor or by the gas bypass streams that at times moved to the middle of the faceplate. Bubbles rose through the dipleg, but their frequency decreased as the solids flux through the dipleg increased. Table 1. Dipleg operation: 20-cm-diameter semicircular dipleg (no exit attachment) Ug m/s
0.15 0.30 0.46 0.61 0.76 0.91
Dipleg Solids Flux kg/s-m2 Diplegs Dipleg 2 1 and 3 (D = 20 cm) 0.20 0.24 1.61 1.95 6.35 6.84 17.1 19.0 36.6 41.0 85.4 96.2
Gas Bypassing in the Fluid Bed? YES YES YES YES YES WEAK
DIPLEG 2 OPERATION No Dipleg Aeration GOOD GOOD GOOD GOOD GOOD GOOD
With Dipleg Aeration GOOD GOOD GOOD GOOD GOOD GOOD
Table 2 shows the results for the 7.6-cm-diameter semicircular dipleg with no exit termination for a static bed height of 1.52 m. With no dipleg aeration, the dipleg functioned well for all solids fluxes up to 88 kg/s-m2. The dipleg flooded for solids
fluxes of 103, 137 and 293 kg/s-m2. At a solids flux of 684 kg/s-m2 solids bridging occurred in the upper part of the dipleg where there the semicircular dipleg joined the round tube. The solids then accumulated and overflowed into the cyclone. When dipleg aeration was present, the dipleg functioned without flooding at all the solids fluxes except at 684 kg/s-m2 when bridging occurred. Bubbles were able to rise through the dipleg for solids fluxes up to about 220 kg/s-m2. The dipleg functioned properly for the 1.07 m static bed tests with or without dipleg aeration except for the highest solids flux that led to dipleg bridging. Table 2. Dipleg operation: 7.6-cm-diameter semicircular dipleg (no exit attachment) Ug m/s
0.15 0.30 0.46 0.55 0.61 0.76 0.91
Dipleg 2 (D = 7.6 cm) Solids Flux kg/s-m2 1.46 13.2 48.8 87.9 136.7 292.9 683.5
Gas Bypassing in the Fluid Bed? YES YES YES YES YES YES NO/WEAK
DIPLEG 2 OPERATION No Dipleg Aeration GOOD GOOD GOOD GOOD FLOODED FLOODED BRIDGED
With Dipleg Aeration GOOD GOOD GOOD GOOD GOOD GOOD BRIDGED
Effect on Diplegs Fitted With a Splash Plate Table 3 gives the results for the 7.6-cm-diameter semicircular dipleg fitted with a splash plate and with and with no dipleg aeration. With no aeration, the dipleg functioned well for solid fluxes of 13 and 49 kg/s-m2 but flooded at solid fluxes of 78, 107 and 137 kg/s-m2. When dipleg aeration was turned on, the dipleg was able to function well at solid fluxes of 107 and 137 kg/s-m2. Flooding occurred for solid fluxes of 200 and 459 kg/s-m2 and the dipleg bridged when the solid flux was increased to 684 kg/s-m2 as was the case was for the straight dipleg discussed above. The dipleg did not flood for tests that were conducted with a static bed height of 1.07 m where no or very weak gas bypassing was occurring. Comparing the dipleg with no attachment with the dipleg with a splash plate under gas bypassing conditions, it appears that with no dipleg aeration a straight open cyclone dipleg worked just as well as the one with a splash plate. However, when there was dipleg aeration the dipleg with a splash plate flooded at a lower solids flux of 200 kg/s-m2, compared to the straight dipleg which did not flood at Gs = 293 kg/s-m2. Effect on Diplegs Fitted With a Trickle Valve Table 4 presents results for the 20-cm-diameter semicircular dipleg fitted with a trickle valve and with and with no dipleg aeration for a 1.52 m static bed. With no dipleg aeration, the dipleg worked well for solids fluxes of 0.24, 1.92 and 6.83 kg/sm2 which corresponded to gas velocities in the bed of 0.15, 1.0 and 0.46 m/s, respectively. The dipleg flooded when the solids flux was raised to 19 kg/s-m2 corresponding to a bed superficial velocity of 0.6 m/s, but functioned well when the
superficial gas velocity was increased to 0.76 m/s. At a superficial gas velocity of 0.76 m/s, the dipleg solids flux was 41 kg/s-m2. It seemed that more air was able to leak from the bed and enter the dipleg at a superficial gas velocity of 0.76 m/s than at a superficial gas velocity of 0.6 m/s, and this amount of air was sufficient to aerate the dipleg and allow the dipleg to operate without flooding. At a superficial gas velocity of 0.9 m/s, for a solid flux of 96 kg/s-m2, the dipleg at first flooded but then recovered and continued to function well. Apparently, enough air was able to leak through the trickle valve and reaerate the flooded dipleg. The dipleg flooded at solid fluxes of 130 and 205 kg/s-m2. These solids fluxes corresponded to superficial gas velocities of 1.0 and 1.1 m/s, respectively. It appeared that even with the higher air leakage from the bed, the solids flux was too high for the dipleg to function without an external supply of aeration. For tests performed with dipleg aeration on the dipleg functioned without flooding at all solids fluxes tested, up to 205 kg/s-m2. Table 3. Dipleg operation: 7.6-cm-diameter semicircular dipleg with a splash plate Ug m/s
0.15 0.30 0.46 0.53 0.58 0.61 0.69 0.84 0.91
Dipleg 2 (D = 7.6 cm) Solids Flux kg/sm2 1.46 13.2 48.8 78.1 107.4 136.7 200.2 458.9 683.5
Gas Bypassing in the Fluid Bed? YES YES YES YES YES YES YES YES NO/WEAK
DIPLEG 2 OPERATION No Dipleg Aeration GOOD GOOD GOOD FLOODED FLOODED FLOODED GOOD GOOD GOOD
With Dipleg Aeration GOOD GOOD GOOD GOOD GOOD GOOD FLOODED FLOODED BRIDGED
Table 4. Dipleg operation: 20-cm-diameter semicircular dipleg with a trickle valve Ug m/s
0.15 0.30 0.46 0.61 0.76 0.91
Dipleg 2 (D = 20 cm) Solids Flux kg/s-m2 0.244 1.92 6.83 19.0 41.0 96.2
Gas Bypassing in the Fluid Bed? YES YES YES YES YES WEAK
0.98 1.07
130.3 204.6
NO NO
DIPLEG 2 OPERATION No Dipleg Aeration GOOD GOOD GOOD FLOODED GOOD FLOODED THEN RECOVERED FLOODED FLOODED
With Dipleg Aeration GOOD GOOD GOOD GOOD GOOD GOOD GOOD GOOD
CONCLUSION Diplegs immersed in poorly fluidized zones caused by gas bypassing can flood and cause solids to back up into the cyclone causing excessive solids losses. The occurrence of flooding was a function of dipleg solids flux and the presence or absence of a dipleg exit attachment. As found in other studies, dipleg aeration significantly increased the operating range of the dipleg solids flux before flooding would occur. Diplegs with no exit attachments functioned well at all conditions tested if they had dipleg aeration. With no dipleg aeration, diplegs with no exit attachments flooded at solids fluxes of 137 kg/s-m2 and above. With no dipleg aeration, the dipleg fitted with a splash plate flooded at solids fluxes of 78 kg/s-m2 and above. The addition of dipleg aeration extended the mass flux operating window of the dipleg. With aeration, the dipleg fitted with a trickle valve functioned well for all conditions tested, up to 205 kg/s-m2. With no dipleg aeration, the dipleg flooded if the solids flux exceeded 98 kg/s-m2. REFERENCES 1. T. C. Bristow and T. Shingles (1989), Cyclone dipleg and trickle valve operation, in Fluidization VI, J. R. Grace, L. W. Shemilt, M. A. Begougnou, eds., Engineering Foundation, 161 – 168 2. Reddy Karri, S. B. and T. M. Knowlton (2004), The effect of aeration on the operation of cyclone diplegs fitted with trickle valves, Ind. Eng. Chem. Res., 43 (18), 5783 - 5789 3. D. Geldart, N. Broodryk and A. Kerdoncuff (1993), Studies on the flow of solids down cyclone diplegs, Powder Technol., 76, 175 – 183 4. H. W. A. Dries and J. H. Bouma (1997), Down flow of class A powder in cyclone diplegs, in Proceeding of 5th Int. Conf on Circulating Fluidized Beds, 585 – 590 5. S. J. Wang, D. Geldart, M. S. Beck and T. Dyakowski (2000), A behaviour of a catalyst flowing down in a dipleg, Chem. Eng. J., 77, 51 – 56 6. J. Wang, J. H. Bouma and H. Dries (2000), An experimental study of cyclone dipleg flow in fluidized catalytic cracking, Powder Technol., 112, 221 – 228 7. J. Wells, Streaming flow in large scale fluidization, Paper presented at the AIChE Annual Meeting, Reno, Nevada, 2001 8. T. M. Knowlton (2001), Video presented at Fluidization IX, Beijing, China. 9. S. B. R. Karri, A. S. Issangya and T. M. Knowlton, (2004), Gas bypassing in deep fluidized beds, in Fluidization XI, U. Arena, R. Chirone, M. Miccio, P. Salatini, eds., Naples, Italy 10. A. S. Issangya, S. B. Reddy Karri and T. M. Knowlton (2008), Effect of baffles on jet streaming in deep fluidized beds of Group A particles, in Circulating Fluidized Bed Technology IX, Hamburg, Germany 11. A. S. Issangya, S. B. Reddy Karri and T. M. Knowlton (2009), Effects of imposed solids flux and pressure on gas bypassing in deep beds of Group A materials (2009), in Fluidization XIII, S. D. Kim, Y. Kang, J. K. Lee, Y. C. Seo (eds.) Engineering Conference International, NY 12. S. Karimipour and T. Pugsley (2010), Study of gas streaming in deep fluidized bed containing Geldart’s Group A particles, Che. Eng. Sci., 65, 3508 – 3517 13. C. O. Bolthrunis, M. Hagan and W. Mukaddam (2010), Spinning straw into gold: Turning academic papers into commercial fluidized bed reactor designs, in Fluidization XIII, S. D. Kim, Y. Kang, J. K. Lee, Y. C. Seo (eds.) Engineering Conference International, NY
Baghouse
P DP
Internal Cyclones D = 41 cm
DP
51-cm-Dia. Cyclones Dipleg Total DP
DP Across 15.2 cm
DP Fluctuations
30°
191 cm
DIPLEGS AERATION
1.52-m-Dia, 5.2 m Tall Semi-Circular Column with Three 41-cm-Dia. Internal Cyclones
10.2-cm-Dia. Semicircular Sparger
FLUIDIZING AIR
7.6-cm-Dia Pipes Manifold
79 cm
Fluidizing Air
Figure 1. Side View of the Test Unit
38 mm Radius 12.7 mm Wall Thickness 102 mm Radius
Aeration
15 cm-Dia Round Pipe
Aeration
89-mm-Dia. Semicircular Splash Plate
Steel Plate Aeration Trickle Valve
89 mm o
4
Figure 3. Schematic Drawing of Diplegs with a Splash Plate and a Trickle Valve
Particle Diameter, micron 0
20
40
60
80
100
100 120 140 160 180 200 12 10
80
FCC Catalyst dp50: 84 μm Fines: 2.5% < 44 μm
60 40
6 4
20 0
8
2
0
20
40
60
80
0 100 120 140 160 180 200
Particle Diameter, micron
Figure 4. Particle Size Distribution of Material Used
Percent Volume Fraction
25.4 mm Thick Faceplate
Semicircular Dipleg With a Trickle Valve
25.4 mm Thick Faceplate
Cumulative Volume Percent Less Than
Semicircular Dipleg With a Splash Plate
Figure 2. Front View of the Test Unit
FLUIDIZATION BEHAVIOR IN A GAS-SOLID FLUIDIZED BED WITH THERMALLY INDUCED INTER-PARTICLE FORCES Jaber Shabanian, Farzam Fotovat, Jonathan Bouffard, Jamal Chaouki Department of Chemical Engineering, Ecole Polytechnique de Montreal, Montreal, Quebec, Canada Corresponding author: Tel.: +1-514-340-4711 X 4034; fax: +1-514-340-4159. E-mail address: [email protected].
ABSTRACT In this work, a new approach for increasing and controlling inter-particle forces (IPFs) was applied. This method used a spherical inert particle coated with a polymer material having a low glass transition temperature. Since IPFs depend on the temperature of the coated particles, they can be easily controlled by the temperature of the inlet air. For this reason, the temperature of the system was varied uniformly near the glass transition temperature of the polymer, between 20 – 40oC, to investigate the effect of IPFs on fluidization behavior at low and high gas velocities. INTRODUCTION Particle size, shape, roughness and density as well as inter-particle forces (IPFs) are among the most important parameters affecting the flow dynamics of powder materials. In regard to the significance of IPFs, for instance, there is no doubt that IPFs dominate the fluidization behavior of Geldart Group C particles, which leads to a completely different behavior compared to the other groups of Geldart’s classification with low or no IPFs. Investigations into the influence of IPFs on the behavior of a gas-solid fluidized bed have been carried out using different methods. Most importantly, however, controlling the level of IPFs to have a uniform cohesion throughout the particulate media is not an easy task. Methods that have been used include the following: increasing the amount of Van der Waals forces by decreasing the mean size of particles (1); increasing the amount of capillary forces by the addition of a cohesive agent into the bed (2); creation of a magnetic field around the bed (3) or raising the bed temperature to a high value (4). Each of these methods has specific difficulties in practice. For the first method it is very difficult to control particle shape and surface roughness. Due to these difficulties, the use of Van der Waals forces to study the effect of IPFs on fluidization characteristics is a complex procedure. By increasing the size of particles, the hydrodynamic forces become dominant compared to the IPFs. For larger particles, an increase in IPFs must be induced by the addition of a cohesive agent into the particulate system. One of the most popular methods to conduct this technique is by the presence of wet capillary bonds created by an interstitial liquid between the particles. The problem with this approach is that it is challenging to have a uniform distribution of the agent throughout the whole bed, which leads to force anisotropy 1
inside the particulate system (5). The other problem with this approach is that it restricts the fluidization study at low superficial gas velocities. To employ the third method an expensive set-up is needed to generate a magnetic field with the help of a costly electromagnetic coil system. The other problem of this approach is that the ferromagnetic particles attract themselves when they are parallel to the magnetic field and repel each other when they are perpendicular, which, consequently, causes anisotropic attraction/repulsion in the bulk materials (6). For the last method, the first problem is that it is costly to have an apparatus operating at high temperatures. Secondly, the lack of proper measurement techniques at high temperatures is the other difficulty with this strategy (7). In this work, a novel approach is proposed to induce IPFs inside a particulate media. It involves large particles that are not significantly influenced by the Van der Walls forces and other colloidal interactions. This technique uses a copolymer of PMMA/PEA (Poly Methyl MethAcrylate/Poly Ethyl Acrylate) contained in a polymer suspension called Eudragit NE30D. The copolymer, which is characterized by a low glass transition temperature, around 9oC, is coated on inert particles by an atomization process. By changing the ambient temperature to which the coated particles are exposed, the polymer adhesion/friction parameters and Young Modulus are modified in a way that the observed cohesive IPFs between the particles are changed significantly. Accordingly, the cohesion between particles can be adjusted by temperature in a stable and reproducible manner. The advantage of this method is that it does not necessitate the addition of any liquid phase, which has to be uniformly distributed into the particulate system. Besides, it allows the fluidization study to be carried out at both low and high superficial gas velocities. In contrast to the magnetic interactions, the forces are not dominant in only one direction, but are located at each contact between the particles. Thus, there is no force anisotropy present except if the temperature is not uniform in the system. The last priority of this technique is that it can be conducted at low temperatures and IPFs are changed in a completely controlled manner by merely a small increment in the system temperature. METHODOLOGY Particle Coating Process The experimental work necessitates having inert particles as base particles, which can accept the copolymer of PMMA/PEA as the coating. A 450-720μm cut of =1575 kg/m3), which lie in Geldart Group B spherical sugar beads (dp=580μm, particles, were chosen as inert particles in this work. Sugar beads were coated by an atomization process with a polymer suspension in water and dried simultaneously to obtain a uniform coating on the particle surface. It was achieved in a spheronizer, which allowed the introduction of air under the rotating disc located inside the bowl. Table 1 presents operating parameters associated with the coating process of the particles. Heated air, which was passed through an electrical heater before entering the processing chamber, was used for adjusting the temperature to the desired operating setpoint and also for concurrent drying of the particles. The temperature was controlled with the help of a controller coupled with an infrared cell, which measured the surface temperature of the particles. In addition, a thermocouple located under the spheronizer disc allowed for measuring the air entrance temperature. The air flow rate was adjusted to the 2
desired value with the help of a rotameter. The coating solution (Water 0.21 kg; PMMA/PEA 0.086 kg; Nonoxynol100 0.004 kg) was added by atomization onto the particles with a Schlick 970 series two-substance atomizer. The atomizer was fed with the solution by a peristaltic pump with a flow rate approximately equal to 1 g/min and compressed air, which allowed the formation of fine droplets. The atomizer gun was arranged in such a manner that the tip of the nozzle was placed at approximately 4 cm from the torus surface to avoid coating losses on the parts of the equipment. The characteristics of the final product are summarized in Table 2. Table 1. Spheronizer’s Operating Parameters Disc rotational rate (rpm) 230 Air flow rate (cfm) 25 Air temperature (oC) 30 Solution flow rate (g/min) 1 Atomization pressure (bar) 2
Table 2. Final Particles Coating Characteristics Materials Quantity Spherical sugar beads 3.0 (kg) PMMA/PEA 0.10 (kg) Mass percentage of coating 3.4 % Coating layer thickness ~ 5
Experimental Set-up and Procedure for Fluidization Study The experimental set-up used for the fluidization study consisted of a fluidization column, which was constructed with a transparent Plexiglas tube with 0.152 m I.D. and 1.5 m in height. Dried and filtered air was introduced into the bed through a perforated plate as the distributor. It contained holes 1 mm in diameter arranged in a triangular pitch. Air was heated with the help of an electrical heater before entering the fluidizing column. Accordingly, it was used to adjust the temperature of the bed to a desired value. Temperature was controlled by means of a PI controller driven by a thermocouple constantly immersed in the bed. A thermocouple located at the windbox allowed measuring the air entrance temperature. Furthermore, the air flow rate was controlled with a calibrated rotameter, which gave rise to a maximum superficial gas velocity of 0.75 m/s in the bed. In this regard, different superficial gas velocities were used for each system and temperature tested, covering both the fixed bed state and bubbling regime. To investigate the effect of IPFs on the fluidization behavior, two systems were studied, uncoated sugar beads and coated sugar beads. Experiments of uncoated sugar beads were carried out at 20oC while the ones for coated sugar beads were conducted at different operating temperatures, 20oC, 30oC and 40oC. Hereafter, for simplicity, we name these systems with their different operating conditions in abbreviated form, SB, CSB20, CSB30 and CSB40, which stand for uncoated sugar beads at 20oC and coated sugar beads at 20oC, 30oC and 40oC, respectively. All experiments were performed at atmospheric pressure. It is worth noting that variations in the air density and viscosity in the 20oC to 40oC temperature range are 6% and 5%, respectively, which are fairly negligible compared to the amount of variation of cohesion, which rises from the new technique for the same temperature range. Moreover, the same amount of material was poured into the bed for both systems, which resulted in an initial bed height of approximately 20 cm at ambient conditions. The pressure drop across the bed was measured using a differential pressure transducer. It was mounted flush with the wall of the bed through 15 micron inline filters and measuring ports. In addition, a local measurement technique was employed to investigate the influence of IPFs on the hydrodynamics of a gas-solid fluidized bed. To study the dynamic local flow structure a parallel optical fiber probe 3
was employed to measure the instantaneous local bed voidage. The probe was located 16 cm in height above the distributor and at the bed center for all the experiments. This axial position for the probe ensured it was far away from the turbulent effects of the distributor. For every temperature tested, the optical fiber probe was calibrated according to the linear interpolation between the read voltage of the air alone ( = 1) and the read voltage of the fixed bed. Analysis Methods The ratio between the measured and calculated pressure drop across the bed, ∆ /∆ , was used to highlight changes in the fluidization behavior of the coated sugar beads with increasing temperature. Analysis of local bed voidage can effectively provide much information about the dynamics of the fluidized beds, which results in a better understanding about the flow behavior in the fluidized bed (8). In this regard, the dynamic local two-phase flow structure was analyzed for beds of different amounts of IPFs at each superficial gas velocity. Instantaneous local bed voidage was at first scrutinized to have a closer picture on gas and solids interaction in the bed for different operating conditions. The time-averaged local bed voidage at the same measurement position was also used to indicate the influence of IPFs on the local flow structure. The probability density function of the local voidage from to 1 was analyzed to quantitatively explain the gas-solid distribution of emulsion and dilute phases and its dependence on the IPFs. According to Cui et al. (9), this is allowed for investigating the dynamic behavior of the dilute and dense phases. RESULTS AND DISCUSSION First and foremost, it was necessary to confirm that the new methodology for the increment of cohesive IPFs can work properly after a slight increase in the system temperature. It was verified in the spheronizer where coated particles were prepared. To execute it, a required amount of inert particles with pharmaceutical excipients was first produced and, subsequently, coated with a 30 layer of the copolymer. Figure 1 shows the modification of the particulate system with temperature. It can be easily demonstrated that by progressively increasing the temperature, the surface of the particulate media, which was characterized by a smooth profile with no observable deviations or clusters relative to the mean position of the interface at 27oC, was gradually changed. It started to create apparent clusters at 36oC, which became more obvious at 39oC, followed by the appearance of significant structure modification at 40oC, the presence of a secondary particle flow structure at the top of the main particle bed at 41oC and, finally, the moving of the particle bed in mass at 43oC. The most common approach for the overall identification of the fluidization status at different velocities is studying the whole bed pressure drop as a function of superficial gas velocity. The ∆ /∆ ratio can be used as a first indication of the effect of IPFs on fluidization behaviour (10). Figure 2 shows that by increasing IPFs, the degree of overshooting in the “fluidization” curve increases, which is accompanied by the increment of minimum fluidization velocity (Umf). For the case of CSB40, IPFs considerably influenced the bed’s behavior and caused the presence of a mass of particles, which lifted as a plug rather than fluidizing. This effect is shown in Figure 2. 4
27 oC
36 oC
39 oC
o 41 oC 43 oC 40 C Figure 1. Modification of the particulate system with temperature in spheronizer.
1.6
Table 3. Variation of Fixed Bed Height with IPFs
ΔPm / ΔPc
1.4 1.2
Fixed bed height (cm)
1.0 0.8 SB CSB20 CSB30 CSB40 ΔPc = Mg/A
0.6 0.4 0.2 0.0 0.0
0.1
0.2
0.3
0.4
0.5
0.6
SB
20.3
CSB20
20.5
CSB30
21.4
CSB40
23
Ug (m/s) Figure 2. Effect of IPFs on normalized pressure drop profile in fluidization path.
Furthermore, a decrement of the measured/theoretical pressure drop ratio from unity when the bed is fluidized shows the growth of the degree of cohesiveness of the bed (1, 10). As can be found in Figure 2, for CSB20 the ∆ /∆ ratio is similar to SB, while a decrement in the ratio is fairly observable for CSB30. For CSB40 fluidization was not attained due to paramount effect of cohesive IPFs. These results show that enhancement of IPFs can cause the behavior of the bed to change from Group B to Group A and even Group C powders. Formisani et al. (11) noted that voidage of the loosely settled bed of particles of Groups A and B increases with temperature. They attributed these findings to the increment of IPFs with temperature. Table 3 reports the variation of fixed bed height with IPFs in this study. It reveals that the fixed bed height and, correspondingly, the fixed bed voidage increase with IPFs. This result affirms Formisani et al. (11) and indicates that a bed with higher IPFs can hold more gas in the fixed state. The variations of local bed voidage with time reflect the interaction between gas and solids phases, which can influence the mass and heat transfer rates in the fluidized beds and, consequently, can affect the overall reaction rate in fluidized reactors (12). Figures 3 – 6 show the evolution of instantaneous local bed voidage with superficial gas velocity and IPFs for SB and CSB40. As can be found in these figures the portion of the dilute phase increases with increasing gas velocity for both cases. Therefore, the fluidizing air prefers to pass through the bed as a bubble phase. However, it seems that IPFs have the opposite influence on local bed behavior. It can be seen from Figures 3 – 6 that by increasing IPFs, the optical probe spends 5
more time in the emulsion phase rather than the dilute phase, which means that the portion of the emulsion phase increases by the enhancement of IPFs. As a result, the tendency of gas to pass the bed in the emulsion phase increases. It is worth noting that the CSB40 has a higher saturated emulsion value ( ) compared to SB, which is a subsequent consequence of holding more gas in the fixed bed state. Moreover, a bed of higher IPFs has a dilute phase with a maximum voidage value lower than unity, while in the case of SB there are some temporal signals of bed voidage equal to unity, which are indications of pure bubbles. This can be referred to the presence of more solid in the dilute phase for the particulate bed of enhanced cohesive IPFs. 1.0
0.9
Local voidage ε (-)
Local voidage ε (-)
1.0
0.8 0.7 0.6 0.5 εmf 0
2
4
6
8
0.9 0.8 0.7 0.6 εmf 0.5 0
10
2
6
8
10
Τime (s)
Τime (s)
Figure 3. Signal of instantaneous local bed voidage for SB (Ug = 0.41 m/s).
Figure 4. Signal of instantaneous local bed voidage for CSB40 (Ug = 0.41 m/s).
1.0
1.0
0.9
Local voidage ε (-)
Local voidage ε (-)
4
0.8 0.7 0.6 0.5 εmf 0
2
4
6
8
10
0.9 0.8 0.7 0.6 εmf 0.5 0
Τime (s)
2
4
6
8
10
Τime (s)
Figure 5. Signal of instantaneous local bed voidage for SB (Ug = 0.74 m/s).
Figure 6. Signal of instantaneous local bed voidage for CSB40 (Ug = 0.74 m/s).
The time-averaged local bed voidage was further investigated to give valuable information about the effect of IPFs on the hydrodynamics of a gas-solid fluidized bed. As it is illustrated in Figure 7 the time-averaged local bed voidage increases with superficial gas velocity for particulate beds with different amounts of IPFs. Moreover, for each superficial gas velocity tested in the bubbling regime the time average local bed voidage decreases with increasing IPFs. This implies that the twophase flow structure at constant superficial gas velocity varies with a greater extent for beds with higher IPFs. Analysis of the probability density function of dynamic local voidage demonstrates that for each superficial gas velocity tested, the probability of the dense phase and dilute phase increases and decreases, respectively by increasing IPFs (Figures 810). These findings indicate that the probability of the flow structure with higher voidage is reduced and the whole flow structure becomes less dilute when IPFs are enhanced. In addition, it can be found that at a given fluidizing gas throughput, with 6
Probability density function (%)
enhancing IPFs, more gas enters into the emulsion phase and dilutes it rather than passing into the bubble phase and increasing its fraction. Therefore, it can be inferred that the behavior of the particulate bed shifts from Group B into Group A by increasing IPFs in the bed.
Local voidage ε (-)
0.80 0.75 0.70 0.65 0.60 SB CSB30 CSB40
0.55 0.50 0.3
0.4
0.5
0.6
0.7
0.8
Probability density function (%)
Probability density function (%)
Ug = 0.41 m/s Ug = 0.55 m/s Ug = 0.74 m/s
30 20 10 0
εmf 0.5 0.6
0.7
0.8
0.9
30 20 10 0ε
mf 0.5
0.6
0.7
0.8
0.9
1.0
Local voidage ε (-) Figure 8. Probability density function of the local voidage of dynamic flow structure for SB.
Ug (m/s) Figure 7. Variation of the average local voidage with IPFs and superficial gas velocity.
40
Ug = 0.41 m/s Ug = 0.55 m/s Ug = 0.74 m/s
40
1.0
Local voidage ε (-)
Ug = 0.41 m/s Ug = 0.55 m/s Ug = 0.74 m/s
40 30 20 10 0
0.5 εmf 0.6
0.7
0.8
0.9
1.0
Local voidage ε (-) Figure 10. Probability density function of the local voidage of dynamic flow structure for CSB40.
Figure 9. Probability density function of the local voidage of dynamic flow structure for CSB30.
Overall, the experimental results of this work are in good agreement with those for Yates and Newton (13) and Rowes et al. (14), who found that increasing the fines (< 45 ) content results in an increase in interstitial gas flow. In the range of IPFs studied in this work, which defluidization phenomenon was not encountered within, the presence of more gas in the fixed bed state and emulsion phase is an indication of the improvement of gas/solid contact in the bed of Group B powders. CONCLUSION This work introduces a new and different approach to study the effect of IPFs on the fluidization behavior of the gas-solid fluidized bed. It was shown that by controlling the temperature of the particulate system of inert particles coated with a layer of a PMMA/PEA copolymer, the degree of cohesiveness of the bed can be easily changed in a selective manner. Interesting aspects of this approach for increment of IPFs are working at low bed temperatures, controlling the level of cohesiveness by merely controlling the bed temperature and facilitating the investigation of the influence of IPFs on fluidization characteristics from low to high superficial gas velocities. Experimental results obtained by local and global measurement techniques show that increment of IPFs can significantly affect the dynamic local flow structure and overall behavior of the gas-solid fluidized bed. Dictating higher amounts of IPFs into 7
the bed causes higher fixed bed voidage and minimum fluidization velocity. Similarly, there is a significant influence of IPFs on the bed behavior in the bubbling fluidization regime. The results obtained in the range of IPFs studied in this work imply that at a given throughput of the fluidizing gas, the relative tendency of gas flowing in the emulsion phase increases with IPFs, which has a positive effect on enhancing chemical conversion in the case of the active catalyst. According to experimental results, it can be concluded that by increasing IPFs the fluidization behavior of the particulate bed can shift from Group B to Group A or even Group C powders. NOTATION A dp M Umf Ug ∆
bed cross-section area (m2) mean particle diameter (μm) bed weight (kg) minimum fluidization velocity (m/s) superficial gas velocity (m/s) Mg/A (N/m2)
∆
measured pressure drop (N/m2)
Greek Letters local voidage (-) minimum fluidization voidage (-) particle density (kg/m3)
REFERENCES 1. Geldart, D., Harnby, N. and Wong, A.C., Fluidization of cohesive powders. Powder Technology, 1984. 37(1): p. 25-37. 2. Seville, J.P.K. and Clift, R., The effect of thin liquid layers on fluidisation characteristics. Powder Technology, 1984. 37(1): p. 117-129. 3. Rhodes, M.J., Wang, X.S., Forsyth, A.J., Gan, K.S. and Phadtajaphan, S., Use of a magnetic fluidized bed in studying Geldart Group B to A transition. Chemical Engineering Science, 2001. 56(18): p. 5429-5436. 4. Lettieri, P., Yates, J.G. and Newton, D., The influence of interparticle forces on the fluidization behaviour of some industrial materials at high temperature. Powder Technology, 2000. 110(1-2): p. 117-127. 5. Fraysse, N., Thomé, H. and Petit, L., Humidity effects on the stability of a sandpile. The European Physical Journal B - Condensed Matter and Complex Systems, 1999. 11(4): p. 615-619. 6. Lumay, G. and Vandewalle, N., Controlled flow of smart powders. Physical Review E, 2008. 78(6): p. 061302. 7. Cui, H. and Chaouki, J., Effects of temperature on local two-phase flow structure in bubbling and turbulent fluidized beds of FCC particles. Chemical Engineering Science, 2004. 59(16): p. 3413-3422. 8. Zhu, H., Zhu, J., Li, G. and Li, F., Detailed measuremnets of flow structure inside a dense gas-solids fluidized bed. Powder Technology, 2008. 180(3): p. 339-349. 9. Cui, H., Mostoufi, N. and Chaouki, J., Characterization of dynamic gas-solid distribution in fluidized beds. Chemical Engineering Journal, 2000. 114(1-3): p. 133-143. 10. Bruni, G., Lettieri, P., Newton, D. and Barletta, D., An investigation of the effect of the interparticle forces on the fluidization behaviour of fine powders linked with rheological studies. Chemical Engineering Science, 2007. 62(1-2): p. 387-396. 11. Formisani, B., Girimonte, R. and Mancuso, L., Analysis of the fluidization process of particle beds at high temperature. Chemical Engineering Science, 1998. 53(5): p. 951961. 12. Mostoufi, N. and Chaouki, J., Local solid mixing in gas-solid fluidized beds. Powder Technology, 2001. 114(2): p. 23-31. 13. Yates, J.G., and Newton, D., Fine particle effects in a fluidized-bed reactor. Chemical Engineering Sceince, 1986. 41(4): p. 801-806. 14. Rowe, P.N., Santoro, L., and Yates, J.G., The division of gas between bubble and interestitial phases in fluidized beds of fine powders. Chemical Engineering Sceince, 1978. 33(1): p. 133-140.
8
CO-COMBUSTION OF HAZELNUT SHELLS WITH A HIGHSULFUR TURKISH LIGNITE IN A CIRCULATING FLUIDIZED BED COMBUSTOR WITH AIR STAGING Aysel T. Atimtaya*, Hayati Olgunb, Murat Varola, Mustafa Can Çelebic, Hüsnü Ataküld, Ufuk Kayahanb, Berrin Bayb, Alper Ünlüb, Gerçek Bardakçıoğlue, Murat Özcane a Middle East Technical University, Dept. of Environ. Eng., 06531 Ankara, Turkey b TUBITAK-MRC, Energy Institute, P.O.Box 21, Gebze 41470, Kocaeli, Turkey c Istanbul Technical University, Energy Institute, Maslak 34469, Istanbul, Turkey d Istanbul Technical University, Dept. of Chem. Eng., Maslak 34469, Istanbul, Turkey e GAMA Power Systems Engineering and Contracting, Inc., 06520 Ankara, Turkey ABSTRACT In this study, a high-S lignite coal was burned with hazelnut shells in a laboratory scale circulating fluidized bed with secondary air injection. The results showed that emissions can be reduced with the addition of secondary air. The objective of the study was to maximize combustion performance, minimize the emissions, and to see the effect of various amounts of secondary air injection on emissions. INTRODUCTION Combustion of mixtures of different fuels, particularly biomass materials with coal, in a fluidized bed combustor is a promising application to produce cleaner energy as well as to dispose waste products of biomass resources. In addition to this, combustion of biomass materials as a supplementary fuel for coal-fired power plants has an advantage of saving money for the fuel cost. Moreover, co-combustion of biomass with coal is also an effective way to reduce CO2 emissions (1). Annual amount of hazelnut shells in Turkey is 566,437 tons and the corresponding total heating value is 8,745,790 GJ (2). This waste can be used in energy production. Youssef, et al. (3) conducted the combustion of four different kinds of biomass in a circulating fluidized bed with 145 mm inner diameter and 2 m height. Different excess air ratios were utilized in experiments. Desroches-Ducarne, et al. (4) investigated the emissions of NO, N2O, HCl, SO2 and CO during co-combustion of coal and municipal refuse mixtures in a circulating fluidized bed boiler. Cliffe and Patumsawad (5) studied the co-combustion of olive cake with coal. They used fluidized bed combustor to research the feasibility of using olive cake as an energy source. Armesto, et al. (6) investigated the co-combustion of coal and olive oil industry residues in fluidized bed. Two different Spanish coals, lignite and anthracite were used for the study. Topal, et al. (7) investigated the olive cake combustion in a CFBC. They used a small scale circulating fluidized bed of 125 mm diameter and 1800 mm height. The results obtained from this combustor were used to compare them with that of coal combustion. Permchart and Kouprianov (8) made an experimental study about the combustion of three different biomass sources in a single fluidized bed combustor (FBC). Atimtay and Topal (9) studied the cocombustion of olive cake with lignite coal in a CFB. Experiments were conducted in the same bed used in the previous work by Topal, et al. (7). In all of these studies, it was found that all biomass materials are good candidates to be used as supplementary fuels to various coals, especially lignite coals. However, combustion and co-combustion of hazelnut shells were not studied before.
MATERIALS AND METHODS A laboratory scale CFBC system was used for small scale tests before going into pilot scale studies of 0.75 MWth. The thermal capacity of the laboratory scale CFBC was 30 kW as shown in Fig.1. The experimental setup consists of a riser, a down comer, a fuel feeding system, electrical heaters, and two cyclones. The lab-scale combustor column (riser) has an inside diameter of 108 mm and a height of 6 m. It consists of 8 modules. Six electrical heaters are used along the combustor column in order to heat the system during the start-up period. The effect of staged air injection on the emissions from several ports along the column has been studied. The temperature of the riser column was kept at 850 oC during the combustion experiments. Combustion tests were carried out with Orhaneli lignite coal as the main fuel. Hazelnut shells were used as the auxiliary fuel (biomass). The fuel particles were between 1-2 mm. The proximate and ultimate analyses of fuels are presented in Table 1. During the experiments, CO, SO2, NO, and O2 emissions were continuously measured with the ABB/AO2000 flue gas analyzer. Calibration of the flue gas analyzer was done with certified calibration gases. Figure 1 shows the experimental system. The measurements were also checked with GASMET DX4000 Flue Gas Analyzer. All emission concentrations measured are expressed on 6% oxygen basis.
Figure 1. Schematic view of laboratory scale experimental system
Table 1. Characteristics of fuels Orhaneli Lignite Proximate Analysis (wet basis)
Hazelnut shell
Ash, wt %
23.09
1.52
Volatile Matter, wt %
34.40
64.73
Fixed Carbon, wt %
20.18
21.78
Moisture, wt %
22.33
11.97
Lower Heating Value, kJ/kg
11,829
18,933
Higher Heating Value, kJ/kg
12,962
20,326
C, wt %
41.48
56.34
H, wt %
2.37
5.35
N, wt %
0.54
0.51
Ultimate Analysis (dry basis)
O, wt %
24.12
36.06
S (Total), %
1.76
0.01
Ash, wt %
29.73
1.73
RESULTS AND DISCUSSION Combustion Experiments Temperature Distribution: The temperature profiles along the combustor with and without secondary air injection (SAR) was between 790-850°C. During the experiments, the total amount of air given to the system was held constant. The ratio of secondary air to total air was changed. The increase in the SAR causes a temperature increase in the dense phase and the maximum temperature was observed at about 240 mm above the distributor plate due to better combustion of volatiles. Later on, as the height increases a decrease in the temperature is observed due to cooling effect of secondary air. Throughout the experiments secondary air was fed to the 3rd, 4th 5th and 6th modules. Emissions: All the emission data presented in this section were taken for the dense phase temperature of 85050°C. Fig. 2 demonstrates the CO emissions resulting from the fuel mixture of Orhaneli Lignite (OL) and Hazelnut Shells (HS) for three different ratios, namely 90 wt% OL-10 wt% HS, 70 wt% OL-30 wt% HS and 50 wt% OL-50 wt% HS. In Fig. 2, CO emissions vs. SAR are plotted for different places at which the secondary air is injected. As it is seen from Fig. 2a, 2b and 2c, CO emissions do not suddenly decrease as the secondary air is introduced. Instead, CO emissions increase up to a certain point as the secondary air is introduced for case (a) and (c). The reason for increasing CO concentration might be the Boudouard reaction where char is reacted with CO2 to give CO (10). It was observed that after the secondary to total air ratio exceeds 20%, CO emissions display a decreasing trend. Highest CO concentrations are observed for the case where the secondary air is injected at a height of 400 mm from the distributor for case (a), and at a height of 220 mm from the distributor for case (c). In both cases SAR value was 20%.
a
b
c Figure 2: CO emissions with different secondary air ratios: (a) 90% by wt. OL -10% by wt. HS with primary air, (b) 70% by wt. OL -30% by wt. HS with primary air, (c) 50% by wt. OL -50% by wt. HS with primary air
a
b
c Figure 3: CO, NO, N2O emissions with different secondary air ratios, when staged air is introduced 139 cm above the distributor plate: (a) 90 wt% OL-10wt% HS, (b) 70 wt% OL-30 wt% HS, (c) 50 wt% OL-50 wt% HS
Fig.3 shows the CO, NO and N2O emissions resulting from co-combustion of various ratios of OL and HS. NO emissions followed a decreasing trend for all SAR values. As it was shown in Fig. 2a and 2c before, CO concentrations increased until SAR value of 20% and then decreased. It was reported in the literature before that CO has an important role in NO reduction (7, 8). Besides, NO emissions slightly dropped for all SAR values. The reason might be the cooling effect of secondary air injection after some values of SAR. Fig. 3a shows CO emissions having a declining trend for different mixtures with SAR after a certain SAR value. It was thought that this may be due to lower volatile matter (VM) content of the hazelnut shells (64.7%) as compared to other biomasses. As the fuel particles are introduced into the combustor, VM in the fuel immediately volatilizes and the medium becomes oxygen deficient. Therefore, some incomplete combustion products like CO can form. When secondary air is injected, this helps the combustion to be better. Since the VM content of the hazelnut shells is not very high, the secondary air injection becomes effective starting with small SAR values. Another point to notice is that as the HS content in the fuel mixture increases, CO emissions also increases. As in the case of 90% coal-10% HS, CO concentration was maximum 300 mg/Nm3, but with the hazelnut shell in the mixture increases to 50%, CO concentration went up to 800-1100 mg/Nm3. Figs. 4, 5 and 6 show the change of NO, N2O and NO2 emissions with height above the distributor plate for 10%, 30% and 50% HS addition to Orhaneli lignite, respectively. In each case, secondary air ratios of 10%, 20% and 30% are tried. In all of these figures, one can see that NO2 concentration is almost zero. N2O concentration is small and NO concentration is the most prominent among these three. As can be seen in Fig.4a, NO concentration starts at 330 mg/Nm3 in the second module and decreases with height along the combustor. The same trend is observed in the other figures. However, as SAR increases from 10 to 30%, NO concentration decreases down to 200-250 mg/Nm3. The reason might be the cooling effect of secondary air injection. In Fig.5, a similar trend is observed for NO concentration where in this case 30% HS is mixed in the fuel mixture. NO concentrations are around 200-250 mg/Nm3. In these cases SAR is 10% and 20%. However, when SAR is increased to 30%, NO concentration increases along the height of the combustor and goes up to 350 mg/Nm3. This behaviour could not be explained. In Fig.6, again a similar trend is observed for NO concentration where in this case 50% HS is mixed in the fuel mixture. NO concentrations decrease slightly along the combustor height. The concentrations are around 200-250 mg/Nm3. Then with the increase of SAR to 30%, the concentration drops to 150 mg/Nm3. These decreases are thought to happen due to dilution effect with increasing SAR. N2O and NO2 concentrations stay almost the same, with very slight changes as SAR increases.
a
b
c Figure 4: Change of NO, N2O and NO2 emissions with height above the distributor plate for 90 wt% OL-10 wt% HS: a) SAR 10%, b) SAR 20%, c) SAR 30%
a
b
c Figure 5: Change of NO, N2O and NO2 emissions with height above the distributor plate for 70 wt% OL-30 wt% HS : a) SAR 10%, b) SAR 20%, c) SAR 30%
a
b
c Figure 6: Change of NO, N2O and NO2 emissions with height above the distributor plate for 50 wt% OL-50 wt% HS : a) SAR 10%, b) SAR 20%, c) SAR 30% Combustion Efficiency Combustion efficiency is calculated according to the Equations (1) to (4). The results of unburnt carbon analysis are given in Table 2. It is clear that, the unburnt carbon is negligible both in the bottom and in the fly ash for all runs. Carbon loss during the combustion is mainly originated from CO formation. Table 2: Unburnt C analysis results Bottom Ash
Fly Ash
Fuel Mixture
LCO %
Lbottom %
0 0.043 0
0 0.182 0.384
90 % wt. OL-10 % wt. HS 70 % wt. OL-30 % wt. HS 50 % wt. OL-50 % wt. HS
2.70 1.55 0.92
0.02 -
Lfly %
η %
97.30 0.05 98.38 0.05 99.03
LCO = CO ppm, measured * V fluegas, measured * HL_CO / HL_fuel / 10,000
(1)
Lbottom = Ashbottom * Cbottom * HL_Char / Mf / HL_fuel * 100
(2)
Lfly= Ashfly* Cfly* H L_Char / Mf / HL_fuel * 100
(3)
η = 100 - (LCO + Lbottom + Lfly)
(4)
where LCO is the percentage of the total carbon loss due to CO formation; Lbottom is the percentage of the total carbon loss in the bottom ash; Lfly is the percentage of the total carbon loss in the fly ash; and η is the total combustion efficiency. As can be seen from the table, the combustion efficiency for all mixtures are found to be greater than 97.3%.
CONCLUSION The results of this study have shown that the co-combustion of lignite coal with hazelnut shells in a CFBC can be performed with minimum emissions. With the injection of secondary air into the combustor, CO emission trends were found to be different for SAR< 20% and SAR> 20%. CO presence in the emissions helped the NO reduction. Further tests with several combinations of sorbent addition with air staging will be carried out to obtain the optimum emission performance for future work. ACKNOWLEDGEMENT The financial support provided by the Turkish Scientific and Technical Research Council (TUBITAK) under the project code of TUBITAK-KAMAG-105G023 is greatly appreciated.
NOTATION SAR: secondary air ratio (secondary air/total air) η: combustion efficiency (%) Lfly: combustion loss due to unburnt C contained in fly ash Lbottom: combustion loss due to unburnt C contained in bottom ash
LCO: combustion loss due to CO emission measured in stack gas HL_CO: Lower heating value of CO (∆Hc 0, Heat of combustion), 12.63 MJ/Nm3 CO (Perry, 2007) HL_Char: Lower heating value of char (∆Hc 0, Heat of combustion), 32.79 MJ/kg char (Perry, 2007) HL_fuel: Lower heating value of the fuel used
REFERENCES 1 Hupa M., “Interaction of Fuels in Co-Firing in FBC”, Fuel, 2005, 84, 1312-1319. 2 Biosynergy Workshop 17-18 April 2008, Petten. 3 Youssef M.A., Wahid S., Mohamed M.A., Askalany A.A., “Experimental Study on Egyptian Biomass Combustion in Circulating Fluidized Bed 4 Desroches-Ducarne E., Eric M., Gerard M., Lucien D., “Co-combustion of Coal and Municipal Solid Waste in a Circulating Fluidized Bed”, Fuel, 1998, 77, 1311-1315. 5 Cliffe K.R., Patumsawad S., “Co-combustion of Waste From Olive Oil Production With Coal in a Fluidised Bed”, Waste Management, 2001, 21, 49-53. 6 Armesto L., Bahillo A., Cabanillas A., Veijonen K., Otero J., Plumed A., Salvador A., “Cocombustion of Coal and Olive Oil Industry Residues in Fluidized Bed”, Fuel, 2003, 82, 993-1000. 7 Topal H., Atimtay T.A., Durmaz A., “Olive Cake Combustion in a Circulating Fluidized Bed”, Fuel, 2003, 82, 1049-1056. 8 Permchart W., Kouprianov V.I., ”Emission Performance and Combustion Efficiency of a Conical Fluidized Bed Combustor Firing Various Biomass Fuels”, Bioresource Technology, 2004, 92, 83-91. 9 Atimtay A. T., Topal H., “Co-combustion of Olive Cake With Lignite Coal in a Circulating Fluidized bed”, Fuel, 2004, 83, 859-867. 10 Xie J, Yang X, Zhang L, Ding T., Song W.,Lin W., “Emissions of SO2, NO and N2O in a circulating fluidized bed combustor during co-firing coal and biomass”, Journal of Environmental Sciences, 2007, 19, 109-116. 11 Gungor A., “Simulation of NOx emission in circulating fluidized bed burning low grade fuels”, Energy and Fuels, 2009, 23, 2475-2481. 12 Adanez J., Diego L.F., Gayan P., Cabanillas A., “Modeling of Sulfur Retention in Circulating Fluidized Bed Combustors”, Fuel, 1996, 75 (3), 262-270.
COMPARISON BETWEEN MEASUREMENTS AND NU MERICAL SIMULATION OF PARTICLE FLOW AND COMBUSTION AT THE DUISBURG CFBC PLANT M. Weng1), J. Plackmeyer2) aixprocess GmbH, Aachen (Germany) 2) Consulting Engineer, Bergisch-Gladbach (Germany) 1)
ABSTRACT The article presents the 3-dimensional numerical simulation of combustion and particle flow as an efficient engineering tool for analysis and optimization of fluidized bed combustion chambers. The comparison with measurements that were carried out within a European Research Project and with operational experience shows very good agreement and provides recommendations for future optimization. SCOPE OF THE STUDY After retrofitting the Duisburg CFB combustion chamber with elongated heat ex changer modules, the plant showed a significant increase in local wear and corro sion. The reason was supposed to be a locally reducing atmosphere from high CO concentrations. In order to verify this hypothesis and to develop adequate optimiza tion measures, a comprehensive simulation study was performed. For this plant, measurements are available concerning local solids volume fraction and temperat ure, Wischniewski et al. (1). Hence, the Duisburg CFBC appears to be adequate to prove the capability of numerical simulation in large scale fluidized bed combustors.
Figure 1: Duisburg CFBC design
1
Fig. 1 shows the combustor design with inlet and recycle ports, integrated heat ex changer modules and the exit to subsequent cyclones. For the simulation study, the primary air is assumed to have a uniform profile at the distributor due to high nozzle pressure head. Secondary air is injected via 4 inlet nozzles with a velocity of appr. 80 m/s. In operation with 100% coal, solid fuel enters through 2 discrete ports loc ated on one side of the combustion chamber. In secondary fuel co-firing operation, coal-substituting matter is injected through 2 inlets located on the opposite side. SIMULATION METHOD Numerical Simulation in Fluidized Beds and Dense Particle Flows Many different fluidized bed simulation studies have been reported historically. How ever, most or all of these studies reveal significant simplifications according to local discretization, coupling between particle and gas phase or the computation of com bustion phenomena. The commercial software code Barracuda® employs a different approach based on the Computational Particle Fluid Dynamics (CPFD®) method, Snider (2). It overcomes the shortcoming traditionally seen in CFD fluidized bed sim ulations. This is done by a bi-directional coupling between the discrete motion of Lagrangian particles and the continous gas phase for which the Navier-Stokes equations are solved. The simulation is strictly transient, thus accounting for the in herently fluctuating character of flows with high solid volume fractions. In the lower part of a CFB the flow is dense with local solid volume fractions > 10%. In this regime, the two-phase flow is governed by particle-particle interactions. Typ ically the fuel is injected into this region, so the proper computation of solids mixing and jet penetration is crucial for the prediction of process characteristics. The simu lation method considers the particle interactions by integrating the discrete particle properties in each computational cell, hence forming a particle stress tensor for which a transport equation is computed within the Eulerian frame of reference. The result of this solution is then mapped back onto the respective position of each single particle. This hybrid particle interaction model then solves the particle equa tion of motion by balancing drag, pressure gradient, volume forces and an additional expression for particle normal stress representing multiple particle interactions. A characteristic feature of the Barracuda® software is that particles displace the sur rounding continuous phase (see Fig. 2). As a consequence, the Eulerian phase computational domain is a highly complex transient 3-dimensional space, thus en abling the realistic representation of large scale fluctuations in dense particle flows.
particle fluid particles displace the fluid
Figure 2: CPFD Barracuda method for the continuous phase region For reacting flows, the continuous phase momentum transport equations and particle motion are coupled with scalar equations for energy and gas phase species (O2, N2, H2O, CO, CO2, …). The set of equations is completed by closure terms for
2
homogeneous and heterogeneous reactions. The respective reaction rates are com puted at each location and time step. The resulting variable sequences provide a deep insight into the complex structure of dense particle flows. Time- and space-averaging of instantaneous values enables quantitative studies and form the base for comparison with measurements. Never theless, all information about variable fluctuations is preserved. Modeling of Coal and Secondary Fuel Combustion In the present simulation study, two fuel compositions were considered. Tables 1 and 2 show the short analysis of coal and Meat-and-Bone-Meal (MBM) as a typical secondary fuel (SF). Table 1: coal composition Component
weight- %
Moisture
14 -16
total carbon
75-80
Ash
8-10
Volatiles
40-43
Table 2: composition meat-and-bone meal (MBM) Component
weight- %
Moisture
4
total carbon
42
ash
21
volatiles
66
Table 3: stoichiometric equations for the reduced combustion mechanism Steam gasification
C(s) + H2O ↔ CO + H2
CO2 gasification
C(s) + CO2 ↔ 2CO
Combustion
λC(s) + O2 → 2(λ-1)CO + (2-λ)CO2
Water gas shift
CO + H2O ↔ CO2 + H2
Volatile combustion
CxHyOz + αO2 → βCO2 + γH2
CO combustion
2CO + O2 → 2CO2
Each solid fuel is represented by its specific particle size distribution (milled coal d50 = 320 micron, MBM d50 = 450 micron). The loss of particle matter due to reaction is considered by a shrinking particle model. At the beginning of the calculation, the ash content is initialized with a typical mass of bed ash and an initial particle size distri bution with 220 micron mean diameter). The homogeneous and heterogeneous fuel reactions are represented by a reduced mechanism (see Table 3).
3
The equilibrium reactions are split into single equations for forward and backward reactions. All kinetic reactions rates are taken from approved references, Syamlal and Bisset, Yoon et al., Bustamante et al. [3-5]. Simplifying the complex heat and mass transfer in partially porous particles, moisture and volatile fuel contents are as sumed as gaseous inlet streams. The model error is considered to be small since drying and volatiles evaporation are very fast at fluidized bed conditions with relat ively small fuel particles. For the presented study, the coal and secondary fuel com bustion system was modeled in the Barracuda code for the first time. COMPARISON BETWEEN SIMULATION AND MEASUREMENTS With a sophisticated experimental setup, local solid phase volume fractions, gasphase temperatures, pressure profiles and concentrations were taken in the inner region of the Duisburg plant. The objective of the study was to investigate the influ ence of secondary fuel co-firing on emissions and solid phase inventory. The opera tional data used as a base for simulation boundary condition definition are slightly different from the load case during the measurement campaign. The exact operation conditions could not be reconstructed with total agreement according to SF compos ition and position of injection. However, the comparison between different load cases with and without co-firing of SF is assumed to sufficiently represent the overall plant characteristic in order to evaluate the simulation quality. The operation conditions were 100% load each. In the first case, 100% coal is used, for the co-firing case 25% of the heating value is substituted by MBM which enters via 2 ports opposite from the coal injection.
Figure 3: local solid phase volume fraction for 100% coal combustion and co-firing conditions: comparison between simulation and measurements Fig. 3 shows profiles of the local solid phase volume fraction vs. height above the primary air distributor. Measurement positions are within the inner chamber region without the influence of the dense near-wall particle wall carpet. Position “Front” is the coal injection side, “back” is opposite in the lateral direction. The heat exchanger modules are located between the measurement positions 17 m and 27 m. In the simulation, virtual sensors were installed at respective positions within the computa tional domain and values were time-averaged to receive statistically sound values. Simulation and measurements are in good agreement and show the strong volume fraction decay in the region 8 m to 13 m above the air distributor. Above secondary air injection, the volume fractions decrease slightly up to the cyclone entrance. Again in good agreement is the tendency towards a higher solid content in the co-fir
4
ing case. The maldistribution between front and back side is given correctly, too. Re garding the difficult measurement conditions and the model simplifications, even the absolute values are in remarkable agreement. Fig. 4 shows the temperature profiles in the combustor. The absolute discrepancy between local measured and simulated values is small, only the front side temperat ure below the heat exchanger being 35° lower compared to the measured value. Again, the profile trends between front and back side and load cases with and without co-firing are given correctly.
Figure 4: local gas phase temperature for 100% coal combustion and co-firing con ditions; comparison between simulation and measurement The corresponding profiles show that a considerable fraction of the overall combus tion takes place in a region higher than 15 m above the distributor and that the burnout is not finalised in the heat exchanger zone. Mean temperature difference between front and back side is 30 – 50°C. This discrepancy decreases for co-firing conditions with SF feeding on the back side. Furthermore the profiles show coincid ently the lack of lateral mixing throughout the whole combustion chamber. SIMULATION RESULTS AND OPERATIONAL EXPERIENCE The internal circulation of solid matter within the combustion chamber plays an im portant role for the overall solids inventory and its residence time distribution. The in ternally recycled solids form a dense layer that flows downwards in a near-wall re gion. For the 100% coal case, fig. 5 gives the time-averaged particle velocities on the left hand side and the solids volume fraction on the right. The penetration depth of the secondary air injected with 80 m/s is below 1.5 m which is remarkably low. The reason is a high momentum stream due to the relatively dense particle flow that is further accelerated by gas-phase mass and heat sources from the combustion re actions. The dense particle wall film can be clearly seen. The mean downward velo city is 2 m/s which is in good agreement with observations from similar plants. The upward flow in the combustion region has an average velocity of 15 m/s and stretches out up to the heat exchanger. The vector plot of instantaneous gas velocit ies in Fig. 6 demonstrates the high fluctuations with maximum velocities above 25 m/s. The effect of high mean velocity combined with large amplitude fluctuations on wear can be seen in Fig. 7. Barracuda explicitly calculates the wear effect from an integration of particle velocity and impact angle from each particle that hits the wall. Although this value is qualitative, it provides useful information about the posi tion of maximum wear in a plant. Fig. 7 shows the wear at the heat exchanger plates
5
as a comparison of the outer (left picture) and a centre position (right picture). The predicted region of maximum wear above the coal injection is in excellent agree ment with operational experience. volume fraction [-]
vel [m/s] 15.0
0.5
0.0
0.0
Figure 5: time-averaged particle velocity vectors and solid volume fraction at the combustion chamber centre plane; coal is injected from the right hand side vel [m/s] 25.0
0.0
t - 2s
t
t + 2s
Figure 6: Instantaneous particle velocities in a plane of the coal inlet (coal injection from the right) In addition to mechanical wear, the heat exchanger region experiences a chemical impact from a locally reducing atmosphere. Fig. 8 shows the time-averaged oxygen mole fraction in the centre plane for 100% coal firing (left) and 25% MBM co-firing (right). The oxygen maldistribution is clearly to be seen. Due to the lack of lateral mixing, nearly the complete fuel stream including the volatiles remains on the coal injection side leading to incomplete burnout and local lack of oxygen. Secondary air injected from the opposite side is not sufficient for mixing and does not reach the zone of high fuel content. The oxygen-poor streak stretches out into the cyclone in
6
let. The cyclone high turbulence intensities will then induce mixing and reaction of the fuel- and oxygen-rich streaks, respectively. This tendency is underlined by oper ational experience. Temperatures of 950° to 1050°C are observed in the cyclone and in the transfer duct to the subsequent heat exchangers.
Figure 7: qualitative wear prediction
Figure 8: comparison of time-averaged oxygen mole fractions for 100% coal firing (left) and 25% MBM co-firing (right) MBM injection on the coal-opposite side leads to a slight equalization in oxygen con centration. There still exists a region with lower oxygen concentration in the center due to the low secondary air penetration depths and the poor lateral mixing. Here, the simulation reveals further optimization potential according to the position of sec ondary air injection. Above the MBM injection port there is a zone with oxygen con
7
centration near zero. This is due to the high MBM volatiles content and the resulting oxygen consumption. The volatiles combustion rate decreases and restarts above the secondary air nozzle. To displace the combustion zone and use the complete height of the combustion chamber, the simulation indicates a relocation of the back side secondary air nozzle in the downward direction (about 3-4 m). CONCLUSIONS The advanced particle flow and reaction simulation of the Duisburg CFBC shows very good agreement with local in-situ measurements and additional operational ex periences. The tendencies of characteristic parameters such as local solids volume fraction and temperatures are predicted correctly, the identification of wear-intensive regions is reliable. The high resolution in time and space enables detailed analysis of the plant characteristics and remarkably enhances the understanding of the com plex coupled flow and reaction behaviour. The high information density including fre quency and amplitude of fluctuations which are characteristic for dense particle flows underlines the relevance of advanced simulation tools for plant optimization. Analogous to well-established simulations of pulverized-coal combustion plants with conventional CFD methods, now by means of the Barracuda method the effect of fuel scenarios and geometric modifications on emissions, wear and local heat im pact can be reliably predicted for fluidized bed applications. NOTATION CFB d50 MBM SF
circulating fluidized bed particle mean diameter meat and bone meal secondary fuel
REFERENCES [1] Wischnewski, R. , Werther, J. and Heidenhof, N. (2006): Synergy effects of co-combustion of biomass and sewage sludge with coal in the CFB-combustor of Stadtwerke Duisburg AG. VGB Power Tech, 86(12), pp 63-70, 2006 [2] Snider, D. M., 2001, An Incompressible three dimensional multiphase particle-in-cell model for dense particle flows. Journal of Computational Physics 170, 523-549 [3] Syamlal, M., Bissett, L.A., 1992, METC Gasifier Advanced Simulation (MGAS) Model, DOE/METC--92/4108, DE92 001111 [4] Yoon, H., Wei, J. And Denn, M.M., 1978, A Model for Moving-Bed Coal Gasi fication Reactors. AIChE Journal 24, 5 [5] Bustamante, F., Enick, R. M., Cugini, A., Killmeyer, R. P., Howard, B. H., Rothenberger, K. S., Ciocco, M. V., Morreale, B. D., Chattopadhyay, S. and Shi, S. (2004), High-temperature kinetics of the homogeneous reverse water–gas shift re action. AIChE Journal, 50: 1028–1041
8
COMPARISON OF ENTRAINMENT RATE IN ACRYLONITRILE REACTORS USING PLANT DATA AND CFD SIMULATIONS S. Moffatt1, S. Ramchandran1, P. Zhao2, and K. Williams2 Ascend Performance Materials, LLC, PO Box 711,FM 2917,Alvin, TX 77512 2 CPFD Software, LLC,10899 Montgomery Blvd. Suite B,Albuquerque, NM 87111
1
ABSTRACT Accurate entrainment rates are important in fluidized bed reactors for several reasons, including determination of cyclone loadings and efficiencies, sizing of diplegs, and inputs to population balance models. Entrainment correlations exist in the literature and from other sources to predict entrainment rates from fluidized beds, but they can vary by orders of magnitude. In addition, many correlations do not take into account effects of internals which are present in many types of industrial reactors. A study was undertaken to better understand entrainment rates from Sohiotype acrylonitrile fluidized bed reactors containing catalyst classified as a Geldart type A powder. As part of this study, full scale CFD models were developed using the Barracuda® computational particle fluid dynamics (CPFD®) software and validated with the help of data collected from multiple plant reactors. These models compared two different sizes of industrial-scale reactors and included all major internals including cooling coils, cyclones, cyclone diplegs and gas spargers. Data on the pressure profile and actual entrainment rate to the cyclones generated by the Barracuda models were compared to the measured pressure data and derived entrainment rate in the plant reactors. The results showed good agreement. Additionally, evaluation of using the slip factor in the model to compare the particle volume fraction in the freeboard to the actual entrainment rate was done to determine if this technique could be used in the plant setting. The slip factor as calculated by Barracuda was between 1.55-1.95 which is similar to other values in the literature. INTRODUCTION Barracuda® CPFD model pressure profiles and entrainment rates were compared to plant reactor data from two different diameter Acrylonitrile reactors. In the industrial setting, accurate entrainment rates are important in fluidized bed reactors for several reasons, including determination of cyclone loadings and efficiencies, sizing of diplegs, and inputs to population balance models. Entrainment correlations exist in the literature and from other sources to predict entrainment rates from fluidized beds, but they can vary by orders of magnitude. In addition, many correlations do not take into account effects of internals which are present in many industrial reactors. In contrast, a full-scale Barracuda model can take into account effects of particle size distribution and effects of internals and reactor walls. In addition to the direct Barracuda model predictions, the idea of using a slip factor applied to actual plant reactor pressure measurements in the upper freeboard near the cyclone inlets was explored within the model. This technique may provide a simplified way to more directly estimate entrainment rates in industrial reactors. REACTOR SYSTEM The two reactors studied were Sohio-type Acrylonitrile reactors, as illustrated in Figure 1. These reactors are described by Kunii and Levenspiel (1) as having a uniform air feed through a bottom distributor and an upper distributor where ammonia and propylene are fed. The Sohio reactors have numerous vertical coolant coils that circulate water and produce steam from the highly exothermic reaction. Internal cyclones collect the catalyst from the effluent gas and return it to the bottom of the reactor.
Product Gas
Table 1. Operating and Model Conditions
Coolant Water
Steam
NH3, C3H6 Distributor
Catalyst dp 50, microns Fines Content, < 44 microns Particle Density, kg/m3 Reactor Diameter, m Operating Pressure, kPa Operating Temperature, °C
50-65 10-35 1400-2000 3.6 (small), 9 (large) 100-200 425-490
Distributor
Air
Figure 1. Sohio-type Acrylonitrile Reactor, Modified from Kunii and Levenspiel (1) Pressure profiles were taken with local digital pressure gauges. Due to the limited nozzle locations on the reactor vessels, the pressure profile was limited to approximately ten locations along the reactor height. The pressures were taken at points approximately 1 foot from the wall of the reactor vessels using internal piping. NUMERICAL MODEL (CPFD) The three-dimensional gas-solids flow inside the two Acrylonitrile reactors operating in the turbulent fluidization mode was numerically simulated using the commercial Barracuda software package. The Barracuda software is an advanced math-physics based numerical tool built on the technology of the computational particle-fluid dynamics (CPFD) developed by CPFD Software. The software's numerical methodology uses a direct element method where solids are modeled using the Lagrangian method as discrete particles with proper size and density distributions, and the fluid is modeled as a continuum solved on a fixed grid using the Eulerian method. The actual solids particles numbering in the order of 1015 to 1018 are typically modeled with 1 to 5 million numerical particles, each of which groups the physical particles with the same properties (size, density, etc) as a single entity. Each of the numerical particles is explicitly tracked and calculated for its position and motion in the Lagrangian scheme. Solutions of the fluid dynamics and solids motion and their interaction are fully coupled. The software has been extensively validated against available theoretical and experimental data, and it is efficient in simulating large commercial scale fluidization units. The reactor simulations included all major internals including cyclones, diplegs, cooling coils and support beams and gas spargers in the numerical model. Figure 2(a) shows the larger reactor loaded with solids particles. The reactor model was discretized with approximately 200,000 cells. A quarter of the gridded model cutting through the center lines is shown in Figure 2(b). The geometry of the smaller reactor was similar with fewer cyclones and coils. The simulation conditions are typical operation conditions of Acrylonitrile reactors. Since fluidization behavior of the bed was the focus of the study, chemical reactions and thermal
dynamics and heat transfer were not considered. The fluid, a gas mixture of air and hydrocarbons, had a superficial gas velocity of approximately 0.6 m/s. The reactor was initially loaded with solids particles at near close-pack volume fraction. The flow boundary conditions included fluidizing gas entering the bed uniformly through the bottom of the reactor and through the gas sparger at a higher elevation inside the reactor, pressure boundary conditions at the cyclone inlets where gas and solids flow exit the system, and solids returned to the bed through the bottom of the primary and secondary diplegs. The rate of solids returned to the bed from the diplegs matched approximately the rate of solids entrainment through the cyclones so the bed inventory was maintained almost constant during the simulation. The variation of bed inventory is within 1~2%. A typical simulation was approximately 200 s of real-time operation; about 100s to reach quasi-steady state and an additional 100 s is run to obtain average properties such as the particle volume fraction, the pressure and gas and solids velocities. Two simulations were performed on different reactor sizes both with a superficial gas velocity of about 0.6 m/s.
(a)
(b)
Figure 2. One of the Two Acrylonitrile Reactors Simulated. (a) The Whole Reactor with Solids Particles Being Fluidized at the Early Stage of the Simulation. (b) Gridded Model.
RESULTS AND DISCUSSION Pressure vs. Elevation Pressure vs. elevation as well as density vs. elevation were measured in the plant and computed by the Barracuda model and were compared in Figures 3-4. The key parameters of the Barracuda models and the plant reactor were chosen to be as close as possible for a good comparison. They are shown on a relative basis to the maximum elevation and pressure. In addition, calculated points corresponding to the relative top pressure accounting only for the weight of the bed as well as the relative top pressure using the bed weight and correcting for an average 6% reduction in cross-sectional area due to the reactor internals are shown for comparison.. From Figures 3, the Barracuda pressure vs. height shows a more gradual decrease in pressure than the plant data. The model shows reasonably good agreement with the pressure survey data over the entire reactor height. A force balance check showed that the pressure drop calculated by the Barracuda model matched the bed weight within 5%. Figure 4 shows the pressure profile of the 3.6-meter diameter reactor compared to the pressure profiled predicted by the Barracuda model. In this case, the pressure profile matched the shape of the plant data closely up to about 40% of the reactor bed height. The top point of the plant reactor survey shows about 2% difference from the Barracuda model. While the overall inventory in the model matched the plant within 5%, the pressure deviation may be due to de-fluidized catalyst in the plant reactor and catalyst below the bottom pressure tap that is not contributing to the overall pressure drop.
1.00 0.90
ΔP used for entrainment rate estimate
0.80
Relative Height, h/H
0.70 Barracuda Average Plant Reactor Static Data Top Pressure Using Bed Weight Only Top Pressure Using Bed Weight and 6% Area Reduction for Internals
0.60 0.50 0.40 0.30 0.20 0.10 0.00 0.90
0.91
0.92
0.93
0.94
0.95
0.96
0.97
Absolute Pressure Relative to Maximum Pressure
Figure 3. Pressure Profile Comparison in the 9-m Diameter Reactor
0.98
0.99
1.00
1.00
ΔP used for entrainment rate estimate
0.90
0.80
Relative Height, h/H
0.70 Barracuda Average Plant Reactor Static Data Top Pressure Using Bed Weight Only Top Pressure Using Bed Weight and 6% Area Reduction for Internals
0.60 0.50 0.40
0.30 0.20
0.10 0.00 0.93
0.94
0.95
0.96
0.97
0.98
0.99
1.00
Absolute Pressure Relative to Maximum Pressure
Figure 4. Pressure Profile Comparison in the 3.6-m Diameter Reactor Entrainment Rate Comparisons One of the key parameters desired from the pressure survey and the Barracuda models was an estimate of the entrainment rate of solids to the cyclones. While the upper limit of the entrainment can be calculated from the upper-most bed density, the actual entrainment to the cyclones would be lower. A question posed during this study was if a slip factor could be applied to the upper-most bed density to better estimate the entrainment rate and cyclone loading. The slip factor, as defined by Patience, et.al. (2), is the ratio of the gas velocity to the particle velocity. In parallel, the Barracuda model offered a way to not only apply this concept within the model, but to also directly compare to the actual entrainment with the model itself. ψ = Uo/(εavgVp)
(1)
The average particle velocity is calculated from Eq. (2) and the voidage from Eq. (3) assuming pressure drop is only due to the hydrostatic head of the solids: Vp = Gs/[ρp(1- εavg)]
(2)
εavg = 1- dP/(ρpgdz)
(3)
The slip factor defined by Eq. (1) was directly calculated from the Barracuda simulation. Figure 5 shows the slip factor for both reactor diameters at various elevations over the equivalent range between the top two pressure taps in the plant reactors. The slip factor was 1.47 to 2.97 over this elevation range. The 9-m reactor average slip factor was higher than the 3.6-m reactor which was also reflected in the higher predicted entrainment rate shown in Table 3. The arithmetic average slip factor around 2 indicated that on average the particle velocity was less than half of the fluid velocity (note that the average void fraction was around 0.97). Due to the difference in
the particle and fluid velocities, the actual entrainment rate was much less than that calculated under the assumption that the particle and fluid flow to the cyclone together at the same velocity. The Barracuda simulation predictions match well with data from Patience, et. al. where the slip factor in the fully developed region of CFB risers from various data sources was calculated as approaching 2. The entrainment flux (Gs) from the acrylonitrile reactors was calculated from Equations 1 to 3 using the average Barracuda model calculated slip factor applied to the top most differential pressure measurement under the operating conditions present. 1.00
0.95 9-m Average Slip Factor = 2.44
3.6-m Average Slip Factor = 1.83 Relative Height (h/H)
0.90
0.85
3.6-m Diameter Barracuda Model 9-m Diameter Barracuda Model
0.80
0.75
0.70 0
0.5
1
1.5
2
2.5
3
3.5
Calculated Slip Factor (Model)
Figure 5. Slip factor calculated by Barracuda at elevations between the top two plant reactor pressure taps The Barracuda model results and the method described above applied to the plant reactor data are compared to several other literature entrainment rate correlations in Table 3. A 5% higher superficial gas velocity was used in the entrainment rate correlations due to the internal blockage inside the reactor around the transport disengagement height (TDH). Evaluating the Barracuda model results in the same manner as the plant data, the average slip factor shown in Figure 5 for each reactor diameter was applied to the pressure drop over height (DP/L) data from the plant to estimate the actual entrainment rate. By using this method, the Barracuda model results include both the actual solids slip and any solids moving down the reactor walls, diplegs, or other structures. Therefore, the Barracuda predicted slip may be reasonably applied to the plant reactor data. Using this same slip factor on the plant DP/L data, the 9-m diameter reactor entrainment at a superficial gas velocity of 0.6 m/s is about 50% of the Barracuda model prediction. The 3.6-m diameter reactor entrainment rate predicted using this method was about 65% of the Barracuda model prediction. A separate independent method of measuring cyclone loading in the plant reactor was reasonably close to these entrainment rate predictions. Ideally, the top two pressure taps would be closer together and closer to the cyclone inlet. Overall the Barracuda model was as good as or better than most literature correlations. Reasons for the deviation could include the lack of inter-particle forces (clustering) in the Barracuda model vs. the expectation that these are present in the plant reactors, or drag model and bed-expansion
differences. Schildermans and Baeyens (3) attempted to correlate entrainment rates Sohio-type Acrylonitrile reactors using a combination of entrainment rate correlations. Using a 3.5-m diameter reactor, they calculated entrainment fluxes of 0.23-0.91 kg/s m2, which are much lower than those calculated in this study. Possible reasons for the higher estimated fluxes from the plant reactor data compared to Schildermans and Bayeyens or FCC systems include less clumping due to different catalyst systems, higher fines content, and that the real slip factors could be higher than predicted by Barracuda. Table 3. Entrainment Flux Comparison to Selected Correlations at Uo = 0.6 m/s kg /s m2 3.6-m plant reactor pressure data with 1.83 slip factor applied 3.6-m Barracuda Model Actual Entrainment 9-m plant reactor pressure data with 2.44 slip factor applied 9-m Barracuda Model Actual Entrainment PSRI Correlation (4) Tasirin and Geldart (4) Zenz, et al. Procedure (5) Kato, et al. (6) Wen and Hashinger (7) Geldart et al. (8) Merrick and Highley (9) Zenz and Weil (10) Sciazko (11)
11.9 18.6 7.81 15.5 2.9 1.8 22.5 7.3 0.4 5.9 35.0 1.7 5.6
Interestingly, Abrahamsen and Geldart (12) showed no impact of bed diameter on entrainment rate, and Wen and Chen (13) indicate directionally that the elutriation rate is expected to be higher for larger diameter vessels. Their work contrasts with this study in which both the plant and Barracuda models show decreasing entrainment flux with increasing column diameter. In the case of the plant reactor data, the smaller diameter plant reactor had a higher fines content which may have dominated this difference. CONCLUSIONS Barracuda CPFD models showed reasonable fit of pressure vs. height compared to Sohio-type Acrylonitrile reactors of 3.6- and 9-m diameter. Slip factors were calculated over the range of the top two pressure taps of the plant reactors and were used to estimate entrainment rates within 10-50% of the Barracuda model predicted entrainment rates. The Barracuda model slip factor approached 1.55 for the 3.6-m diameter reactor and 1.95 for the 9-m diameter reactor. The Barracuda model fit the 3.6-m diameter reactor data better than the 9-m diameter data. Overall, using the calculated slip factor from the Barracuda models applied to the density above TDH or the Barracuda model directly to estimate entrainment rates from large diameter industrial reactors appears to be a reasonable approach. ACKNOWLEDGEMENTS The authors would like to acknowledge the contributions of the plant personnel at Ascend Performance Materials, LLC including Bart Propst, Stephen Pope, Brian Manis, and Dean Murphy for collecting the data and helping with the analysis. In addition, we would like to acknowledge Ted Knowlton from PSRI and Todd Pugsley from University of Saskatchewan for their input on entrainment rate considerations as well as comments from Mayank Kashyap of Ascend Performance Materials.
NOTATION dp 50 H h ψ Uo εavg Vp Gs ρp g P z dP/dz
= = = = = = = = = = = = =
Mass Mean Particle Diameter Total Reactor Height Local Reactor Height Slip Factor Superficial Gas Velocity Average Void Fraction Particle Velocity Solids Flux (mass/area-time) Particle Density Gravitational Constant Pressure Vertical Coordinate Pressure Gradient
REFERENCES 1. Kunii, D., and O. Levenspiel, Fluidization Engineering, 2nd Ed., Butterworth-Heinmann, Boston,1991, pp. 31-32. 2. Patience, G.S., J. Chaouki, F. Berruti, and R. Wong, Powder Technology, 72 (1992) 31-37. 3. Schildermans, I., and J. Baeyens, The Carry-over of Catalyst from Large Fluidized Bed Gascatalytic Reactors, Powder Handling and Processing, Vol. 14, No. 4, (2002) 246-251. 4. PSRI Fluidization Seminar, February 20-23, 2006, Sugarland, TX. 5. Tasirin, S.M., and D. Geldart, Powder Technology, 95 (1998) 240-247. 6. Kato, K., T. Tajima, M. Mao, and H. Iwamoto, In: Kwauk M, Kunii D, Zheng J, Hasatani M, eds. Fluidization ’85 – Science and Technology, Elsevier, Amsterdam, 1985, pp. 134-147. 7. Wen, C.Y., and R.F. Hashinger, AIChE J., 6 (1960) 220-226. 8. Geldart, D., J. Cullinan, S. Gilvray, and D.J. Pope, Trans. Inst. Chem. Eng., 57 (1979) 269-275. 9. Merrick, D. and J. Highley, AIChE Symposium Ser., 70 (1974), 366-378. 10. Zenz, F.A. and N.A. Weil, AIChE J, 4, (1958) 472. 11. M. Sciazko, Powder Technol., 66 (1991) 33-39. 12. Abrahamsen, A.R., and D. Geldart, Powder Technology, 26 (1980) 47-55. 13. Wen, C. Y., and L.H. Chen, AIChE Journal, 28 (1982) 117-128.
EFFECT OF WALL BOUNDARY CONDITIONS AND MESH REFINEMENT ON NUMERICAL SIMULATION OF PRESSURIZED DENSE FLUIDIZED BED FOR POLYMERIZATION REACTOR P. Fede1,2, O. Simonin1,2, R. Ansart1,2, H. Neau1,2 and I. Ghouila3 1
Université de Toulouse; INPT, UPS ; IMFT ; 31400 Toulouse, France e-mail : [email protected] 2 CNRS; Institut de Mécanique des Fluides de Toulouse ; 31400 Toulouse, France 3 INEOS; Innovene; CTL/PRO Ecopolis Lavéra, 13117, Lavéra, France ABSTRACT The effect of the mesh refinement and the solid phase boundary condition are investigated for numerical simulation of dense pressurized fluidized bed. Two fluidized beds have been considered: a laboratory-scale device and a pilot-scale facility. A relation is proposed to scale-up the numerical simulation and preserving the spatial resolution accuracy. As expected the boundary condition of the solid phase modifies the behaviour of the fluidized bed. Compared to free-slip wall boundary condition, a no-slip condition improves the numerical predictions with respect to available experimental data. INTRODUCTION Pressurized gas-solid fluidized beds are used in a wide range of industrial applications such as coal combustion, catalytic polymerization, uranium fluoration or biomass pyrolysis. The numerical modelling of such industrial fluidized beds is challenging because many complex phenomena take place (particle-turbulence interaction, particle-particle and particle-wall collision, heat and mass transfers) and the large-scale geometry of the industrial facilities. The development of numerical modelling of fluidized bed hydrodynamic started about two decades ago. Nowadays it is possible to perform 3D realistic simulations of industrial configurations by using unsteady Eulerian reactive multi-fluid approach. Numerical simulations of industrial and pilot-reactor were carried out with such an approach showing a good agreement with the qualitative knowledge of the process (bed height, pressure drop, local mass flux). However the size of industrial reactor and the computer resources imposed too coarse meshes. Recent studies have emphasized the role of the spatial resolution on the prediction of the fluidized bed behaviour (Agrawal et al (1); Igci et al (2); Parmentier et al (3-4)). Also it has been shown that for dilute (Benyahia et al (5)) and dense (Fede et al (6)) gas-solid flow the boundary condition of the solid phase may modify the structure of the flow. According to Fede et al (6), in dense fluidized bed, the no-slip wall boundary improves the prediction of the radial profile of mean vertical particle velocity compared to the results predicted by using a free-slip wall boundary condition. In the present study, 3D numerical simulations of dense pressurized fluidized beds have been carried out using an Eulerian n-fluid modelling approach for fluid-particle turbulent polydispersed flows developed and implemented by IMFT (Institut de Mécanique des Fluides de Toulouse) in the NEPTUNE CFD V1.07@Tlse version.
Table. 1. Mesh characteristics for laboratory- and pilot-scale reactors.
NEPTUNE CFD is a multiphase flow software developed in the framework of the NEPTUNE project, financially supported by CEA (Commissariat à l’Energie Atomique), EDF (Electricité de France), IRSN (Institut de Radioprotection et de Sûreté Nucléaire) and AREVA-NP. The multiphase Eulerian approach is derived from a joint fluid-particle PDF equation allowing to derive the transport equations for the particle velocity’s moment (Simonin (7)). In the proposed modelling approach, separate mean transport equations (mass, momentum, and fluctuating kinetic energy) are solved for each phase and coupled through inter-phase transfer terms. The drag law is modified according to Gobin et al (8). The collisional particle stress tensor is derived in the frame of the kinetic theory of granular media (Boëlle et al (9)). The turbulence modelling is achieved by the standard k−ε model extended to the multiphase flows (i.e. accounting for additional source terms due to the inter-phase interactions). For the dispersed phase, a coupled transport equation system is solved on particle fluctuating kinetic energy and fluid-particle fluctuating velocity covariance (Simonin (7)). In the present paper, we analyze the effect of the solid wall boundary conditions and mesh size for several fluidized bed systems: first a laboratory-scale bed (0.9m of height) and a pilot-scale bed (9m of height). The numerical simulation of an industrial-scale bed (34m of height) is discussed and the results will be shown at the conference. GEOMETRY, MESH AND SCALING The numerical simulations were carried out for several isothermal reactors: first a laboratory-scale reactor with a diameter of 0.077m and an height of 1.7m, second a pilot-scale device with a diameter of 0.74m and a height about 9 meters. For the comparison, simulations were carried out for an industrial-scale polymer reactor has a diameter of 5m and a height of 34m. In the last few years, the sensitivity of the numerical simulation with respect to the mesh has been investigated. Agrawal et al (1) showed that a very fine mesh (typically with a cell size of the order of a few particle diameters) permits to capture some meso-scale structures. These structures have a strong influence on the hydrodynamic of the fluidized bed especially on the bed height, on the vertical solid mass flux and the mixing process. Recent studies (Heynderickx et al (10); Igci et al (2)) pointed out the role of the drag force in the formation of the meso-scales. These studies were made by a priori analysis of mesh-independent numerical simulation.
Fig. 1. Sketches of the reactor. From the left to the right: the laboratory-scale reactor, the pilot-scale reactor and the large industrial facilities.
Parmentier et al (3) have shown that the error related to the mesh can be scaled. Indeed, for similar operating conditions and particle properties, considering a numerical simulation of a dense fluidized bed of diameter Dr with a cell size of ∆, the ~ required mesh for a larger reactor of diameter Dr and conserving the error due to the mesh is given by:
~ ~ ∆ = ∆ Dr / Dr
(1)
The meshes have been constructed using O-grid technique in order to have nearly uniform cells in a horizontal section ( dx ≈ dy ). The Table 1, giving the main characteristics of the meshes for the laboratory- and pilot-scale reactors, shows that the cell size dx is nearly in accordance with Equation (1) when passing from the laboratory-scale to the pilot-scale reactors. The numerical simulations are performed in two steps. First the column of the fluidized bed is filled of solid. The solid volume fraction is uniformly distributed and determined in order to fit the solid mass of the experiment. Then a transitory phase, corresponding to the destabilization of the bed, is computed during 20 seconds (the
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Fig. 2. Laboratory-scale: radial profile of the mean vertical particle velocity normalized by the fluidization velocity (Vf). The operating pressure is P=12bar and the fluidization velocity Vf=0.32m/s. On the left-panel the data has been extracted at z=0.75×Dr and the right panel z=1.7×Dr.
time step is approximately 10-3s). In a second step, the time-averaged statistics are computed during 80s in order to obtain converged statistics. LABORATORY-SCALE DEVICE The experimental investigation of dense fluidized bed is a trickiest challenge. Because of the medium opacity, the experimental data are commonly reduced to pressure drop at the wall or local solid mass flux. However, recently a novel technique has been developed called Positron Emission Particle Tracking allowing to access to the local properties of the solid flow. This non-invasive technique consists of tracking a single particle (up to now it is possible to have multiple particle tracking) and using specific algorithm to reconstruct the Lagrangian trajectory of the particle inside the fluidized bed. This technique has been successfully employed to investigate the hydrodynamic of dense fluidized bed (Link et al (11); Fede et al (6)). The drawback of the PEPT method is that the entire reactor has to be equipped of sensor. Then, the using of PEPT to investigate an industrial reactor is still quite complicated. However the PEPT can be used on a laboratory-scale fluidized bed. Such experimental works are crucial to improve the knowledge on the local mechanisms taking place in a fluidized bed. This is also relevant to assess a modelling approach and closure laws used in Euler-Euler models. Fede et al (6) made an analysis of experimental data obtained by PEPT. On the radial profiles of the time-averaged mean vertical particle velocity they observed that in the near-wall region the solid velocity is nearly equal to zero. It suggests that a no-slip wall boundary condition for the solid phase is more suitable than a free-slip condition. However, a no-slip boundary condition for the mean particle velocity and zero flux for the particle fluctuating kinetic energy are very questionable but could represent elastic bouncing on the wall with an isotropic angle distribution. Such a situation could correspond to spherical particles bouncing on very rough wall or to
Fig. 3. Pilot-scale: streamtraces of the time-averaged simulated particle velocity. Left panel: free-slip wall boundary condition for solid phase, right-panel: no-slip boundary condition. The operating pressure is P=12 bar and the fluidization velocity Vf=0.32m/s.
very irregular particles bouncing on a smooth wall (Konan et al (12)). However the experimental observations have shown a downward solid flux at the wall. Then one may emphasize that wall boundary condition for the mean solid velocity is between the free-slip and the no-slip condition. The Figure 2 shows the radial profile of the time-averaged mean vertical particle velocity using the two meshes and both boundary condition. The operating pressure is 12bar and the fluidization velocity 32cm/s and the particles are Geldart’s B type. As shown by Figure 3 such a dense pressurized fluidized bed exhibits a peculiar hydrodynamic with a 3-dimensional recirculating loop. The figure shows that the modification of the wall boundary condition for the mean particle velocity changes the topology of the flow. The free-slip condition leads to a one-loop system whereas a no-slip boundary condition to a two counter-rotating recirculating loop. The recirculating loop is clearly shown by the radial profiles in Figure 2. Indeed, we observe an upward mean vertical particle velocity at the bed centre (r = 0) and in the near-wall region a downward particle velocity. We observe that the mesh refinement does not have a significant effect on the time-averaged mean particle vertical velocity. In contrast, the Figure 3 shows that the no-slip wall boundary condition gives the same trend than the experimental data. In particular, the slope break appearing close to wall is well predicted even if the position of the slope break is not correct. Similar trends have been observed with the same particles but differing in terms of fluidization velocity and operating gas density.
Fig. 4. Pilot-scale: radial profile of the time-averaged mean vertical gas (left) and solid (right) velocity normalized by the fluidization velocity. The radial profile is extracted at z=3.2×Dr.
PILOT-SCALE FACILITY The pilot-scale reactor investigation is particularly important because the operating conditions are very close to the ones in industrial process. In addition, the flow simulation conditions of such a device are well controlled (in terms of temperature, velocity of fluidisation, particle properties) and experimental data are available (pressure drops, temperature, gas composition). Fede et al (13) performed numerical simulations of such a polymerization pilot reactor located at INEOS Lavéra (France). As for the laboratory-scale they found a large recirculating loop. The Figure 4 shows the radial profile of the time-averaged mean vertical gas and solid velocity in the pilot-scale fluidized bed. We observe that the structure of the flow is similar to the one observed in the laboratory-scale reactor. Namely, we notice a large upward particle velocity at the reactor centre (up to 4 times the fluidisation velocity) and a downward particle velocity near the wall. This typical profile indicates the presence of the recirculating loop. The figure 4 shows that the profile of the gas velocity profile has the same shape than the profile of the particle velocity. In such a fluidized bed, the near-wall region is characterized by large downward solid mass flux. Then, the particles drag the gas in their motion leading to negative vertical gas velocity. The modification of the wall-boundary condition for the solid phase clearly modifies this effect. Indeed, Figure 4 exhibits a strong reduction of the downward particle velocity at the wall and consequently a reduction of the downward gas velocity. The mass conservation leads to decrease the particle velocity at the reactor centre. The vertical mean pressure distribution is shown by Figure 5. The pressure distribution exhibits a slope break corresponding to the top of the fluidized bed. With the free-slip wall boundary condition the pressure distribution inside the fluidized bed is found slightly bended whereas a no-slip condition gives linear profiles. This effect may be explained by the recirculating loop identified in such a fluidized bed. Indeed, the free-slip boundary condition should lead to a too large rotating motion of the
Fig. 5. Vertical mean gas pressure distribution measured at the wall in pilot-scale reactor.
recirculating loop. Then, the gas and the particles are accelerated at the centre of the bed modifying the pressure distribution. The mesh refinement does not significantly modify the pressure distribution even if the top of the fluidized bed is slightly decreased with the mesh refinement. It was expected because the particles correspond to Geldart’s B particle type. For such kind of particles the sensitivity of the numerical solution with the mesh is less important than for Geldart’s A particles. INDUSTRIAL-SCALE FACILITY In the two previous sections, we have briefly described the hydrodynamic of isothermal laboratory-scale and pilot-scale device. For the considered operating points and powder properties, Equation (1) gives the required mesh size to ensure a good spatial resolution. If we apply such a scaling-rule, the mesh for the industrialscale configuration requires about 4,000,000 cells. Nowadays such a numerical simulation is realizable using High Parallel Computing. In particular the parallel efficiency of NEPTUNE CFD has been demonstrated up to 1,024 CPU (Neau et al (14)). Such an industrial-scale numerical simulation is currently running and the results will be presented. CONCLUSION Numerical simulations of pressurized dense fluidized bed for polymerization have been carried out and compared with experimental data for laboratory-scale and pilot scale. The effect of boundary conditions on the solid phase has been investigated and the results show that the prediction of the gas pressure vertical distribution is improved with a no-slip boundary condition for the mean solid velocity. ACKNOWLEDGMENT This work was granted access to the HPC resources of CINES under the allocation 2010-026012 made by GENCI (Grand Equipement National de Calcul Intensif) and by CALMIP under the project P0111.
NOTATION Dr diameter of the reactor Hr height of the reactor Vf operating fluidization velocity
Wg Wp P
mean vertical gas velocity mean vertical solid velocity gas pressure
REFERENCES (1) Agrawal, K., Loezos, P., Syamlal, M., and Sundaresan, S. (2001). “The role of mesoscale structures in rapid gas-solid flows”, J. Fluid Mech., 445, 151–185. (2) Igci, Y., Andrews IV, A. T., Sundaresan, S., Pannala, S., and O’Brien, T. (2008). “Filtered two-fluid models for fluidized gas-particle suspensions”, AIChE Journal, 54, 1431–1448. (3) Parmentier, J.-F., Simonin, O., and Delsart, O. (2008). “A numerical study of fluidization behavior of Geldart B, A/B and A particles using an Eulerian multifluid modeling approach”, Proc. of the 9th Int. Conference on Circulating Fluidized Beds, Circulating Fluidized Bed Technology IX. 331–336. (4) Parmentier, J.-F., Simonin, O., and Delsart, O. “A functional subgrid drift velocity model for filtered drag prediction in dense fluidized bed”, Submitted to AIChE Journal. (5) Benyahia, S., Syamlal, M., and O’Brien, T. J. (2005). “Evaluation of boundary conditions used to model dilute, turbulent gas/solids flows in a pipe”, Powder Technology, 156, 62 – 72. (6) Fede, P., Moula, G., Ingram, A., Dumas, T., and Simonin, O. (2009). “3D numerical simulation and PEPT experimental investigation of pressurized gas-solid fluidized bed hydrodynamic”, Proceedings of ASME 2009 Fluids Engineering Division Summer Meeting, ASME. (7) Simonin, O. (1996). “Combustion and turbulence in two-phase flows”. Lecture Series 1996-02, Von Karman Institute for Fluid Dynamics. (8) Gobin, A., Neau, H., Simonin, O., Llinas, J. R., Reiling, V., and Sélo, J. L. (2003). “Fluid dynamic numerical simulation of a gas phase polymerisation reactor”, International Journal for Numerical Methods in Fluids, 43, 1199–1220. (9) Boëlle, A., Balzer, G., and Simonin, O. (1995). “Second-order prediction of the particlephase stress tensor of inelastic spheres in simple shear dense suspensions”, in Gas-Particle Flows, Vol. 228, ASME FED. 9 – 18. (10) Heynderickx, G. J., Das, A. K., Wilde, J. D., and Marin, G. B. (2004). “Effect of clustering on gas-solid drag in dilute two-phase flow”, Ind. Eng. Chem. Res., 43, 4635–4646. (11) Link, J. M., Deen, N. G., Kuipers, J. A. M., Fan, X., Ingram, A., Parker, D. J., Wood, J., and Seville, J. P. K. (2008). “PEPT and discrete particle simulation study of spout-fluid bed regimes”, AIChE Journal, 54(5), 1189–1202. (12) Konan, N., Kannengieser, O., and Simonin, O. (2009). “Stochastic modeling of the multiple rebound effects for particle-rough wall collisions”, International Journal of Multiphase Flow, 35(10), 933 – 945. (13) Fede, P., Neau, H., Simonin, O., and Ghouila, I. (2010). “3D unsteady numerical simulation of the hydrodynamic of a gas phase polymerization pilot reactor”, 7th International Conference on Multiphase Flow, ICMF 2010, Tampa, FL. (14)Neau, H., Laviéville, J., and Simonin, O. (2010). “Neptune CFD high parallel computing performances for particle-laden reactive flows”, 7th International Conference on Multiphase Flow, ICMF 2010, Tampa, FL, May 30 - June 4.
ELUTRIATION FROM FLUIDIZED BEDS: COMPARISON BETWEEN EXPERIMENTAL MEASUREMENTS AND 3D SIMULATION RESULTS
2
Renaud Ansart1 2 , Herve´ Neau 1 2 , Philippe Accart3 4 Alain de Ryck3 4 and Olivier Simonin1 2 1 Universite ´ de Toulouse; INPT, UPS; IMFT ; F-31400 Toulouse, France ´ CNRS; Institut de Mecanique des Fluides de Toulouse; F-31400 Toulouse, France 3 Universite ´ de Toulouse; Mines Albi; RAPSODEE; F-81013 Albi, France 4 CNRS, Centre RAPSODEE, Campus Jarlard, F-81013 Albi, France [email protected], [email protected]
ABSTRACT This paper presents comparisons between experimental measurements and unsteady threedimensional numerical simulations performed by an unstructured parallelized CFD multiphase flow code of elutriation and transport of mono-dispersed glass beads type B and A/B in a fluidized bed. These comparisons show a satisfactory agreement between experimental measurements and numerical predictions. The numerical results also show a strong dependence on the mesh size, especially for fine particles. A model accounting for influences of mesoscales structures on overall bed hydrodynamics, which are not resolved by coarse mesh simulations, is applied. Results obtained with the model show improvements for the dense fluidized bed and even for a transport step.
INTRODUCTION Gas-solid fluidized beds are used in a wide range of industrial applications such as coal combustion, catalytic polymerization and uranium fluoridation. Many of the fluidized bed industrial processes involve poly-dispersed powder and even multi-species of powders. In bubbling fluidized bed combustion and catalytic cracking, elutriation is a major cause of inefficiency, while it could be highly desirable in specific case. Whether the intention is to minimize or to promote elutriation, the involved phenomena must be properly known if the process has to be efficiently controlled. Numerical simulation is becoming an efficient approach to study the separation and entrainment processes observed in an industrial fluidized bed. In the literature, there is a lack of experimental data to validate CFD simulations of these phenomena. Thus, a joint experimental and numerical project between RAPSODEE Centre and IMFT was initiated. The object of this paper is to present comparisons between threedimensional numerical simulation predictions and experimental data of particle gas pressure drop and entrainment in a fluidized bed.
Particle properties Solid mass (kg) Density (kg/m3 ) Diameter d50 (µm) 10 Span= d90d−d 50 −1 Vt (m · s ) Umf (m · s−1 ) Fig. 1: Experimental set up.
Fine 2.5 2470 84 0.38 0.41 5 · 10−3
Coarse 2.5 2470 213 0.414 1.51 36 · 10−3
Table. 1: Powder properties.
EXPERIMENTAL SET UP The column in the laboratory experimental set up was 10 cm in diameter and 59 cm high (Fig. 1) and was fitted with a conical outlet. The column material was stainless steel to avoid electrostatic charge (Ansart et al. (1)). The bronze distributor had a pressure drop of 6 kPa at a superficial gas velocity of 0.18 m · s−1 . Fluidizing air was supplied by a Brooks smart mass flow meter and controllers 5853S with an accuracy of ±0.7 % of the rate and ±0.2 % of full scale (2.32 m · s−1 ). The process was divided into two parts: the first allowed the fluidization of particles by a homogeneous superficial gas velocity (Vf 1 < Vt ), in order to obtain a bubbling fluidized bed regime. According to a linear ramp-up of 5 s during the second part, the fluidization velocity was increased to entrain the particles (Vf 2 > Vt ). The particles entrained were collected through a vessel at the outlet of a cyclone. The mass of particles collected was continuously weighed during the process with a resolution time of 1 s and an accuracy of 0.01 g. Pressure variations along the pipe were monitored by severals sensors located every 1.5 cm. Honeywell DC pressure instrumentation was used with an accuracy of ±0.25 % of full scale. The resolution time was 0.1 s. The measurement of gas pressure on the wall was made through an 8 mm diameter opening with a filter. The powder was glass beads with properties described in Table 1. For 2.5 kg of solid mass, the bed at rest in the column is approximatively 21 cm. Two particle sizes called fine (Geldart type A/B) and coarse (Geldart type B) were used. The mean diameters of powder were determined using a Mastersizer 2000 with 1.5 bar of dispersion. The bulk material was sieved to ensure an almost mono-dispersed distribution. The terminal settling velocity Vt of the particle was calculated by the expression of the drag coefficient, Equation (5). According to Remf , an estimation of minimum fluidization velocity Umf was computed using the expression recommended by Wen and Yu: Remf = (33.72 + 0.0408
ρg (ρp − ρg )d3p g 0.5 ) − 33.7 µ2g
Umf =
Remf µg . ρp dp
(1)
MATHEMATICAL MODEL Simulations were carried out using an Eulerian n-fluid modeling approach for polydispersed fluid-particle flows implemented in the NEPTUNE CFD software which was ´ developed and implemented by IMFT (Institut de Mecanique des Fluides de Toulouse). This software is a multiphase flow code developed in the framework of the NEP´ TUNE project, financially supported by CEA (Commissariat a` l’Energie Atomique), ´ ´ EDF (Electricit e´ de France), IRSN (Institut de Radioprotection et de Suret ˆ e´ Nuceaire) and AREVA. In the proposed modeling approach, the mean transport equations (mass, momentum and fluctuant kinetic energy) are solved for each phase and coupled through interphase transfer terms. These equations are derived by phase ensemble averaging weighted by the gas density for the continuous phase and by using kinetic theory of granular flows supplemented by fluid and turbulence effects for the dispersed phase (Balzer et al. (2), Gobin et al. (3)). In the following development, subscript k = g, refers to the gas phase and k = p refers to the particle phase. The mass balance equation is: ∂ ∂ αk ρk + αk ρk Uk,i = 0, ∂t ∂xi
(2)
where αk is the k th phase volume fraction, ρk the density and Uk,i the ith component of the velocity. In equation (2), the right-hand-side is equal to zero without mass transfer. The mean momentum transport equation for the phase k is written: αk ρk
∂Uk,i ∂Uk,i ∂Pg ∂ + Uk,j = −αk + αk ρk gi + Ik,i + −αk ρk u0k,i u0k,j + Θp,ij , ∂t ∂xj ∂xi ∂xj (3)
where u0k,i is the fluctuating part of the instantaneous velocity of phase k, Pg is the mean gas pressure, gi the ith component of the gravity acceleration and Ik,i the mean gas particle interphase momentum transfer without the mean gas pressure contribution. Finally, Θk,ij is for k = g the molecular viscous tensor and for k = p the collisional particle stress tensor. Due to the large particle to gas density ratio, only the drag force was assumed to be acting on the particles. Hence, the mean gas-particle interphase momentum transfer can be written: Ip,i = −Ig,i = −αp ρp
Vr,i F τgp
with
1 3 ρg h|vr |i = Cd,WY . F τgp 4 ρp dp
(4)
with Cd,WY given by Wen & Yu’s correlation: ( Cd,W Y =
−1.7 1 + 0.15Re0.687 αg p −1.7 0.44 αg 24 Rep
Rep < 1000 Rep ≥ 1000
Rep = αg
ρg h|vr |idp µg
(5)
The mean relative velocity Vr,i between gas and particle is expressed in terms of the mean gas velocity, mean particle velocity and drift velocity. In equation (3), the collisional particle stress tensor is derived in the frame of the kinetic theory of granular media (Boelle et al. (4)).
For the gas turbulence, a standard k − ε model extended to the multiphase flows accounting for additional source terms due to the interfacial interactions was used. For the dispersed phase, a coupled transport equation system is solved on particle fluctuating kinetic energy and fluid-particle fluctuating velocity covariance (qp2 − qf p ). In this paper, the influence of mesh size on the numerical predictions was studied. The mesh size required to fully resolve all of the fine-scale structures decreased as a function of the mean particle relaxation timescale (Parmentier et al. (5)). Because of limited computational resources, a filtered approach can be used to model the drag term accurately (Agrawal (6)). In the framework of a filtered approach for gas-solid flows, Parmentier et al. (7) proposed that the filtered drag can be modeled by:
αp ρp Vr,i F τgp
=
αp ρp (δij + h(αp )Kij f (∆∗G )) Ver,j , F τegp
(6)
where Ver,j = is the resolved relative velocity. Kij = δij Kh + δi3 δj3 (Kv − Kh ), the model coefficient is the same in x and y direction (Kh and Kv are determined by a dynamic √ adjustment performed by a second filter). h(αp ) = − u (1 − u)2 (1 − 1.88 u + 5.16 u2 ), where u = αp /αm et αm = 0.64 is the maximum compacting. The forms of the h and f functions are derived from a highly-resolved simulation of mono-dispersed gas-solid flow. The function f is modeled as the following equation: f (∆∗G ) =
∆∗G 2 a2 + ∆∗G 2
with ∆∗G =
∆G √ . τpSt gL
(7)
where a = 0.084, ∆∗G is a dimensionless mesh size, ∆G the cube root of the cell volume, L the bed diameter and τpst stokes relaxation time. NUMERICAL PARAMETERS To study the influence of mesh refinement, we used three 3D meshes based on Ogrid technique were used. The reference mesh contained 428 451 hexahedra with approximately ∆x = ∆y = ∆z = 3.7 mm. The mesh was uniformly refined by a factor of 1.5 which consisted of 1 477 060 cells. The coarse mesh was made up of 123 816 cells constructed from the reference mesh by coarsening by a factor of 1.5. The numerical simulations were performed on parallel computers with 8 cores for the coarse mesh, 64 cores for the reference mesh and 128 cores for the fine mesh, because of mesh size and physical time needed (Neau et al. (8)). At the bottom (z = 0), the fluidization grid was an inlet for the gas, with an imposed superficial velocity corresponding to the fluidization velocity vf , and a wall for the particles. At the top of the fluidized bed, a free outlet for both the gas and the particles was defined. The wall-type boundary condition was friction for the gas and a no-slip for the particle. Fede et al. (9) have shown that the gas pressure drop predictions are improved with a no-slip boundary condition for the mean particle velocity and zero-flux condition for the mean particle agitation q2p . RESULTS AND DISCUSSION First of all, a comparison between the numerical predictions for the coarse particles and the experimental results was obtained. Then, the same comparison was carried
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Fig. 2: Comparison between experimental data and numerical simulation predictions for coarse particles. In the left plot the data were obtained during the bubbling step Vf 1 = 2.5 Umf and in the right plot during the entrainment step Vf 2 = 1.03 Vt . out for the results of fine particles. In the experiments, the particle phase was slightly poly-dispersed (span ≈ 0.4). However, the numerical simulations were carried out with a monodisperse particle distribution having a median diameter equal to d50 . Coarse particles Gas pressure drop along the wall during the bubbling phase and the mass of particles collected obtained by numerical predictions, for the coarse and reference mesh sizes, were compared with the experimental measurements. To study the wall gas pressure drop during the bubbling step, the numerical simulations were carried out as follows: at t = 0 the fluidized bed was filled up with a uniform solid mass fraction according to the experimental solid mass. A transitory step takes place for t ∈ [0 s, 20 s] corresponding to the destabilization of the fluidized bed. Then, the statistics were computed for t ∈ [20 s, 60 s] insuring a statistical convergence. As Fig. 2 shows, the mesh refinement had no effect on the bed expansion or the mass of particles entrained for the coarse particles (type B). The coarse mesh was sufficient to predict bed dynamics, and no further mesh refinement was needed. Moreover, a very good agreement between numerical predictions of the wall gas pressure drop and the experimental data during the bubbling step (Vf 1 = 2.5 Umf ), Fig. 2(a), was obtained. Above the bed particles, the gradient of gas pressure was equal is negligible for the numerical results and for the experimental measurements. Inside the bed, both distributions were linear. The numerical results predicted the same bed height as the experimental data. Fig. 2(b) shows the evolution of the mass of particles collected during the entrainment step (Vf 2 = 1.03 Vt ). A good agreement between the experimental measurements of the mass flow rate of coarse particles and the numerical results was obtained. Indeed, at the start of the entrainment process the flux of particles was slightly overestimated, and during the following progress of the process the numerical predictions of the flux were very close to the measurements.
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3000
(b) Effect of sub-grid model for drag force.
Fig. 3: Comparison between experimental data and numerical simulation predictions of wall mean gas pressure for fine particles during the bubbling step. Vf 1 = 5 Umf . Fine particles For fine particles, the numerical simulations were performed on three mesh sizes. Fig. 3(a) shows the wall distribution of time averaged gas pressure, during the bubbling step (Vf 1 = 5 Umf ), for the experimental measurements and for the numerical predictions. As shown, mesh refinement had a strong effect on the numerical results for the fine particles. For the coarse and reference mesh sizes, the bed expansion was overestimated. Only, the fine mesh case was able to predict a similar wall gas pressure as the experimental measurements, and to correctly estimate the bed height. The structures of the gas-particle fluidized bed were not able to be resolved by the coarse and reference mesh simulations and they had a drastic influence on the macroscopic flow. To account for the effect of unresolved structures on the macroscopic behavior in the coarse and reference mesh sizes, the sub-grid model described before was used to model the effective drag term. The Fig. 3(b) shows that the wall gas pressure drop predictions are greatly improved by using the sub-grid model. With the sub-grid model, the bed height was almost independent of the mesh size. A satisfactory agreement was obtained between the numerical predictions (fine grid and large grid with sub-grid model) and experimental measurements of the bed height, Table 2. The mean bed height was defined as the height where the gas pressure distribution slope was modified. The Fig. 4 shows the mass of fine particles collected during the entrainment step (Vf 2 = 1.53 Vt ). In the bubbling phase, mesh refinement had a strong effect on the mass flow rate of particles entrained. Comparisons with the fine mesh simulation results showed that the coarse and reference mesh simulations overestimated the mass flow rate of the particles entrained. With the sub-grid model, the numerical simulations were approaching to the experimental measurements.
100
Mass of particles collected (%)
90 80 70
Coarse mesh Reference mesh Fine mesh Coarse mesh + sub-grid Reference mesh + sub-grid Fine mesh + sub-grid Exp. data
60 50
Exp mean data Coarse mesh Reference mesh Fine mesh Coarse mesh +sub grid Reference mesh +sub grid
40 30 20 10 0
50
100
150
200
250
300
350
400
450
Bed height (cm) 27 26.2 24.1 24.5 24.2 23.6 24.2
500
Time (s)
Table. 2: Time-averaged bed height.
Fig. 4: Mass of fine particles collected during the entrainment step. Vf 2 = 1.53 Vt . The simulation for the reference mesh with sub-grid model appeared to give similar results as with the simulation of fine mesh but with less expensive computational resources. As for the coarse particles, the flux of particles at the start of the process was slightly overestimated, and was similar to the experimental measurements for the following test duration. CONCLUSION An experimental test unit was designed and built to study particle separation and entrainment in a fluidized bed by measuring the gas pressure along the column and the mass of particles leaving the column. A three dimensional, unsteady numerical predictions was carried out with the unstructured parallelized CFD multiphase flow NEPTUNE CFD were compared with experimental measurements. Comparisons were obtained for two types of particles (B and A/B). The numerical predictions for type A/B have strong dependency on the mesh size. To account for the effect of unresolved structures on the macroscopic behavior for coarse grid simulations, a sub-grid model was used to model the drag term. Accordingly, the numerical results were greatly improved and were in good agreement with the fine grid simulation. Coarse numerical simulations with the sub-grid model were hugely much less expensive from the point of view of the computational resources. The numerical predictions of the bed height and of the entrainment rate were in good agreement with experimental measurements. The next step of this study is to focus on the elutriation of a mixture of fine and coarse particles. ACKNOWLEDGMENTS This work was granted access to the HPC resources of CINES under the allocation 2010-026012 made by GENCI (Grand Equipement National de Calcul Intensif) and ´ ees) ´ CALMIP (Centre de Calcul Midi-Pyren under the allocation P0111.
NOTATION Cd,W Y Wen ’n Yu drag coefficient dp particle diameter g gravitational constant Pg mean gas pressure 2 qp mean particle agitation Rep particle Reynolds number Uk,i mean velocity of phase k Umf minimum fluidization velocity Vf superficial gas velocity
Vr Vt u0k,i αk ∆∗G ∆G µg ρk F τgp
relative velocity terminal settling velocity fluctuating velocity of phase k volume fraction of phase k dimensionless mesh size cube root of the cell volume gas viscosity density of phase mean gas-particle relaxation timescale
REFERENCES 1. Ansart, R., Neau, H., Accart, P., de Ryck, A. and Simonin, O. (2010). “Separation and Taking Off in a Fluidized Bed: Comparison between Experimental Measurements and Three-dimensional Simulation Results”, Proceedings of 7th International Conference on Multiphase Flows, Florida USA. 2. Balzer, G. and Boelle, A. and Simonin, O. (1995). “Eulerian Gas-Solid Flow Modelling of Dense Fluidized Bed”, FLUIDIZATION VIII, Proc. International Symposium of the Engineering Foundation, J.F. Large and C. Lagu´ erie (Editors). ´ J.L. (2003). 3. Gobin, A., Neau, H., Simonin, O., Llinas, J.R. and Reiling, V. and Selo, “Fluid Dynamic Numerical Simulation of a Gas Phase Polymerization Reactor”, Int. J. for Num. Methods in Fluids, 43, 1199-1220. 4. Boelle, A., Balzer, G. and Simonin, O. (1995). “Second-order prediction of the prediction of the particle-phase stress tensor of inelastic spheres in simple shear dense suspensions”, In Gas-Particle flows, Vol. 228, ASME FED. 9-18. 5. Parmentier, J.F., Simonin, O. and Delsart O. (2008). “A numerical study of fluidization behavior of Geldart B, A/B and A particles using an eulerian multifluid modeling approach”, 9th International Conference on circulating fluidized beds. Hamburg, Germany. 6. Agrawal, K., Loezos, P., Syamlal, M. and Sundaresan, S. (2001). “The Role of Mesoscales Structures in Rapid Gas-solid Flows”, J. Fluid Mech., 445, 151-185. 7. Parmentier, J.F., Simonin, O. and Delsart O. (2010). “A functional subgrid drift velocity model for filtered drag prediction in dense fluidized bed”, Submitted to AIChE Journal. ´ 8. Neau, H., Lavieville, J. and Simonin, O. (2010). “NEPTUNE CFD high parallel computing performances for particle laden reactive flows”, Proceeding 7th International Conference on Multiphase Flows, Florida USA. 9. Fede, P., Moula, G., Ingram, T. and Simonin, O. (2009). “3D numerical simulation and pept experimental investigation of pressurized gas-solid fluidized bed hydrodynamic”, Proceedings of ASME 2009 Fluids Engineering Division Summer Meeting. ASME.
EROSION IN SECOND STAGE CYCLONES: EFFECTS OF CYCLONE LENGTH AND OUTLET GAS VELOCITY S. B. Reddy Karri*, Ray Cocco and Ted M. Knowlton Particulate Solid Research, Inc. 4201 W. 36th Street, Chicago, IL 60632, USA ABSTRACT Severe erosion in the lower cone and in the upper dipleg of second stage cyclones have been observed in commercial cyclones. The main objective of this study is to shed light on the mechanism by which this erosion takes place, and how different design and operating parameters affect the erosion. Experimental data on how parameters such as the cyclone length-to diameter ratio (L/D), inlet solids loading and gas outlet velocity affect second stage cyclone erosion are presented. The outlet gas velocity was varied by changing the size of the vortex tube diameter. The effect of a vortex stabilizer on cyclone cone erosion is also discussed. INTRODUCTION Petroleum refineries have increased their focus on improving unit reliability, and reducing operational and maintenance costs. Because their cyclones have high efficiencies, fluidized catalytic cracking unit (FCCU) process operators are now concerned with longer campaign durations, and would like to improve cyclone reliability. The 2008 NPRA survey and other surveys (1,2) revealed that FCCU cyclone reliability (3,4) was a major concern for refineries. The most pervasive problem is erosion in secondary cyclones in the lower cone and upper dipleg, which is the focus of this study. There is a fundamental difference between first and second stage FCC cyclones in regard to erosion patterns. Highly-loaded first stage cyclones normally experience no cone erosion, whereas lightly-loaded second stage cyclones can have severe cone erosion. This seems to be counter-intuitive, but can be explained by the differences in the solids flow patterns and vortex lengths, as shown in Figure 1. Due to the high solids loading and the low gas inlet velocity in a typical FCCU primary cyclone, gravitational force plays a key role. As a result, the solids appear to fall rapidly down into the cyclone cone and dipleg, as shown in Fig. 1, taking only one to two full turns before exiting the cyclone. The vortex length in a highly-loaded
Primary Cyclone (High Loading)
Secondary Cyclone (Low Loading)
Inner Vortex
Longer , Energetic, Inner Vortex
Solids Flow Path
Many More Turns on Tighter Radius: Spins Faster – Hence More Potential for Erosion
(Outer Vortex)
Figure 1. Schematic Depiction of First and Second-Stage Cyclone Operation primary cyclone is much shorter, because the high solids loading dampens the formation of a long vortex. Therefore, the vortex does not “whip” the solids at a high velocity around the cone as in a primary cyclone. In a typical second stage cyclone, the solids loading is approximately 1/1000 to 1/10,000 of the loading in the first stage cyclone. Due to the light solids loading and high gas velocity, the inner vortex is relatively long and energetic. As the swirling solids in the outer vortex approach the cone in a second stage cyclone, the long, rapidly-rotating vortex accelerates the solids stream and causes it to intensify its rotation because of the conservation of angular momentum. The solids in a secondstage cyclone typically take four to seven turns before exiting the bottom cone, and the spinning continues into the top portion of the dipleg below the cone. Most of those spins are located in the lower part of the cone and in the upper dipleg where the small diameters result in high angular velocities. The rapidly-rotating solids stream coupled with the unstable, continuous movement of the vortex causes significant erosion in the cone and at the top of the dipleg of second-stage cyclones. EXPERIMENTAL The testing was structured to benchmark three possible solutions to mitigate the erosion occurring in second-stage cyclones: 1) increasing cyclone length-to-diameter ratio (L/D), 2) increasing the angle of the cone, and 3) adding a vortex stabilizer. The cyclone test facility used in the study is shown in Fig. 2. It consisted of a 0.91m-diameter fluidized bed, a 0.2-m-diameter standpipe approximately 17 m in length; a slide valve to control the solids flow rate around the unit; a 0.2-m-diameter riser approximately 21 m tall; a 0.48-m-diameter first stage cyclone; and the 0.43-mdiameter second stage cyclone.
2
17 16
15
Air was used as the conveying gas in the test unit. The solids used were equilibrium FCC catalyst with a median (dp,50) particle size of 75 m. The fines (material < 44 microns) concentration in the catalyst was approximately 8 wt.%. The particle density of the catalyst was 1490 kg/m3. Loadings to the second stage cyclone were varied between 0.001 to 0.21 kg/m3. The secondary cyclone was constructed modularly for easy change of dimensions. A schematic drawing of the second stage cyclone is shown in Fig. 3 for several different barrel lengths. Multiple coatings of drywall joint compound were added to the inside of the cyclone before each test. The amount of erosion occurring in the cyclone was measured by the weight loss of the drywall compound occurring over a certain period of time.
18
2nd Stage Cyclone 14R
1st Stage Cyclone
20-cm Riser
Diverter Valve 13R
12R
Automatic L-Valve 0.91-m -Bed 11R
Receiving Tank on Load Cells
20-cm Standpipe 10R
Figure 2. Schematic Drawing of Cyclone Erosion Test Unit 1" 44 [10.80]
7°
7° 1'-5" [43.18]
9" [22.86]
9" [22.86] 6" [15.41]
7° 1'-5" [43.18]
1'-9" [53.34]
1'-5" [43.18]
1'-9" [53.34]
1'-9" [53.34]
1'-5" [43.18]
1" 2'-84 [81.92]
1'-5" [43.18] 1'-5" [43.13]
1" 2'-84 [81.92]
4" [10.16] 4" [10.16]
1" 2'-84 [81.92] 79° 4"
L/Db = 3.1
L/Db = 4.1
L/Db = 5.1
Figure 3. Schematic Drawing of Different Cyclone L/D's [cm] RESULTS AND DISCUSSION Effect of Increased Cyclone L/D
3
The study found that the erosion took place primarily in the bottom 1/3 of the cone of the secondary cyclone”. A photograph illustrating this effect is shown in Fig. 4. This figure shows that the drywall coating was completely eroded from the bottom 1/3 of the cone, whereas the remaining drywall was mostly intact.
Figure 4. Photograph of Erosion of Drywall Joint Compound in the Cone of a Second-Stage Cyclone Barrel erosion rates were also measured in the tests and were found to be much lower than the erosion rates in the cone (Fig. 5). Measured barrel erosion rates ranged between 85 to 105 g/h, which was about 15% of the cone erosion rate for the cyclone with an L/D of 3.1, and about 20% for a cyclone with an L/D of 5.1.
Cyclone lengths were increased by increasing the length of the cyclone barrel to give length-to-diameter (L/D) ratios of 3.1, 4.1 and 5.1. In these tests, the inlet gas velocity to the cyclone was 19.8 m/s and the outlet gas velocity was 27 m/s. The results of the testing to determine the effect of cyclone L/D is shown in Fig. 5. As can be seen, the erosion rate decreased with increasing cyclone L/D. The measured erosion rate at an L/D of 5.1 was about 70% of the erosion rate of the cyclone with an L/D of 3.1.
800
Material: FCC Eq. Catalyst Ugi: 19.8 m/s Ugo: 27 m/s Cyclone Size: 43-cm Inlet Type: Tangential Li : 0.011 kg/m3 L/Db:
700 h / g , s e t a R n o i s ro E
600 500 400
3.1
300
4.1 5.1
200 100 0 Cone
Barrel
Figure 5. The Effect of Second-Stage Cyclone L/D on Cone Erosion and Barrel Erosion
Effect of Cone Length The effect of cone length on cyclone cone erosion was tested by adding a longer cone so that the cone angle from the horizontal increased from 79 to 84º. This increased the cone length from 0.82 to 1.68 m. When comparing the two cone configurations, the overall length of the cyclone was held constant.
4
Cone Erosion Rate, g/h
As shown in Fig. 6, the cyclone with the longer cone had a higher erosion rate at low outlet gas velocities than the cyclone with the shorter cone but longer barrel. However, the erosion rate became approximately equal to the erosion rate of the shorter cone at the highest outlet gas velocity. The trend of the two curves was exactly opposite. For the cyclone with the shorter cone, the erosion rate increased with gas velocity, whereas for the longer cone the erosion rate decreased with gas velocity. For an outlet gas velocity of approximately 27 m/s, the erosion rate for the short cone cyclone Ugo, m/s was approximately 0 10 20 30 40 50 60 2500 800 g/h, while the erosion rate for the 2000 long cone cyclone was approximately 1500 Longer Cone 1800 g/h, a factor of shorter cone 2.25. However, 1000 Material: FCC Eq. Catalyst even at the highest Ugi: 19.8 m/s 500 Cyclone Size: 43-cm outlet gas velocities, Inlet Type: Tangential which were outside Li : 0.032 kg/m3; L/Db: 5.1 0 typical operating 0 50 100 150 200 outlet velocities of Ugo, ft/s secondary cyclones, Figure 6. The Effect of Cone Length on Second-Stage the longer cone did Cyclone Cone Erosion not have a significant advantage over the shorter cone in regard to cone erosion Vortex Stabilizers To determine the effect of adding a vortex stabilizer on cone erosion, a flat-disk vortex stabilizer was added in the cyclone cone, approximately 1/3 1" Stabilizer 74 of the cone length from its Disk [18.42] bottom, at the top of the region in which the cone erosion was most 1" 102 79° significant. A photograph of the [26.78] disk is shown in Fig. 7. The location of the vortex stabilizer was selected to be at the top of the high-erosion section of the cone. It was thought that adding the vortex stabilizer 1/3 of the cone height from the bottom of the cone would prevent highvelocity spinning solids in that region and reduce erosion. The purpose of the flat plate (or the vortex stabilizer) was to stabilize the central vortex. It was expected that the influence of the vortex would end at the plate, and
4" [10.16]
No Erosion Three Supports
Figure 7. Photographs and Schematic Drawing of the Vortex Stabilizer and Supports
5
the number and intensity of the solids spirals below the plate would be reduced.
Cone Erosion Rate, g/h
The effect of adding the vortex stabilizer disk on cyclone cone erosion is shown in Figs. 8 and 9 for cyclones with L/Ds of 3.1 and 5.1, respectively. It was found that cone erosion for a cyclone with a vortex stabilizer was significantly lower than that for a cyclone without a vortex stabilizer. Cone erosion was found to increase linearly with increasing gas velocity for a cyclone without a vortex stabilizer. However, cone erosion in a cyclone with a vortex stabilizer decreased slightly with increasing gas outlet velocity. The decrease in erosion is counter-intuitive. However, this can be explained by the fact that the vortex diameter is smaller when the diameter of the outlet tube is decreased. This Ugo, m/s increases the distance between 0 20 40 60 the vortex and the cone wall, 3500 w/o disk which then reduces the 3000 w/ disk centrifugal force (and, therefore, 2500 Material: FCC Eq. Catalyst the solids velocity) on the solids Ugi: 19.8 m/s 2000 rotating in the cone. The Cyclone Size: 43-cm reduction in force on the solids 1500 Inlet Type: Tangential Li : 0.032 kg/m3; L/Db: 3.1 appears to explain the decrease 1000 of cone erosion vs. gas outlet 500 velocity for a cyclone with a 0 vortex stabilizer (Figs. 8 and 9). 0
50
100 Ugo, ft/s
150
200
C o n e E ro si o n R a te , g / h
For the shorter cyclone, the cone erosion rate was Figure 8. The Effect of Gas Outlet Velocity on approximately 2100 g/h for the Second-Stage Cyclone Cone Erosion for cyclone without a vortex Cyclones With and Without a Flat-Plate Vortex stabilizer at an outlet gas Ugo, m/s Stabilizer velocity of 15.2 m/s. The 0 10 20 30 40 50 60 corresponding cone erosion 1600 Material: FCC Eq. Catalyst rate for the cyclone with a 1400 Ugi: 19.8 m/s vortex stabilizer at the same 1200 Cyclone Size: 43-cm Inlet Type: Tangential outlet gas velocity was about 1000 Li : 0.032 kg/m3; L/Db: 5.1 1400 g/h. The cone erosion 800 rate with the vortex stabilizer 600 w/o disk 400 was about 67% of the cone w/ disk 200 erosion rate for the cyclone 0 without the vortex stabilizer. 0 50 100 150 200 However, at an outlet gas Ugo, ft/s velocity (45.7 m/s) closer to actual practice, the cone Figure 9. The Effect of Gas Outlet Velocity on erosion rate for a cyclone with Second-Stage Cyclone Cone Erosion Rate for the vortex stabilizer was only Cyclones With and Without a Flat-Plate Vortex about 600 g/h. The Stabilizer corresponding cone erosion rate for a conventional cyclone without a vortex stabilizer was about 2900 g/h. This was a factor of about 4.8. For the cyclone with an L/D of 5.1, the overall cone erosion rates were lower. This was expected because the tests with the longer cyclone described above gave lower cone erosion rates than shorter cyclones. As with the shorter cyclone, the trendlines
6
of cone erosion rate vs. outlet gas velocity were linear. Similarly, the curve for the conventional cyclone erosion rate without a vortex stabilizer increased with increasing gas velocity, and the curve for the cyclone erosion rate with the vortex stabilizer decreased slightly with increasing gas velocity. However, as with the shorter cyclone, the cyclone with the vortex stabilizer was found to have much lower erosion rates than the conventional cyclone without the vortex stabilizer. Comparing the cone erosion rates at an outlet gas velocity of 45.7 m/s, the conventional cyclone without a vortex stabilizer had a cone erosion rate of approximately 1200 g/h, while the cyclone with the vortex stabilizer had a cone erosion rate of about 240 g/h. This is a factor of approximately 5 - similar to what was found for the shorter cyclone. Why does the vortex stabilizer decrease cone erosion? It appears that the stabilizer prevents the vortex from "whipping" the solids around at high velocities below the stabilizer in the region where high cone erosion rates are experienced for a cyclone without a vortex stabilizer. Below the stabilizer, the high-velocity central vortex does not really exist. Therefore, this reduction in the spinning solids velocity at the wall leads to a significant reduction in erosion. A comparison of the cone erosion rates for various second-stage cyclone configurations is given in Table 2. Drywall joint compound was also added to the disk to see if the upper surface of the vortex stabilizer would erode. However, essentially no erosion was measured on the upper surface of the disk, and no erosion was found on the supporting rods. Shell Experience with Vortex Stabilizers In the 1980’s, Shell had over 30 FCC units, mostly with cyclones without vortex stabilizers, which were found to be the number one cause of all FCC unscheduled shutdowns. Shell started using the vortex stabilizer (5) in the early 90’s. Fig. 11 shows the result of how the vortex stabilizer reduced overall FCC unscheduled down time. Table 2. Comparison of cone erosion rates for different cyclone configurations L/D (-)
Velocity Inlet, m/s
Velocity Outlet, m/s
Erosion Reduction Factor
Cone Erosion Rate, g/h
Short Cyclone
3.1
19.8
45.7
Base
2850
Long Cyclone
5.1
19.8
45.7
>2
1200
Long Cone
5.1
19.8
45.7
>2
1200
Vortex Stabilizer
3.1
19.8
45.7
>4
650
Vortex Stabilizer
5.1
19.8
45.7
>11
240
Using 1992 data as the base line, Fig. 11 shows that cyclones with the vortex stabilizer reduced the total unit down time of all FCC units in the Shell system by a factor of 10 over a period of 8 years.
7
Severity (Incl. near misses) [Number of Events * Duration] 1992 = 100%
Total Severity of Cyclone Problems 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1992
1993 1994 1995 1996 1997 Figure 10. Cyclone Severity vs. Time
1998
1999
CONCLUSIONS Second stage cyclone cone erosion is a pervasive problem in FCCU operation, which can be significantly improved by incorporating the use of a vortex stabilizer. Vortex stabilizers were more effective in reducing second stage cyclone cone erosion than increasing cyclone barrel or cone length. REFERENCES 1. Solomon Associates LLC , 2006 Solomon FCC Events Determining TAR Timing Survey, 2006 2. Grace Davison Survey, FCC Conference in Dublin, California, USA, 2002 3. Zenz, F.A., Cyclone Separators, in Manual on Disposal of Refinery Wastes Volume on Atmospheric Emissions, Chapter 11, American Petroleum Institute, Pub. No. 931, Washington, D.C. (1975). 4. Tenney, Ed., FCC Cyclone Problems And How They Can Be Overcome With Current Designs, Presented at the Grace-Davison FCC Technology Conference, Toledo, Spain (1992). 5. Chen, Y., Karri, S.B.R., Knowlton, T.M., Winning in the Downturn: How to Improve FCC Unit Reliability and Reduce Costs via Improved Cyclone Technology, NPRA Meeting, Phoenix, AZ (2010). 6. Sexton, J., Karri, S.B.R., Knowlton, T.M., “What is Happening Above Your Fluidized Bed?” Tools to Maximize FCC Unit Reliability and Turnaround Cycles, NPRA Meeting, Phoenix, AZ (2010).
8
STUDY OF CALCINATION-CARBONATION OF CALCIUM CARBONATE IN DIFFERENT FLUIDIZING MEDIUMS FOR CHEMICAL LOOPING GASIFICATION IN CIRCULATING FLUIDIZED BEDS Bishnu Acharya*, Animesh Dutta** and Prabir Basu
**Department of Mechanical Engineering, Dalhousie University, Halifax, Nova Scotia, B3J 1Z1 Canada**School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada ABSTRACT Chemical looping gasification (CLG) of biomass in a circulating fluidized bed is an excellent option for production of separate streams of hydrogen rich and carbon dioxide rich gases. This process uses H2O (steam) instead of air or oxygen for gasification, and thereby produces high value nitrogen free product gas. An important feature of this process is in-process removal of carbon dioxide from the reaction site by CaO. This allows the reaction to move towards higher yield of hydrogen. Circulating fluidized bed (CFB) provides an ideal reactor configuration for such a looping reaction. A CFB based CLG unit operates somewhat similar to the FCC reactor except that the bubbling bed in the loopseal serves as the gasifier where calcined limestone absorbs carbon dioxide forming calcium carbonate. The riser works as the regenerator of CaCO3, the CO2 sorbent. Thus, the sorbent particles move back and forth between the riser calciner and loopseal carbonizer. To study this process closely and to determine how well the sorbent retains its reactivity through such a cyclical process, an experiment was carried out in a Quartz wool matrix reactor (QWM), which closely simulates the highly expanded ambience of a fast-fluidized bed. An empirical relation was developed for the conversion of calcium carbonate (CaCO3) as a function of temperature and residence time. A simple reaction kinetic model for calcination in presence of N2, CO2 and H2O has been developed and compared. Loss in effectiveness of the sorbent has been studied and another empirical relation was developed for the estimation of extent of carbonation with the number of cycle. INTRODUCTION Chemical looping gasification (CLG) is an innovative approach for production of hydrogen-rich gas with in-process capture and regeneration of carbon dioxide. It consists of two fluidized bed reactors, one operating as a gasifier in bubbling bed mode, and another as a regenerator in fast fluidized bed regime, together forming a circulating fluidized bed reactor. Calcium oxide (CaO) is used as the sorbent in the gasifier. It absorbs the carbon dioxide produced during gasification and is subsequently regenerated in the regenerator. Thus, from the gasifier a CO 2 free hydrogen-rich gas is obtained while from the regenerator, a relatively pure stream of carbon dioxide is produced that can be taken directly for sequestration or other value added use thus avoiding the cost and efficiency penalty for post capture. The main focus of this paper will be on the study of regeneration behaviour of calcium carbonate. The calcination reaction (CaCO3 = CaO + CO2 +183 kJ/mol) is highly endothermic in nature. Also its capacity for regeneration of CaO depends on the fluidizing medium in the fast fluidized bed. The fluidizing medium in the riser for the study was chosen to
1
be carbon dioxide because the CFB riser is to use a part of the CO 2 produced (Acharya et al, 2009). The partial pressure of carbon dioxide in this case will naturally be very high, and it might be above the equilibrium pressure of CO2 for calcination. Therefore, one would expect a very low regeneration of the sorbent, CaO. This necessitates an alternative medium that could not be air due to potential nitrogendilution of the product gas from the nitrogen in air. So, steam is identified as the next best and yet inexpensive fluidizing medium. The main objective of this research is to determine the level of regeneration with nitrogen (N2), carbon dioxide (CO2) and steam (H2O) as the fluidizing medium and compare them. Ideally the work should be done in a fast-fluidized bed, but it is extremely difficult to follow the calcination process while the particles are constantly moving around the CFB loop. Standard Thermo Gravimetric (TGA) test is more representative of a fixed bed than of a fast bed reactor. Fast bed reactor is characterized by it is highly expanded bed, where reacting particles are widely apart. For this reason experiments were done in the quartz wool matrix reactor developed by Wu and Basu (1993), where particles were dispersed on a quartz wool matrix with a voidage similar to that of a fast bed. The gas velocity was adjusted to represent the gas-solid slip velocity in a fast bed. Thus, this closely simulates the reaction in the riser of a CFB. A reaction kinetic model was developed for calcination in three different mediums. The comparisons of different medium were made on the basis of activation energy and the reaction rate constant. The results obtained could provide a basis for the choice of fluidizing medium for the regenerator in a chemical looping gasification system. In a CFB-CLG reactor the sorbent will continuously move back and forth between the gasifier and the regenerator undergoing calcination and carbonation. So, the calcination-carbonation cycle was studied to identify the loss in activity of sorbent over different cycle. This will help to identify the requirement of fresh sorbent into the system. EXPERIMENTAL SETUP The quartz wool matrix reactor comprises a stainless steel reactor of 50 mm internal diameter heated externally by electric heaters (Fig 1). A precision electronic balance sits at the top of the reactor. Temperature was measured at three different locations of the reactor with K type thermocouple. Data from the balance, thermocouple and all flow meters were recorded by a data acquisition system. Reactor was heated to the desired temperature and then the gas was supplied from the bottom of the reactor. Flow of gas, (or gas mixtures) going into the reactor was measured by electronic regulating type flow meters. A measured weight of calcium carbonate of size 45 micron was sprinkled over and inside the quartz wool that was placed inside a wire basket suspended on the balance. The particle size was deliberately chosen small to reduce the mass transfer effect as well as to make sure that the particle size was significantly smaller than the reactor size. Once the system was stabilized and a constant temperature in the reactor was noted, the basket with quartz wool and calcium carbonate was lowered into the reactor. The change in mass of the sample was measured continuously. After allowing the reaction (calcination and carbonation) for 30 minutes it was taken out, cooled and was weighted.
2
After calcination for 30 min, the sample was moved out into a cooler ambient to quench the calcination reaction. Thereafter, the medium in the QWM was changed to carbon dioxide and the temperature of reactor was reduced to 650oC. Once the temperature drops to 650oC, the previously calcined sample was lowered into the reactor, and was left there for another 30 min for carbonation. Thus the sample undergoes alternative calcination and carbonation. It is repeated for 5 cycles of calcination and carbonation. The calcination reaction (CaCO3 = CaO + CO2) was studied at four temperatures: 800oC, 900oC, 950oC and 1000oC for each of the three media, CO2, H2O and N2. The carbonation reaction (CaO + CO2 = CaCO3) in a biomass gasifier generally takes place at a relatively lower temperature in a CLG. So, in QWM reactor, the carbonation was studied at 650oC with CO2 alone. Experiments in CO2 alone ensure that complete conversion of calcium oxide. Any loss in conversion over consecutive cycle could occur due to sintering of the particle.
Figure 1: Schematic of QWM experimental setup To examine the kinetics of calcination as well as to compare the rate for three media a first order reaction kinetic model was used.
(1- X)(Peq - PCO2 ) dX =K dt Peq
(1)
(Stanmore and Gilot, 2005) Where, K = intrinsic rate constant (s-1), X = conversion (-), Peq = equilibrium decomposition pressure (atm), PCO2 = partial pressure of CO2 (atm), ko = reaction rate constant (s-1), Ea = activation energy (kJ/mol), R = universal gas constant
3
(kJ/mol K), T = temperature (K), Wo = initial weight of calcium carbonate (gm), Wt = weight of calcium carbonate after time t (gm). RESULTS AND DISCUSSION Effect of Temperature and Residence Time on Calcination Table 1 shows the results of calcination at different temperatures in presence of three different media. A conversion greater than 99% was obtained at 900oC in a N2 environment in 12.5 min. The same level of conversion was obtained in a shorter time if the temperature increased to 950oC. In CO2 environment this conversion at 900oC was only 72.89%. Even at 1000oC the maximum conversion was 92.95% in 30 min. A higher partial pressure of CO2, close to the equilibrium decomposition pressure would inhibit the calcination reaction. Therefore at such high CO 2 partial pressure the conversion is much lower even at very high temperatures. With steam, complete conversion was obtained in shorter residence time of 10 min at a temperature of 1000oC. Table 1: Calcination obtained during calcination under three different mediums
Temperature (oC) 600 700 800 900 950 1000
N2 Conversion (%)
Time (mins)
52.29 96.32 99.39 99.31 100
30 25.50 12.5 10 10
CO2 Conversion Time (%) (mins)
7.58 20 72.89 92.95
30 30 30 30
H2O Conversion Time (%) (mins) 8.78 30 73.22 30 96.94 30 100 25 100 19.16 100 10
Supply of N2 and steam quickly removes carbon dioxide from the system, thus lowering its partial pressure resulting in higher conversion. However, steam offers higher conversion than N2. At 700oC, 73.22% conversion was obtained which was 28% more than that obtained with N2 at the same temperature. Steam seems to have a catalytic effect that lowers the equilibrium decomposition temperature for calcination reaction to occur, thus conversion has been complete even at very low temperature. Wang and Thompson (1995) found that the surface adsorption of H2O molecules weakens the CaO-CO2 bond resulting in enhanced calcination rates. Iyer et al (2005) attributed this phenomenon to higher gas-solid heat transfer properties of steam. Therefore the calcination obtained was much higher with steam. The calcination with steam can occur at a lower temperature, but it needs longer residence time. As shown in Fig 6, it will take a long time to achieve full conversion with CO2 as medium, while it takes 10 min with N2 and 19 min with steam at 950oC. Therefore, to study the combined effect of temperature and residence time, a linear regression model was developed. The fractional conversion of CaCO3, X is expressed empirically as a function of temperature θ and time τ.
4
For H2O: [R2 = 88.8%]
X = -1627 + 1.68 θ +46.1 τ – 0.0416 θ τ
(2)
For CO2: X = -359 + 0.446 θ
[R2 = 79.3%]
(3)
For N2: X = 186 – 0.126 θ – 14.6 τ + 0.0188 θ τ
[R2 = 99.1%]
(4)
Here, θ = Temperature (oC), τ = time (min) and X = conversion of CaCO3 (%). From Table 1 it is apparent that the conversion of CaCO3 in presence of H2O reduces with reduction in temperature, but there was a sharp order of magnitude drop in conversion when the temperature drops from 700oC to 600oC. Reasons for this large reduction below 700oC could not be explained at this time.
Figure 2: Calcination obtained with time at T = 950oC for three different media Kinetics of Calcination The intrinsic rate constant (Eq. 1) for calcination was calculated at each temperature and by the use of the Arrhenius plot; the activation energy was determined. The activation energy and the reaction rate constant for the calcination reaction occurring in presence of three media is shown in Table 2. Table 2: Kinetics of calcination under three different media
Ea (kJ/mol) ko (1/s)
N2
CO2
H2O
257.78
180.56
248.62
4.82x1010
2.12 x106
3.63 x1010
5
Figure 3: Arrehenius plot for calcination done in presence of three medium Fig 3 shows the comparison of reaction kinetics in presence of different media. it suggests that the kinetic rate in presence of steam is closer to that in presence of nitrogen. On the other hand for CO2 the kinetics rates are significantly lower. Calcination and Carbonation Reaction Cycle In a chemical looping system, the sorbent (CaO) undergoes through number of calcination-carbonation cycle and with each cycle its performance decrease. This could occur due to sintering and deposition of tar/char on sorbent particles. The change in percentage of CaO in the sorbent particle during calcination-carbonation cycle is plotted against time in Fig 4. Calcination occured in presence of N2, CO2 or H2O while carbonation in CO2.
Figure 4: Calcination-carbonation cycle
6
Figure 5: Calcination and carbonation conversion obtained at the end of each cycle Fig 5 shows the total conversion obtained at end of each calcination and carbonation cycle studied. Calcination in presence of steam and N2 shows nearly complete conversion at the first cycle studied and then drops slightly for latter cycles. In presence of CO2, the conversion was only partial even for the first cycle and drops quickly in the subsequent cycle. The carbonation study shows that irrespective of the medium used for calcination, the ability of the sorbent CaO to capture carbon dioixde decreases with number of cycle. Stanmore and Gilot (2005) inferred that in high temperature calcination, the calcium particles sinters leading to decrease in porosity and surface area compounded by the pore closure due to carbonation (due to difference in molar volume of CaO and CaCO3) resulting in loss of conversion. Thus to quantify this loss in ability of the sorbent to capture CO2, an emperical relation was developed between the conversion, XCaO obtained and the number of cycle, N. XCaO = - 18.63 ln (N) + 62.598
(5)
Using this relation one can estimate the frequency at which the fresh sorbent needs to be charged into a CFB based chemical looping gasifer. Limitation of this emperical relation is that it only includes the effect of high temperature sintering phenomenon but does not include other effects that may influence in a commercial chemical looping gasification system. The deposition of tar and carbon on the sorbent are examples of such inhibiting factors. These can only be studied in the real chemical looping gasification system. CONCLUSION The study on calcination of CaCO3 in presence of different media shows that the calcination shown in presence of H2O is similar to that in N2. The kinetic of calcination in N2 and H2O are much higher than that for CO2. Compared to other media steam offer higher conversion at a relatively low temperature. Thus steam reduces the energy requirement of calcination, which may partially offset the energy required to produce the additional steam.
7
Figure 6: Conversion obtained during carbonation with number of cycles Considering this and the potential dilution of the product gas with N2, steam could be the best medium in the riser in a chemical looping gasification system. However one can expect a lower conversion in a large chemical looping CFB system than that obtained in the present bench scale study. Therefore, a study on calcinationcarbonation looping cycle in a pilot lab scale chemical looping gasification plant is underway. ACKNOWLEDGEMENT The authors thank Natural Science Engineering Council financial support to the first author. It also thanks Greenfield Research Incorporated for financial support and use of the QWM reactor for this study. REFERENCES 1. Wu, S. and Basu, P, 1993. Surface reaction rates of coarse bituminous char particles in the temperature range 600 to 1340K. Fuel 72, 1429-1433 2. Acharya B, Dutta A, Basu P, 2009. “Chemical-Looping Gasification of Biomass for Hydrogen-Enriched Gas Production with In-Process Carbon Dioxide Capture”, Energy Fuels, 23, 5077–5083. 3. Y. Wang, W.J. Thomson, 1995. “The effects of steam and carbon dioxide on calcite decomposition using dynamic X-ray diffraction”, Chem. Eng. Sci. 50, 1373– 1382. 4. M, Gupta H, Wong D, and Fan L.S., 2005. Enhanced Hydrogen Production Integrated with CO2 Separation in a Single-Stage Reactor”, DOE annual progress report DE-FC26-03NT41853 5. Stanmore.B.R and Gilot P, 2005. “Review-calcination and carbonation of limestone during thermal cycling for CO2 sequestration”, Fuel Processing Technology 86 (2005) 1707– 1743
8
BENCH-SCALE INVESTIGATION OF LIMESTONE SIZE EVOLUTION IN A FLUIDIZED BED COMBUSTOR Xuan Yao 1, Nan Hu 1, Hairui Yang 1*, John H. Chiu2, Pierre Gauville2, and Shin G. Kang2 1 Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Thermal Engineering, Tsinghua University, Beijing, 100084, China 2 Boiler Combustion Systems, Alstom Power Inc., Windsor, CT, 06095 U.S.A. ABSTRACT The influence of temperature, heating rate and chemical reaction on fragmentation and attrition of limestone in a fluidized bed (FB) was investigated. The intensity of fragmentation and attrition was measured in the same apparatus but at different fluidizing velocities and fluidizing media. It was found that the heating rate has a positive effect on fragmentation for the tested limestones. The effect of bed temperature on limestone fragmentation was inconclusive. The influence of chemical reaction on the fragmentation seems to be complicated; CO2 release due to calcination would prompt fragmentation while the sulfation would increase the gas diffusion resistance and depresses the fragmentation intensity. On the other hand, the CaSO3/CaSO4 layer was found to be attrition-resistant leading to small attrition rates. Attrition rate constant showed to decay exponentially with time and approaching a constant for all limestone particles. Particle sizes between 200-400 m have larger attrition rate constant than coarse ones perhaps due to their large specific surface area. INTRODUCTION Limestone is commonly used as a desulfurization sorbent in fluidized bed and circulating fluidized bed (CFB) boilers to reduce SO2 emissions. After limestone is added to the furnace, substantial changes in the sorbent particle size distribution, namely comminution, can be caused by limestone fragmentation and attrition [1-7]. Based on previous studies, fragmentation is often classified into primary and secondary steps [6]. The primary fragmentation refers to the generation of fragments, either coarse or fine, immediately after the injection of limestone particles into the hot furnace [4, 6, 8, 9]. This process often occurs in the dense bed or in the splashing zone of either FB or CFB combustors. The secondary fragmentation refers to the generation of fragments mostly from high-velocity collisions against bed material or reactor walls and internals. Attrition refers to the generation of fine particles by abrasion and depends upon the resistance of the bed particles to surface wear. Size evolution caused by fragmentation and attrition is strongly coupled with the calcinations and sulfation processes as well as the overall mass balance in the CFB system. Thus, factors impacting fragmentation and attrition play important roles in the boiler performance. Previous studies have investigated the impact of limestone type and size [2,4,7,10-13], fluidizing gas velocity [6,8,9] and temperature [7,14] on fragmentation. For a CFB boiler, scholars also found that fragmentation is influenced by the solids circulation rate [2,3,8] and by the inventory of inert bed material [13-14], etc. However, an investigation on the effect of chemical reaction, especially sulfation, on
1
limestone fragmentation is an issue of practical significance. In addition, the impact of particle size, temperature and heating rate on fragmentation remains a controversial topic in the literature [7,14]. Therefore, the aim of this work was to conduct a systematic study of limestone fragmentation and attrition in the FB reactor in order to further explore some of these factors. A set of experiments on the primary fragmentation and attrition of two different limestones was conducted in this study. The effects of heating rate, temperature and initial particle size of the limestone were studied. Different fluidizing media (air, SO2, CO2) were used in the experiment to study the influence of calcination and sulfation on fragmentation and attrition behavior. The jet effect near the distributor was not studied. EXPERIMENTAL The bench-scaled fluidized bed reactor used in this study was described in detail previously [15]. The reactor was a round tube made of silica glass, with an inner diameter of 54 mm and height of 800 mm. The reactor was electrically heated and the main section could be maintained at a desired temperature with a deviation of ±5oC. An air distributor (a porous plate type) was placed near the middle of the reactor. The section below the distributor was used to preheat the fluidizing gases. During the fragmentation experiments, inert bed material, i.e., quartz sand (90-125 m) was pre-laid on the air distributor. The limestone was then added in batch mode (20 g), with a particular initial size cut, and mixed with the quartz sand. The initial static height of the bed material was kept at about 40 mm for all tests. A rather low, superficial gas velocity, Ug, was set for the tests at about 0.1-0.2 m/s, at a reactor temperature of 850oC, to minimize the attrition and secondary fragmentation. Thus, all of the fragments formed could be attributed to the primary fragmentation. After the limestone was added into the reactor for a prescribed time interval, all of the materials, including limestone fragments and bed material, were aspirated out and collected by a solids collection system. The bed material was then separated from the collected mixture by a sieve shaker, and the particle size distribution (PSD) of the residual fragments was obtained by sieving and weighing. The mass of the fragments in the size range of the inert bed material (90-125 m) was estimated by measuring the weight loss during calcination in a muffle furnace. As in a previous study [16], the coefficient of average particle size variation, Fd, was used to characterize the degree of particle size change, which can be expressed as Fd = df/do, where df is the average Sauter diameter of new PSD and do is the average Sauter diameter of the particles in the original sample. For purposes of discussion, another parameter called the fragmentation intensity coefficient (FIC), defined as FIC = 1 - Fd, was introduced to describe the intensity of fragmentation. The FIC can vary between zero (no change in particle size) and near unity (denoting a substantial decrease in particle size). To measure the attrition rate by abrasion, a relatively high Ug (e.g., Ug 0.5 m/s) was used such that fines with a diameter less than 80 m, generated by attrition, could be elutriated and then collected by the solids collection system. Before the adding of the testing sample, a certain amount of bed material of quartz sand (~160 g, 250-300 m) was pre-loaded in the reactor. The quartz sand was abrasion resistant and
2
remained in the reactor under the selected Ug. The bed was put into operation with a preset Ug and bed temperature for a prescribed period until it was steady. The testing sample, with a mass of 50 g, was then added to the bed. At 10 minute intervals, the attrition rate, Rs, was obtained [1] by weighing the fine particles collected in the cyclone with a total sampling time of about one minute duration. From these data, the attrition rate constant, Ka, was calculated. The experiment was continued until the attrition rate became steady. In this study, the influence of chemical reaction was also evaluated by changing the fluidizing media. Limestone was first calcined in the FB for 10 minutes under an air atmosphere; and then different mixtures of CO2 and SO2 were subsequently used to study the influence of sulfation. Two kinds of limestone (types A and B) were studied. The CaCO3 and MgCO3 compositions measured by x-ray diffraction are listed in Table 1. Each limestone was classified into 3 size groups of 200-400 m, 400-600 m, and 600-800 m in order to study the effects of the initial particle size. Table 1. The composition of the limestone samples (% in mass) Sample Ca Mg CaCO3 MgCO3 Other A 39.4048 98.51 1.49 B 38.5512 0.7811 96.38 2.73 0.89 RESULTS AND DISCUSSION 0.5
Effect of Limestone Type and Original Size
0.45 0.4
Limesteone A Limesteone B
FIC
Figure 1 shows the FIC of both limestone types 0.35 at a temperature of 850ºC under an air 0.3 0.25 atmosphere in the FB furnace. A higher FIC 0.2 value represents a higher fragmentation intensity 0.15 and a smaller average particle size produced. 0.1 For both limestone types, the enhanced 0.05 fragmentation of the tests with a larger initial 0 particle size classification is apparent. 200-400 400-600 600-800 origin particle size, m However, a previous experimental study [15] indicates that the effect of particle size on Figure 1. Fragmentation intensity coefficient fragmentation is not always the same, and depends (FIC) of limestones A and B under air atmosphere in FB (850°C) on the limestone type. The micro-structure of the particles could be very different for different limestone samples and could cause significant differences in fragmentation and attrition [8]. In this study, it is believed that the impact of the particle micro-structure, combined with other effects such as heating rate, could be the reason for the ambiguous effect of initial particle size. This phenomenon should be studied in more detail in future research. Effect of Temperature on Fragmentation In order to study the influence of temperature on the fragmentation of limestone, a test was conducted under different temperatures (850 and 900ºC) in the FB reactor. Figure 2 and Figure 3 show the changes in the cumulative particle size distributions of limestone A with different initial sizes. The data appears to indicate, albeit not conclusively, that the average particle size dS increases with temperature except
3
sizes between 400-500 m. Other study [14] showed that high temperature enhances the attrition of particles but lessens the primary fragmentation. The author explained that enhancement of CO2 release at high temperature is not strong enough to prompt increased fragmentation in the bed. More studies are necessary in the future. 1
1
limestone
0.9
limestone
0.9
FB-850 (ds=336 m) FB-900 (ds=356 m)
0.8
FB-850 (ds=418 m) FB-900 (ds=474 m)
0.8
0.7
Cumulative mass,%
Cumulative mass (%)
0.6 0.5 0.4 0.3 0.2 0.1
0.7
0.6 0.5 0.4 0.3 0.2 0.1
0
0 0
100
200 300 400 particle size( m)
500
600
Figure 2. Effect of temperature on the PSD of fragmentation product (A, 400-600 m)
0
100 200 300 400 500 600 700 800 particle size( m)
Figure 3. Effect of temperature on the PSD of fragmentation product (A, 600-800 m)
Effect of Heating Rate on Primary Fragmentation In order to simulate the effect of heating rate on the primary fragmentation of limestone, various experimental conditions were used. In Figures 4 and 5, FB refers to the baseline operational condition, C refers to the calcination of limestone in the reactor without inert bed material (silica sand), and FB-H refers to the introduction of a higher limestone weight (50g) to decrease the heating rate in the FB. 1
0.7
limestone C FB FB-H
0.9 0.8 Cumulative mass (%)
0.8 Cumulative mass (%)
1
limestone C FB FB-H
0.9
0.6 0.5 0.4 0.3 0.2 0.1
0.7 0.6 0.5 0.4 0.3 0.2 0.1
0
0 0
100
200 300 400 particle size( m)
500
600
Figure 4 Effect of heating rate on the PSD of fragmentation product (A. 400-600 µm)
0
100
200 300 400 particle size( m)
500
600
Figure 5 Effect of heating rate on the PSD of fragmentation product (B. 400-600 µm)
As shown in Figures 4 and 5, compared with the initial limestone particle size, lime calcined under FB condition is much finer than that of lime PSD obtained under the ‘C’ experimental condition. Under the ‘C’ experimental condition, where no inert bed material was used, the thermal heating rate was mainly caused by radiation from the wall and convection of high temperature gas, which induced a smaller heating rate than that provided by silica sand bed. Therefore, the heating rate could enhance the fragmentation of the limestone, especially for limestone B. The relative contribution of the higher heating rate on fragmentation, provided by the silica sand particles, is difficult to quantify, because it is coupled with the mixing-induced attrition caused by
4
the purely mechanical stresses of the sand particles on the limestone. The comparison between the PSDs from the FB and FB-H conditions also illustrates the promotion of thermo-mechanical and chemical effects by the heating rate on the primary fragmentation intensity. Limestone is calcined into lime in the furnace (endothermic process) and at the same time experiences thermal stresses as it is heated up. The time required for complete fragmentation is about 4 to 6 minutes longer for the larger sample weight (FB-H). But the effect of such a small difference on attrition and particle size evolution is probably negligible. Because the output of the electrical furnace is constant, less limestone sample means a higher heating rate. In general, an improved heating rate can enhance calcination and prompt primary fragmentation. Therefore, as shown in Figures 4 and 5, the fragmentation product size of FB-H is less than that of FB condition. Effect of Fluidizing Media on Fragmentation The product gas from industrial CFB boilers is composed of CO2, SO2, NOX, etc. These gas species may have a great impact on limestone fragmentation in the boilers. Previous studies have found that the existence of a high CO2 concentration will suppress the calcination and fragmentation of limestone [9,15,16]. SO2, calcined lime absorbs SO2 to form CaSO3/CaSO4, which may also suppress fragmentation. 0.5 0.45
0.45 0.4
A-2000ppmSO2
0.35
FIC
FIC
0.4
0.5
A-air A-800ppmSO2
0.3
B-air B-800ppmSO2 B-2000ppmSO2
0.35 0.3 0.25
0.25
0.2
0.2
0.15
0.15 0.1
0.1
0.05
0.05 0
0 200-400 400-600 limestone particle size(µm)
600-800
Figure 6 Fragmentation intensity of limestone A with SO2 atmosphere
200-400
400-600
600-800
limestone particle size(µm)
Figure 7 Fragmentation intensity of limestone B with SO2 atmosphere
Figures 6 and 7 show FIC values of two kinds of limestone under different atmospheres at a reactor temperature (850°C). It’s obvious that the SO2 suppressed the fragmentation of both kinds of limestone under the conditions used in this study, at least for the larger particle sizes Both limestone types have the largest fragmentation intensity under an air atmosphere. The possible reason may be that fresh lime on the surface of the particle reacted with SO2 and produced a harder, attrition-resistant layer of CaSO3/CaSO4 product over the particle surface and may hinder the CO2 release. Figure 8a shows CO2 concentration variation during the fragmentation process under different fluidizing media. The release of CO2 is faster with air initially as comparing to the SO2 atmosphere. But with the uncertainty of reading in low value the trend seems reversed later. Figure 8b shows the accumulated CO2 released (normalizing to 100%). As shown for the same percent of calcinations completed, fluidizing media with air requires less time than that with SO2 case. This seems to confirm the
5
Accumulative CO2 Concentration (%)
suppressing effect of the SO2 on CO2 release. On the other hand, the variation of SO2 concentration has no obvious impact on the FIC of limestone with short time duration. The sulfation reaction of CaO and SO2, as a relatively long duration process, will take hours to complete. Therefore, during the short time of primary fragmentation, the variation of SO2 concentration from 800 ppm to 2000 ppm may have no appreciable effect on the thickness of the layer and subsequently on the FIC. CO2 concentration (%)
12
100 90 80 70 60 50 40 30 20 10 0
10 8 6
2000ppm SO2
4
800ppm SO2
2
Air
0 0
2
4
6
8 10 Time [min]
12
14
Figure 8a CO2 concentration variation during fragmentation process (A, 400-600mm)
16
2000ppm SO2 800 ppm SO2 air
0
2
4
6 8 10 Time (min)
12
14
16
Figure 8b Normalized CO2 concentration accumulated during fragmentation process (A, 400-600mm)
Cumulative mass
Generally, three mechanisms are attributed to the 1 limestone fragmentation: thermal stress at high limestone 0.9 FB temperature, high internal pressure caused by 2000ppmSO2 0.8 organics or water evaporation and the CO2 release 2000ppmSO2+15%CO2 0.7 during calcination [5]. Calcination is a chemical 0.6 reaction process that will produce CO2 and lime 0.5 (CaO). At a certain temperature, calcination 0.4 reaction occurs only if the partial pressure of CO2 in 0.3 the environment is lower than the CO2 pressure at 0.2 chemical equilibrium [9,18]. The existence of 0.1 high CO2 concentration in the environment will 0 0 100 200 300 400 500 600 suppress the calcination reaction [16]. As shown in particle size( m) Figure 9, fragmentation was suppressed under an Figure 9 PSD of fragmentation atmosphere of 15% CO2+2000 ppm SO2, as evidenced product ( A, 400-600 µm) by the fact that the size change of limestone was insignificant. The results further confirmed the contribution of the 1 calcination reaction to the fragmentation of limestone 0.9 Fragmentation(ds=336 m) limestone. 0.8 Cumulative mass
Effect of the Sulfation on Attrition of Limestone
0.7
Attrition
(ds=313 m)
0.6 0.5
The degree of (secondary) fragmentation that can 0.4 be attributed to sulphation processes other than 0.3 calcinations and rapid gas release is trivial. Thus, 0.2 comminution of lime and its sulphate products is 0.1 0 mainly attributed to attrition by abrasion [6]. 0 100 200 300 400 500 600 Attrition by abrasion generates fine particles that are particle size( m) quickly elutriated by the high fluidizing gas velocity Figure 10. PSD comparisons of attrition and collected by the cyclone at the outlet of furnace. and fragmentation product (A, 400-600µm) The average size of the product remaining in the
6
reactor after the attrition process is finer than with the primary fragmentation product, except for particle 450 m as shown in Figure 10. In this study, the attrition process lasted for about 150 minutes at a temperature of 850°C and a fluidizing velocity of 0.5 m/s in the FB reactor. Figure 11 shows the change of Ka for limestone A with three initial particle sizes. Ka is found to decay exponentially with time, and then approaches a constant (K ). Regardless of the initial value of Ka for limestone, finer limestone samples less than 400 m eventually have a higher K . The attrition rate constant of K for particles larger than 400 m shows small difference. Finer particles have a larger specific surface area for the same inventory and may experience more abrasion. 2.5E-05
1.2E-04
8.0E-05
2.0E-05
800ppmSO2 2000ppmSO2
1.5E-05
K (m-1)
Ka(m-1)
Air
200-400 m 400-600 m 600-800 m
1.0E-04
6.0E-05
1.0E-05
4.0E-05
5.0E-06
2.0E-05 0.0E+00
0.0E+00
0
20 40 60 80 100 time (min) Figure 11. Change of attrition rate constant of limestone A (air atmosphere)
200-400 400-600 600-800 limestone particle size( m)
Figure 12. Attrition rate constant K of limestone A
The influence of sulfation on the attrition of limestone and its products was shown in Figure 12 for limestone sample A. After the limestone was calcined in the FB for ten minutes under an air atmosphere, the fluidizing gas was switched to a mixture of air and SO2. The attrition rate of the sample under an SO2 atmosphere was found to exhibit a trend that was similar to that under an air atmosphere in size effect up to a factor of 2. Similar to other studies [6,8], the higher SO2 concentration produces lower K due to more attrition resistant. CONCLUSION Two limestone samples were selected and tested in a bench-scale bubbling fluidized bed reactor under various conditions to study the limestone fragmentation and attrition. Experimental results confirmed that fragmentation and attrition could be significantly affected by limestone type, initial size, heating rate, fluidizing media, and temperature. The average size of the particles decreased during the fragmentation process. The heating rate and mechanical collision in the FB have been found to enhance the fragmentation, while the temperature seems to have negative effect on fragmentation. Sulfation reactions suppress the fragmentation of limestone due to the gas diffusion resistance of the CaSO3/CaSO4 layer. The layer of CaSO3/CaSO4 surface is attrition resistant. The sulfation reaction leads to a smaller attrition rate of limestone products under an SO2 atmosphere than under an air atmosphere. ACKNOWLEDGEMENT Financial support of this work by Alstom is gratefully acknowledged.
7
NOTATION Ug gas superficial velocity in tube, ms-1 Ra attrition rate, kgs-1 -1 Ka attrition rate constant, m K final attrition rate constant, m-1 do average Sauter mean diameter of the original PSD, µm df average Sauter mean diameter of the new PSD, µm REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
13. 14. 15. 16.
D. Merrick, J. Highley, Particle size reduction and elutriation in a fluidized bed process, AIChE Symposium Series, 137 (1974) 366-378. R. Chandran, J. Duqum, Attrition characteristics relevant for fluidized-bed combustion, in: J. Grace, L. Shemile, M. Bergougnou (Eds.), Fluidization VI, M. Couturier, I. Karidio, F. Steward, Study on the rate of breakage of various Canadian limestones in a circulating transport reactor, in: A. Avidan (Eds.), Circulating Fluidized Bed Technology IV, 1993, pp.672-680. I. Karidio, Sulfation and breakage characterization of various Canadian limestones, University of New Brunswick, Canada, PhD Thesis, 1994 N. Hu, A.W. Scaroni, Fragmentation of Calcium-based Sorbents under High Heating Rate, Short Residence Time Conditions, Fuel, 74 (1995) 374-382. F. Scala, A. Cammarota, R. Chirone, et al., Comminution of limestone during batch fluidized-bed calcination and sulfation, AIChE Journal, 43 (1997) 363–73. A. Benedetto, P. Salatino P. Modelling attrition of limestone during calcination and sulfation in a fluidized bed reactor, Powder Technology, 95 (1998) 119–28. F. Scala, P. Bareschino, R. Boerefijn, et al., Attrition of sorbents during fluidized bed calcination and sulfation, Powder Technology, 107 (2000) 153-167. F. Scala, F. Montagnaro, P. Salatino, Sulfation of Limestones in a Fluidized Bed Combustor: The Relationship between Particle Attrition and Microstructure, Canadian Journal of Chemical Engineering, 86 (2008) 347-355. J .Wang, S. Li and H. Yang, et al., Study of the Explosive Cracking and Wear Characteristics of Limestone, Journal of Engineering for Thermal Energy & Power, 21 (2006) 366-369 (In Chinese). F. Scala, P. Salatino, The Influence of Sorbent Properties and Reaction Conditions on Attrition of Limestone by Impact Load in Fluidized Beds, The Proceedings of the 20th FBC Conference, Xi’an China, 2009, 486-491. F. Montagnaro, P. Salatino, F. Scala, M. Urciuolo, Sorbent Inventory and Particle Size Distribution in Air-blown Circulating Fluidized Bed Combustors: The influence of Particle Attrition and Fragmentation, The Proceedings of the 20th FBC Conference, Xi’an China, 2009, 696-671. F. Scala, F. Montagnaro, P. Salatino, Attrition of Limestone by Impact Loading in Fluidized Beds, Energy and Fuels, 21 (2007) 2566–2572. F. Montagnaro, P. Salatino, F. Scala. The influence of temperature on limestone sulfation and attrition under fluidized bed combustion conditions, Experimental Thermal and Fluid Science, 34 (2010) 352–358. Xuan Y, Hai. Zhang, Hairui Yang, et al. An experimental study on the primary fragmentation and attrition of limestones in a fluidized bed, Fuel Processing Technology, 91 (2010) 1119–1124. G. Hu, Kim Dam-Johansen, Stig Wedel, et al., Review of the direct sulfation reaction of limestone, Progress in Energy and Combustion Science, 32 (2006) 386-407.
8
HYDRODYNAMICS OF A CLUSTER DESCENDING AT THE WALL OF A CFB RISER: NUMERICAL STUDY Subhashini Vashisth and John R. Grace Department of Chemical and Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver, Canada V6T 1Z3
ABSTRACT The incompressible hydrodynamics of a single parabolic cluster descending at the wall of a CFB riser was numerically simulated using a 2-D Eulerian-Granular model and a segregated time-dependent unsteady solver. Numerical predictions of the velocity of descent and the evolution of cluster shape are in reasonable agreement with experimental results available in the literature. INTRODUCTION Clustering of particles is an important feature observed in CFB risers. Grace and Tuot (1) showed that vertical flow of homogeneous particle suspensions is unstable, causing the particles to gather in ‘clusters’, ‘strands’ or ‘packets’, mostly falling down along the riser wall (2,3). The frequent formation, descent and dissolution of clusters causes axial dispersion of particles and gas, thereby having a negative impact on the performance of CFB catalytic and gas-solid reactions. Clustering also strongly influences particle holdup and pressure drop (4). In order to understand cluster dynamics, several researchers (e.g. 5, 6) have measured the velocity of descent of particle clusters near the wall of CFB risers. Except for very large risers, the descent velocities have almost always been found to be between 0.3 to 2.0 m/s, despite wide variations in operating conditions. Experimental investigations have also been reported on flow characterization of clusters (7, 8, 9), cluster porosity, cluster occurrence frequency, as well as cluster residence time and size (10, 11, 12). Despite significant advances in the visualization of flow, there is no clear definition of cluster shape. Yerushalmi (13) adopted the terms “streamers”, “strands” and “ribbons”. Rhodes et al. (14) described clusters as `swarms or `strands, depending on their shape and the operating conditions. Elliptical or ellipsoidal frontal shapes were observed by Lim et al. (6). Similarly, rounded-bottom assemblies descending near the riser wall were captured by the infrared images of Noymer and Glicksman (11). Even though the clusters are far from spherical in shape, some researchers (e.g. 15) have approximated clusters as spheres in order to simplify the analysis of gas flow around clusters. Considering possible shapes of clusters, it seems reasonable from the existing information to assume aerodynamic bluff bodies. In the present work we have attempted to investigate a single cluster which, based on the experimental observations of Zhou et al. (5), is initially parabolic in shape. The leading edge of the descending cluster is assumed to be similar to that of a large liquid drop (6). A two-dimensional computational fluid dynamic model employing the Eulerian-
granular model is used to simulate the behaviour of clusters. Numerical simulations based on the commercial CFD code solver, Ansys-Fluent 12.1, were carried out and are compared with the experimental results of Zhou et al. (5) and Lim et al. (6). EULERIAN-GRANULAR GAS-SOLID FLOW MODEL The gas-solid two-phase incompressible flow is modeled in an Eulerian-Granular framework, with the Syamlal-O’Brien (16) drag model employed for interphase momentum exchange. The solid phase stress was computed from the kinetic theory of granular flow. A laminar viscous model was assumed. External forces, lift and virtual mass forces were neglected. For the sake of brevity, the model equations are not presented here. Instead, the complete set of governing equations may be obtained from the manuals of Ansys-Fluent 12.1 (17). Table 1 specifies the granular parameters for the riser flow simulations. The value of the granular temperature was adopted from the literature (17, 18). Its value was kept constant as the core region of the riser was very dilute compared to the cluster. Hence, random fluctuations were neglected. Incorporation of an appropriate turbulence model and effect of varying the granular temperature will be considered in future work. MODEL SET-UP AND NUMERICAL SOLUTION PROCEDURE The CFB riser geometry and grid were created using Ansys-Workbench 12.1. The primary objective of the present work was to simulate the descent of an initially parabolic-shaped cluster of width Dc in a two-dimensional calculation region. Key parameters are listed in Table 2. Air and particles are fed into the riser from the bottom at a specified superficial gas velocity and at a given mass flux, respectively. Both particles and air leave the riser at the top. Initially, the riser was completely filled with air, and then the solids were introduced. At t = 0 s, the cluster was initiated. QUICK and second-order upwind differences were used to discretize the continuity and momentum equations respectively, whereas time was second-order implicitly discretized. The Phase-Coupled SIMPLE algorithm was used for pressure-velocity coupling. Each case was simulated for 1.5 s. The velocity of descent of each cluster was calculated as a mass-weighted-average velocity over all of the particles which initially belonged to the cluster, i.e.
n n U cl = ∑ ε pi ρ pU pi / ∑ ε pi ρ p i =1 i =1 Boundary Conditions: Uniform-velocity inlet conditions were imposed: Ug,y =Ug; Ug,x = 0 ; Up,y =Up; Up,x = 0 At the outlet: ∂U g , y / ∂y = ∂U g , x / ∂y = 0 No slip was imposed on the gas velocity at both side walls: Ug = 0 Transient simulations with a time step of 0.0001 s were carried out based on the governing equations and boundary conditions until steady state was obtained. In order to accurately account for the motion of clusters at the wall, refined grid spacing was used near the wall. Sensitivity to the grid spacing and time step were checked in the initial numerical experiments. The numerical computations
were confirmed to be converged by checking the time-averaged mass residual (<10−4) at different planes along the height of the riser. Simulation experiments were performed for 25 x 80 (width x height), 36 x 100 and 60 x 200 mesh resolutions and compared with the experimental results of Zhou et al. (5). This comparison showed that the 36 x 100 grid (with a time step of 0.0001 s) was able to provide mesh-independent results, as shown in Figure 1. This mesh was then adopted for further parametric studies. RESULTS AND DISCUSSION Experimental investigations of particle velocity profiles and motion of clusters near the wall of a 146 mm x 146 mm square by 9.14 m tall riser conducted by Zhou et al. (5) and Lim et al. (6) were compared with the CFD predictions. As can be seen from Figure 1, the air and particle velocity distributions were predicted reasonably well. The particles inside the riser are not uniformly distributed, despite the imposed uniformity at the inlet. The clusters are characterised by local high particle concentration. The predicted spatial-temporal structure of clusters depends on the local velocities and concentrations of both gas and solids. Figure 2 shows predicted contours of particle volume fraction at various times for Ug = 5.5 m/s and Gs = 20 kg/m2.s. A section of the riser is shown with a cluster at the wall. At t = 0.05 s, the volume fraction at the core of the cluster is 0.335, decreasing to 0.035 on its outer surface, and further to 0.029 at the centre of the riser. The cluster is predicted to descend along the wall of the riser and to deform under the influence of gravity and drag due to the upward flow of gas and solids. After t = 0.4 s, the outer surface of the cluster is pulled upwards, while the core maintains a drop-like elongated shape. The cluster is predicted to become more and more dilute with time as it expands, but it still keeps itself intact as a cluster, in practice probably influenced by inter-particle forces (20). The influence of frictional forces was neglected in this study. At t = 0.7 s, a petal-like shape was observed, after which the cluster starts to recede to a parabolic-drop shape, before detaching from the wall at t ≈ 0.8 s. Similar shape evolution was predicted for other gas velocities and solid mass fractions. The descent velocities of clusters for different gas velocities and solid mass fluxes are plotted in Figure 3 as a function of time. These velocities are predicted to be in the range of 0.1 to 2 m/s, in accordance with experimental values (e.g. 5, 6). The cluster velocity increases with time as the cluster descends from rest, before detaching from the wall. Increasing either the superficial air velocity or the solid mass flux in the upward direction increases the drag resistance on the descending cluster, thereby reducing its velocity of descent. Moreover, clusters are predicted to detach earlier with increasing upward suspension velocities and solids fluxes. The predicted particle concentration profiles at and near the left wall as a function of time in Figure 4 show higher particle concentration near the wall, falling as the centre of the riser is approached. Similar observations were reported by Manyele et al. (12) and Li Huilin et al. (21). The present model correctly predicts the trend of particle concentrations. As time progresses, their concentration drops from 0.48 at t = 0 s to 0.054 at t = 0.7 s, while descending from a cluster mid-point coordinate, z, of 0.9 m to z = 0.2 m, respectively. It is evident that considerable dilution of the cluster is predicted to take place. Note that the present model does not take inter-particle forces into account, which may in practice help to
keep clusters intact (20). The change in cluster voidage did not greatly affect the evolution of cluster shape. Figures 5(a) and (b) show average lateral distributions of gas and particle velocities at various times. The particle motion closely follows the upward-flowing carrier gas in the centre, but, near the left wall, particles were observed to descend. Hence, both graphs can be divided into two regions: (a) Region I, distance from left wall < 0.05 m (presence of cluster at wall) and, (b) Region II, distance from left wall > 0.05 m (towards the center of the riser). It was observed that with increasing time, the velocity profiles for both gas and solids approach symmetry at the centre. Figure 6 shows the lateral variation of cluster width for Ug = 5.5 m/s and varying solids flux. Initially centered at height, z = 0.9 m above the inlet, clusters at the wall were considered with an initial width of 30 mm. As a cluster descends along the wall, its maximum width progressively increases and reaches nearly 120 mm at z = 0.2 m. Beyond this point, the cluster detaches from the wall and is large enough to either fall down or be entrained by the incoming gas. Similar monotonically increasing cluster dimensions were observed for different inlet solid mass fluxes. Mostoufi and Chaouki (22) and Li Huilin et al. (21) found a similar trend from their experimental and numerical investigations, respectively. Figure 7 shows the cluster velocity of descent as a function of initial cluster width. Simulations were first carried out for widths of 22.2, 26.6, 28.1, 30.0 and 35.6 mm. Both the experimental data (6) and CFD predictions were observed to fluctuate. Interestingly, the predicted data followed a similar trend to the experimental results. When further simulations were conducted for widths of 19.9, 21.0, 23.4 and 31.9 mm, the model captured the experimental trend reasonably well. It is recommended that further investigations be carried out to understand the reasons for the fluctuations. Figures 8(a) and (b) plot the velocity of descent of a cluster as a function of superficial gas velocity and solid mass flux rate. As the gas velocity and solid flux increase, there is little variation in the predicted descent velocity of the clusters. However, descending clusters experience some drag resistance owing to the rapid upward suspension flow, and hence a small decrease in cluster descending velocity is observed. Similar observations were made by Zhou et al. (5), Lim et al. (6) and Noymer (23). NOTATION Dc dp Gs Ucl Ug Up t z
ε
ρ
width of the cluster [mm] particle diameter [µm] solid mass flux [kg/m2.s] cluster descent velocity [m/s] gas superficial velocity [m/s] particle velocity [m/s] time [s] height of the riser [m] solid volume fraction [-] density [kg/m3]
CONCLUSIONS A gas-solid Eulerian-Granular CFD model was developed to predict the motion of initially parabolic-drop-shaped clusters at the wall of a CFB riser. The predicted velocity of cluster descent is in reasonable agreement with experimental data of Zhou et al. (6) and Lim et al. (7). Clusters are predicted to distort while descending, from parabolic to drop-shape to petal and back to parabolic, before detaching from the wall. They also increase in size and become more dilute as they accelerate from rest while descending. The present model can be refined in the future by incorporating such additional features as frictional forces, turbulence and interactions between multiple clusters. REFERENCES [1] Grace, J.R. and Tuot, J.A., 1979. A theory for cluster formation in vertically conveyed suspension of intermediate density Trans. Int. Chem. Eng., 57, 49– 54. [2] Yerushalmi, R.G.J., Cankurt, N.T., Geldart, D., Liss, B., 1978. Flow regimes in vertical gas– solid contact systems, AIChE Symp. 74, 1–13. [3] Chen, C.J., 1999. Experiments that address phenomenological issues in fast fluidization, Chem. Eng. Sci. 54, 5529–5539. [4] Guenther, C. and R. Breault, R., 2007. Wavelet Analysis to Characterize. Cluster Dynamics in a Circulating Fluidized Beds, Powder Technol., 2007; 173, 163–173. [5] Zhou, J., Grace, J.R., Lim, C.J., Brereton, C.M.H., 1995. Particle velocity profiles in a circulating fluidized red riser of square cross-section, Chem. Eng. Sci., 50 (2), 237-244, 1995. [6] Lim, K.S., Zhou, J., Finley, C., Grace, J.R., Lim, C. J., & Brereton, C. M. H., 1997. Cluster descending velocity at the wall of circulating fluidized bed risers. Proceedings of the fifth international conference on circulating fluidized beds. Beijing, People’s Republic of China. [7] Horio, M. and Kuroki, H., 1994. Three-dimensional flow visualization of dilutely dispersed solids in bubbling and circulating fluidized beds, Chem. Eng. Sci. 49, 2413–2421. [8] Soong,C.H., Tuzla,K., Chen, J.C., 1995. Experimental determination of clusters size and velocity in circulating fluidized beds, in: J.F. Large, C. Laguerie (Eds.), Fluidization VIII, AIChE, New York, pp. 219–227. [9] Sharma, A.K., Tuzla, K., Matsen, J., Chen, J.C., 2000. Parametric effects of particle size and gas velocity on cluster characteristics in fast fluidized beds, Powder Technol. 111, 114–122. [10] Moortel, V.D., Azario,E., Santini, R., Tadrist, L.1998. Experimental analysis of the gas particle flow in a circulating fluidized bed using a phase Doppler particle analyzer, Chem. Eng. Sci. 53, 1883–1899. [11] Noymer, P.D., Glicksman, L.R., 1998. Cluster motion and particle-convective heat transfer at the wall of a circulating fluidized bed, Int. J. Heat Mass Transfer 41, 147–158. [12] Manyele, S.V., Parssinen, J.H., Zhu, J.X., 2002. Characterizing particle aggregates in a high-density and high-flux CFB riser, Chem. Eng. J. 88, 151– 161. [13] Yerushalmi, R.G.J., Turner, D.H., Squires, A.M., 1976. Ind. Eng. Chem. Process Des. Dev., 15, 47–53. [14] Rhodes, M., Mineo, H., & Hirama, T., 1992. Particle motion at the wall of a circulating fluidized bed. Powder Technology, 70, 207-214.
[15] Shuyan, W., Guodong, L., Yanbo, W., Juhui, C., Yongjian, L., Lixin, W., 2010. Numerical investigation of gas-particle cluster convective heat transfer in circulating fluidized beds, Int. J. Heat and Mass Transfer, 53, 3102 -2110. [16] Syamlal, M., Rogers, W., O’Brien, T.J., 1993. MFIX Documentation: Theory Guide. National Technical Information Service, vol. 1. Springfield, VA, DOE/METC-9411004, NTIS/DE9400087. [17] Ansys-Fluent 12.1 Users Guide, Fluent Inc., Lebanon, NH, 2009. [18] Kuipers, J. A. M., Prins,W., Van Swaaij, W. P. M., 1992. Numerical calculation of wall-to-bed heat transfer coefficients in gas-fluidized beds, AIChE J., 38 (7), 1079 – 1091. [19] Lun, C.K.K., Savage, S.B., Jeffrey, D.J., Chepurniy, N., 1984. Kinetic theories for granular flow: inelastic particle in Couette flow and slightly inelastic particles in a general flow field. Journal of Fluid Mechanics 140, 223–256. [20] Shaffer, F., Gopalan, B., Breault, R., Cocco, R., Hays,R., Karri, R., Knowlton, T. A., New View of Riser Flow Fields using High Speed Particle Imaging, NETL Multiphase Flow Workshop May 4-6, 2010. [21] Huilin, L., Qiaoqun, S., Yurong, H., Yongli, S., Ding, J., Xiang, L., 2005. Numerical study of particle cluster flow in risers with cluster-based approach. Chem. Eng. Sci. 60, 6757-6767. [22] Mostoufi, N., Chaouki, J., 2004. Flow structure of the solids in gas–solid fluidized beds. Chem. Eng. Sci. 59, 4217 – 4227. [23] Noymer, P. D., 1997. Heat transfer by particle convection at the wall of a circulating fluidized bed. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA. Table 1. Kinetic model specifications Granular temperature, m2/s2 Granular viscosity, kg/m. s Granular bulk viscosity, kg/m. s Frictional viscosity, kg/m. s Solids pressure , Pa Radial distribution correction factor for inter-particle collisions) Elasticity modulus, Pa Packing limit, [-]
10-5 [17,18] Syamlal-O’Brien (16) Lun et al. (19) None Lun etal. (19) Lun etal. (19) Derived (17) 0.6
Table 2. Parameters used in the CFD simulations Parameter Height of riser computational domain, m Width of riser, m Particle diameter, dp, µm Width of cluster, Dc, mm Density of particles, ρp, kg/m3 Inlet solids mass flux, Gs, kg/m2.s Superficial gas velocity, Ug, m/s Particle-particle coefficient of restitution, [-] Volume fraction of particles in cluster, [-]
Value(s) 8, 1.0 0.146 and 0.285 213 19.1, 21, 22.2, 23.4, 26.6, 28.1, 30.2, 31.9 and 35.6 2640 10, 20, 30, 40 and 60 4.5, 5.5, 6, 7 and 8 0.95 0.48 (base case)
0.0 Gs = 20 kg/m2.s Ug = 5.5 m/s
-0.4 -0.8
Gs = 40 kg/m2.s Ug = 7 m/s
Zhou et al. (1995) 25 x 80 30 x 100 60 x 200
Zhou et al. (1995) 25 x 80 30 x 100 60 x 200
-1.2 -1.6 -2.0 -2.4 -2.8 -3.2 0.00
0.04
0.08
0.12
0.00
0.04
Distance from left wall [m]
0.08
0.00
0.12
0.04
Distance from left wall [m]
(a)
0.08
0.12
Distance from left wall [m]
(b)
(c)
Figure 1. Grid independent test: Lateral profiles of particle velocities for Dcl = 30 mm, dp = 213 µm, z= 6.2 m, t = 5.2 s]
0.035
0.029 0.007
0.02 0.335
0.011
0.218
0.144
0.478 z = 0. 88 m
z = 0. 9 m
( c) t = 0.3 s
0.003
0.005
0.008 0.024
0.025
0.106 z = 0. 3 m
z = 0. 2 m
z = 0. 25 m
(f) t = 0.7 s
(e) t = 0.5 s
(d) t = 0.4 s
0.004
0.034 z = 0. 5 m
z = 0. 6 m
z = 0. 7 m
(b) t = 0.05 s
(a) t = 0 s
(g) t = 0.75 s
(h) t = 0.8 s
2
Figure 2. Volume fraction of particles at instantaneous time, Gs = 10 kg/m .s; Ug = 5.5 m/s; Dcl = 30 mm Gs = 20 Kg/m2.s Ug = 5.5 m/s Dcl = 30 mm
0.5 Particle volume fraction [-]
2.0
1.5
Ucl [m/s]
Particle velocity [m/s]
Gs = 40 kg/m2.s Ug = 5.5 m/s
Zhou et al. (1995) 25 x 80 30 x 100 60 x 200
1.0
Gs (kg/m2.s) Ug (m/s) 10 5.5 60 5.5 20 8
0.5
0.0 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Time [s]
Figure 3. Descent velocity of cluster versus time with varying Ug and Gs
0.4
t=0s t = 0.05 s t = 0.1 s t = 0.2 s t = 0.3 s t = 0.4 s t = 0.5 s t = 0.6 s t = 0.7 s
0.3 0.2 0.1 0.0 0.00
0.01
0.02
0.03
0.04
0.05
0.06
Distance from the left wall [m]
Figure 4. Lateral profile of concentration at and near left wall
particle
8 7 Particle velocity [m/s]
Air Velocity [m/s]
6 5 4 3 2
Gs= 10 kg/m2.s
1
t = 0.05 s t = 0.4 s t = 0.7 s
Ug = 5.5 m/s
0 0.00
0.05
0.10
0.15
0.20
0.25
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 0.00
Gs= 10 kg/m2.s
t = 0.05 s t = 0.4 s t = 0.7 s
Ug = 5.5 m/s 0.05
0.10
0.15
0.20
0.25
Distance from left wall [m]
Distance from left wall [m]
(b)
(a)
Figure 5. Lateral distributions of (a) Gas velocity and, (b) Particle velocity at z = 0.5 m, Dcl = 30 mm 1.6
1.0
Ug = 5.5 m/s
0.9
Gs = 40 kg/m2.s Gs = 30 kg/m2.s Gs = 20 kg/m2.s
0.8 0.7
1.2
0.6
Ucl [m/s]
Height [m]
Lim et al. (1997) Predicted
1.4
0.5
1.0
0.4
0.8 0.3 0.2
0.6
0.1
20
40
60
80
100
120
16
140
20
24
28
32
36
Maximum width of cluster, Dcl [mm]
Maximum width of cluster [mm]
Figure 7. Comparison of predicted cluster descent velocity with experimental data of Lim et al. (1997) including 15% error bars, 2 Gs = 10 kg/m .s, Ug = 5.5 m/s.
Figure 6. Variation of maximum width of cluster versus height of riser
1.12
1.1
Ug = 5.5 m/s; Dcl = 30 mm 1.10
Ucl [m/s]
Ucl [m/s]
1.08
1.0 1.06 1.04
0.9 1.02 2
Gs=20 kg/m .s ; Dcl = 30 mm 0.8 4.0
4.5
5.0
5.5
6.0
6.5
7.0
Gas Velocity, Ug [m/s]
(a)
1.00
7.5
8.0
8.5
10
20
30
40
50
60
Solid mass fraction, Gs [kg/m2.s]
(b)
Figure 8. Variation of cluster velocity as a function of (a) Gas velocity and, (b) Solid mass flux at z = 0.5 m
DESIGN CRITERIA OF UNIFLOW CYCLONES FOR THE SEPARATION OF SOLID PARTICLES FROM GASES Ulrich Muschelknautz, Paul Pattis, Michael Reinalter, Michael Kraxner MCI Management Center Innsbruck Egger-Lienz-Str. 120, A-6020 Innsbruck, AUSTRIA
ABSTRACT The main advantages of uniflow cyclones compared to standard reverse flow cyclones are their compact design and their capability of being easily integrated into pipelines. Experiments show that a 0.2m-diameter horizontal uniflow cyclone removes 88.6% of 2 g/m3 of fine mineral powder (d10=3 m, d50=20 m) from 1000 m3/h air at a pressure loss of 2510 Pa (0.364 psi). INTRODUCTION Uniflow cyclones for the separation of particles from gases have solids and gas passing through them in only one direction, Fig. 1, left. At the pipe entrance a rotational flow is generated by curved blades. Subsequently the particles are moved towards the pipe wall due to the centrifugal force. At the end of the separation chamber the particles are discharged through an opening in the wall. The clean gas leaves the separator through a central gas outlet pipe. The main difference to the standard reverse flow cyclone (Fig. 1, right) is, that in a uniflow cyclone the gas flow does not reverse.
Fig, 1. Uniflow cyclone (left) and standard reverse flow cyclone (right).
This property leads to several essential advantages of uniflow cyclones compared to standard reverse flow cyclones: Easy installation into pipelines Much smaller volume (diameter and length) needed for cleaning a given gas volume flow rate at a given pressure loss, i.e. at a given energy consumption Short residence time of the gas
1
Applications of uniflow cyclones are amongst others dust removal from gases on compact space pre-separation of particles from process gases in order to vastly increase the operation time of subsequent filters combustion and melting processes in metallurgy (Weng (1)) short-contact time reaction processes (Gauthier et al. (2, 3)) Approved design criteria as well as calculation models for uniflow cyclones, which are valid for a wide range of applications, are missing up to now. Progress has been made for a few special applications: Baluev and Troyankin (4, 5) studied uniflow cyclones for application to combustion chambers. On the basis of experiments they developed design criteria as well as calculation formulas for the tangential velocity and the pressure loss. Gauthier et al. (2, 3) extensively investigated uniflow cyclones for separating catalyst particles from the process gas of carbon hydrogen cracking. Tests with uniflow cyclones (diameter DC = 0.05 m) at high solids loadings (above 1wt. solids /wt. gas) and with glass beads (Sauter diameter of 29 m) showed, that excellent collection efficiencies require short separator lengths LC/DC of approximately 1.5 and a proper design below the gas outlet. They showed the strong influence of air humidity on the separation efficiency. Experiments of Zhang et al. (6) with horizontal uniflow cyclones (diameter 0.168 m) at very low crude gas particle concentrations of a few mg/m3 showed that particles of 10 m can be separated by an efficiency of 90%. Muschelknautz (7) derived design criteria for vertical uniflow cyclones (diameter 0.292 m) with very low pressure loss below 2 mbar for collecting coarse particles. Weng (1) developed a uniform calculation method for the pressure loss of different uniflow cyclone types (diameter 0.19 m), based on measurements of the gas flow patterns with Laser Doppler Velocimetry (LDV) and CFD simulations. Also calculation models for uniflow cyclones have been published (Brunazzi and Paglianti (8), Tan (9, 10)). Applying these models on measurements at MCI showed good agreement with the measured overall separation efficiencies. However the calculated fractional efficiency curves deviated from the measured ones (11). MCI intends to develop design fundamentals of uniflow cyclones for a wide range of industrial applications. Extensive studies have been performed at test facilities for vertical (diameter 0.3m) and horizontal uniflow cyclones (diameters 0.3m and 0.2m). For vertical cyclones several design parameters have been studied, such as the gas volume flow rate, the particle size distribution of the feed, the geometries of swirl vane inserts and of the vortex finder by Würtl (12), and the feed material (steel, sand, food powders, wolfram, molybdenum and others) by Leitner (13). Foidl (14) achieved a pressure recovery of 43% by installing swirl vane inserts in the gas outlet. Open questions remaining from those investigations were: 1) how re-entrainment of particles from the dust bunker back into the clean gas can be minimized and 2) how the geometry of the swirl generator can be improved. Those problems were investigated at a test facility for horizontal uniflow cyclones (Pattis (15), Reinalter (16)).
2
EXPERIMENTAL SET UP Fig. 2 shows the test facility for cyclones with an inner diameter of DC = 192 mm.
Fig. 2. Scheme of the test plant. 1. Injector (1.1 pressurized air), 2.Vibrating feeder (2.1 Potentiometer for mass flow control), 3. Swirl generator, 4. Dust bunker, 5. Filter, 6. Orifice measuring the gas flow rate, 7. Sound absorber, 8. Compensator, 9. Radial fan.
The solid particles are fed into the gas flow by a hopper, a vibrating feeder (2) and an injector (1). The gas streams first through a 2 m long inlet pipe in order to uniformly distribute the gas velocity as well as the particle concentration over the pipe cross section before entering the cyclone. To allow visual observations the inlet pipe, the cyclone body and the particle collection chamber are made from Plexiglas. As test dusts, limestone powders with two different particle size distributions have been chosen: Carolith 20-R with d10 = 3 m and d50 = 20 m as well as Carolith 00,2 with d10 = 5 m and d50 = 65 m. The collection efficiency has been determined from the mass of the collected particles mCollected and the mass of the feed mFeed.The fractional efficiency was calculated from the particle size distributions of the feed Q3,Feed and of the fines Q3,Fines. The latter has been measured in-line on probes by isokinetic sampling 4 m after the entrance of the gas outlet. At this position the former swirling flow has already been transformed into a pure axial flow by a flow straightener and the diameter has been widened onto 300 mm. Probes at 5 positions distributed over the cross section have been taken. Q3,Feed has been measured off-line on a probe taken from the hopper. The cyclone pressure loss p has been measured as the difference of the static pressures p1 at a position 0.2 m before the swirl generator, and p3 at 1.4 m (i.e. 12 pipe diameters) after the entrance of the gas outlet with diameter 117 mm (Fig. 2). All tests presented here were performed with a gas flow rate of 1000 m3/h ( 30 m 3 /h ) and a solids concentration in the crude gas of 2g/m3 ( 0.08 g/m 3 ). The variations during one test are shown in brackets. Every test was performed at least twice. All tests showed that, for a given set of cyclone-geometry and operation data, the measured separation efficiencies, , deviated from the mean value by a maximum of 0.5% . In all tests a feed mass of 250 g was used. During all tests the air humidity was between 25 and 35%, and the temperature between 12 and 18°C.
3
RESULTS Three uniflow cyclone types have been investigated: Type 1: Swirl vane inserts (SVI), ring slot for solids discharge (15, 16) Type 2A: Swirl vane inserts (SVI), window for solids discharge (16) Type 2B: Spiral (SP) as swirl generator, window for solids discharge (15) Two swirl vane inserts have been tested: SVI-30° with a vane angle of 30° and SVI50° with a vane angle of 50°, measured between the vane line at the core and normal to the cyclone axis. The vortex finder diameter DVF was in all cases 117mm. Uniflow cyclone type 1: Swirl vane inserts and ring slot for solids discharge The swirl generator consists of 6 curved vanes arranged around a cylindrical core, the solids discharge opening is a ring slot, see Fig. 3.
Fig. 3. Uniflow cyclone with ring-slot shaped solids discharge opening.
Maximum separation efficiencies achieved by this device were: SVI-30°: = 87.9% at p = 4125 Pa (0.598 psi) for LC/DC = 1, LVF = 0 mm. SVI-50°: = 82.0% at p = 1860 Pa (0.270 psi) for LC/DC = 1, LVF = 0 mm. For optimum separation efficiencies a short separator length, LC, between the end of the swirl generator and the beginning of the discharge opening is required, Fig. 4.
Fig. 4. Performance data of Uniflow Cyclone type 1 as a function of separator length LC (Carolith 20-R, LVF=0mm).
4
The vortex finder length, LVF, crucially influences the collection efficiency. The optimum length is LVF=0 mm, see Fig. 4. Sticking the vortex tube into the separation chamber up to 100 mm (positive values of LVF, see Fig. 3) leads to a small decrease of by 2%. If LVF<0, then separation passes first a local minimum at LVF=-200mm and breaks down for LVF<-600mm. The pressure loss depends only weakly on LVF.
Fig. 5. Performance data of Uniflow Cyclone type 1 as a function of vortex finder length LVF for swirl generator SVI-30° (Carolith 20-R, Lc/Dc = 7).
The collection efficiency depends strongly on the feed’s particle size distribution:
p
% Pa
Carolith 20-R (d10 = 3 m, d50 = 20 m) 86.7 3651
Carolith 0-0.2 (d10 = 5 m , d50 = 65 m) 94.1 3420
Table 1: Separation efficiency and pressure loss for different particle size distributions of the feed (Uniflow Cyclone with SVI-30°, LC/DC = 7, LVF = 150 mm).
Uniflow cyclone type 2: Window for solids discharge This uniflow cyclone type has a window for the solids discharge in connection with a rectangular box as dust bunker, similar to that investigated by Zhang (6). Two variants of this cyclone type have been tested: Variant 2A with swirl vane inserts (Fig. 6, left) and variant 2B with a spiral (Fig. 6, right) as a swirl generator.
Fig. 6 Uniflow cyclone with window-shaped opening for solids discharge (LB = length of bunker). Left: Variant 2A with swirl vane inserts (16), right: Variant 2B with spiral (15).
5
The spiral inlet of variant 2B, called SP-40°, is a single vane assembled as a spiral around a core, with a vane angle of 40° at the spiral core against the normal to the cyclone axis. Maximum values for the separation efficiency of variant 2A (without core) were - SVI-30°: = 90.0 %, p = 4050 Pa (0.587 psi) for LC/DC=4.6, LVF = 400 mm - SVI-50°: = 82.9 %, p = 1782 Pa (0.258 psi) for LC/DC=3.6, LVF = 400 mm Best values for the separation efficiencies of variant 2B were - SP-40°: = 88.6 %, p = 2510 Pa (0.364 psi) for LC/DC=3.6, LVF = 400 mm If in variant 2A (LVF=400mm) a core is installed, which elongates the swirl vane inserts core up to the vortex finder, the separation efficiency decreases with increasing core length by up to 2%. At a core length of 1000 mm the separation efficiency for Carolith 20-R decreases to 87.5%, but also the pressure loss decreases to 3250 Pa. The separator length LC (see Fig. 6) should again not be too large. The best performances are obtained for LC/DC = 3 to 5, cf. Fig. 7.
Fig. 7. Performance data of Uniflow Cyclone type 2A (left) and type 2B (right) as a function of separator length LC for swirl generators SVI-30° and SVI-50° (both types with Carolith 20R, LVF = 400 mm, without core).
The vortex finder length LVF (see Fig. 6) has its optimum value at 400 mm (Fig. 8).
Fig. 8. Performance data of Uniflow Cyclone type 2A as a function of vortex finder length L VF for swirl generator SVI-30° (Carolith 20-R, core length 1000 mm).
6
Influence of the particle size distribution of feed on the cyclone performance:
p
% Pa
Carolith 20-R (d10 = 3 m, d50 = 20 m) 88.6 2510
Carolith 0-0.2 (d10 = 5 m , d50 = 65 m) 96.0 2425
Table 2: Cyclone performance of Uniflow Cyclone type 2B, LC/DC = 3.6, LVF = 400 mm.
Fig. 9 shows the fractional separation efficiency F of the uniflow cyclone variant 2B. The minimum of F may be explained as the result of particle agglomeration.
Fig. 9. Fractional separation efficiency of Uniflow Cyclone type 2B (Lc/Dc =3.6, Carolith 20-R, = 88.6%, p = 2510 Pa). Dashed curves: Cumulative fractions undersize of feed and fines (Measurements by Laser Diffraction).
CONCLUSIONS Compared to standard cyclones, uniflow cyclones are able to clean a given gas particle flow using a much more compact cyclone body. A spiral as swirl generator needs much less pressure loss than swirl vane inserts, for the same collection efficiency. Future studies will be focused on the influence of the solids loading. ACKNOWLEDGMENT Financial support from MCI is gratefully acknowledged. NOTATION d d50 d10 DC DVF
Particle size ( m) Particle diameter for 50% cumulative fraction undersize ( m) Particle diameter for 10% cumulative fraction undersize ( m) Diameter of cyclone (mm) Diameter of vortex finder (mm)
7
LC LVF mcollected mfeed mfines Q3,Feed Q3,Fines Greek: p F
Length of separation chamber (mm) Length of vortex finder (mm) Mass of collected particles (g) Mass of feed (g) Mass of fines (g) Cumulative volume fraction undersize of feed (%) Cumulative volume fraction undersize of fines (%) Pressure loss (Pa) Total separation efficiency (%) Fractional separation efficiency (%)
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
Weng M. Experimentelle und numerische Untersuchung von Gleichstromzyklonen. Dissertation, RWTH Aachen University, 2002. Gauthier T.A., Briens C.I., Bergougnou M.A., Galtier P. Uniflow cyclone efficiency study. Powder Technol. 62, 217-225, 1990. Gauthier T.A., Briens C.I., Crousle O, Galtier P, Bergougnou M.A. Study of the gas circulation patterns in a uniflow cyclone. Can. J. Chem. Eng. 70, 209215, 1992 Baluev E. D. and Troyankin Y. V. The effect of design parameters on the aerodynamics of cyclone chambers. Teploenergetika 14, 67-71, 1967. Troyankin Y. V. and Baluev E. D. The aerodynamic resistance and efficiency of a cyclone chamber. Teploenergetika 16, 29–32, 1969. Zhang Y., Wang X., Riskowski G.L., Christianson L.L., Ford S.E. Particle separation efficiency of a uniflow deduster with different types of dust. Transactions of ASHRAE, 1999. Muschelknautz U. Separation of coarse particles: New Separator with low pressure drop. Proceedings of CFB-7-Conference, Niagara Falls, 2002. Brunazzi E., Paglianti A. Simplified Design of Axial-Flow Cyclone Mist Eliminators. AIChE Journal, Vol 49, No. 1, 41-51, 2003. Tan Z. Mechanisms of Particle Separation in an Aerodynamic Air Cleaner. Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Illinois, 2004. Tan Z. An analytical model for the fractional efficiency of a uniflow cyclone with tangential inlet. Powder Technology 183, 147-151, 2008. Krimbacher T. Evaluierung von Gleichstromzyklon-Rechenmodellen anhand von Messdaten. Bachelor Thesis, MCI Innsbruck, 2010. Würtl R. Experimental investigation of uniflow cyclone performance, Diploma Thesis. MCI Innsbruck, 2007. Leitner A. Einfluss der Staubart auf den Trenngrad eines Gleichstromzyklons. Diploma Thesis, MCI Innsbruck, 2009. Foidl S. Entwicklung von Leitapparaten zur Druckverlustminimierung in Gleichstromzyklonen. Diploma Thesis, MCI Innsbruck, 2007. Pattis P. Entwicklung eines Gleichstromzyklons mit gezielter Strähnenführung. Master Thesis, MCI Innsbruck, 2010. Reinalter M. Minimierung des Partikelwiedereintrages in den Reingasstrom von Gleichstromzyklonen. Master Thesis, MCI Innsbruck, 2010.
8
UNDERSTANDING STANDPIPE HYDRODYNAMICS USING ELECTRICAL CAPACITANCE TOMOGRAPHY Changhua Qui1, Robert Joachim, S.B1. Reddy Karri2,3 1Industrial
Tomography Systems 39 Deangate, Manchester, UK M32BA 2Particulate
Solid Research, Inc. 4201 W 36th Street, Suite 200 Chicago, IL, USA 60632
3Corresponding
author ([email protected]) Tel.: +1 773 523 7227 Fax: +1 773 299 1007
ABSTRACT Standpipes are often the bottleneck in a circulating fluidized bed processes. Understanding the pressure build and dissipation in a standpipe is critical in designing and operating a standpipe that can meet production needs. However, this critical component of a circulating fluidized bed (CFB) is often neglected in the design process which usually results in an underperforming unit operation. In an effort to better design new standpipe and to better optimize existing ones, electrical capacitance tomography (ECT) was evaluated in a 7-inch (18-cm) diameter standpipe to understand the gas-solid hydrodynamics in a standpipe with respect to circulation rates and aeration strategies. INTRODUCTION Standpipes are the pressure balance in a circulating fluidized beds. It is the pressure build and dissipation in a standpipe that determined the maximum circulation rate or solids flux in a circulating fluidized bed (CFB). How a standpipe is aerate determined the level of pressure build that can be obtained. For Geldart Group A powders, uniform aeration is recommended. For Geldart Group B powders, only aeration near the slide valve is recommended [1]. Even the level of aeration should be considered as an important operating parameter [2]. Too much, and the bubbles can defeat any possible pressure build and too little makes the aeration ineffective. Yet, how aeration enhances the standpipe pressure build is unclear. Does the aeration provide a reduction in friction at the wall or does the gas penetration deep into the standpipe and keeps the solids fluidized or perhaps both? To better understand how aeration effects solids hydrodynamics in a standpipe, electrical
capacitance tomography (ECT) was installed on an 7-inch (18-cm) diameter standpipe of circulating fluidized bed cold-flow unit. EXPERIMENTAL Standpipe Tests were conducted in a 8-inch (20-cm) diameter circulating fluidized bed equipped with a 8-inch (20-cm) diameter standpipe and a 7-inch (18-cm) diameter spool piece for the ECT electrodes, as shown in Figure 1. The riser was 22-m tall and terminated with an elbow with an r/D of 1.5 to a 48-cm diameter primary cyclone, a secondary cyclone, and diplegs to route the solids collected by the cyclones into the 3-ft (0.9-m) diameter fluidized storage hopper. A 20-cm diameter by 16.8-m tall standpipe returns the solids back to the riser. A 7-inch (18-cm) diameter spool piece was incorporated into the riser near the slide valve to accommodate the ECT system. A diverter valve in the first-stage cyclone dipleg allows solids to be diverted into a collection hopper on load cells so that the solids flow rate can be measured. The facility operated at ambient temperature and pressure. It can operate at gas velocities of up to 70 ft/s (21.3 m/s) and at solids fluxes in the riser of 200 lb/s-ft2 (980 kg/s-m2). The unit was filled with equilibrium FCC catalyst powder having a particle density of 1500 lb/ft3 and median particle size of 72 microns.
Figure 1: Schematic and picture of 8-inch (20-cm) diameter CFB with 7-inch (18-cm) diameter spool piece for ECT system. Two trials were conducted, the first test was to establish optimal measurement parameters; the second was to observe and evaluate the ECT system over a test matrix of 16 fluidized beds flowing process conditions. These conditions are presented in Table 1.
Table 1: Experimental design for ECT evaluation in a 7-inch (18-cm) diameter standpipe. Test Case
Solids Flux
Aeration Rate Aeration Injection Points
lb/ft2-sec
kg/m2-sec
SCFH
SCMH
1
0
0
30
0.850
All
2
0
0
50
1.416
All
3
5
24
25
0.708
All
4
5
24
20
0.566
All
5
10
49
15
0.425
All
6
28
137
20
0.566
All
7
28
137
20
0.566
All but Increasing Qg from 20-200 @ #9,#10
8
60
294
30
0.850
All
9
50
245
35
0.991
All
10
50
245
40
1.133
All but Qg @ #9 increasing from 40 to 200
11
76
372
35
0.991
All
12
76
372
35
0.991
All but Increasing Qg from 35 up to 200 @ #7,#9
13
115
563
35
0.991
All
14
115
563
35
0.991
All but Increasing Qg from 35 up to 200 @ #7
15
115
563
200
5.664
All
16
115
563
200
5.664
All but decreasing the Qg from 200 down to 40@ #7
Electrical Capacitance Tomography The tests were performed using an ITS m3000c Electrical Capacitance Tomography system (ECT). The instrument operates by taking measurements from the multiple electrodes arranged around a pipe to obtain information on the material(s) within the pipe by dielectric imaging. The ITS capacitance sensor was 7-inch (18-cm) internal diameter with 12 electrodes, the PSRI fluidized bed circulation pipe at either side (top and bottom) was 8-inch (20-cm) diameter. It driven by an m3000c system, - which applies a voltage to an electrode and measures voltages between this and all other electrodes. Measurements are repeated rapidly (in approx. 20 ms) until all combinations have been measured for a single frame of data. Overall, 66
capacitance measurements points are taken per frame. These are combined using proprietary software to provide a cross-sectional map of the capacitance distribution through the pipeline. Including on line image processing data is presented at 12 Hz. The measurements are very sensitive (measuring capacitances in order of picoFarads). To ensure data is collected properly high and low thresholds must be taken with homogeneous material in the sensor. After a number of tests (calibrations), it was determined that the optimal threshold settings were: the low reference with the gas filled into the sensor and high reference with the fully packed FCC catalytic power filled with the sensor during the real time trial tests. RESULTS AND DISCUSSION
Figure 2: ECT scans for a standpipe at rest with uniform aeration and the slide valve fully closed (Test Case 1).
Figure 3: ECT scans for a standpipe at a solids flux of 76 lb/ft2-sec (385 kg/m2sec) with 35 SCFH (0.991 SCMH) uniform aeration and the slide valve at the 0.5inch (1.25-cm) opening position (Test Case 3).
Figure 4: ECT scans for a standpipe at a solids flux of 115 lb/ft2-sec (563 kg/m2sec) with 200 SCFH (5.7 SCMH) uniform aeration and the slide valve at the 1.9inch (4.8-cm) opening position (Test Case 15). Figure 2 shows the results of the ECT scan for a 7-inch (18-cm) diameter riser at a zero solids flux (slide valve was fully closed) with 25 SCFH (0.71 SCMH) uniform aeration, which corresponds to Test Case 1. Under these conditions, the standpipe behaved more like a bubbling fluidized bed. The ECT scans started with the startup of the aeration. The images are plotted on a 32 x 32 grid with 820 pixels within the circular geometry of the sensor. The lower image of Figure 2, shows 3D tomographic images in a ‘stack’ of 400 images. This represents a time period of 32 seconds and allows temporal flow features to be observed over this time period. The ECT scans revealed the expected behavior for a bubbling fluidized bed. Gas bubbles were clearly resolved in the center of the standpipe with respect to time. Figure 3 shows the ECT scans for an operating standpipe at a solids flux of 76 lb/ft2sec (385 kg/m2-sec) with 35 SCFH (0.991 SCMH) uniform aeration, which corresponds to Test Case 11. For the results shown in Figure 3, the ECT scans started with the startup of the standpipe and the aeration. During the next 30 seconds, gas from the aeration ports appeared to from a core-annulus profile with low concentrations of solids in the center of the standpipe.8 This was an expected profile determined by the ECT scans. The slide valve position for this test case had only 0.5-inch (1.25-cm) opening and was the limiting factor for the solids circulation rate. The amount of aeration far exceeded that needed to enhance the standpipe flow. This resulted in the generation large pockets of gas flowing up the standpipe. Opening the slide valve such that it no longer limited the flow, changed the gas-solid distribution completely, as shown in Figure 4. With the slide valve opened to the 1.9inch (4.8-cm) position, solids fluxes increased to 115 lb/ft2-sec (563 lb/m2-sec). As shown in Figure 4, the aeration gas quickly migrated to the wall, even with the aeration increased to 200 SCFH (5.7 SCMH). Thus, it appears that at low solids flow rates in standpipes, bubbles have enough momentum to push the solids to the wall and generate a core-annulus profile. At
Figure 5: ECT scans for a standpipe at a starting solids flux of 115 lb/ft2-sec (563 kg/m2-sec) with 35 to 200 SCFH (1 to 5.7 SCMH) bottom aeration (Top - Test Case 14) and 200 to 40 SCFH (5.7 to 1.1 SCMH) bottom aeration (Bottom - Test Case 16)and the slide valve at the 1.9-inch (4.8-cm) opening position. high solids flows, the solids momentum restricts the gas to the walls. In this mode, it is suspected that the gas may act as a lubricant against the solids shear stress and friction at the wall and thereby further enhance the standpipe flow and pressure build. These results are consistent with earlier ECT studies done in a 3-inch (4.6-cm) diameter standpipe [2]. In addition, PETP studies in a 1-inch and 3-inch (2.5-cm and 4.5-cm) diameter standpipe by Chan et. al. [3] that with good standpipe operation, the solids flow seemed to dominate the center of the riser as well. They also noted that velocity distribution of solids is asymmetrical within the standpipes cross-section with a faster motion away from the aeration point but a slower motion closer to the aeration point. Where the aeration comes into the standpipe is equally important. For Geldart Group A powders, uniform aeration is recommended where as for Geldart Group B powders, only aeration near the slide valve is recommended [1]. As shown in Figure 5, increasing gas flow from 35 to 200 SCFH (1 to 5.7 SCMH) in only one aeration port at the bottom of the standpipe resulted in flow instabilities in the standpipe aeration despite that similar, relatively high levels of aeration were deemed useful for Test Case 15 shown in Figure 3 for uniform aeartion. Stability was regained when the aeration was lowered to 40 SCFH (1.1 SCMH). The contrast between the ECT scanned images between Figures 4 and 5 suggest that for Geldart Group A powders, too much aeration in one location could lead to a core-annulus profile with low solids concentration in the center of the riser, indicative of unstable standpipe flow. CONCLUSIONS ECT imaging of standpipes appeared to generate expected results and highlighted the role aeration has on standpipe behavior. Too much aeration or aeration in the wrong position can lead to the gas migrating to the center of the standpipe and impede solids circulation rates, and standpipe pressure builds. Well positioned aeration and the correct levels results in the gas migrating to the wall region and perhaps reduce solids stress and friction at the wall, which enhances the solids circulation rates.
REFERENCES 1. Knowlton, T.M.. “Standpipes and return systems” in Fluidization Solids Handling and Processing (Yang, W-C, ed.) Noyes Publications, NJ (1999) pp. 435-486 2. Srivastava, A., Agrawal, K., Sundaresan, S., Karri, S.B.R., Knowlton, T.M., Dynamics of gas-particle flow in circulating fluidized beds. Powder Technology, 100 (1998) pp. 173-182. 3. Chan, C.W, Seville, J., Fan X., Baeyens, J., Solid particle motion in a standpipe as observed by Positron Emission Particle Tracking. Powder Technology 194 (2009) pp. 58-66.
