Advances in Chemical Engineering, Volume 33: Automotive Emission Control

CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contribution begin.

M. AHMADINEJAD, Johnson Matthey Technology Centre, Blount’s Court, Sonning Common, Reading, RG4 9NH, UK (47) D. CHATTERJEE, Daimler AG, Department GR/VPE, 70546 Stuttgart, Germany (103) M. R. DESAI, Johnson Matthey Technology Centre, Blount’s Court, Sonning Common, Reading, RG4 9NH, UK (47) A. GU¨THENKE, Daimler AG, Department GR/VPE, 70546 Stuttgart, Germany (103) E. KLADOPOULOU, Aerosol & Particle Technology Laboratory, CERTH/CPERI, P.O. Box 361, Thermi Thessaloniki 57001, Greece (213) P. KOCˇI´, Institute of Chemical Technology at Prague, Department of Chemical Engineering, CZ 166 28 Prague, Czech Republic (103) A. G. KONSTANDOPOULOS, Aerosol & Particle Technology Laboratory, CERTH/ CPERI, P.O. Box 361, Thermi Thessaloniki 57001, Greece; and Department of Chemical Engineering, Aristotle University, P.O. Box 1517, 54006 Thessaloniki, Greece (213) M. KOSTOGLOU, Aerosol & Particle Technology Laboratory, CERTH/CPERI, P.O. Box 361, Thermi Thessaloniki 57001, Greece; and Division of Chemical Technology, Department of Chemistry, Aristotle University, Univ. Box 116, 541 24 Thessaloniki, Greece (213) B. KRUTZSCH, Daimler AG, Department GR/VPE, 70546 Stuttgart, Germany (103) M. MAREK, Institute of Chemical Technology at Prague, Department of Chemical Engineering, CZ 166 28 Prague, Czech Republic (103) S. MATSUMOTO, Toyota Motor Corporation, Material Engineering Div.1; 1 Toyota-cho, Toyota, Aichi, 471-8572 Japan (1) I. NOVA, Dipartimento di Chimica, Materiali e Ingegneria Chimica ‘‘G. Natta’’, Politecnico di Milano, Milan I-20133, Italy (103) H. SHINJOH, Toyota Central R&D Labs., Inc., Nagakute, Aichi, 480-1192 Japan (1) E. TRONCONI, Dipartimento di Chimica, Materiali e Ingegneria Chimica ‘‘G. Natta’’, Politecnico di Milano, Milan I-20133, Italy (103) N. VLACHOS, Aerosol & Particle Technology Laboratory, CERTH/CPERI, P.O. Box 361, Thermi Thessaloniki 57001, Greece (213)

ix

x

CONTRIBUTORS

T. C. WATLING, Johnson Matthey Technology Centre, Blount’s Court, Sonning Common, Reading, RG4 9NH, UK (47) M. WEIBEL, Daimler AG, Department GR/VPE, 70546 Stuttgart, Germany (103) A. P. E. YORK, Johnson Matthey Technology Centre, Blount’s Court, Sonning Common, Reading, RG4 9NH, UK (47)

PREFACE Automotive Emission Control Automotive emission control is certainly one of the success stories of our profession. Faced with both acute and chronic environmental problems such as smog and acid rain, in the seventies of the last century industry in general and car manufacturers in particular engaged in a major research and development effort. Since then, environmental regulations have become more stringent with so-called zero emission vehicles as ultimate goal. To meet this challenge it was not sufficient to limit progress to the internal combustion engine front alone. It soon appeared that catalytic oxidation of the non-combusted hydrocarbons (function 1) and carbon monoxide (function 2) as well as the reduction of nitrogen oxides (function 3) was required. The three-way catalytic converter was born. It can be safely stated that today this is the most abundant catalytic reactor in the world. From it a lot of other technologies evolved, the most common being NOx storage and reduction catalysts and selective catalytic reduction (SCR) catalysts both for lean NOx removal, diesel oxidation catalysts for lean CO and hydrocarbon removal and (coated) diesel particulate filters for soot removal, or combinations of those. Moreover the range of conditions at which these devices are operating is much broader than that of any chemical reactor in the process industry. Of course, a key to this success has been the design of catalysts combining different functions and resistant to the harsh reaction conditions. That is why the first contribution of this issue, the most ‘‘chemical’’ one, illustrates the present approach with respect to so-called three-way catalysts and NOx storage practised by the largest car manufacturer in the world. Issues related to oxygen storage and release, to the sintering of the precious metals group catalyst components as well as to the details of the NOx storage and reduction are discussed. The present level of understanding is impressive and the expectation is for further progress. This volume, however, emphasises the role of chemical engineering in automotive emission control. Clearly the development of mathematical models describing the different functions of the converter(s), as well as their interaction, has been and still is crucial. Each of the contributions advocates the implementation of the latter, combined with experimental validation, rather than engaging into elaborate experimental programs. The developed reactor models are capable of covering the wide range of operating conditions. Even more challenging, they cover quite a spectrum of time and length scales. They xi

xii

PREFACE

are, e.g. accounting for the transient operation connected to both the cold startup of the engine and the periodic changes of the composition of the engine outlet induced by the interaction between the engine and the converter. Needless to say, in view of the computational costs, any implemented feature of the models has been carefully assessed as to its benefits. This has, e.g. lead to the choice of Langmuir–Hinshelwood–Hougen–Watson rate equations rather than of microkinetics, i.e. of kinetics accounting implicitly rather than explicitly for every significant elementary reaction. Still, these models have empowered the involved research themes in their efforts aimed at catalyst and/or reactor development and optimization. In doing so, it was and is important to realize that the emissions control system does not function in isolation, but in combination with the engine. Combining computational fluid dynamics with chemical kinetics, e.g. to investigate the effect of flow maldistributions at the diesel particulate filter, is emerging. The second contribution comes from a major catalyst manufacturer and illustrates how insight in the reaction paths involved in three-way conversion leads to a fundamental, i.e. based on first principles, model. The emphasis in this contribution is on the chemistry rather than on the reactor model, i.e. on the description of the physical phenomena occurring in the monolith reactor. In this sense, this contribution is the bridge from the first to the third contribution. The latter elaborates further on the monolith reactor model. It is the result of a long collaboration between a major car manufacturer and two academic groups indicating the degree of effort required to reach the present state-of-theart. It elaborates further on the monolith reactor model and is the result of a long collaboration between a major car manufacturer and two academic groups indicating the degree of effort required to reach the present state-of-the-art. It is in this section that the reader will find the detailed description and modelling of different lean exhaust aftertreatment technologies. The reactions taking place in the diesel oxidation catalyst and the different steps involved in the removal of NOx from the lean engine exhaust are covered. The NOx storage and reduction catalyst as well as SCR technology is discussed in depth. The last contribution illustrates the progress concerning the removal of particulate matter. It is the most ‘‘physical’’ of the four. In contrast to the first three it comes exclusively from an academic group. Its very strong collaboration with the automotive industry is clearly apparent, among other things from the long list of Society of Automotive Engineers (SAE) technical papers. I am convinced that the present volume will help to position this work in the archival literature. The combination of state-of-the-art computational techniques with heuristics is an example for the chemical engineering community. Before concluding, let me remind you that the aim of Advances in Chemical Engineering is to provide the reader with personal views of authorities in the field. These should allow assessment of the state-of-the-art in a particular domain and to develop a feeling of its further evolution without claiming to be exhaustive. This is particularly true for the present volume. I was very happy

PREFACE

xiii

that major players in the field have accepted to contribute and to allow us to have a look into their ‘‘kitchen’’. For the involved companies this is not obvious and I am very grateful to them. Finally, I would like to dedicate this issue to Jozef Hoebink, my former colleague at Eindhoven University of Technology. Jozef has been active in the field of automotive emission control for more than fifteen years before he left us too soon. Those who have met him will not forget his warm personality and his enthusiasm for our profession. Guy B. Marin Ghent, Belgium, July 2007

DYNAMIC BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS Shinichi Matsumoto1, and Hirofumi Shinjoh2 1

Toyota Motor Corporation, Material Engineering Div.1; 1 Toyota-cho, Toyota, Aichi, 471-8572 Japan 2

Toyota Central R&D Labs., Inc., Nagakute, Aichi, 480-1192 Japan I. Introduction II. Automotive Exhaust Catalyst and its Specific Features A. Introduction of Exhaust Catalyst B. Catalytic Performance in Fluctuating Condition III. Oxygen Storage and Release in TWC A. Introduction B. OSC of Mixed Oxide: (Ce,La)O2
x, (Ce,Zr)O2 C. Heat Resistant Oxygen Storage Material: ACZ D. Atomic Arrangement of Oxygen Storage Materials and their OSC E. Dynamic Oxygen Mobility in Pt/CeO2–ZrO2 IV. Sintering of PGM A. Introduction B. Sintering Inhibition Mechanism of Platinum Supported on Ceria-based Oxide C. Re-dispersion of Platinum Supported on Ceria-based Oxide V. NOx Storage and Reduction Catalyst and Reaction Mechanism A. NOx Reduction Method Under Lean Conditions B. Outlook of NSR Catalyst C. Mechanism of NSR Catalyst D. SOx Poisoning VI. Improvement of NSR Catalyst and Engine System A. NSR Catalyst Formulation B. Improvement of Durability against Sulfur Poisoning C. Combination of Catalysts VII. Conclusions References

2 3 3 5 8 8 9 10 12 15 18 18 19 22 23 23 25 26 30 32 32 33 39 42 44

Corresponding author. Tel.: +81-565-72-0213, +81-55-997-0501; Fax: +81-565-72-6887, +81-55-997-7879. E-mail: [email protected]

1 Advances in Chemical Engineering, vol. 33 ISSN 0065-2377 DOI 10.1016/S0065-2377(07)33001-9

Copyright r 2008 by Elsevier Inc. All rights reserved.

2

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Abstract Automotive catalyst technology is now faced with very difficult problems. The recent progress of research on the dynamic behavior and characterization of automobile catalysts, and their development to solve these problems are reviewed in this chapter. The oxygen storage and release phenomena under the non-steady atmosphere are investigated in terms of the oxygen storage capacity, the oxygen mobility and the local structure of oxygen storage materials. These parameters are in good correlation with each other. The sintering and re-dispersion phenomena of Platinum Group Metals (PGM) on metal oxides are studied by X-ray absorption analysis. The sintering of Pt is suppressed by making a bond between Pt and surface oxygen of oxides such as CeO2 under the oxygen rich atmosphere, and sintered Pt particles on CeO2 are re-dispersed under appropriate conditions. The NOx storage-reduction (NSR) catalyst, which was developed for automotive lean-burn engines, can reduce NOx under the oxygen-rich atmosphere. The NOx reduction phenomena are investigated by four steps. NO is oxidized on Pt under the oxygen-rich atmosphere. NO2 reacts with basic materials and then is stored in the NSR as nitrate. The stored NOx is released after the decomposition of nitrate under the oxygen deficient atmosphere. The released NOx is reduced into N2 on PGM by the reaction with a reducing component such as HC, CO and H2. The main cause of deterioration for the NSR is sulfur poisoning. The sulfur-poisoning mechanism and the way for an NSR with high tolerance to sulfur poisoning is studied in storage materials, support materials, substrate structures and the arrangement of catalysts.

I. Introduction Nowadays automobile prevails across the globe as the most popular and important mode of transportation in our daily life. About 50 million cars are produced each year, and totally over 700 million cars are being used worldwide. Thus, the application of automotive catalysts for detoxifying the pollutants, such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx) and particulate matter (PM) in the exhaust gases is absolutely indispensable in every vehicle. The composition, temperature and flow amount of automotive exhaust gases vary continuously with driving conditions. The circumstance of the exhaust catalysts exposed is far more different from that in chemical factories. The automotive catalysts are requested to purify infinitesimally small amount of the

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

3

toxic gases in the exhaust perfectly under the unsteady condition, and also have a long-term durability during an automobile life. It is very important to manage and control the exhaust gas transient, in particular the fluctuation of atmosphere. The technology of automotive catalysts had already developed for the practical application, while it still needs to be paid more efforts for reaching the final goal of zero-emission. In this chapter, following a general illustration on the automotive exhaust catalyst, we tried to discuss the recent progress and catalyst development for controlling the atmosphere fluctuation in the exhaust gases based on our research results.

II. Automotive Exhaust Catalyst and its Specific Features A. INTRODUCTION

OF

EXHAUST CATALYST

In early 1970s, the serious photochemical smog in California brought the strict regulations of automotive exhaust emissions to both USA and Japan. Through lots of trials including engine modifications, the catalytic system for exhaust gases had been recognized as the only efficient method to meet the regulations. The oxidation catalyst for CO and HC was first adopted in 1974, and then in 1977, the 3-way catalyst (TWC) with the oxygen sensor was introduced for simultaneously detoxifying the three pollutant gases, CO, HC and NOx. The catalytic methods for exhaust gases have established and most of automobiles have equipped the catalysts for emission control from then on. The composition, temperature and flow amount of the exhaust gases from automobiles are very variable with the driving conditions. Figure 1 exemplifies an exhaust gas composition emitted from a gasoline engine as a function of air to fuel ratio (A/F) (Kummer, 1980). Much CO and HC are emitted under the rich condition, and there appears a NOx emission peak around A/F=16. The temperature of the catalyst ranges from sub-zero to more than 1,0001C, and the flow rate varies at different order of magnitude. The exhaust catalyst is requested to purify these toxic gases over such a wide range of these gas conditions as earlier. Figure 2 illustrates a conventional monolith-type catalyst. Exhaust catalysts are composed of several components, including noble metals, Pt, Rh and Pd as active site, alumina-based supports with a high surface area even at high temperature, and metal oxides as promoter materials. Cerium oxides as oxygen storage material and basic materials for NOx storage are typical promoter materials in the catalysts. The catalysts component is some hundreds micrometers thick and loaded on the substrate, usually made from cordielite (2MgO2  Al2O3  5SiO2).

4

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

14

7

12

6

CO, CO2, O2 concentration / %

10

5 HC Stoichiometric A/F

8

4

NOx 6 O2

CO 4

2

2

0 10

3

1

12

14

16

18

NOx concentration /103 ppm, HC concentration / 102 ppm

CO2

0 20

A/F FIG. 1. Concentrations of CO, NOx, HC (as hexane), O2 and CO2 emitted by spark-ignited engine as a function of the intake A/F.

Active site : Pt, Rh, Pd Support : alumina, etc 3-way catalyst Promoter : CeO2 compound, alkaline materials, etc

Monolithic substrate : cordielite FIG. 2. Outline of 3-way catalyst.

Removal Efficiency (%)

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

O2 sensor

Feed back system

Fuel-rich 100

5

Stoichiometric Fuel-lean

NOx

50

HC or CO

Operating Window

0 Oxygen-lean 14.6 Oxygen-rich Air-to-fuel Ratio (wt./wt.)

(b)

3-way catalyst

O2 sensor

(a) Air

Air flow meter

Engine E

3 way catalyst

Fuel Injecter

(c)

Exhaust gas

CPU

FIG. 3. 3-way catalyst system. (a) Diagram; (b) performance and (c) feedback system.

Figure 3 shows a TWC system and a typical performance of the TWC. The three components are highly purified over the catalyst around the stoichiometric point. The oxidizing and reducing components have almost the same chemical equivalent in the narrow shadowed region, and CO, HC and NOx are converted into H2O, CO2 and N2 (Fig. 3b). The atmosphere of the TWC is automatically controlled around the stoichiometric point by the TWC system. The flow rate of air is monitored and the fuel injection is controlled by a computerized system to obtain a suitable A/F ratio (Fig. 3c). The signal from oxygen sensor is used as a feedback for the fuel and air injection control loop. Therefore, the exhaust gases are fluctuating streams between oxidizing and reducing periodically and alternatively.

B. CATALYTIC PERFORMANCE

IN

FLUCTUATING CONDITION

The oscillations of atmosphere fluctuation occur with a frequency in the order of 1 Hz, and the catalytic activities are greatly affected by the species of noble metals and exhaust gas conditions. Figure 4 shows the NOx conversion efficiency on a Pd catalyst as a function of oscillating periods and amplitudes in an engine test (Yokota et al., 1985). This figure indicates that there are suitable cycling conditions, which the catalytic activities are superior to that under the static condition, and the catalyst performance depends on the cycling

6

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

NOx Conversion / %

100

50

0

0

1

2 Period / s

3

FIG. 4. NOx reduction behavior on Pd/alumina catalyst as a function of oscillation periods and amplitudes in an engine test. Engine: 2 L, 1,600 rpm and
440 Torr; catalyst: Pd 0.05 g/L. A/F amplitude of oscillation: 0.4(W), 0.7(J) and 1.0(&).

conditions. These behaviors also found in the simulated exhaust gases and even in the simple binary reaction gas system such as CO–O2, and Pd and Pt catalysts are particularly improved by controlling the cycling characteristics. Figure 5 illustrates an example of the periodic operation effect on simple binary gas systems (Muraki et al., 1985). This figure shows the periodic operation effect for Pt catalyst in C3H6 oxidation (Shinjoh et al., 1989). The maximum conversions are observed for any temperature, and the optimum period for maximum conversion decreases with increasing temperature. From the results of kinetics and evolution pattern analysis, the periodic operation effects arise from a difference of adsorption capability between the two reactants on the catalyst surface, that is, the self-poisoning reactant is the one more strongly adsorbed on the catalyst surface (Shinjoh et al., 1987). Accordingly, the catalyst surface under static conditions is almost covered by the stronger ad-species, and the desired reactions are suppressed. Conversely, under optimum cycling conditions, these ad-species are eliminated and surface compositions are suitable for reaction to take place. Under these circumstances, the reaction rate reaches the maximum value. These periodic operation effects can be applied to improve the reactivity of TWCs by a selection of suitable cycling condition. Figure 6 shows CO oxidation reaction with various oscillation periods. The dashed line indicates the best operation for high CO conversion, that is, the oscillation period should be longer at low temperature (20 s at 501C), and turns shorter

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

7

100

C3H6 Conversion / %

450 °C

330 °C 50

270 °C

225 °C

0

1 Period / s

0

2

FIG. 5. Periodic operation effect on Pt/Al2O3 catalyst in C3H6–O2 reaction.

CO Conversion / %

100

50

0 50

100

150 Temperature / °C

200

250

FIG. 6. Periodic operation effect on Pd/Al2O3 catalyst in CO–O2 reaction. Oscillation periods: 0 s(K), 1 s(Q), 5 s(S), 10 s(J) and 20 s(P). Periodic operation effect on Pd/Al2O3 catalyst in CO–O2 reaction.

8

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

with increasing temperature (10 s at 1001C, 5 s at 1501C and 1 s at 2001C), and keep a static condition at high temperature. However, these controls must be very difficult because exhaust gas conditions are changeable by individual driving mode and catalysts also change, which means catalyst degradation, with time. However, Rh catalysts show generally high catalytic performance with static condition. Rh is a requisite component for exhaust catalyst for its high NOx reduction activity into N2. Therefore, the other way to improve catalytic activity, in the opposite direction, is reducing the fluctuation by addition of oxygen storage materials as cerium compounds to the catalysts. These materials keep a stoichiometric atmosphere widely by adsorbing excess oxygen in oxidizing atmosphere, and releasing oxygen in reducing atmosphere. The oxygen storage materials are going to be discussed in the next chapter in more detail. As the atmosphere fluctuation is one of the characteristic features for exhaust gases, it is very important for automotive use to optimize the catalysts under these conditions. One way is to overcome the demerit of these conditions for decreasing fluctuation, the other way is to utilize this operation in a positive manner. In this chapter, the way and the material for this purpose in automotive catalyst use was introduced as following: (1) oxygen storage material and its improvement, (2) Pt sintering and re-dispersion, (3) NOx storage and reduction catalyst and NOx reduction mechanism and (4) improvement on NOx storage-reduction (NSR) catalyst system.

III. Oxygen Storage and Release in TWC A. INTRODUCTION TWCs have been continuously developed to improve their performance and durability since commercialized in 1977. The performance of TWCs is maximized in exhaust gas conditions close to the stoichiometric point. That is, the normal 14.5:1 (stoichiometric) ratio produces exhaust gas that contains the right balance of CO, H2 and HC to reduce NOx and O2. CO, H2 and HC are oxidized and NOx and O2 are reduced simultaneously into harmless CO2, H2O and N2 on a TWC. However, the air–fuel ratio occasionally fluctuates in actual vehicle-driving conditions. In an actual vehicle driving in the LA#4 mode test, for example, NOx emission increases during acceleration when the air–fuel ratio fluctuates out of the stoichiometic ratio (Fig. 7). To moderate the fluctuating atmosphere experienced by catalysts, materials with oxygen storage capacity (OSC) such as CeO2 have been used in conventional TWCs (Gandhi et al., 1976; Nagai et al., 2002; Ozawa et al., 1993). CeO2 releases oxygen in an oxygen deficient atmosphere, and stores oxygen in an oxygen excess atmosphere as

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

9

14 NOx Emission 13

100

A/F

Car Speed / km h-1

15

12 50 11

0

0

50 Time / s

10

FIG. 7. Fluctuation of the air-fuel (A/F) ratio (solid line) and NOx emissions (hatched line) of a vehicle in the LA#4 driving mode (dotted line).

described by the following reversible reaction: CeO2 ! CeO2ð1
xÞ þ xO2

(1)

under the oxygen excess atmosphere, and CeO2ð1
xÞ þ xO2 ! CeO2

(2)

under the oxygen deficient atmosphere, where 0pxp0.25. CeO2 had been used in TWCs, but the thermal stability of pure CeO2 is insufficient to meet the new requirements for low-emission vehicles. The investigations on the mechanism of the oxygen storage phenomena to develop a new oxygen storage material with high thermal stability are described in this chapter.

B. OSC

OF

MIXED OXIDE: (Ce,La)O2
x, (Ce,Zr)O2

Complex oxides of ceria were formed by pyrolysis of mixed precipitation from aqueous solution. Table I shows the XRD results of Ce–La and Ce–Zr mixed oxides calcined at 1,0001C (Matsumoto et al., 1991). These results

10

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

PHASES FORMED x (Ce1
x,Lax)O2
x/2 0 CeO2 0.1 CeO2 0.2 CeO2 0.3 CeO2 0.5 CeO2

IN

CeO2–La2O3

TABLE I CeO2–ZrO2 SYSTEMS (MEASURED

AND

x

Phase

s.s. s.s. s.s.+C1 s.s.+La2O3+C1

(Ce1
x,Zrx)O2 0 0.1 0.2 0.3 0.4

BY

XRD) Phase

CeO2 CeO2 CeO2 CeO2 CeO2

s.s. s.s. s.s.+C2 s.s.+ZrO2 s.s.+ C2

Note: s.s., solid solution with La or Zr oxide; C1 and C2, cubic fluorite-type oxide.

indicate the formation of solid solution (Ce1
x,Lax)O2
x/2, complex oxides and La2O3 in the CeO2–La2O3 system, and the formation of solid solution (Ce1
x,Zrx)O2 (xo0.2), complex oxides and Zr-rich solid solution (Zr1
x, Cex)O2 in the CeO2–ZrO2 system. Lattice constant of (Ce1
x,Lax)O2
x/2 increased as La content, whereas that of (Ce1
x,Zrx)O2 decreased as Zr content. The result is explained by different cation radii of Ce4+, La3+ and Zr4+ in oxide solid solution with the same fluorite-type structure. CO oxidation activity of (Ce0.9,La0.1)O1.95 and (Ce0.8,Zr0.2)O2 heated at 1,0001C are shown in Fig. 8. The activity of CeO2 is extremely improved by the addition of La and Zr into CeO2. The reaction kinetics is controlled by the diffusion of lattice oxygen and is described by the following equation: kt ¼ f1
ð1
aÞ1=3 g2

(3)

where a is non-stoichiometric parameter in CeO2
a and (Ce0.8,La0.2)O1.9
a obtained by in situ XRD data (Ozawa et al., 1991). This suggests that La addition enhances the activity of lattice oxygen for CO oxidation. Meanwhile, the particle diameter of Ce0.8Zr0.2O2 heated at 1,0001C is 12 nm, which is much smaller than 1,100 nm of CeO2. It is concluded that Zr addition prevents the sintering of CeO2 and enhances the diffusion of lattice oxygen.

C. HEAT RESISTANT OXYGEN STORAGE MATERIAL: ACZ A new material, that we named ACZ was developed for a TWC with high activity based on a novel concept, i.e. the diffusion barrier concept as described in Fig. 9 (Kanazawa et al., 2003). Diffusion barrier layers of Al2O3 are built up between CeO2–ZrO2 mixed oxide (CZ) particles to inhibit the coagulation or grain growth of CZ. After an aging test at 1,0001C for 10 h in air, the surface area of ACZ was 29 m2/g compared with that of CZ: 2 m2/g. Table II shows the precious metal particle diameter and the crystal diameter of CZ in two catalysts.

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

11

100


/%

(c)

50 (b)

(a)

0 200

300

400 T/°C

500

600

FIG. 8. Conversion (j) of CO pulsed onto (a) CeO2, (b) Ce0.9La0.1O1.95 and (c) Ce0.8Zr0.2O2 heated at 1,0001C for 5 h in air.

CeO2-ZeO2 solid solution Al2O3

Thermal aging

(a) ACZ

Thermal aging

(b) CZ

FIG. 9. The diffusion barrier concept for ACZ compared with CZ. (a) ACZ: the sintering of CZ is inhibited by Al2O3 particles dispersed among CZ particles and (b) CZ: sinter easily without any dispersal.

12

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

TABLE II THE PRECIOUS METAL PARTICLE DIAMETER AND THE CRYSTAL DIAMETER OF CZ IN TWO CATALYSTS: CATALYST A CONTAINED 1.5 g/dm3 Pt, 0.35 g/dm3 Rh WITH CZ AND CATALYST B CONTAINED THE SAME AMOUNT OF PRECIOUS METALS WITH ACZ Samplea

Particle (diameter/nm)b

A B a

CZ

Pt

17.2 8.7

23.7 19.6

Aged at 9501C for 100 h. Measured by XRD.

b

TABLE III LIGHT-OFF TEMPERATURE OF AGED CATALYSTS: CATALYST A CONTAINED 1.5 g/dm3 Pt, 0.35 g/dm3 Rh WITH CZ AND CATALYST B CONTAINED THE SAME AMOUNT OF PRECIOUS METALS WITH ACZ Samplea

A B a

Light-off temperatureb (1C) HC

CO

NOx

332 318

322 307

319 302

Aged at 9501C for 100 h. Temperature at 50% conversion.

b

These catalysts were aged under the exhaust gas stream at 9001C for 100 h. Catalyst A contained 1.5 g/dm3 Pt, 0.35 g/dm3 Rh with CZ and Catalyst B contained the same amount of precious metals with ACZ. As shown in the table, both the sintering of the precious metals and the CZ particles is inhibited in Catalyst B compared with Catalyst A. Table III also shows the light-off temperature of those catalysts. The light-off temperature of Catalyst B is about 151C lower than that of Catalyst A. In addition, the NOx emission of a vehicle equipped with Catalyst B was about 20% less than that with Catalyst A.

D. ATOMIC ARRANGEMENT

OF

OXYGEN STORAGE MATERIALS

AND THEIR

OSC

Three types of CZ were prepared by Suda et al. to investigate the relations between the structures of these materials and their OSC properties (Suda et al., 2001, 2002). M–CZ was prepared by the hydrolysis of an aqueous solution of ZrO(NO3)2 with ammonia on CeO2 powder, followed by the calcination in air at 7001C. The structure of M–CZ was a mixture of CeO2, ZrO2 and CeO2–ZrO2 solid solution. S–CZ was prepared by an attrition-milling process of CeO2

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

COORDINATING CATIONS

FOR

Ce

AND

ZR

TABLE IV CZ MATERIALS MEASURED

OF THE

BY

13

XAFS ANALYSIS

Sample A–Ba

M–CZ

S–CZ

R–CZ

Ce–Ce Ce–Zr Zr–Ce Zr–Zr

11.9 0.0 0.0 12.0

8.0 3.6 4.0 3.0

6.0 6.0 6.0 6.0

a

A(center cation)–B(coordinating cation).

powder with ZrO2 spheres in ethanol. The structure of S–CZ was a solid solution of CeO2 and ZrO2. R–CZ was prepared by the calcination of M–CZ with graphite at 1,2001C under a reducing atmosphere followed by the re-oxidation in air at 5001C. The structure of R–CZ was a solid solution of CeO2 and ZrO2 when the mole fraction of ZrO2 was below 0.3. The k-phase structure of CeZrO4 was appeared when the mole fraction of ZrO2 was above 0.3. The arrangement of atoms in the local structure of CZ with 50 mole% of ZrO2 was studied by Nagai et al. (2001, 2002). The number of nearest neighbor cations around Ce4+ or Zr4+ is shown in Table IV, which were determined from the data of Ce and Zr K-edge EXAFS. Both a Ce and a Zr atoms in R–CZ are surrounded by six Ce and six Zr atoms, respectively, via Ce–O–Ce, Zr–O–Zr or Ce–O–Zr bonds. The crystal structure of R–CZ was determined by XRD to be a pyrochlore-type structure, that is, Ce and Zr atoms were arranged regularly. On the contrary, the local structure around Ce is different from that around Zr in S–CZ and in M–CZ. Based on these experimental results, the schematic atomic arrangement of each CZ is shown in Fig. 10. The amount of OSC for these oxygen storage materials described earlier was measured according to the following procedure. Platinum-loaded catalysts were prepared by impregnating M–CZ, S–CZ and R–CZ with a solution of Pt(NH3)2(NO2)2 into M–CZ, S–CZ and R—CZ, respectively. Pt of 1% was loaded onto each CZ material. These catalysts were treated at 9001C in air for 15 min, followed by reduction under a stream of 20% hydrogen in nitrogen at 5001C until the decrease of their mass ceased. Subsequently, they were re-oxidized under a stream of 50% oxygen in nitrogen until the increase of their mass ceased. The amount of mass decrease was almost equal to the increase for each catalyst, thus we defined this quantity as OSC of these materials. Figure 11 shows the specific OSC of the CZs, i.e. the amount of OCS per mole of Ce. The specific OSC of R–CZ has the maximum value of 0.22 mol(O2)/mol(Ce), close to the theoretical value of 0.25, at 50 mole% of ZrO2. The regular arrangement of Ce and Zr atoms in R–CZ with 50 mole% of ZrO2 probably eases the oxygen release from it. In the oxygen-release process, the volume of the CZs increases in proportion to the change in the ratio of Ce4+ (smaller cation: 0.094 nm) to

14

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Ce

(a)

Zr

(b)

(c)

FIG. 10. Schematic figure of the atomic configuration of three types of CeO2–ZrO2 mixed oxide (CZ). (a) M-CZ was a mixture of CeO2, ZrO2 and CeO2–ZrO2 solid solution; (b) S–CZ was a solid solution of CeO2 and ZrO2 and (c) R–CZ was a solid solution of CeO2 and ZrO2 with a pyrochloretype structure, that is, Ce and Zr atoms were arranged regularly.

Specific OSC (mol-O2 / mol-Ce)

0.3

0.2

0.1

0 0

0.5 Mole fraction of ZrO2

1.0

FIG. 11. Specific OSC of CeO2–ZrO2 mixed oxide (CZ) as a function of ZrO2 content. J, M–CZ; &, S–CZ; ’, R–CZ.

Ce3+ (larger cation: 0.114 nm). The stress energy caused by this volume change would suppress further valence change of Ce. The substitution of the smaller ion Zr4+ (0.084 nm) for Ce4+ could compensate for the volume change, which then could ease the valence change of Ce. The 8 in 64 oxygen atoms in the unit

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

15

cell of pyrochlore-type R–CZ crystal are surrounded by 4 Zr atoms. The other oxygen atoms are surrounded by both Ce and Zr atoms or by four Ce atoms. The 8 oxygen atoms would be easier to move than the other 56 atoms because they are completely surrounded by all of smaller Zr ions. The ratio of the mobile 8 oxygen atoms (4 oxygen molecules) to 16 Ce atoms in the unit cell of R–CZ with 50 mole% of ZrO2 is consistent with the theoretical value of 0.25 mol(O2)/mol(Ce) for the amount of OSC.

E. DYNAMIC OXYGEN MOBILITY

IN

Pt/CeO2–ZrO2

The oxygen mobility on three types of CeO2–ZrO2 mixed oxide (M–CZ, S–CZ and R–CZ) was studied by the 18O/16O isotopic exchange reaction combined with CO oxidation reaction (Dong et al., 2004a, b).

1. Isotopic Exchange Reaction Pt of 1 wt% was supported by conventional impregnation on M–CZ, S–CZ and R–CZ, respectively. The catalyst sample was reduced under hydrogen for 15 min at 5001C followed by the oxidation for 60 min at 5001C. The sample was cooled down to room temperature. A 50 Torr dose of pure 18O2 was then introduced at the reaction temperature. The pressure of the oxygen isomers, P-36 (18O2), P-34 (18O16O) and P-36 (16O2) was continuously measured at the temperature of reaction. The partial pressure curves of three molecules evolved from Pt/R–CZ along with the exchange reaction at 4601C are plotted in Fig. 12 (Dong et al., 2004a). At 4601C, the adsorption and decomposition of oxygen molecules on Pt would be very fast, thus the diffusion of oxygen atoms to the top surface from the bulk oxide would be the control step. The initial reaction region is referred to as Zone 1 corresponded with the surface diffusion, and the reaction region approaching equilibrium is referred to as Zone 2 ascribed to the bulk diffusion. Isotopic exchange reaction rates and oxygen diffusion coefficients of Pt/M–CZ, S–CZ and R–CZ at 4111C are listed in Table V (Dong et al., 2004a). The surface diffusion rate for R–CZ (R0E ) is nearly 4 times larger than the bulk diffusion rate or the equilibrium isotopic exchange rate (R1 E ), while it is 3.8 times larger for S–CZ and 26 times larger for M–CZ. These results indicate that the contribution from bulk could also be counted as an important function in the total oxygen and release performance. As listed in Table V, the derived surface and bulk oxygen diffusion coefficients of R–CZ are apparently larger than those of S–CZ and M–CZ. This indicates that the surface and bulk diffusion coefficients relate closely with the nature and structure of the oxygen storage material.

16

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

60 P-36

Partial pressure, torr

50

P-34 P-32

40 30 20 10 0 0

6

12 Time, min

18

24

FIG. 12. Partial pressure evolution during isotopic exchange reaction on CZ–R at 4601C.

ION EXCHANGE REACTION

AND

Catalysts Initial ion exchange reaction rate, R0E , (atoms/s/g) Equilibrium ion exchange reaction rate, R1 E , (atoms/s/g) Surface diffusion coefficient, DS (m2/s) Bulk diffusion coefficient, DB (m2/s)

TABLE V OXYGEN DIFFUSION COEFFICIENTS

AT

4111C

Pt/R–CZ

Pt/S–CZ

Pt/M–CZ

1.504  1018

3.899  1017

1.578  1018

3.55  1017

1.029  1017

5.7321  1016

2.98  10
13 4.33  10
19

1.48  10
18 1.20  10
20

4.23  10
19 7.77  10
22

2. Dynamic Oxygen Storage and Release Performance and the Role of Zr Following the 18O2/16O2 isotopic exchange reaction of the catalyst sample, CO was introduced into the system to conduct CO oxidation reaction as shown in Fig. 13. From the sum of the production of C16O2 and C16O18O and their production rate in Fig. 13, the oxygen storage capacity and the oxygen release rates are derived as shown in Fig. 14 (Dong et al., 2004b). The obtained oxygen storage capacity and oxygen release rate are well matched with the results measured by thermogravimetric analysis (TGA) method (Suda et al., 2000, 2001), and are in good agreement with the oxygen mobility obtained from the isotopic exchange reaction. Moreover, these parameters are consistent with the structural homogeneity of Zr in the framework of the oxides, as R–CZ W S–CZ W M–CZ.

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

17

25

Partial pressure, torr

20

15 CO CO2-44

10

CO2-46 5

0

0

5

10 Time, min

15

20

1800

2.1

1200

1.4

600

0.7

0

Pt /M-CZ

Oxygen release rate (umol /min)

Oxygen Storage Capacity (umol/g)

FIG. 13. Subsequent CO oxidization at 5001C after isotopic exchange reaction on R–CZ at 4601C.

0 Pt/S-CZ

Pt/R-CZ

FIG. 14. Oxygen storage capacity and oxygen release rates at 5001C.

Based on the isotopic exchange reaction, the oxygen diffusion coefficients of the pure CeO2, Ce0.15Zr0.85O2 oxide and the pure ZrO2 were measured as in Table VI (Dong et al., 2004a). The table clearly indicates that the surface diffusion coefficient of CeO2 is much higher than that of ZrO2, while the bulk diffusion coefficient is smaller than that of ZrO2. The CeO2–ZrO2 solid solution

18

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH TABLE VI OXYGEN DIFFUSION COEFFICIENT IN CeO2–ZrO2 OXIDES

Oxides DS at 3001C (10
19 m2/s) Ea (kJ/mol) DB at 3001C (10
23 m2/s) Ea (kJ/mol)

CeO2

Ce0.15Zr0.85O2

ZrO2

21 41 12 15

33 39 53 10

2 49 41 14

gives both a much higher surface and bulk diffusion coefficient. These results suggest that zirconium atoms in the framework not only contribute to the enlargement of the lattice space for the migration of oxygen atoms as described in the last section but also help to capture oxygen atoms and transfer them to cerium atoms.

IV. Sintering of PGM A. INTRODUCTION Platinum group metals (PGM) such as Pt, Pd and Rh have been used in conventional TWCs. The catalytic performance of TWCs deteriorates accompanied with sintering of PGM caused by thermal aging during car driving. The effect of aging temperature and atmosphere on the catalytic activity and the sintering of Pt, Pd and Rh on alumina support were investigated under the synthesized gas stream (Shinjoh et al., 1990). For Pt and Rh, the catalytic activity decreases and the particle size increases with increasing both aging temperature and the oxygen concentration in the aging atmosphere. However, for Pd, the effect of the oxygen content on the catalytic activity and the sintering is the reverse as compared with that of Pt and Rh. The characterization of Pt/Rh TWCs aged under the engine exhaust stream and the improvement of their heat resistance were reported by Miyoshi et al., 1989. The particle size of PGM increases with increasing aging temperature and aging time. The surface area of alumina support and the catalytic performance decreases simultaneously. The addition of about 1 mole% of La into alumina suppresses the loss of its surface area, and then the sintering of Pt and Rh is highly restricted. The performance of TWC and its durability should be further improved to meet the new requirements for low-emission vehicles. It is described in this chapter that the sintering of Pt depends on the characteristic of supports and the re-dispersion of Pt is occurred on CeO2 through the interaction between Pt and the surface oxygen of CeO2.

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

B. SINTERING INHIBITION MECHANISM OXIDE

OF

PLATINUM SUPPORTED

ON

19

CERIA-BASED

The sintering of Pt after high-temperature aging in an oxidative atmosphere is much higher than in a reductive atmosphere (Fiedorow et al., 1978; Harris, 1986). The sintering of Pt particles on various oxides in an oxidative atmosphere was investigated at the atomic level using X-ray absorption analysis (Nagai et al., 2006). Figure 15 shows the transmission electron microscopy (TEM) images of the Pt/Al2O3 and Pt/CZY catalysts after aging treatment at 8001C in air for 5 h. In the aged Pt/Al2O3, large Pt particles ranging from 3 to 150 nm are observed. In contrast, no explicit Pt particles are observed on the aged Pt/CZY. In the aged Pt/CZY, Pt was detected by energy dispersive X-ray (EDX) analysis. This indicates that Pt particles are highly dispersed on the CZY support. The average size of Pt metal particles for these catalysts determined by XRD and the CO pulse method is shown in Table VII. Pt particle size on the Al2O3 support before the aging treatment, as estimated by the CO pulse method, is almost the same as that on CZY (about 1 nm diameter). Pt particle size increased significantly in Pt/Al2O3 during the aging treatment; particle sizes in the aged Pt/Al2O3 determined by XRD and CO pulse methods are 61 and 23.6 nm, respectively. However, Pt particles in the Pt/CZY cannot be observed by XRD even after aging, suggesting that the Pt particles on CZY support remain highly dispersed. The Pt particle size of 1.1 nm in the aged Pt/CZY estimated by CO pulse is the same as that in the fresh catalyst. The local structure around a target element is revealed by EXAFS analysis. The Fourier transforms (FTs) of the aged catalysts and reference samples

(A)

(B) Pt

200 nm

5 nm

FIG. 15. TEM images of Pt supported catalysts after 8001C aging in air for 5 h. (A) Pt/Al2O3 catalyst and (B) Pt/CZY.

20

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

AVERAGE PLATINUM PARTICLE SIZES

TABLE VII CATALYSTS ESTIMATED

OF THE

Sample

Pt/Al2O3 Fresh Aged Pt/CZY Fresh Aged a

BY

XRD

AND

CO ADSORPTION

Average Pt particle size (nm) XRDa

CO adsorption

NDb 61

1 23.6

NDb NDb

1.1 1.1

Average particle size was estimated from Pt(111) line width. The diffraction peak from the Pt particles could not be detected.

b

performed on the Pt L3-edge EXAFS spectra in the ca. 3.0–16 A˚–1 region are presented in Fig. 16. The quantitative curve-fitting analysis of the EXAFS spectra was performed for the inverse FTs on the Pt–oxygen and Pt–cation (cation=Pt, Ce and Zr) shells, respectively. In the FT spectrum of Pt foil, the peak at 2.76 A˚ is assigned to the Pt–Pt bond. FT spectrum of PtO2 powder, the peaks at 2.04 and 3.10 A˚ are assigned to the Pt–O and Pt–O–Pt bonds, respectively. For an aged Pt/Al2O3 sample, only an intense peak at 2.76 A˚, which corresponds to the Pt–Pt bond, was observed. The FT spectrum of the Pt/Al2O3 coincides with that of Pt foil. The coordination number (CN) of the Pt–Pt shell in the aged Pt/Al2O3 sample was 11.5. This indicates that the Pt metal particles on Al2O3 after aging are at least 20 nm in size (Greegor and Lytle, 1980). For Pt supported on CZY (solid solution of 50 wt% CeO2, 46 wt% ZrO2 and 4 wt% Y2O3) analyzed after the aging treatment, two peaks are observed. The first peak at 2.02 A˚ is assigned to the Pt–O–Ce bond, and the second peak at 1.7 A˚ is assigned to the Pt–O bond by comparison to a PtO2 reference. The value of the CN of the Pt–Ce shell in aged Pt/CZY was 3.5. This coordination number of 3.5 is lower than 12, which is the maximum coordination number in the cubic fluorite structure. This indicates that Pt ions exist on the surface of the CZY support. In addition, intense Pt–Pt or Pt–O–Pt peaks could not be observed in the aged Pt/CZY sample, suggesting that there are no large Pt metal or oxide particles on the CZY surface. In other words, highly dispersed Pt oxides are present on the surface of CZY. Based on the observation earlier, the sintering inhibition mechanism of Pt supported on CZY is proposed as follows (Nagai et al., 2006). In the case of Pt/Al2O3, since the interaction between Pt and Al2O3 is weak, mobile Pt particles migrate across the surface of the Al2O3 support and sinter during an 8001C aging treatment in an oxidizing atmosphere. In contrast, Pt supported on CZY has a strong interaction with the CZY support. Therefore the CZY support stabilizes a high-oxidation state of Pt, and then the formation of the

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

21

80 Pt-Pt

Magnitude of FT / Å-4

60

Pt foil

Pt/Al2O3

40 Pt-O

Pt-O-Ce Pt /CZY

20 Pt-O-Pt PtO2 0

0

1

2

3 R /Å

4

5

6

FIG. 16. Fourier-transformed k3x data of Pt L3-edge EXAFS for supported Pt catalysts after 8001C aging in air and the standard samples of Pt foil and PtO2 powder.

rigid Pt–O–Ce bond acts as an anchor. The formation of the Pt–O–Ce bond on the CZY suppresses the sintering of Pt. The average size of the Pt metal particles after the aging treatment under the oxidizing atmosphere on SiO2, Al2O3, ZrO2, TiO2, CeO2 and CZY was estimated using the CO pulse method. Also, the binding energy of the O(1s) electron in those support oxides was measured by XPS analysis. The XANES spectra of the L3-edge of Pt on these oxides were investigated. There is a linear relationship between the white-line intensity in the XANES spectra and the oxidation state of PtOx on some metal oxide supports (Yoshida et al., 2005). On the basis of this linear relationship, the oxidation state of Pt on Al2O3 was estimated as 0, and as 3.53 on CZY, etc. Figure 17 shows the correlation between these three factors on the various supported Pt catalysts. It is clear that there is a very strong correlation between these three factors. The oxidation state of Pt after aging increased with the decreasing binding energy of the O(1s) electron. This indicates that the Pt-oxide-support interaction strengthens as the electron density of oxygen in support oxide increases, that is, Pt particles on the electron dense support oxide are oxidized easily. Subsequently, the Pt

22

50

4 SiO2

CZY TiO2 CeO2 Al2O3

25

SiO2

2 ZrO2

ZrO2

CeO2 CZY 532 530 531 Binding Energy of O1s in support oxide / eV Al2O3

0 533

TiO2

0

Oxidation state of Pt after aging

Pt particle size after ageing /nm

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

529

FIG. 17. Pt-oxide-support interaction and its relation to Pt sintering in an oxidizing atmosphere.

particle size after the aging decreases as the electron density of oxygen in the support increased. As a result, the sintering inhibition effect on Pt can be controlled by the electron density of oxygen in the support oxide through the Pt-oxide-support interaction. That is the key parameter of the Pt-oxide-support interaction and its relation to Pt sintering in an oxidizing atmosphere.

C. RE-DISPERSION

OF

PLATINUM SUPPORTED

ON

CERIA-BASED OXIDE

Nishihata et al. (2002) reported the re-dispersion of Pd in a Perovskite-type oxide. They investigated the oxidation state and the local structure of Pd by using X-ray absorption analysis. Pd occupies the B-site in La2PdCoO6 in the oxidized sample. For the reduced catalyst, the XAD and XANES measurements suggested the segregation of metallic Pd from the perovskite crystal. They imply that Pd also moves back and forth between the B-site in the perovskite structure and sites within the lattice of Pd metal clusters dispersed on perovskite surface when the catalyst is exposed to fluctuations in the redox characteristics of the emission exhaust. The re-dispersion phenomena of sintered Pt on CZY were observed by in situ time-resolved Turbo-XAS in fluorescence mode at ID-24 of ESRF (Nagai et al., 2006). H2 (3%) (He balance) and O2 (20%) (He balance) gases were introduced to the in situ sample cell alternately every 60 s. The white-line peak height of the normalized Pt L3-edge XANES for the sintered Pt/CZY catalyst was collected every 1 s, which is plotted in Fig. 18. The white-line peak height changes very quickly, within 1 s, between the values of reduced and that of oxidized Pt after switching the gas atmosphere. This result indicates that the reduction and oxidation of Pt is very fast. While the white-line peak height under the reducing atmosphere is constant, the height under the oxidizing atmosphere increases

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

23

20% O2/He (60 s)

3% H2/He (60 s)

1.6

∆I

Height of XANES Peak

1.8

1.4

1.2

0

500

1000

1500

Time (s)

Pt atom CZY support 5 nm

Pt particle size: 7 nm 30 s

3 nm 1000 s

FIG. 18. Time resolved height of the XANES peak for the sintered Pt/CZY catalyst (Pt particle size; 7 nm) under oxidizing/reducing atmosphere at 6001C.

gradually. The difference between the white-line peak height of the oxidized and reduced samples corresponds with the particle size of Pt as shown in Fig. 19 (Nagai et al., 2006). Therefore, it is indicated that the Pt particle size of the aged catalyst decreases from 7 to 5 nm after 60 s, and then to 3 nm after 1,000 s. This kind of Pt re-dispersion was not observed in a conventional Pt/Al2O3 catalyst. These results suggest that the Pt particle on CZY is easily oxidized under the 20% O2 gas stream through the strong Pt-ceria-support interaction followed by breaking into small pieces of oxide particles, i.e. Pt re-dispersion.

V. NOx Storage and Reduction Catalyst and Reaction Mechanism A. NOX REDUCTION METHOD UNDER LEAN CONDITIONS Along with the concern in the pollutants of NOx, CO and HC, the care about the emission of CO2 is also increasing. Today, fuel-efficient lean-burn engines

24

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

1.5

∆I

∆ I (a.u.)

Normalized XANES

0.6

1.0 20% O2 /He 0.5

0.4

0.2

3% H2 /He

0.0 11520

0 11560 11600 Photon Energy (eV)

0

5

10

15

Pt particle size (nm)

FIG. 19. Pt L3-edge XANES of a Pt/CZY catalyst under oxidizing/reducing atmosphere at 4001C (left). Relationship between Pt particle size and DI (right).

Stoichiometric point

Oxidizing condition 10 Oxygen concentration (%)

Conversion (%)

100

HC NOx

CO

0 12

14

16

18 A/F

20

22

24

0 26

FIG. 20. Performance of a conventional 3-way catalyst.

have been widely adopted. Figure 20 shows the performance of a conventional TWC. Under oxidizing conditions in the lean-burn exhaust gas, the TWC acts as an oxidation catalyst efficiently controlling CO and HC emissions, but the conversion of the NOx emissions falls to very low levels. The NOx reduction for the exhaust from lean-burn engine is one of the greatest challenges in environmental protection, and a lot of researchers have strived to develop more effective catalysts by many ways. Their efforts could be possibly categorized into four approaches: (1) NOx direct decomposition, (2) selective catalytic reduction on NOx with hydrocarbons (HC–SCR), (3) selective catalytic reduction on NOx with ammonia (NH3–SCR) and (4) NOx storage and reduction (NSR).

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

25

(1) NOx direct decomposition seems the most attractive solution in emission control, because the reaction does not require any reductants added and potentially could lead to the formation of only N2 and O2. Cu–zeolite is one of the best catalysts for NOx direct decomposition, but the activity is very poor and needed to be improved several orders of magnitude high (Iwamoto et al., 1981). (2) HC–SCR is a desirable way for the use of very similar type of hydrocarbons that could be found in exhaust gases, and lots of papers have been published (Burch and Watling, 1996; Hamada et al., 1990; Held and Konig, 1990; Iwamoto et al., 1990). Many types of catalyst, in particular Cu–zeolites, alumina, Pt-loaded catalysts, show HC–SCR activities and have an operation window for NOx reduction as a function of temperature, respectively. That is because NOx reduction with HC is competitive to HC oxidation with O2 in the oxidizing conditions. Catalyst for automotive exhaust is subjected to have high activity through a wide operating condition, therefore, HC–SCR catalyst has not yet used practically for automotive pollution. (3) NH3–SCR is a mature technology for steady-state conditions from power plant and stationary diesel engines (Forzatti, 2001), while is not sufficient for mobile applications, in particular motor vehicles, the catalyst is operated under fast transient conditions, and exposed to exhausts of a large temperature range. Though V–W/Ti, Cu(or Fe)–zeolite catalysts are candidates for NH3–SCR, some problems are sticking out, such as the facility for carrying urea as NH3 source on board, and the social facility for urea supply (Johnson, 2001). (4) NSR is very attractive method for NOx removal by storing NOx under lean conditions and then reducing the stored NOx to N2 under rich excursions by engine operation: this technology is also referred as a lean NOx trap. In this chapter, NSR catalyst and the mechanism for NOx reduction is described.

B. OUTLOOK

OF

NSR CATALYST

The NSR catalysts are mainly composed of precious metals as active site, basic materials such as barium compounds for NOx storage, and large surface oxides as support. Figure 21 illustrates the reaction mechanism of NOx storage and reduction on the catalyst. Under oxidizing conditions, NO is first oxidized on precious metals and stored as nitrates on the neighboring NOx storage compounds. Subsequently, the stored NOx is reduced to nitrogen by the following two steps: decomposition of nitrates and then reaction with reducing agents on the precious metals. Figure 22 (a) illustrates the schematic diagram of lean-burn gasoline system commercialized in Japan, and Fig. 22 (b) shows an

26

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Oxidizing condition

Reducing condition CO2,H2O HC,CO,H2

NO3-

NO+O2 (a)

N2

(b) nitrate

NO2 M

PM

(c)

(d)

NO2 M

PM

support

support PM: precious metal, MNOx storage compound

FIG. 21. NOx storage and reduction scheme on NSR catalyst.

example of air and fuel mixture control methods during constant cruising (Kato et al., 1994). As the figure shows, NOx concentration in the outlet gas becomes higher with time in lean-burn condition at A/F=23, which means the catalyst becomes to be saturated with NOx. A rich mixture supply at A/F=10 at a certain interval reduces the stored NOx effectively with very little fuel penalty.

C. MECHANISM

OF

NSR CATALYST

Recently, the mechanism of NOx adsorption and desorption, reduction on NSR catalyst has been investigated by a lot of scientists (Epling et al., 2004; Fridell et al., 1999, 2000; Lietti et al., 2001; Takahashi et al., 1996, 2006). It appears now clearly the storage/reduction is rather complex process due to the complex nature of the exhaust gases. However, four reactions described later are believed to be the main routes with the NSR reaction: (a) NO oxidation to NO2 (oxidizing condition), (b) NOx storage on basic material (oxidizing condition), (c) NOx release from the basic material (reducing condition) and (d) NOx reduction to N2 (reducing condition). (a) This route proceeds on oxidation catalysts at low temperature being restricted due to the thermodynamic equilibrium of NO and NO2. It is well known that Pt catalysts promote NO oxidation significantly in oxidizing condition, while its behavior in lean-burn exhaust is a little bit different (Piacentini et al., 2005). The residual agents of CO and hydrocarbons in oxidizing condition reduces NO2 to NO on the catalyst; hence, the outlet NO2 concentration falls lower than the value merely derived from thermodynamic equilibrium with NO oxidation reaction (Fig. 23). With CO or hydrocarbons existing in the reaction atmosphere, it does not matter

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

27

(a)

(b) FIG. 22. (a) Schematic diagram of lean-burn gasoline system with NSR catalyst. (b) Rich mixture control method during constant speed driving.

if NO2 or NO is used as the inlet NOx species; the outlet NO2 concentration would turn out the same. Thus, the NOx storage amount with NO2 as an inlet NOx source is approximately the same as that with NO. If CO or hydrocarbons is not present in the reaction condition, the outlet NO2 concentration equals to the thermodynamic value with NO oxidation reaction, resulting in the increase of NOx storage amount on the catalyst. Therefore, both NO oxidation to NO2 and the removal of the residual reducing agents from exhaust gas are necessary for improving the NOx storage performance at low temperature.

28

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

500 No catalyst NO2 conc.(ppm)

400

300

Without reducing gases

Thermodynamic equilibrium

200 With reducing gases 100

0 250

300

350

400

Temperature FIG. 23. Effect of H2 and CO coexistence for NO2 formation on the NSR catalyst in oxidizing condition.

(b) Comparing the Fourier transform infrared (FTIR) spectra before and after NOx storage on the Pt/Ba catalyst, the majority of NOx stored is a nitrate species, in particular at high temperature due to the low thermal stability of nitrite (Takahashi et al., 1996). The pathways for NOx storage exist in both nitrite and nitrate routes (Epling et al., 2004; Fridell et al., 2000). The nitrite route implies a stepwise oxidation of NO on precious metal followed by adsorption at a neighboring Ba site to form a nitrite ad-species that is then oxidized to a nitrate. The nitrate pathway implies nitrates form through the disproportionation reaction of NO2. The nitrite route seems to occur on Ba site near precious metal, so that is considered more effective and the main route under the actual exhaust conditions. The earlier consideration was derived from a simple reaction gas system, for example NO–O2 reaction, although there exists much amount of CO2 and H2O in the exhaust gases. The kinetics and amount of NOx storage are highly affected by the presence of CO2 and H2O (Epling et al., 2004; Fridell et al., 1999; Lietti et al., 2001). It was shown that BaO, Ba(OH)2 and BaCO3 coexist at the surface on the Pt/ Ba/Al2O3. NOx storage occurs first at the Ba sites in the oxide form, then the hydroxide and finally the carbonate. CO2 strongly competes with NOx for the adsorption sites, thus decreases the NOx storage amount on the catalyst. The effect of H2O is reported to have both faces, promote or suppress depending on temperature. The promotion effect of H2O was observed at low temperature, whereas at high temperature an inhibition effect was observed. There is a general agreement that the fraction of Ba involved in the NSR process is a small portion of the total loading in the catalyst, and the Ba loading amount affects the NSR behavior. Ba loading effect of Pt–Ba/Al2O3 catalysts was investigated with calcinations of the catalysts

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

29

(Piacentini et al., 2005). It was resulted that there were three different Ba-containing species; amorphous BaO on Al2O3 surface, amorphous carbonates and crystalline carbonate. Amorphous carbonate showed relatively low thermal stability and possesses high reactivity for NOx storage. (c) NOx release from the catalyst is caused by atmosphere change from oxidizing to reducing condition. There are two primary forces for NOx release on the catalyst. One is generated by exothermic reactions such as oxidation of the reductant in the stream. NOx can be released since the nitrate stability decreases with increasing temperature. The second cause is the introduction of the reductant species to achieve a net reducing environment. That is, the equilibrium stability of nitrate species is dramatically reduced. This route is also accelerated by Pt coexistence with promoting the reactions. (d) The bottleneck step for the entire NSR process should be a reduction of the stored NOx as low fuel penalty as possible, that means of a restricted rich condition. It is very important to clarify the reduction reaction in the NOx reduction process. Figure 24 shows NOx reduction abilities of reducing agents, H2, CO and C3H6 (Takahashi et al., in press). The order of the activities is the following: H2 W CO W C3H6. And, the H2 generation is more apparent and efficient by water gas shift reaction than that through the steam reforming reaction on the NSR catalyst. As described earlier, CO2 competes with NOx for adsorption on Ba site. This competition increases the rate of NOx releases under reducing condition.

600

NOx Concentration / ppm

500 400 300 200 100 0

0

100

200 Time / s

300

400

FIG. 24. Effect of reducing gas species for NSR reaction behavior at 2501C. Reducing gas: none (), C3H6 (X), CO (B) and H2 (~).

30

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Pt / Ba / SiO2 NOx storage ratio (%)

100

Catalyst A

Catalyst A

50

Ba / SiO2

Pt / SiO 2

Catalyst B 0 500

550 600 Temperature (K)

650 Catalyst B

FIG. 25. Effect of arrangement with precious metal and NOx storage compound to NOx storage ratio.

Reaction (b) and (d) involves the spillover processes of NOx between precious metal and NOx storage site. The importance of close contact between Pt and Ba material was investigated through comparison of catalyst systems in which the two components are arranged differently (Fig. 25) (Takahashi et al., 1996). Catalyst A was synthesized by supporting Pt and Ba on identical SiO2 support particle. However, Catalyst B was prepared by blending Pt/SiO2 and Ba/SiO2. Figure 25 indicates the NOx storage on the Catalyst A and B. This result suggested that Pt and Ba should be well contacted for high NOx storage and the spillover process of NOx between them is very important for NSR reaction. Under NOx storage conditions, isotopic exchange between 15NO and stored NOx was investigated, and the exchange rate was much faster for a close Pt/ BaO coexisting on the Al2O3 support than for a combined Pt/SiO2+BaO/Al2O3 system (Cant et al., 2006). These results demonstrate that Pt and BaO in close proximity are essential for high spillover of NOx between them. The distance between Pt and BaO available for NSR reaction was investigated using a simple model catalyst on a flat substrate and a thin film by several analysis methods; EPMA, SEM, TEM, EELS, XPS and AES. The stored NOx was reduced in the region of a few micrometers around the Pt particle, after NOx adsorption on the catalyst followed by the H2 reduction for 1 h (Sakamoto et al., 2006).

D. SOX POISONING The NSR technology is regarded as the most reliable and attractive de-NOx method. However, catalyst degradation by SOx poisoning is a big problem. Sulfate was detected on the NSR catalyst after the aging test. SO2 is oxidized and reacts with the NOx storage materials to form sulfates, which means the

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

31

occupation of NOx storage site. In general, sulfates are invariably more thermal stable compared to the nitrates, and a sulfated catalyst requires high temperature to be reduced, i.e. to be regenerated, that may induce a thermal degradation of the catalyst. The model catalyst of flat substrate with Pt and BaO as described earlier was used to investigate the difference between NOx and SOx adsorption–desorption behavior (Sakamoto et al., 2006). Based on these experiments for the model catalyst, Fig. 26 shows that the NOx adsorption–desorption site around Pt edge is about a few micrometers in the storage-reduction process of 1 h, however, the SOx site is decreased for a few nanometers. This means that BaO within a few micrometers around the Pt can work as a NOx storage material without SOx poisoning. However, few nanometers of BaSO4 close to Pt can be reduced, that is, only few nanometers of Ba layer around Pt edge can work as a storage material after SOx poisoning. The length of diffusion of NOx and reductants in a real vehicle driving condition, i.e. storage-reduction process in few seconds, should be much shorter than a few micrometers. Duprez (2006) reported that oxygen diffusion coefficient at 4001C on a series of Rh/oxide catalysts was in the range of 2  10
20–4  10
16 m2/s. It means that oxygen atoms may migrate over distances around 10
10–10
8 m for 1 s. It will be a future challenge to quantify an effective site for the NSR process, however, the remarkable difference of adsorption–desorption behavior between NOx and SOx suggests that the NSR process should be blocked significantly after SOx poisoning. Figure 27 shows the relationship between the particle size of sulfate and their decomposition rate on an NSR catalyst (Takahashi et al., 1996). The smaller the sulfate size is, the easier the decomposition. It was also reported that the sulfate size was strongly related to SOx amount in the exhaust. As for reducing

a few micro meter BaOx

Pt

Ba(NO3)2

Si substrate

a few nanometer BaOx

Pt

BaSO4

Si substrate FIG. 26. Image of reduction area of nitrate and sulfate of barium around Pt.

32

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

FIG. 27. The relationship between particle size and decomposition rate of barium sulfate on the NSR catalyst.

species, hydrogen has a much greater reducing power among reducing agents such as CO and HC (Matsumoto, 2004). From these results, though it is not easy to decompose the sulfates, there are some directions to solve the problem. They are, (1) increase the number of active site by loading more and dispersing finely on the support, (2) improve the reducing condition which the NSR catalyst is exposed and (3) decrease the S content in fuel. However, increasing precious metal usage and reducing condition are not preferred from the point of view of environmental protection and effective use of valuables. So it is important to develop NSR catalysts with higher the SOx durability, meanwhile, more efforts in oil industry are necessary to lower sulfur content in the fuel. In the next chapter, the progress on improving NSR catalysts and on NSR catalyst system will be described.

VI. Improvement of NSR Catalyst and Engine System A. NSR CATALYST FORMULATION An NSR catalyst consists of precious metal as active site, basic material as NOx storage site, and support for highly dispersing these sites. As active site, Pt and Rh are much effective elements for NSR catalysts. One important role of the active site is to oxidize NO under oxidizing condition. Our early catalyst test showed that, precious metals and Mn oxide loaded on alumina are indicated a high NO oxidation activity. Particularly, Pt showed the highest turn over

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

33

FIG. 28. The relationship between basicity of NOx storage compounds and NOx storage amount.

frequency compared with other elements. The other role of the active site is to reduce NOx to N2. Precious metals, in particular, Rh is well known as the best element for NOx reduction activity. In addition, oxidation activities for HC and CO in exhaust gases are also necessary as automotive catalysts, Pt and Pd are suitable for oxidation. Therefore, NSR catalyst in general contains Pt and Rh, possibly Pd. As NOx storage material, basic materials as alkaline and alkaline earth compounds are popular in use. Figure 28 shows the influence of basicity of NOx storage compounds on NOx storage amount (Takahashi et al., 1996). A close correlation between the NOx storage amount and electron negativity is obtained, and stable nitrates can be formed with the strong basicity of NOx storage compounds. Barium compounds have been used widely for high NOx storage ability and its thermal stability. Potassium compounds are another component with a significant benefit at higher temperatures where the potassium nitrate is more stable than the typical barium nitrate (Konsolakis and Tentekakis, 2001; Toops et al., 2006). Support for NO oxidation reaction was reported that SiO2 seemed to be preferable (Xue et al., 1996). However, NSR catalysts are subjected high thermal stability like as the conventional automotive catalyst. Alumina supports is popular for the catalyst for its high thermal durability with a high surface area. Some other supports have been developed for improving SOx durability as described later.

B. IMPROVEMENT

OF

DURABILITY

AGAINST

SULFUR POISONING

One of the big problems for NSR catalyst is SOx poisoning. The storage material forms stable sulfates in lean conditions and loses NOx adsorption

34

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

capability. To improve SOx durability of NSR catalyst, a variety of approaches had been tried, such as additives, supports and substrate structure. The effect of the addition of transition metal elemets to Pt/Ba/Al2O3 catalyst was investigated for improving SOx durability (Yamazaki et al., 2001, 2004). NOx reducing activity of the catalyst was improved by some additives, and the highest activity was gained upon Fe-compound addition after aging test containing SO2. However, the addition of a Cu-compound had a negative effect on the NSR catalysis. Figure 29 shows SOx desorption performance of different Fe loading amount to Pt/Ba/Al2O3 exposed to an oxidizing atmosphere containing SO2. This result indicates that the Fe-compound promotes the SO2 desorption from the NSR catalyst. Figure 30 shows the BaSO4 particle size on the Pt/Ba/Al2O3 varies against Fe loading. The BaSO4 particle size of Fe-containing catalysts is far smaller than that of Pt/Ba/Al2O3, and the BaSO4 particle was smaller with increasing the amount of Fe loading. These results indicate that Fe-compound inhibits the growth in size of BaSO4 particles under oxidizing condition in the presence of SO2. Therefore, the Fe-compound promotes the decomposition of BaSO4 particles and the sulfur desorption when exposed to reducing conditions. The effect of Fe-compound addition on its TWC performance was also studied with Pt/Ba/Al2O3–CeO2 catalyst to improve the durability against thermal deterioration. The catalyst with Fe-compounds showed a higher catalytic performance after thermal aging at 8501C. It was assumed that, Fe-compound addition suppressed CO poisoning on precious metal of the catalyst. Therefore, the Fe-compound plays two important roles; (1) acting as oxygen storage site more effectively, (2) weakening the CO self-poisoning under reducing conditions, probably by providing oxygen to CO from iron oxide and/or Fe in Pt–Fe alloy.

FIG. 29. SO2 desorption profile of TPD measurement on the PtBa/Al2O3 with Fe-compounds exposed to a simulated oxidizing condition containing SO2.

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

35

FIG. 30. Effect of Fe loading to BaSO4 particle size on Pt/Ba/Al2O3 catalysts.

H2 has a great potential for reducing sulfates on the catalyst as described earlier. Rh loaded on ZrO2 was found to provide the high catalytic activity for H2 formation by steam reforming reaction (Duprez, 2006). The most effective way to sustain the reaction of H2 with sulfate is probably to produce H2 in the proximity of barium sulfate. Figure 31(a) shows the effect of Rh/ZrO2 addition to the NSR catalyst after an engine test of sulfur poisoning. Sulfur was easier to desorb from the catalyst with Rh/ZrO2 in reducing condition. Figure 31(b) illustrates a scheme of Rh/ZrO2 addition to Pt/Ba/alumina catalyst. H2 is produced on Rh/ZrO2 by steam reforming reaction, then react and decompose sulfate in reducing condition, resulting in the enhancement SOx removal on the catalyst. As for support material, TiO2 addition to the NSR catalysts leads to improve the durabily of the catalyst against sulfur poisoning (Duprez, 2006). TiO2 is an acidic material, and sulfates on TiO2 are less stable than that on alumina. Therefore, TiO2 particle dispersed in the alumina-based catalyst promotes the decomposition and removal of sulfates in the reducing condition. The promotion for SOx desorption is much more facilitated by smaller particle size of TiO2, which could be dispersed highly in the catalyst. ZrO2 is also effective for a support on K-compounds for high temperature use, for K-compound is apt to interact with alumina and TiO2 to loose its NOx storage ability. Recently, ZrO2–TiO2 mixed oxide as a support for K-compounds was found to improve the NOx removal activity (Takahashi et al., in press). Figure 32 shows NOx storage activity after aging with SO2 as a function of ZrO2 content using simulated exhaust gases. A relative high ability above 5001C was obtained with 60–80 wt% ZrO2 content, particularly at 70%. Figure 33 shows the state of potassium after the aging test with SO2 of the three catalyst, TiO2, ZrO2 and ZT70 (70% ZrO2–30% TiO2) as a support. The results indicate that the catalyst with ZT70 retained the highest amount of remaining active potassium,

36

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH (a) HC+H 2 O flow

Amount of Sulfur Desorption(a.u.)

Sulfur aging at A/F=22, 600 °C, 5h

With Rh/ZrO2

Conventional NSR cat. 300

400

700

500 600 Temperature / °C

(b)

SO2

BaSO4

HC, H2O H2

Ba

Pt

Rh/ZrO2

Al2O3

FIG. 31. (a) Effect of Rh/ZrO2 addition for sulfur desorption. (b) Schematic effect of Rh/ZrO2 addition to NSR catalyst for sulfur desorption.

NOx storage amount (a.u.)

100

50

500 °C 600 °C

0

0

20

60 40 ZrO2 content (wt%)

80

100

FIG. 32. NOx removal performance of several ZrO2–TiO2 catalysts after the sulfur aging test.

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

37

100

Ratio (%)

sulfate

Solid phase reaction

50

Remaining active 0

K /Pt/TiO2

K/Pt/ZT70

K /Pt/ZrO2

NH3 desorption amount ( µmol L)

FIG. 33. The state of potassium over the sulfur-aged catalysts.

600

400

200

0

0

20

40 60 ZrO2 content (wt%)

80

100

FIG. 34. NH3 desorption amount for several ZrO2–TiO2 oxides.

which was neither the formed-sulfate nor the solid-phase-reacted potassium. The acidity of support is an important factor regarding the SOx tolerance of the NSR catalyst. The catalyst containing ZT70 was verified to have the highest acid amount by NH3 Temperature Programmed Desorption (TPD) measurement (Fig. 34), and was supposed to be the best against SOx poisoning among the ZrO2–TiO2 supports. Furthermore, nano-level composite between alumina and ZT was found to improve thermal durability of the ZT support (Fig. 35) (Imagawa et al.). Figure 35b illustrates the scheme of this material (AZT). At high temperature, ZT particle is easy to agglomerate each other. As the result, the surface area of the ZT decreases, causing degradation of NSR activity. Alumina for a stable substance inhibits the sintering of ZT particle in high temperature region. NOx storage amount of the catalyst containing AZT was higher than that of the catalyst of ZT after SOx aging test as shown at Fig. 36.

38

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

ZrO2-TiO2 (ZT) High temperature Sintering

Alumina (Barrier) (

Alumina

ZT

ZT High temperature Suppress the sintering

AZ T

(a) Scheme of the AZT concept

Particle size of ZT (nm)

30

20

10

0

AZT

ZT

(b) Particle size of Z T after thermal ageing test at 1273 K. FIG. 35. (a) The concept of complex oxide of zirconia, titania and alumina (AZT) and (b) its effect on thermal durability after thermal aging test at 1,0001C.

One of the rate-determining processes on the whole NSR reaction is a diffusion of NOx and SOx in the catalyst. The thickness of catalyst component coat on the substrate also affects the NSR activity. The conventional catalyst converters have square-shaped cells for the substrate of catalyst support. The thickness of coating usually becomes a much greater thickness at the corner (Fig 37a). However, the thickness of coatings with a hexagonal cell substrate (Fig. 37b) is almost the same compared to that on the square cell substrate. From the results of engine test, SOx desorption of the catalyst was great improved with the hexagonal-type cell substrate, and NSR activity on the catalyst after durability test was also superior to the conventional catalyst with the square cell substrate.

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

39

40

NOx storage amount (µmol)

AZT catalyst

20 ZT catalyst

0 400

500 Temperature (°C)

600

FIG. 36. The NOx storage performance of ZT catalyst and AZT catalyst after sulfur-poisoning test.

FIG. 37. Photographs of wash-coat layer on monolithic substrate with square cells (left) and hexagonal cells (right) (see Plate 1 in Color Plate Section at the end of this book).

C. COMBINATION

OF

CATALYSTS

Among the many published reports on the applications of the catalytic system, the addition of an oxidation catalyst upstream of an NSR catalyst improved NOx storage activity through promoting NO2 formation under oxidative atmosphere. As described earlier, coexistence of reducing gas such as HC, CO is reduced NO2 into NO even in oxidizing atmosphere. Therefore, oxidation catalyst placed in front of NSR catalyst is necessary.

40

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Through investigating the effect from the combination of several types of catalysts, it was found that a particular catalyst combination, Pd/Al2O3, NSR catalyst and Cu/zeolite in turn, showed a high NOx reduction activity under oxidative atmosphere with periodic deep rich operation (Shinjoh et al., in press). NOx conversion with catalyst combinations of Pd/Al2O3+NSR catalyst and Pd/Al2O3+NSR catalyst+Cu/zeolite were measured in simulated automotive exhaust gases with periodically fluctuation between oxidative and reductive atmospheres. Comparing NOx conversions on them under the oxidative atmosphere in Fig. 38, the effect of Cu/zeolite addition for improving NOx conversion was remarkably indicated between 130 and 3101C. From the evolution patterns of gases, NOx, NH3 and HC, at 3101C on Pd/alumina+NSR catalyst and Pd/alumina+NSR catalyst+Cu/zeolite in the measurement (Fig. 39), it was suggested that NH3 and HC were adsorbed under the reductive atmosphere and reacted with NOx under the oxidative atmosphere on Cu/zeolite. Figure 40 shows NOx reduction activities on Cu/zeolite with the preadsorbed NH3 or C3H6 under oxidative atmosphere. These results indicated that the adsorbed NH3 selectively reacted with NOx at equal molar ratio, while the adsorbed C3H6 reacted with both NOx and O2. From these results, the high NOx reduction activity with this combination of catalysts could be elucidated by the following reaction mechanism (Fig. 41): Pd catalyst upstream of the NSR catalyst improved NOx storage ability by enhancing NO2 formation under oxidative atmosphere, the stored NOx was apt to be reduced to NH3 on the NSR catalyst. The generated NH3 was adsorbed on Cu/zeolite downstream of

100

NOx conversion (%)

90

Pd/alumina + NSR catalyst + Cu/ Zeolite

80 Pd /alumina + NSR catalyst 70

60

50 150

200

250 Temperature (°C)

FIG. 38. NOx reduction activity in cycled feed stream.

300

350

41

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

(a)

3000

300

3000

(b)

NOx , NH3 (ppm)

Inlet NOx 2000

200 NOx NH3 100

1000

HC (ppmC)

NOx, NH3 (ppm)

Inlet NOx 200

2000 NOx

100

1000 HC

HC

0

0

3 6 Re Ox. Red. Ox. Time (min)

0

0

0

HC (ppmC)

300

NH3

3 6 Re Ox. Red. Ox. Time (min)

0

FIG. 39. Evolution pattern of several gases in cycled feed stream at 3101C. (a) Pd/Al2O3+NSR catalyst and (b) Pd/Al2O3+NSR catalyst+Cu/Zeolite.

300 (a)

Inlet NOx

200

NOx , NH3 (ppm)

NOx , NH3 (ppm)

300

NOx

100 NH3 0 100

200 300 400 Temperature (°C)

500

Inlet NOx

(b) 200

NOx

100 HC 0 100

200 300 400 Temperature (°C)

500

FIG. 40. NOx reduction activity with pre-adsorbed NH3 or C3H6 on Cu/Zeolite. After saturating the adsorbtion of a reductant: (a) NH3 and (b) C3H6, on Cu/Zeolite, NOx reduction activity was measured with increasing temperatures at 201C/min under the oxidizing atmosphere.

the NSR catalyst under the reductive atmosphere, and easily reacted with NOx on the Cu/zeolite under the subsequent oxidative atmosphere. The NSR technology has been also applied to diesel engines, and is most reliable and attractive method for lean-burn combustion vehicles. Diesel particulate-NOx reduction system (DPNR) method is used to realize the simultaneous and continuous reduction of particulate and NOx is also recommended. This catalyst system is DPF combined with NSR catalyst. Soot on catalyst is removed during NOx reduction by occasional rich engine modification. Many other catalyst systems with NSR catalyst have been also developed. With decreasing S content in fuel and successive development of

42

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Synergic effect

storage NO+O2 → NOx

oxidative exhaust gas with periodic deep rich operation

oxidation catalyst

NOx+NH3(ad) → N2

NSR catalyst

NOx(ad) → NH3

Cu/zeolite

high NOx reduction activity

adsorb

FIG. 41. Schematic concept of the synergy effect of the catalyst combination.

catalyst will lead to the worldwide prevailing of this kind of catalyst. We expect for great progress and big improvements in cleaning the exhaust gases, as well as the fuel economy by using these techniques in the near future.

VII. Conclusions This chapter reviews the recent progress of research on the dynamic behavior and characterization of automobile catalysts mainly done in our laboratory. Basically, automobile catalysts are used under the non-steady condition in which a gas concentration, a gas flow rate and a temperature fluctuate with a car-driving mode. Therefore, it is very important for developing automobile catalysts to clarify the mechanism of their dynamic behavior and characterize the structure and state of materials under dynamic conditions. Four topics on the dynamic phenomena for TWC and NSR catalysts were described in this chapter. (1) Oxygen storage and release in TWCs The precise local structure around Ce and Zr atoms of CeO2–ZrO2 mixed oxide (CZ) was investigated to clarify their effects on OSC. The amount of OSC per Ce atom of R–CZ (prepared by the calcination of M–CZ with graphite at 1,2001C under a reducing atmosphere) is much higher than that of S–CZ (prepared by an attrition milling process of CeO2 powder with ZrO2 spheres in ethanol) and M–CZ (prepared by the hydrolysis of an aqueous solution of ZrO(NO3)2 with ammonia on CeO2 powder), and is close to the theoretical value of 0.25, which means almost all of the quadruvalent Ce are reduced into trivalent Ce. The oxygen mobility in CZ materials is of similar magnitude. The regular arrangement of Ce and Zr atoms in R–CZ probably eases the release of oxygen. A new material ACZ for a TWC with high thermal stability is developed based on a novel concept, i.e. the diffusion barrier concept. Diffusion

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

43

barrier layers of Al2O3 are built up between CZ particles to inhibit the coagulation or grain growth of CZ. (2) Sintering of PGM The X-ray absorption analysis of Pt catalysts suggests that the Pt-oxidesupport interaction inhibits the sintering of Pt particles and the sintering inhibition effect on Pt can be controlled by the electron density of oxygen in the support through the Pt–O–Ce bond. The real-time observation of sintered Pt on CZ materials by in situ timeresolved Turbo-XAS demonstrates the re-dispersion of Pt particles. The Pt particle size of the aged catalyst decrease from 7 to 5 nm after 60 s and then to 3 nm after 1,000 s under the alternating flow of reducing and oxidizing gases at 6001C. These results suggest that the sintering and re-dispersion of Pt is highly affected by the characteristics of the surface oxygen of supports. (3) NSR catalyst and reaction mechanism Four reaction steps, (a) to (d), are the main routes in the NSR reaction and the most difficult problem to be solved for the NSR catalyst is the deactivation caused by sulfur poisoning. The course of improvement of the NSR catalyst is proposed. (a) NO oxidation to NO2 (oxidizing condition): both NO oxidation to NO2 and the removal of the residual reducing agents from exhaust gas are necessary for improving the NOx storage performance at low temperature, (b) NOx storage in basic materials (oxidizing condition): amorphous barium carbonate shows relatively low thermal stability and possesses high reactivity for NOx storage, (c) NOx release from the basic material (reducing condition): accelerated by Pt coexistence with promoting the reactions and (d) NOx reduction to N2 (reducing condition): H2 generated by water gas shift reaction is quite effective for the reduction. BaO within a few nanometers around the Pt can work as NOx storage material in the reaction containing SO2. Also, the smaller the sulfate particle size in the aged catalyst is, the easier its decomposition. (4) Improve NSR catalyst and system The NOx storage amount has a good correlation with the electron negativity of storage materials. Barium compounds have been used widely for high NOx storage ability and its thermal stability. Potassium compounds are another component with a significant benefit at higher temperatures. The addition of a Fe-compound inhibits the growth in size of BaSO4 particles under oxidizing conditions in the presence of SO2, and then the Fe-compound promotes the decomposition of BaSO4 particles and the sulfur desorption when exposed to reducing conditions. Also, the catalyst with Fe-compounds shows a higher catalytic performance after thermal aging at 8501C. This suggests that the addition of a Fe-compound suppresses CO poisoning on precious metal of the catalyst. That is, the

44

SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Fe-compound plays two important roles: (1) acting as oxygen storage site more effectively, (2) weakening the CO self-poisoning under reducing conditions, probably by providing oxygen to CO from iron oxide and/or Fe in Pt–Fe alloy. Rh/ZrO2 has the high catalytic activity for H2 formation by steam reforming reaction. In the NSR catalyst contained Rh/ZrO2, H2 is produced on Rh/ZrO2 by steam reforming reaction, and then sulfates are decomposed in reducing condition, resulting in the enhancement of SOx removal from sulfur-poisoned catalyst. TiO2 addition to the NSR catalysts leads to an improvement of SOx resistance of the catalyst. TiO2 is an acidic material, and sulfates on TiO2 are less stable than that on alumina. Hence, TiO2 promotes the decomposition and removal of sulfates in reducing conditions. Also, ZrO2–TiO2 mixed oxide as a support for K-compounds improves the NOx removal activity. One of the rate-determining processes on the whole NSR reaction is a diffusion of NOx and SOx in the catalyst. The thickness of coatings with a hexagonal cell substrate is almost uniform, therefore, SOx desorption of the catalyst is greatly improved with the hexagonal cell substrate compared to that on the conventional square cell. An oxidation catalyst placed upstream of an NSR catalyst improves NOx storage activity through promoting NO2 formation under oxidative atmosphere and oxidizes coexisting reducing gas such as HC, CO which reduces NO2 into NO even in oxidizing atmosphere. The NSR technology is also applied to diesel engines. DPNR which combines a DPF with an NSR catalyst realizes the simultaneous reduction of particulate and NOx. Soot on catalyst is removed during the NOx reduction process by an occasional rich engine operation. We have a dream of realizing a car which purifies the air of the earth through its driving. We will be improving an automobile catalyst on and on as described in this chapter: oxygen storage capacity, NSR ability, etc. In addition, we would be making efforts to clarify the nature of catalysis in automobile catalysts, i.e. electronic interaction between PGM, supports and exhaust gases, dynamic observations of an electronic state of catalysts and reaction kinetics, etc. for realizing our dream.

REFERENCES Burch, R., and Watling, T. C. Catal. Lett. 37, 51 (1996). Cant, N. W., Liu, I. O. Y., and Patterson, M. J. J. Catal. 243, 309 (2006). Dong, F., Suda, A., Tanabe, T., Nagai, Y., Sobukawa, H., Shinjoh, H., Sugiura, M., Descorme, C., and Duprez, D. Catal. Today 90, 223 (2004a).

BEHAVIOR AND CHARACTERIZATION OF AUTOMOBILE CATALYSTS

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Dong, F., Suda, A., Tanabe, T., Nagai, Y., Sobukawa, H., Shinjoh, H., Sugiura, M., Descorme, C., and Duprez, D. Catal. Today 93, 827 (2004b). Duprez, D. Catal.Today 112, 17 (2006). Epling, W. S., Campbell, L. E., Yezerets, A., Currier, N. W., and Parks, J. E. Catal. Rev. 46, 163 (2004). Fiedorow, R. M. J., Chahar, B. S., and Wanke, S. E. J. Catal. 51, 193 (1978). Forzatti, P. Appl.Catal. A 222, 221 (2001). Fridell, E., Persson, H., Westerberg, B., Oksson, L., and Skoglundh, M. Catal. Lett. 66, 71 (2000). Fridell, E., Skoglundh, M., Westerberg, B., Johansson, S., and Smedler, G. J. Catal. 183, 196 (1999). Gandhi, H. S., Piken, A. G., Shelef, M., and Delesh, R. G., SAE Technical Paper 760201 (1976). Greegor, R. B., and Lytle, F. W. J. Catal. 63, 476 (1980). Hamada, H., Kintaichi, Y., Sasaki, M., Ito, T., and Tabata, M. Appl. Catal. 64, L1 (1990). Harris, P. J. F. J. Catal. 97, 527 (1986). Held, W., and Konig, A., SAE Technical Paper 900496 (1990). Imagawa, H., Tanaka, T., Takahashi, N., Matsunaga, S., and Shinjoh, H., TOCAT5, PI-430, Tokyo, Japan (2006). Iwamoto, M., Yahiro, H., Yu-u, Y., Shundo, S., and Mizuno, N. Shokubai 32, 430 (1990). Iwamoto, M., Yokoo, S., Sakai, K., and Kagawa, S. J. Chem. Soc. Faraday Trans. 1 77, 1629 (1981). Johnson, T. V. Soc. Automot. Eng. SP-1581, 23 (2001). Kanazawa, T., Suzuki, J., Takada, T., Suzuki, T., Morikawa, A., Suda, A., Sobukawa H., and Sugiura, M., SAE Technical Paper 2003-01-0811 (2003). Kato, K., Kihara, T., Asamuna, T., Gotoh, M., and Shibagaki, S. Toyota Tech. Rev. 44, 2 (1994). Konsolakis, M., and Tentekakis, I. V. Appl. Catal. B 29, 103 (2001). Kummer, J. K. Prog. Energy Combust. Sci. 6, 177 (1980). Lietti, L., Forzatti, P., Nova, I., and Tronconi, E. J. Catal. 204, 175 (2001). Matsumoto, S. Catal. Today 90, 183 (2004). Matsumoto, S., Miyoshi, N., Kanazawa, T., Kimura, M., and Ozawa, M., in ‘‘Catal. Sci. Technol.’’ (S. Yoshida, N. Takezawa, and T. Ono Eds.), Vol. 1, p. 335. Kodansha, Tokyo (1991). Miyoshi, N., Matsumoto, S., Ozawa, M., and Kimura, M., SAE Technical Paper 891970 (1989). Muraki, H., Sobukawa, H., and Fujitani, Y. Nippon Kagaku Kaishi 176, (1985). Nagai, Y., Hirabayashi, T., Dohmae, K., Takagi, N., Minami, T., Shinjoh, H., and Matsumoto, S. J. Catal. 242, 103 (2006). Nagai, Y., Takagi, N., Ikeda, Y., Dohmae, K., Tanabe, T., Pascarelli, S., Guilera, G., Newton, M., Shinjoh, H., and Matsumoto, S., PI-439, TOCAT5, 2006, Tokyo, Japan. Nagai, Y., Yamamoto, T., Tanaka, T., Yoshida, S., Nonaka, T., Okamoto, T., Suda, A., and Sugiura, M. J. Synchrotoron Rad. 8, 616 (2001). Nagai, Y., Yamamoto, T., Tanaka, T., Yoshida, S., Nonaka, T., Okamoto, T., Suda, A., and Sugiura, M. Catal. Today 74, 225 (2002). Nishihata, Y., Mizuki, J., Akao, T., Tanaka, H., Uenishi, M., Kimura, M., Okamoto, T., and Hamada, N. Nature 418, 164 (2002). Ozawa, M., Kimura, M., and Isogai, A. J. Mater. Sci. 26, 4818 (1991). Ozawa, M., Kimura, M., and Isogai, A. J. Alloys Comp. 193, 73 (1993). Piacentini, M., Maciejewski, M., and Baiker, A. Appl. Catal. B 59, 187 (2005). Sakamoto, Y., Okumura, K., Kizaki, Y., Matsumnaga, S., Takahashi, N., and Shinjoh, H. J. Catal. 238, 361 (2006). Shinjoh, H., Muraki, H., and Fujitani, Y., Stu. Surf. Sci. Catal. 30, 187, Elsevier, Amsterdam (1987). Shinjoh, H., Muraki, H., and Fujitani, Y. Appl.Catal. 49, 195 (1989). Shinjoh, H., Muraki, H., and Fujitani, Y., Proceedings of the 2nd International Symposium (CAPOC2), Brussels, September 10–13 (1990). Shinjoh, H., Takahashi, N., and Yokota, K., Topics Catal. (in press).

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SHINICHI MATSUMOTO AND HIROFUMI SHINJOH

Suda, A., Kandori, T., Sobukawa, H., and Sugiura, M. J. Ceram. Soc. Jpn. 108, 473 (2000). Suda, A., Sobukawa, H., Suzuki, T., Kandori, K., Ukyo, Y., and Sugiura, M. J. Ceram. Soc. Jpn. 109, 177 (2001). Suda, A., Ukyo, Y., Sobukawa, H., and Sugiura, M. J. Ceram. Soc. Jpn. 110, 126 (2002). Takahashi, N., Shinjoh, H., Iijima, T., Suzuki, T., Yamazaki, K., Yokota, K., Suzuki, H., Miyoshi, N., Matsumoto, S., Tanizawa, T., Tanaka, T., Tateishi, S., and Kasahara, K. Catal. Today 27, 63 (1996). Takahashi, N., Suda, A., Hachisuka, I., Sugiura, M., Sobukawa, H., and Shinjoh, H., Appl. Catal. B (in press). Takahashi, N., Yamazaki, K., Sobukawa, H., and Shinjoh, H. J. Chem. Eng. Jpn. 39, 437 (2006). Takahashi, N., Yamazaki, K., Sobukawa, H., and Shinjoh, H., Appl. Catal. B (in press). Toops, T. J., Smith, D. B., and Partridge, W. P. Catal. Today 114, 112 (2006). Xue, E., Seshan, K., and Roth, J. R. H. Appl. Catal. B 11, 65 (1996). Yamazaki, K., Suzuki, T., Takahashi, N., Yokota, K., and Sugiura, M. Appl. Catal. B 30, 459 (2001). Yamazaki, K., Takahashi, N., Shinjoh, H., and Sugiura, M. Appl. Catal. B 53, 1 (2004). Yokota, K., Muraki, H., and Fujitani, Y., SAE Technical Paper 850129 (1985). Yoshida, H., Nonoyama, S., Yazawa, Y., and Hattori, T. Phy. Scr. T 115, 813 (2005).

PLATE 1. Photographs of wash-coat layer on monolithic substrate with square cells (left) and hexagonal cells (right) (for Black and White version, see page 39).

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS Mehrdad Ahmadinejad, Maya R. Desai, Timothy C. Watling and Andrew P.E. York Johnson Matthey Technology Centre, Blount’s Court, Sonning Common, Reading, RG4 9NH, UK I. Introduction II. Applications of Modelling/Rational Design of Emissions Control System A. Catalyst System Design B. Modification of Engine Calibration III. Monolith Reactor Model IV. Model Development, Illustrated by 3-Way Catalysis A. Approach/Methodology B. Reactions Involved C. Developing Kinetics from Microreactor Data D. Model Validation V. Other (Diesel) Aftertreatment Systems A. Diesel Oxidation Catalysts B. Ammonia SCR C. NOX Traps D. Filters VI. Future Directions Acknowledgement List of Symbols Abbreviations References

48 49 50 53 56 59 59 62 63 71 76 78 83 88 91 97 98 98 99 100

Abstract As emissions requirements become evermore stringent, designing a system to meet the legislation subject to packaging and cost constraints becomes progressively more difficult. This is further exacerbated by increasing system complexity, where more than one technology may be required to control all the legislated pollutants and/or an active Corresponding author. Tel.: +44(0)118 924 2139; Fax: +44(0)118 924 2106. E-mail: [email protected]

47 Advances in Chemical Engineering, vol. 33 ISSN 0065-2377 DOI 10.1016/S0065-2377(07)33002-0

Copyright r 2008 by Elsevier Inc. All rights reserved.

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MEHRDAD AHMADINEJAD ET AL.

regeneration strategy is involved. By using computer simulation to aid design, this process can be considerably shortened, saving time and money. As an added bonus, the exercise of model development and application can lead to a greater understanding of the chemistry and physics of the system. This chapter gives an overview of some of the work done at Johnson Matthey on simulating emissions control devices. It covers both model development and the application of modelling to aid the design of exhaust aftertreatment systems. Simulation of the majority of emissions control technologies is covered, viz. 3-way catalysts for gasoline exhaust; and oxidation catalysts, urea/ammonia selective catalytic reduction, NOX traps and particulate filters for diesel exhaust. Examples of the application of modelling to consider parameters such as substrate (monolith) type, catalyst size and aspect ratio, engine calibration and precious metal loading and distribution are given.

I. Introduction As emissions requirements become evermore stringent (Heck and Farrauto, 2001), designing a system to meet the legislation subject to packaging and cost constraints becomes progressively more difficult. This is further exacerbated by increasing system complexity, particularly for diesel exhaust, where separate technologies may be used for NOX and particulate control and active regeneration strategies involved. Even with the well-established 3-way catalysts (TWCs) used for gasoline exhaust, there is likely to be more than one catalyst, each with a different precious metal loading and combination/selection of platinum, palladium and rhodium and/or support (washcoat) to obtain optimum reactivity. Thus, designing an aftertreatment system by a ‘‘trial and error’’ approach becomes more protracted and expensive; it is not just testing that is time consuming, but also preparation of catalysts, aging and mounting. By using computer simulation to aid design, this process can be considerably shortened. As an added bonus, the exercise of model development and application can lead to a greater understanding of the chemistry and physics of the system. In recent years there has been an increase in interest in simulation of automotive aftertreatment systems both among academics (Marin and Hoebink, 1997), catalyst manufacturers (Ahmadinejad et al., 2006; York et al., 2005), vehicle/engine producers (Baba et al., 2000; Laing et al., 1999; Oh and Cavendish, 1982) and consulting companies (Wanker et al., 2002). A lot of this work is published in the open literature, however, there is a limit to what industry will publish, in order to maintain a competitive advantage. In particular, kinetic equations and especially kinetic parameters are generally

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

49

regarded as proprietary information and hence are rarely seen in published work. This chapter discusses both the development of models and their application. One way of organising this chapter would be to discuss model development first and then go on to consider the applications. However, as the entire reason for developing these models is to have a practical tool for system design, it was decided to start with the application of the models. The next section discusses the physical model for a monolith reactor, which is common to all technologies (except diesel particulate filters) discussed later. Our approach to model development will then be covered in detail, using TWCs as an example. The final section will outline work done on the various technologies used for diesel exhaust aftertreatment.

II. Applications of Modelling/Rational Design of Emissions Control System As we develop aftertreatment system models to be used as a practical tool for system design rather than as an exercise in itself, this chapter will start by discussing the applications of modelling. Since catalyst kinetics must be empirically determined (until molecular modelling advances considerably), modelling cannot help in catalyst design, i.e. to design the chemistry. However, it can be used to aid designing an aftertreatment system, i.e. to use the chemistry to its best advantage. In an age where computer simulation is used in someway to design almost every component of a vehicle, it seems logical that computer simulation should be used to assist in the design of the aftertreatment system. Given the complexity of the chemistry involved, it seems unlikely that computers will replace testing in the near term, and indeed experimental testing is generally required by legislation. However, computer simulation can be used to examine a wide range of alternatives quickly and cheaply, enabling testing to be confined to only the most promising systems, thereby leading to a faster design process. Figure 1 illustrates some of the many variables in an emissions system that can be investigated with computer simulation. Computer simulation can be used to consider not only the effect of the emissions system itself, but also the effect of engine design. For example, the effect of recalibrating the engine to produce hotter exhaust gas or a different gas composition can be considered without actually doing this on the engine until the result is known to be worthwhile. The rest of this section will consider these two aspects separately. All the examples shown in this section are for TWCs.

50

MEHRDAD AHMADINEJAD ET AL.

Length Catalyst type

Diameter

Formulation

Flexible

Size

Insulated

Aspect ratio

Etc.

Substrate PGM loading Etc. Engine calibration

FIG. 1. Schematic illustrating possible configurations of engine (dark square), catalyst(s) (grey rectangles) and connecting pipes together with potential system variables.

A. CATALYST SYSTEM DESIGN Before discussing system parameters, which modelling can help define, it is worth briefly outlining the construction of the catalytic converter for those not familiar with them. The converter consists of a substrate (the physical support), which is generally either a ceramic monolithic honeycomb with square channels or a metallic monolith formed by winding flat and corrugated metal foils together, onto which the active component, known as the washcoat, is coated. The washcoat consists of the chemical support onto which the active metal is dispersed (Twigg, 2007). Computer simulation can be used to consider a wide range of system parameters, such as
different formulations, precious metal loading and aging (if suitable kinetics






available); number of catalysts; proximity to engine; exhaust pipe (single or dual skin, flex coupler, length, diameter); catalyst size and aspect ratio and substrate choice (cell density, wall thickness; ceramic or metallic).

It is worth expanding on some of these points. Modelling can be used to look at the (support) formulation, precious metal loading and catalyst aging,

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

51

provided suitable catalyst kinetics are available. It is an unfortunate fact that chemical kinetics must be empirically determined for each catalyst formulation and aging, and that this can be a time consuming process even for just one catalyst/aging. However, this process can be simplified by perhaps doing a detailed kinetics study (as described in Section IV) on a sample of a given formulation with a representative aging and precious metal loading and then doing a smaller kinetics study on other samples with different aging and/or metal loadings, and fitting the data using a subset of the kinetics parameters. Typically, it is assumed that the metal loading and aging affects only the number of sites, but not the activity of individual sites. In the absence of better information, it is often assumed that the catalyst activity is linearly proportional to precious metal loading. While this approximation often works well, it is not always correct (Evans et al., 1999). Finally, it is worth remembering that sometimes catalyst activity is not the limiting factor in controlling emissions. For example, in a vehicle where the exhaust temperature ramps-up rapidly after key-on, catalyst light off may be limited not by catalyst activity, but by the rate at which the catalyst warms up. In this case, increasing the rate of the kinetics (equivalent to increasing metal loading or using a more advanced washcoat (support)), will have no effect on the predicted emissions. Heat losses from the exhaust gas, as it travels down the exhaust pipe from the engine to the catalyst, result in slower catalyst warm-up and reduced conversion. These heat losses can be reduced by moving the catalyst closer to the engine or by using a dual skin (insulated) exhaust pipe rather than a standard single skin pipe. The diameter of the exhaust pipe will also have an effect. By using a model for heat losses from the exhaust pipe, such as that published by Ansell et al. (1996), such effects can be quantified. Clearly substrate type and catalyst size can have a significant effect on postcatalyst emissions. Figure 2 shows an example of a study looking at this on a TWC. The simulations were run for the first 200s of the FTP (Federal Test Procedure; a standard US drive cycle) test as catalyst light off was of prime interest. Only predicted THC (total hydrocarbon) emissions are discussed here. The base case (line 1) was a 5.66  6 in (144  152 mm), 400/6 (i.e. 400 cells per square inch with 6/1,000 in wall thickness) catalyst. This was replaced by a so-called ‘‘cascade’’ system, consisting of a 4  4 in (102  102 mm) catalyst followed by a 5.66  4 in (144  102 mm) catalyst in the same can. This system has the same total catalyst volume as the original system. In the simulations, it was assumed that the cone between the two substrates was ideally designed so that the gas flow across the front face of the rear catalyst was even. Line 2 is the predicted THC emissions for just the front catalyst. The smaller front face area of the 4  4 in catalyst compared to the original 5.66  6 in catalyst, means that the heat of the exhaust is taken up by a smaller area, leading to earlier light off. However, the smaller volume of the 4  4 in part results in larger breakthrough after light off, so the benefit of earlier light off is soon lost. With the full cascade system (line 3), the extra volume of the second catalyst results in reduced

52

MEHRDAD AHMADINEJAD ET AL.

Cumulative THC Emissions / g

0.8 Engine Out

1 2

3

0.6 4 5 6

0.4

Engine Out 1: 5.66x6" 400/6 (Base Case) 2: 4x4" 400/6 3: 4x4" 400/6 + 5.66x4" 400/6 4: 4x4" 600/3 + 5.66x4" 600/3 5: 4x4" 900/2 + 5.66x4" 600/3 6: 4x4" 1200/2 + 5.66x4" 600/3

0.2

0 0

50

100 Time / s

150

200

FIG. 2. Predicted THC emissions during the first 210s of the FTP showing the effect of substrate type and of having one or two catalysts. The single catalyst (5.66  4 in/144  152 mm) and two catalyst (4  4+5.66  4 in/102  102 mm+144  102 mm) systems have the same total volume. Substrates are denoted as cells per square inch/wall thickness in thousandths of an inch.

breakthrough after light off, maintaining the advantage of the reduced front face area. The remaining simulations in Fig. 2 look at changing the substrate type. Line 4 shows the predicted effect of changing both 400/6 substrates for 600/3, while lines 5 and 6 show the predicted effect of changing the front substrate to a 900/2 and a 1,200/2, respectively. Substrate choice has two main effects. Lower thermal mass (associated with thinner walls, use of less dense ceramic or even a lower catalyst washcoat loading) leads to faster warm-up and hence earlier light off. Increasing cell density increases geometric surface area and reduces hydraulic diameter, resulting in improved heat and mass transfer between the exhaust gas and the substrate, leading to faster warm-up and hence reduced breakthrough after light off. Examination of Fig. 2 reveals that increasing cell density and reducing wall thickness, does indeed result in earlier light off and reduced breakthrough after light off. Note that going from a 900/2 front catalyst to a 1,200/2 (lines 5 and 6) is predicted to have a relatively small effect. This is because the higher thermal mass of the 1,200/2 (more walls of the same thickness) offsets the advantage of greater heat transfer. It is worth noting that all the simulation results shown in Fig. 2 were run with the same engine out data, i.e. it was assumed that changing the substrate(s) did not affect the emissions from the engine. In reality, increasing the substrate cell density is likely to increase the backpressure, which in turn may affect the engine out emissions; this effect is not included in this model. On the plus side, running each simulation with the same engine out data avoids any problems

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

53

with test reproducibility, which is found with experimental chassis dynamometer data, especially if a human driver is used. Another example of the use of modelling to consider substrate choice can be found in Lafyatis et al. (2000).

B. MODIFICATION

OF

ENGINE CALIBRATION

It is important to remember that the aftertreatment system does not work in isolation, but is part of a system with the engine. Clearly, the composition of the exhaust gases, their temperature and flow rate influence the catalyst performance. With a gasoline engine, the combustion must take place close to stoichiometry, if a TWC is to be able to control CO, NOX and THC emissions. With diesel engines, some complicated engine management may be required for regeneration, e.g. running the engine rich to regenerate a NOX trap, or making the exhaust hotter to regenerate a soot filter. Exhaust temperature is, of course, another key factor affecting rate of reaction over the catalyst. By using simulation, the effect of a change in the engine calibration (i.e. the way the engine runs) can be assessed simply by modifying existing engine out data to see if such a change in the calibration is worthwhile. Only if the calibration change is likely to be useful, do the calibration engineers need to make the necessary changes, thus potentially saving much time and effort. This section looks at two examples for a gasoline engine, considering the influence of the initial temperature ramp and of reducing engine out HC levels before light off. The predicted effect of initial temperature ramp on THC emissions during the first 210s of the FTP test is shown in Fig. 3. These simulations were run by modifying the original engine out data, which had an initial ramp of about 6 K/s, to one with a faster, linear ramp (Fig. 4). As expected, increasing the initial ramp rate results in faster catalyst warm-up and hence earlier light off. However, the extent of improvement in light off with increasing temperature ramp decreases as the ramp rate is increased. Thus, while going from 6 to 10 K/s has a large effect, doubling the ramp rate to 20 K/s has a smaller, but still significant effect; little is to be gained from increasing the ramp rate beyond 35 K/s. With these results, the cost/difficulty of getting the increased temperature ramp from the engine can be weighed up against the potential savings from the use of a smaller and/or lower precious metal loaded catalyst. These predictions also make it clear that there is a limit on the maximum ramp rate that is worth considering with this system. It may be that if a higher cell density substrate were used, the heat transfer to the catalyst would be improved, and more benefit would be seen for the more rapid temperature ramps. It is interesting to observe that, increasing the initial temperature ramp not only results in earlier light off, but also results in reduced breakthrough after light off. Figure 3 clearly shows that the predicted breakthrough at

54

MEHRDAD AHMADINEJAD ET AL. 0.8

Cumulative THC Emissions / g

Engine Out

0.6

≈6K s-1 (Original) 10K s-1

0.4

20K s-1 35K s-1 50K s-1

0.2

100K s-1

0.0 0

50

100

150

200

250

Time / s

FIG. 3. Predicted effect of initial temperature ramp on HC emissions over the first 210s of the FTP test. The inlet temperatures used are shown in Fig. 4. 600

Inlet Temperature / °C

500

400

300 6K s-1 (Original) 10K s-1 20K s-1 35K s-1 50K s-1 100K s-1

200

100

0 0

50

100

150

200

250

Time / s

FIG. 4. Inlet temperatures fed to the model to predict the effect of initial temperature ramp. The measured data have an initial temperature ramp of B6 K/s.

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

55

600

Average Bed Temperature / °C

500

400

300 6K s-1 (Original) 10K s-1 20K s-1 35K s-1 50K s-1 100K s-1

200

100

0 0

50

100

150

200

250

Time / s FIG. 5. Predicted average catalyst temperature as a function of initial temperature ramp rate.

about 100s decreases with increasing ramp rate, even though the inlet temperature for all simulations is the same after 50s (Fig. 4). Examination of the predicted average catalyst temperature (Fig. 5) reveals that the effect of the brief initial temperature ramp still has an influence on catalyst temperature until 195s into the test. This is presumably due to extra heat generated by CO and HC combustion, on account of earlier light off. Since the catalyst is hotter, the size of the breakthrough at 100s is reduced. This example illustrates how a better understanding of the system can be gained by examining parameters predicted by the model. The second example considers changing the engine calibration to reduce engine out HC. Since a large portion of post-catalyst HC emissions is released before the catalyst lights off, reducing HC output at the beginning of the test can be very beneficial. Figure 6 shows the predicted effect of reducing engine out HC to one-half and one-third of the measured values. As expected, reducing the engine out HC resulted in reduced catalyst out HC. However, the catalyst out emissions are actually slightly lower than expected from the reduction in engine out HC. This is because HC inhibits CO and HC oxidation, so reducing the HC going into the catalyst results in a faster rate of reaction, and hence more heat generation leading to a further increase in the reaction rate.

56

MEHRDAD AHMADINEJAD ET AL.

0.7

Cumulative THC Emissions / g

0.6 As-measured 0.5 0.4 0.3

As-measured / 2

0.2 As-measured / 3 0.1 0.0 0

50

100

150

200

250

Time / s FIG. 6. Predicted effect of reducing engine out HC to one-half and one-third of the as-measured levels on THC emissions during the first 210s of the FTP.

III. Monolith Reactor Model In this section, the physical model used to describe the flow-through monolith reactor is outlined. Such a reactor is common to all the emissions control strategies discussed in this chapter, apart from soot filters. A brief review of the development history of monolith reactor models for TWC applications can be found in Koltsakis and Stamatelos (1997). Various workers have looked at 1-, 2- and 3-dimensional models considering both the whole monolith and just a single channel. A multidimensional model for the whole monolith is required for investigating the effects of a flow maldistribution across the front face of the monolith, but is probably unnecessary when the flow is uniform. Other workers have studied multidimensional single channel models, where the gas flow within the channel is modelled in detail. In general, for a model to be useful in practice, some compromise has to be made between having a reasonable runtime versus detail/complexity, both in terms of the chemical kinetics and the description of the flow field within the channels of and across the monolith. Most of our work has focused on 1-dimensional models, as these give a reasonable runtime, at the expense of ignoring radial gradients; for a

57

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

well-designed system, where the flow across the front face of the catalyst is uniform, this is a good approximation. Our model has been previously published (Ansell et al., 1996) and is similar to that of other workers in the field (e.g. Baba et al., 2000; Marin and Hoebink, 1997; Oh and Cavendish, 1982); such models are well established, hence our main focus is on the chemistry/kinetics of our aftertreatment catalysts. The model is a 1-dimensional lumped parameter model with the following assumptions:













Uniform flow distribution at the entrance to monolith. Negligible radial temperature gradient across the monolith. Axial transport of mass and energy in the gas phase by convection only. Axial transport of energy in the solid phase by conduction. Transfer of mass and energy between the gas and solid phases described by mass and heat transfer terms with coefficients calculated from a correlation based on Ullah et al. (1992). No radiative transport of energy to or from the monolith faces. No diffusion resistance in the washcoat. Reactant gases are dilute so that changes in the amount of substance (and hence gas volume) with reaction can be ignored, i.e. this is a trace system. Negligible pressure drop along the channels. Ideal gas behaviour. The equations describing the 1-dimensional model are

0 ¼ rg C pg v

0¼

@T g 2 þ h Sv ðT g  T s Þ þ ho ðT g  T surr Þ Gas energy balance r @z

@vC gi þ km;i S v ðC gi  C si Þ @z

ð1 
Þrs C ps

X @T s @2 T s ¼ ð1 
Þl 2  L H i Ri @t @z i þh S v ðT g  T s Þ

ð1 
Þ
wc

Gas mass balance

@C si ¼ km;i S v ðC gi  C si Þ þ Ri L @t

(1)

(2)

(3) Solid energy balance Solid mass balance

(4)

where Cgi Csi Cpg

concentration of species i in the gas phase concentration of species i in the solid phase specific heat capacity of gas phase at constant pressure

mol/m3 mol/m3 J/kg/K

58 Cps Hi h ho km,i L r Ri SV t Tg Ts TSurr V Z e eWC l rg rs

MEHRDAD AHMADINEJAD ET AL.

specific heat capacity of solid phase at constant pressure heat of formation of species i (negative for exothermic reaction) heat transfer coefficient for transport between solid and gas phases heat transfer coefficient for heat loss to the surroundings mass transfer coefficient for species i mass of catalyst per unit volume of monolith radius of monolith rate of formation of species i per unit mass of catalyst geometric surface area (surface area of channels/volume of monolith) time temperature of the gas phase temperature of the solid (monolith) temperature of surroundings mean velocity of gas in channel axial distance open frontal area of monolith washcoat porosity  volume of washcoat/solid volume of monolith thermal conductivity of coated monolith density of gas solid density of monolith

J/kg/K J/mol W/m2/K W/m2/K m/s kg/m3 m mol/kg/s m1 s K K K m/s m   W/m/K kg/m3 kg/m3

These equations are the same as for the model presented by Ansell et al. (1996), with the addition of a term for heat loss to the surroundings in the gas energy balance and with the correction of minor typographical errors. While Eq. (1) includes a term for heat losses to the surroundings, it is important that this term is relatively minor as significant heat losses will result in a significant radial temperature gradient, which in turn invalidates the use of a 1-dimensional model. In practice, the small surface area to volume ratio of the monolith, and the fact they have some insulation, means that the heat losses are indeed small. The gas energy and mass balance equations, unlike the corresponding solid balances, do not have a term for accumulation. This is because the high convective flow of gas through the channels of the monolith makes accumulation of heat or reactants in the gas phase negligible. In practice, the accumulation term in the solid mass balance could also be removed as, in general, it also tends to be small. However, it is included in our models as it enables the equations to be solved numerically more easily. It is worth noting, that while this model would generally be described as a 1-dimensional model, because it contains a single spatial coordinate (z), mass and heat transport between the gas and solid phases within the channel and

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

59

perpendicular to the axial direction are included in the model using a lumped parameter approach. Thus, this model might better be described as a single channel model.

IV. Model Development, Illustrated by 3-Way Catalysis In this section the stages and some of the considerations in developing a model are discussed. All the examples are taken from 3-way catalysis, but the same general principles apply for any other emissions control technologies. TWCs are used on gasoline vehicles and are so called because they remove three pollutants from the exhaust gas, viz. CO, hydrocarbons and NOX. They work by reacting the oxidising components of the exhaust (NOX and O2) with the reducing components (CO and hydrocarbons). Provided the fuel/air mixture combusted in the engine is stoichiometric, the pollutants effectively ‘‘cancel each other out’’. More information on the chemistry and history of the development of these catalysts can be found in Chorkendorff and Niemantsverdriet (2003), Gandhi et al. (2003), Koltsakis and Stamatelos (1997) and Twigg (2007).

A. APPROACH/METHODOLOGY Ultimately a model is a simplification of real-world behaviour. To be useful, it must produce predictions of sufficient accuracy in a reasonable time span; if it is quicker to do the experiments than to develop and run the model, then the model is worthless (except as an academic exercise). In choosing an approach for modelling a system, a compromise must be made between complexity and simplicity. A very complex model, which includes a detailed description of the many physico-chemical processes involved, may give an accurate prediction over a wide range of conditions. However, there will be many parameters to set from experimental data, and so development and validation will be time consuming. An over complex model may also have a protracted run-time. However, a very simple model may be quick to develop and run, but if the predictions are far from reality, this model is also useless. The simplest approach used for autocatalyst modelling is the so-called look up table (Laing et al., 1999). Essentially, the model is populated with a database of conversions for various species as a function of temperature and space velocity, from which conversions can be predicted by interpolation. This, coupled with a simple thermal model for catalyst temperature and some way of allowing for mass transport control, constitutes the simplest type of model. Once this sort of model has been written, adapting to another formulation is a relatively quick process of measuring new conversion curves and adding these to the model.

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Since the models are so simple, a whole test cycle can be simulated in a very short time. The disadvantage of such a model is that it is not based on the underlying chemical processes and so the range of applicability is likely to be limited. At the other end of the spectrum lies micro-kinetic modelling (Kocˇi et al., 2004; Marin and Hoebink, 1997). Here not just the individual reactions, but also all the intermediate mechanistic steps are modelled. This leads to a vast number of parameters that must be fixed either from experiment or by theoretical calculations. As a very detailed description of the chemistry is included, an accurate prediction over the widest range of conditions can be expected, provided the correct mechanistic steps and parameters have been chosen. The problem with this approach lies in the time taken to run and, in particular, to develop such a model; an entire PhD thesis can be spent just determining the kinetics of a few of the many reactions required. In the commercial world, the catalyst formulation may well have changed over this period. While such models are interesting and important for understanding in detail the complex chemistry occurring on the catalyst surface, the protracted development time compared to the time taken to bring a commercial catalyst to market makes them unsuitable for use in industry. At Johnson Matthey, we have adopted an approach somewhere in between these two extremes. The model should reflect the underlying physicochemical processes involved, to be as predictive as possible over as wide a range of conditions as possible, but yet not be too time consuming to develop and run. A single global rate equation is used for each reaction, thus the chemistry of the catalyst can be included, without creating excessive parameters to define. Typically, Langmuir-Hinshelwood kinetics (Chorkendorff and Niemantsverdriet, 2003) are used, as is normal for reactions occurring on the surface of a heterogeneous catalyst. These equations also behave well numerically; unlike a power law equation, there is no danger of division by zero when concentrations go to zero. A typical rate equation would be

Aj exp rj ¼




E j RT s



½Am ½Bn

ð1þK a ½AÞp ð1 þ K b ½B þ K c ½CÞq

(5)

where rj is the rate of reaction, Aj the pre-exponential factor, [A], [B], [C] concentrations of A, B and C, Ej the activation energy, R the molar gas constant, Ka, Kb and Kc are Langmuir-Hinshelwood constants, and p, q, m and n are positive constants, ideally integers. Note that the rate of reaction depends not only on the concentrations of the reactants, but also on the concentrations of other species that may inhibit the reaction. Other workers using this approach include Baba et al. (2000) and Oh and Cavendish (1982). The methodology devised for model development is shown in Fig. 7. Data for developing the model come from SCAT (simulated catalyst activity test,

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61

Prediction of of Prediction Behaviour Behaviour onon Vehicle Vehicle SCATTests Tests SCAT

SCATModel Model SCAT

Adsorption Adsorption Studies Studies

Rate Equations Rate Equations Parameters & &Parameters AdsorptionHeats Heats Adsorption Coefficients &&Coefficients

Adsorption Adsorption Model Model Engine Test Engine Test

Monolith Monolith Model Model

Adsorption Adsorption Kinetics Kinetics Model Model Validation Validation

FIG. 7. Model development methodology.

i.e. microreactor) tests, adsorption studies and from real-world engine or vehicle tests. SCAT data (laboratory microreactor data) are used for development of kinetics. Catalyst testing with synthetic gas mixtures allows for greater and more reproducible feed than is possible when using the exhaust of a real engine. This control is essential for separating out the effects of the many reactions occurring over the catalyst in the complete system. To minimise the influence of heat and mass transfer on the measured conversions, and hence to measure as close to the intrinsic kinetics as possible, these tests are normally done using a packed bed obtained by grinding and sieving an aged monolith catalyst to give tiny catalyst pellets of well-defined size. The SCAT data is then modelled using a model of the SCAT reactor. This is similar to the model described in Section III, except that different correlations, appropriate for a packed bed, are used for the heat and mass transfer coefficients. By trying various forms of kinetic equations and optimising the parameters in these, a series of rate equations with parameters is obtained. This process is discussed in more detail in Section IV.C. For some models adsorption or storage is important. For example, oxygen storage is important in a 3-way catalysis, a catalyst may contain a hydrocarbon storage component for improved low-temperature performance, and ammonia storage is important for ammonia SCR (selective catalytic reduction). Clearly, this sort of behaviour needs to be included in the final model. The nature of the measurements depends on the exact system being studied and will be discussed in more detail later. Suffice to say, from measurements at steady state, the heats of adsorption and coefficients of

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adsorption, i.e. thermodynamics of adsorption, can be determined, and by fitting the rate at which steady state is achieved with a model, a description of the kinetics of adsorption/desorption can be found. Once the above-discussed components of the model have been determined, they are added to the final model of a monolith (or even filter) reactor. The monolith reactor model has already been described in Section III. The next stage is to validate the model by comparing the predictions of the model based on laboratory data, with the real-world data measured on an engine bench or chassis dynamometer. At this stage the reason(s) for any discrepancies between the prediction and experiment need to be determined and, if required, further work on the kinetics done to improve the prediction. This process can take a number of iterations. Model validation is described in more detail in Section IV.D. Once all this has been done the model can be used predictively with confidence.

B. REACTIONS INVOLVED Vehicle exhaust is a complex mixture of many components. This leads to there being potentially a huge number of chemical reactions occurring on the surface of the catalyst, all competing for common reactants and active sites. When developing a model, the trick is to select the salient reactions so that the main features of the catalytic performance can be predicted, without making the model unduly complicated. In this section, the typical reactions included in a TWC model are outlined. Firstly, reactions of hydrocarbons will be discussed. Vehicle exhaust contains a complex mixture of hydrocarbons (Kubo et al., 1993). These will have different reactivity and their different molecular masses lead to different propensities for mass transport, the larger molecules diffusing more slowly to the catalyst coating. While the full (vast) range of hydrocarbons in the exhaust could not practically be included in a model, it is desirable to include a small number of representative hydrocarbons to emulate the range of activity and transport properties of the full mixture. Potential reactions of hydrocarbons are total oxidation, partial oxidation and steam reforming. These are shown below, using propene as an example, but similar equations would apply for other hydrocarbons. C3 H6 þ 4 12 O2 ! 3CO2 þ 3H2 O C3 H6 þ 3O2 ! 3CO þ 3H2 O

Total oxidation Partial oxidation

C3 H6 þ 3H2 O ! 3CO þ 6H2

Steam reforming

Under lean conditions, total oxidation would be expected to be the most important reaction. However, under rich conditions partial oxidation and

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63

steam reforming (especially at higher temperatures) are important contributors to hydrocarbon conversion. Note that partial oxidation and steam reforming produce CO and H2, which can go on to react with, for example, NOX. Oxidation is an important route for CO removal. CO can also react with water in the so-called water gas shift reaction to form H2. This reaction is important for conversion under rich conditions, at higher temperatures. CO is also an important reductant for NOX. CO þ 12 O2 ! CO2

CO oxidation

CO þ H2 O Ð CO2 þ H2

Water gas shift reaction

Nitrogen oxides (NOX) are removed by reaction with the reducing species present, viz. CO, H2 and hydrocarbons. The activity for NOX reduction increases in the order hydrocarbonoCOoH2. NO þ CO ! 12 N2 þ CO2 NO þ 2 12 H2 ! NH3 þ H2 O

NOX reduction by CO NOX reduction by H2

9NO þ C3 H6 ! 4 12 N2 þ 3CO2 þ 3H2 O

NOX reduction by hydrocarbon

Finally, H2 can be oxidised to water. H2 þ 12 O2 ! H2 O H2 oxidation Figure 8 is a schematic diagram showing the main reactions together with the temperature required for reaction and the stoichiometry (lean/rich) for which they are important.

C. DEVELOPING KINETICS

FROM

MICROREACTOR DATA

Having good kinetics is the key to having a good model. As already mentioned in Section III, the physical model for a monolithic reactor is well established (validated) and comparable to that used by other workers in the field. It is thus the kinetics used that determine how good the prediction of the model will be. For this reason, the focus of our modelling effort is to develop the best possible kinetics. Chemical kinetics must be determined empirically. In the future, with developments in molecular modelling, it may one day be possible to predict the kinetic behaviour of a new catalyst formulation without actually making it. However, that day is a long way off, especially given the complex formulation

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Steam reforming of HC Water gas shift Oxidation of Less Reactive HC

Partial oxidation of HC

Oxidation of Reactive HC Reduction of NOX by CO and H2

CO Oxidation H2 Oxidation

Rich

λ

Lean

FIG. 8. Schematic showing the importance of the main reactions for a TWC as a function of stoichiometry and temperature required for reaction.

of a modern TWC. Until then, a new set of kinetics will have to be developed for each formulation of interest, and developing good kinetics will remain a time consuming process. It is worth noting that a given set of kinetics is valid not just for a specific formulation but also for a specific aging; as a catalyst ages, so the kinetics will change. Probably the most cited paper on autocatalyst kinetics is the pioneering work of Voltz et al. (1973). This reports kinetics for carbon monoxide and propene oxidation over Pt/Al2O3 under oxidising conditions. Many subsequent workers (e.g. Baba et al., 2000; Oh and Cavendish, 1982), without the time and/or equipment to develop new kinetics for their own catalysts, have adapted these kinetics to match either microreactor or engine data. However, given that kinetics are catalyst specific and that generally the formulation is more complicated than just Pt/Al2O3, this approach is somewhat unsatisfactory. Moreover, Schweich (1995) has pointed out some shortcomings of these kinetics, for example the heat of adsorption of NO is endothermic, which seems unlikely. There are a number of kinetics studies of TWCs in the literature. However, those that actually give kinetic expressions and parameters tend to be of model, rather than fully formulated, catalysts. Catalyst manufacturers tend to regard catalyst kinetics as proprietary information and so do not publish them in the open literature. Having established the importance of generating kinetics, a methodology, which has been found to work well, will be discussed. As already mentioned in

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65

Section IV.A, packed bed microreactor light-off data are the main source of information for generating kinetics. All these measurements are done with CO2 and water in the feed, as this is always present in vehicle exhaust. These components have an inhibiting effect, but once the concentration is above a certain amount, a further increase in concentration has no significant further inhibiting effect, so the concentration dependence of CO2 and water can be neglected, provided they are present at a representative level in all experiments. Figure 9 shows a typical series of CO light-off curves measured under lean conditions. The first curve (squares) is for a simple feed of CO and O2 (plus CO2 and water, with N2 as the balance). In reality, a series of these curves would be measured with different CO and O2 concentrations, to determine the concentration and temperature dependence of CO oxidation. By modelling this data, a rate expression for CO oxidation for this catalyst can be determined. The other curves in Fig. 9 show the effect of adding extra components into the feed, which are not actually involved in CO oxidation. These extra components also adsorb on active sites on the catalyst surface and hence inhibit CO oxidation. Thus, adding NO (triangles) or C3H6 (circles) to the feed inhibits CO oxidation, shifting the light-off curve to higher temperature. When both NO and C3H6 are present (diamonds), the reaction is even further inhibited and the light-off curve is shifted to even higher temperature. Having fixed the rate equation for CO oxidation alone from the CO+O2 only data, further terms can be added to the rate equation to allow for the inhibiting effect of other

100 CO

O2

NO C3H6

CO Conversion / %

80

60

40

20

0 50

100

150

200

250

300

Temperature / °C

FIG. 9. CO light off under lean conditions as a function of feed composition. CO2 and H2O were also in the feed.

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components. In this way, the influence of different components can be separated out (as far as possible) and their effect correctly accounted for in the rate equations. Figure 10 is similar to Fig. 9, but looks at C3H6 oxidation. Note that C3H6 lights off at a higher temperature than CO as it is less reactive. As before, adding other components, viz. CO, NO and both CO and NO together, inhibits the reaction of C3H6, shifting the light off to higher temperature. A complication to the above is when competing reactions occur. For example, when NO is added to the CO+O2 feed, reduction of NO by CO can occur as well as CO oxidation. In this case, a rate expression for the CO–NO reaction would first be determined from experiments with CO and NO (as well as CO2 and water), but no O2 in the feed before considering mixtures of CO, NO and O2. When modelling the latter, terms for O2 inhibition may be necessary in the kinetics expression for the CO–NO reaction as well as for NO inhibition in the CO oxidation expression (as already mentioned earlier). Simultaneous reaction of C3H6 and CO can be handled in a similar way. The two figures discussed so far show data measured under lean conditions, i.e. with excess O2. Under rich conditions, there are two further complications. Firstly, it is no longer possible to completely oxidise all the CO and C3H6 present, so other reactions, such as partial oxidation and steam reforming of hydrocarbons and the water gas shift reaction, need to be considered. Secondly, there is competition for O2 between the different oxidation reactions and the

100 CO

O2

NO

C3H6

C3H6 Conversion / %

80

60

40

20

0 50

100

150

200

250

300

Temperature / °C

FIG. 10. C3H6 light off under lean conditions as a function of feed composition. CO2 and H2O were also in the feed.

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

exact selectivity to different oxidation reactions depends critically on the kinetics. These two points are discussed more fully later. Again, by careful choice of experiments, it is possible, to some extent, to study these reactions in isolation. For example, the kinetics for total oxidation of C3H6 can be fixed using data measured under lean conditions, before considering rich data for modelling partial oxidation and steam reforming. Microreactor data for C3H6 under rich conditions are shown in Fig. 11. In this example, it is very easy to distinguish between total oxidation and steam reforming of C3H6, as there is a significant temperature interval between the O2 running out and the onset of steam reforming. However, it is not always so easy to distinguish between different reactions. An example of simultaneous reaction of CO and C3H6 under rich conditions is shown in Fig. 12. As the temperature is increased, CO starts to react and the CO and O2 conversions rise. Clearly, CO starts to react before C3H6, because it is more reactive. Soon the O2 conversion reaches 100%, and the CO conversion stops rising with temperature, because there is no more O2 available for CO oxidation. As the temperature rises further, the C3H6 starts to react. C3H6 and CO are now competing for O2, with C3H6 winning out. Thus, the conversion of C3H6 rises, while that of CO falls off. This could be explained by the activation energy for C3H6 oxidation being greater than that for CO oxidation, so that the rate of C3H6 oxidation increases faster with increasing temperature than that of CO. Eventually, C3H6 conversion reaches 100%, while the CO conversion

100

Conversion / %

80

60

40 Experiment: O2 Experiment: C3H6

20

Model: Oxidation only Model: Oxidation & Steam Reforming

0 100

150

200

250 300 350 Temperature / °C

400

450

500

FIG. 11. C3H6 oxidation under rich conditions showing model predictions for a model including total oxidation only and for one with both oxidation and steam reforming together.

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100 CO C3H 6

Conversion / %

80

O2

60

40

20

0 100

150

200

250 300 350 Temperature / °C

400

450

500

FIG. 12. Light-off curves for a rich mixture of CO, CO2, C3H6, H2O, O2. Points are experiment, lines are simulation.

continues to fall. At still higher temperatures, the CO conversion rises again due to onset of the water gas shift reaction. Obtaining kinetics which are able to describe this competition between these finely balanced reactions over a range of feed compositions and temperatures can prove to be very challenging. Figure 12 also shows SCAT model prediction, as well as the experimental data. An example of the importance of getting the activation energies correct when it comes to simulating competing reactions will now be discussed. Figure 13 shows a series of C3H6 light-off curves together with model predictions using the same C3H6 kinetics that worked well for the simultaneous CO and C3H6 oxidation shown in Fig. 12. The fit to these C3H6 light-off curves can be improved by decreasing the activation energy of C3H6 oxidation. Figure 14 shows the fit with the ‘‘improved’’ kinetics, which were obtained using an optimisation routine to adjust the kinetic parameters to minimise the least squares difference between experiment and simulated conversions. The fit has clearly been improved. However, when these kinetics were used on the CO and C3H6 oxidation data (Fig. 15), the fit was rather disappointing. The CO starts to light off as before, as the CO oxidation kinetics were unchanged. However, because the activation energy for C3H6 oxidation is now less than that of CO oxidation, no competition for O2 between CO and C3H6 oxidation occurs. Hence, the predicted CO conversion does not fall off with increasing temperature, as observed experimentally, and C3H6 light off is not predicted to occur until a much higher temperature. In fact, analysis of the model shows

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69

100

C3H6 Conversion / %

80

60

40 0.65% O2 230ppm C3 H6 0.67% O2 1000ppm C3 H6

20

0.25% O2 1000ppm C3 H6 0.17% O2 500ppm C3 H6 0 100

150

200

250 300 350 Temperature / °C

400

450

500

FIG. 13. C3H6 light-off curves with experimental data (points) and model prediction (lines) for a series of feed compositions. The kinetics are the same as used for Fig. 12.

100

C3H6 Conversion / %

80

60

40 0.65% O2 230ppm C3 H6 0.67% O2 1000ppm C3 H6

20

0 100

0.25% O2 1000ppm C3 H6 0.17% O2 500ppm C3 H6 150

200

250 300 350 Temperature / °C

400

450

500

FIG. 14. The same C3H6 light-off curves as in Fig. 13 (points), but fitted with different kinetics (lines), which give a better fit here, but which are much less good when CO is added to the feed (Fig. 15).

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100 CO

Conversion / %

80

C3H6 O2

60

40

20

0 100

150

200

250 300 350 Temperature / °C

400

450

500

FIG. 15. The light-off data for a rich mixture of CO, CO2, C3H6, H2O, O2 as in Fig. 12 (points), but fitted (lines) using the same C3H6 kinetics as Fig. 14.

that this predicted C3H6 conversion is solely due to steam reforming, while with the Fig. 12 kinetics it was due to total oxidation. This example, illustrates the care that needs to be taken when fixing kinetic parameters and that as more complicated gas mixtures are considered, it may be necessary to reconsider kinetics, which at first sight seemed entirely adequate with simpler gas mixtures. So far this discussion has covered the effect of kinetics on reaction selectivity. However, it is important to realise that selectivity can also be determined by thermodynamics. It is thus necessary to write kinetics expressions that ensure the thermodynamic limitations are fulfilled for reactions where thermodynamic limitation is likely. For example, if the experiment shown in Fig. 12 were continued to higher the temperature, the CO conversion would eventually fall off with increasing temperature due to the water gas shift reaction reaching equilibrium. As a final example of fitting SCAT data, Fig. 16 shows experimental data and SCAT model prediction for a more complicated gas mixture including CO, C3H6, NO and O2. Once this sort of fit is achieved for a series of different feed compositions, the kinetics are ready to be added to the model of a monolith reactor and tested against data for a real system. So far only steady-state kinetics have been discussed. It is also important to include a description of oxygen storage into the model. Oxygen storage components (OSC) in the catalyst, typically ceria-zirconia, serve to store and

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71

100 CO NOx

Conversion / %

80

C3H6 O2

60

40

20

0 150

200

250 300 Temperature / °C

350

400

FIG. 16. Measured (points) and simulated (lines) conversions for a mixture of CO, CO2, C3H6, H2O, NO, O2.

release oxygen to compensate for changes in reactant stoichiometry (Koltsakis and Stamatelos, 1997). When the feed gas is rich (reducing), oxygen is released to improve oxidation of CO and HC, and when the feed is lean (oxidising), oxygen is stored to improve NOX reduction. The accessible oxygen storage capacity of a catalyst increases with temperature, up to a maximum value (Fig. 17). This is presumably related to increased mobility of oxygen in the lattice of the oxygen storage component with temperature. The importance of including a description of OSC in the model is shown in Figs. 18 and 19. These show data from a light-off experiment in which the feed was perturbed about stoichiometric composition. When OSC was not included (Fig. 18), the model gives a good prediction of light off, but at higher temperatures, it under estimates the CO, NO and O2 conversions. The HC prediction is good throughout the whole test because HC conversion is not that sensitive to stoichiometry. Adding a description of OSC to the model (Fig. 19) gives a much improved prediction after light off.

D. MODEL VALIDATION It is crucial that the model is able to predict catalyst performance with real vehicle data; unless tested against real data the model is only of academic interest rather than a tool of practical application. So far development of

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Oxygen Storage Capacity

MEHRDAD AHMADINEJAD ET AL.

300

400

500 Temperature / °C

600

700

FIG. 17. Oxygen storage capacity as a function of temperature.

100

Conversion / %

80

60

HC Expt CO Expt NO Expt O2 Expt HC Sim O2 Sim NO Sim CO Sim

40

20

0 150

200

250

300

350

400

450

Temperature / °C FIG. 18. Measured and simulated conversions during light off using a perturbed feed (l=170.06, frequency 0.5 Hz). OSC was not included in the model.

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100

Conversion / %

80

60

HC Expt CO Expt NO Expt O2 Expt HC Sim O2 Sim NO Sim CO Sim

40

20

0 150

200

250

300

350

400

450

Temperature / °C FIG. 19. Measured and simulated conversions during light off using a perturbed feed (l=170.06, frequency 0.5 Hz). OSC was included in the model.

kinetics from laboratory microreactor (SCAT) data has been discussed. The next stage of model development is to test these kinetics against real vehicle/ engine data, typically measured on a rolling road or a bench-mounted engine. At this stage, the reason for any discrepancy between the model and reality needs to be determined and the model improved and re-validated accordingly. It is important to stress that the validation process is about testing microreactor-generated kinetics against real-world vehicle/engine data, rather than about tuning the kinetics to match the data. Generally, the lab-generated kinetics are found to work well, although sometimes slight changes to the preexponential factor are required, for example, because the real hydrocarbons in the exhaust differ from the small selection investigated in the laboratory. Ideally, the model should be validated over all the vehicle/engine tests of interest, e.g. over both European and FTP (US) drive cycles. An example of model validation is shown in Figs. 20–23. Predicted THC emissions for two generations of a TWC model are compared with the measured tailpipe emissions over a FTP test in Fig. 20. With the first model (labelled ‘‘Original Model’’), the prediction of light off was very good, however the level of HC slip after about 400s is excessive. Clearly, this model failed the validation and it was necessary to identify the cause of the HC slip. Analysis of the model output revealed that most of the HC slipped was unreactive short chain alkanes and methane. These were being slipped because steam reforming of these hydrocarbons had not been included in the model. When these

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Cumulative THC Emissions / g mile -1

0.15

0.12

0.09

0.06 Engine Out Tailpipe: Measured

0.03

Tailpipe: Simulated (Original Model) Tailpipe: Simulated (Final Model) 0.00 0

500

1000 Time / s

1500

2000

FIG. 20. Validation of a TWC model over the FTP test. Predicted THC emissions are shown for an early and an improved model.

Cumulative NOX Emissions / g mile -1

0.5

0.4

0.3

0.2

Engine Out Tailpipe: Measured Tailpipe: Simulated

0.1

0.0 0

500

1000 Time / s

1500

FIG. 21. Validation of a TWC model over the FTP test for NOX emissions.

2000

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

75

Cumulative CO Emissions / g mile -1

1.0 Engine Out Tailpipe: Measured Tailpipe: Simulated

0.8

0.6

0.4

0.2

0.0 0

500

1000 Time / s

1500

2000

FIG. 22. Validation of a TWC model over the FTP test for CO emissions.

800 Inlet Catalyst: Measured Catalyst: Simulated

Temperature / °C

600

400

200

0 0

500

1000 Time / s

1500

2000

FIG. 23. Validation of a TWC model over the FTP test for temperature. Catalyst temperatures are measured 25 mm from the front face of the catalyst.

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reactions were added (‘‘Final Model’’ in the figure), a much better agreement between measured and simulated emissions was obtained. In general, with TWC modelling, HC is the easiest emission to reliably predict. This is believed to be because it is the least sensitive to the air–fuel ratio. The NOX validation plot is shown in Fig. 21. In general, NOX is harder to predict reliably than THC. In this case, the model gives a good prediction of light off, slips too much NOX between 900s and 1,600s, but then under predicts the breakthrough at 1,600s to join up with the measured cumulative emissions at the end of the test. Essentially, the closer the measured and simulated emissions are throughout the whole test cycle, the more confidence one can have that the model will correctly predict the effect of a system change. It is worth noting that the model predicts all the breakthroughs to be in the right place, even if the magnitude of the breakthrough is not always correct. The CO prediction is shown in Fig. 22. The model gives a good prediction of light off, but then fails to correctly predict the size of some of the breakthroughs, particularly those at around 200s, 1,400s and 1,600s. CO has always been found to be the most difficult emission to predict correctly. The reason for this is unclear. From a practical point of view the CO prediction is less important than the THC and NOX predictions as CO is rarely the limiting emission for meeting the legislation with gasoline engines. Finally, predicted and simulated catalyst temperatures are compared in Fig. 23. These temperatures were measured by a thermocouple inserted into the catalyst 25 mm from the front face, as measured in the axial direction. The agreement between measurement and prediction is good, indicating that the thermal properties used in the model for the catalyst/substrate are reasonable.

V. Other (Diesel) Aftertreatment Systems So far this review has concentrated on model development methodology, and the application of modelling to the design of systems for gasoline vehicles using TWCs. However, of increasing importance over the past decade, and also looking into the future, will be the need for modelling to address diesel emissions control. In this field, the choice of technologies is wider than for gasoline vehicles, and a number of different aftertreatment technologies are available (Johnson, 2004; Koltsakis and Stamatelos, 1997; Twigg, 2007): (1) Diesel oxidation catalysts (DOCs), which convert CO and hydrocarbons to more benign CO2 and H2O, and can also convert NO to NO2 for possible downstream use or be used to generate higher temperatures from the

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oxidation of post-injected hydrocarbon, again for downstream use. Under some circumstances a DOC can also lead to some NOX reduction. (2) Urea/ammonia SCR, in which a reductant, usually urea solution (Adblue, 32.5% urea (BASF, 2003)), is injected into the exhaust stream for reduction of NOX to N2 and H2O. (3) Lean NOX trap (LNT), in which NOX is stored on the catalyst and then the catalyst regenerated intermittently using engine control. (4) Diesel particulate filters (DPF), which remove particulate from the exhaust stream. They can also be coated with catalyst, e.g. DOC, LNT or SCR, to enable removal of gaseous pollutants in the same system component. Clearly with so many systems available, and tightening diesel legislation demanding a combination of various technologies, it is important to be able to design the most efficient solution. Examples of ways in which these aftertreatment technologies can be combined are shown in Fig. 24, however this is certainly not exhaustive and many other system ordering arrangements and combinations can be imagined. Furthermore, in addition to the aftertreatment technologies described, engine control strategies are also important, and

Engine

DOC

DPF

CDPF

EGR

UREA SCR

LNT

FIG. 24. Schematic diagram showing examples of possible aftertreatment systems.

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systems such as exhaust gas recirculation (EGR) can be incorporated into the system design; in EGR some of the exhaust gas is recirculated into the engine intake, reducing the oxygen content and therefore the NOX formed in the engine cylinder (Stone, 1999). With so many options and design parameters, modelling can play a critical part in diesel aftertreatment system design and optimisation. In the following sections, the application of modelling to diesel aftertreatment will be addressed. However, given that these diesel models are developed using a similar approach and methodology to the TWC models, the emphasis in these sections will be on application of the models to system design and understanding. A. DIESEL OXIDATION CATALYSTS DOCs are important for reducing the emissions of CO and hydrocarbons from diesel vehicles (Clerc, 1996). Furthermore, these catalysts are active for the oxidation of NO to NO2, which can be used in downstream systems, such as SCR and filters, where NO2 concentration can be key to the total system performance. Modelling can play a role in designing optimum systems for CO, HC and NO oxidation. In addition, DOCs are also capable of reducing NOX, under certain conditions. Early work on these ‘‘lean NOX catalysts’’ concentrated on Cu/ZSM-5 catalysts (Amiridis et al., 1996; Walker, 1995), but platinum (Amiridis et al., 1996; Burch and Millington, 1995) and silver (Breen and Burch, 2006) -based catalysts, with better hydrothermal resistance than the zeolite systems, are also available. Unfortunately, NOX reduction under lean conditions only occurs over a narrow temperature range and therefore modelling can aid in optimisation of the catalyst and emissions system. Because of the lean NOX reduction function of these catalysts, it is important to include not only the oxidation reactions CO þ 12 O2 ! CO2

y y Cx Hy þ x þ O2 ! xCO2 þ H2 O 4 2 NO þ 12 O2 Ð NO2

CO oxidation Hydrocarbon oxidation NO oxidation ðreversibleÞ

but also NO reduction reactions
y Cx Hy þ 2x þ NO !
2y
y x þ N2 þ xCO2 þ H2 O NO reduction by hydrocarbon 4 2 in a model of a DOC for system simulation.

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

Diesel exhaust consists of a complex mixture of hydrocarbons (Wajsman et al., 1996) with different reactivities and mass transport propensities. Figure 25 compares the reactivities of some hydrocarbons typically found in diesel exhaust. As with TWCs (Section IV.B), it is desirable to include a small selection of representative hydrocarbons in a DOC model to emulate the reactivity and transport properties of the real exhaust. As discussed for TWC models (Section II), a DOC model can be used for catalyst sizing and system design. Figure 26 shows a validation plot comparing model prediction with measured data over the ESC (European Stationary Cycle); excellent agreement is observed. Good agreement has also been obtained with this model over other test cycles (York et al., 2005). Further functionality can be added to the DOC by the incorporation of a hydrocarbon storage component, typically a zeolite, to the catalyst formulation. This serves to trap hydrocarbon in the exhaust from engine start-up, when the temperature of the catalyst is too low for oxidation reactions to proceed at the required rate. This stored hydrocarbon is later released as the temperature increases, ideally at a temperature at which oxidation to CO2 can occur. The overall result is that hydrocarbon slip is reduced even when the system is cold at start up, resulting in much lower total emissions over the drive cycle. It is thus necessary to include a description of the thermodynamics and kinetics of hydrocarbon storage in a model of such a DOC. Typically, we have based this on hydrocarbon breakthrough experiments. In this experiment, hydrocarbon in an inert carrier is passed over an outgassed DOC core sample.

1.0

Conversion

0.8

0.6

0.4

C3H6 C7H8

0.2

n-C10H22 0.0 100

120

140 160 Temperature / °C

180

200

FIG. 25. SCAT light off comparison of the activity of different hydrocarbons for oxidation over a Pt-based DOC. All hydrocarbons were present at the same concentration on a C1 basis.

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MEHRDAD AHMADINEJAD ET AL.

100

NO conversion / %

80

80 60

70

40

NO CO

20

CO, HC conversion / %

90 100

60

HC 0 0

200

400

600

800

1000

1200

1400

50 1600

Time / s FIG. 26. Modelled (lines) and measured (points) component conversions for a DOC over the ESC (European Stationary Cycle). Measured data for a bench mounted 10 L, 210 kW turbocharged engine.

Initially, all the hydrocarbon is adsorbed on the core and none is observed at the outlet. Once the core is saturated, hydrocarbon breakthrough is observed. Example breakthrough curves for two temperatures are shown in Fig. 27. The amount of hydrocarbon adsorbed is given by the area above the breakthrough curve (after correction for the residence time of the reactor). By conducting experiments with different hydrocarbon concentrations and at different temperatures, the temperature and concentration dependency of the amount stored can be determined and hence isotherms generated. Typically, the isotherms can be fitted to a Langmuir isotherm (Fig. 28), where the amount stored is given by QEquil;i ¼ QSat;i

bC si 1 þ bC si

(6)

The temperature dependence of the constant, b, is given by the Van’t Hoff relationship, viz. bi ¼ bi;0 expðDH ads;i =RTÞ

(7)

where bi

thermodynamic equilibrium constant for adsorption of i

m3/mol

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

bi,0

m3/mol

thermodynamic equilibrium constant of i at infinite temperature concentration of species i in the solid phase the amount of i adsorbed at equilibrium per unit mass of washcoat the maximum amount of i that can be adsorbed per unit mass of washcoat heat of adsorption of i

Csi QEquil,i QSat,i DHads,i

mol/m3 mol/kg mol/kg J/mol

The thermodynamics of adsorption of a given species are thus characterised by bi,0 and DHads,i. By fitting the breakthrough curves, expressions for the kinetics of adsorption/desorption can be developed; Fig. 27 shows simulated, as well as measured, breakthrough curves. An example of the application of this DOC model is shown in Figs. 29 and 30. The original system used a 300/8 substrate. There was interest in knowing if the emissions would be improved if the substrate was changed to a 400/4.5, and whether the advantage would still be maintained if the length were reduced by 10%; this length was predicted to give the same backpressure as the original system. Predicted CO emissions are shown in Fig. 29. Changing the substrate from a 300/8 to a 400/4.5 is predicted to result in earlier light off, due to reduced thermal mass, and reduced breakthrough after light off, due to improved mass transfer. Reducing the length by 10% does not, of course, affect the light off, but results is greater breakthrough after light off, with the CO

Mole fraction as C1

0.008

0.006

0.004

0.002

100°C 25°C

0 0

200

400

600

800

1000

Time / s FIG. 27. Iso-octane (2,2,4-trimethylpentane) breakthrough curves on a DOC core sample. Measured (points) and simulated (lines) data are shown for two temperatures.

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Amount Adsorbed

MEHRDAD AHMADINEJAD ET AL.

25°C 50°C 100°C 150°C 0

0.01

0.02

0.03

0.04

0.05

[i-C8H18] / mol m-3 FIG. 28. Iso-octane adsorption isotherms on a DOC core sample. Points measured, lines fit to Langmuir isotherm.

Cumulative CO Emissions / g

5

4 Engine Out 3

300/8

Standard length

400/4.5 Standard length 400/4.5 10% Shorter

2

1

0 0

200

400

600 Time / s

800

1000

1200

FIG. 29. Predicted effect of substrate type and length on CO emissions over the European test cycle for a Pt-based DOC containing a HC storage component.

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

83

Cumulative THC Emissions / g

1.0

0.8

0.6 Engine Out 300/8

0.4

Standard length

400/4.5 Standard length 400/4.5 10% Shorter

0.2

0.0 0

200

400

600 Time / s

800

1000

1200

FIG. 30. Predicted effect of substrate type and length on THC emissions over the European test cycle for a Pt-based DOC containing a HC storage component.

emissions at the end of the test predicted to be higher than with the original system. The corresponding THC prediction is given in Fig. 30. Storage of hydrocarbon in the zeolite in this formulation means that the predicted tailpipe emissions are below the engine out from time zero. Hence, catalyst light off is unimportant for THC emissions in this case. The substrate, however, still has an effect on breakthrough after light off. Thus changing the substrate from a 300/8 to a 400/4.5 results in lower emissions, due to improved mass transfer. Reducing the catalyst length by 10% is predicted to increase HC breakthrough, but, unlike the CO, the emissions at the end of the test are still lower than for the original system. It is worth noting that the substrate has a bigger effect on THC than CO emissions. This is because hydrocarbons have a greater molecular mass than CO, and so their conversion is more susceptible to mass transfer limitation, and hence more sensitive to substrate cell density.

B. AMMONIA SCR Urea/ammonia SCR, in which NOX is reduced by ammonia, typically introduced as an aqueous urea solution sprayed into the exhaust stream upstream of the catalyst, is one technology that is being seriously considered by

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MEHRDAD AHMADINEJAD ET AL.

many engine/vehicle manufacturers to meet future tight NOX emissions legislation. In addition, when combined with filter technology, SCR can provide a way to reduce both particulate matter (PM) and NOX emissions; for example the SCRTs system (Walker et al., 2000) shown in Fig. 31. Typical catalysts for SCR include supported vanadia, and iron or copper supported on zeolite. Here the application of a model to the design and understanding of vanadia catalyst systems is presented. Over the vanadiabased catalyst system, a Rideal–Eley approach has been adopted by most workers in the field, in which the first step is ammonia adsorption on the catalyst. This stored ammonia can then either react with NOX or be desorbed. Some important contributions to the SCR modelling literature are Andersson et al. (1994), Lietti and Forzatti (1994), Dumesic et al. (1996), Lietti et al. (1997), Maurer et al. (1999), Kleemann et al. (2000), Nova et al. (2000), Koebel et al. (2001), Nova et al. (2001), Forzatti (2001), York et al. (2004) and

SCR Catalyst

DOC + Filter

Urea solution FIG. 31. Schematic diagram showing the SCRTs system for removing PM and NOX.

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

Chatterjee et al. (2006). The most important reactions for consideration in the model are 4NH3 þ 4NO þ O2 ! 4N2 þ 6H2 O

SCR reaction of NO

4NH3 þ 2NO2 þ O2 ! 3N2 þ 6H2 O SCR reaction of NO2 2NH3 þ NO þ NO2 ! 2N2 þ 3H2 O SCR reaction of NO þ NO2 4NH3 þ 3O2 ! 2N2 þ 6H2 O

Oxidation of NH3 to N2

Urea decomposition/hydrolysis to ammonia may also be rate limiting under very specific conditions, but is not considered in this discussion, i.e. conversion of urea to ammonia is considered to be fast. ðNH2 Þ2 CO ! NH3 þ HNCO

Urea decomposition

HNCO þ H2 O ! NH3 þ CO2

Hydrolysis of cyanic acid

One of the primary uses for an accurate SCR model is in the sizing of catalyst systems. The model has been shown to be accurate for the prediction of NOX emissions and conversions, as is shown in Table I for 267 mm diameter vanadiabased catalysts of various lengths. Excellent agreement was obtained between the model and the experimental data, especially for the 17 L system. Furthermore, in combination with the DOC model described earlier, it is possible to study the effect of NO2, formed over the DOC, on the performance of the SCR catalyst (Table II). In the case shown, the extra NO2 appears to have a deleterious effect on NOX conversion. Indeed, it is now well known that too much NO2 in the vanadia SCR catalyst results in a decrease in performance, while when optimised the NO2 leads to a significantly improved performance (Chandler et al., 2000; Gieshoff et al., 2000). Therefore, it is clear to see how the models can be used for SCR system design. Using this model, it has also been possible to probe the behaviour of the catalysts in detail. For example, experiments were conducted wherein each TABLE I EFFECT OF CATALYST LENGTH/VOLUME ON THE OVERALL WEIGHTED NOX EMISSIONS AND CONVERSIONS OVER 267  152 mm (8.5 L), 267  229 mm (12.75 L) AND 267  305 mm (17 L) 400/6 SCR CATALYST SYSTEMS Catalyst volume (L)

8.5 12.75 17

NOX emissions after SCR system (g/kWh)

NOX conversion (%)

Experiment

Model

Experiment

Model

1.341 – 0.796

1.637 1.108 0.794

82.6 – 87.6

78.8 85.6 87.7

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MEHRDAD AHMADINEJAD ET AL.

TABLE II THE EFFECT OF AN UPSTREAM DOC ON THE PREDICTED PERFORMANCE OF A 267  305 mm (17 L) 400/6 SCR CATALYST OVER THE ESC NOX emissions after SCR system (g/kWh) NOX conversion (%)

Without pre-oxidation catalyst With pre-oxidation catalyst

Experiment

Model

Experiment

Model

0.796 1.379

0.794 1.586

87.6 82.0

87.7 79.3

mode in the ESC cycle was run individually and the ammonia injection rate increased stepwise, giving an ammonia concentration from 70% to 120% of the inlet NOX concentration in 5% steps. This was done to investigate the effect of NH3:NOX ratio for each ESC mode, so enabling optimisation of the NH3 injection rate to achieve the highest possible NOX conversion accompanied by the lowest possible NH3 slip. Figure 32 shows the results obtained for a 267  305 mm, 400/6 catalyst for modes 6, 7 and 10 (medium, low and high temperature modes, respectively), and the model prediction; the catalyst was conditioned in ammonia before each run to ensure steady state ammonia coverage. For all three modes shown, very good agreement between the experimental data and the simulations was seen; similar agreement was obtained for all the remaining ESC modes. From this information it is possible to build up an understanding of the relationship between NH3:NOX ratio and NOX conversion and NH3 slip for use in system design. It was interesting to note that increasing the ammonia injection rate did not affect the NOX conversion to any great extent in both modes 7 and 10, while the expected increase in conversion up to 100% was seen for mode 6. This effect was further investigated. Figure 33 presents the predicted fractional ammonia coverage for the three modes at three different positions in the catalyst: viz. (1) at the front; (2) in the middle and (3) at the end. For the mid-temperature mode 6, increasing the ammonia injection rate had a marked effect on the coverage, and this was especially noticeable at the end of the catalyst bed; this leads to an increase in the reaction rate (and hence NOX conversion (Fig. 32a)), since this is directly proportional to the ammonia coverage. At higher temperature (mode 10), the coverage did not increase markedly on increasing the injection rate, and therefore nor did the conversion; it seems that at these temperatures the ammonia is desorbed before it can react (and hence the NH3 coverage remains close to zero). Perhaps the most interesting result was that obtained for mode 7 (low temperature), where the ammonia coverage was close to unity even at the start of the reaction with 70% injection rate. Since increasing the NH3:NOX ratio cannot further increase the coverage, the reaction rate (and hence NOX conversion (Fig. 32b)) remains unchanged. This example illustrates how

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

(a) 100

200 120% 100%

70%

120

40

80 X[NO] NH3 slip

20

0 1000

1500

40

2000

2500 Time / s

3000

3500

0 4000 350

(b) 100 X[NO]

120%

NH3 slip

80 NO Conversion / %

NH3 slip / ppm

60

160

300 250

100% 60

200 70% 150

40

NH3 slip / ppm

NO Conversion / %

80

100 20 50 0 1000

1500

2000

2500 Time / s

3000

3500

(c) 100

0 4000

200 X[NO]

NO Conversion / %

120% 100%

60

160

120

70% 40

80

20

40

0 1000

1500

2000

2500

3000

3500

NH3 slip / ppm

NH3 slip

80

0 4000

Time / s

FIG. 32. Measured (symbols) and simulated (lines) NOX conversion and NH3 slip for (a) Mode 6 (3851C), (b) Mode 7 (2851C) and (c) Mode 10 (5001C) of the ESC cycle with progressively increasing ammonia injection rate.

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MEHRDAD AHMADINEJAD ET AL.

1.0

(1),(2),(3) Mode 7, 285°C

Fractional coverage

0.8 (1) 120%

0.6

100% 70%

(2)

0.4

(3)

0.2

(1)

0.0 1000

(2) (3) 1500

2000

2500

3000

3500

Mode 6, 385°C

Mode 10, 500°C 4000

Time / s FIG. 33. Plot of calculated fractional ammonia coverage for the three ESC modes at the front (1), middle (2) and rear (3) of the catalyst brick, with progressively increasing ammonia injection rate.

modelling can be a powerful tool for rationalising and understanding complicated (and unexpected) phenomena.

C. NOX TRAPS Another technology under consideration for NOX abatement in diesel vehicles is the LNT, which is also commonly called the NOX adsorber catalyst (NAC) or NOX storage/reduction catalyst (NSRC). Some of the processes occurring in an LNT are somewhat similar to those in the TWC systems discussed earlier (see Section IV.B), and indeed LNT systems typically contain platinum and rhodium, as do TWCs. However, a TWC will not operate as an efficient NOX reduction system in the presence of excess oxygen. In order to enable operation under lean conditions (e.g. diesel or gasoline direct injection engines (GDI)), a NOX storage material is added to the catalyst: NOX is adsorbed and stored on this material under the lean conditions usually associated with diesel exhaust, and is then released and reduced to nitrogen during a brief rich (regeneration) period of operation. By careful choice of the storage material, catalysts with differing storage capacities and thermal properties can be designed for applications with different temperature ranges. Typical adsorber materials are the alkali and alkaline-earth metal oxides, e.g. barium, magnesium, potassium and cesium.

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

89

Clearly, when modelling an LNT it is important to include the most important processes occurring in this relatively complex catalyst system. Kinetic and experimental studies of lean NOX trap catalysts, including those describing chemical principles, have been published previously (Brogan et al., 1995; Dou and Bailey, 1998; Fekete et al., 1997; Miyoshi et al., 1995; Takami et al., 1995). These processes can be summarised as follows: 1. Storage (Lean Operation) Under lean (normal) conditions, NOX is stored by oxidation of NO to NO2, followed by NO2 adsorption on metal oxide sites (MO) to form nitrites or nitrates (Kocˇi and Marek, 2004; Nova et al., 2004). 2NO þ O2 Ð 2NO2 MO þ 2NO2 þ

1 2 O2

Oxidation of NO ! MðNO3 Þ2

Storage of NO2 as nitrate

Understanding the storage capacity of the metal oxide material and the NO2 adsorption rate is therefore also very important. Furthermore, oxides of sulphur in the exhaust gas, formed from combustion of sulphur compounds in the fuel (or lubricant oil), are stored in a similar manner. Since sulphates are thermodynamically more stable than the corresponding nitrate, this results in a reduction in the NOX storage capacity of the LNT. 2. Regeneration (Rich Operation) Eventually, after a period of normal engine operation, the LNT will become saturated and hence no longer able to store NOX. The LNT is regenerated by subjecting it to a short period of rich operation. This reduces the nitrate, releasing NOX, which is subsequently reduced to nitrogen, in accord with the equations below: MðNO3 Þ2 þ 3CO ! MO þ 2NO þ 3CO2

Nitrate reduction by CO

MðNO3 Þ2 þ 3H2 ! MO þ 2NO þ 3H2 O

Nitrate reduction by H2

MðNO3 Þ2 þ 13 C3 H6 ! MO þ NO þ CO ! 12 N2 þ CO2 NO þ 2 12 H2 ! NH3 þ H2 O

Nitrate reduction by HC NO reduction by CO

2NO þ H2 O þ CO2

NO reduction by H2

Due to the importance of controlled switching of the engine operation from lean to rich conditions and back again, the need to monitor the NOX storage efficiency, and the desire to minimise the fuel penalty associated with regeneration, the design of LNT systems involves a high degree of integration with the engine management system. Modelling can significantly assist in

90

MEHRDAD AHMADINEJAD ET AL.

designing LNT system parameters, such as catalyst volume, precious metal loading and regeneration strategy. An example of the performance of the LNT model, which has been described previously (Ahmadinejad et al., 2006), for predicting NOX removal during a pulsed switching microreactor experiment is shown in Fig. 34. In this experiment, the gas composition is switched between lean and rich (120s lean, followed by 5s rich) under isothermal conditions using a small monolith core in a microreactor. Good agreement between the model and the experimental data has been achieved. By performing a series of such experiments, it is possible to investigate the NOX conversion efficiency over a range of temperatures. Figure 35 shows the experimental NOX conversion (calculated from the analysis of the last three lean/rich cycles) and the simulated conversion; good agreement is observed across the whole temperature window. Similar simulations could be performed to probe the regeneration strategy of a full-sized LNT system. The results of a modelling study looking at the effect of reducing the LNT catalyst volume on the NOX conversion are shown in Table III. For the ‘‘original length’’ system, experimental data measured on a heavy-duty diesel engine was available; good agreement between measured and predicted emissions was observed. As expected, decreasing the catalyst volume leads to a decrease in predicted NOX conversion. The model has also been used to investigate the effect of reducing the engine out NOX on the volume of the LNT system required to meet a set NOX emissions level. This type of information can be used to weigh up economic

1000 NOx Slip Exp NOx Slip Sim

NOX Slip / ppm

800

600

400

200

0 0

200

400

600

800 1000 Time / s

1200

1400

1600

FIG. 34. Microreactor data for pulsed lean/rich cycling using an LNT at 2801C, 50 k/h, 500 ppm NOX inlet. Experimental data in black, simulation in grey.

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

100

NOX Conversion / %

Experimental Simulated 80

60

40

20

0 250

300

350

400 450 Temperature / °C

500

550

600

FIG. 35. Overall NOX conversion during lean/rich cycling microreactor experiments over a range of temperatures for an LNT (50 k/h, 500 ppm NOX inlet).

EFFECT

OF

CATALYST LENGTH

Catalyst length Original length Length reduced Length reduced Length reduced Length reduced

TABLE III NOX CONVERSION

ON

OVER THE

FTP CYCLE NOX conversion (%)

by by by by

10% 20% 30% 40%

68 62 57 51 45

advantages of engine re-calibration versus the size (and hence cost) of the LNT catalyst. For example, the model predicts (Fig. 36) that the same cumulative emissions can be obtained with a 16% smaller LNT, if the engine out emissions are reduced by 25%. D. FILTERS DPFs are important aftertreatment devices, enabling significant reduction in the emissions of PM. Typically, the filters are made of cordierite or silicon carbide materials, and can be either coated with a catalyst or left uncoated. The exhaust stream is forced to flow from one channel of the filter to an adjacent channel (see Fig. 37): the PM is filtered out by the wall of the filter.

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Cumulative NOX emissions / g mile-1

Simulated, original NOX out, original trap size Simulated, 75% NOX engine out, reduced trap size by 16% Simulated, 75% NOX engine out, original trap size

0

500

1000

1500

2000

Time / s

FIG. 36. Predicted effect of catalyst size and engine out NOX on tailpipe NOX emissions.

FIG. 37. Schematic showing the operating principle of a DPF.

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

93

The PM is generally removed from the filter by oxidation, either with oxygen or nitrogen dioxide, a more reactive species, as shown in the equations below: C þ NO2 ! CO þ NO

Soot oxidation by NO2 to CO

C þ 2NO2 ! CO2 þ 2NO C þ 12 O2 ! CO

Soot oxidation by NO2 to CO2 Soot oxidation by O2 to CO

C þ O2 ! CO2

Soot oxidation by O2 to CO2

Regeneration using nitrogen dioxide is the basis for the operation of the CRTs diesel particulate filter system (Allansson et al., 2002; Cooper et al., 1990). DPFs have generated a huge amount of interest recently, and a number of research groups worldwide have concentrated on modelling their performance (Bissett, 1984; Konstandopoulos and Johnson, 1989; Konstandopoulos and Kostoglou, 2000; Konstandopoulos et al., 2000, 2001, 2002; Kandylas and Koltsakis, 2002; Rumpf and Gupte, 1971; Zhang et al., 2002). Filter modelling can be divided up into a number of areas, namely: (1) physical model: flow through the filter, pressure drop prediction, etc.; (2) soot accumulation and removal by oxidation; and (3) wall filtration modelling: filtration efficiency/soot capture probability in the wall and the effect of captured soot on wall porosity, etc. In addition, when the filter is coated with a catalyst layer we also need to consider: (4) gaseous component conversion, i.e. CO, HC, NO oxidation and (5) NO2 diffusion from the catalyst in/on the filter wall back to the soot layer (so-called ‘‘back diffusion’’). 1–3 and 5 have been covered extensively (Bissett, 1984; Haralampous et al., 2004; Huynh et al., 2003; Konstandopoulos and Johnson, 1989; York et al., 2005, 2007), and are also covered in part elsewhere in this volume (Konstandopoulos et al., this volume). The application of a coated filter as a gaseous emissions control device has received far less attention and will be discussed now. In most modelling studies to date, diffusion has been ignored so that the reactant molecules simply flow with the bulk gas flow along the inlet channel and through the wall to the catalyst coating on/in the filter wall. This means that the concentrations of the reactants along the length of the inlet channel, and hence that seen by the catalyst, will be constant (when volume changes due to temperature are ignored). Such a model tends to predict reaction to occur evenly over the length of the catalyst. While this sort of model appears to perform well compared to measured data for filters evenly loaded with PGM (platinum group metal) along their length, they fail to predict the effect of PGM distribution (i.e. zoning). In these zoned systems, the filter is loaded with a higher PGM loading at the front than at the rear. Experimentally, these give improved emissions reduction compared to an evenly loaded part with the same total amount of metal. However, the ‘‘convection model’’ fails to predict this advantage

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MEHRDAD AHMADINEJAD ET AL.

as the activity gain at the front of the filter is offset by the activity loss at the rear. Similarly, when an evenly loaded filter is subjected to real-world aging, often the rear of the filter experiences higher temperatures than the front (during regeneration/soot removal), leading to non-uniform aging and hence non-uniform activity gradients within the filter system; again the effect of this cannot be predicted using a model that only includes transport of reactants by convection. With a monolith reactor, diffusion from the channels to the catalyst coated on the channel walls is the sole means by which reactants are able to reach the catalyst (Section III). It seems reasonable that a similar diffusion process occurs in a coated filter. In a new approach to modelling catalysed DPF (CDPF) systems, transport equations have been incorporated to account for the transfer of reactants to the catalyst surface by diffusion from the bulk flow from both the gas flowing along the coated channels and the gas passing through the filter wall. With this model, the reaction zone is moved towards the front of the filter compared with the ‘‘convection model’’. Figure 38 shows hydrocarbon and NOX concentrations as a function of axial position. It can be seen that, for example, the THC concentration drops (Fig. 38a) from its inlet value to close to zero in the inlet channel as the hydrocarbons react, but in the outlet channel a small amount of hydrocarbon is also present. This is because some of the hydrocarbons are able to pass through the wall unreacted, implying the conversion is mass transfer limited. Mass transfer limited hydrocarbon conversion has been shown earlier with monolith reactors (Section V.A). Figure 38b shows the concentration of NO and NO2 along the length of the filter channel. Oxidation of NO to NO2 over the catalyst coating results in a decrease in NO concentration and a corresponding rise in NO2 concentration along the inlet channel. Along the outlet channel, the NO concentration decreases due to dilution by gas entering from the inlet channel with a lower NO concentration. For most of the length of the filter the NO concentration is higher in the outlet channel than in the inlet channel as the rate of dilution along the outlet channel is slower than the rate of NO reaction along the inlet channel. With a good description of the mass transfer processes occurring in a CDPF now in place, it should be possible to predict the effects of PGM zoning and non-uniform aging on the performance of a CDPF. To illustrate the way in which this model can help in optimising the placement of the PGM washcoat in a CDPF systems, simulations were carried out over the European drive cycle for: (i) Uniform PGM loading along the inlet channel (‘‘Uniform PGM loading’’). (ii) Uniformly loaded along the front half of the inlet channel and the rear half of the outlet channel (‘‘Front 1/2 inlet, rear 1/2 outlet’’). (iii) A higher PGM loading on the front half of the inlet channel than that on the rear half (5:1), while keeping the overall PGM levels the same as in examples i and ii (‘‘Loaded 5:1 PGM’’).

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SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

(a)

[THC] / mmol m-3

8 7

Inlet channel

6

Outlet channel

5 4 3 2 1 0 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Axial distance / m (b) 2 Inlet channel

[NO] and [NO2] / mmol m-3

Outlet channel

NO2

1.5

1

0.5 NO 0 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Axial distance / m FIG. 38. Snapshot of the (a) hydrocarbon and (b) NO/NO2 concentrations as a function of the axial distance along a catalysed filter (5.66  6 in/144  152 mm) at a temperature of 1651C.

The simulation results are shown in Fig. 39. It can be seen that lower CO and THC emissions are obtained with both the ‘‘zoned’’ CDPF systems, i.e. ‘‘Loaded 5:1 PGM’’ and ‘‘Front 1/2 inlet, rear 1/2 outlet’’. In the case of the ‘‘Loaded 5:1 PGM’’ a slightly earlier light off is observed, due to higher activity at the front of the filter (the first part to heat up), resulting in lower total

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MEHRDAD AHMADINEJAD ET AL.

Cumulative CO / g

(a)

Engine out Uniform PGM loading Front 1/2 inlet, rear 1/2 outlet Loaded 5:1 PGM 0

200

400

600

800

1000

1200

Time / s

Cumulative THC /g

(b)

Engine out Uniform PGM loading Front 1/2 inlet, rear 1/2 outlet Loaded 5:1 PGM 0

200

400

600

800

1000

1200

Time / s FIG. 39. Illustrative examples showing the effect of PGM distribution on the emissions of (a) CO and (b) THC over the European test cycle.

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97

emissions. For the ‘‘Front 1/2 inlet, rear 1/2 outlet’’ system the advantage over the uniform system is due to the ability to oxidise CO and THC, which has passed into the outlet channel unreacted, over the catalyst coated onto the rear half of the outlet channel. This illustrative study, using the new approach to CDPF emissions modelling, shows that PGM distribution can have a significant effect on emissions. In later work, with improved kinetics that more closely describe the ‘‘real-world’’ system performance, it has been shown that very great cost savings can be achieved by correctly zoning the CDPF (York et al., 2008).

VI. Future Directions Interest in computer simulation as a means of accelerating the design process for aftertreatment systems has grown rapidly in recent years and this growth is likely to continue. A few years ago, when aftertreatment was only required for gasoline engines, there was only a single product to model, viz. the TWC. With the recent introduction of emissions legislation for diesel engines, the range of emissions control technologies to model has increased to include diesel oxidation catalysts, ammonia SCR, ammonia slip catalysts, NOX traps, particulate filters, etc. There are now many groups working on modelling these components. In the future, aftertreatment systems are likely to become more complicated, particularly for diesel engine exhaust, as legislation becomes evermore stringent. Separate components may be required for NOX, particulate, and CO and HC control. Alternatively, for reducing cost and packaging volume, more than one component may be combined in a single unit. This may involve coating one catalyst technology onto a filter, to control both particulate and gaseous pollutants in a single unit, or coating two (or more) different catalysts onto a single substrate, either as front and rear zones or as a series of layers (e.g. Nakanishi et al., 2006) along the catalyst length. Diesel systems also frequently require some regeneration strategy, e.g. to remove soot from a filter or to regenerate a NOX trap. Ammonia SCR requires a strategy for determining the urea injection rate. In designing such complicated systems, the number of variables to be optimised for a given system is large, as is the complexity of comparing different system types. Hence computer simulation becomes increasing useful to improve the design of the emissions system and to reduce the development time. It is important to realise that the emissions control system does not function in isolation, but is part of a system with the engine and the rest of the vehicle/ machine. There is thus interest in modelling the whole vehicle/machine (Assanis et al., 2000; Fluga, 1993; Fussey et al., 2001; Sahraeian et al., 2004), so that the

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engine calibration, emissions (Fussey et al., 2001) and indeed the rest of the vehicle can be designed/optimised together. As computers become faster, the complexity of problem that can be usefully simulated increases. Areas of interest include combining computational fluid dynamics (CFD) modelling with chemical kinetics to investigate (and hence reduce) the effect of flow maldistributions on aftertreatment system efficiency, and simulating catalyst deactivation over the lifetime of the catalyst. In the distant future, developments in molecular modelling may enable catalyst performance/reaction kinetics to be predicted without actually making the catalyst. This would make catalyst design a real possibility. However, this is still a long way off.

ACKNOWLEDGEMENT We would like to thank Johnson Matthey PLC for permission to publish this paper.

LIST OF SYMBOLS [A] Aj bi bi,0 Cgi Csi Cpg Cps Ej Hi h ho ka,kb,kc km,i L m, n p, q

concentration of species A (mol/m3) pre-exponential factor for reaction j (m3(m+n)/mol(m+n1)/kg/s) thermodynamic equilibrium constant for adsorption of i (m3/mol) thermodynamic equilibrium constant of i at infinite temperature (m3/mol) concentration of species i in the gas phase (mol/m3) concentration of species i in the solid phase (mol/m3) specific heat capacity of gas phase at constant pressure (J/kg/K) specific heat capacity of solid phase at constant pressure (J/kg/K) activation energy of reaction j (J/mol) heat of formation of species i (negative for exothermic reaction) (J/mol) heat transfer coefficient for transport between solid and gas phases (W/m2/K) heat transfer coefficient for heat loss to the surroundings (W/m2/K) Langmuir–Hinshelwood coefficients (m3/mol) mass transfer coefficient for species i (m/s) mass of catalyst per unit volume of monolith (kg/m3) indices in rate equation (-) indices in rate equation (-)

SIMULATION OF AUTOMOTIVE EMISSION CONTROL SYSTEMS

QEquil,i QSat,i r R Ri rj SV t Tg Ts TSurr v z DHads,i e eWC l rg rs

99

the amount of i adsorbed at equilibrium per unit mass of washcoat (mol/kg) the maximum amount of i that can be adsorbed per unit mass of washcoat (mol/kg) radius of monolith (m) molar gas constant (J/mol/K) rate of formation of species i per unit mass of catalyst (mol/kg/s) rate of reaction j per unit mass of catalyst (mol/kg/s) geometric surface area (surface area of channels/volume of monolith) (m1) time (s) temperature of the gas phase (K) temperature of solid (monolith) (K) temperature of surroundings (K) mean velocity of gas in channel (m/s) axial distance (m) heat of adsorption of i (J/mol) open frontal area of monolith (-) washcoat porosity  volume of washcoat/solid volume of monolith (-) thermal conductivity of coated monolith (W/m/K) density of gas (kg/m3) solid density of monolith (kg/m3)

ABBREVIATIONS CDPF DOC DPF EGR ESC FTP HC LNT OSC PGM PM SCAT SCR THC TWC

catalysed diesel particulate filter diesel oxidation catalyst diesel particulate filter exhaust gas recirculation European stationary cycle; test cycle for heavy-duty diesel engines Federal Test Procedure; a standard US drive cycle hydrocarbon lean NOx trap oxygen storage component/capacity platinum group metal particulate matter simulated catalyst activity test, i.e. a microreactor test selective catalytic reduction total hydrocarbon 3-way catalyst

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REFERENCES Ahmadinejad, M., Watling, T. C., York, A. P. E., Walker, A. P., Chen, H.-Y., Hess, H. S., and Cox, J. P. SAE 2006-01-3445 (2006). Allansson, R., Blakeman, P. G., Cooper, B. J., Hess, H. S., Silcock, P. J., and Walker, A. P. SAE 2002-01-0428 (2002). Amiridis, M. D., Zhang, T., and Farrauto, R. J. Appl. Catal. B: Environ. 10, 203 (1996). Andersson, S. L., Gabrielsson, P. L. T., and Odenbrand, C. U. I. AIChE J. 40, 1911 (1994). Ansell, G. P., Bennett, P. S., Cox, J. P., Frost, J. C., Gray, P. G., Jones, A. -M., Rajaram, R. R., Walker, A. P., Litorell, M., and Smedler, G. Appl. Catal. B: Environ. 10, 183 (1996). Assanis, D., Filipi, Z., Gravante, A., Grohnke, D., Gui, X., Louca, L., Rideout, G., Stein, J., and Wang, Y. SAE 2000-01-0288 (2000). Baba, N., Yokota, K., Matsunaga, S., Kojima, S., Ohsawa, K., Ito, T., and Domyo, H. SAE 2000-01-0214 (2000). BASF ‘‘Urea solution 32.5% AdBlue’’, Technical Leaflet, BASF AG, Ludwigshafen, Germany (2003). Reference obtained from www.dieselnet.com/tech/cat_scr.html Bissett, E. J. Chem. Eng. Sci. 39, 1233 (1984). Breen, J. P., and Burch, R. Top. Catal. 39, 53 (2006). Brogan, M. S., Brisley, R. J., Walker, A. P., Webster, D. E., Boegner, W., Fekete, N. P., Kra¨mmer, M., Krutzsch, B., and Voigtla¨nder, D. SAE 952490 (1995). Burch, R., and Millington, P. J. Catal. Today 26, 185 (1995). Chandler, G. R., Cooper, B. J., Harris, J. P., Thoss, J. E., Uusimaki, A., Walker, A. P., and Warren, J. P. SAE 2000-01-0188 (2000). Chatterjee, D., Burkhardt, T., Weibel, M., Tronconi, E., Nova, I., and Cristian, C. SAE 200601-0468 (2006). Chorkendorff, I., and Niemantsverdriet, J. W., ‘‘Concepts of Modern Catalysis and Kinetics’’. Wiley-VCH, Weinheim (2003). Clerc, J. C. Appl. Catal. B: Environ. 10, 99 (1996). Cooper, B. J., Jung, H. J., and Thoss, J. E. US Patent 4,902,487 (1990). Dou, D., and Bailey, O. H. SAE 982594 (1998). Dumesic, J. A., Topsøe, N.-Y., Topsøe, H., Chen, Y., and Slabiak, T. J. Catal. 163, 409 (1996). Evans, J. M., Ansell, G. P., Brown, C. M., Cox, J. P., Lafyatis, D. S., and Millington, P. J. SAE 1999-01-3472 (1999). Fekete, N., Leyrer, J., Kemmler, R., and Krutzsch, B. SAE 970746 (1997). Fluga, E. C. SAE 931179 (1993). Forzatti, P. Appl. Catal. A: Gen. 222, 221 (2001). Fussey, P. M., Goodfellow, C. L., Oversby, K. K., Porter, B. C., and Wheals, J. C. SAE 200101-0938 (2001). Gandhi, H. S., Graham, G. W., and McCabe, R. W. J. Catal. 216, 433 (2003). Gieshoff, J., Schafer-Sindlinger, A., Spurk, P. C., van den Tillaart, J. A. A., and Garr, G. SAE 200001-0189 (2000). Haralampous, O. A., Koltsakis, G. C., Samaras, Z. C., Vogt, C.-D., Ohara, E., Watanabe, Y., and Mizutani, T. SAE 2004-01-0696 (2004). Heck, R. M., and Farrauto, R. J. Appl. Catal. A: Gen. 221, 443 (2001). Huynh, C. T., Johnson, J. H., Yang, S. L., Bagley, S. T., and Warner, J. R. SAE 2003-01-0841 (2003). Johnson, T. V. SAE 2004-01-0070 (2004). Kandylas, I. P., and Koltsakis, G. C. Ind. Eng. Chem. Res. 41, 2115 (2002). Kleemann, M., Elsener, M., Koebel, M., and Wokaun, A. Appl. Catal. B: Environ. 27, 231 (2000). Kocˇi, P., Marek, M., Kubı´ cˇek, M., Maunula, T., and Ha¨rko¨nen, M. Ind. Eng. Chem. Res. 43, 4503 (2004).

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CURRENT STATUS OF MODELING LEAN EXHAUST GAS AFTERTREATMENT CATALYSTS Anke Gu¨thenke1,, Daniel Chatterjee1, Michel Weibel1, Bernd Krutzsch1, Petr Kocˇ´ı2, Milosˇ Marek2, Isabella Nova3 and Enrico Tronconi3 1

Daimler AG, Department GR/VPE, 70546 Stuttgart, Germany

2

Institute of Chemical Technology at Prague, Department of Chemical Engineering, CZ 166 28 Prague, Czech Republic 3

Dipartimento di Chimica, Materiali e Ingegneria Chimica ‘‘G. Natta’’, Politecnico di Milano, Milan I-20133, Italy I. Introduction II. Simulation of Combined Exhaust Gas Aftertreatment Systems III. Monolith Reactor Modeling A. Monolith Channel B. Spatially 1D Model C. Heat and Mass Transfer between Bulk Gas and Catalytic Washcoat D. Internal Diffusion in the Washcoat E. Spatially 2D (1D+1D) Model with Internal Diffusion F. Detailed 3D Model of Porous Catalytic Washcoat G. Numerical Solution IV. Development of Global Reaction Kinetics A. Microreactor Scale B. Monolith Reactor Scale C. Engine Test Bench Scale V. Diesel Oxidation Catalyst A. Functions of DOC B. Development of a DOC Global Kinetic Model C. Validation and Applications of the DOC Global Kinetic Model VI. NOx Storage and Reduction Catalyst A. NOx Storage and Reduction Principles B. Discussion on Surface Reaction Mechanisms

104 109 111 112 114 115 117 119 121 122 124 124 129 129 130 130 131 139 142 142 144

Corresponding author. Tel.: +49 711 17 21911; Fax: +49 711 3052 158 692. E-mail: [email protected]

103 Advances in Chemical Engineering, vol. 33 ISSN 0065-2377 DOI 10.1016/S0065-2377(07)33003-2

Copyright r 2008 by Elsevier Inc. All rights reserved.

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ANKE GU¨THENKE ET AL. C. Development of Effective NSRC Kinetic Model D. NSRC Model Validation and Simulation Results VII. Selective Catalytic Reduction of NOx by NH3 (Urea–SCR) A. Microreactor Scale B. Monolith Reactor Scale C. Engine Test Bench Scale VIII. Combined Aftertreatment Systems IX. Summary and Conclusions List of Symbols Abbreviations Acknowledgments References

150 158 164 166 188 192 198 201 202 205 206 206

Abstract Decreasing emission limits lead to the development of combined aftertreatment systems, consisting of combinations of different catalyst technologies and particulate filters. Modeling such systems can contribute considerably in reducing development time and cost. The methodology for developing catalyst models is reviewed and models for the diesel oxidation catalyst (DOC) with hydrocarbon (HC) adsorption, the NOx storage and reduction catalyst (NSRC) and the urea–selective catalytic reduction system (urea–SCR) are developed. Applications for exhaust aftertreatment system modeling are shown.

I. Introduction Worldwide emission legislation has become increasingly stringent over the last years. European emission limits for passenger cars and heavy-duty trucks are displayed in Figs. 1 and 2, respectively. For passenger cars, the final values and date for EU V are still under discussion. For heavy-duty trucks, since 2000, particulate emissions have been reduced by 80%, NOx will be reduced by 60% in 2008 with EU V. A further reduction of emission limits with EU VI is being considered for 2013 (Schulte-Braucks, 2006). In the USA, for Tier 2 emission limits different bins have been defined. Passenger cars are certified in any of the available bins by choice of the vehicle manufacturer. With full implementation in 2009, the average NOx emission of the entire light-duty fleet sold by each manufacturer must meet 0.07 g/mile. For the individual bins, the full useful life emission limits are displayed in Table I. The very strict California low emission vehicle (LEV) II emission standards are covered in the lower number bins to make certification easier for vehicle manufacturers.

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CURRENT STATUS OF MODELING LEAN EXHAUST GAS Gasoline

Diesel PM (g/km)

CO (g/km) 2.3

0.05 0.

EU 3 (2000)

THC (g/km)

CO (g/km) 1.0 1.0

EU 3 (2000)

0.025 0.025 EU44 (200 (2005) 5)

4

0.2 0.1

0.6

EUEU 5?5? (2009?)

EU 4 (2005)

0.07 0.08

0.5

0.005 0.005

EU 5 ?(2009?) ?(2009?) EU 0.23 0.3

0.15 NOx(g/km)

0.56 HC+NOx(g/km)

FIG. 1. European emission legislation for passenger cars (Dieselnet, 2007).

Heavy Duty Trucks

PM (g/kWh) 0.1 EU III (2000)

CO (g/kWh) 2.1

1.5

0.02 EU IV 2005

EUEU VV 2008 2008 2.0

3.5

5.0

NOx (g/kWh)

FIG. 2. European emission legislation for heavy-duty trucks—ESC limits (Dieselnet, 2007).

For heavy-duty trucks, US07 legislation demands particulate matter (PM) below 0.01 g/bhp-hr (grams per brake horsepower-hour), NOx below 0.20 g/bhp-hr and non-methane hydrocarbon (NMHC) below 0.14 g/bhp-hr. The NOx and NMHC limits will be phased in for diesel engines between 2007 and 2010, based on a percentage of vehicle sales. Additionally, not-to-exceed values of 1.5 times the emission limits are defined, which have to be met for a very large range of engine operating conditions. Test cycles differ widely for European and US legislation and also for passenger cars and commercial vehicles. For European passenger car

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106

TABLE I US EPA PASSENGER CAR TIER 2 EMISSION STANDARDS (DIESELNET, 2007) Bin

Full useful life NMOGa(g/mile)

CO (g/mile)

NOx (g/mile)

PM (g/mile)

HCHO (g/mile)

0.125 0.090 0.090 0.090 0.070 0.055 0.010 0.000

4.2 4.2 4.2 2.1 2.1 2.1 2.1 0.0

0.2 0.15 0.10 0.07 0.04 0.03 0.02 0.00

0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.00

0.018 0.018 0.018 0.018 0.011 0.011 0.004 0.000

8b 7 6 5 4 3 2 1 a

For diesel fueled vehicle, NMOG (non-methane organic gases) means NMHC (non-methane hydrocarbons).

Velocity (km/h)

120 100 80 60 40 20 0 0

200

400

600

800

1000

time [s]

FIG. 3. NEDC—New European Driving Cycle (Dieselnet, 2007).

certification, the new European driving cycle (NEDC) is employed, which is also known as the MVEG-A cycle. Tested on a chassis dynamometer, it consists of four ECE segments to represent urban driving conditions, followed by one EUCD to account for extra urban conditions, as shown in Fig. 3. Emissions are sampled during the cycle according to the constant volume sampling technique and expressed in grams per kilometer for each pollutant. In the USA, the FTP-75 (Federal Test Procedure) is used for emission certification of light-duty vehicles, consisting of a cold start phase, a transient phase and a hot start phase. It is complemented by two additional test procedures to account for high-speed driving (US06) and the use of air conditioning (SC03). Compared to the NEDC, the FTP-75 is more transient and covers a wider range of engine operating conditions. For heavy-duty commercial vehicles emission certification, the engine is tested on an engine dynamometer. In the European Stationary Cycle (ESC),

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

100

Load [%]

75

50

25

0

15% 15% Idle

50

8% 8%

9%

8%

5%

10%

5%

5%

10%

5%

5%

10%

5%

B

C

A

75

107

100

Engine Speed [%]

FIG. 4. ESC—European Stationary Cycle, weighing factors are given for the individual points (Dieselnet, 2007).

this is done in 13 steady-state modes, in which emissions are measured and are averaged over the cycle using weighing factors, cf. Fig. 4. Three additional test points are chosen by the tester. The ESC represents relatively high average load and therefore high exhaust gas temperatures. The stationary test is complemented by the European transient cycle (ETC) and the European load response (ELR) test to also account for transient conditions and for the purpose of smoke opacity measurements. The USA FTP-75 transient test for heavy-duty commercial vehicle engines consists of driving conditions representative of urban and freeway traffic, including a cold start. The average load factor is about 20–25% of the maximum horsepower at a given engine speed. A global harmonization of test cycles is under discussion. To fulfill the increasingly stringent emission limits, two routes are being followed today, mostly simultaneously, which are lowering the engine raw emissions and increasing the efficiency of the exhaust gas aftertreatment. To lower engine raw emissions, the main parameters are air–fuel ratio, the optimization of the combustion chamber shape and intake flow movement, ignition/injection timing, in-cylinder pressure and exhaust gas recirculation.

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In commercial vehicle engines, by means of cooled exhaust gas recirculation, in-cylinder temperatures are reduced, leading to lower NOx, but mostly also to higher particulate emissions. To fulfill future emission standards, a combination of raw emission reduction and exhaust gas aftertreatment is necessary. Decreasing fuel economy with raw emission reduction has to be weighed against catalyst—and especially noble metal—price for exhaust gas aftertreatment. Exhaust aftertreatment systems are becoming more and more advanced. Combined aftertreatment systems consisting of different catalyst technologies and particulate filter are developed by several vehicle manufacturers. The Bluetec system uses a different combination of technologies for different applications, i.e. vehicles and markets. Since October 2006, the passenger car Mercedes-Benz E320 Bluetec is sold in the US equipped with a combined system consisting of diesel oxidation catalyst (DOC), diesel particulate filter (DPF), NOx storage and reduction catalyst (NSRC) and selective catalytic reduction (SCR) catalyst to comply with EPA (Environmental Protection Agency, USA) Bin 8 standards. Since 2005, the Mercedes-Benz Actros heavyduty truck is equipped with EU IV/V compliant Bluetec system, which uses urea–SCR for NOx reduction. For the design and optimization of such increasingly complex exhaust aftertreatment systems, a purely experimental approach is very time and cost consuming. The investigation of all possible combinations of catalyst geometries, their position on the exhaust line and the resulting interactions leads to a huge testing effort, which can hardly be conducted in a tightscheduled development process. Exhaust aftertreatment simulation can make a considerable contribution to the development of complex combined systems. Different simulation tools are employed in the development of exhaust aftertreatment systems. Spatially 2D or 3D CFD simulation is commonly used to assess the effect of non-uniform flow conditions in pipes and housing and of non-uniform catalyst inlet flow on temperature distribution and reactions inside the monolith, thus enabling geometry optimization. Spatially one-dimensional (1D) simulation has the advantage of shorter calculation times, enabling the modeling of complete exhaust aftertreatment systems consisting of a combination of different catalysts and particulate filter. The 2D or 3D effects are mostly neglected. The simulation enables a preselection of catalyst or particulate filter sizes and geometries before going onto the test bench. Variations of engine out parameters, i.e. raw emissions or exhaust gas temperature and their influence on catalyst or particulate filter performance are assessed. In combined systems, exhaust conditions for a catalyst or particulate filter are influenced by its upstream component and changes in one component affect all others further down the line. Furthermore, the development of operating, regeneration or control strategies for a complete exhaust aftertreatment system can be supported.

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II. Simulation of Combined Exhaust Gas Aftertreatment Systems For the modeling and simulation of combined exhaust gas aftertreatment systems, it is necessary to develop models for the different catalyst and particulate filter technologies. The combination of the individual models in a common software environment facilitates system definition by the user and data transfer between the individual models. A software environment called ExACT (Exhaust Aftertreatment Components Toolbox) has been developed and is used at Daimler AG (Chatterjee et al., 2005, 2006; Gu¨thenke et al., 2007a,b) for this purpose. The software environment is based on Matlab/Simulink (2006) and includes models for different types of catalysts and particulate filters, cf. Fig. 5. Models for three-way catalyst (TWC), NSRC, SCR catalyst and coated DPF ((C)DPF) are included, as well as models for pipes, pipes with changing cross-sections, etc., to enable modeling of a complete exhaust line. Raw emissions are as yet not modeled but specified based on engine out measurements. Temperature and exhaust composition are analyzed between catalysts and at end-of-pipe. Post-processing also allows the analysis of the conditions inside a catalyst or particulate filter, e.g. concerning temperature or species concentration profiles.

FIG. 5. Example of a combined exhaust gas aftertreatment system model.

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Surface Reactions: (Chemistry)

Single Channel: (Mass and Heat Transport)

Variation Variation of of coatings coatings // noble noble metals metals Variation Variation of of geometrical geometrical parameters: parameters: e.g. e.g. length, length, diameter, diameter, cell cell density, density, wall wall thickness,.. thickness,..

FIG. 6. Scheme of the catalyst model structure.

The catalyst and particulate filter models were developed individually with different university partners. They are described in the following sections. A key issue for all models is robustness and scalability as the applications range from passenger cars to heavy-duty commercial vehicles. The models are physical and chemically based, consisting of a transport model for heat and mass transfer phenomena in the monolith in gas and solid phases, cf. Fig. 6. The monolith reactor modeling is discussed in more detail in Section III. In the washcoat, reaction rates are modeled via global reaction mechanisms. In such a global or macrokinetic reaction mechanism, several microkinetic adsorption, reaction and desorption steps are ‘‘lumped’’ together, reducing the overall number of kinetic parameters considerably. For some catalysts, e.g. TWC, the elementary steps are relatively well known and information on a microkinetic reaction mechanism including parameters can be found in literature (Braun et al., 2000; Chatterjee et al., 2001). For other catalysts however, e.g. NSRC, there is still a discussion in academia about the elementary steps taking place in the washcoat (Epling et al., 2004a). Therefore, for the development of the catalyst models discussed in the following sections, global reaction kinetic approach was chosen. The developments of global reaction kinetics were however based on chemical knowledge of the system investigated, from literature and laboratory experiments. In the case of NH3–SCR, the development of detailed, chemically consistent kinetic schemes has been paralleled by a dedicated investigation of their catalytic mechanism under the specific conditions of mobile applications, which differ considerably from those of conventional SCR applications for stationary plants, widely investigated in the past (Ciardelli et al., 2004b; Nova et al., 2006b; Tronconi et al., 2007). The development of global reaction kinetic mechanisms is discussed in Section IV, the reaction mechanisms for the individual catalyst models for selective catalytic reduction catalyst (urea–SCR), NSRC and DOC are explained in Sections V, VI and VII, respectively. The employed physical and chemically based modeling approach enables a relatively large variation of geometrical catalyst parameters, e.g. catalyst length, diameter, etc., with the global reaction kinetics and therefore the overall

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catalyst model still remaining valid. Due to the global reaction kinetic approach employed, for a variation of coatings or noble metals, new reaction kinetics or at least kinetic parameters need to be found. As the models presented in this chapter are relatively complex, they are not used for control purposes in their current status. They can however be used for the development of control models, either by linearization and simplification, or as ‘‘virtual test bench’’. This way, a control model is pre-tuned on the catalyst or system model before parameterization on the ‘‘real’’ test bench, thus saving development time and costs. The simulation of combined exhaust aftertreatment systems has also been undertaken by Wurzenberger and Peters (2003) and Wurzenberger and Wanker (2005). They focus on the 1D simulation of an urea–SCR system. The system includes a model for NO2 production on a DOC, a model for urea injection, urea decomposition and hydrolysis catalyst, a model for a vanadium-type SCR catalyst and a model for NH3 decomposition on a clean-up catalyst. The catalyst models consist of a 1D monolith model with global kinetic reactions on the washcoat surface, kinetic parameters have been taken from literature or adjusted to experimental data from literature. The complete model was implemented in AVL BOOST (2006). AVL BOOST is an engine cycle and gas exchange simulation software tool, which allows for the building of a model of the entire engine. Another engine cycle and gas exchange simulation software tool which has been extended for exhaust aftertreatment simulation is GT-POWER (2006). This software includes models for engine components as well as templates for DOC, SCR catalyst, NSRC and TWC. Reaction kinetics can be provided by the user, based on templates. Kinetic parameters adaptation is supported with a built-in optimizer tool. The hydraulic simulation tool AMESim (2006) has also been extended for exhaust aftertreatment simulation, by including routines developed together with IFP (2006). The software includes models for TWC, hydrocarbon (HC) trap, NSRC, oxygen storage, DOC and DPF as well as pipes, etc. Catalysts are modeled via 0D approach, hence all transport effects are lumped into reaction kinetic parameters. These kinetic parameters can be adapted by the user.

III. Monolith Reactor Modeling In this section the models employed for simulation of catalytic monolith reactor are discussed, focusing on effective description of heat and mass transfer phenomena in monolith channel. The number of different mathematical models developed for converters of automobile exhaust gases over the last decades is huge—cf., e.g. Heck et al. (1976), Young and Finlayson (1976), Oh and Cavendish (1982), Zygourakis and Aris (1983), Chen et al. (1988),

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Tronconi and Forzatti (1992), Montreuil et al. (1992), Leclerc and Schweich (1993), Lie et al. (1993), Pinkas et al. (1995), Groppi et al. (1995a, b), Kirchner and Eigenberger (1996), Koltsakis et al. (1997), Koltsakis and Stamatelos (1997), Jahn et al. (1997), Dubien et al. (1997), Jira´t et al. (1999a, b, 2001), Balakotaiah et al. (2000), Wanker et al. (2000), Hoebink et al. (2000), Chatterjee et al. (2001), Mukadi and Hayes (2002), Kocˇı´ et al. (2004a–d) and Tischer and Deutschmann (2005), the reviews by Groppi et al. (1999) and Hoebink et al. (2006) and numerous references given there. Until mid-1990s the modeling was mostly devoted to simple oxidation or TWCs, however, the developed fundamental physical models and selected reaction kinetics can be utilized also in the modeling of newer catalyst technologies. Two sub-levels can be recognized in all the models (i) the description of heat and mass transport, ranging from simplified lumped models (cf., e.g. Balakotaiah et al., 2000), via classical heterogeneous 1D plug flow reactor models, to fully distributed models (Tischer and Deutschmann, 2005; Wanker et al., 2000) and (ii) the description of chemical reactions, ranging from single reaction (typically CO oxidation), via multiple reactions with pseudostationary Langmuir–Hinshelwood kinetics, addition of non-stationary storage sub-models (e.g. Jira´t et al. 1999a; Kocˇı´ et al., 2004c, 2007b; Koltsakis et al., 1997; Kryl et al., 2005), up to large, completely microkinetic schemes (e.g. Chatterjee et al., 2001; Kocˇı´ et al., 2004a, b, d; Mukadi and Hayes, 2002). For the routine use in automotive industry, it is necessary to consider the model as simple as possible, with reasonable computation times, while still retaining the essential reliability. The choice depends on the studied problem and the range of operating conditions.

A. MONOLITH CHANNEL In many situations, the monolith reactor can be represented by a single channel. This assumption is correct for the isothermal or adiabatic reactor with uniform inlet flow distribution. If the actual conditions in the reactor are significantly different, more parallel channels with heat exchange have to be simulated (cf., e.g. Chen et al., 1988; Jahn et al., 1997, 2001; Tischer and Deutschmann, 2005; Wanker et al., 2000; Young and Finlayson, 1976). In this section we will further discuss effective single channel models. The most important processes in monolith channel—convection of exhaust gas, heat and mass transfer between the flowing gas and the washcoat, internal diffusion, catalytic reactions in the washcoat, heat and mass accumulation and heat conduction—are schematically depicted in Fig. 7. The size of the entire monolith converter varies for individual applications (from small close-coupled ones in passenger cars to large ones in heavy-duty trucks) to meet similar space velocities and conversions for differently sized engines. However, cross-sectional channel density around 400 cpsi, diameter

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113

FIG. 7. Monolith channel topology and physical and chemical processes considered in the models (Kocˇı´ , 2005).

of the channel around 1 mm, thickness of the washcoat layer in the range 20–80 mm and wall thickness 100–200 mm are similar for most of the applications. The following, well-acceptable assumptions are applied in the presented models of automobile exhaust gas converters: Ideal gas behavior and constant pressure are considered (system open to ambient atmosphere, very low pressure drop). Relatively low concentration of key reactants enables to approximate diffusion processes by the Fick’s law and to assume negligible change in the number of moles caused by the reactions. Axial dispersion and heat conduction effects in the flowing gas can be neglected due to short residence times (E0.1 s). The description of heat and mass transfer between bulk of flowing gas and catalytic washcoat is approximated by distributed transfer coefficients, calculated from suitable correlations (cf. Section III.C). All physical properties of gas (cp, r, m, l, Dk) and solid phase heat capacity are evaluated in dependence on temperature. Effective heat conductivity, density and heat capacity are used for the entire solid phase, which consists of catalytic washcoat layer and monolith substrate (wall). The reactions take place only in active catalytic layer, the rates Rj are considered individually for each type of the converter (DOC, SCR, NSRC, TWC). The development of suitable reaction schemes and the evaluation of kinetic parameters are discussed generally in Section IV. The details for DOC, NSRC and SCR of NOx by NH3 are given in Sections V, VI and VII, respectively. The important species deposited on the catalyst surface are balanced (e.g. HC adsorption in DOC, oxygen and NOx storage in NSRC, NH3 adsorption in SCR). Heat transfer by radiation and homogeneous reactions

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can be omitted for temperatures below 6001C, which is well satisfied for lean exhaust catalysts. Additional assumptions special to the individual models are discussed in the respective sub-sections.

B. SPATIALLY 1D MODEL In the spatially 1D model of the monolith channel, no transverse concentration gradients inside the catalytic washcoat layer are considered, i.e. the influence of internal diffusion is neglected or included in the employed reaction-kinetic parameters. It may lead to the over-prediction of the achieved conversions, particularly with the increasing thickness of the washcoat layer (cf., e.g., Aris, 1975; Kryl et al., 2005; Tronconi and Beretta, 1999; Zygourakis and Aris, 1983). To overcome this limitation, the effectiveness-factor concept can be used in a limited extent (cf. Section III.D). Despite the drawbacks coming from the fact that internal diffusion effects are implicitly included in the reaction kinetics, the 1D plug-flow model is extensively used in automotive industry, thanks to the reasonable combination of physical reliability and short computation times.

1. Model Equations The following balances are considered in spatially 1D, heterogeneous model of a monolith channel with plug-flow and surface storage of gas components: mass balances in the flowing gas, including accumulation, convection and external mass transfer, Eq. (1); mass balances in the washcoat pores, including accumulation, external mass transfer and catalytic reactions, Eq. (2); mass balances on the catalyst surface, including accumulation, and catalytic reactions, Eq. (3); enthalpy balance of the flowing gas, including accumulation, convection and gas–solid heat transfer, Eq. (4); enthalpy balance of the solid phase, including accumulation, axial heat conduction, gas–solid heat transfer, heat exchange with the surroundings (usually negligible for full-size monolith, more important for small laboratory reactor) and heat source from catalytic reactions, Eq. (5) @ck ðz; tÞ @ðuck Þ kc a s ¼
þ g cðyk
yk Þ; @t @z


k ¼ 1...K

J @csk ðz; tÞ kc a 1X s ¼ s cðy
y Þ þ nk; j Rj ; k
ð1

g Þjs k
s j¼1 @t

k ¼ 1...K

(1)

(2)

CURRENT STATUS OF MODELING LEAN EXHAUST GAS J @cm ðz; tÞ 1 X ¼ cap nc Rj ; @t Cm j¼1 m; j

rcp

rs csp

m ¼ 1...M

@Tðz; tÞ @T kh a ¼
u rcp þ g ðT s
TÞ @t @z


115 (3)

(4)

J X @T s ðz; tÞ @2 T s kh a ¼ ls 2 þ ðT
T s Þ
W ðT s
T e Þ
js DH r; j Rj g @t ð1

Þ @z j¼1

(5)

2. Boundary Conditions ck jz¼0 ¼ cin k ðtÞ;

k ¼ 1...K

(6)

Tjz¼0 ¼ T in ðtÞ; ujz¼0 ¼ uin ðtÞ

(7)


@T s

¼ 0; @z
z¼0


@T s

¼0 @z
z¼L

(8)

The inlet gas linear velocity uin is calculated from the following equation: uin ¼

in V_ S
g

(9)

The apparent gas hourly space velocity (SV) is defined at the standard temperature and pressure
V_

SV ¼
V STP

C. HEAT

AND

MASS TRANSFER

BETWEEN

BULK GAS

(10)

AND

CATALYTIC WASHCOAT

Mass and heat transfer between flowing gas and catalytic washcoat layer along the monolith channel are in 1D gas models approximated by distributed

ANKE GU¨THENKE ET AL.

116

transfer coefficients kc(z) and kh(z), respectively—cf. Eqs. (1), (2), (4) and (5). The evaluation of transfer coefficients for laminar flow monolith reactors is still under debate. Various experimental and theoretical approaches have been proposed in the literature (cf., e.g. Balakotaiah et al., 2000; Balakotaiah and West, 2002; Groppi et al., 1995a, b; Hayes et al., 2004; Heck et al., 1976; Ramanathan et al., 2003; Shah and London, 1978; Tronconi and Beretta, 1999; Tronconi and Forzatti, 1992; West et al., 2003). Generally, the transfer coefficients can be calculated from the dimensionless Nusselt and Sherwood numbers, Eq. (11), which are determined from the empirically or theoretically derived correlations of the following general type, Eq. (12):

kh ðzÞ ¼

Nu lg ; d

Nu ¼ f 1 ðRe; Pr; d; zÞ;

Re ¼

ud ; mg

Sc ¼

Sh D d

(11)

Sh ¼ f 2 ðRe; Sc; d; zÞ

(12)

kc ðzÞ ¼

mg ; D

Pr ¼

cgp mg rg lg

(13)

Transfer coefficients in catalytic monolith for automotive applications typically exhibit a maximum at the channel inlet and then decrease relatively fast (within the length of several millimeters) to the limit values for fully developed concentration and temperature profiles in laminar flow. Proper heat and mass transfer coefficients are important for correct prediction of coldstart behavior and catalyst light-off. The basic issue is to obtain accurate asymptotic Nu and Sh numbers for particular shape of the channel and washcoat layer (Hayes et al., 2004; Ramanathan et al., 2003). Even if different correlations provide different kc and kh profiles at the inlet region of the monolith, these differences usually have minor influence on the computed outlet values of concentrations and temperature under typical operating conditions. Recently, a set of correlations including the effect of channel shape has been proposed by Ramanathan et al. (2003) on the basis of solution of the Navier– Stokes equations in the channel, with different solutions derived for ignitedreaction and extinct-reaction regimes. The comparison of various empirical and theoretical correlations with experimentally evaluated mass transfer coefficients is given by West et al. (2003). The correlations by Ramanathan et al. (2003) or Tronconi and Forzatti (1992) have been used in most simulations presented in this chapter.

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

D. INTERNAL DIFFUSION

IN THE

117

WASHCOAT

The catalyst operation not only above, but also around the light-off temperature has been shown to be significantly affected by washcoat diffusion (cf., e.g. Hayes et al., 2004, 2005; Kocˇı´ et al., 2004a, b; Kryl et al., 2005; Ramanathan et al., 2003; Tronconi and Beretta, 1999; Zygourakis and Aris, 1983). The internal diffusion effects cannot be simply included into the reaction kinetics particularly in the case of parametric studies on the varying washcoat thickness. There are two major types of diffusion contributing to mass transport in the monolith washcoat (cf., e.g. Aris, 1975; Froment and Bischoff, 1979, 1990; Poling et al., 2001): volume (molecular) diffusion, Eq. (14), and Knudsen diffusion, Eq. (15), the latter one being dominant in small pores. Dvol k ¼

144:9 T 1:75 =p 2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1=3 1=3 ok þ oref 2=ð10
3 =W k þ 10
3 =W ref Þ

DKnudsen k

2 ¼ rp 3

sffiffiffiffiffiffiffiffiffiffiffiffi 8Rg T pW k

(14)

(15)

Using resistance-in-series model (Bosanquet formula), the diffusivity in pore with radius rp is

Dk ðrp Þ ¼

1 1 þ Knudsen ðrp Þ Dvol D k k

!
1 (16)

Porous catalytic washcoat exhibits bimodal pore size distribution with larger macropores (rpE100–500 nm) among individual support material particles (e.g. g-Al2O3, zeolites), and small meso-/micropores (rpE3–6 nm) inside the particles. Typical pore size distribution and electron microscopy images of g-Al2O3-based washcoat can be found, e.g. in Stary´ et al. (2006) and Kocˇı´ et al. (2006). Different theoretical models applied to this pore size distribution can give relatively large variations of the calculated effective diffusivity value (Deff). The most commonly used approximations are (i) random-pore model (Wakao and Smith, 1962) using two characteristic transport pores (micropores m and macropores M) 2 2 Deff k ¼
M DM;k þ
m Dm;k þ

4ð
M

2M Þ ðð1=DM;k Þ þ ðð1

M Þ2 =
2m Dm;k ÞÞ

(17)

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ANKE GU¨THENKE ET AL.

and (ii) mean transport-pore model using one characteristic pore size rp and tortuosity parameter (cf., e.g. Froment and Bischoff, 1979, 1990) Deff k ¼


s Dk ðrp Þ t

(18)

Experimental determination of effective washcoat diffusivity in cordierite monolith coated by g-Al2O3-based catalyst for automobile exhaust treatment has been done using either chromatographic impulse-response method (Monolith, 2007; Stary´ et al., 2006) or measurements of steady countercurrent diffusion by modified Wicke–Kallenbach method (Hayes et al., 2000; Zhang et al., 2004). The experimentally observed values of effective diffusivity are around 1  106 m2/s for CH4 in g-Al2O3-based washcoat at 298 K and standard pressure. Effective diffusion coefficients in SCR monolith catalysts for stationary deNOx operation were experimentally determined by Beeckman (1991), who measured the steady diffusion through SCR catalyst walls and found it in agreement with the Wakao–Smith random pore model. Calculated reaction rates can be in the spatially 1D model corrected using the generalized effectiveness factor (Z) approach for non-linear rate laws. The effect of internal diffusion limitations on the apparent reaction rate Reff is then lumped into the parameter Z evaluated in dependence on Deff, d and Rj (cf. Aris, 1975; Froment and Bischoff, 1979, 1990; Leclerc and Schweich, 1993). Reff j ¼ Zj R j

(19)

Under excess of the second reactant (in automobile exhaust gas typically H2O, CO2 and for lean-burn engines exhaust specifically also O2), the effectiveness factor calculation can be simplified by approximating the reaction rate Rj by a pseudo-first-order rate law with respect to the component k using new rate constant kref, j (evaluated from the original rate law) Rj ¼ kref; j csk

(20)

Then the classical Thiele modulus (F) and the effectiveness factor (Z), expressing the extent of internal diffusion limitations in the catalytic washcoat layer of thickness d, can be calculated according to (cf. Aris, 1975; Froment and Bischoff, 1979, 1990) sffiffiffiffiffiffiffiffiffiffi kref; j Fj ¼ d Deff k

(21)

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

Zj ¼

tanh Fj Fj

119 (22)

Values of effectiveness factors in washcoat layers with non-uniform thickness around the channel perimeter have been studied by Hayes et al. (2005). However, the applicability of (even the generalized) effectiveness factor approach is quite limited in complex systems with competing reactions, surface deposition of reaction components, non-linear rate laws and under transient operating conditions (e.g. periodically operated NSRC). Typically, the effectiveness factor method can be used for more accurate prediction of CO, H2 and HC oxidation light-off and conversions in DOC.

E. SPATIALLY 2D (1D+1D) MODEL

WITH

INTERNAL DIFFUSION

When the internal diffusion effects are considered explicitly, concentration variations in the catalytic washcoat layer are modeled both in the axial (z) and the transverse (radial, r) directions. Simple slab geometry is chosen for the washcoat layer, since the ratio of the washcoat thickness to the channel diameter is low. The layer is characterized by its external surface density a and the mean thickness d. It can be assumed that there are no temperature gradients in the transverse direction within the washcoat layer and in the wall of the channel because of the sufficiently high heat conductivity, cf., e.g. Wanker et al. (2000). For the bulk gas, the spatially 1D description with the distributed heat and mass transfer coefficients is used, similarly as in the spatially 1D model. 1. Model Equations The following balances are considered in the spatially 2D (1D+1D), heterogeneous model of a monolith channel with plug-flow, internal washcoat diffusion and surface deposition of gas components (Kocˇı´ et al., 2004a): mass balances in the flowing gas, including accumulation, convection and external mass transfer, Eq. (23); mass balances in the washcoat pores, including accumulation, internal diffusion and catalytic reactions, Eq. (24); mass balances on the catalyst surface, including accumulation, and catalytic reactions Eq. (25); enthalpy balance of the flowing gas, including accumulation, convection and gas–solid heat transfer, Eq. (26); enthalpy balance of the solid phase, including accumulation, axial heat conduction, gas–solid heat transfer, heat exchange with the surroundings and heat source from catalytic reactions, Eq. (27) @ck ðz; tÞ @ðuck Þ kc a s

¼
þ g cðyk r¼d
yk Þ; @t @z


k ¼ 1...K

(23)

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120

J @csk ðz; r; tÞ Deff @2 csk 1 X ¼ ks þ nk; j Rj ;
s j¼1 @t
@r2

J @cm ðz; r; tÞ 1 X ¼ cap nc R j ; @t Cm j¼1 m; j

rcp

rs csp

k ¼ 1...K

m ¼ 1...M

@Tðz; tÞ @T kh a ¼
u rcp þ g ðT s
TÞ @t @z


@T s ðz; tÞ @2 T s kh a ¼ ls 2 þ ðT
T s Þ
W ðT s
T e Þ @t ð1

g Þ @z J Z d X a
DH j Rj dr ð1

g Þ j¼1 r¼0

(24)

(25)

(26)

ð27Þ

2. Boundary Conditions Boundary conditions at the inlet (z ¼ 0) and at the outlet (z ¼ L) of the monolith are the same as for the 1D model, cf. Eqs. (6)–(8). In the catalytic washcoat layer, r ¼ 0 corresponds to the wall boundary and r ¼ d is on the external surface of the washcoat. Boundary conditions for the washcoat layer are then Deff k



@csk

¼ kc cðyk
ysk
r¼d Þ;
@r r¼d

@csk

¼ 0; @r
r¼0

k ¼ 1...K

k ¼ 1...K

(28)

(29)

There exists a relation between the volume of catalytic washcoat layer (represented in the 1D model by the volume fraction js in the solid phase) and the characteristic thickness of the layer d used in the spatially 2D model. This relation depends on the chosen washcoat geometry—for slab geometry used here it is da ¼ js ð1

g Þ

(30)

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

F. DETAILED 3D MODEL

OF

121

POROUS CATALYTIC WASHCOAT

For the detailed study of reaction–transport interactions in the porous catalytic layer, the spatially 3D model computer-reconstructed washcoat section can be employed (Kocˇı´ et al., 2006, 2007a). The structure of porous catalyst support is controlled in the course of washcoat preparation on two levels: (i) the level of macropores, influenced by mixing of wet supporting material particles with different sizes followed by specific thermal treatment and (ii) the level of meso-/ micropores, determined by the internal nanostructure of the used materials (e.g. alumina, zeolites) and sizes of noble metal crystallites. Information about the porous structure (pore size distribution, typical sizes of particles, etc.) on the micro- and nanoscale levels can be obtained from scanning electron microscopy (SEM), transmission electron microscopy (TEM), or other high-resolution imaging techniques in combination with mercury porosimetry and BET adsorption isotherm data. This information can be used in computer reconstruction of porous catalytic medium. In the reconstructed catalyst, transport (diffusion, permeation, heat conduction) and combined reaction–transport processes can be simulated on detailed level (Kosek et al., 2005). The computer-reconstructed catalyst is represented by a discrete volume phase function in the form of 3D matrix containing information about the phase in each volume element. Another 3D matrix defines the distribution of active catalytic sites. Macroporosity, sizes of supporting articles and the correlation function describing the macropore size distribution are evaluated from the SEM images of porous catalyst (Kocˇı´ et al., 2006; Kosek et al., 2005). Spatially 3D reaction–diffusion system with low concentrations of reactants and products can be described by mass balances in the form of the following partial differential equations (Kocˇı´ et al., 2006, 2007a). For gaseous components k @ck ðX ; Y ; Z; tÞ Deff ¼ ks @t




@2 c k @2 c k @2 c k þ þ @X 2 @Y 2 @Z2

 þ

J 1X nk;j Rj ; s
j¼1

k ¼ 1...K (31)

For surface-deposited components m J @cm ðX ; Y ; Z; tÞ 1 X ¼ cap nc R j ; @t Cm j¼1 m;j

m ¼ 1...M

(32)

the given set of partial differential equations is then solved within the spatially 3D section of digitally reconstructed porous catalyst (XAoX0,X1W, YAoY0,Y1W, ZAoZ0,Z1W). Examples of the obtained concentration and reaction rate profiles are given in Fig. 8.

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ANKE GU¨THENKE ET AL.

FIG. 8. Typical CO concentration and reaction rate profiles in the porous Pt/g-Al2O3 catalyst reconstructed by particle-packing method. Mean hydraulic diameter of macropores dM h =300 nm, macroporosity eM=18.1%. Free space corresponds to macropores, solid gray corresponds to mesoporous g-Al2O3 with dispersed Pt. Length of the section edge 10 mm. Boundary Z1:yCO=1%, yO2 =0.5%. (a) T=513 K, (b) T=533 K (Kocˇı´ et al., 2007a) (see Plate 2 in Color Plate Section at the end of this book).

Two types of concentration gradients can be recognized: (i) the longer range concentration gradient in the local direction Z (corresponding to the global transverse direction r in the 2D model of entire monolith) and (ii) the shortrange local gradients within the individual meso-/microporous catalyst particles (Kocˇı´ et al., 2007a). This methodology can be used for the calculation of local reaction rates and effectiveness factors in dependence on gas components concentrations, temperature and porous catalytic layer structure (cf. Fig. 9). The results can then be used as input values for simulations at a larger scale, e.g. the effective reaction rates averaged over the studied washcoat section can be employed as local reaction rates in the 1D model of monolith channel.

G. NUMERICAL SOLUTION The detailed 3D model of porous catalyst is solved in pseudo-steady state. A large set of non-linear algebraic equations is obtained after equidistant discretization of spatial derivatives. This set can be solved by the Gauss–Seidel iteration method (cf. Kocˇı´ et al., 2007a). The system of hyperbolic and parabolic partial differential equations representing the 1D or 2D model of monolith channel is solved by the finite differences method with adaptive time-step control. An effective numerical solution is based on (i) discretization of continuous coordinates z, r and t, (ii) application of difference approximations of the derivatives, (iii) decomposition of the set of equations for T s, T, c and cs, (iv) quasi-linearization of

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

123

FIG. 9. Influence of local temperature T and varying catalyst structure on the CO oxidation effectiveness factor Z. Each Pt/g-Al2O3 catalyst was reconstructed by packing of g-Al2O3 particles of two different sizes with small-to-large particles number 16. Small particles size d1=1 mm was kept constant, while large particles size d2 and particles fractional overlap o (level of sintering) were varied. Gas concentrations the same as in Fig. 8 (Kocˇı´ et al., 2007a).

the reaction terms Rj and (v) solving the resulting systems of linear algebraic equations (Kubı´ cˇek et al., 1997; Kocˇı´ , 2005). Quasi-linearization of reaction rates (first-order Taylor’s expansion) and (semi-)implicit approximations of derivatives with respect to time are used for stable integration and to prevent the use of very short time-steps for stiff system. Another numerical approach involves spatial discretization of coordinates z, r and approximation of derivatives based on orthogonal collocation techniques (Finlayson, 1980; Villadsen and Michelsen, 1978; Villadsen and Stewart, 1967). The solver is implemented in Fortran, using optimized treatment of diagonalband matrices and analytical derivatives of reaction rates to minimize computation time. The software structure is modular, so that different reaction-kinetic modules for individual types of catalysts can be easily employed in the monolith channel model. The compiled converter models are then linked in the form of dynamic libraries into the common environment (ExACT) under Matlab/Simulink. Such combination enables fast and effective simulation of combined systems of catalytic monolith converters for automobile exhaust treatment.

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IV. Development of Global Reaction Kinetics The aim of the present section is to illustrate the procedures employed for the derivation of dynamic kinetic models appropriate for simulation of exhaust aftertreatment devices according to the converter models illustrated in the previous section. In particular, it will be shown how to derive global reaction kinetics which are based on a fundamental study aimed at the elucidation of the reaction mechanism. In principle, this approach enables a greater model adherence to the real behavior of the reacting system, which should eventually afford better results when validating the model in a wide range of operating conditions, as typically required for automotive applications. The experimental setup and the testing procedures specifically needed for the dynamic kinetic investigation will be discussed in the following sections, as well as the relevant methods for data analysis and for parameter estimation. A stage-wise scale-up approach is presented: (A) transient kinetic experiments are first performed in a microreactor over a small sample of catalyst in powder form in order to identify the reaction network and to evaluate the intrinsic kinetics in the absence of diffusional disguises; (B) such rate expressions, as well as the relevant geometrical and morphological characteristics of the monolith catalysts, are then incorporated into a transient mathematical model of monolithic reactors, developed along the lines presented in the previous Section III: this enables an intermediate validation of the kinetics against transient runs performed over small honeycomb catalyst samples (e.g., up to 10 cm3 in volume) still operated in a lab-scale rig using synthetic gas mixtures; (C) the final validation is eventually based on engine test bench data collected over full-scale monolith converters, using real engine exhaust gases. This approach closely reflects the procedures recently applied in the development of simulation models for the SCR technology (see Section VII). A. MICROREACTOR SCALE 1. Experimental In the first stage of the investigation the catalyst can be considered in the form of powder in order to derive intrinsic transient kinetics of all the relevant reactive processes. To this purpose, dynamic reactive experiments can be performed in a simple tubular fixed-bed microreactor over small quantities (50–200 mg) of finely powdered catalyst: in principle, this guarantees negligible transport limitations and more controlled conditions (e.g. isothermal catalyst bed), hence enabling a direct estimation of intrinsic rate parameters by kinetic fit. Internal diffusion limitations are particularly relevant to the case of bulk (extruded) monolith catalysts, such as vanadium-based systems for NH3/urea SCR; however, they

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may play a role also in washcoated honeycomb catalysts, depending on the thickness of the washcoat layers and on the local reaction rates. According to this approach, a portion consisting of several grams from the original extruded or washcoated monolith is crushed and sieved to a fine powder of controlled size (e.g. with average particle diameter o 100 mm), from which the catalyst sample required for testing should be extracted randomly: this procedure helps to rule out problems related to possible non-homogeneous distributions of the active species in the original monoliths. In the case of washcoated honeycomb catalysts, differences in mechanical strength between the washcoat layer and the monolith substrate may favor maldistributions of the active phase when grinding and sieving: this should be carefully checked to prevent erroneous evaluations of the intrinsic catalytic activity. For example, iterative grinding of the cuts with bigger particles may be helpful to avoid segregation of sturdier cordierite chunks. Alternatively, one could consider of running kinetic experiments directly over the washcoat precursor powder, if available. While this rules out any maldistribution issue, the observed behavior could still be not fully representative of the intrinsic activity of the monolith catalyst due to the effects of thermal and chemical treatments associated with the washcoat deposition procedures (Chatterjee et al., 2007). The absence of significant physical limitations during reactive processes in the bed of crushed catalyst powder can be checked a posteriori by classical dimensionless diagnostic criteria (Kapteijn and Moulijn, 1997; Mears, 1971): notice however that such evaluations rigorously apply to steady-state conditions only. An important requirement of kinetic studies for automotive aftertreatment devices is the capability of performing dynamic reactive experiments. Steadystate tests provide useful information for identification of reaction pathway and stoichiometry, but cannot capture the real operating behavior of catalytic converters for vehicles, which is transient in nature. Indeed, this is so not only because of the continuously changing conditions (temperature, composition, flow rate) of the engine exhausts: as extensively addressed in the following sections, the principles of NSRC and SCR applications largely rely on the storage/reaction/release dynamics of NOx and of NH3, respectively. Dynamic kinetic runs can be performed according to, e.g. the transient response method (TRM) (Lietti et al., 1997): it consists in executing stepwise changes of the concentration of one or more species in the feed mixture while continuously monitoring the temporal evolution of the system response. The perturbation of the feed composition is best realized using the fast 4-way pulse valves described in Section VII, which assure constant conditions of pressure and total flow. In this type of experiment the temperature is normally kept constant throughout the run. However, the temperature can be also increased at different constant heating rates during transient experiments of different nature (T-ramps), involving, e.g. temperature programmed desorption (TPD) or temperature programmed reaction (TPR). TPD runs provide information on the storage/release kinetics of species adsorbed onto the catalyst surface,

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including their T-dependent storage capacity: this is an important element for the design of deNOx aftertreatment systems such as NSRC or SCR converters; TPR runs as well can supply information concerning the T-dependence of the reaction rates. For all such experiments, an obvious but critical prerequisite is that dead volumes in the test rig be minimized. Transient experiments also require that the analysis of the outlet gas mixture must be continuous; this determines the choice of suitable gas analyzers with high time resolution, which should allow to monitor the temporal evolution of the largest possible number of species involved in the considered reactions. Measured composition dynamics typically need to be corrected for the transfer functions of the test rig and of the analyzers, as done, e.g. by Oh and Cavendish (1985), Siemund et al. (1996) and Nova et al. (2006a) on the basis of blank composition step change experiments. The important role of suitable gas analyzers in understanding the dynamic behavior of SCR systems is specifically discussed in (Ciardelli et al., 2007b). Operating conditions adopted in the transient kinetic runs are selected as similar as possible to those prevailing in the real aftertreatment device: this applies particularly to temperature ranges, feed gas compositions and reactant concentrations. In view of the typical composition of engine exhausts, the kinetic influence of species such as O2, H2O, CO2, CO and HCs should be always considered in principle. When several species and reactions are involved in the investigated process, the experimental work is best organized according to a hierarchical approach of growing complexity: first the simplest reacting systems (e.g. adsorption/ desorption of individual species) are addressed, then the other reactants are included one by one, if possible, to address separately the main reactions. This approach offers a good insight into the individual reaction pathways prevailing in the global system, and can be helpful in reducing the correlation among the parameters estimates in the regression analysis. In fact the intrinsic rate expressions are derived for each step of the process, and estimation of the intrinsic kinetics is obtained from the fit of different sets of data, according to a specific strategy which reflects the stage-wise approach mentioned above. The general data set can then be complemented by additional dedicated runs: for example, specifically designed transient experiments can be implemented, too, to explore the system response to perturbations of the reaction conditions (e.g. sensitivity of the global stoichiometries to reaction temperature) and/or to gain more detailed dynamic information on the process. 2. Data Analysis: Microreactor Model In order to estimate the rate parameters of the kinetic expressions derived in the fundamental study, the kinetic runs performed over the powdered catalyst are typically analyzed according to a heterogeneous 1D plug-flow dynamic

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model of the test microreactor, assuming the catalytic bed to be isothermal and isobaric and based upon the following unsteady material balance equations for gaseous (k) and adsorbed (m) species. Gaseous phase:
g

J X @ck @ck ¼
u
ð1

g Þ nk; j Rj @t @z j¼1

k ¼ 1...K

(33)

Adsorbed phase: Om

J X @ym ¼
nm; j Rj @t j¼1

m ¼ 1...M

(34)

where ck is the gas-phase concentration of species k, ym the surface coverage of species m, eg the void fraction of the catalyst bed, u the gas linear velocity (m/s), Om the catalyst adsorption capacity of species m (mol/m3cat), Rj the intrinsic rate of reaction j (mol/m3cat/s) and nk, j the stoichiometric coefficient of species k in reaction j. Examples of derivation of rate expressions Rj ¼ Rj ðT; ck ; ym ; xÞ are presented in the following sections, depending on the amount of knowledge on the catalytic chemistry, either empirical or mechanistically consistent forms can be adopted. 3. Data Analysis: Parameter Estimation The rate expressions Rj ¼ Rj ðT; ck ; ym ; xÞ typically contain functional dependencies on reaction conditions (temperature, gas-phase and surface concentrations of reactants and products) as well as on adaptive parameters x (i.e., selected pre-exponential factors k0, j, activation energies Ej, inhibition constants K, effective storage capacities ceq and adsorption capacities Ccap and O). Such rate parameters are estimated by multiresponse non-linear regression according to the integral method of kinetic analysis based on classical leastsquares principles (Froment and Bischoff, 1979). The objective function to be minimized in the weighted least squares method is f ðxÞ ¼

NS X NY X

 2 sim wk yexp i;k
yi;k ðxÞ

(35)

i¼1 k¼1

Here x is the vector of kinetic parameters to be fitted, i the index of the experimental data point and of the corresponding simulation result, wk denotes the weight of the component k in the sum and yexp and ysim stand for NY experimentally measured and simulated outlet components concentrations, respectively. Experimental responses yexp are typically the temporal evolutions

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of all the species concentrations detected by the analytical systems at the test reactor outlet for all the NS data points collected in transient runs. Optimized parameter estimates yield the best possible match between yexp and the corresponding responses ysim calculated according to the test reactor model, Eqs. (33)-(34). In relation to kinetic modeling of aftertreatment devices, the following specific aspects may be worth of note:  The number of adaptive kinetic parameters x can become quite large, due to

the large number of reactions involved, each of them being associated at least with one pre-exponential factor and with one activation energy. Accordingly, a sequential approach, aimed at estimating independently the rate parameters of selected blocks of reactions, is generally more advantageous than addressing simultaneously the parameter estimation for the global reacting system, as discussed above.  Although there are many rate parameters, the degrees of freedom available for their estimation can be also quite large, since they result from the product between the number of experimental responses NY and the number of measured data points NS during the transient kinetic runs. Thus, one can expect in principle a reasonable statistical significance for the parameter estimates in spite of their large number. This should be, however, always checked (Himmelblau, 1970).  Robust, multimethod regression codes are required to optimize the rate parameters, also in view of possible strong correlations. For example, the BURENL routine, specifically developed for regression analysis of kinetic schemes (Donati and Buzzi-Ferraris, 1974; Villa et al., 1985) has been used in the case of SCR modeling activities. The adaptive simplex optimization method Amoeba was used for minimization of the objective function Eq. (35) when evaluating kinetic parameters for NSRC and DOC.  Values of kinetic constants are calculated in dependence on temperature according to the Arrhenius law:   Ej kj ¼ k0; j exp
RT

(36)

A useful re-parametrization of Eq. (36) is as follows:  kj ¼ exp Aj
Bj



1; 000 1; 000
T T ref

 (37)

Here Tref is a suitable reference temperature (e.g. the mean temperature in the experimental field) and Aj and Bj the modified adaptive parameters obviously related to the original parameters k0, j and Ej. Replacing Eq. (36) with Eq. (37) can

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significantly reduce the correlation among the parameter estimates. The physicochemical consistency of the rate parameter estimates generated by the regression procedure should be always examined in addition to their statistical significance.

B. MONOLITH REACTOR SCALE 1. Experimental In a second and possibly alternative stage of the kinetic investigation, laboratory experiments are performed over the same catalyst as for the microreactor tests, but now in the form of small monolith samples with volumes of few cubic centimeter. Flow rates, as well as catalyst size, are thus typically increased about by a factor of 100 with respect to the microreactor kinetic runs. This experimental scale provides data either for intermediate validation of the intrinsic kinetics from stage one, or directly for kinetic parameter estimation if runs over catalyst powders are omitted. The up-scaling from microreactor to small monoliths principally deals with the change of geometry (from powdered to honeycomb catalyst) and fluid dynamics (from turbulent flow in packed-bed to laminar flow in monolith channels). In this respect, it involves therefore moving closer to the conditions prevailing in the real full-scale monolithic converter, while still operating, however, under well controlled laboratory conditions, involving, e.g. the use of synthetic gas mixtures. If kinetic runs over the same catalyst in powder form are available, comparing them with tests over small monolith samples at the same conditions permits also a direct experimental evaluation of the role of diffusion processes in determining the catalytic performances. 2. Dynamic Monolithic Reactor Model This intermediate scale affords a preliminary validation of the intrinsic kinetics determined on the basis of microreactor runs. For this purpose, the rate expressions must be incorporated into a transient two-phase mathematical model of monolith reactors, such as those described in Section III. In case a 2D (1D+1D) model is adopted, predictive account is possible in principle also for internal diffusion of the reacting species within the porous washcoat or the catalytic walls of the honeycomb matrix.

C. ENGINE TEST BENCH SCALE In a third stage, test bench experiments are performed over full-scale monolith catalysts feeding real engine exhausts, and the data are used for final

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validation both of the kinetics and of the converter model. The up-scaling from small honeycombs to the full-scale monolithic converter does not involve changes in the catalyst morphology and in the flow regime, but the catalyst size is increased by two-to-three orders of magnitude. Besides, real engine exhausts are used as feed gases, enabling at this stage verification of possibly overlooked composition effects on the reaction kinetics.

V. Diesel Oxidation Catalyst A. FUNCTIONS

OF

DOC

The composition of diesel emissions is more complex than that of the spark ignited gasoline engines. The emissions from diesel engines contain solids (dry carbon and ash), liquids (liquid phase hydrocarbons, H2SO4) and gases (HC, CO, NOx, SO2). The combinations of solids and liquids in the exhaust form particulates or total particulate matter (TPM). The particulates are composed mainly of dry carbon (soot), liquid phase hydrocarbons and adsorbed sulfuric acid and/or sulfates from the fuel (DieselNet, 2007). Most of the sulfur in the fuel is present in the exhaust as gaseous SO2, only a small part is oxidized to SO3 which with water forms condensable sulfuric acid. The liquid hydrocarbons in the particulates originate either from unburned fuel, or from the lubricating oil and other additives. They form the soluble organic fraction (SOF) or volatile organic fraction (VOF). A standard DOC is able to reduce the TPM emission mass by approximately 30%, but no significant reduction in the number of PM particles is achieved (only SOF is abated). Higher PM conversions up to 70% can be achieved in special flow-through DOC monoliths employing substrates with a complex channel geometry enforcing turbulent flow (Jacobs et al., 2006). Recently, closed wall-flow monoliths—DPF—providing more than 95% efficiency in TPM abatement became standard for particulates aftertreatment (cf., e.g. DieselNet, 2007; Gulati et al., 2006). Thus, the main function of the DOC is to oxidize CO and unburned HCs. The secondary function, utilized in combined exhaust aftertreatment systems, is the oxidation of NO to NO2, which then enables optimum operation of the NOx aftertreatment catalysts placed down the exhaust line (NSRC and/or SCR, cf. Sections VI, VII and VIII, and also DPF). Exhaust temperatures and concentrations of combustibles from diesel engines are generally lower than those met in gasoline engines, hence a highly active catalyst with low light-off temperature for CO and HC oxidation is required (Farrauto and Kenneth, 1996; Koltsakis et al., 1997). However, the catalyst should have low catalytic activity for the oxidation of sulfur dioxide as resulting sulfates can adsorb onto soot and/or catalytic surface and deactivate the DOC

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or other catalysts down the exhaust treatment line. Recently, diesel fuels with significantly reduced sulfur content are available in the market—50 ppm wt. S in standard fuel and 15 ppm wt. S in ‘‘sulfur-free’’ fuel (DieselNet, 2007). At the cold start of the engine the catalyst is not able to oxidize carbon monoxide and hydrocarbons present in the exhaust. Therefore, zeolites are added into g-Al2O3-based catalytic washcoat for HC adsorption at low temperatures, resulting in an integrated adsorber–reactor system (Jira´t et al., 2001; Kryl et al., 2005). For optimum operation of such a system, the consecutive HC desorption induced by increasing temperature should not occur earlier than the catalyst light-off.

B. DEVELOPMENT

OF A

DOC GLOBAL KINETIC MODEL

In this section we shall discuss the development of a global kinetic model for DOC. The basic model reactions considered in the DOC model are summarized in Table II. Here the real HC mixture is modeled by two characteristic hydrocarbons—propene and decane. Propene represents more reactive, light hydrocarbons, which practically do not adsorb during cold start, while decane is a representative of heavier hydrocarbons with significant adsorption on MODEL REACTIONS

TABLE II DIESEL OXIDATION CATALYST

FOR THE

Reaction step

Reaction rate

CO+12O2-CO2

R1 ¼

H2+12O2-H2O

R2 ¼

C3H6+92O2-3H2O+3CO2

R3 ¼

C10H22+31 2 O2-11H2O+10CO2

R4 ¼

NO+12O22NO2

R5 ¼

2NO+C3H6+72O2-3CO2+N2+3H2O C10H22+Ze2C10H22*Ze

No.

k1 yCO yO2 G1 k2 yH2 yO2 G1 k3 yC3 H6 yO2 G1 k4 yC10 H22 yO2 G2 k5 ðyNO y0:5
yNO2 =K eq Þ y;5 O

R1 R2 R3 R4 R5

2

G1

R6 ¼

K 6 R2 yNO G3

R7 ¼

cap kads 7 CHC yC10 H22 ð1 des cap
k7 CHC cHC

R6
cHC Þ

R7

Note: s G1 ¼ ð1 þ K a;1 yCO þ K a;2 yC3 H6 Þ2 ð1 þ K a;3 y2CO y2C3 H6 Þð1 þ K a;4 y0:7 NOx ÞT

G2 ¼ 1 þ K a;5 yC10 H22 ; G3 ¼ ð1 þ K a;6 yO2 Þð1 þ K a;7 yNO Þ All molar fractions y are considered locally in catalyst pores, the superscript ‘‘s’’ is omitted for brevity. If the DOC is operated temporarily also under fuel-rich conditions (e.g. during regeneration of the NSRC or DPF in a combined system) the reactions R6-R7, R8-R9 and R11–R14 in Table III (Section VI) should also be considered.

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zeolites and higher light-off temperature. Additional hydrocarbons may be considered to describe the mixture more accurately, e.g. toluene for aromates (cf. Kryl et al., 2005). Only the reactions taking place under excess of oxygen (lean conditions) are discussed in this section. However, the reactions taking place in the DOC under the fuel-rich conditions should also be considered in the case of a specifically controlled diesel engine with combined exhaust aftertreatment system containing, e.g., periodically operated NSRC or DPF with active regeneration. These additional reactions include: (i) water gas shift and steam reforming, (ii) NO reduction by CO and H2 and (iii) oxygen storage and reduction. These reactions are discussed in Section VI dedicated to the NSRC (cf. Table III, reactions R6-R7, R8-R9 and R11–R14, respectively). In the next sections, the reactions from Table II will be discussed in the sequence corresponding to the procedure of kinetic parameter evaluation. At first, parameters of each single reaction are evaluated separately using the data obtained from laboratory experiments with the simplest inlet gas composition (i.e., the basic components plus one variable component). The resulting parameter values are then further tuned according to the results from the measurements focused on particular reaction subsystems (e.g. HC+O2+NO), where also the inhibition and selectivity constants are evaluated. The complete reaction system is considered in the final step of the data fitting (cf. Kryl et al., 2005). In the laboratory experiments, DOC monolith samples (length 7.5 cm, diameter 1.4 cm) with rather thin catalyst layer coating (E25 mm) were employed to minimize the internal diffusion effects. The samples were placed into a thermostat to suppress the formation of temperature-gradients along the channels. In the course of each experiment, the temperature of the inlet gas and the monolith sample was increased at a constant rate of 10 K/min within the range of 300–800 K. The exhaust gases at the inlet of the converter were simulated by synthetic gas mixtures with defined compositions and flow rates (cf. individual figure captions; all gas mixtures contained 6% CO2 and 6% H2O). 1. HC Adsorption Transient deposition of hydrocarbons on zeolites during a cold start operation of the DOC can be modeled by the rate laws based on Langmuir or Temkin mechanism for physical adsorption/desorption (cf., e.g. Goralski et al., 2000; Koltsakis and Stamatelos, 2000; Kruglov and Aris, 1995; Kryl et al., 2005; Twigg, 2006). The rate laws for the adsorption and desorption of hydrocarbons are then RHC; ads ¼ kHC; ads Ccap HC yHC ð1
cHC Þ

(38)

RHC; des ¼ kHC; des Ccap HC cHC

(39)

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FIG. 10. Evaluation of kinetic parameters for the DOC model—HC adsorption/desorption (reaction R7 in Table II). Comparison of the measured and simulated outlet C10H22 concentrations in the course of the adsorption/desorption experiment. Synthetic gas mixture, other gases: 6% CO2, 6% H2O, N2 balance, SV=30,000 h
1 (Kryl et al., 2005). Reprinted with permission from Ind. Eng. Chem. Res. 44, 9524, r 2005 American Chemical Society.

A typical experiment, arranged for the evaluation of kinetic parameters Ccap HC (HC adsorption capacity), kHC; ads (rate constant for the non-activated adsorption of hydrocarbons) and the rate constant of HC desorption kHC; des ðTÞ increasing with temperature according to Eq. (36), is depicted in Fig. 10. First, the catalytic washcoat was purified at 673 K using an inlet gas mixture of 14% O2 in nitrogen for 10 min to remove all adsorbed HCs and NOx. Then, after cooling the catalyst in pure N2 feed, a mixture of HC in nitrogen was introduced into the monolith at a constant temperature of 383 K for 15 min. In the next step, the inlet gas was replaced by pure N2, and after 10 min, constant temperature conditions were changed to a temperature ramp increasing with 10 K/min. The space velocity was kept constant at 30 000 h
1 throughout the experiment (Kryl et al., 2005). 2. CO and HC Oxidation Detailed microkinetic models are available for CO, H2 and HC oxidation on noble metal(s) (NM)/g-Al2O3-based catalysts (cf., e.g. Chatterjee et al., 2001; Harmsen et al., 2000, 2001; Nibbelke et al., 1998). The model for CO oxidation on Pt sites includes both Langmuir–Hinshelwood and Eley–Rideal pathways (cf., e.g., Froment and Bischoff, 1990). Microkinetic description of the hydrocarbons oxidation is more complicated, particularly due to a large number of different reaction intermediates formed on the catalytic surface. Simplified mechanisms, using just one or two formal surface reaction steps,

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are thus usually employed for the HC decomposition, and only simpler hydrocarbons are considered—cf., e.g., Harmsen et al. (2000, 2001). The microkinetic models provide quite detailed description of the transients in catalyst operation. However, the number of balanced species and reaction steps is quite high for a realistic exhaust gas composition, due to the explicit consideration of all surface-deposited reaction intermediates. The models using microkinetic reaction schemes may also exhibit quite complex non-linear dynamic behavior (cf., e.g., Kubı´ cˇek and Marek, 1983; Marek and Schreiber, 1995), including multiplicities of steady states, hysteresis and stable autonomous oscillations. Such behavior has been described and analyzed for a certain range of operation parameters in monolith models ranging from a lumped one (CSTR), to a spatially 2D (1D+1D) one, both for CO oxidation alone and the complete CO+O2+HC+NOx system (Kocˇı´ et al., 2004a, d). When a simple, fast and robust model with global kinetics is the aim, the reaction kinetics able to predict correctly the rate of CO, H2 and hydrocarbons oxidation under most conditions met in the DOC consist of semi-empirical, pseudo-steady state kinetic expressions based on Langmuir–Hinshelwood surface reaction mechanism (cf., e.g., Froment and Bischoff, 1990). Such rate laws were proposed for CO and C3H6 oxidation in Pt/g-Al2O3 catalytic mufflers in the presence of NO already by Voltz et al. (1973) and since then this type of kinetics has been successfully employed in many models of oxidation and three-way catalytic monolith converters RCO ¼

RC3 H6 ¼

kCO yCO yO2 G1

kC3 H6 yC3 H6 yO2 G1

G 1 ¼ ð1 þ K a;1 yCO þ K a;2 yC3 H6 Þ2 ð1 þ K a;3 y2CO y2C3 H6 Þð1 þ K a;4 y0:7 NOX ÞT

(40)

(41)

(42)

The values of kinetic parameters (pre-exponential factors k0, j and activation energies Ej of rate constants k and inhibition constant Ka) can for a particular catalyst be determined by weighted least squares method, Eq. (35), from the light-off or complete ignition–extinction curves measured in experiments with slowly varying one inlet gas variable—temperature or concentration of one component (cf., e.g., Ansell et al., 1996; Dubien et al., 1997; Dvorˇ a´k et al., 1994; Kryl et al, 2005; Kocˇı´ et al., 2004c, 2007b; Pinkas et al., 1995). Examples of light-off experiments for CO and C10H22 are given in Fig. 11 together with the results of the 1D model using global DOC kinetics. In the case of decane, the adsorption has to be considered with the kinetic parameters already evaluated from the adsorption/desorption experiments

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FIG. 11. Evaluation of kinetic parameters for the DOC model—CO and HC oxidation. Comparison of experimentally observed and simulated outlet concentrations in the course of the oxidation light-off for simple mixtures: (a) CO, reaction R1; (b) C10H22, reactions R4 and R7 (cf. Table II). Lab experiments with isothermal monolith sample using synthetic gas mixtures (14% O2, 6% CO2, 6% H2O, N2 balance). Rate of temperature increase 10 K/min, SV=30,000 h
1 (Kryl et al., 2005). Reprinted with permission from Ind. Eng. Chem. Res. 44, 9524, r 2005 American Chemical Society.

(cf. Fig. 10). During the kinetic parameter evaluation procedure, the experiments are performed first for individual reactions. After the evaluation of the rate and self-inhibition constants for CO and individual HCs, the remaining inhibition constants are evaluated from the experiments with the combined mixtures. Particular care should be given to the inhibition parameters if the DOC is operated also under temporarily rich conditions (i.e., with NSRC or DPF). The light-off temperature varies significantly for lean and rich exhaust gas and depends also on NOx concentration (cf. Fig. 21 in Section VI on the NSRC kinetics).

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FIG. 12. Comparison of the measured and simulated light-off of a more complex HC mixture. DOC monolith sample with pre-adsorbed hydrocarbons (initial cHC=0.3). Lab experiment with isothermal monolith sample using synthetic gas mixture (1,200 ppm CO, 55 ppm C3H6, 10 ppm C10H22, 10 ppm C6H5CH3, 140 ppm NO, 14% O2, 6% CO2, 6% H2O, N2 balance). Rate of temperature increase 10 K/min, SV=30,000 h
1 (Kryl et al., 2005). Reprinted with permission from Ind. Eng. Chem. Res. 44, 9524, r 2005 American Chemical Society.

The typical evolution of the outlet HC concentrations in the course of the complex mixture light-off is shown in Fig. 12. Here the values of kinetic parameters obtained from the experiments with individual mixtures have been kept constant and the experiment was used for the kinetics validation. We can observe the adsorption at lower temperatures, followed by the two steps corresponding to the ignition of the ‘‘fast’’ (C3H6) and ‘‘slow’’ (C10H22) hydrocarbons, respectively. Eventually, an HC desorption peak can be observed during the light-off for the catalysts with high HC adsorption capacity and lower noble metal activity (Jira´t et al., 1999b; Kryl et al., 2005).

3. NO Oxidation to NO2 The NO oxidation to NO2 is a reversible reaction limited by thermodynamic equilibrium. The typical dependence of the NO2 outlet concentration on temperature is shown in Fig. 13. At low temperatures, NO2 is thermodynamically more stable than NO but the reaction rate is rather slow. At higher temperatures, the reaction rate increases, but concurrently the NO2 formation becomes limited by thermodynamic equilibrium. Thus, the outlet NO2 concentration from the DOC typically exhibits a maximum at intermediate temperatures.

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FIG. 13. Evaluation of kinetic parameters for the DOC model—NO oxidation (reaction R5 in Table II). Comparison of measured and simulated outlet NOx concentrations in the course of temperature ramp (2 K/min) for two different space velocities (SV=50,000 and 100,000 h
1). Lab experiment with isothermal monolith sample using synthetic gas mixture (100 ppm CO, 100 ppm C3H6, 500 ppm NO, 8% O2, 8% CO2, 8% H2O, N2 balance).

The actual NO/NO2 ratio in the exhaust gas significantly influences the NOx reactions in catalysts positioned downstream the exhaust treatment line, particularly the NOx storage in the NSRC (cf. Section VI), the oxidation of PM in DPF (cf., e.g., the review by Gulati et al., 2006) and the SCR of NOx by ammonia in the urea–SCR catalyst (cf. Sections VII and VIII). The global rate law considering the reaction equilibrium constant Keq is employed quite often in automotive exhaust catalyst models because it ensures the following thermodynamic limitations in a wide range of operating conditions:

RNO=NO2 ¼ kNO=NO2

eq ðyNO y0:5 O2
yNO2 =K Þ G

(43)

The term G—e.g., in the form given in Eq. (42)—accounting for the inhibition by HC and CO should not be omitted on the right-hand side of Eq. (43). The NO oxidation rate is influenced by these components present in the complete exhaust gas mixture, which can be observed during cold start and in rich exhaust peaks. The value of reaction rate Eq. (43) can be negative when NO2 present in the mixture is transformed to NO via backward reaction, typically at higher temperatures. A comparison of measured and simulated outlet NO2 concentrations in dependence on temperature can be seen for two different space velocities in Fig. 13. The pre-exponential factor k0, j and activation energy Ej of the kinetic constant kNO=NO2 in the global rate law were evaluated by the weighted least squares method, Eq. (35).

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4. Lean NOX Reduction by HC NOx reduction conversions met in the DOC are quite low. Excess of air in burned lean fuel mixture results in excess of oxygen in the exhaust. Under such conditions, the reducing components naturally present in diesel exhaust (CO, H2 and HC) are readily oxidized by the excessive oxygen and NOx remains unreduced. However, the unburned hydrocarbons still exhibit a certain activity for NO reduction on NM/g-Al2O3 and NM/zeolites catalysts under lean conditions (HC–SCR). Many efforts have been put into the investigation of different NM-based or alternative catalysts tailored for the HC–SCR reaction and the development of reliable reaction mechanisms—cf., e.g., Joubert et al. (2006) and the reviews by Burch et al. (2002) and Burch (2004). However, there are several major drawbacks that hinder practical application of this NOx reduction method in automobile exhaust aftertreatment: (i) The NO reduction activity is typically limited to a certain temperature window, for NM-based catalysts it is around the light-off—cf. Fig. 14 and Ansell et al. (1996), Jira´t et al. (1999b), Burch et al. (2002) and Joubert et al. (2006). (ii) With low HC concentrations and the exhaust composition met in modern diesel engines, the achieved NOx conversions in real driving cycles are quite low (typically around 5–10%, cf., e.g., Kryl et al, 2005). (iii) The selectivity of NOx reduction is problematic, N2O may form up to 50% of the product (Burch et al., 2002; Joubert et al., 2006). Alternative (Cu-, Co-, Ag-, etc., based) catalysts may provide a wider temperature window or better selectivity for

FIG. 14. Evaluation of kinetic parameters for the DOC model—lean NOx reduction (reaction R5 in Table II). Comparison of measured and simulated outlet NOx concentrations in course of a temperature ramp (10 K/min). Lab experiment with isothermal monolith sample using synthetic gas mixture (1,200 ppm CO, 55 ppm C3H6, 10 ppm C10H22, 10 ppm C6H5CH3, 140 ppm NO, 14% O2, 6% CO2, 6% H2O, N2 balance), SV=30,000 h
1 (Kryl et al., 2005). Reprinted with permission from Indus. Eng. Chem. Res. 44, 9524, r 2005 American Chemical Society.

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HC–SCR under specific situations, but none of them exhibits sufficient activity and durability in the entire range of operating conditions met in automobile exhaust treatment (cf. the reviews by Burch, 2004; Burch et al., 2002). Thus, special converters and advanced engine control techniques are necessary to meet the upcoming stringent NOx emission limits. Two types of catalytic deNOx systems for mobile applications—NSRC using periodic lean/rich operation and the SCR of NOx by NH3 (urea–SCR)—are discussed in Sections VI and VII, respectively. A global kinetic expression for HC–SCR of NO was proposed by Ansell et al. (1996). Starting from the selectivity approach, the NO reduction rate is derived from the total rate of HC oxidation under lean conditions, i.e., the sum of the HC oxidation by O2 (major part) and the HC oxidation by NO (minor part). The rate of NO reduction is then calculated from RNO;HC
SCR ¼ RC3 H6 oxidation

K NO;HC
SCR yNO ð1 þ K a;O2 Þð1 þ K a;NO Þ

(44)

This type of rate law is employed in the global DOC kinetic model given in Table II (cf. reaction R5). A typical evolution of the outlet NOx concentration in the course of a slow temperature ramp is shown in Fig. 14. From this type of experiment, the selectivity and inhibition constants K(T) are evaluated, considering exponential temperature dependence, Eq. (36). Again, simpler HC+O2+NO reaction mixtures with single hydrocarbon are examined first, followed by more complex inlet gas compositions. C. VALIDATION

AND

APPLICATIONS

OF THE

DOC GLOBAL KINETIC MODEL

Application studies with the DOC model were performed using engine test bench measurements with a 6-cylinder turbocharged passenger car diesel engine. A small DOC (with the volume of approximately 0.6 dm3) was located close to the engine, directly after the turbocharger, resulting in high space velocities (average SV ¼ 115,000 h
1, maximum up to SV ¼ 450,000 h
1) and very steep temperature gradients. These conditions made it quite demanding for simulation study of the dynamic behavior. The measured inlet and outlet temperatures in the course of an NEDC can be seen in Fig. 15, together with the evolution of the outlet temperature predicted by the 1D DOC model. We can observe that even for the close-coupled catalyst, the exhaust temperature during the first, urban driving part is quite low. On the contrary, relatively high temperature peaks can be observed in the final, extra-urban driving part. Downstream this close-coupled DOC, several configurations of other catalysts—a larger DOC, NSRC or SCR—were tested for research purposes (they will not be discussed further in this section). Several DOCs based on similar washcoat formulation but with varying noble-metal loading (90–150 g/ft3 monolith) and catalyst ageing status were

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FIG. 15. DOC model validation—Measured and simulated temperatures during the NEDC. Close-coupled monolith.

studied. All monoliths used the same cordierite substrate with 400 cpsi. The original set of kinetic parameters evaluated from the laboratory experiments with the standard catalyst was calibrated individually for each catalyst by fitting only the pre-exponential factors k0, j of the rate constants kj to one set of the NEDC measurements including cold start. Considering catalysts with the same washcoat structure and comparable NM dispersion (ageing level), the preexponential factors k0, j can be estimated as approximately proportional to the noble metal concentration in the washcoat. With the DOC model calibrated for each catalyst with respect to the individual ageing status and noble metal loading, different driving cycles were studied, including FTP, US-06 and NEDC. The simulation results were validated by the corresponding measurements. The examples of simulation and experimental results for the NEDC are given in Fig. 16. The model describes the HC adsorption effect during cold start, light-off and the achieved HC conversions throughout the driving cycle for varying NM loadings and catalyst ageing levels very well. A set-up with pre-heating during the cold start was also examined. In this case, a small uncoated metal catalyst (Emitec) was mounted before the DOC as an electrical pre-heater with power 2,000 W. The aim was to study the effect of temperature increase on DOC performance during the driving cycles. The effect of increased temperature on HC light-off after cold start follows from the comparison of Fig. 16 left top and left bottom. A typical evolution of the concentration profiles of the adsorbed hydrocarbons in course of the NEDC, predicted by the DOC model, is illustrated in Fig. 17. This simulation is performed for a main under-floor DOC

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FIG. 16. DOC model validation—Measured and simulated HC concentrations during the NEDC for a close-coupled DOC monolith. Integral HC conversions X evaluated from experiment and simulation data are compared in parentheses. Top left: Pt loading 90 g/ft3, fresh (X exp=74.0%, X sim=75.7%). Top right: Pt loading 140 g/ft3, fresh (X exp=86.7%, X sim=87.6%). Bottom left: Pt loading 90 g/ft3, fresh, with electrical pre-heater upstream (X exp=83.1%, X sim=84.9%). Bottom right: Pt loading 150 g/ft3, aged, different engine control (raw emissions) used for this driving cycle test (X exp=55.6%, X sim=59.9%).

FIG. 17. Typical evolution of the concentration profiles for the adsorbed HC, predicted by the DOC model in course of the NEDC driving cycle (Kryl et al., 2005).

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without a close-coupled pre-catalyst, resulting in lower temperatures throughout the driving cycle. We can observe first the adsorption of hydrocarbons during the cold-start, which is reflected in the increase of the surface concentration cHC. As the temperature increases, desorption takes place and the surface HC concentration gradually decreases. The complete removal of the heavier hydrocarbons can be observed in the extra-urban part of the driving cycle (after t ¼ 800 s), when higher temperatures are met (cf. also Fig. 15).

VI. NOx Storage and Reduction Catalyst From the reaction-kinetic modeling point of view, the NSRC, sometimes called lean NOx trap (LNT) or NOx adsorber, is the most complex of the currently used automobile exhaust converters. A variety of different physical and chemical processes and the number of gas and surface components participating in typical periodic lean/rich operation form a large and closely linked system.

A. NOX STORAGE

AND

REDUCTION PRINCIPLES

Primary application of the NSRC is the elimination of NOx emissions from diesel and lean-burn gasoline engines, where direct NOx reduction is difficult. It is operated in periodic lean/rich regime: in the course of a long lean phase (economical engine operation with lean fuel mixture, excess of air and O2, typically lasting for several minutes) NOx are adsorbed (stored) on the catalyst surface. Then, the accumulated NOx are reduced within a short rich phase (injection of rich fuel mixture, excess of reducing components—CO, H2 and HC, lasting for several seconds). A scheme of the processes on the NSRC surface during lean/rich operation is given in Fig. 18. The typical evolution of outlet NOx concentrations in the course of stabilized periodic lean/rich operation of the NSRC is depicted in Fig. 19. Rich conditions in the exhaust are achieved by advanced control of fuel injection, exhaust gas recirculation (EGR) management, or fuel by-passing (Dieselnet, 2007). The enrichment phases increase fuel consumption slightly, usually by 3–4%. In gasoline lean-burn engines, fuel enrichment is natural within peak-load operation (acceleration). Catalyst durability is influenced mainly by the decrease of active NOx storage capacity, caused by thermal ageing and by sulfur poisoning. Thus, a low-sulfur fuel and proper catalyst de-sulfurization strategies have to be used (DieselNet, 2007). The NSRC concept has been derived from the TWC—cf. Takahashi et al. (1996). Thus, the catalyst is also active in CO and HC oxidation reactions, as

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FIG. 18. Scheme of adsorption, desorption and reaction processes on the surface of the NSRC during lean and rich conditions (Kocˇı´ , 2005) (see Plate 3 in Color Plate Section at the end of this book).

FIG. 19. Typical evolution of outlet NOx concentrations in the course of stabilized periodic lean/ rich operation of the NSRC. Tin=3501C, lean phase 180s, rich phase 2s, SV=30,000 h
1 (Kocˇı´ et al., 2007b).

well as NOx reduction under stoichiometric conditions. Typical NSRC formulation is NM/AM/Ce–ZrOx/g–Al2O3, where NM=Pt, Pd and/or Rh and AM=alkali or alkaline-earth metals (Ba, K, etc.). The noble metals form active catalytic centers for redox reactions, while the AM provide the NOx storage capacity. Oxygen storage capacity of the Ce–Zr oxides complicates the desired NOx reduction function during the rich phase by competitive consumption of the supplied reductants. However, the mixed Ce–Zr oxides are still present in most NSRC due to their stabilization effect on washcoat porous structure and dispersion of active components (Kasˇ par et al., 2003).

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144 B. DISCUSSION

ON

SURFACE REACTION MECHANISMS

Since the first introduction of NSRC in Japan in 1994 (Takahashi et al., 1996), there has been a large, exponentially increasing number of publications dealing with different aspects of the NOx storage and reduction catalysis—cf., e.g., the reviews by Epling et al. (2004a) and Burch (2004). Here we shall discuss briefly only the issues important for the development of an effective and robust mathematical model of an NSRC, which can be used for simulations in the ExACT. Many apparent discrepancies can be found in the experimental results reported in literature for NSRC operation. They are usually caused by inconsistent experimental conditions, which have to be taken into account carefully (cf. Burch, 2004). Actual temperature, non-isothermal conditions in the test reactor, the composition of the gas mixture (presence of CO2 and H2O, ratio of NO/NO2 at the inlet, the used reducing components), transport limitations and dynamics of the measurements are the most important ones. 1. NOX Storage: Lean Phase Several NOx adsorbing components with particular temperature dependence of the effective NOx storage capacity can be used in the NSRC washcoat. Typically, the compounds of alkali and alkaline-earth metals are employed— mostly Ba (lower-temperature operation) and K (high-temperature operation), but also Na, Ca, Li, Mg, etc. The NOx storage capacity is in relation to the basicity of the used component (Kobayashi et al., 1997). More active NOx storage components can be (and often are) combined in one washcoat, or separated into different catalytic layers to achieve better performance (Kocˇı´ et al., 2004c; Maunula et al., 2001). However, the majority of the researchers consider a model catalyst with only Ba as the NOx storage component (cf. the review by Epling et al., 2004a). A detailed sequence for NO2 storage on BaO has been proposed after experimental observations and density-functional theory calculations by Broqvist et al. (2004). It involves the formation of nitrites, followed by the formation of nitrite–nitrate and nitrate–nitrate pairs on the surface, the latter being most stable. The Ba-nitrites–nitrates (NO2–BaO–NO2) are then transformed to Ba(NO3)2 via redox reaction, including consumption of NO2 and release of NO. Thus, the overall reaction for the NO2 storage on BaO with this disproportion mechanism can be written as 3NO2 þ BaO ! BaðNO3 Þ2 þ NO The same overall reaction can be obtained using a reaction mechanism with intermediate formation of Ba peroxide—cf., e.g., Lietti et al. (2001) and Olsson et al. (2001). However, it was shown that the formation of the surface peroxide is an endothermic process with respect to the formation of the NO2–BaO–NO2 configuration. Based on that, it was suggested that the

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peroxide formation is unstable toward surface nitrate formation (Broqvist et al., 2004). Experimental observations indicating the occurrence of multiple NOx storage sites with possibly different storage mechanisms were reported by Epling et al. (2004b). NOx speciation experiments showed that the NO2 disproportion mechanism dominates at the later stages of the adsorption process. However, at the early stages of adsorption the stoichiometric relationships for this mechanism are not observed. Experimental evidence strongly suggests that this is due to presence of two distinct types of Ba-storage sites, most likely based on the proximity of Ba and Pt components. For the sites with close contact between Pt and Ba, the adsorbed oxygen can be used for the oxidation of surface nitrites to nitrates, giving the overall reaction 2NO2 þ 12 O2 þ BaO ! BaðNO3 Þ2 The effect of proximity between Pt and BaO on uptake, release and reduction of NOx on storage catalysts was investigated by Cant et al. (2006). Szanyi et al. (2005) studied morphological changes of BaO/Al2O3 during NO2 uptake and release. Other authors (Kabin et al., 2006; Nova et al., 2004, 2006b) proposed alternative pathways in which, in the presence of oxygen, NO is directly adsorbed to form Ba nitrites which are progressively oxidized to nitrates, without a previous formation of gaseous NO2. The summary reaction for this NO adsorption route (including the oxidation of nitrites to nitrates) is 2NO þ 32 O2 þ BaO ! BaðNO3 Þ2 In such a mechanism, the presence of noble metals appears to facilitate the formation of intermediate nitrite species, while a close proximity between Pt and Ba seems to play an important role in enhancing this route against the NO2 disproportion route (Nova et al., 2005). The NO storage mechanism was already included in older semi-empirical NSRC models (e.g., Kocˇı´ et al., 2004c; Kojima et al., 2001) reflecting the fact that the experimentally observed NOx storage cannot be described solely by the NO2 adsorption route (particularly at lower temperatures). In contact with gas mixtures containing water and carbon dioxide (which is the case in real automobile exhaust gas), the NOx storage sites can be in the form of hydroxyls or carbonates, respectively. The following order of stability for different Ba-species have been observed by Lietti et al. (2001): BaO o Ba(OH)2 o BaCO3 o Ba(NO3)2. The NOx storage is then competitive and it is accompanied by the release of H2O and CO2. The inhibition of NOx storage by H2O and CO2 was studied, e.g., by Epling et al. (2004b). Because H2O and CO2 are always present in automobile exhaust gas, their influence on NOx storage capacity is usually implicitly included in the description of NOx storage.

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FIG. 20. Typical temperature dependence of the effective NOx storage capacity for low- and hightemperature NOx storage components (here denoted MIIO and MI2O, respectively) in the washcoat of a NSRC (Kocˇı´ et al., 2004c).

A study dealing with the effect of oxygen content in the gas mixture was carried out already by Takahashi et al. (1996). They found that the amount of the stored NOx increases with the increasing oxygen content in the gas phase. This increase is significant for lower O2 concentrations, while for higher O2 content the observed NOx storage capacity is practically constant. This was confirmed by many other studies (cf. the review by Epling et al., 2004a). Thermogravimetric measurements of NOx storage in Ba/Pt/g-Al2O3 catalyst were performed by Muncrief et al. (2004). The weight of the sample was followed in the course of NOx adsorption at different temperatures, and also in the course of periodic lean/rich operation. Formation of carbonates, nitrites and nitrates was considered. From the mass balances it followed that only a minor part of total Ba moles is effectively utilized in the NOx storage under common operating conditions. The maximum NOx storage capacity was found to be a decreasing function of temperature (due to decreasing stability of the surface nitrates). However, a major part of the NOx storage capacity at low temperatures turned out to be inaccessible in practical operation, due to extremely slow saturation (in the order of hours). Considering the effective (utilizable) NOx storage capacity, a typical dependence with a maximum at intermediate temperatures is observed (cf. Fig. 20). 2. Reduction of the Stored NOX: Rich Phase Two mechanisms contribute to the removal of the stored NOx from the surface: (i) chemical reactions with reducing components (CO, H2, HC) present during the rich phase and (ii) thermal decomposition of surface nitrites and nitrates (heat is liberated by the reaction of CO, H2 and HC with spare oxygen).

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The latter mechanism is significant only for higher temperatures (W 3001C). Nova et al. (2006c) demonstrated that the co-presence of the NOx storage element and noble metal on the same support is necessary for the reduction of the stored nitrates. The effects of various reducing components and noble metals on NOx storage and reduction operation of NM/BaCO3/g-Al2O3 (NM=Pt, Pd, Rh) were studied, e.g., by Kobayashi et al. (1997) and Abdulhamid et al. (2006a). The order of the observed NOx storage capacity in dependence on the used noble metal was PtWPdWRh, while the order of NOx reduction ability was opposite. Typically, bi-metallic formulations are used in NSRCs. They provide better results than single metal ones in terms of oxidation activity, NOx trapping, NOx reduction activity and also in removal of surface sulfur compounds inhibiting the NSRC process (Epling et al., 2004a). The order of activity observed by Abdulhamid et al. (2006a) for the NOx reducing components was H2WCOWC3H6WC3H8. The influence of rich-phase duration, gas composition and temperature on NOx conversion over periodic lean/rich operation in the presence of H2O and CO2 was studied also by Kocˇı´ et al. (2007b). They reported in situ production of H2 by the reactions of CO and C3H6 with water. A similar extent of NOx reduction was observed at temperatures above 3001C when using equivalent amounts of H2, CO and C3H6. However, the reduction by hydrogen still resulted in highest NOx conversions. This observation suggested the presence of internal diffusion limitations in the porous catalytic washcoat (hydrogen exhibits the highest effective diffusivity of the reductants). The typical dependence of the achieved integral NOx conversion on inlet temperature and length of the rich phase is depicted in Fig. 21 (Kocˇı´ et al., 2004c). If the rich

FIG. 21. Typical dependence of the integral NOx conversion on the length of the rich phase for different temperatures of the inlet gas. Periodic lean/rich operation of the adiabatic, hightemperature NSRC. tlean=95s, SV=30,000 h
1 (Kocˇı´ et al., 2004c).

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phase is too short, then the stored nitrogen oxides are not fully reduced and the number of free storage sites for the next lean phase is lower, resulting in lower NOx conversion. However, as soon as the NOx storage capacity is fully regenerated, the application of a longer rich phase is pointless—it increases the fuel consumption and brings no improvement to NOx storage efficiency. Choi et al. (2006) studied the intra-channel evolution of concentration and temperature profiles during regeneration of monolithic Pt/K/Al2O3 NSRC by CO in the presence of CO2 and H2O and they observed two regeneration phases. The first one was related to the consumption of spare oxygen and evolution of corresponding exotherms caused by CO+O2 and CO+NOx reactions, with N2 as major product of NOx reduction. The second one was characterized by the production of H2 (water gas shift) and NH3 as major product of NOx reduction. Pihl et al. (2006) explained the absence of NH3 as a by-product of the NOx reduction during the first part of the regeneration phase by a surface-reduction front moving downstream the reactor, in front of which the formed NH3 can be re-oxidized back to N2. Thus, an NH3 peak in the exhaust is expected to occur after the surface-reduction front reaches the monolith outlet. Cumaranatunge et al. (2007) proved experimentally that ammonia is an active intermediate in the regeneration of NSRC with H2., i.e. H2 can react with NOx producing NH3, which in turn is able to reduce the remaining NOx stored downstream the reactor. When NH3 is used directly at the reactor inlet instead of H2, the NOx reduction process is equivalent and equally effective. The dynamics of the entire NSRC regeneration process is further influenced by: (i) the actual status of the surface prior to enrichment (NOx saturation, ratio of nitrites and nitrates, cf. Forzatti et al., 2006); (ii) surface oxygen storage effects in the NSRC washcoat (Kocˇı´ et al., 2007b); (iii) internal transport in the porous catalytic layer, possibly including the transport within the storage nanoparticles (Hepburn et al., 1996, 1998; Tuttlies et al., 2004) and (iv) the presence of two types of NOx storage sites—‘‘fast’’ ones in proximity of noble metal sites and ‘‘slow’’, isolated ones (Epling et al., 2004b). The simultaneous removal of NOx and soot on Pt-Ba/Al2O3 NSRC was investigated by Castoldi et al. (2006). They concluded that the presence of soot does not affect the NOx reduction activity of the NSRC, while the soot combustion is enhanced by the presence of NO2. This principle has been already utilized by Toyota in the integrated DPNR (diesel particulate and NOx reduction) system (Nakatani et al., 2002). Long-term poisoning of the NOx storage components by sulfur and phosphorus contained in fuel and lubricants leads to gradual decrease of the effective NOx adsorption capacity. The sulfates formed on the NOx storage sites are more stable than the nitrates, and special de-sulfurization techniques need to be applied from time to time to keep the NSRC effectiveness on a reasonable level (cf., e.g., Dieselnet, 2007). The NSRC poisoning by different sulfur compounds (SO2, H2S and COS) was examined by Amberntsson et al. (2002).

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Abdulhamid et al. (2006b) studied by means of in situ FTIR spectroscopy the effects of water on the interaction of SO2 with Pt/BaCO3/Al2O3 NSRC. The mechanisms of sulfur poisoning and regeneration of a commercial NOx storage catalyst for lean-burn gasoline engines were discussed by Rohr et al. (2005). Sakamoto et al. (2006) used a Pt/Ba thin-film model catalyst to study NOx and SOx adsorption and demonstrated that NOx and SOx were adsorbed all over the model catalyst. NOx desorption occurred preferentially in an area of a few micrometers around the platinum and was suppressed at all other sites, whereas SOx desorption occurred preferentially in an area a few nanometers wide around the platinum. Matsumoto et al. (2000) proposed more sulfur-tolerant NOx sorbents based on the addition of Ti in the NSRC washcoat (Pt/Ba/TiO2/Al2O3). 3. NSRC Kinetic Models Several different models were proposed for the slow NOx storage process, while only few details and approximate models are available for the highly transient NOx reduction within the rich phase, lasting only several seconds. The models can be divided into two groups, depending on whether the internal diffusion in the particles of the NOx storage material is considered explicitly, or this effect is included implicitly into the evaluated kinetic parameters. The models can be further differentiated by the level of complexity for the reaction kinetics description, i.e., either (simplified) microkinetic scheme or the global kinetics. One of the first mathematical models for the NSRC was developed by Hepburn et al. (1996, 1998), who introduced a shrinking-core model of the mass transport in the NOx storage material particles (Ba) and validated the results by both laboratory experiments and vehicle test data. Jira´t et al. (1999a) proposed a model using a global and straightforward description of the NOx storage and reduction processes incorporating the possible internal diffusion effects into the effective values of the reaction kinetic parameters. This simplified approach was further developed and validated with laboratory and engine test bench data for different types of NOx storage catalysts by Kocˇı´ et al. (2004c, 2007b) and Gu¨thenke et al. (2007a, b). The intra-particle diffusion model was implemented in global kinetic models by, e.g., Tuttlies et al. (2004), Olsson et al. (2005) and Scholz et al. (2007). In the latter work, three different forms of the NOx storage sites (surface, bulk and semi-bulk) with independent kinetic parameters were defined. Olsson et al. focused on microkinetic models aiming to describe in detail the transient steps in the NOx storage and reduction process. First, the NO oxidation sub-model on Pt/g-Al2O3 and Pt/BaO/g-Al2O3 was developed (Olsson et al., 1999), then the NOx storage sub-model (Olsson et al., 2001) and finally the mean-field microkinetic NOx storage and reduction model (Olsson et al., 2002), where NOx reduction by propene was considered. Intra-particle diffusion was not assumed in this case. However, the detailed microkinetic models, even

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using a simplified exhaust gas composition and a limited number of possible reaction steps, turned out to be too complicated and time-consuming for routine simulations of automotive exhaust gas converters under the wide range of real operating conditions. This resulted in the development of an independent global kinetic model (Olsson et al., 2005). Concurrently with the work on the first microkinetic models, the semiempirical models with global kinetic approach were further developed, using transient kinetics only for the oxygen and NOx storage/reduction (cf., e.g., Kojima et al., 2001; Kocˇı´ et al., 2004c). In this case, a more detailed composition of the exhaust gas (different HCs, CO, H2, water, etc.) and a wider set of reactions were considered while keeping a relatively low number of kinetic parameters and reasonable computation times. The extensions included the NOx reduction by individual reductants with different light-off temperatures (CO, H2 and HC) and the implementation of oxygen storage effects and reactions with water (water gas shift and steam reforming). This type of models focused also on the correct prediction of CO and HC conversions and the corresponding temperature effects, particularly during cold start and rich peaks (Kojima et al., 2001). Empirical temperature dependence of the effective NOx storage capacity was obtained by interpolating the experimentally evaluated values from isothermal adsorption experiments at individual temperatures. Sharma et al. (2005) developed a 1D two-phase model for the analysis of periodic NOx storage and reduction by C3H6 in a catalytic monolith, based on a simplified kinetic scheme. They focused on the evaluation of temperature and reaction fronts along the monolith and their effect on NOx conversion. Kim et al. (2003) proposed a phenomenological control-oriented lean NOx trap model. The global kinetic models using empirical temperature dependence of the effective NOx storage capacity can be calibrated for different types of the NSRC, containing, e.g., Ba- or K-based NOx storage components optimized for the lowand high-temperature operation, respectively. Simulations of dynamically operated NSRC with differentiated washcoat (containing two different catalytic layers in one channel) were also performed (Kocˇı´ et al., 2004c). C. DEVELOPMENT

OF

EFFECTIVE NSRC KINETIC MODEL

We can observe from the above references that different NSRCs share some common properties, but actual kinetic behavior depends on the washcoat structure details and actual state of the catalyst evolved under reaction conditions and also varies due to effects of ageing and poisoning mainly by sulfur-containing species. Hence, robust, partly phenomenological models appear to be useful in modeling commercial catalysts under varying exhaust conditions following from different engine operation characteristics. We shall discuss in the following an example of such a model, its development and application (Kocˇı´ et al., 2004c, 2007b; Gu¨thenke et al., 2007a, b).

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1. Selection of Model Reactions for the NSRC The robust NSRC model has to be valid in the complete range of operating conditions encountered in vehicles, considering particularly the wide temperature range from cold start up to 500–6001C. Quite often NSRC models are developed and tested only for the intermediate temperatures (250–4001C), where effective NOx storage is practically independent of temperature, and the catalyst is above light-off temperature. Furthermore, the model has to describe correctly not only the NOx storage and reduction phenomena, but also CO and HC light-off and conversions and the related temperature effects (reaction exotherms) that are closely linked. For example, an incorrect prediction of the CO, H2 and HC reaction rates with the spare oxygen in the rich phase results in (i) an inaccurate temperature profile inside the reactor with possible differences in the order of several tens of Kelvins and (ii) misleading concentrations of the CO, H2 and HC available for the reduction of the stored NOx. These discrepancies may in turn result in a wrong prediction of the NSRC regeneration extent and thus also in an incorrect simulation of the NOx emissions in the next lean/rich period, regardless of how sophisticated and well calibrated the employed NOx storage and reduction model is. The other reactions involved in the network are oxygen storage and reduction on the catalyst surface (influencing the balance of the reductants during the rich phase) and reactions of CO and HC with water (in situ production of H2). The balance of H2 is quite important from the point of view of NOx reduction selectivity toward NH3. The NSRC model reaction scheme can be seen in Table III. The model contains basic CO, H2, HC and NO oxidation reactions that take place also in the DOC or TWC (reactions R1–R5 in Table III, cf. also Section III), and an oxygen storage sub-model typical for the TWC (reactions R11–R14 in Table III, cf. Kocˇı´ et al., 2007b; Koltsakis et al., 1997). NOx storage, desorption and reduction are described by reactions R15–R23 in Table III. An effective combination of pseudo-stationary kinetics for CO, H2, HC and NO oxidation, water gas shift and steam reforming (reactions R1–R9) and transient kinetics with an explicit consideration of the most important surface components (oxygen and NOx storage, reactions R11–R23 in Table III) is proposed (Kocˇı´ et al., 2007b). Possible internal diffusion effects for the NOx storage processes (cf. Section VI.B) are implicitly included in the evaluated kinetic parameters. In this way, the complexity of the reaction scheme and the number of kinetic parameters are kept on a low level, while the model is still able to describe and quantify the most important phenomena of NSRC. This enables fast and efficient dynamic simulations of the NSRC necessary for the routine use in the ExACT software environment (Gu¨thenke et al., 2007a, b). The oxygen and NOx storage phenomena are characterized by the maximum effective storage capacities Ccap, temperature-dependent relative saturation coverages ceq(T) and kinetic constants kj(T) determining the dynamics of the

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MODEL REACTIONS

FOR

TABLE III NOX STORAGE AND REDUCTION CATALYST

Reaction step

Reaction rate

CO+12O2-CO2

R1 ¼

H2+12O2-H2O

R2 ¼

C3H6+92O2-3H2O+3CO2

R3 ¼

C10H22+31 2 O2-11H2O+10CO2

R4 ¼

NO+12O22NO2

R5 ¼

k1 yCO yO2 G1 k2 yH2 yO2 G1 k3 yC3 H6 yO2 G1 k4 yC10 H22 yO2 G2 k5 ðyNO y0:5
yNO2 =K eq Þ O y;5 2

R6 ¼ k6 ðyCO yH2 O
yCO2 yH2 =K eq y;6 Þ

NO+CO-CO2+12N2 NO+H2-H2O+12N2 9NO+C3H6-3CO2+92N2+3H2O Ce2O3+12O2-2CeO2 CO+2CeO2-CO2+Ce2O3 H2+2CeO2-H2O+Ce2O3

R8 ¼ k8 yCO y0:5 NO R9 ¼ k9 yH2 y0:5 NO R10 ¼ k10 yC3 H6 y0:5 NO eq R11 ¼ k11 Ccap O2 yO2 ðcO2
cO2 Þ cap R12 ¼ k21 CO2 yCO cO2 R13 ¼ k13 Ccap O2 yH2 cO2 R14 ¼ k14 Ccap O2 yC3 H6 cO2

5CO+Ba(NO3)2-BaO+5CO2+N2 5H2+Ba(NO3)2-BaO+5H2O+N2 5 5 5 9C3H6+Ba(NO3)2-BaO+3CO2+3H2O+N2

R7 ¼ k7 ðyC3 H6 yH2 O


eq 2 0:1 R16 ¼ k16 Ccap NOx yNO yO2 ðcNOx
cNOx Þ

R18 ¼ R19 ¼

3CO+Ba(NO3)2-BaO+3CO2+2NO

R20 ¼

3H2+Ba(NO3)2-BaO+3H2O+2NO

R21 ¼

1 3C3H6+Ba(NO3)2-BaO+H2O+2NO+CO2

BaO+CO2-BaCO3

2 y3CO y6H2 =ðK eq y;7 yH2 O ÞÞ

eq 2 0:1 R15 ¼ k15 Ccap NOx yNO2 yO2 ðcNOx
cNOx Þ

R17 ¼

R22 ¼ R23 ¼

R1 R2 R3 R4 R5

G1

H2O+CO2H2+CO2 3H2O+C3H6-6H2+3CO

1 1 1 9C3H6+2CeO2-3CO2+Ce2O3+3H2O 1 2NO2+2O2+BaCO3-Ba(NO3)2+CO2 2NO+32O2+BaCO32Ba(NO3)2+CO2

No.

k17 Ccap y c2 NOx CO NOx G3 k18 Ccap y c2 NOx H2 NOx G3 k19 Ccap y c2 NOx C3 H6 NOx G3 k20 Ccap y c2 NOx CO NOx G4 k21 Ccap y c2 NOx H2 NOx G4 k22 Ccap y c2 NOx C3 H6 NOx G4 22 P

Rj

R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23

j¼17

Note: s G1 ¼ ð1 þ K a;1 yCO þ K a;2 yC3 H6 Þ2 ð1 þ K a;3 y2CO y2C3 H6 Þð1 þ K a;4 y0:7 NOx ÞT

G2 ¼ 1 þ K a;5 yC10 H22 ; G3 ¼ 1 þ K a;6 yO2 ; G4 ¼ ð1 þ 0:1K a;6 yO2 Þð1 þ K a;7 yNOx Þ All molar fractions y are considered locally in catalyst pores, the superscript ‘‘s’’ is omitted for brevity.

processes. For the oxygen storage, only the ‘‘fast’’ oxygen storage capacity (Lambrou et al., 2004; Yao and Yu Yao, 1984) is considered because of the rich phase time-scale (several seconds). For NOx storage, a model with two types of the NOx storage sites (the ‘‘fast’’ and ‘‘slow’’ sites) was also considered, however, the average NOx storage site

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model employing the empirical second-order rate law (cf. reactions R15–R22 in Table III and Gu¨thenke et al., 2007a, b; Kocˇı´ et al., 2007b) turned out to be sufficiently accurate in most of applications, while having less tunable parameters. The NOx storage rate laws given in Table III are able to predict both the fast NOx uptake at the beginning of the adsorption phase, and the slow storage process close to the saturation, in agreement with experimental observations (cf. the review by Epling et al., 2004a). The NOx storage process cannot be described only by the reaction pathway assuming first NO oxidation to NO2 and then the consecutive NO2 storage (the nitrate route only, cf. Section VI.B.1), particularly at lower temperatures when the NO oxidation is slow. Based on experimental results, a second pathway considering the storage of NO (the nitrite route) is present in most of the NSRC models (cf., e.g., Gu¨thenke et al., 2007a, b; Kocˇı´ et al., 2004c, 2007b; Olsson et al., 2005; Scholz et al., 2007). In the presented model (Gu¨thenke et al., 2007a, b; Kocˇı´ et al., 2007b) the decomposition of the stored NOx in the rich phase is considered in two ways—(i) NO desorption (reactions R20–R22 in Table III) followed by the catalytic reduction of the desorbed NO reactions R8–R10 in Table III) and (ii) direct reduction of the stored NOx (reactions R17–R19 in Table III) accounting for the spill-over from the NOx storage site to an adjacent NM site (cf. Fig. 18). Three different NOx reductants are distinguished—H2, CO and C3H6. Individual kinetic parameters, light-off temperatures and effective diffusivities are considered for these model components (cf. Kocˇı´ et al., 2007b).

2. Evaluation of Model Kinetic Parameters The model kinetic parameters—rate constants kj(T), inhibition constants Ka(T), maximum effective storage capacities Ccap and temperature-dependent relative saturation coverages ceq(T)—were fitted by minimization of the weighted least-squares objective function from the measured and simulated data, Eq. (35), employing an adaptive simplex optimization algorithm. This was done successively for the kinetic reactions in Table III to minimize the number of parameters estimated simultaneously (Kocˇı´ et al., 2007b): (i) CO, H2 and HC oxidation light-off, first individually under both lean and rich conditions, then in mixtures including inhibition effects, (ii) water gas shift and steam reforming (hydrogen production) under rich conditions, (iii) NO reduction by CO, H2 and C3H6, (iv) NO/NO2 transformation, (v) NOx storage, including temperature dependence of effective NOx storage capacity, (vi) oxygen storage and reduction, including temperature dependence of effective oxygen storage capacity and (vii) NOx desorption and reduction by CO, H2 and C3H6 under rich conditions. In each step of the evaluation, the already obtained kinetic parameters have been kept constant.

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Laboratory experiments designed to study the transient behavior of an NSRC (Kocˇı´ et al., 2007b; Waldbu¨sser, 2005) were performed to obtain suitable data for the evaluation of the global kinetic parameters. A commercial NOx storage catalyst material of the type NM/Ba/CeO2/g-Al2O3 washcoated on a cordierite substrate with wall thickness of 0.11 mm and channel hydraulic diameter of 1.06 mm was investigated. Monolith samples of the size approximately 8 cm3 were studied in two arrangements: (i) one sample in a quasi-adiabatic microreactor (Daimler AG, Stuttgart; cf. Waldbu¨sser, 2005) and (ii) three identical samples arranged in series, placed in modular, nearly isothermal steel microreactor (Institute of Chemical Technology, Prague; cf. Kocˇı´ et al., 2005, 2007b; Monolith, 2007). One set of experiments was conducted on fresh catalyst material. Pretreatment of the fresh catalyst samples was 1 h on stream at 500 1C, lean conditions and then 1 h lean/rich operation at 300 1C. After that, no significant change of catalyst activity and very minor change of storage capacity were observed in the course of the experimental series. A second set of experiments was conducted on specifically aged catalyst material, equivalent to a catalyst after 80,000 km passenger car use. The ageing level was obtained by applying DPF regenerations and de-sulfurizations consecutively (Gu¨thenke, 2007b; Waldbu¨sser, 2005). The laboratory experiments were performed either with slowly increasing temperature ramp (the light-off experiments for CO, H2 and HC oxidation, water gas shift and steam reforming, cf. Fig. 22), or with constant inlet temperature (the storage experiments and lean/rich switching, cf. Figs. 24 and 25). Lean exhaust gas was provided by synthetic gases (Kocˇı´ et al., 2007b), or by a one cylinder engine at a constant load (Waldbu¨sser, 2005), rich exhaust was supplied using synthetic gas mixtures. Examples of light-off experimental data for CO and C3H6 oxidation are given in Fig. 22a b, together with the simulated outlet concentrations. The respective kinetic parameters—the rate constants kj(T) and the inhibition constants Ka(T)—were evaluated from experimental data by the weighted least squares method, Eq. (35). At first, the rate constants were determined from experiments with individual simple lean mixtures (i.e., CO+O2 and C3H6+O2), then the self-inhibition constants were evaluated employing the data measured for simple rich mixtures and finally the cross-inhibition constants were optimized using the results of light-off experiments with complex lean and rich mixtures, without and with NO (Kocˇı´ et al., 2007b). The effects of lean vs. rich composition (i.e., selfinhibition by high CO and HC concentrations) and the inhibition by NO can be clearly seen from Fig. 22a and b. The observed light-off temperatures for the individual reductants were always in the order H2 o CO o C3H6 at corresponding conditions. The global kinetic model was able to describe the large variations of reaction rate and light-off temperature under the studied lean and rich conditions.

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FIG. 22. Experimentally observed and simulated light-off curves for fresh NSRC. (a) CO oxidation, influence of lean (excess of O2) and rich (excess of CO) conditions. Composition of inlet gas (mol. fractions)—lean mixture: 0.25% CO, 7% O2; rich mixture: 0.25% CO, 0.05% O2. (b) C3H6 oxidation, influence of inhibition by NO. Composition of inlet gas (mol. fractions): 200 ppm C3H6, 0.25% CO, 7% O2 and 0 or 150 ppm NO. (c) Water gas shift—production of hydrogen, composition of inlet gas (mol. fractions): 4.2% CO, 0.0% O2. All gas mixtures: 10% H2O, 10% CO2 and balance N2. Temperature ramp 3 K/min (Kocˇı´ et al., 2007b).

Water gas shift and steam reforming reactions producing H2 under rich conditions (reactions R6 and R7 in Table III, respectively) start to be significantly active at the temperatures above 300 1C (cf. Fig. 22c. These reactions result in a different actual CO:C3H6:H2 concentration ratio inside the monolith in comparison with the raw exhaust gas, or the synthetic rich inlet gas mixture used in the lab experiments (Kocˇı´ et al., 2007b). The reactions with water are characterized by the evaluated rate constants kj(T) as well as by the thermodynamic equilibrium constants Keq(T). The optimum NOx storage catalyst should exhibit quite low oxygen storage capacity to efficiently utilize the excess of CO, H2 and HC in the rich phase for the NOx reduction. However, it is practically impossible to produce the catalyst with zero oxygen storage capacity because Ce–Zr oxides are used for the stabilization of the washcoat structure (Kasˇ par et al., 2003). Experiments with step changes between oxygen and individual reductants (CO, H2 and C3H6) were performed at different temperatures to study the oxygen storage effects (reactions R11–R14 in Table III). It can be seen from the example given in Fig. 23 that the effective oxygen storage capacity increases monotonously with

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ANKE GU¨THENKE ET AL.

FIG. 23. Experimentally observed and simulated oxygen storage and reduction; fresh NSRC. Composition of inlet gas (mol. fractions)—lean mixture: 7% O2, 0% H2; rich mixture: 1.4% H2, 0% O2; both mixtures: 10% H2O, 10% CO2, balance N2 (Kocˇı´ et al., 2007b).

temperature, which is in agreement with other experimental observations (e.g., Boaro et al., 2004). At higher temperatures, the amount of reductants consumed during the rich phase by reactions with stored oxygen cannot be eq neglected. From these experiments, the model parameters Ccap O2 and cO2 ðTÞ and k11(T)–k14(T) are evaluated (Kocˇı´ et al., 2007b). The NOx storage experiments were performed for temperatures ranging from 1501C to 5001C and space velocities from 30,000 to 90,000 h
1. Prior to the measurements, the catalyst was fully regenerated at 4001C by rich gas mixture with the composition corresponding to an air/fuel ratio of 12.8 for 60 s. For constant lean inlet gas composition, temperature and space velocity, isothermal NOx adsorption as well as NO/NO2 transformation were measured to evaluate eq the NOx storage dynamics and the storage capacity (Ccap NOx and cNOx ðTÞ) of the catalyst, cf. Fig. 24. To investigate the regeneration behavior, lean-rich switches were applied periodically. Temperature and space velocity were varied as specified above. Lean and rich phase durations were varied in the range of 60–300 s and 3–7 s, respectively, with the rich phase composition corresponding to an air/fuel ratio between 12.8 and 13.6 (Waldbu¨sser, 2005). For each experiment, lean/rich switching was repeated until stabilized periodic operation was obtained, cf. Fig. 25. The kinetic parameters for the desorption and reduction of the stored NOx (reactions R17–R22 in Table III) were evaluated from a set of experiments conducted with several different rich-phase lengths at individual temperatures (Gu¨thenke et al., 2007b). For the fresh and the specifically aged catalyst materials, the dependence of the normalized NOx storage capacity on temperature could be kept the same (Gu¨thenke et al., 2007b). This minimized the number of parameters to be re-adapted for two catalysts with different ageing level. Thus, only the maximum NOx storage capacity Ccap and the pre-exponential factors for the reactions R1–R22 had to be re-evaluated, cf. Table III and Eq. (36).

157

CURRENT STATUS OF MODELING LEAN EXHAUST GAS 0.00016

NOx- / NO2- mole fractions [-]

0.00014 NO in NOx out meas NO2 out meas NOx out calc NO2 out calc

0.00012 0.0001 0.00008 0.00006 0.00004 0.00002 0 0

50

100

150

200

250 time [s]

300

350

400

450

500

FIG. 24. Fitting of kinetic parameters to isothermal adsorption experiment—comparison of measured (meas) and calculated (calc) outlet NOx concentrations. Fresh catalyst, SV=60,000 h
1, T=2001C, lean gas composition: 145 ppm NOx, 4% CO2, 4% H2O, 18% O2 (Gu¨thenke et al., 2007a).

0.00014

NOx-, NO2-mole fractions [-]

0.00012 0.0001 0.00008 0.00006

NO in NOx out meas NOx out calc NO2 out meas NO2 out calc

0.00004 0.00002 0 350

450

550

650

750

time [s] FIG. 25. Evaluation of the kinetic parameters for the stored NOx reduction. Comparison of the measured and calculated (calc) outlet NOx concentrations; fresh NSRC. Lean/rich experiment (180s/5s), SV=60,000 h
1, T in=3501C (Gu¨thenke et al., 2007b). Reprinted with permission from SAE Paper # 2007-01-1117 r 2007 SAE International.

ANKE GU¨THENKE ET AL.

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D. NSRC MODEL VALIDATION

AND

SIMULATION RESULTS

Examples of the results obtained with the spatially 1D model utilizing global NSRC kinetics are given in Figs. 26–33. The reaction kinetic model was validated with real exhaust measurement data from passenger car and heavyduty commercial vehicle to ensure applicability in the full range of operating conditions encountered. The kinetic equations and the parameters were kept constant for all validation calculations. For passenger car applications, FTP-75 driving cycle data obtained during vehicle dynamometer test (Waldbu¨sser, 2005) are shown in Figs. 26–28 (Gu¨thenke et al., 2007a). The NSRC was operated at an average space velocity of 42,000 h
1. The exhaust temperature in front of and behind the NSRC is shown in Fig. 26, the measured and predicted outlet temperatures are in a good agreement. Evolution of the predicted and measured instantaneous NOx emissions at the NSRC outlet during the test cycle is given in Fig. 27. The corresponding integrated (cumulative) NOx emissions, including the effect of fluctuating flow-rate, are then depicted in Fig. 28. The cumulative values are normalized by the integrated inlet (raw) NOx emissions over the complete test cycle. The simulated emissions match the measurement results very well; the agreement was reached also for the CO and HC conversions. The evolution of the spatially averaged NOx coverage cNOx (i.e., the amount of the NOx stored in the converter) in the course of the FTP cycle can be seen in Fig. 28. At the test start, the catalyst is fully regenerated. Time intervals with

Exhaust Temperature behind Catalyst [°C]

500 T in front of catalyst meas T behind catalyst meas T behind catalyst calc

450 400 350 300 250 200 150 100 0

200

400

600

800

1000

1200

1400

1600

1800

time [s] FIG. 26. Validation of the NSRC model for passenger car application—comparison of measured (meas) and calculated (calc) outlet temperatures during the FTP-75 driving cycle; fresh catalyst (Gu¨thenke et al., 2007a) (see Plate 4 in Color Plate Section at the end of this book).

159

CURRENT STATUS OF MODELING LEAN EXHAUST GAS 0.00020 NOx out meas NOx out calc

NOX - mole fractions [-]

0.00015

0.00010

0.00005

0.00000 0

200

400

600

800

1000

1200

1400

1600

1800

time [s]

1

1 NOx in front of cat. integrated meas. NOx behind cat. integrated meas. NOx behind cat. integrated calc. NOx storage fraction covered

0,9 0,8 0,7 0,6 0,5

0,9 0,8 0,7 0,6 0,5

0,4

0,4

0,3

0,3

0,2

0,2

0,1

0,1

0

NOx storage fraction covered [-]

Integrated NOx emissions / integrated NOx raw emissions over complete test cycle [-]

FIG. 27. Validation of the NSRC model for passenger car application—comparison of measured (meas) and calculated (calc) evolution of instantaneous NOx emissions during the FTP-75 driving cycle; fresh catalyst (Gu¨thenke et al., 2007a). For comparison of respective integral emission data cf. Fig. 28. Integral NOx conversions X evaluated from experiment and simulation data: Xexp=88.9%, Xsim=88.3%.

0 0

500

1000 time [s]

1500

FIG. 28. Validation of the NSRC model for passenger car application—comparison of measured (meas) and calculated (calc) cumulative NOx emissions for the FTP-75 driving cycle; fresh catalyst (Gu¨thenke et al., 2007a). The covered fraction of the NOx storage capacity (spatially averaged cNOx) is calculated by the model. Integral NOx conversions X evaluated from experiment and simulation data: X exp=88.9%, X sim=88.3% (see Plate 5 in Color Plate Section at the end of this book).

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NOx conversion over FTP-75 driving cycle

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

0. 1

0.3

NOx storage fraction covered at the beginning of the cycle

FIG. 29. Predicted variation of the NOx conversion over the FTP-75 driving cycle in dependence on the catalyst regeneration status at the beginning (i.e., initial cNOx); fresh NSRC (Gu¨thenke et al., 2007a).

0.0005 NOx in meas NOx out meas NOx out calc

0.00045

NOX - mole fractions [-]

0.0004 0.00035 0.0003 0.00025 0.0002 0.00015 0.0001 0.00005 0 1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

time [s]

FIG. 30. Validation of the NSRC model for heavy-duty commercial vehicle application— comparison of measured (meas) and calculated (calc) outlet NOx concentrations for the ESC load point B50; fresh catalyst (Gu¨thenke et al., 2007a).

161

CURRENT STATUS OF MODELING LEAN EXHAUST GAS NOx int in meas

0.7

NOx int out meas

0.9

NOx int out calc

0.8

NOx stor.fract.covered

0.6 0.5

0.7 0.6

0.4

0.5 0.3

0.4 0.3

0.2

0.2

NOx storage fraction covered [ ]

Integrated NOx emissions/ integrated NOx raw emissions over complete test cycle [-]

1

0.1

0.1 0 0

200

400

600

800

1000

0 1200

time [s]

FIG. 31. Validation of the NSRC model for passenger car application—comparison of measured (meas) and calculated (calc) cumulative NOx emissions for the NEDC driving cycle; aged catalyst (Gu¨thenke et al., 2007b). The covered fraction of the NOx storage capacity (spatially averaged cNOx) is calculated by the model. Reprinted with permission from SAE Paper # 2007-01-1117 r 2007 SAE International. Integral NOx conversions X evaluated from experiment and simulation data: Xexp=42.0%, Xsim=44.3%.

FIG. 32. Computed spatiotemporal concentration profile of the stored NOx for the NEDC driving cycle; aged NSRC. The z/L stands for the dimensionless spatial coordinate along the monolith: 0 is at the inlet, 1 at the outlet (Gu¨thenke et al., 2007b). Reprinted with permission from SAE Paper # 2007-01-1117 r 2007 SAE International.

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Integrated NOx conversion over NEDC [-]

0.7 0.65

Variation of catalyst length Variation of NO NOxx raw raw emissions emissions Variation of exhaust temperature

L*2

0.6 0.55

NO x raw * 0.5

L * 1.5 T + 100K

0.5 0.45

T + 50K

base case

0.4 0.35 0.3 0.25

NO x raw * 1.5

NO x raw * 2 T - 50K L * 0.5

0.2 FIG. 33. Influence of catalyst length, NOx raw emissions and exhaust temperature on the integral NOx conversion over NEDC; aged NSRC (Gu¨thenke et al., 2007b). Reprinted with permission from SAE Paper # 2007-01-1117 r 2007 SAE International.

monotonously increasing NOx coverage correspond to lean engine operation with NOx adsorption, while a sudden decrease of the NOx coverage (i.e., catalyst regeneration) is observed during the short enrichment phases. Variations in rich phase conditions (duration, temperature and gas composition) influence the extent of catalyst regeneration (cf., e.g., Gu¨thenke et al., 2007a, b; Kocˇı´ et al., 2004c, 2007b). Effects of different operating/regenerating strategies on the conversion can thus be investigated. The presented example FTP test is finished with a non-zero NOx coverage, i.e., the catalyst is not fully regenerated at the end of the driving cycle (cf. Fig. 28). The extent of the regeneration has a significant influence on the NOx conversions during a NSRC operation, yet it cannot be easily measured. The NSRC model balances the most important surface components (NOx and O2) and thus delivers information on the covered NOx storage fraction. Figure 29 shows the result of a simulation study on the influence of the initial NOx coverage on the conversions over the following FTP driving cycle (Gu¨thenke et al., 2007a). When the catalyst is not fully regenerated at the end of the previous cycle, the NOx conversion varies considerably. Such results help to explain unexpected measurement results during the development process and improve long-term reproducibility and predictability of the exhaust system. For heavy-duty commercial vehicle application, validation data from an engine test bench were used. Two different NSRC volumes were employed, 7 times and 3.5 times larger than that for the passenger car application. Various operating

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

163

points from ESC measurements (cf. Section II) were simulated, resulting in space velocities ranging from 36,000 to 135,000 h
1. The engine operating points were stationary concerning load and rotations and controlled lean/rich switches were applied until stabilized behavior of the catalyst was reached. The evolution of the NOx concentrations in front of and behind the NSRC for the operating point B50 is depicted in Fig. 30 (Gu¨thenke et al., 2007a). An example of the results for the aged NSRC is given in Fig. 31, where the predicted and measured NOx emissions are compared for a passenger car NEDC. The simulation was performed employing the model parameterization for the specifically aged catalyst material and NEDC vehicle dynamometer measurements (Waldbu¨sser, 2005) were used for the validation. Keeping the same temperature dependence of the normalized NOx storage capacity ceq(T) for the fresh and the aged catalyst, and using the maximum NOx storage capacity Ccap and the pre-exponential factors for the reactions R1–R22 evaluated from the laboratory experiments, a good agreement between measurements and simulations can be reached (Gu¨thenke et al., 2007b). The corresponding spatio-temporal concentration profile of the stored NOx calculated for the studied case is depicted in Fig. 32. The evolution of the exhaust gas temperature is similar to that given for the DOC in Fig. 15 (Section V.C), but at generally lower temperature level than for the closecoupled DOC. Thus, it is quite a demanding task to effectively regenerate the aged NSRC during cold start operation. During the first rich phase only the front of the catalyst is regenerated and the concentration of the stored NOx decreases only slightly in the rear part of the monolith, where the lowest temperature and concentrations of reductants are met. For the second and the third rich phase, the regeneration is also better at the front than at the rear part of the catalyst. During the last rich phase, almost the entire length of the catalyst is fully regenerated. This is due to a longer rich phase and higher catalyst temperatures met in the extra-urban part of the driving cycle. On the base case given in Fig. 31, several parametric studies were conducted. Catalyst length and NOx raw emissions were varied between 0.5 and 2 times the original value. Figure 33 shows that the integrated conversion over the driving cycle increases with increasing catalyst length and decreases with increasing raw emissions. These effects are quite intuitive, however, note that the dependence is not linear. The exhaust temperature was varied with respect to the base case between 50 K less and 100 K more. With the temperature increased by 50 K, the NOx conversion increases. For the base case temperature profile plus 100 K, it decreases again, as the optimum working temperature of the NSRC is exceeded. Decreased thermal stability of barium nitrates leads to a lower storage capacity (cf. the discussion in Section VI.B.1). The model also gives the possibility to study the effect of concurrent changes. For example, if the NOx raw emissions are increased by a

ANKE GU¨THENKE ET AL.

164

factor of 1.5 for the studied case, a 2.3 times long NSRC is necessary to achieve the same cumulative NOx emissions behind catalyst over the NEDC (Gu¨thenke et al., 2007b).

VII. Selective Catalytic Reduction of NOx by NH3 (Urea–SCR) In this section the methods described in Sections III and IV are applied to derive a dynamic numerical model of the SCR of NO–NO2 with NH3 over a commercial V2O5/WO3/TiO2 extruded monolith catalyst. The extension of the same dynamic model to a zeolite-based catalyst is currently in progress (Chatterjee et al., 2007). The SCR with NH3/urea is emerging as the most promising technology for the abatement of NOx emissions from diesel vehicles (ACEA, 2003; Heck et al., 2002). This has stimulated a renewed interest in the investigation of fundamental aspects of the SCR catalytic chemistry, also in view of the need of the transportation industry to develop design and simulation tools incorporating SCR kinetic schemes. Indeed, NH3–SCR over vanadia-type catalysts, wherein one molecule of NO is reduced by one molecule of ammonia in the presence of oxygen to give harmless dinitrogen and water, according to the standard SCR reaction, R1 in Table IV, has represented for the last two decades the most effective commercial deNOx process for stack gases from power plants and other stationary sources (Forzatti et al., 2002). However, the specific demands of mobile applications, associated, e.g., with onboard ammonia storage, volume limitations, dynamic operation and extensive functional T-windows, do not permit a straightforward transposition of the technology. Particularly, since the working conditions for mobile applications may be much colder than in stationary installations, the increase of deNOx activity at low temperatures represents a major development goal. A possible solution to this issue is represented by the so-called fast SCR reaction known since the early 1980s, when Kato and co-workers (Kato et al., 1981) found that the reaction involving an equimolar NO and NO2 feed mixture, R2 in Table IV, is remarkably faster than the standard SCR, reaction, in the field of low temperatures (T o 300 1C). In practical terms, a preoxidation catalyst located upstream of the SCR catalyst

STANDARD

TABLE IV FAST SCR REACTIONS

AND

Reaction step 2NH3+2NO+12O2-2N2+3H2O 2NH3+NO+NO2-2N2+3H2O

No. Standard SCR reaction Fast SCR reaction

R1 R2

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

165

could convert a fraction of NO in the engine exhausts to NO2 in order to approach the optimal NO/NO2 equimolar feed ratio of the fast SCR reaction (Koebel et al., 2002). Nevertheless addition of NO2 to the SCR reacting system introduces considerable complexity resulting from the multiplication of primary and secondary reaction routes, and may result in the formation of such undesired byproducts as NH4NO3 and N2O (Ciardelli et al., 2007a; Madia et al., 2002). In this section we present the derivation of a detailed kinetic model of the full NO/NO2–NH3 reacting system over a commercial V2O5/WO3/TiO2 catalyst, whose intrinsic rate expressions have been then incorporated into a transient 2D (1D+1D) mathematical model of the SCR honeycomb monolith reactor to predict the dynamic behavior of real full-scale converters. The overall reaction scheme adopted in the kinetic model is summarized in Table V, and discussed in details in the following paragraphs. It is worth emphasizing that the present modeling effort relies on (and complements) a fundamental investigation of both the standard and the fast SCR catalytic mechanisms, whose elucidation proved to be critical for effective design and operation of deNOx aftertreatment devices. In line with the general guidelines presented in Section IV, the catalyst in powder form has been at first considered in order to estimate the intrinsic SCR kinetics. For the sake of clarity, the experimental work was organized according to a stage-wise approach of growing complexity: first the simplest reacting system NH3/O2 was addressed, then NO was included to study the standard SCR reaction only, and finally, with the addition of NO2, the full NH3–NO– NO2/O2 system was investigated. In subsequent stages validation experiments were performed over monolith catalyst samples at two different scales: (i) monolith core samples (up to 10 cm3) in a laboratory rig for integral reactor experiments and (ii) full-scale honeycomb monoliths (up to 43 L in size) in engine test bench runs. MODEL REACTIONS

TABLE V FOR V-BASED SCR CATALYSTS

Reaction step NH3-NH3* NH3*-NH3 NH3*+34O2-12N2+32H2O NH3*+NO+14O2-N2+32H2O 2NO2+H2O3HONO+HNO3 NH3*+HONO-N2+2H2O NH3*+HNO33NH4NO3 HNO3+NO3HONO+NO2 NH3*+HNO3-N2O+2H2O NH3*+34NO2-78N2+34H2O

Reaction rate NH3 adsorption NH3 desorption NH3 oxidation Standard SCR NO2 disproportion Decomposition of Ammonium Nitrite Ammonium nitrate formation/ decomposition HNO3 reduction by NO N2O formation NO2 SCR

No.

Rads Rdes Rox RNO RAmm RNit RDec

R3 R4 R5 R6 R7 R8 R9

RFst RN 2 O RNO2

R10 R11 R12

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While the practical implementation of the SCR deNOx technology for vehicles relies on using an urea aqueous solution as ammonia carrier, NH3 being generated by decomposition and hydrolysis of urea, the present investigation has been focused on the reactivity of ammonia only: it is believed in fact that decoupling urea decomposition from NH3–NOx reactions is quite helpful in effectively elucidating SCR chemistry and kinetics.

A. MICROREACTOR SCALE 1. Testing Procedure The transient experiments herein described were carried out over powdered catalyst in a microreactor: a portion consisting of several grams from the original extruded monolith was crushed and sieved to a powder (140–200 mesh). One hundred and sixty milligrams of this powder, diluted with 80 mg of quartz were eventually loaded in the microreactor. Intraparticle gradients and gas–solid mass transfer limitations were ruled out by theoretical criteria (Mears, 1971). For reproducibility purposes, a specific pre-treatment was performed whenever a fresh catalyst sample was loaded in the reactor. This typically consisted in running a temperature ramp from 501C to 6001C at 151C/min while feeding to the reactor 2% of oxygen in helium, with a flow of 150 cm3/min (STP). After that the temperature was kept at 6001C for about one hour. This treatment assured total desorption of sulphate species, often present in fresh commercial V-based SCR catalysts. The feed mixture was prepared by combining the pure synthetic reacting gases, namely ammonia, NO, NO2 and oxygen. Contrary to the usual approach in the literature, helium instead of nitrogen was used as inert carrier gas, so that N2, which is the main product of the SCR reaction, could be easily evaluated by the analytical system. Argon was also used as a tracer and internal standard for the analysis. The flow of each component was controlled by means of seven mass-flow meters (Brooks Inst. 5850S) connected to switchboards. Downstream of the mass-flow meters the rig was operated at atmospheric pressure. Water vapor was fed by means of a saturator through which part of the global feed stream was bubbled before entering the reactor. Transient experiments were performed by means of two 4-way switch valves which allow a rapid cross change between inlets and outlets, so that the reactants are instantaneously fed to or released from the reactor. The same flow rate is always set for the two streams entering each valve, thus ensuring that the only variation affecting the reacting mixture is the change of reactant concentrations. The microreactor, schematically represented in Fig. 34, consists of a quartz tube (internal diameter 6 mm) inserted in a furnace, through which the reacting

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167

NO, NO2 in

NH3 in

gas in: He, O2, H2O capillaries thermocouple

corundum

Catalyst bed

gas out FIG. 34. Schematic diagram of the microreactor for NH3–SCR kinetic runs.

mixture flows. The catalyst powder is placed at the depth corresponding to the isothermal zone of the furnace, between two layers of inert corundum to favor good mixing of the gaseous species. The reaction temperature is measured and controlled by a thermocouple immersed in the catalyst bed. The outlet gas is then sent to the analytical system. The entire line downstream of the reactor is traced and heated to 2001C in order to prevent deposition of ammonium nitrate, which is a possible product of the reaction between NO2 and NH3. In order to secure a continuous quantitative analysis of the outlet gas mixture, including all the species necessary for the evaluation of the nitrogen atomic balance, namely NH3, NO, NO2 (reactants), N2 and N2O (products), the gases exiting the reactor were analyzed both by a Mass Spectrometer (MS) (Balzers QMS200) and by a UV analyzer (ABB Limas 11HW) in a parallel

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ANKE GU¨THENKE ET AL.

arrangement. Moreover a specific cold trap was designed for the quantification of ammonium nitrate, which is formed under particular conditions. Operating conditions as similar as possible to those of real aftertreatment systems were chosen for the experiments: temperature range ¼ 50–5501C, NOx concentration range ¼ 0–1,000 ppm, presence of water and oxygen. While real engine exhausts typically contain about 10% v/v of water and oxygen, it was not possible to feed such high concentrations in microreactor runs because of limitations related to the mass spectrometer. The effect of water and oxygen on the SCR reactivity was in any case addressed, and a good compromise was found using feed contents of 1% H2O and 2% O2 v/v: such conditions well represent the SCR reactivity under real conditions and at the same time allow the use of a MS analyzer. The effects of higher oxygen and water feed contents were studied at the intermediate scale over monolith core samples. Likewise, hydrocarbons and CO2 were not added to the feed at the microreactor scale, but their effect on the SCR reactivity over V-based catalysts is known to be negligible, as later confirmed by test bench experiments. 2. NH3/O2 Reacting System The experimental investigation started from the study of the simplest reacting system, i.e. including only ammonia and oxygen as reactants (Ciardelli et al., 2004a). Two main catalytic processes are expected to occur in this case, namely the adsorption–desorption of NH3 (R3 and R4 in Table V), and, at higher temperatures, its oxidation by gaseous oxygen (R5 in Table V). The two processes have been addressed sequentially, as discussed in the following sections. 3. NH3 Adsorption–Desorption Experimental runs - In order to obtain intrinsic kinetics of the NH3 adsorption–desorption process as a function of temperature, the dynamics of NH3 adsorption–desorption were studied over the commercial V-based SCR catalyst adopting the TRM: specifically, the experiments consisted in stepwise feeding 1,000 ppm of NH3 while flowing oxygen (2%), water (1%) and balance helium at constant temperature. Depending on temperature, different amounts of ammonia were adsorbed onto the catalyst at this stage. When the catalyst adsorption capacity was saturated (outlet ammonia signal approaching the feed level), NH3 was shut off and desorption of weakly adsorbed ammonia occurred. Then, after interrupting the oxygen feed in order to prevent ammonia oxidation, a temperature ramp at 151C/min from 501C to 5501C was run so to provoke complete thermal desorption of ammonia. Such experiments were typically performed at an SV of 92,000 h
1. Typical results obtained upon imposing a step change of the NH3 feed concentration are presented in Fig. 35. The figure shows the evolution of the

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169

FIG. 35. Fitted results of adsorption
desorption+TPD run: SV=92,000 h
1; NH3=1,000 ppm, H2O=1%, O2=2%. (A) T=501C; (B) T=1001C; (C) T=1501C; (D) T=2001C. Symbols: experimental; solid line: calculated.

NH3 outlet concentration (symbols) monitored upon opening and shutting down the NH3 feed (dotted line), at four different adsorption temperatures, namely T ¼ 50, 100, 150, 2001C, followed by a TPD run. Considering the experiment performed at 501C, upon the NH3 step addition (at t ¼ 0 s) the NH3 reactor outlet concentration shows a dead time, i.e. a period during which the fed NH3 is completely adsorbed onto the catalyst surface, and then it increases with time, approaching the inlet value of 1,000 ppm after E3,500 s. Upon NH3 shut-off (t ¼ 4,000 s) the reactor outlet NH3 concentration starts decreasing with time due to the desorption of previously adsorbed NH3. However, complete desorption of NH3 is achieved only by performing a TPD run. Indeed, as soon as the catalyst is heated, the NH3 signal increases again reaching a maximum value at about 2501C, then it drops back to zero for temperatures higher than 4501C. On increasing the adsorption temperature, shorter dead-times are observed in Fig. 35 (respectively E280, 220, 190 and 140 s for Tads ¼ 50, 100, 150 and 2001C): thus the amount of NH3 adsorbed onto the catalyst surface is reduced, in line with the exothermic NH3 adsorption process. Likewise the TPD runs, whose areas decrease on increasing the adsorption temperature, also confirm the lower storage capacity of the system at higher temperatures. Kinetic analysis—The model of the test microreactor was based on the following equations, whose symbols are defined in the Notation. They represent the adaptation of the general Eqs. (33) and (34) in Section IV to the specific reacting system herein considered.

ANKE GU¨THENKE ET AL.

170

NH3 mass balance (adsorbed phase): ONH3

@yNH3 ¼ Rads
Rdes @t

(45)

NH3 mass balance (gas phase):
g

@cNH3 @cNH3 ¼
u
ð1

g ÞðRads
Rdes Þ @t @z

(46)

Based on literature indications (Lietti et al., 1997, 2000) and supported by preliminary fits of the experimental data, a non-activated NH3 adsorption process and Temkin-type NH3 desorption kinetics have been assumed, i.e. Rads ¼ kads cNH3 ð1
yNH3 Þ

Rdes

  E des ð1
ayNH3 Þ yNH3 ¼ k0;des exp
RT

(47)

(48)

Figure 35 illustrates the comparison between experimental data (symbols) and model fitting (solid lines) after global non-linear regression on the four runs: a good agreement is evident in all cases. Particularly, the model well reproduces the dead time of the outlet NH3 gaseous concentration, which is representative of the NH3 storage capacity. In addition, TPD runs are fairly well fitted in terms of concentration profiles and temperature peaks in a large range of temperatures. The parameter estimates associated with the fit in Fig. 35 well compare with the corresponding estimates obtained in previous works performed over both model and commercial V-based SCR catalysts for stationary applications (Lietti et al., 1997, 2000). 4. NH3 Oxidation The reaction of ammonia with oxygen over V-based catalysts produces mainly nitrogen, according to the stoichiometry of R5 in Table V. Analogously to the case of the ammonia adsorption–desorption, specific runs were carried out in order to extract the intrinsic kinetics of ammonia oxidation and at the same time to validate the previously fitted kinetics of the ammonia adsorption– desorption process. Experiments including both the phases of adsorption–desorption and oxidation of ammonia were hence designed and performed. A typical run is shown in Fig. 36: at temperature of 1751C 1,000 ppm of NH3 were stepwise fed in a stream of water (1%), oxygen (2%) and balance He, with an SV of 230,000 h
1. At time ¼ 3,000 s a temperature ramp at 121C/min was started. Adsorption of ammonia occurred as soon as NH3 was admitted to the reactor,

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171

FIG. 36. Fitting of the NH3 oxidation TPR: SV=210,000 h
1; NH3=1,000 ppm, H2O=1%, O2=2%. T ramp 175–4251C at 121C/min. Symbols: experimental; solid lines: calculated.

while its desorption was evident during the temperature ramp. Moreover, ammonia oxidation started when the catalyst temperature exceeded 3501C, as apparent from the consumption of ammonia and the production of nitrogen. The experiment in Fig. 36 has been analyzed according to the usual plug-flow model of the test microreactor. In this case, the ammonia mass balance equations were modified in order to include the oxidation reaction R5 in Table V, which was considered to proceed via adsorbed ammonia NH3*. Moreover, the mass balance for gaseous nitrogen was introduced NH3 mass balance (adsorbed phase): O

@y ¼ Rads
Rdes
Rox @t

(49)

NH3 and N2 mass balances (gas phase):
g

@cNH3 @cNH3 ¼
u
ð1

g ÞðRads
Rdes Þ @t @z

(50)

@cN2 @cN2 1 ¼
u þ ð1

g Þ Rox 2 @t @z

(51)


g

For the rate of ammonia oxidation a simple first-order rate expression in the surface concentration of ammonia has been assumed, which also considers the effect of oxygen, i.e. 

Rox

 E ox  yO2 b ¼ k0;ox exp
yNH3 RT 0:02

(52)

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ANKE GU¨THENKE ET AL.

The kinetic parameters of ammonia oxidation were fitted by multiresponse nonlinear regression, while the parameter estimates for the ammonia adsorption– desorption kinetics were kept unchanged with respect to those obtained from the fit in the previous section. Notably, in this case both the NH3 and the N2 outlet concentrations were regarded as regression responses. Figure 36 shows the result of the fitting in terms of experimental (symbols) and calculated (solid lines) outlet concentrations of NH3 and nitrogen as a function of time. It is worth of note that the kinetic model is capable to capture both the onset temperature of ammonia oxidation and the slope with which it proceeds upon increasing the temperature. Moreover the ammonia adsorption– desorption dynamics, which are very demanding especially during the T-ramp, were very well predicted, thus validating the fit performed in the previous paragraph at different operating conditions of SV and heating rate. The parameter estimates associated with the fitting in Fig. 36 well compare with other estimates for NH3 oxidation over V-based SCR catalysts reported in the literature. 5. NH3–NO/O2 Reacting System This represents the typical feed mixture to SCR converters when no oxidation precatalyst is applied, as for instance in the case of NOx abatement from stationary sources. With such a feed mixture the main deNOx reaction occurring over V-based catalysts is the so-called standard SCR (R6 in Table V). Transient experiments in a wide range of temperatures (50–5501C) were performed in order to develop a suitable kinetic model of the NH3–NO/O2 reacting system. The study of the standard SCR kinetics was particularly focused on the characteristics that are critical for mobile applications, namely the behavior during transient operation and the reactivity in the low temperature region. 6. Experimental The reactivity of the NH3–NO/O2 system was studied first by TPR experiments in order to explore the effects of some operative conditions, namely temperature, water and oxygen feed contents and space velocity, on the standard SCR reaction. In this case NH3 (1,000 ppm)+NO (1,000 ppm) with O2 (2 or 6% v/v), H2O (1%) and balance He were initially fed at 501C and then the catalyst temperature was continuously increased at 2 1C/min up to 4501C. No significant influence of the water feed content on the standard SCR reaction between 1% and 10% H2O was found, while a moderate promoting effect of oxygen on the SCR activity was clearly apparent (Chatterjee et al., 2005; Ciardelli et al., 2004a; Nova et al., 2006a; Tronconi et al., 2005). Afterwards the reactivity in dynamic conditions was systematically studied by means of TRM experiments, i.e. by performing step changes of the NH3 feed

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173

FIG. 37. Transient SCR microreactor experiments with step feed of NH3 (0–1,000 ppm) in NO (1,000 ppm)/He+H2O (1% v/v)+He+O2 (2% v/v) (A) and O2 (6% v/v (B) and at different temperatures. Symbols: measured concentrations of NH3, NO, N2 at reactor outlet. Lines: kinetic fitting using the modified redox rate law, Eq. (57). Reprinted with permission from Nova et al. (2006a).

concentration (0-1,000-0 ppm) at constant NO feed content (1,000 ppm) in the presence of water (1% v/v) and oxygen (2 and 6% v/v). The system was operated at atmospheric pressure with an SV ¼ 9.2  104 h
1. Figure 37A (symbols) displays selected step-response TRM experiments performed with 2% O2 at different temperatures, namely at 200, 225, 250 and 275 1C, in terms of NH3, NO and N2 outlet concentrations vs. time. In the run performed at T ¼ 2501C (squares), upon NH3 step feed at t ¼ 0 s the NH3 outlet concentration trace exhibited a dead time (E250 s) and then slowly grew with time on stream, eventually approaching a steady-state value of about 200 ppm, that is much lower than the ammonia feed concentration level (1,000 ppm). In correspondence to the NH3 admittance to the reactor, a sudden drop of the NO outlet concentration was observed together with a mirror-like increment of the N2 concentration, associated with the start-up of the SCR reaction. The levels of NH3, NO and N2 at steady state were in fact consistent

174

ANKE GU¨THENKE ET AL.

with the stoichiometry of the standard SCR reaction (R6, Table V), with a conversion close to 80%. Following shutdown of the ammonia feed (t ¼ 1,500 s) the concentrations of NH3, NO and N2 slowly recovered their feed levels, as all the ammonia still adsorbed on the catalyst surface was progressively depleted by the reaction with nitric oxide. The dynamic features described above are common to all the experiments performed in the high temperature range (above 2501C): Fig. 37A (diamonds) shows, e.g., also the run at 2751C, at which temperature almost complete steady-state conversion of the reactants was approached. On the contrary, the experiments performed at lower temperatures, i.e. 2251C (Fig. 37A, triangles) and 2001C (circles), exhibited a different dynamic behavior of NO and N2 during both the NH3 start-up phase and the NH3 shut-off transient. The more significant effect was observed when the NH3 feed concentration was restored to 0 ppm (t ¼ 1,500 s): the NO outlet concentration first decreased, passed through a minimum and then began to increase due to the depletion of adsorbed ammonia. A symmetrical evolution was observed for N2, thus proving that during the transient phase of ammonia shutdown, when only adsorbed NH3 was reacting with NO continuously fed to the reactor, the deNOx activity of the system was temporarily enhanced until complete depletion of the residual NH3 on the catalyst surface. This evidently confirms that excess ammonia inhibits the SCR reaction, as already pointed out by several authors (Kapteijn et al., 1993; Koebel and Elsener, 1998; Nova et al., 2000; Willey et al., 1991). A minor transient feature was also manifested when ammonia was admitted to the reactor (t ¼ 0 s): the NO outlet concentration immediately decreased, went through a weak minimum near 150 s and finally slightly increased, reaching steady state in correspondence of the end of the ammonia feed phase (tE2,800 s). Again, the nitrogen evolution was symmetrical to that of NO. The same ammonia inhibition effect invoked to explain the enhancement in the deNOx conversion at ammonia shutdown can explain this transient behavior, too. In fact both features suggest the existence of an optimal ammonia surface concentration, which is lower than the coverage established at steady state. It is worth of note that such transient effects due to NH3 inhibition were most evident at the lowest investigated temperature (2001C), but gradually vanished upon increasing the reaction temperature, i.e. reducing the amount of adsorbed NH3 present on the catalyst surface, and were no longer visible at T Z 250 1C. Transient kinetic experiments were performed also in the presence of a higher concentration of oxygen, namely 6% v/v. Results collected at different temperatures are displayed in Fig. 37B (symbols) in terms of NH3, NO and N2 outlet concentration traces vs. time (T ¼ 200, 225, 250, 2751C). They are qualitatively similar to those with 2% v/v oxygen feed in Fig. 37A and discussed above. Particularly, two different behaviors of the NO and the N2 concentration traces were again observed when the NH3 feed was opened up/shut down. In the high-T range, monotonic temporal evolutions were observed: e.g. at NH3

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

175

shutdown they slowly recovered their feed levels, using up the ammonia still adsorbed on the catalyst surface. In the low T-range, again maxima–minima behaviors became apparent: thus, at ammonia shutdown the NO concentration (and symmetrically the N2 production) decreased at first, went through a minimum, then began to increase and approached its steady-state value. Thus, the higher oxygen content did not alter the dynamic features of the transient experiments, and in particular the ammonia inhibiting action was still evident to a similar extent, particularly at ammonia shut-down. The main effect of the higher oxygen concentration was a slight enhancement of the SCR conversion, as measured by the steady state levels of ammonia, NO and nitrogen: for example at 2501C, 780 ppm of nitrogen were measured at steady state when 2% oxygen was fed to the reactor (see Fig. 37A), while in the presence of 6% of oxygen the SCR reaction produced 820 ppm of nitrogen (see Fig. 37B). 6. Kinetic Analysis The kinetic analysis of the whole set of transient data collected over the powdered SCR catalyst has been addressed using the dynamic 1D isothermal heterogeneous plug-flow model of the test microreactor (Chatterjee et al., 2005; Ciardelli et al., 2004a) described in Section IV. The following reactions were included in the kinetic model: NH3 adsorption (R3 in Table V), NH3 desorption (R4 in Table IV), NH3 oxidation (R5 in Table IV) and standard SCR (R6 in Table V). Mass balances for adsorbed ammonia and nitrogen now include the standard SCR reaction. Moreover, the mass balance of gaseous NO was introduced, too (a) adsorbed phase: @yNH3 ¼ Rads
Rdes
Rox
RNO @t

(53)

@cNH3 @cNH3 ¼
u
ð1

g ÞðRads
Rdes Þ @t @z

(54)

NH3 O (b) gas phase: NH3
g

  @cN2 @cN2 1 g ¼
u þ ð1

Þ RNO þ Rox N2
2 @t @z g

NO
g

@cNO @cNO ¼
u
ð1

g ÞRNO @t @z

(55)

(56)

ANKE GU¨THENKE ET AL.

176

The reactions of adsorption–desorption of NH3 and ammonia oxidation to N2 were considered with the kinetic expressions shown in Section V.A.2.b. The peculiar dynamic effect attributed to the inhibition effect of ammonia was accounted for by a dual-site modified redox (MR) rate law (Nova et al., 2006a) RNO ¼

k0;NO e
E NO =RT cNO yNH3 

 1=4 1 þ K NH3 ðyNH3 =1
yNH3 Þ 1 þ kO2 ðcNO yNH3 =yO2 Þ

(57)

The MR rate law relies on the assumption that the SCR reaction is governed by a redox mechanism and therefore predicts a kinetic dependence on oxygen. It has been derived assuming that: (i) two types of sites for NH3 adsorption (acidic non-reducible sites) and for NO+NH3 activation/reaction (redox sites, associated with vanadium), respectively, prevail on the catalyst surface; (ii) NH3 blocks the redox sites; (iii) reoxidation of the redox sites is rate controlling. A global multiresponse non-linear regression was performed to fit Eq. (57) to all the runs with both 2% and 6% v/v O2 feed content to obtain the estimates of the kinetic parameters (Nova et al., 2006a). Figure 37 (solid lines) illustrates the adequacy of the global fit of the TRM runs with 2 and 6% O2: the MR rate law can evidently capture the complex maxima–minima NO and N2 traces (symbols) at low T at both NH3 startup, that a simple Eley–Rideal (ER), approach based on the equation   E NO RNO ¼ k0;NO exp
cNO yNH3 RT

(58)

was found unable to reproduce (Nova et al., 2006a). The MR kinetic model has been then validated on a predictive basis by comparing its simulations with experimental data from microreactor runs consisting of high frequency NH3 feed pulses in a stream of 1,000 ppm of NO, 2% O2 and 1% H2O at 180 1C (Nova et al., 2006a; Tronconi et al., 2005). The experimental signals (see Fig. 38, symbols) were in line with the abovementioned ammonia inhibition effect, exhibiting a characteristic transient behavior characterized by the greatest NO conversion after the NH3 shut down. The experiments were simulated using both the MR, Eq. (57), and the ER, Eq. (58), rate expressions. The results confirmed that changing the deNOx rate equation from the ER- to the new MR kinetic model improved significantly the description of fast SCR transients similar to those associated with the operation of SCR aftertreatment devices for vehicles at low temperature. Accordingly, we conclude that the dual-site MR approach is compatible with the ammonia inhibition effects observed during unsteady SCR experiments, as well as with the oxygen dependence of the SCR kinetics at low temperatures, and can be successfully applied to simulate the complex dynamic behavior of

177

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

NO

1000

A

800

Concentration (ppm)

600

NH3

400

200

N2 0

B

NO

1000

Concentration (ppm)

800

600

NH3 400

200

N2 0 0

1000

2000

3000 Time (s)

4000

5000

6000

FIG. 38. Transient SCR microreactor experiments with high frequency NH3 feed pulses (1,000 ppm) in flowing NO (1,000 ppm)+O2 (2% v/v) and H2O (1% v/v)+He at 1801C, with pulse frequency: 5 min on/5 min off. Symbols: outlet concentration of ammonia (circles), NO (squares) and N2 (triangles)—Dotted lines: feed ammonia concentration. Solid lines: (A) simulation using the MR rate law, (B) simulation using the ER rate law.

178

ANKE GU¨THENKE ET AL.

real exhaust gas aftertreatment systems for vehicles. It is also worth noticing that the redox rate model, Eq. (57), predicts the existence of an optimal ammonia surface coverage at low temperatures, but reduces to the well established Eley–Rideal form at 250 1C and above, i.e. in the range of temperatures typical of SCR stationary applications. In fact, Eley–Rideal kinetics have been successfully adopted in the past for steady-state modeling of SCR installations for power plants (Forzatti et al., 2002; Tronconi and Forzatti, 1992). 7. NH3–NO–NO2/O2 Reacting System The inclusion of NO2 among the reactants of the SCR process is strictly connected with the adaptation of such a technology to mobile sources. As a matter of fact NOx mixtures in combustion exhausts are mainly composed (95%) of NO, thus in the past years the research on SCR has been focused on the reactivity of the NH3–NO/O2 system. With regard to onboard applications, however, the presence of a DOC system allows the conversion of part of NO to NO2 upstream of the SCR catalyst. Thus the amount of NO2 in the NOx mixture that enters the SCR catalyst is increased. This becomes an advantage for the deNOx system as it enhances the low temperature activity, thus helping to overcome one of the biggest limitations of mobile SCR, namely the low NOx conversions below 2501C. In fact it has been well known since the 1980s that the reaction involving an equimolar NO and NO2 feed mixture is considerably faster than the standard SCR in the field of low temperatures. In order to develop a suitable kinetic model of the full NH3–NO–NO2/O2 SCR reacting system, first the active reactions depending on NO/NO2 feed ratio and temperature were identified; then a dedicated study was performed aimed at clarifying the catalytic mechanism of the fast SCR reaction; on the basis of such a reaction chemistry a detailed kinetic model was eventually derived, whose intrinsic rate parameters were estimated from global non-linear regression of a large set of experimental transient runs. 8. Experimental Methods and Reaction Chemistry The reactivity study of the NH3–NO/NO2 SCR system was at first focused on identifying all of the relevant reactions occurring within the range of operating conditions of industrial interest, as well as on the definition of a global reaction scheme suitable to account for the distribution of the major observed products, namely N2, NH4NO3 and N2O. Accordingly, a systematic kinetic investigation was carried out over a representative range of temperatures (160–4251C) and over the full range of NO/NOX feed ratios (from zero to unity) (Ciardelli et al., 2007a). TRM experiments were performed with the usual procedure, i.e. feeding O2 (2% v/v), H2O (1% v/v) and balance He at constant temperature and

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179

performing a step change of NOx inlet concentration (0-1,000 ppm using different NO/NO2 mixtures) while feeding 1,000 ppm of NH3. After approaching steady state, the NH3 feed was stepwise shut down. The system was operated at atmospheric pressure and with an SV of 210,000 h
1. Figure 39 shows, e.g., the transient data collected at 2001C and at 2751C when feeding 1,000 ppm NH3+500 ppm of NO+500 ppm of NO2 (NO/NO2 feed ratio ¼ 1/1) or 1,000 ppm NH3+330 ppm of NO+670 ppm of NO2 (NO/NO2 feed ratio ¼ 1/2), respectively, in terms of outlet concentrations of NO, NO2, N2 and NH3 (symbols) vs. time. In the first case, conversions of roughly 700 ppm of NH3, 350 ppm of NO, 350 ppm of NO2, along with production of about 700 ppm of N2 were measured at steady state. Such values agree with the stoichiometry of the fast SCR reaction. In the second example, at t ¼ 2,800 s the NOx mixture (330 ppm of NO+670 ppm of NO2) was added to the 1,000 ppm ammonia feed stream at 2751C. At this temperature all of the NO in the feed was converted, while the steady-state concentrations of the other species were about 700 ppm of N2, 250 ppm of NH3, 250 ppm of NO2 and 30 ppm of N2O. Such values are readily explained if we assume that all NO in the feed reacted according to the fast SCR stoichiometry, whereas the remaining ammonia and NO2 reacted to give N2 and N2O according to the ammonium nitrate formation (R7+R8+R9 in Table V) and ammonium nitrate decomposition to N2O (R11 in Table V). The TRM experiments performed varying temperature in the range 175–2751C and the NO/NOx feed ratio are summarized in Fig. 40 in terms of steady-state deNOx performances (symbols) at different temperatures vs. the NO/NOx feed ratio. It appears that in all cases, in agreement with results reported by other authors (Kato et al., 1981; Koebel et al., 2002), the highest NOx conversion was achieved with a 1/1 NO2/NO feed ratio. Notice that at low temperature a gain of over 50% in NOx conversion can be obtained moving from NO/NOx ¼ 1, that is with only NO in the feed, to NO/NOx ratio ¼ 0.5, i.e. the stoichiometric feed for the fast SCR reaction. However, for T Z 3501C, in the ‘‘lean-NO2’’ zone 100% of NOx conversion could be reached because at this high temperature the standard SCR (R6 in Table V) is active enough to consume all of the excess NO, hence the promoting effect of NO2 was not apparent. Moving toward the left part of Fig. 40, that is the ‘‘NO2-rich’’ zone, the decrease in conversion is due to the low fraction of NO in the feed, which becomes the limiting reactant of the fast SCR: at low temperature the excess NO2 then reacts with ammonia according to the ammonium nitrate formation route (R7+R8+R9 in Table V) while for T W 2751C the NO2–SCR, reaction R12 (cf. Table V), occurs. Both these reactions are less effective than the fast SCR (R7+2*R8+R10 in Table V): reaction (R7+R8+R9, cf. Table V) results in a conversion of about 50% independent from the operating temperature, while reaction (R12, cf. Table V) cannot reach total NOx conversion because ammonia becomes the limiting reactant.

ANKE GU¨THENKE ET AL.

180 1200

NO/NOx= 0.5; T = 200°C

Concentration (ppm )

1000

NH3 N2

800

NO2

600

400

NO

200

0 N2O

0

2000

4000

6000 8000 Time (s)

10000

12000

1200 NO/NOx= 0.33; T = 275°C

Concentration (ppm)

1000

NH3

800

N2

NO2

600

400

NO

200 N2O 0

0

2000

4000

6000

8000

10000

Time (s) FIG. 39. NOx+NH3 TRM. Feed: 1,000 ppm NH3, 1,000 ppm NOx, 1% H2O, 2% O2, balance He, T=2001C, 2751C, SV=210,000 h
1. Symbols—outlet concentration of NH3 (triangles), NO2 (full circles), N2 (squares) and N2O (stars); solid lines—kinetic fitting.

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

100

181

275°C

NOx conversion (%)

80 225°C 200°C

60

175°C

40

20

0 0.0

0.2

0.4 0.6 NO / NOx feed ratio

0.8

1.0

FIG. 40. Measured (symbols) and simulated (lines) steady-state SCR NOx conversion at different T for varying inlet NO2/NOx ratios. Feed: 1,000 ppm NH3, 1,000 ppm NOx, 1% H2O, 2% O2, balance He, SV=210,000 h
1.

The analysis of the reactivity data herein reported provided significant insight into the chemistry prevailing over V-based SCR catalysts when feeding NH3–NO/NO2 mixtures, which was further extended and validated on the basis of transient response experiments addressing both the overall reactivity in the fast SCR reaction and also individual reaction steps (Ciardelli et al., 2004b; Nova et al., 2006b). One of the most significant findings was the identification of the key role of nitrates as intermediates in the fast SCR: this was clearly demonstrated by a set of specifically designed dynamic experiments, during which NH4NO3 was first formed upon the catalyst surface, and then reduced in the presence of NO. Notably, ammonium nitrate had been regarded so far only as a possible undesired by-product formed by a side reaction in parallel to the fast SCR (Madia et al., 2002). One of such experiments is illustrated in Fig. 41 (Nova et al., 2007). At 1501C, when 1,000 ppm NH3, 1,000 ppm NO, 500 ppm NO2 (so with excess NO with respect to fast SCR stoichiometric feed) were fed to the reactor, the steady-state outlet concentrations indicated the simultaneous occurrence of the fast SCR (R7+2*R8+R10 in Table V) (25% NO conversion) and of the ammonium nitrate formation (R7+R8+R9 in Table V) (25% NH3 conversion): under these conditions ammonium nitrate was being partially stored onto the catalyst surface, as demonstrated in (Ciardelli et al., 2004b, 2007a; Nova et al., 2006b). Afterwards, at t ¼ 3,850 s a T-ramp at 201C/min was started: increasing consumption of NO and production of N2 were observed, meaning

ANKE GU¨THENKE ET AL.

182

600

1800 1600

500

N2

1200

400

1000 300 800

T

600

NO

400

200

NH3

200

Temperature (°C)

Concentration (ppm)

1400

100

NO2

0

0

N2O 3800

3900

4000

4100 4200 Time (s)

4300

4400

4500

FIG. 41. TPR experiment: 1,000 ppm NH3, 500 ppm NO2, 1,000 ppm NO in He at T=1501C; T-ramp 150–3001C at 201C/min; SV=92,000 h
1. Reprinted from Nova et al. (2007) with kind permission of Springer Science and Business Media.

that the rate of the fast SCR reaction was growing. As the T-ramp started, desorption of NH3 was also observed, as well as a small production of NO2. Both such species were then consumed as the temperature was increased further. The analysis of the transient behavior during the temperature ramp points out that another reaction involving NO consumption must have been occurring in addition to the fast SCR: with a feed mixture of 1,000 ppm NH3, 1,000 ppm NO and 500 ppm NO2, the highest possible conversion of NO according to the fast SCR reaction should have been 50%, with a production of 1,000 ppm of N2 (the dashed lines in Fig. 41 indicate such values). Nevertheless, the measured NO conversion exceeded 50%, reaching a peak of about 80% with a simultaneous production of 1,600 ppm of N2. Such a behavior is explained by invoking the reaction between ammonium nitrate (which had been previously formed and stored onto the catalyst surface, as mentioned above) and NO, according to the stoichiometry resulting from the combination of R9 reverse and R10 (
R9+R10 in Table V). The depletion of NH4NO3 is responsible for the subsequent drop of NO conversion observed after t ¼ 4,150 s, and indeed at the end of the experiment steady-state concentrations of reactants and products were eventually in agreement with the occurrence of the fast SCR reaction only. It appears thus that the fast SCR reaction was proceeding only before t ¼ 4,000 s while, as it became limited by the NO2 feed concentration, the reaction (
R9+R10,

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

183

cf. Table V) occurred afterwards. However, no discontinuity and no change of slope can be observed in the neighborhood of t ¼ 4,000 s, thus indicating that the fast SCR reaction and the reaction between NO and NH4NO3 share the same rate as well as the same apparent activation energy. The same conclusion was also achieved performing a transient experiment at constant temperature (1701C) (Ciardelli et al., 2004b; Nova et al., 2006b): the NO conversion levels measured either when the fast SCR reaction was occurring or when the reaction between NO and surface nitrates was occurring were practically unaltered, indicating that the two reactions were actually progressing at the same rate. Accordingly, it can be ruled out that the reaction between NH4NO3 and NO and the fast SCR reaction proceed in parallel: we have proposed an alternative, sequential scheme, whereby the fast SCR reaction results from the formation of nitrate surface species related to NH4NO3, reactions (R7+R8+R9, Table V), and their subsequent reduction by NO, reaction (R10 in Table V), which is rate determining at low temperature (Ciardelli et al., 2004b). Thus, the overall reaction scheme of the fast SCR is depicted in Fig. 42, and the roles of the reactants in such scheme can be summarized as follows (Ciardelli et al., 2007a; Nova et al., 2006b): (i)

NO2 is responsible for the formation of nitrate and nitrite adspecies, likely via disproportion and heterolytic chemisorption (R7 in Table V). (ii) NO acts as a reductant, converting nitrates to nitrites (R10 in Table V). (iii) NH3 eventually reacts with nitrites to give harmless nitrogen and H2O via decomposition of the unstable ammonium nitrite (R8 in Table V). (iv) In defect of NO, NH3 can also react with nitrates to form ammonium nitrate, which, under specific low-T conditions (To1701C), builds up onto the catalysts (cf. R9, Table V).

FIG. 42. Chemistry of NH3–NO2 (upper dashed box) and NH3–NO/NO2 (solid box) SCR reacting systems over V2O5–WO3/TiO2 SCR catalysts.

184

ANKE GU¨THENKE ET AL.

A similar reaction scheme has been recently proposed on the basis of an IR study of the NO/NO2–NH3 reactivity over a BaNa–Y zeolite SCR catalyst (Yeom et al., 2005). It is worth emphasizing that the reaction scheme above is able to explain not only the stoichiometry of the fast SCR reaction, and specifically the optimal equimolar NO to NO2 feed ratio, but also the selectivity to all of the observed products, namely N2, NH4NO3 and N2O, which derives from thermal decomposition of ammonium nitrate (Ciardelli et al., 2004b, 2007a; Nova et al., 2006b); furthermore it is in agreement with the observed kinetics of the fast SCR reactions, which at low temperature is limited by the rate of the reaction between nitrate and NO. In additional dedicated mechanistic experiments we found that, contrary to the NH4NO3 formation, the rate limiting step (i.e. reaction R10 in Table V) as well as the global fast SCR reaction did not proceed over a V-free WO3/TiO2 catalyst, thus identifying the catalytic role of vanadium redox sites in the fast SCR mechanism (Tronconi et al., 2007). It was also proved that surface nitrate species, formed from NO2, are much more effective than gaseous oxygen in reoxidizing the reduced V-sites, which explains the greatly enhanced deNOx activity with respect to the standard SCR at low temperature. Accordingly, a unifying mechanistic redox scheme could be proposed for both standard and fast SCR reactions, i.e. for the global NH3–NO/NO2 SCR process (Tronconi et al., 2007). Reactions R13, Table VI and R14, Table VI describe the redox cycle in the NO + NH3 standard SCR, where reaction (R14, Table VI) is the rate limiting reoxidation step involving gaseous oxygen. In the case of the NO+NO2+NH3 fast SCR, the reduction of the V-catalyst sites still occurs according to the same global reaction (R13, Table VI), but the rate determining step in the redox process, i.e. the reoxidation of V-sites, is radically changed, being carried out in this case by nitrates according to reaction (R15, Table VI). Nitrates are formed together with nitrites through reaction (R16, Table VI) via disproportion of NO2, while reaction (R17, Table VI) accounts for the decomposition of nitrites to N2 via reaction with NH3. In agreement with experimental observations (Tronconi et al., 2007), notice that no redox catalyst function is involved in steps (R16, Table VI) TABLE VI UNIFYING MECHANISTIC REDOX SCHEME FOR STANDARD Standard SCR NO+NH3*+V5+=O-N2+H2O+V4+
OH V4+
OH+14O2-V5+=O+12H2O Fast SCR NO+NH3*+V5+=O-N2+H2O+V4+
OH + 5+ =O+NO2+H2O V4+
OH+NO
3 +H -V
2NO2+O2
2NO
2 +NO3 2
+ NO
2 +NH3 -N2+H2O+O +H

AND

FAST SCR REACTIONS

R13 R14 R13 R15 R16 R17

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

185

and (R17, Table VI), which are assumed to occur over non-reducible oxidic sites (herein represented schematically as O2
) possibly associated with the W- or Ti-catalyst components. A similar sequence has been recently invoked to explain the formation of ammonium nitrate observed over TiO2 and over zeolites (Yeom et al., 2005), as well as the formation of nitrates from NO2 over Al2O3 (Apostolescu et al., 2004). Notably, the kinetic scheme herein presented is in complete agreement with the findings on the role of nitrates in the chemistry of the fast SCR reaction presented before. 9. Kinetic Analysis For a comprehensive kinetic description of the NH3+NO/NO2 reacting system in a wide range of temperatures and NO2/NOx feed ratios a global kinetic model was developed, based on the whole set of reactions in Table V. The extension of the present model to include the redox features presented in the previous section is currently ongoing. The ammonia, NO and nitrogen mass balance equations were modified with respect to those already presented in previous sections, in order to include all of the reactions observed in the NH3–NO/NO2 reacting system. Moreover, additional mass balances for adsorbed nitrates (yHNO3 ) and for gaseous NO2, N2O and HNO3 were introduced (a) adsorbed phase: NH3

O

@yNH3 ¼ Rads
Rdes
Rox
RNO
RNO2
Rnit
Rdec
RN2 O (59) @t NH4 NO3

O

@yNH4 NO3 ¼ Rdec @t

(60)

(b) gas phase: NH3

N2


g

@cNH3 @cNH3 ¼
u
ð1

g ÞðRads
Rdes Þ @t @z

  @cN2 @cN2 1 7 ¼
u þ ð1

g Þ Rox þ RNO þ Rnit þ RNO2 2 8 @t @z

NO

NO2


g


g

@cNO @cNO ¼
u
ð1

g ÞðRNO þ RFST Þ @t @z

  @cNO2 @cNO2 3 g ¼
u
ð1

Þ 2Ramm
RFST þ RNO2
4 @t @z g

(61)

(62)

(63)

(64)

ANKE GU¨THENKE ET AL.

186
g

N2 O

HNO3


g

@cN2 O @cN2 O ¼
u þ ð1

g ÞRN2 O @t @z

(65)

@cHNO3 @cHNO3 ¼
u þ ð1

g ÞðRamm
RFST
RN2 O
Rdec Þ (66) @t @z

A pseudo-steady-state assumption was introduced for HONO, in view of its ready decomposition to N2 HONO

0 ¼ ðRamm
Rnit þ RFST Þ

(67)

Consistently with what reported in the previous sections the NH3 adsorption, desorption and oxidation rates were fitted by the following expressions, respectively: Rads ¼ kads cNH3 ð1
yNH3
yNH4 NO3 Þ

(68)

  E des Rdes ¼ k0;des exp
ð1
ayNH3 Þ yNH3 RT

(48)



Rox

 E ox  yO2 b ¼ k0;ox exp
yNH3 RT 0:02

(52)

For the standard SCR reaction, the following rate expression was considered,    y b E NO RNO ¼ k0;NO exp
gNO cNO yNH3 O2 RT 0:02 gNO ¼

1 1 þ K LH ðyNH3 =ð1
yNH3
yNH4 NO3 ÞÞ

(69)

which represents a simplified form of the MR dual-site redox rate expression, Eq. (57). The following expressions were adopted for the rates of reactions (R7–12, Table V)    E amm cHNO3 cHNO2 Ramm ¼ k0;amm exp
(70) yH2 O c2NO2
RT K eq amm Rnit

  E nit ¼ k0;nit exp
yNH3 cHNO2 RT

  E dec Rdec ¼ kadsnit yNH3 cHNO3
k0;dec exp
yNH4 NO3 RT

(71)

(72)

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

187

    E FST cNO2 cHNO2 RFST ¼ k0;FST exp
gFST cNO cHNO3
RT K eq FST

gFST ¼

1 1 þ K LH2 ðyNH3 =ð1
yNH3
yNH4 NO3 ÞÞ

(73)

  EN O RN2 O ¼ k0;N2 O exp
2 yNH3 cHNO3 RT

(74)

  E NO2S RNO2 ¼ k0;NO2 exp
yNH3 cNO2 RT

(75)

For Eqs. (48), (52) and (68)–(69), the same rate parameter values were herein adopted as used in the previous work addressing the standard SCR reaction only (Chatterjee et al., 2005; Ciardelli et al., 2004a). A set of 32 experimental runs was used for the estimation of the additional rate parameters for the reactions involving NO2 according to the procedures described in (Chatterjee et al., 2005, 2006; Ciardelli et al., 2004a). With respect to the large number of fitting parameters required to account for the comprehensive reaction scheme, and in order to minimize correlations, a sequential fitting strategy was followed (Chatterjee et al., 2006). First, the rate parameters associated with formation, adsorption–desorption of ammonium nitrate, in Eqs. (70)–(72), were estimated by regressions of results from runs with feeds containing NO2+NH3 only. In a subsequent stage, the estimates of the rate parameters in Eq. (73) were secured by regression on runs involving NO+NO2+NH3 at temperatures below 2501C, where the NO2 SCR reaction is not active. Finally, the rate parameters for Eqs. (74) and (75), i.e. for the NO2 SCR and the formation of N2O, were estimated from the high-temperature TRM runs. As an example, the quality of the fit is illustrated in Fig. 39, where the calculated (lines) and measured (symbols) temporal evolutions of NH3, NO, NO2, N2 and N2O are reported. A good correlation is indeed obtained between experimental traces and model predictions, thus supporting the adequacy of the kinetic model. Most important for the practical applications herein discussed, Fig. 40 confirm that the proposed rate equations are able to represent effectively the observed influence of the NO2 feed content on the whole T-range. A first validation was obtained simulating the results collected at different ammonia to NOx feed ratios: as an example, in Fig. 43 some of the experimental results (symbols) are compared with the model predictions (line).

ANKE GU¨THENKE ET AL.

188 100

NOx Conversion (%)

80

60

40

20 C°NH3 = 550 ppm 0 0.4

0.6

0.8

1.0 1.2 1.4 NH3/NOx feed ratio

1.6

1.8

2.0

FIG. 43. Steady-state NO conversions vs. NH3/NOx feed ratio. SV=210,000 h
1; T=200 1C; CO2 =2%; CH2 0 =1%; NO/NOx=0.5. Symbols: experimental, solid line: model predictions.

B. MONOLITH REACTOR SCALE Validation at intermediate scale was first performed by comparing the results of kinetic runs over small honeycomb catalyst samples (volumes in the range 5–20 cm3) with corresponding model simulations. For these purposes the intrinsic kinetics derived above were incorporated into a fully transient twophase adiabatic 2D (1D+1D) mathematical model of SCR monolith reactors (Chatterjee et al., 2005, 2006; Tronconi et al., 1998) specifically adapted for mobile SCR applications, similar to the one described in Section III. Such a model accounts also for intraporous diffusion of reacting and product species within the porous catalytic walls of the honeycomb matrix. Effective intraporous diffusivities were estimated according to Wakao and Smith (1962) on the basis of the experimentally determined morphological characteristics of the catalyst. Additional assumptions include negligible pressure drop and axial dispersion. 1. Experimental Kinetic runs over small monolithic honeycomb catalyst samples were performed in two different rigs at Politecnico di Milano and in the Daimler

189

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

AG laboratories in Stuttgart. The experiments were carried out using monolith samples (order of magnitude of honeycomb volumes: 10–20 cm3) of 200 or 300 cpsi cell density made of the same catalyst material used for the microreactor runs. Runs were carried out under isothermal conditions within the T-range 150–4501C. The space velocity was set between 36,000 and 72,000 h
1 by changing the flow rate and the monolith length (22 or 44 mm). The synthetic feed mixture was composed by oxygen (2–10% v/v), water (1–10% v/v), NO (1,000 ppm), NH3 (800–1,200 ppm) and balance nitrogen. Specific gas analyzers were used to follow the transient behavior of the reactants: in Stuttgart NH3 was detected by microwave process analysis (Mipan), while NOx by chemiluminescence techniques using a modified low temperature NO2 converter (CLD Ecophysics). In Milano a second UV analyzer (ABB Limas 11) was adopted for NO, NO2 and NH3, similar to the one installed in the microreactor rig. Figure 44 illustrates the results of steady-state experiments at different temperatures over a small honeycomb sample (volume E 20 cm3, 300 cpsi) with a feed flow rate of 9,800 cm3/min, with composition 1,000 ppm NO, 1,000 ppm

1100 1000

NO, monolith NH3, monolith NO, powder NH3, powder

900

Concentration (ppm)

800 700 600 500 400 Monolith

300 200 Powder

100 0 150

200

250

300

350

400

450

Temperature ( °C) FIG. 44. Steady-state NO and NH3 concentrations vs. temperature in runs over a small monolith catalyst with 300 cpsi and over the same catalyst crushed to powder. Feed: 1,000 ppm NH3, 1,000 ppm NO, 1% H2O, 2% O2 in N2; SV=45,000 cm3/g/h (STP).

ANKE GU¨THENKE ET AL.

190

NH3, 2% O2, 1% H2O and balance nitrogen. The solid symbols indicate measured outlet NO and NH3 concentrations. For comparison purposes, the figure shows also the outlet concentrations measured during identical experiments over the catalyst powder obtained by crushing and sieving the same monolith (open symbols). In this case the flow rate was adjusted to achieve the same space velocity (45,000 cm3/g/h
1) with reference to the active catalyst mass. It is apparent that the data over the monolith and over the powder catalyst are overlapped in the low-T region. At T W 225 1C, however, deviations become apparent, corresponding to lower conversions over the monolith catalyst. Such deviations are attributed to the onset of diffusion limitations in the honeycomb catalyst, which therefore play a significant role in controlling the deNOx performance over a wide range of temperatures. As shown in Fig. 45, two series of experiments were performed with different O2 feed contents: higher NO conversions were observed when 10% O2 was fed (squares), thus confirming at the monolith scale the promoting effect of oxygen on the standard SCR reaction (R6, Table V) noted in microreactor experiments over the powdered catalysts and discussed in previous sections. Solid lines in Fig. 45 represent the results of model simulation: a very good agreement between experimental and calculated NOx conversions is observed. The promoting effect of oxygen was also very well predicted. 1200

NO Concentration (ppm)

1000

800

600 2% O2 400 10% O2 200

0 150

200

250

300

350

400

450

Temperature ( °C) FIG. 45. Steady-state NO concentrations vs. temperature in validation runs over small monolith catalyst sample with 300 cpsi. Feed: 1,000 ppm NH3, 1,000 ppm NO, 1% H2O in N2; black: 2% O2, red: 10% O2; SV=25,000 h
1; symbols: experimental, solid line: model predictions.

191

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

1000

Concentration (ppm)

800

600 NH3 NO

400

200

0 150

200

250

300 Temperature ( °C)

350

400

FIG. 46. TPR validation run over small monolith catalyst with 200 cpsi. Feed: 1,020 ppm NH3, 960 ppm NO, 10% H2O, 10% O2 in N2, SV=36,000 h
1; symbols: experimental, solid line: model predictions.

Figure 46 presents the comparison between experimental results (symbols), obtained over a different monolith sample (volumeE10 cm3) upon performing a TPR run, and the corresponding model predictions (solid lines): 1,020 ppm of NH3 and 960 ppm of NO were fed in a stream of 10% H2O, 10% O2, balance nitrogen, with an SV ¼ 36,000 h
1. A temperature ramp (from 1001C to 4501C) was performed. Equimolar conversion of NO and NH3 was observed, in agreement with the stoichiometry of the standard SCR reaction (R6, Table V). The observed reactivity was again well predicted by the model simulation in the whole T-range. A third transient experiment is shown in Fig. 47, where again experimental results (symbols) are compared with model simulation (solid lines). At time ¼ 0 s, 1,000 ppm of NH3 and 1,000 ppm of NO were simultaneously fed to the honeycomb catalyst (5 cm3) in a stream of 10% H2O, 10% O2, balance nitrogen, with an SV ¼ 72,000 h
1. Temperature was set at 250 1C. After the initial transient, NO and ammonia signals pointed to a steady-state conversion of about 40%. Again the standard SCR stoichiometry was respected. At time of about 300 s, the NH3 feed was shutoff. Consequently its outlet signal slowly decreased, while NO, after a short transient, reached its feed value of 1,000 ppm. Again, a good correlation between experiment and predictive simulation on the integral monolith reactor level was obtained: the model was able to correctly predict both the steady-state levels of the reactants and their temporal evolution.

ANKE GU¨THENKE ET AL.

192

1000

800 Concentration (ppm)

NO

600

400

NH3

200

0 0

100

200

300 400 Time (s)

500

600

FIG. 47. TRM validation run over a small monolith catalyst with 200 cpsi. Feed: 1,000 ppm NH3, 1,000 ppm NO, 10% H2O, 10% O2 in N2, T=2501C, SV=72,000 h
1; symbols: experimental, solid line: model fitting.

Altogether, the data reported in this section indicate a very good predictive quality of the model simulations: this implies in the first place that the SCR kinetics estimated over powdered catalyst were successfully validated at this bigger scale. However, the excellent agreement between monolith data and model predictions based on intrinsic kinetics also confirms the accurate model description of physical phenomena, specifically external and intraporous mass transfer, which were not significant in the microreactor runs over the powdered catalyst, but played an important role in the monolith runs, as pointed out by the direct comparison in Fig. 44. C. ENGINE TEST BENCH SCALE 1. Heavy-duty Diesel Engine Runs, No DOC Different sets of experimental data were used for model validation at real gas scale. Urea was used to supply NH3: an adequate residence time in the exhaust

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

193

gas stream was allowed to secure its complete conversion to NH3 before reaching the SCR catalyst. A first data set was measured on a heavy-duty diesel engine test bench. Extruded monoliths with 300 cpsi, a wall thickness of 0.32 mm and a diameter of 144 mm were used in these runs. By varying the number of monolith catalyst samples it was possible to test different catalyst volumes (25, 32 and 43 L). The complete SCR catalyst setup resulted in SVs ranging from 21,000 to 40,000 h
1. In these experiments no oxidation catalyst was positioned upstream of the SCR catalysts; accordingly the NO2 concentration was always negligible (r5% v/v of the total NOX feed content). A set of 100 different engineoperating points was measured and a sampling time of 2 Hz before and after the catalyst system was adopted to measure both temperatures and emissions using a chemiluminescence spectrometer for NO and NO2 and a diode laser spectrometer for NH3. A typical test bench experiment started when the engine ran at constant load and speed. After a given time the urea dosing system introduced the reducing agent onto the catalyst, which resulted in an increase of the NOx conversion after the SCR-catalyst. After that the NH3 slip behind the catalyst could be measured. Typically, the experiment ran until total NOx conversion or steady state was established. Validation maps, as illustrated, e.g., in Fig. 48, were generated using the steady state measured engine operating points. In this case the deviation between simulated NOx conversion and experimental data was typically below 4% (Chatterjee et al., 2005). Standard European test cycles (ESC and ETC), during which the catalyst outlet temperature varied between 2001C and 3501C, were also performed at this scale. Examples of related simulation results are plotted together with data taken from the transient heavy-duty engine test bench runs in Fig. 48, where NOx concentrations have been normalized by the maximum inlet value during the test cycles. The comparison between measurement and simulation for the NOx concentrations downstream of the catalyst reveals an excellent correlation, as pointed out by Fig. 49A (ESC) and B (first 500s of ETC). The overall deNOx efficiency within the ESC or ETC test cycle is predicted with an error of 3–4% (Chatterjee et al., 2005; Tronconi et al., 2005). Notably, the overall satisfactory agreement of the predictive simulations with data from test runs with real engine exhausts also confirms the negligible influence of such species as CO, CO2 and hydrocarbons, whose effects were not included in the kinetic investigation. 2. Heavy-duty Diesel Engine Runs, with DOC In a second set of experiments, DOC was placed upstream of the SCR catalyst in order to generate NO2 by partial oxidation of NO in the engine

ANKE GU¨THENKE ET AL.

194

ηerror, NO , 25L, 300cpsi x 18 1000

16 14 12 10

600

[%]

mexhaust[kg/h]

800

8 6

400

4 2 250

300

350

400

Texhaust[°C]

ηerror, NO , 32L, 300cpsi x 1800 12 1600

mexhaust[kg/h]

8

1200 1000

[%]

10

1400

6

800 4 600 2 400 200

300

400

500

Texhaust[°C]

FIG. 48. Mean error for the simulation of the NOx conversion at steady-state and constant urea dosing conditions as a function of exhausts mass flow and catalyst inlet temperature. Reprinted with permission from SAE Paper # 2005-01-0965 r 2005 SAE International (see Plate 6 in Color Plate Section at the end of this book).

195

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

NOx [-]

NOx [-]

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

0

(A)

500

1000

time [s]

1500

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

(B)

100

200

300

400

500

time [s]

FIG. 49. Normalized NOx concentration at SCR catalyst inlet and outlet of an ESC (A) and an ETC (B) test cycle. Dotted black lines—inlet values, solid black lines—outlet measurement, gray lines—outlet simulation. Reprinted with permission from SAE Paper # 2006-01-0468 r 2006 SAE International.

exhausts. Twelve HD engine operating points were measured, covering exhaust temperatures at SCR converter inlet from 2001C to 4701C and NO2/NOx feed contents from 3% to 40%. As shown, e.g., in Fig. 50, a good correlation between simulation and experiment was apparent in all cases, which confirms the good prediction quality of the SCR converter model also in the presence of NO2. It should be noted that the NOx conversion curves of these experiments (see e.g. Fig. 50) reveal a typical behavior. After the start of the urea dosage a steep increase in the NOx conversion rate appears, which is mainly determined by the fast SCR reaction converting all of the available NO2. In the following time period, the increase of the NOx conversion is much slower, with the limiting factor being the buildup of stored NH3. 3. Passenger Car Diesel Engine Runs, with DOC Finally, a passenger car diesel engine was used in a third set of test bench experiments to test the model under more dynamic conditions. Because of restrictions on the residence time between DOC and SCR catalyst and due to the higher dynamics of the experiments, complete conversion of urea could not be assured in this case, therefore gaseous NH3 instead of urea was injected after the oxidation catalyst. The SCR catalyst used for these experiments was an extruded monolith with 300 cpsi, a wall thickness of 0.32 mm in a race track shape. The studied SVs ranged from 70,000 to 211,000 h
1, the measured 14 engine operating points covering exhaust temperatures from 2301C to 4601C and NO2/NOx ratios from 10% to 44%. In addition to the measurement setup used for the heavy-duty test bench, a mass spectrometer (CIMS) was used for NO, NO2 and NH3 (Chatterjee et al., 2006).

196

ANKE GU¨THENKE ET AL.

FIG. 50. Heavy-duty engine test bench measurements. Dotted lines—measurement of NOx conversion (black) and NH3 slip (gray); solid lines—simulation of NOx conversion (black) and NH3 slip (gray). Reprinted with permission from SAE Paper # 2006-01-0468 r 2006 SAE International.

Because of the short NH3 pulses, no steady state within the SCR catalyst was reached during the experimental runs. Yet, the example presented in Fig. 51 reveals once more a good agreement between simulation and experiment, even under conditions with very short NH3 pulses. The deviations

CURRENT STATUS OF MODELING LEAN EXHAUST GAS

197

FIG. 51. Passenger car diesel engine test bench measurements. Solid lines—measured NOx conversion (gray), simulated NOx conversion (black); dashed line—measured NH3 dosing ratio a. Reprinted with permission from SAE Paper # 2006-01-0468 r 2006 SAE International.

between measured data and simulations can mainly be attributed to the fact that the measured NH3 signal at the SCR catalyst inlet was used to calculate the NH3/NOx feed value for the simulation. Because of the limited measuring accuracy under such highly transient conditions, some deviations of

198

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the measured to the real NH3/NOx feed value could not be avoided. The combined exhaust treatment system DOC+SCR is further discussed in the next section.

VIII. Combined Aftertreatment Systems The different catalyst models within the simulation environment ExACT are used for the simulation of combined aftertreatment systems, when exhaust conditions for a catalyst are influenced by its upstream component and changes in one catalyst affect all components further down the line. An application example is given in the following section and in Chatterjee et al. (2006). It investigates a combined system of DOC and SCR catalyst. Further examples for such combined systems to be investigated are DOC and NSRC or combinations of different catalyst technologies with DPF. As discussed, the low temperature deNOx efficiency of SCR converters for automotive exhaust aftertreatment can be significantly enhanced by converting part of the nitric oxide to NO2, e.g. by means of a DOC located upstream of the SCR. In fact, the so-called ‘‘fast’’ SCR reaction, involving the reaction between NH3 and equimolar amounts of NO and NO2, can be faster by one order of magnitude than the ‘‘standard’’ SCR in the low-T region (Ciardelli et al., 2007a; Koebel et al., 2001). Effective exploitation of fast SCR reactivity is certainly important in order to meet the stringent forthcoming automotive NOx emission limits. To study the influence of NO2 on SCR efficiency, ESC and ETC test cycles with 0% and 50% NO2/NOx ratio in front of SCR catalyst were simulated. However, during transient test cycle operating conditions, a constant supply of optimum 50% NO2/NOx ratio will be difficult to achieve. NO to NO2 conversion over a DOC is dependent on exhaust temperature, space velocity and exhaust composition. Because of transient operating conditions, it becomes furthermore a function of time. Exploiting the fast SCR reaction is thus limited by the realistically achievable NO2/NOx ratio in front of the SCR catalyst. To investigate this, ESC and ETC test cycles were also simulated for a combined system of DOC and SCR, where the NO2/NOx ratio in front of the SCR is defined by the NO2 conversion over the DOC (Fig. 52). In the simulations, extruded monoliths of commercial V2O5–WO3/TiO2-type SCR catalyst material with medium–high V content were investigated, with 300 cpsi and a total SCR volume of 18 L. A NH3/NOx,in dosing ratio of a ¼ 1 was used for all simulations, this makes the different simulation cases easier to compare, otherwise the results would be biased by the respective dosing strategy. For the DOC, a Pt-type catalyst was chosen. DOC geometry and volume was adapted for optimum DOC and SCR system performance.

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FIG. 52. Combined system model for DOC and SCR.

TABLE VII TOTAL SIMULATED NOX CONVERSION EFFICIENCIES FOR SCR WITH 0% NO2/NOX INLET, 50% NO2/NOX INLET AND FOR DOC AND SCR COMBINED SYSTEM NOx conversion 0% NO2/NOx 50% NO2/NOx Combined DOC and SCR

ESC (%)

ETC (%)

90.7 94.0 93.6

87.5 94.9 91.8

Source: Chatterjee et al. (2006). Reprinted with permission from SAE Paper x 2006-01-0468 r 2006 SAE International.

As already discussed, the optimum catalyst efficiency is obtained for a NO2/NOx ratio of 50%. The ESC and ETC simulation outputs for these input conditions can be regarded as a benchmark result, disregarding the NH3 slip. The comparison with the baseline simulation without NO2 in Fig. 54 reveals that NO2 improves the SCR efficiency significantly. The total conversion efficiency is increased from 90.7% to 94.0% for the ESC and from 87.5% to 94.9% for the ETC, cf. Table VII. The improvements can mainly be attributed to the higher conversion rates in the colder parts of the test cycles (e.g., 850s–1,000s in the ESC). ESC and ETC diesel oxidation catalyst simulation results in Fig. 53 show that the NO2/NOx ratio behind the DOC varies from 30% to 60% over the ESC and from 10% to 80% over the ETC for the configuration studied, with a mean value of approximately 40%. Using these simulated NO2/NOx ratios behind the DOC as input for the SCR test cycle simulations, lower conversion efficiencies are obtained compared to the 50% NO2 case. However, Fig. 54 and the values in Table VII indicate that there is still a significant increase in the total NOx conversion compared to the simulation without NO2 in the inlet feed. This also confirms that the chosen DOC geometry and volume is quite well adapted to the specific application.

500

100 90 80 70 60 50 40 30 20 10 0

400 350 300 250 200 150 0

500

1000 1500 time [s]

450 400 350 300 250 200 150 100 50 0

NO2 /NO [%]

450 temperature [°C]

NO2 /NOx [%]

100 90 80 70 60 50 40 30 20 10 0

100 2000

0

500

1000 time [s]

temperature [°C]

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200

1500

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

NOx [-]

NOx [-]

FIG. 53. Simulation of transient NO to NO2 conversion and temperature at DOC outlet during ESC (left) and ETC (right) test cycle, simulated NO2/NOx ratio: solid black line, temperature: dashed gray line (Chatterjee et al., 2006). Reprinted with permission from SAE Paper # 2006-010468 r 2006 SAE International.

0

500

1000 time [s]

1500

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

100

200 time [s]

300

400

FIG. 54. Normalized NOx concentrations before and after SCR during ESC (left) and ETC (right) test cycle simulation with a=1 (concentrations normalized by the maximum inlet value). Measured inlet concentration: dotted black line, simulated outlet concentration for NO2/NOx inlet=0: dashed gray line, simulated outlet concentration for NO2/NOx inlet simulated with pre-DOC: solid black line (Chatterjee et al., 2006). Reprinted with permission from SAE Paper # 2006-01-0468 r 2006 SAE International.

Overall, the simulation results show that NO2 in the inlet feed of the SCR catalyst offers the potential for improving the NOx conversion efficiency. Up to 7% improvement were obtained for best case conditions (a ¼ 1, NO2/NOx ¼ 50%) in the simulation. How much of this improvement can be achieved under application conditions is determined by the design of the DOC (e.g., volume, noble metal loading) and the optimum urea dosage strategy which is needed in order to avoid NH3 slip.

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IX. Summary and Conclusions Exhaust emission legislation has become more and more stringent over the last years, demanding for lower engine raw emissions and more efficient exhaust converters. Simultaneous low emission limits for different species, e.g. PM and NOx, lead to the development of combined aftertreatment systems, consisting of different catalyst technologies and particulate filter. Simulation can make a considerable contribution to shorten the time and lower the cost of the system development. In this publication, the current status of exhaust aftertreatment simulation tools used in automotive industry is reviewed. The developed models for DOC with HC adsorption, NSRC and catalyst for SCR of NOx by NH3 (urea) were included into the common simulation environment ExACT, which enables simulation of complete combined exhaust aftertreatment systems. To model mass and energy transport in monolith systems, several approaches are discussed, leading from a representative channel spatially 1D approach to 2D (1D+1D) modeling explicitly including washcoat diffusion. Correlations are given to describe heat and mass transfer between bulk gas phase and catalytic washcoat. For the detailed study of reaction–transport interactions in the porous catalytic layer, the spatially 3D model of the computerreconstructed washcoat section can be employed. The process of the derivation of global reaction kinetics is explained, based on a stage-wise approach. Microreactor experiments on washcoat powder enable the study of the reaction mechanism without diffusion limitations. Reactions are studied individually, first the simplest reacting systems, then adding components toward the full gas mixture. Parameter estimation can be greatly aided by numerical optimization of the resulting least-squares objective function. The resulting kinetic equations can be directly validated on lab scale, performing experiments on small monolith samples with synthetic gas mixtures. The final validation is undertaken with driving cycle dynamometer or engine test bench data, showing the applicability of the model under real exhaust conditions. The developed model for the DOC describes the most important reactions taking place under lean operating conditions: HC adsorption, CO, H2, HC and NO oxidation reactions as well as lean NOx reduction by HC. The kinetic parameters were evaluated from monolith sample lab experiments and validated on passenger car driving cycle measurement data, showing that the model after calibration is well able to describe temperature and species conversion in DOC over wide range of noble metal loadings and catalyst ageing levels. The NSRC is operated in periodic lean/rich regime: in the course of a long lean phase, typically lasting for several minutes, NOx are stored on the

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catalyst surface. Then, the accumulated NOx are released and reduced within a short rich phase, lasting for several seconds. An effective model for this catalyst has been developed, describing NOx storage and release on BaCO3 and also including CO, H2, HC and NO oxidation reactions, water gas shift and steam reforming, oxygen storage and release and NO reduction reactions. The model was designed to efficiently describe the most important phenomena taking place on the NSRC, while at the same time keeping the complexity of the reaction scheme and the number of kinetic parameters on a low level. It was adapted to monolith sample lab experiments and validated on passenger car driving cycle and commercial vehicle engine test bench measurement data, showing its applicability for a wide range of catalyst sizes, operating conditions and catalyst ageing levels. The model was applied to study the influence of catalyst regeneration status, catalyst geometry, exhaust temperature and NOx raw emissions on NSRC conversion over a complete driving cycle. The SCR of NOx with urea has over the last two decades proven to be the most effective commercial deNOx process for stack gases from power plants and other stationary sources. It is increasingly studied and applied in diesel passenger cars and commercial vehicles, where mostly colder, non-stationary conditions and more complex gas mixtures are encountered. A detailed kinetic model of the complete NO/NO2–NH3 reacting system was derived on microreactor scale and validated on monolith samples with synthetic gas mixtures, as well as on commercial vehicle engine test bench and on diesel passenger car dynamometer measurement data. The model is well applicable over this wide range in catalyst sizes and operating conditions. Finally, an application example for a combined aftertreatment system simulation has been given and discussed, investigating a combination of a DOC and a SCR catalyst. It shows how NOx conversions in the urea–SCR converter over the ESC and ETC driving cycles can be greatly improved by NO2 formation in the DOC placed upstream. The system model is used to evaluate DOC size under steady state, but also under transient cycle operation. Further examples for such combined systems which can be investigated are DOC and NSRC or combinations of different catalyst technologies with DPF.

LIST OF SYMBOLS A. Latin Letters a k0 c

density of external surface area in monolith, m2/m3 pre-exponential factor of reaction, dimension depends on the reaction concentration (bulk gas), mol/m3

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cs cp c ps d D E G J k kc kh K Ka,KLH Keq L M p r rp Rj R S SV t T Ts Te u V V˙ w W X,Y,Z X x y ys z

203

concentration in washcoat pores, mol/m3 specific heat capacity of gas, J/kg/K effective specific heat capacity of solid phase, J/kg/K monolith channel diameter, m diffusivity, m2/s activation energy of reaction, J/mol kinetic inhibition term number of reactions kinetic constant of reaction, dimension depends on the reaction mass transfer coefficient, m/s heat transfer coefficient, J/m2/K/s number of gas components kinetic inhibition constants thermodynamic equilibrium constant length of monolith, m number of surface-deposited components pressure, Pa transverse spatial coordinate in catalytic washcoat layer, m pore radius, m reaction rate, mol/m3/s (related to washcoat volume) universal gas constant, 8.31434 J/mol/K frontal area of monolith, m2 gas hourly space velocity, 1/s (defined at standard T ¼ 273.15 K and p ¼ 101,325 Pa) time, s temperature of gas, K temperature of solid phase, K temperature of surroundings, K linear gas velocity, m/s volume, m3 volumetric flow rate, m3/s weight of component in sum of squares, 1 effective heat loss coefficient, J/m3/K/s orthogonal spatial coordinates in 3D washcoat section, m component conversion, 1 vector of kinetic parameters to be optimized molar fraction (bulk gas), 1 molar fraction in washcoat pores, mol/m3 spatial coordinate along monolith (axial), m

B. Greek Letters a a

NH3 dosing ratio in NH3–SCR, 1 parameter for surface coverage dependence in NH3–SCR kinetics, 1

204 b g DHr d eg es Z y m n l r rs t js F c Ccap o O

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O2 reaction order in NH3–SCR kinetics, 1 inhibition term in NH3–SCR kinetics, 1 standard reaction enthalpy, J/mol thickness of catalytic washcoat layer, m open frontal area fraction (monolith), void fraction (catalyst bed), 1 porosity of catalytic washcoat layer, 1 effectiveness factor, 1 surface coverage of adsorbed component, 1 dynamic viscosity, Pa.s stoichiometric coefficient, mol//mol heat conductivity, J/m/K/s gas density, kg/m3 apparent density of solid phase (incl. pores), kg/m3 tortuosity, 1 volume fraction of catalytic washcoat in entire solid phase, 1 Thiele modulus, 1 relative surface concentration of stored component, 1 storage capacity, mol/m3 (related to catalytic washcoat volume) volume diffusion constant, m3/mol adsorption capacity, mol/m3 (related to catalytic washcoat volume)

C. Subscripts and Superscripts a ads adsnit amm calc dec des eq exp FST g in j k LH m meas M nit

denotes inhibition constant in DOC and NSRC kinetics denotes NH3 adsorption in NH3–SCR denotes NH4NO3* adsorption in NH3–SCR denotes NO2 disproportion in NH3–SCR calculation denotes NH4NO3* desorption in NH3–SCR denotes NH3 desorption in NH3–SCR equilibrium experiment denotes HNO3 reaction in NH3–SCR gas inlet index of reaction index of gas component denotes inhibition constant in NH3–SCR kinetics index of surface-deposited component measurement macro denotes HONO reduction in NH3–SCR

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NO NO2 N 2O m out ox ref s sim vol w wt

205

denotes NO–deNOx reaction in NH3–SCR denotes direct NO2–deNOx reaction in NH3–SCR denotes N2O formation in NH3–SCR micro outlet denotes NH3 oxidation in NH3–SCR reference solid phase (washcoat and monolith substrate) simulation volume washcoat weight

ABBREVIATIONS 1D cpsi (C)DPF DOC deNOx EPA ESC ETC ExACT FTP g/bhp-hr HC LEV LNT MR NEDC NM NMHC NMOG NOx NSRC PM SC03 SCR SOF

spatially one-dimensional channels per square inch (cross-sectional monolith channel density) (Coated) diesel particulate filter diesel oxidation catalyst abatement of nitrogen oxides Environmental Protection Agency (USA) European stationary driving cycle (for heavy-duty vehicles) European transient driving cycle (for heavy-duty vehicles) Exhaust Aftertreatment Components Toolbox Federal test procedure, US driving cycle grams per brake horsepower-hour hydrocarbon(s) low emission vehicle lean NOx trap (equivalent to NSRC) modified redox mechanism in SCR new European driving cycle noble metal(s) non-methane hydrocarbons non-methane organic gases nitrogen oxides, NO and NO2 only NOx storage and reduction catalyst particulate matter US driving cycle with air-conditioning device switched on selective catalytic reduction of NOx soluble organic fraction in TPM

206 TPM TPD TPR TRM TWC US06

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total particulate matter temperature-programmed desorption (temperature ramp) temperature-programmed reaction (temperature ramp) transient response method three-way catalyst US highway driving cycle

ACKNOWLEDGMENTS Milosˇ Marek and Petr Kocˇı´ thank to Teuvo Maunula and Matti Ha¨rko¨nen from Ecocat Oy for the co-operation and discussions that supported the development of the NSRC and DOC kinetic models.

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PLATE 2. Typical CO concentration and reaction rate profiles in the porous Pt/g-Al2O3 catalyst reconstructed by particle-packing method. Mean hydraulic diameter of macropores dM h =300 nm, macroporosity eM=18.1%. Free space corresponds to macropores, solid gray corresponds to mesoporous g-Al2O3 with dispersed Pt. Length of the section edge 10 mm. Boundary Z1:yCO=1%, yO2 =0.5%. (a) T=513 K, (b) T=533 K (Kocˇı´ et al., 2007a) (for Black and White version, see page 122).

PLATE 3. Scheme of adsorption, desorption and reaction processes on the surface of the NSRC during lean and rich conditions (Kocˇı´ , 2005) (for Black and White version, see page 143).

Exhaust Temperature behind Catalyst [°C]

500 T in front of catalyst meas T behind catalyst meas T behind catalyst calc

450 400 350 300 250 200 150 100 0

200

400

600

800

1000

1200

1400

1600

1800

time [s] PLATE 4. Validation of the NSRC model for passenger car application—comparison of measured (meas) and calculated (calc) outlet temperatures during the FTP-75 driving cycle; fresh catalyst (Gu¨thenke et al., 2007a) (for Black and White version, see page 158).

1 NOx in front of cat. integrated meas. NOx behind cat. integrated meas. NOx behind cat. integrated calc. NOx storage fraction covered

0,8 0,7 0,6 0,5

0,9 0,8 0,7 0,6 0,5

0,4

0,4

0,3

0,3

0,2

0,2

0,1

0,1

0

NOx storage fraction covered [-]

Integrated NOx emissions / integrated NOx raw emissions over complete test cycle [-]

1 0,9

0 0

500

1000 time [s]

1500

PLATE 5. Validation of the NSRC model for passenger car application—comparison of measured (meas) and calculated (calc) cumulative NOx emissions for the FTP-75 driving cycle; fresh catalyst (Gu¨thenke et al., 2007a). The covered fraction of the NOx storage capacity (spatially averaged cNOx) is calculated by the model. Integral NOx conversions X evaluated from experiment and simulation data: X exp=88.9%, X sim=88.3% (for Black and White version, see page 159).

ηerror, NO , 25L, 300cpsi x 18 1000

16 14 12 10

600

[%]

mexhaust[kg/h]

800

8 6

400

4 2 250

300

350

400

Texhaust[°C]

ηerror, NO , 32L, 300cpsi x 1800 12 1600

mexhaust[kg/h]

8

1200 1000

[%]

10

1400

6

800 4 600 2 400 200

300

400

500

Texhaust[°C]

PLATE 6. Mean error for the simulation of the NOx conversion at steady-state and constant urea dosing conditions as a function of exhausts mass flow and catalyst inlet temperature. Reprinted with permission from SAE Paper # 2005-01-0965 r 2005 SAE International (for Black and White version, see page 194).

ADVANCES IN THE SCIENCE AND TECHNOLOGY OF DIESEL PARTICULATE FILTER SIMULATION Athanasios G. Konstandopoulos1,2,, Margaritis Kostoglou1,3, Nickolas Vlachos1 and Evdoxia Kladopoulou1 1

Aerosol & Particle Technology Laboratory, CERTH/CPERI, P.O. Box 361, Thermi Thessaloniki 57001, Greece

2

Department of Chemical Engineering, Aristotle University, P.O. Box 1517, 54006, Thessaloniki, Greece

3

Division of Chemical Technology, Department of Chemistry, Aristotle University, Univ. Box 116, 541 24 Thessaloniki, Greece I. Introduction II. The Many Scales of DPF Systems III. Filter Wall Scale A. Classic Flow in Porous Media Descriptors B. Microflow Simulation Technology C. Soot Deposit Microstructure D. Soot Deposit Compaction E. Deep-bed Filtration F. Microflow Deposition in Filter Wall G. Effect of Microstructure on Oxidation Kinetics H. The Role of NO2 Turnover/Recycling IV. Filter Channel Scale A. Inertial Losses at Channel Inlet/Outlet B. Asymmetric Channel Geometries C. Ash and Soot Entrainment Phenomena D. Channel to Porous Wall Heat Transfer V. Entire Filter Scale A. The Effective Conductivity of DPFs B. Multichannel Phenomena VI. Conclusion Nomenclature Greek Letters Abbreviations Acknowledgments References Appendix. Microstructural Model of Soot Oxidation: The Effect of Catalyst

214 216 216 217 219 223 226 228 231 234 238 242 242 245 246 250 254 254 257 261 265 266 267 267 268 270

Corresponding author. Tel.: + 30 2310 498192; Fax: + 30 2310 498190. E-mail: [email protected]

213 Advances in Chemical Engineering, vol. 33 ISSN 0065-2377 DOI 10.1016/S0065-2377(07)33004-4

Copyright r 2008 by Elsevier Inc. All rights reserved.

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Abstract As diesel emissions regulations are becoming more stringent, diesel particulate filters (DPFs) have become possibly the most important and complex diesel emission control device. A traditional design of experiments approach becomes very time consuming and costly if one wants to address DPF design, system integration, regeneration control strategy optimization and ash ageing assessment, due to the very large number of tests needed. The application of simulation tools provides a promising alternative and hence simulation is increasingly being used for the design of exhaust emission control systems. In a DPF many coupled physico-chemical phenomena occur over widely disparate spatial and temporal scales and the simulation approach should account for and exploit these features. This becomes possible with the introduction of certain defensible assumptions and/or simplifications to arrive at an accurate but computationally tractable DPF simulation approach, for the needs of industrial users. The present chapter summarizes the current state of the science and technology of DPF simulation, from the Aerosol & Particle Technology Laboratory (APTL), addressing consistent and rigorous refinements in a number of areas including: the aerodynamics at the filter and channel inlet, flow, filtration and reaction phenomena at the DPF material microstructure scale, and in coupling/interfacing DPF simulation with three-dimensional (3D) Computational Fluid Dynamics (CFD) codes. Experimental results are cited where appropriate, to re-enforce/validate the points brought forward by the simulation approach.

I. Introduction Advanced fuel injection technology in conjunction with an inherent high thermal efficiency have lead to increased market share of diesel powered vehicles especially in Europe, where the modern diesel passenger car is increasingly perceived by consumers, as an environmentally friendly and cost-effective transport means. This increasing market penetration of the diesel powertrain is expected to continue, if the diesel engine is able to meet even more stringent emission limits in the future. The first successful market introduction of vehicles equipped with diesel particulate filters (DPFs) and fuel-borne catalyst assisted regeneration took place in 2001 (Blanchard et al., 2004) and since then DPFs have become possibly the most important and complex diesel emission control device. The deployment of DPFs is also necessary for complying with heavy-duty engine emission standards while retrofit applications of passive DPF systems for heavy-duty vehicles are already widespread during the last decade.

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Recent developments (Bardon et al., 2004; Boretto et al., 2004; Ogyu et al., 2004; Young et al., 2004) in passenger car emission control systems are focusing on so-called ‘‘fit-for-life’’ solutions, eliminating the servicing of the DPF (ash removal) during the vehicle’s lifetime. Durability of the engine and emission control system is also very important in heavy-duty vehicles. Consequently, the emission control engineer must find practical and cost-effective solutions, which in addition to particulate emission reduction should not hamper the also challenging task of reduction of nitrogen oxide (NOx) emissions. NOx reduction has relied up to now predominantly on advanced in-cylinder reduction (exhaust gas recirculation, new combustion concepts) approaches, but aftertreatment technologies such as lean NOx traps (LNT) and lean NOx converters (LNC) are actively researched for passenger car application, while urea-based selective catalytic reduction (SCR) systems have already been introduced in the heavy-duty market in Europe (Johnson, 2004). Nextgeneration diesel emission control systems will be complex assemblies of chemical reactors and separators (Fig. 1) sometimes integrating different functionalities on the same monolithic support to achieve demanding requirements in space and cost, especially in passenger cars (Nakatani et al., 2002). Such complex emission control systems require for their cost-effective, design, development, system-level integration and optimization advanced simulation tools. In addition, requirements for robust on-board monitoring and control generate the need for efficient algorithms, implementable in computationally limited engine control units (ECUs), which will provide accurate knowledge of the state of emission control system during vehicle operation, and which can be used in control loops for the management of the integrated powertrain-emission control system. ECU

CAN bus

T, P, ∆P Soot, NOx sensor inputs

ENGINE SENSOR A /D INTERFACE

DOC

DPF

DeNOx tailpipe

HC, CO removal

Soot removal

NOx removal

FIG. 1. An example of a future integrated soot-NOx emission control system. DPF stands for diesel particulate filter. Sensory inputs of temperature (T), pressure (P), pressure drop (DP), soot and NOx concentration will be required along the exhaust pipe.

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The aim of the present work is to review some recent advances in DPF simulation science and technology from the Aerosol & Particle Technology Laboratory (APTL). While our current focus is on DPFs some of the mathematical/computational methods (especially those related to porous media and microstructures) may be applicable in other catalytic converter systems. Advances in DPF simulation are presented according to a taxonomy based on different spatial scales, originally introduced from this laboratory in Konstandopoulos and Kostoglou (1999a) and exploited in Konstandopoulos and Kostoglou (1999b, 2000, 2004), Konstandopoulos et al. (2000, 2001, 2003) and Kostoglou et al. (2003) to build hierarchically a state-of-the-art DPF simulation approach. Experimental results are cited where appropriate, to re-enforce/validate the points brought forward by the simulation approach.

II. The Many Scales of DPF Systems The multiscale nature of DPFs has been already addressed in Konstandopoulos and Kostoglou (1999b, 2000, 2004), Konstandopoulos et al. (2000, 2001, 2003) and Kostoglou et al., 2003, and spans several orders of magnitude, as shown in Fig. 2. This multitude of scales provides a useful way to organize a ‘‘divide-andconquer strategy’’ for the simulation of DPFs, with typical examples given in Konstandopoulos and Kostoglou (1999b); Konstandopoulos et al. (2001, 2003), Bissett (1984) and Bissett and Shadman (1985). Generally, there are three simultaneous length scales that need to be considered for the modeling of DPFs. The first one refers to the phenomena occurring across the soot layer and the porous filter walls, the second one to the phenomena occurring along the filter channels and the third one is the macroscopic scale of the entire DPF. Recent developments from our work at each scale are presented in the following sections.

III. Filter Wall Scale This section summarizes the advances in the description of flow, transport and reaction phenomena in the filter wall scale. The filter wall scale is the one which triggers the material development in the field: new filter structures, multifunctional catalyst coatings and ash interactions (SAE International, 2004). In addition, at this scale the structural aspects of the deposited soot particles need to be considered.

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O(10 µm)

O(m)

O(100 µm)

O(10 cm)

O(mm)

FIG. 2. DPF simulation has to face multiple scales. Figure from Konstandopoulos and Kostoglou (2004).

A. CLASSIC FLOW

IN

POROUS MEDIA DESCRIPTORS

Flow resistance descriptors of DPF walls, experimental protocols for their determination and useful correlations have been previously presented in Konstandopoulos (2003). At the filter wall scale flow resistance is determined by the Darcy permeability k and the Forchheimer coefficient b of the wall. DPwall ¼

m uw ws þ bru2w ws k |fflfflffl{zfflfflffl} |fflfflfflffl{zfflfflfflffl} Darcy

(1)

Forchheimer

A general correlation extending over five orders of magnitude for the permeability of porous DPF walls made of extruded, sintered ‘‘grains’’ was given in Konstandopoulos (2003), which can be expressed as: kwall ¼ f w ðeÞ
d 2c
SCFðd c Þ

(2)

where SCF is a factor accounting for slip-flow and free molecular effects (based on the prevailing mean free path and the grain diameter dc of typical filter

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media), and the function fw(e) was given in several equivalent forms (Konstandopoulos, 2003). It was also suggested in Konstandopoulos (2003) and Konstandopoulos et al. (2001) that the Forchheimer coefficient b and the Darcy permeability k were interconnected in accordance with: b¼

constant pffiffiffi e1:5 k

(3)

The value of the constant in Eq. (3) depends on the actual morphology of the elements making up the porous medium and it was estimated for beds of spheres as 0.143, based on the original Ergun’s correlation or as 0.134 for smooth and 0.298 for rough particles of packed beds, based on newer compilations (Dullien, 1979). The Forchheimer contribution to the pressure drop is negligible unless filters are operated at relatively high filtration velocities, which can occur for some filter designs based on sheets of sintered metal materials. Experiments performed with granular sintered metal filter media (Konstandopoulos et al., 2005) have shown that Eq. (3) can be applicable with a value of the constant equal to 0.34 (Fig. 3). This is in good agreement with the value of 0.3 mentioned in Konstandopoulos (2003), Konstandopoulos et al. (2001) by analogy to macroscopic rough granular media (Dullien, 1979). Equation (3) is also applicable for fibrous filter media (Fig. 4) with the constant being 16.6. The scatter observed can be attributed to the fact that the porosity of fibrous filters shows higher local inhomogeneity, meaning that the variations of each sample to the nominal specified porosity are likely to be higher.

Forchheimer coefficient, (m-1)

2.5E+06 2.0E+06 1.5E+06 y = 0.34x 1.0E+06 5.0E+05 0.0E+00 0.0E+00 1.0E+06 2• .0E+06 3 • .0E+06 4.0E+06 5.0E+06 •6.0E+06 1/(ε3/2•k 1/2) (m-1) FIG. 3. Validity of Eq. (3) for sintered metal granular filters.

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Forchheimer coefficient, (m-1)

•1.E+07 1.E+07 8.E+06 6.E+06 4.E+06 y = 16.6x

•2.E+06 0.E+00 0.E+00

1.E+05

2.E+05

3.E+05

1/(ε 3/2 k1/2)

4.E+05

5.E+05

•6.E+05

(m-1)

FIG. 4. Validity of Eq. (3) for fibrous filters.

B. MICROFLOW SIMULATION TECHNOLOGY The characterization of the flow in existing DPF materials has been assessed by experiments and macroscopic continuum flow in porous media approaches. However, when it comes to material design it is essential to employ flow simulation techniques in geometrically realistic representations of DPF porous media. Some first applications were introduced in Konstandopoulos (2003) and Muntean et al. (2003) and this line of research is especially important for the development of new filter materials, the optimization of catalyst deposition inside the porous wall and for the design of gradient-functional filter microstructures where multiple functionalities in terms of particle separation and catalyst distribution (for combined gas and particle emission control) can be exploited. Using advances in computer reconstruction methods (see e.g. Kikkinides and Burganos, 2000; Torquato, 2001) and past experience with discrete particle deposit simulations (Konstandopoulos, 2000), we have developed algorithmic as well as process-based reconstruction techniques to generate three-dimensional (3D) ‘‘digital materials’’ that are faithful representations of DPF microstructures. We refer to this approach as DPF microflow simulation (MicroFlowS). MicroFlowS is thus a short name for a computational approach, which combines (i)

Statistical methods and descriptors (see e.g. Kikkinides and Burganos, 2000; Torquato, 2001) to characterize the microstructure of DPF porous materials (bare as well as coated). (ii) Computer reconstruction algorithms for the realistic representation of DPF porous media and particle deposit microstructures (see e.g.

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Kikkinides and Burganos, 2000; Konstandopoulos, 2000; Tassopoulos, 1991; Tomadakis and Sotirchos, 1991a, b; Torquato, 2001). (iii) Fluid flow and particle transport, deposition and reaction algorithms in the reconstructed porous materials (employing Lattice Boltzmann (LB) techniques, see e.g. Chen and Doolen, 1998). Examples of computer reconstructed DPF porous media are given in Fig. 5 and encompass all currently available filtration media: extruded ceramic filters (including reaction formed media as cordierite and grain-sintered media as SiC), fibrous filters, foams and sintered metal powder/wiremesh. A distinctive advantage of having ‘‘digital materials’’ at our disposal is the ability to study catalyst deposition processes in these media. Depending on the way the catalyst is applied (e.g. from a solution or from a slurry) the catalyst coating degree of uniformity (Fig. 6) is expected to lead to different flow resistance behavior of the DPF. To illustrate the quantitative nature of MicroFlowS, we describe the application of the approach to SiC filter structures with pore sizes in the range

Granular (SiC)

Foam

Reaction formed (e.g. cordierite)

Sintered Metal

FIG. 5. Computer reconstruction of various porous filter structures.

Fibrous

Acicular

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(a)

221

(b)

1.0 Normalised Permeability (-)

0.9 0.8 0.7 Non-uniform coating Uniform coating

0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

0.2 0.1 0.3 Fraction of Pore Volume Filled (-)

0.4

(c) FIG. 6. Simulation of catalytic coating deposition at the same integral quantity of catalyst load. (a) Uniform coating and (b) non-uniform coating. The color code (blue to red) assists to visualize the local catalyst coating thickness from the filter surface. (c) Effect of coating distribution (uniform vs. non-uniform) on the DPF permeability (see Plate 7 in Color Plate Section at the end of this book).

from 10 to 90 mm and porosities in the range of 40–50%. At first scanning electron microscope (SEM) images of the filter wall are taken at different magnifications, as shown in Fig. 7(a) and (b), to identify the range of homogeneity of the material. Then a number of polished cross sections of the material embedded in a special resin are prepared and additional images in the

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100 µm

1 mm

(a)

(b) 100 µm

(d)

Auto correlation (-)

(c)

(e)

(f)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Real medium Reconstructed medium

0

10 20 Correlation distance (m)

30

FIG. 7. (a) and (b) SEM images of filter walls, (c) backscattered SEM image of a filter wall, (d) binary thresholded image. The pore space is denoted in black color. (e) Computer reconstruction of filter shown in (d) and (f) Comparison of autocorrelation function of reconstructed medium to that of the real filter.

SEM backscattered image mode are obtained, e.g. Fig. 7(c). The SEM image is converted into a binary image (with appropriate thresholding algorithms) as shown in Fig. 7(d). The binary image is then analyzed with respect to different statistical descriptors (e.g. porosity, autocorrelation function). Subsequently an appropriate reconstruction algorithm is employed (in the particular case as in the method of Kikkinides and Burganos, 2000) producing the reconstructed ‘‘digital material’’, shown in Fig. 7(e). The ‘‘real’’ and ‘‘reconstructed’’ materials

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have in a statistical sense the same ‘‘porosity’’ and ‘‘pore size’’, as manifested through the very good agreement of their autocorrelation functions (Kikkinides and Burganos, 2000), shown in Fig. 7(f). The flow resistance behavior of the reconstructed medium can now be examined by performing 3D flow simulations with the Lattice Boltzmann method (Chen and Doolen, 1998), and obtaining the permeability of the material (Konstandopoulos, 2003). Figure 8(a) depicts a visualization of 3D flow ‘‘tubes’’ and flow velocity distributions at different cross sections in a reconstructed filter material. Figure 8(b) shows the comparison of computer simulated and experimental permeabilities obtained with the experimental protocol described in Konstandopoulos (2003). C. SOOT DEPOSIT MICROSTRUCTURE In the literature, the assumption that the soot layer grown on the filter wall could be described by a uniform density (equivalently porosity), surface area and permeability has been common practice. These are parameters that had to be tuned according to experiments, leading to widely varying values among different publications, that for the sake of brevity will not be reviewed here. Konstandopoulos et al. (2002) demonstrated that during filter loading the microstructure of the soot cake is determined by the convective-diffusive transport of the soot aggregates toward the deposit and it was shown that soot cake packing density (r) and permeability (ksoot) were related to the local value of the dimensionless mass transfer Peclet number, Pe. Moreover, these parameters can be related to the porosity e and primary particle size (dpr) of the soot aggregates as follows (Konstandopoulos et al., 2002): r ¼ r~
ð1  eÞ

(4)

ksoot ¼ f ðeÞ
d 2pr
SCF

(5)

where SCF is the Stokes–Cunningham factor calculated with a Knudsen number (Kn) based on the primary soot particle size (dpr) by: SCF ¼ 1 þ Kn
ð1:257 þ 0:4e1:1=Kn Þ

(6)

and f(e) the Kuwabara function of porosity e, given as (Konstandopoulos and Johnson, 1989): 2

3 9 1 1=3 2 ð1  eÞ ð1  eÞ 2   e  26 7 5 5 f ðeÞ ¼ 4 5 9 ð1  eÞ

(7)

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

(a)

Experimental Permeability, k (m2)

1E-11

8E-12

6E-12

4E-12

y = 1.02x 2E-12

R2 = 0.99

0 0 (b)

2E-12

4E-12

6E-12

8E-12

Simulated Permeability by LB, kLB

1E-11

(m2)

FIG. 8. (a) Velocity fields at different sections through the reconstructed filter wall (gray denotes the solid material, violet denotes lowest and red denotes highest velocity) and visualization of flow paths in the reconstructed filter wall and (b) comparison of experimental and simulated filter permeabilities (see Plate 8 in Color Plate Section at the end of this book).

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These parameters should not therefore be interpreted as constant, intrinsic physical properties of the soot cake, but rather as dynamic properties that depend on the deposit growth mechanism and its history. Further insight into soot cake properties has been obtained by model experiments with soot aggregates generated by a Combustion Aerosol Standard (CAST) burner (Matter Engineering, Switzerland). The CAST is a quenched diffusion flame gas (propane) burner that allows the stable and controlled generation of soot aggregates over a much larger size range than that found in diesel exhaust. Figure 9 depicts the hydrodynamic resistance factor (r  k)soot of CAST soot deposited cakes on flat disk-shaped glass-fiber filters as a function of Peclet number and aggregate mobility diameter dag. The values of the aggregate mobility diameters shown in Fig. 9 are values measured by the Scanning Mobility Particle Sizer (SMPS). The data are consistent with a scaling relation of the form: ðr  kÞsoot ¼ r~
ð1  eÞ
f ðeÞ
d 2pr
SCF

(8)

where the porosity of the deposits follows a power law in terms of the Peclet number (Rodriguez-Perez et al., 2004):   Pe0 n eðPeÞ ¼ 1  ð1  e1 Þ
1 þ Pe

(9)

2.5E-12 95 nm 115 nm 129 nm 145 nm 161 nm 197 nm

(ρxk)soot (kg/m)

2.0E-12

1.5E-12

1.0E-12

5.0E-13

0.0E+00 0

5

10

15

20

25

30

35

Peclet FIG. 9. Experimental measurements of CAST soot hydrodynamic resistance factor as a function of Peclet number and aggregate mobility diameter. The continuous lines are plotted using the scaling relation form Eqs. (8) and (9).

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

1.00 0.90 0.80 (1-ε) /(1ε-inf)

0.70 0.60 0.50 0.40

95 nm 115 nm 129 nm 145 nm 161 nm 197 nm

0.30 0.20 0.10 0.00 0.01

0.10

1.00 Peclet

10.00

100.00

FIG. 10. Dependence of the soot cake porosity for different soot aggregate sizes at the prevailing Peclet number.

Equation (9) generalizes earlier porosity–Peclet number power-law correlations (Konstandopoulos et al., 2002) obtained at Pe W 0.3 down to the diffusion limited deposition limit. Pe0 is a characteristic cross-over Peclet number defining the scale beyond which the convective mechanism will take over the diffusive mechanism of deposition and ep the large Peclet number asymptote of the porosity. ep has a dependence on the aggregate size and it is described in a forthcoming publication (Konstandopoulos, 2007). Using Eq. (9) the experimental data of Fig. 9 can be collapsed on a single curve as shown in Fig. 10.

D. SOOT DEPOSIT COMPACTION The experiments performed with the CAST burner that were reported in the previous section involved the loading of flat disk-shaped glass-fiber filters at different Peclet numbers, which was achieved by changing the sampling flow for each case. The data were analyzed using our filter pressure drop model accounting for gas compressibility, originally introduced in Skaperdas and Konstandopoulos (2001) and Konstandopoulos and Kladopoulou (2003) as shown in Fig. 11, since experimental pressure drops sometimes reached 300 mbar at the high Peclet number limit. The effect of gas compressibility forces the pressure drop signal to deviate from a linear behavior, however, the experimental pressure drop as a function of mass loading observed, remained linear throughout the experimental range of pressure drops. This is an indication that the hydrodynamic resistance factor (r  k)soot of the deposit

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350 experiment model including effect of gas compressibility model including effect of gas compressibility and soot depsoit compaction

Pressure drop (mbar)

300 250 200 150 100 50

dag = 129 nm 0 0

1

2

4 5 3 Mass loading (g/m2)

6

7

8

FIG. 11. Experimental filter pressure drop as a function of soot mass loading compared with the model taking into account the effect of gas compressibility (dashed line) and the effect of soot deposit compaction (continuous line). The indicative example is given for a soot aggregate size of 129 nm.

changed at higher values of the pressure drop, hence soot cake compaction was taking place. To account for deposit compaction a power law of the form was introduced: f ¼ f0



DP  DPcr DP

d (10)

where f denotes the solid fraction and f0 the solid fraction of the deposit in the uncompacted stage. DPcr is the critical or yield pressure drop for deposit compaction to set-in and DP* a scaling constant to make the equation dimensionally correct. This simple deposit compaction model treats the deposit as a Bingham-type of material that remains undeformed below a yield pressure and that deforms with a power law in the post-yield region. The result is shown in Fig. 11 as the solid line and illustrates that at high pressure drops the effect of gas compressibility and soot cake compaction are opposing each other. Figure 11 gives an indicative example of the effect of gas compressibility and soot deposit compaction on the filter pressure drop for a soot aggregate size of 129 nm, but the same phenomenon was observed for all aggregate sizes at sufficiently high values of the pressure drop. Based on application of the extended pressured drop model that accounts for deposit compaction, we were able to obtain for the first time a quantitative demonstration of the effect of pressure drop on soot deposit compaction. As shown in Fig. 12 critical pressure drops for soot cake compaction are setting in the vicinity of 200 mbar causing a gradual increase

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

0.09

1-ε

0.08

0.07

95 nm 115 nm 129 nm 145 nm 161 nm 197 nm

0.06

0.05 0

100

200 300 Pressure drop (mbar)

400

500

FIG. 12. Effect of pressure drop on the solid fraction of soot cake deposits as a function of soot aggregate size.

of the solid fraction (1e) of the soot deposit, which can reach 25–30% for some aggregates at high enough values of the pressure drop (W400 mbar). These pressure drops are non-typical for regular DPF operation but could instantaneously occur in a heavily loaded, undersized filter, say at the start of a forced regeneration event. Our results demonstrate that such phenomena when present can be quantitatively incorporated within the general simulation framework using the simple deposit compaction model mentioned above. DPcr and the exponent d in Eq. (10) have a dependence on aggregate size and morphology as is described in a forthcoming publication (Konstandopoulos, 2007).

E. DEEP-BED FILTRATION The introduction of more porous wall structures necessitates a closer look at the soot particle fate inside the wall and the deep-bed filtration mode starts to be important. Flow in porous media represents a challenging area of fluid mechanics and initial approaches (Bissett, 1984; Konstandopoulos et al., 2001, 2003) have employed so-called unit-cell models, where the porous filter wall is approximated as a collection of ‘‘cells’’, each hosting an object of simple geometry (a sphere for granular filters and a cylinder for fibrous filters). This filtration theory and a local re-computation of the evolving unit-cell geometry due to deposition of particles (Fig. 13) was employed and a transient filtration model has been derived and tested with very good success against experimental data with ceramic, metallic and fibrous filters (Bissett and Shadman, 1985; Zarvalis et al., 2003). In addition, the same unit-cell-based

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Clean unit cell

Partially loaded unit cell

229

Completely loaded unit cell

unit collector b 0

“empty” envelope

time

FIG. 13. Unit-cell filtration model. The collector size dc and the empty envelope b are matched to the macroscopic porosity of the filter. The unit-cell blocks when the size of the collector becomes a fraction c of b (Konstandopoulos et al., 2000; Vlachos et al., 2002).

90000 80000

B

Pressure drop (Pa)

70000

A

60000

C

50000 40000 30000 D

20000 10000 0 0

10

20 Mass load (g/m2 )

30

40

FIG. 14. Comparison of simulated pressure drop for different filters (A–D) due to catalyst deposition (solid lines) to the experimental data (points) (Karadimitra et al., 2004).

filtration model has been successfully employed as a virtual sensor to describe on-line the deposition process of inorganic nanoparticles on different filter media (Karadimitra et al., 2004). As seen in Fig. 14 the loading of deep-bed filters A–C is described equally successfully as the loading of the cake filter D (Karadimitra et al., 2004). The evolution of the non-uniform spatial profile of the inorganic nanoparticles deposited, across the filter wall, can be seen in Fig. 15 along with a typical SEM line scan of the distribution of an element of the

230

ATHANASIOS G. KONSTANDOPOULOS ET AL.

Filter wall thickness

Element map

Line scan across wall

Flow Mass load (mg/m2 )

250 142 s 282 s 482 s 682 s 882 s 1482 s

200 150 100 50 0 0

0.2

0.4 0.6 0.8 Normalized filter wall thickness

1

FIG. 15. Typical evolution of the spatial deposition of inorganic nanoparticles inside a porous filter wall.

nanoparticle that serves as a tracer. Both simulated and experimental profiles exhibit a sharp decay along the wall as expected from filtration theory for the particular filter structure employed. Employing our transient filtration unit-cell model for parametric studies we can then obtain the spatial distribution and amount of nanoparticles (soot or inorganic e.g. ash or catalysts) deposited in a porous wall as a function of flow velocity and filter microstructure. This can be achieved for different types of porous materials in terms of porosity, pore size and wall thickness. Table I shows the test matrix employed for the parametric study where a wide range of filtration velocities is covered (2–10 cm/s), for a low porous (dpore=12 mm, e=40%) and a high porous material (dpore=40 mm, e=70%), and for a small and a large wall thickness (ws=0.305 and 0.432 mm corresponding to 12 and 17 mil, respectively). The filtration model gives the mass deposited across the wall thickness at different stages of the deep-bed filtration, which are reported as the utilized capacity of the filter wall, defined as: Utilized capacity ¼

V solid V void

(11)

where Vsolid is the total volume of the mass collected inside the filter wall at each stage and Vvoid is the empty space inside the porous wall.

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TEST MATRIX Filtration velocity (cm/s)

FOR

PARAMETRIC STUDY

231

TABLE I WITH TRANSIENT FILTRATION UNIT-CELL MODEL

Pore size dpore (mm)

Porosity e (%)

Wall thickness ws (mm)

12

40

40

70

12

40

40

70

12

40

40

70

0.305 0.432 0.305 0.432 0.305 0.432 0.305 0.432 0.305 0.432 0.305 0.432

2

4

10

Figure 16 shows the normalized mass distribution inside the filter wall vs. the normalized wall thickness as a function of the utilized capacity of the filter wall, for the low porous and the high porous material (small wall thickness) at a filtration velocity of 4 cm/s. The line of the highest utilized capacity gives the state of loading inside the filter wall when the transition from the deep-bed to cake filtration has occurred and there is no more mass entering inside the filter wall. This final state of the mass distribution along the filter wall thickness was calculated for all the cases listed in Table I and the results are shown in Fig. 17. Figure 17 shows that a more uniform mass distribution can be achieved at higher filtration velocities, both for the low and the high porous material. Finally, Fig. 18 shows the utilized capacity of the filter wall for all the cases in study, computed with a gas temperature of 2801C and a primary and aggregate particle size of 20 and 90 nm, respectively, as a function of the Peclet number. It is seen (Fig. 18) that the more porous materials with a smaller wall thickness can attain a better usage of the capacity of the filter wall, when the Peclet number increases.

F. MICROFLOW DEPOSITION

IN

FILTER WALL

The reconstructed ‘‘digital materials’’ previously mentioned are another way to simulate soot deposition apart from the unit-cell models. An example of soot deposition in such a ‘‘digital material’’ (reconstructed with a process-based algorithm that creates and sinters grains from a pre-computed ‘‘grain library’’) is shown in Fig. 19(a). As time passes the transition from deep bed to cake filtration as the top regions of the filter gets filled with soot is evident in

232

ATHANASIOS G. KONSTANDOPOULOS ET AL.

1.0

ε = 40 %, dpore = 12 µm, ws = 0.305 mm

0.9

0.11% 6.12% 10.55% 13.59% 17.50%

Normalized mass

0.8 0.7 Utilized capacity

0.6 0.5 0.4 0.3 0.2 0.1 Fitration velocity = 4 cm/s

0.0

0.0

0.2

1.0

0.4 0.6 Normalized wall thickness

1.0

ε = 70 %, d pore = 40 µm, ws = 0.305 mm

0.9

Utilized capacity

0.8 Normalized mass

0.8

0.7 0.6 0.5 0.4

1.48% 10.05% 20.13% 26.69% 33.58%

0.3 0.2 0.1

Filtration velocity = 4 cm/s

0.0 0.0

0.2

0.4

0.6

0.8

1.0

Normalized wall thickness FIG. 16. Normalized mass distribution inside the filter wall vs. the normalized wall thickness as a function of the utilized capacity of the filter wall. Low porous material (top) and high porous material (bottom), for a filtration velocity of 4 cm/s and a wall thickness of 0.305 mm.

Fig. 19(b). The jumps in the curve are due to the filling of distinct ‘‘pores’’ which is studied in detail using this technique. Over a macroscopic filter area these ‘‘incoherent’’ jumps would average out each other leading to a smooth evolution of the pressure drop as that in Fig. 14. The type of simulation shown in Fig. 19 is expected to be largely applicable in the near future for industrial use exploiting grid-computing environments (http://www.unizar.es/flowgrid/).

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1.0 0.9

Normalized mass

0.8 0.7 0.6

ε = 70 %, d

= 40 µm, w = 0.305 mm

0.5

ε = 70 %, d

= 40 µm, w = 0.432 mm

0.4

ε = 40 %, d

= 12 µm, w = 0.305 mm

ε = 40 %, d

= 12 µm, w = 0.432 mm

0.3 0.2 0.1 0.0

Filtration velocity= 2 cm/s

0.0

0.2

0.4

0.6

0.8

1.0

Normalized wall thickness 1.0 0.9

Normalized mass

0.8 0.7

ε = 70 %, d

= 40 µm, w = 0.305 mm

0.6

ε = 70 %, d

= 40 µm, w = 0.432 mm

0.5

ε = 40 %, d

= 12 µm, w = 0.305 mm

0.4

ε = 40 %, d

= 12 µm, w = 0.432 mm

0.3 0.2 0.1 Filtration velocity=4 cm/s

0.0

0.0

0.2

0.4

0.6

0.8

1.0

Normalized wall thickness 1.0 0.9

Normalized mass

0.8 0.7 0.6 0.5

ε = 70 %, d

= 40 µm, w = 0.305 mm

ε = 70 %, d

= 40 µm, w = 0.432 mm

ε = 40 %, d

= 12 µm, w = 0.305 mm

ε = 40 %, d

= 12 µm, w = 0.432 mm

0.4 0. 3 0.2 0.1 Filtration velocity= 10 cm/s

0.0 0.0

0.2

0.4

0.6

0.8

1.0

Normalized wall thickness FIG. 17. Normalized mass distribution inside the filter wall vs. the normalized wall thickness for the different porous materials. From top to bottom: filtration velocity of 2, 4 and 10 cm/s.

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

40% 35%

Utilized capacity

30% 25% 20% 15% ε = 70 % , dpore = 40 µm, w s= 0.305 mm ε = 70 % , dpore = 40 µm, w s= 0.432 mm ε = 40 % , dpore = 12 µm, w s= 0.305 mm ε = 40 % , dpore = 12 µm, w s= 0.432 mm

10% 5% 0% 0

0.2

0.4

0.6 Peclet

0.8

1

1.2

FIG. 18. Utilized capacity of the filter wall vs. the Peclet number for all the loading cases in study.

Current research efforts are concentrating on computationally efficient implementations of the energy equation within the MicroFlowS framework to allow realistic simulations of soot particle reaction in the porous structures. The next section shows a parallel line of development that started in Konstandopoulos and Kostoglou (2004), which tries to extend continuum models of soot oxidation to account for microstructural effects.

G. EFFECT

OF

MICROSTRUCTURE

ON

OXIDATION KINETICS

The simplest phenomenological macroscopic description of a catalytic coating (Fig. 20) is represented by the so-called two-layer model introduced in Konstandopoulos and Kostoglou (1998, 1999b, 2000). The catalyst coating can interpenetrate/overlap partially with the top part of the filter wall. Hence a spatial ‘‘field of catalyst activity’’ can be assigned to Layer I. Above the catalystinfluenced layer a normal soot deposit exists that can only react through a thermal oxidation mechanism. The model of soot oxidation incorporating the soot layer microstructure, previously developed in Konstandopoulos and Kostoglou (2004) is combined with the two-layer model of the catalytic coating. The mathematical model is presented in detail in the appendix section. The dimensionless microstructural parameter a accounts for the penetration of the thermal layer into the catalytic one, caused by the progressive reaction of the soot, which is in contact with the catalyst. Results are presented for different values of a (see appendix) in Fig. 21. The two layers remain separate for values of a close to 1. As a decreases the degree of penetration between the layers

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235

Pressure drop (Pa)

2500 2000 1500 1000 500 0 time

(a)

(c)

(d)

0.00 g/m2

0.77 g/m2

0.00 g/m2

(b)

1.56 g/m2

0

1

2 3 4 6 7 5 Areal challenge mass (gr/m2)

2.27 g/m2

2.27 g/m2

2.81 g/m2

8

3.30 g/m2

3.30 g/m2

FIG. 19. Simulation of soot deposition on a filter wall. (a) Evolution of soot deposits (gray) in the wall (black is solid, white is pore space) and incipient cake formation; (b) pressure drop as function of challenge soot mass demonstrating the deep-bed to cake filtration transition; (c) visualization of soot deposition in an extruded ceramic (granular) filter wall and (d) development of soot deposits (black) and soot mass fraction in the wall (solid material is gray) to the onset of cake formation. Soot mass fraction scale is from 0 (violet) to the inflow value (red). In (d) the velocity on a section through the filter wall is shown, with overlay of the soot deposit shapes (see Plate 9 in Color Plate Section at the end of this book).

increases, leading to enhanced oxidation rates as more soot from the top layer enters the field of activity of the catalyst. For a=1, soot in the catalytic layer is oxidized fast leaving the soot in the thermal layer unreacted. This has been observed with some early catalytic filters. As a decreases the soot from the top layer replaces more rapidly the soot oxidized in the catalytic layer increasing the global oxidation rate. The corresponding soot layer thickness evolution is shown in Fig. 22. For values of a close to 1 (e.g. 0.9) the catalytic layer is totally depleted from soot at some instances, followed by sudden penetration events from the soot of the thermal layer. These events are clearly shown in the thickness evolution for a=0.9 in

236

ATHANASIOS G. KONSTANDOPOULOS ET AL.

FIG. 20. Schematic of the two-layer model of catalytic coating.

1

Normalized soot mass

0.9 0.8 0.7 0.6 0.5 0.4 α=1 α=0.9 α=0.7 α=0.5

0.3 0.2 0.1 0 0

500

1000

1500

2000

Time (s) FIG. 21. Normalized soot mass evolution for different values of the microstructural parameter a.

Normalized deposit thickness

1 0.9 0.8 0.7 0.6 0.5 0.4 α=1 α=0.9 α=0.7 α=0.5

0.3 0.2 0.1 0 0

500

1000 Time (s)

1500

2000

FIG. 22. Normalized soot deposit thickness evolution for different values of the microstructural parameter a.

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237

1 Normalized deposit density

0.9 0.8 0.7 0.6 0.5 0.4

t=140 t=262 t=326 t=531 t=778

0.3 0.2 0.1 0 0

0.2

0.4 0.6 Normalized deposit thickness

0.8

1

FIG. 23. Normalized soot deposit density profile for different times.

Fig. 22. For smaller values of a, equilibrium between the soot entering the catalytic layer and the soot oxidized in it is established leading to a constant global oxidation rate. The evolution of the dimensionless density profile across the soot layer is shown in Fig. 23. The initial gradual replenishment of the soot in the catalytic layer (at t=140 s) is followed by sudden penetration events (t=262 and 326 s) before the establishment of a steady state profile (at t=531 and 778 s). Regarding the non-catalytic (thermal) layer only a gradual reduction of its thickness, accompanied by a very small reduction of its uniform density is observed. This simple microstructural model exhibits a rich dynamic behavior, however we have also established an experimental program to study the soot cake microstructure under reactive conditions. This is achieved by performing in situ permeability experiments on partially reacted soot cakes. Figure 24 shows an example of such experiments, where the evolution of the soot cake hydrodynamic resistance (r  k)soot as a function of remaining soot mass in the filter is depicted. The experiments are performed exposing small filter samples (preloaded with soot from a modern passenger car diesel engine), in a dedicated flow reactor (Konstandopoulos et al., 2000) at constant temperature. By cycling between an oxidative (10% O2) exhaust gas and an inert (N2) gas stream we obtain at each step controlled, partially oxidized soot cakes. These are then analyzed in situ with the method described in Konstandopoulos et al. (2002) to obtain their hydrodynamic resistance, (r  k)soot by varying the inert gas flow rate and recording the induced pressure drop. The method has a good resolution since it allows the distinction of (r  k)soot for catalyzed and uncatalyzed filters. Such data in conjunction with the

238

ATHANASIOS G. KONSTANDOPOULOS ET AL.

7.0E-12 catalyzed

6.0E-12 (ρxk)soot (kg /m)

uncatalyzed

5.0E-12 4.0E-12 3.0E-12 2.0E-12 1.0E-12 0.0E+00 0.0

1.0

2.0 3.0 Soot mass in filter (g /m2)

4.0

FIG. 24. Experimental determination of the change of (r  k)soot during oxidation. At the soot mass load of 1 g/m2 the soot cake starts to vanish and its hydrodynamic resistance starts to increase rapidly.

theoretical developments on continuum microstructural models of soot oxidation are expected to improve our description of the relevant phenomena in state-of-the-art DPF simulation tools.

H. THE ROLE

OF

NO2 TURNOVER/RECYCLING

The influence of NO2 on soot oxidation, in conjunction with a highly selective NO to NO2 oxidation promoting catalytic coating on a DPF was studied for the first time experimentally and theoretically in Konstandopoulos et al. (2000). At that time NO2-regenerative technologies were practiced with uncatalyzed filters (Hawker et al., 1997; Lueders et al., 1999) and it was suggested that combinations of NO2-regenerative technologies with catalytic filters could lower the dependence of NO2-regenerative technologies on high engine-out NOx concentrations, as NOx emission standards become tighter. This has now become industrial practice (Konstandopoulos et al., 2004). The following reactions occur in presence of an NO oxidation promoting catalyst (such as Pt) in the catalytic layer of the two-layer model (Konstandopoulos et al., 2000): C þ NO2 ! CO þ NO

(12)

NO þ 12 O2 2NO2

(13)

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239

The reaction of NO2 with soot has attracted considerable interest in the literature (Jacquot et al., 2002; Olsson et al., 2001) and complex reaction schemes are being developed to account for the influence of H2O, SO2, the collaborative effect of O2–NO2, presence of various catalysts on the NO to NO2 oxidation (precious and non-precious metals), inhibition and transient kinetic phenomena, or diffusional transport limitations (Dardiotis et al., 2006; Knoth et al., 2005; Olsson et al., 2001). While a detailed kinetics approach may lead to more elaborate rate laws, it is acceptable to consider that the soot-NO2 and NO oxidation reaction rates are first order in NO2. Furthermore, we consider that the catalyst and soot cake are overlaping over a spatial region of finite dimensions and no sharp boundary exists between them. As a consequence NO2 can always find soot to react with and there are no transport limitations. This is a consequence of the realistic morphology of ‘‘rough’’ catalyst coatings but it also allows a great simplification in the mathematical formulation, leading to an analytic global expression for the NO2 turnover (or recycling) factor within the framework of the two-layer model of a catalytic coating as shown in the following: Let X and Y be the molar concentrations of NO2 and NO and Xo and Yo be these concentrations in the inlet stream, respectively. Then, Z=X+Y is the total NOx concentration. Let k1 be the reaction rate constant of the soot-NO2 oxidation reaction and k’A and kB the forward and backward rate constants for the NO oxidation, respectively. For the case of constant oxygen concentration across the soot layer the forward reaction can be considered of pseudo-first order with a modified rate constant kA=k’A[O2]0.5. The two-layer model for soot oxidation with NO2 in a catalytic filter can be written as follows: Soot layer (thickness w2) In this layer only soot oxidation by NO2 happens: uw

dX ¼ k1 X dz

(14)

with initial condition X(0)=Xo The resulting NO2 concentration distribution is: X ¼ X o eðk1 z=uw Þ

(15)

The NO2 and NO concentrations leaving the soot layer and entering the catalyst layer are Xi and Yi, respectively and they can be computed as: X i ¼ X o eðk1 w2 =uw Þ and Y i ¼ Z o  X i

(16)

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

Soot þ catalyst layer (thickness w1) In this layer both soot and NO oxidation take place: uw

dX ¼ k1 X þ kA Y  kB X dz

(17)

dY ¼ k1 X  kA Y þ kB X dz

(18)

uw

with initial conditions X(0)=Xi and Y(0)=Yi The resulting NO2 concentration distribution is:   zw2 kA Z o kA Z o þ Xi  X¼ eðk1 þkA þkB Þ uw k1 þ kA þ kB k1 þ kA þ kB

(19)

The total reaction rate for each layer is given by integrating the local rate along the respective layer thickness (i.e.): dmi ¼ M c k1 dt

Zwi X dz

(20)

wi1

where w is obtained from the local value of the soot density r and the area mass load mi and w0=0 is taken as the filter surface. The evolution equation for the two layers in the thin soot deposit limit (used here for analytical convenience) are: dm1 k1 ¼ M c dt k1 þ kA þ kB   k A Z o w 1 þ uw X i 

  kA Z o ððk1 þkA þkB Þw1 =uw Þ Þ ð21Þ ð1  e k1 þ kA þ kB

dm2 ¼ M c uw X o ð1  eðk1 w2 =uw Þ Þ dt

(22)

From the above equations the evolution equation of the catalytic layer can be written in terms of that of a non-catalytic layer multiplied by a recycling factor R given as following: R¼

  C1C2C3 1 C2C3 þ 1 ð1  eð1þC 3 þC 4 ÞC 1 Þ (23) 1 þ C3 þ C4 1 þ C3 þ C4 1 þ C3 þ C4

where C1=k1w1/uw, C2=Zo/Xi, C3=kA/k1, C4=kB/k

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The NO2 turnover or recycling factor R then means the number of times an NO2 molecule is re-used to oxidize soot compared to the absence of a catalytic coating promoting NO into NO2 oxidation. The recycling factor for the case of an exhaust stream containing NO2 entering the catalytic layer at equilibrium conditions is shown in Fig. 25 vs. the parameter C3 (affected mainly by the design variable catalyst content which affects kA) for several values of C1 (affected by design and operational variables wall velocity, catalyst layer thickness). In the case of large C3 the recycling factor R attains its maximum value and therefore it is a useful number to know for design/optimization purposes. R1 ¼ C 1

½NO2 eq ½NO2 i

(24)

Based on the NO2-soot oxidation data promoted by the catalytic coating tested in Konstandopoulos et al. (2000) an R value of about 3.4 has been computed. This represents a significant enhancement. The above approach of integrating analytically (under certain assumptions) across the porous wall the species balances to obtain local soot consumption rates can be extended for the case of more reactions occurring in the porous wall. In the presence of a precious metal catalyst, the hydrocarbons and the carbon monoxide of the exhaust gases are also oxidized. It can be assumed that all the reactions in the porous wall occur hierarchically (according to their 6

Turnover/recycling factor, R

5

C1= 0.5 C1= 2

4

C1= 5

3

2

1

0 0.01

0.1

1 C3

10

FIG. 25. Recycling factor R vs. parameter C3 for several values of parameter C1.

100

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

kinetics) permitting the analytical integration of the mass balances and the derivation of closed form relation for the local reaction rates (Konstandopoulos et al., 2007). A further extension of this approach to include mass transfer effects which may be important in case of high reaction rates is also possible.

IV. Filter Channel Scale The next relevant spatial scale is that of the filter channel. If the inlet conditions to the DPF can be assumed to be spatially (radially) uniform, formulating the DPF simulation model for two representative channels (inlet and outlet) is equivalent to solving it for the entire DPF (assumed radially homogeneous). It is not therefore surprising that this scale claims most of the published works in the literature, starting with Bissett (1984) who formulated the first comprehensive model which takes into account the detailed flow distribution in the channel. As the transient behavior of the monolith is slow with respect to the residence time of the exhaust gas as it travels across the filter, the quasi-steady state approximation for the exhaust flow in the channels simplifies considerably the computational burden. Although the flow in the filter channels is actually 3D, it can be accurately approximated by a (perimeter averaged) one-dimensional (1D) configuration. This has been confirmed by Konstandopoulos et al. (1999, 2003) who demonstrated excellent agreement of analytical solutions of a 1D single channel flow model with exact 3D Computational Fluid Dynamics (CFD) results as well as with many experiments for square and triangular shaped channels. New developments in the filter channel scale phenomena simulation include: the study of different geometrical factors and soot loading on inertial inlet/outlet channel losses, the implications of asymmetric channel geometries (for ash tolerance), the modeling of particle entrainment phenomena (for ash and soot) and the refinement of the channel-to-porous wall heat transfer modeling. These are discussed in more detail in the next sections.

A. INERTIAL LOSSES

AT

CHANNEL INLET/OUTLET

The contraction of the exhaust gas at the entrance of the inlet channels and its expansion at the exit of the outlet channels causes inertial losses. It has been shown in Konstandopoulos et al. (2001) that CFD calculations or experiments are the only means to evaluate these losses, since estimations on literature correlations (which are not specifically developed for the honeycomb structure of the DPF) are not sufficiently accurate for such evaluations. Moreover, the influence of the soot cake on these losses has never before been studied in a thorough fashion. To improve the inclusion of such phenomena in DPF

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simulation tools, the effect of different cell densities and soot loadings on the DPF contraction/expansion losses coefficient z (Konstandopoulos et al., 2001) is summarized in the present section. 3D CFD calculations were performed with a commercial CFD package (Fluent Inc., 2001) taking advantage of the symmetry of the problem. The physical domain modeled is the one shown in Fig. 26, which includes part of the inlet, the outlet channel and the wall. The computational domain also includes sufficiently large sections upstream and downstream of the channel to obtain well-developed flow. This approach for modeling DPFs has been used and validated in the past (Konstandopoulos et al., 2001, 2003) where the reader can find more detailed descriptions of the procedure followed for the domain and mesh creation. To investigate how the blocking of the channel affects the contraction/ expansion losses coefficient z various cases were studied in terms of filter cell density, channel size and soot mass loading inside the filter. These parameters are shown in Table II, along with the frontal open area fraction (OAF or eA defined as the open channel area over the total filter face area) corresponding to each case. The channel length was fixed to 150 mm (which corresponds to a typical 6 in DPF), and the channel plug length was initially set to 5 mm. The calculations were performed for a Reynolds number based on the channel hydraulic diameter (Reh) of 1,800. This high Reynolds number was chosen since a more accurate determination of z can be obtained at these flow conditions (either experimental or computational) keeping in mind that inertial effects are more pronounced at high Reh. The permeabilities of the filter wall

FIG. 26. Cross section of filter channels showing the front view of the CFD computational domain.

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TEST MATRIX Cell density (cpsi) 100

TABLE II FOR CFD CALCULATIONS

Wall thickness (mm)

Channel size (mm)

Soot layer thickness (mm)

Open area fraction

0.3356

2.184

0 100 200 0 100 200 0 100 200

0.370 0.305 0.247 0.322 0.238 0.168 0.287 0.193 0.117

200

1.440

300

1.111

9 8

100 cpsi 200 cpsi 300 cpsi

200 µm 200 µm

7

ζ (-)

6 5 200 µm

4

100 µm

3

100 µm

2

0 µm 0 µm 100 µm

0 µm

1 0 0.10

0.15

0.20 0.25 0.30 Open area fraction (-)

0.35

0.40

FIG. 27. Contraction/expansion losses coefficient z vs. open area fraction for different cell densities and soot mass loadings.

and that of the soot layer were fixed to the representative values of 1  1012 and 3.3  1013 m2, respectively. The inertial loss coefficient z was determined by a single CFD calculation of the pressure distribution along the system, for each case, and by subtraction of the pressure loss due to the porous channel region, and the porous soot layer (when present). The results are shown in Fig. 27 as a function of the open area fraction for the different filter cell densities and soot mass loadings. The data clearly can be only approximately correlated in terms of the open area fraction as shown in Fig. 27. Subsequently we studied the effect of the plug length on the inertial losses coefficient z for a clean filter at the three different

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4 3.5

100 cpsi 200 cpsi 300 cpsi

15 mm

5 mm

2.5 ζ (-)

15 mm 10 mm 5 mm

10 mm

3

15 mm 10 mm 5 mm

2 1.5 1 0.5 0 0.10

0.15

0.20

0.25

0.30

0.35

0.40

Open area fraction (-) FIG. 28. Contraction/expansion losses coefficient z vs. open area fraction for different cell densities and plug lengths.

cell densities listed in Table II. The plug length value ranged from 5 to 15 mm and the results are shown in Fig. 28. As it is seen the task of deriving a theoretical description for z requires a substantial amount of computations, but it is not intractable. Pending the derivation of a generalized correlation (that takes into account the combined effect of open area fraction, cell density, soot thickness and plug length) we employ in our simulations tabular interpolations between the data of Figs. 27 and 28, which cover a sufficiently large parameter space for practical applications. The advent of other than square channel shapes (Konstandopoulos et al., 2003) or asymmetric channel designs for ash tolerance (Bardon et al., 2004; Ogyu et al., 2004; Young et al., 2004) poses no problem as it can be handled within the same computational framework. Our current efforts with asymmetric channel geometries (see next section), however, focus on the more important problem of extending our single and multichannel DPF simulation models to account for such effects.

B. ASYMMETRIC CHANNEL GEOMETRIES The Peclet number dependence of the soot cake microstructure has important implications for the morphology of the soot (and ash) deposits in the square channels of DPFs, as illustrated in Fig. 29, obtained by a mixed Ordinary Differential Equations (ODE)–Monte Carlo simulation (Rodriguez-Perez et al., 2004). Since the introduction of asymmetric designs (Bardon et al., 2004; Ogyu et al., 2004; Young et al., 2004) the inclusion of such shape effects on DPF

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

FIG. 29. Morphology of simulated soot deposit grown in a square channel for Pe ¼ 0.1 (left) and Pe ¼ 100 (right), at the same soot mass load in the filter (Rodriguez-Perez et al., 2004).

simulation tools employed by the industry becomes necessary. As this inevitably will entail approximations these will have to be assessed with more detailed studies. Soot deposit growth and microstructure evolution is not presently accounted for in general purpose flow solvers. The ‘‘hooks’’ provided in all general purpose flow solvers through user defined functions/subroutines (although often quite awkward) can be used to interface in the solver phenomena occurring at the sub-grid scale and hence study the aforementioned channel shape effects. Our interfacing of local soot deposit growth to large scale CFD simulations is presented in Konstandopoulos et al., 2005. In the present section, we provide examples of the current state of our developments with asymmetric filter channel shapes. Figure 30 lists schematics of three asymmetric channel geometries (A, B and C) inspired by the designs presented in Ogyu et al. (2004), Young et al. (2004) and Bardon et al. (2004) studied (at the same value of open area fraction). Figures 31 and 32 demonstrate the growth of soot cakes in each structure shown in Fig. 30. While a detailed account of soot deposit growth dynamics in asymmetric channel designs will be given elsewhere we can remark that the soot deposits grown bear a very good similarity to those shown on SEM pictures of some of the designs (Ogyu et al., 2004). All filter designs after an initial stage exhibit a nearly uniform soot deposit. This demonstrates the utilization of the entire internal surface area of the inlet channels, as the flow readjusts to transport the soot particles along the path of least resistance (which is not necessarily the shortest geometric path between the inlet and outlet channel, i.e. the wall thickness). This self-regulated behavior is currently advantageously exploited to bring such asymmetric designs within the multichannel simulation framework. C. ASH

AND

SOOT ENTRAINMENT PHENOMENA

The mechanistic modeling of ash transport and deposition dynamics was originally introduced in Konstandopoulos et al. (2003). The same formalism can

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FIG. 30. Cross section of asymmetrical designs showing the front view of the CFD computational domain.

be applied to account for shear-induced soot particle re-entrainment and further downstream deposition (Peters, 2004). Experimental studies of ash deposition and transport in filters require extensive and costly engine runs and are scarce in the literature. A dynamic ash transport-deposition model is then an attractive alternative for the assessment of DPF systems with respect to ash accumulation.

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

FIG. 31. Soot deposits grown on the different channel geometries a, b and c of Fig. 30.

The ash simulation model (Konstandopoulos et al., 2003; Rodriguez-Perez et al., 2004) consists of ash transport and ash layer evolution equations describing the interaction between ash deposition and re-entrainment in the channels along with the gas mass balance and momentum balance equations in the inlet and outlet channels of the DPF. Ash re-entrainment is initiated by flow

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wall

wall soot cake

wall

wall soot cake

wall

wall soot cake

FIG. 32. Path lines (spaced at intervals of equal flux) of the wall flow in different asymmetric shape cells in clean (left) and loaded (right) states. From the top, geometries a, b and c of Fig. 30 are shown.

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

shear stresses along the channel, whenever the shear stress in the channel exceeds locally a critical shear stress characteristic of the ash type (as e.g. determined by composition, particle size, etc). The kinetics of ash reentrainment are determined by the ash stickiness that for a given type of ash depends on the previous filter temperature history (currently accounted by the maximum temperature that the filter wall has experienced), as obtained by a separate filter regeneration simulation (Konstandopoulos et al., 2003). Ash stickiness is described by a critical ash sticking temperature by analogy to the ash fusion temperature employed in studies of ash fouling in heat transfer equipment. The dynamic ash transport and deposition model can exhibit a rich behavior, depending on the ash quality, and prevailing flow conditions leading to different ash deposition profiles inside DPFs, despite its apparent simplicity. Sticky ash leads to deposition along the walls while non-sticky ash can be transported towards the end of the filter. A comparison to the experimental data of Bardasz et al. (2003) in Fig. 33 shows good quantitative agreement for the ash layer thickness resulting from different oil consumption protocols. The sulfur content of the oil and whether it is doped into the fuel (to accelerate the ash ageing test) determines its ‘‘stickiness’’, and this in turn is described by the model of ash re-entrainment. Optimized filter cell densities accounting for different ash accumulation modes and soot loading of filters have been presented in Konstandopoulos and Kladopoulou (2004). The same approach is employed to describe shear-induced transport of soot particles. Based on limited amount of experimental information for such phenomena in the literature we have established a flow cell where soot entrainment from the surface of preloaded filters from the engine exhaust can be studied. Preliminary experiments at ambient conditions reveal that no soot entrainment is observed up to relevant shear rates at the entrance of DPFs. We attribute this to the moisture content in ambient conditions of the soot deposits that due to capillary condensation increases adhesive forces between the particles. In the future experiments at high temperatures are planned to evaluate experimentally the shear-entrained fluxes for soot and ash deposits.

D. CHANNEL

TO

POROUS WALL HEAT TRANSFER

A basic element of the thermal dynamics of the DPF is the heat transfer between the gas in the channel and the porous wall. In case of a porous wall having small wall thermal Peclet number PeT (as is always the case for a DPF as shown by Bissett and Shadman (1985)) the problem degenerates to the following modified Graetz problem: ~ ¼ ath r2 T ~ urT

(25)

wash (µm)

ADVANCES IN THE SCIENCE AND TECHNOLOGY OF DPF SIMULATION 90 80 70 60 50 40 30 20 10 0

251

Bardasz et al. (2003)

0

0.2

0.4

0.6

0.8

1

z /L

(a) 140

Bardasz et al. (2003)

wash (µm)

120 100 80 60 40 20 0 0

0.2

0.4

160

wash (µm)

0.6

0.8

1

0.6

0.8

1

z /L

(b)

Bardasz et al. (2003)

140 120 100 80 60 40 20 0 0

(c)

0.2

0.4 z /L

FIG. 33. Simulated ash profile along the filter length vs. experimental data of Bardasz et al. (2003). (a) High sulfur oil doped into fuel, (b) low sulfur oil doped into fuel and (c) high sulfur oil, regular use undoped into fuel.

with BC’s T=Ti at channel inlet and T=Tw at porous wall, where the velocity field is obtained from the solution of the Navier–Stokes equations with the boundary condition of a constant suction velocity uw. Our goal in developing DPF simulation tools is not the direct solution of Eq. (25) but its replacement with a 1D equation for the cross-section average (mixing-cup) temperature along the channel. This problem has been solved for the cases of a slit (two-dimensional (2D) planar geometry) and a circular pipe. Here we draw some general conclusions for arbitrary channel cross sections based on the behavior of existing solutions. For the purpose of presentation let us assume a

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

planar geometry with a slit width of 2L. This geometry would be relevant for example to the DPF design studied in Konstandopoulos et al. (2005). A first necessary condition for the existence of the 1D analog of Eq. (25) is the existence of a self-similar (asymptotic) velocity profile (itself equivalent to the existence of a 1D equation for the flow field). This self-similar profile depends only on the wall Reynolds (Rew) number and has the following form (planar slit geometry):  zuw  2 uz ¼ uzo  f ðy ; Rew Þ L

uw uy ¼ L

Zy

f ðy2 ; Rew Þdy

(26)

(27)

0

The above self-similar velocity profiles exists only for a Re number smaller than a critical value (e.g. 4.6 for a circular pipe). The self-similar velocity profiles must be found from the solution of the Navier–Stokes equations. Then they have to be substituted in Eq. (25) which must be solved to compute the local Nusselt number Nu(z). The asymptotic Nusselt number Nuas for a pipe flow and constant temperature boundary condition is given by Kinney (1968) as a function of Rew and Prandtl (Pr) numbers. The complete Nu(z) curve for the pipe and slit geometries and constant temperature or constant flux boundary conditions were given by Raithby (1971). This author gave Nuas as a function of Rew and fluid thermal Peclet (PeT) number. Both authors solved Eq. (25) via an eigenfunction expansion. The heat balance for the slit geometry in terms of gas mixing-cup temperature Tc is 2Lrg cp

duz T c ¼ 2hðT c  T w Þ  2rg cp uw T w dz

(28)

From the Figs. 7 and 8 of Raithby (1971) one can infer that Nu ¼ AðRew Þ þ PeT BðRew ; PeÞ

(29)

Substituting in the heat balance leads to Luz

dT c ath ¼ AðRew ÞðT c  T w Þ þ ð1  BðRew ; PeÞÞuw ðT c  T w Þ dz 2L

(30)

The function A stands for the modification to the usual Nusselt number for non-porous wall (the so-called Graetz problem) due to the suction-distorted

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axial velocity profile. Its value in the limit Rew-0 is the well-known asymptotic Nusselt number (3.6568 for pipe). The function B stands for the heat quantity transferred by the fluid stream entering the wall. The temperature of the stream leaving the wall is actually a proper average over the cross section. This averaging process has to do with the flow field (i.e. from where this fluid is coming from) and the temperature field which is imposed by the conduction process and it must be crossed by the transversely flowing fluid. From the Raithby (1971) it seems that the function B takes values around 0.5 for small values of Rew and PeT and increases as PeT increases for a given Rew. The large PeT asymptote for B is BðRew ; PeT Þ ¼ 1  AðRew Þ=PeT

(31)

The asymptotic form of B given above is valid in every geometry since it results from the requirement for the correct heat flux direction. The success of the 1D fluid dynamic model to describe the flow field in the DPF channel (Konstandopoulos and Johnson, 1989; Konstandopoulos et al., 1999, 2003) is an indication for the existence of a (nearly) self-similar flow field. A necessary condition for the application of the 1D model for the heat transfer problem as well, is that the wall velocity uw variation must be small along the characteristic channel length required for establishment of a steady heat transfer pattern (i.e. a length of a2 u¯ z =ath ). In transferring the above to the case of flow and heat transfer in a DPF channel we may formally write the heat balance as a2 uz

dT c ¼ 4ath AðRew ÞðT c  T w Þ þ ð1  BðRew ; PeT ÞÞ4auw ðT c  T w Þ dz

(32)

It must be noticed that the relative contribution of the conductive to convective heat transfer is given by the ratio ð1  BÞPeT =Að0Þ

(33)

which can take appreciable values (close to 1) in some practical conditions, so an accurate computation of the functions A and B is desirable and is currently in progress. The functions A and B need to be computed for the particular geometry of the DPF channels by solving the complete flow and temperature fields for different values of the wall Reynolds, Rew, and thermal Peclet, PeT, numbers and fitting the 1D Eq. (32) to the resulting average perimeter-averaged temperature evolution along the channel. Alternatively, the technique of Raithby (1971) and Kinney (1968) involving the solution for the self-similar flow field and eigenvalue analysis of the resulting energy equation (of the convection diffusion type) can be followed. For large PeT an asymptotic form

254

ATHANASIOS G. KONSTANDOPOULOS ET AL.

for B valid in any geometry is given by BðRew ; PeT Þ ¼ 1  AðRew Þ=PeT

(34)

Assuming that Nu ¼ A(0) (i.e. B ¼ 0, suction does not influence the temperature profile, Bissett, 1984) can lead to an inconsistency for Pe W A(0) since the heat flux is reversed from the hot wall to cold gas in the channel. In Kostoglou et al. (2003) the assumption of Nu ¼ A(0)+Pe (i.e. B ¼ 1) was employed. This is equivalent to assuming that the gas entering the porous wall has a temperature equal to the local mixing-cup temperature of the gas flowing in the channel. This assumption ensures the correct direction of heat flux but overestimates it a bit, since the actual value of B is between 0 and 1. A very good assumption consistent with the 1D flow model is then to use A ¼ A(0) i.e. a Nusselt number applicable to a solid wall but put B ¼ 0.5 as an estimation based on Figs. 7 and 8 of Raithby, 1971. This approximation leads to the following relation for the case of Prandtl number equal to 0.7: Nu ¼ 2:72 þ 0:35Rew

(35)

The above relation is very similar to the first-order expansion with respect to Rew of the polynomial fitting of numerically obtained data for heat transfer in a square channel with one porous and three solid walls (Hwang et al., 1990).

V. Entire Filter Scale A. THE EFFECTIVE CONDUCTIVITY

OF

DPFS

The axial effective conductivity la,eff of the DPF is well known to be the product of the solid fraction (1eA) and the conductivity of the wall material lw. The open area fraction eA (equivalent to the porosity) of a square honeycomb structure is  2 ws eA ¼ 1  (36) a þ ws This wall material conductivity lw is also an effective conductivity since the wall is porous and it is determined from the wall porosity and intrinsic material thermal conductivity, usually through a power law relation known as Archie’s Law. The transverse effective conductivity lt,eff requires a more involved computation. In this direction the DPF is a periodic medium, so its effective conductivity can be found by solving the heat conduction equation in the primary geometric unit (Fig. 34).

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(a + ws) / 2

(a + ws) / 2

ws / 2

FIG. 34. Primary geometric unit for the effective conductivity of DPF (see Plate 10 in Color Plate Section at the end of this book).

A unit temperature difference is imposed to the top wall of Fig. 34 and the conductance is computed. The boundary conditions are T=1 at the upper side, T=0 at the bottom side and insulation for all the other sides. The gas conductivity is considered through the gas phase heat balance equation so is not taken into account here. Taking into account the gas conductivity to compute the transverse effective conductivity for the DPF structure is not correct. Groppi and Tronconi approximate the conductivity of monoliths with square channels using a network of resistances analog as Gropi and Tronconi (1996): pffiffiffiffiffi lt;eff 1  eA ¼ pffiffiffiffiffi 1 þ eA  eA lw

(37)

A close inspection of the derivation in Gropi and Tronconi (1996) reveals that it does not retain the square network symmetry. A larger element length was assigned to the lower resistance path than the one of the higher resistance path leading to a 13% underestimation of the total resistance. Correcting the network analog assigning equal lengths to the two heat transfers paths (i.e. parallel and perpendicular to the temperature gradient in Fig. 34) leads to:  2 !1   !1 lt;eff a þ ws a þ ws 2 2 2 1þ ¼ ¼ pffiffiffiffiffi 1 þ pffiffiffiffiffi 1 þ eA 1 þ eA lw a þ ws =2 a þ ws =2 (38)

256

ATHANASIOS G. KONSTANDOPOULOS ET AL.

The derivation of the above expression is straightforward following the technique described in Gropi and Tronconi (1996). The value of lt,eff/lw for a typical filter with ws/(a+ws)=0.2 is: lt,eff/lw=0.212 (based on Fluent finite volume calculations), lt,eff/lw=0.238 (network approximation of Hwang et al., 1990), lt,eff/lw=0.211 (improved network approximation). Commercial filters are of similar (or smaller) wall to channel ratios so Eq. (38) can be safely used for design purposes. The validity of the effective homogeneous medium approximation to describe heat transfer in the multichannel scale will be assessed following the derivation of the Eq. (38) for the computation of the effective conductivity. For this purpose a DPF segment of square shape containing 24  24 channels is considered. A unit temperature difference between the upper and the other three sides is assumed to achieve macroscale 2D temperature distribution in addition to the microscale one. The problem is solved using Fluent, with the results shown in Fig. 35. Heat fluxes through the right (equal to the left due to symmetry) and bottom sides are shown in Fig. 36. The fluxes from Fluent are averages along

FIG. 35. Temperature distribution computed over a DPF segment with a unit temperature difference between the top and the other three sides (see Plate 11 in Color Plate Section at the end of this book).

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4000 3500

q ( W/m2)

3000 2500 2000

Side wall-exact computation Side wall –effective medium Bottom wall (x30) exact computation Bottom wall (x30) –effective medium

1500 1000 500 0

0

0.2

0.4 0.6 Normalized side coordinate, x or y

0.8

1

FIG. 36. Comparison between approximate and numerically computed heat fluxes on the two sides of a heated DPF segment with 24  24 channels.

the side of the unit cell. The approximation is very successful despite the singularity exhibited by the side flux close to the upper corner. Some inconsistencies are observed close to the singularity. This is expected due to the failure of the local averaging procedure in regions where the macroscopic rate of change of the flux is important on the scale of channel. The problem was also solved with the effective homogeneous medium theory with effective conductivity given by Eq. (38). The side length of the simulated segment is L=35 mm and the ratio ws/(a þ ws) is 0.2. The fluxes from the homogeneous medium approximation shown in Fig. 36 are computed by: Side boundary :

1 X q‘ sinhðipyÞ ¼4 lt;eff sinhðipÞ i¼1;3;5...

Bottom boundary :

1 X q‘ sinðipxÞ ¼4 lt;eff sinhðipÞ i¼1;3;5...

(39)

(40)

Having a validated model for the effective thermal conductivity of DPFs, it is possible to study segmented DPF designs. An example showing the influence of the segment’s gluing material thermal conductivity on the inter-segment heat transfer is shown in Fig. 37. B. MULTICHANNEL PHENOMENA A single channel description is no longer applicable and the entire DPF needs to be simulated in case of no radially uniform inlet conditions to the trap.

258

ATHANASIOS G. KONSTANDOPOULOS ET AL.

1/20

1/50

1/100

860 810 770 720 670 620 FIG. 37. Influence of the segment gluing material thermal conductivity to that of the segment (computed with the effective conductivity approach mentioned in the text) on the inter-segment heat transfer is shown (see Plate 12 in Color Plate Section at the end of this book).

A filter of realistic size consists of several thousand channels so its direct simulation via the numerical solution of a coupled discrete multichannel problem is an intractable task with the currently available computational resources. Alternatively the scale-up problem may be faced employing a continuum model of the filter honeycomb structure. Rigorous scale homogenization procedures lead to continuum models for the entire DPF (Bissett, 1984; Konstandopoulos et al., 2001, 2003); exploiting (as is common in continuum descriptions) a suitable scale disparity, namely the ratio of the channel hydraulic radius to the entire DPF diameter. The smallness of this parameter is invoked to formulate a perturbation expansion of the discrete multichannel equations. The continuum multichannel description of the DPF can accommodate various regeneration methods (thermal, catalytic and NO2-assisted) and can provide spatio-temporal information of several quantities of interest (e.g. filter temperature, soot mass distribution, flow distribution, etc.) as illustrated in Fig. 38. During incomplete regeneration radial non-uniformities might develop in a DPF as it was demonstrated in references Konstandopoulos et al. (2001, 2003). These non-uniformities will lead to flow redistribution during subsequent loading. A network analogy (Amador et al., 2004) suggests itself as an obvious approximation to provide an alternative to CFD but we have not up to now evaluated its accuracy. We have to end this performed CFD calculations with Fluent of the flow distribution at the face of a DPF after incomplete regenerations leading to radial non-uniformities (Fig. 39) computed by a multichannel simulation (Konstandopoulos et al., 2003) and these are compared to the flow distributions obtained from a network approximation in Fig. 40. The obtained flux-averaged error is 15% in the worst case. Current work is focusing on a composite network model, which employs a limited

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r =R

r=0 z (Axis of symmetry) 1200 inlet temperature Probe 1 simulation Probe 2 simulation

temperature (C)

1000 800 600 400 200 0 0

50

100

150

200 250 time (sec)

300

350

400

450

1100 1050 1000 950 900 850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100

FIG. 38. Temporal evolution of temperature probes inserted in a regenerating DPF (bottom) along with the spatial distribution of filter temperature at 145 s, i.e. the moment of exothermic peak (top). Locations (r,z) in millimeter of temperature probes: Probe 1, (24.25,40) and Probe 2, (24.25,142) (see Plate 13 in Color Plate Section at the end of this book).

FIG. 39. Flow distribution at the entrance of a DPF with radially non-uniform soot distribution, resulting from incomplete regeneration (see Plate 14 in Color Plate Section at the end of this book).

260

ATHANASIOS G. KONSTANDOPOULOS ET AL. 16

normalised axial velocity

14

resistor network model axisymmetric CFD

12

Rtrans = 0.25

10 8 6 4 2 0 0.0

0.2

0.4 0.6 0.8 normalized radial position

1.0

4.5 resistor network model normalised axial velocity

4.0

axisymmetric CFD

3.5

Rtrans = 0.5

3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.0

0.2

0.4 0.6 0.8 normalized radial position

1.0

2.0

normalised axial velocity

1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4

axisymmetric CFD resistor network model Rtrans = 0.75

0.2 0.0 0.0

0.2

0.4 0.6 0.8 normalized radial position

1.0

FIG. 40. Comparison of flow distribution at the entrance of a DPF with different radially non-uniform soot distributions computed by CFD and a simple network model.

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amount of information from CFD simulation of an unloaded DPF to improve the above error figure. The coupling of the DPF simulation to CFD appears inescapable in case of 3D flow distributions. We have interfaced our DPF multichannel model with Fluent employing the mechanism of user defined functions/subroutines. As an example of this coupling, we show in Fig. 41 the coupled CFD-multichannel simulation of a DPF placed downstream of a 451 bend in the exhaust. To make the simulation tractable the CFD solution is updated at every 0.5 gr increase of the filter soot load. From the snapshots of soot mass distribution and inlet velocity shown in Fig. 41 it becomes clear that the DPF inlet face velocity field moves in a self-regulated fashion toward a homogeneous distribution, while the soot mass distribution exhibits a stronger ‘‘memory’’ of the initial inlet distribution non-uniformity. An example of the DPF temperature field evolution during 3D simulation of regeneration is shown in Fig. 42. As we proceed to the entire exhaust system scale we face the task of interfacing the DPF behavior to that of other emission control devices in the exhaust (e.g. diesel oxidation catalysts (DOC) and NOx reduction devices). An example of a coupled simulation of a DOC and a DPF in series is shown in Fig. 43. We observe how a hydrocarbon pulse injection upstream of the DOC raises the exhaust temperature and causes regeneration of the DPF. Such simulation tools are very useful for the development and optimization of postinjection strategies for DPF regeneration. Closing the section on the entire filter scale we may remark that at the multichannel level the dynamic ash model of Konstandopoulos et al. (2003) and Rodriguez-Perez et al. (2004) can reproduce ash deposition patterns occurring in fuel-additive assisted regeneration (Blanchard et al., 2004) as seen in Fig. 44 as well as describe the pressure drop evolution due to ash accumulation as seen in Fig. 45.

VI. Conclusion Experience has proven that a DPF should be treated as an integral part of the powertrain and that it requires a systemic approach for its optimal design and operation, the latter being matched to the engine management strategy. The identification of optimum combinations of emission control components by the process of hardware building and testing entire emission control system, often requires a prohibitive amount of man power and cost. In addition, deep knowledge of emission control component behavior is required to perform an effective system-level integration and optimization. Advanced computational tools are now recognized as tractable alternatives for the rapid assessment of various design options for a particular vehicle platform.

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(a) 4 gr

8 gr

velocity

soot load

1 gr

(b)

12000

3.5E-03

8 gr 7 gr 6 gr 5 gr 4 gr 3 gr 2 gr 1 gr

2.5E-03 2.0E-03 1.5E-03 1.0E-03 5.0E-04

8000 6000 4000 2000

0.0E+00 -1.00 -0.50

(c)

CFCFD solution update every 0.5 gr of DPF load

1000

3.0E-03 PRESSURE DROP (Pa)

channel loading (gr/channel)

4.0E-03

0.00

0.50

normalised position

1.00

0

0

2

6 4 FILTER LOAD (gr)

8

FIG. 41. (a) 3D simulation of a DPF placed downstream of a 451 bend in the exhaust. (b) Soot mass and velocity distribution at the inlet face at different soot loads of the filter. (c) Evolution of radial soot mass profile (along the dotted line) at different soot loads of the filter and resulting pressure drop (see Plate 15 in Color Plate Section at the end of this book).

ADVANCES IN THE SCIENCE AND TECHNOLOGY OF DPF SIMULATION

t = 50s

263

t = 200s

Time

C t = 102s

t = 230s

t = 160s

t = 260s

FIG. 42. 3D–DPF simulation. Example of temperature field evolution in a 11.25  12 in DPF during regeneration (see Plate 16 in Color Plate Section at the end of this book).

Doc + DPF

700

6 DOC out /DPF inTemp HC injection profile Engine out / DOC in Temp Exhaust flow Pressure Drop Soot mass load

500

5 4

400 3 300 2

200

Pressure drop (kPa) Soot mass load (gr/m2)

Temperature (C) Exhaust flow (kg / hr)

600

1

100 0

0 0

200

400

600

800

1000

1200

1400

Time (s)

FIG. 43. Coupled DOC–DPF simulation. Filter regeneration by hydrocarbon injection upstream of the DOC over transient engine operation.

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

-S-SiC-DPF : 5,66”x6” (2,47 liter) / 200cpsi

90000 km evaluation

85 80 -S-SiC-DPF : 5,66”x6” (4,12 liter) / 200cpsi 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

55 50 45 40 35 30 25 20 15 10 5 0

FIG. 44. Multichannel dynamic ash model. Simulation of additive ash accumulation at the end of the filter. Comparison with X-ray images of Blanchard et al. (2004) (see Plate 17 in Color Plate Section at the end of this book).

10 9

pressure loss [kPa]

8 81.0 mm

56.0 mm

7 6 35.7 mm

5

12.4 mm

4 3 Dynamic Ash Model

2 1 0 0

20

40

60 ash mass [g]

80

100

120

FIG. 45. Simulation of the effect of ash accumulation of pressure drop. The experimental data are courtesy of Ibiden Co. Ltd.

For basic filter material development wall scale models especially the MicroFlowS approach can be applied. During early development of particulate emission control systems single channel models provide a fast and sufficient means for basic DPF design and sizing analysis. In addition, when applicable

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(i.e. in the absence of radial non-uniformities) they represent a valuable tool for analyzing experimental data collected under well-defined conditions and extraction of relevant physicochemical parameters of the system. Finally, detailed analysis and refinement of the selected DPF system can be achieved with the use of the multichannel models. In the present chapter, we have outlined the current state of progress in DPF simulation drawing from our recent work in the area. Employing a convenient spatial scale classification of the various models we have provided a framework for the consistent integration of the information from the various models across all scales. While computing limitations still remain the barrier for the routine deployment of detailed simulations of coupled emission control components at the entire exhaust system scale, we anticipate that in the near future such simulations will be widely employed by the industry, exploiting grid-computing environments. This means that from a research point of view, DPF simulation will focus into providing a deeper understanding and more detailed description of the coupled transport, structural and reaction microphenomena occurring at the wall and pore scales, to materialize the vision for an a priori design of advanced microstructures, hosting multifunctional catalysts for the highly compact and efficient emission control devices of the future.

NOMENCLATURE a cpg cps cpw dc dpr Ecat Euf Ef Eth f cat CO f th CO G Fox k k1 k’A,kB kA Kcat

side length of channel cross section specific heat capacity of exhaust gas specific heat capacity of soot specific heat capacity of filter wall material grain diameter soot primary particle size activation energy for catalytic soot oxidation parameter in catalytic CO selectivity Eq. (11) parameter in thermal CO selectivity Eq. (5) activation energy for thermal soot oxidation catalytic CO selectivity thermal CO selectivity exhaust mass flux (with respect to filtration area) local soot oxidation rate permeability volumetric reaction constant for soot oxidation with NO2 forward and reverse volumetric reaction constants for NO oxidation modified forward volumetric reaction constant for NO oxidation rate coefficient of catalytic soot oxidation

266 kuf kf ko,cat ko,th Kth ksoot kwall ‘ L Mc MCO M CO2 Mox mw mi PeT q Rew Rg R S Tc To uz uw w ws X,Y,Z y Y YCO Y CO2

ATHANASIOS G. KONSTANDOPOULOS ET AL.

parameter in catalytic CO selectivity equation parameter in thermal CO selectivity equation preexponential factor for catalytic soot oxidation preexponential factor for thermal soot oxidation rate coefficient of thermal soot oxidation soot permeability wall permeability length of segment size slit half width molecular weight of soot molecular weight of CO molecular weight of CO2 molecular weight of oxygen filter wall mass per unit filtration area soot mass in layer i thermal Peclet number for gas uwa/ath local heat flux wall Reynolds number auw/n ideal gas density recycling factor for soot oxidation by NO specific surface area (per unit mass) of soot layer channel cup mixing temperature filter and gas temperature at t=0 mean axial channel velocity superficial filtration velocity soot layer thickness filter wall thickness molar concentration of NO2, NO and total NOx respectively (Appendix). oxygen molar fraction in exhaust gases oxygen mass fraction in exhaust gases CO mass fraction in exhaust gases CO2 mass fraction in exhaust gases

GREEK LETTERS a, g ath b DH DHCO DH CO2

generalized soot layer structural descriptors thermal diffusivity of the gas index for the completeness of soot oxidation (Appendix), Forchheimer coefficient (Eqs. 1 and 3) heat of soot oxidation reaction (per unit mass of soot) specific heat of CO formation specific heat of CO2 formation

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e wall porosity eA open area fraction y parameter in thermal CO selectivity equation yu parameter in catalytic CO selectivity equation la,eff effective axial thermal conductivity of the filter lt,eff effective radial thermal conductivity of the filter lw wall thermal conductivity n kinematic viscosity of the gas m gas viscosity r soot layer density r~ intrinsic soot density rg exhaust gas density t dimensionless time f solid fraction in the deposit -Subscript o denotes the initial value -Overbar denotes dimensionless quantity -O() denotes ‘‘on the order of’’

ABBREVIATIONS 1, 2, 3D CAST CFD DOC DPF ECU LNC LNT OAF ODE SCR SEM SMPS

one, two, three dimensional combustion aerosol standard computational fluid dynamics diesel oxidation catalyst diesel particulate filter engine control unit lean NOX converter lean NOX trap open area fraction ordinary differential equations selective catalytic reduction scanning electron microscope scanning mobility particle sizer

ACKNOWLEDGMENTS This work has been supported in part by the European Commission through the (ART-, PSICO-, SYLOC-, STYFF-) DEXA cluster projects as well as projects COMET, IMITEC and FLOWGRID. An earlier account of this work has been presented in the 2005 SAE World Congress. We thank Dr. E. Kikkinides for sharing his expertise on porous media reconstruction and

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Mr. A. Tsakis for his assistance with computations. We are also thankful to all of our colleagues at the APT Lab for their support in carrying out the work presented. Ibiden Co. Ltd. Japan is acknowledged for providing the experimental data in Fig. 45.

REFERENCES Amador, C., Gavriilidis, A., and Angeli, P. Chem. Eng. J. 101(1–3), 379–390 (2004). Bardasz, E., Mackney, D., Britton, N., Kleinschek, G., Olofsson, K., Murray, I., and Walker, A. P. Investigations of the interactions between lubricant-derived species and aftertreatment systems on a state-of-the-art heavy duty diesel engine. SAE Technical Paper No. 2003-011963 (2003). Bardon, S., Bouteiller, B., Bonnail, N., Girot, P., Gleize, V., Oxarango, L., Higelin, P., Michelin, J., Schuerholz, S., and Terres, F. Asymmetrical channels to increase DPF lifetime. SAE Technical Paper No. 2004-01-0950 (2004). Bissett, E. J. Chem. Eng. Sci. 39(7/8), 1233–1244 (1984). Bissett, E. J., and Shadman, F. AIChE J. 31(5), 753–758 (1985). Blanchard, G., Colignon, C., Griard, C., Rigaudeau, C., Salvat, O., and Seguelong, T. Passenger car series application of a new diesel particulate filter system using a new ceria-based, fuel-borne catalyst: from the engine test bench to European vehicle certification. SAE Technical Paper No. 2002-01-2781 (2004). Boretto, G., Imarisio, R., Rellecati, P., Barucchi, E., and Sanguedolce, A. ‘‘Serial Application of a Catalyzed Particulate Filter on Common Rail DI Diesel Engines For Passenger Cars.’’ F2004V068, FISITA World Automotive Congress, Barcelona, Spain, 2004, May 23–27. Chen, S., and Doolen, G. Ann. Rev. Fluid Mech. 30, 329 (1998). Dardiotis, C. K., Haralampous, O. A., and Koltsakis, G. C. Ind. Eng. Chem. Res. 45, 3520–3530 (2006). Dullien, F. A. L., ‘‘Porous Media’’. Academic Press Inc, San Diego (1979). Fluent Inc. Fluent 6 User’s Guide, December (2001). Gropi, G., and Tronconi, E. AIChE. J. 42(8), 2382–2387 (1996). Hawker, P., Myers, N., Huthwohl, G., Vogel, H., Bates, B., Magnusson, L., and Bronneberg, P. Experience with a new particulate trap technology in Europe. SAE Paper No 970182, (1997). http://www.unizar.es/flowgrid/ Hwang, G. J., Cheng, Y. C., and Ng, M. L. Int. J. Heat Mass Transfer 36, 2429–2440 (1990). Jacquot, F., Logie, V., Brilhac, J. F., and Gilot, P. Carbon 40(3), 335–343 (2002); See also, Setiabudi, A., Makkee, M., and Moulijn, J. A. Appl. Catal. B: Environ. 50(3), 185–194 (2002). Johnson, T. V. Diesel emission control technology-2003 in review. SAE Technical Paper No. 200401-0070 (2004). Karadimitra, K., Lorentzou, S., Agrafiotis C., and Konstandopoulos A. G. ‘‘Modeling of Catalytic Particle Synthesis via Spray Pyrolysis & In-Situ Deposition on Porous Materials’’. PARTEC 2004, International Conference for Particle Technology, Nuremberg, Germany, 2004, March 16–18. Kikkinides, E. S., and Burganos, V. N. Phys. Rev. E 62(5), 6906 (2000). Kinney, R. B. Int. J. Heat Mass Transfer 11, 1393–1401 (1968). Knoth, J. F., Drochner, A., Vogel, H., Gieshoff, J., Ko¨gel, M., Pfeifer, M., and Votsmeier, M. Catal. Today 105, 598–604 (2005); See also, Votsmeier, M., Gieshoff, J., Ko¨gel, M., Pfeifer, M., Knoth, J. F., Drochner, A., and Vogel, H. ‘‘Wall-flow filters with wall-integrated oxidation catalyst: A simulation study’’ Appl. Catal. B: Environ., in press, Corrected Proof, Available online 28 July 2006.

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Konstandopoulos, A. G. Powder Tech. 109(1–3), 262–277 (2000). Konstandopoulos, A. G. Flow resistance descriptors for diesel particulate filters: Definitions, measurements and testing. SAE Technical Paper No. 2003-01-0846 (SP-1755) (2003). Konstandopoulos, A. G. ‘‘Convective-diffusive deposition of fractal-like aggregates and the microstructure and properties of the resulting nanostructured deposits’’ (to be submitted) (2007). Konstandopoulos, A. G., and Johnson, J. H. Wall-flow diesel particulate filters-their pressure drop and collection efficiency. SAE Technical Paper No. 890405, SAE Trans. 98 sec. 3 (J. Engines), pp. 625–647 (1989). Konstandopoulos, A. G., and Kladopoulou, E. ‘‘A Virtual Sensor for On-board Diagnostics and Control of Diesel Particulate Filters’’. CDAuto03A2028, CD AUTO 03, 4th International Conference on Control and Diagnostics in Automotive Applications, Sestri-Levante, Italy, 2003, June 18–20. Konstandopoulos, A. G., and Kladopoulou, E. The optimum cell density for wall-flow monolithic filters: Effects of filter permeability, soot cake structure and ash loading. SAE Technical Paper No. 2004-01-1133 (2004). Konstandopoulos, A. G., and Kostoglou, M. ‘‘A mathematical model of soot oxidation on catalytically coated ceramic filters’’. Advances in Vehicle Control and Safety (AVCS’98), Amiens, France, July 1–3, 1998. Konstandopoulos, A. G., and Kostoglou, M. ‘‘Theory and Practice of Soot Filter Operation’’. Proceedings of Global Powertrain Congress 99 (invited paper), Powertrain Int., Stuttgart, 1999a, October 5–7. Konstandopoulos, A. G., and Kostoglou, M. Periodically reversed flow regeneration of diesel particulate traps. SAE Technical Paper No. 1999-01-0469 (1999b). Konstandopoulos, A. G., and Kostoglou, M. Combust. Flame 121(3), 488–500 (2000). Konstandopoulos, A. G., and Kostoglou, M. Microstructural aspects of soot oxidation in diesel particulate filters. SAE Technical Paper No. 2004-01-0693 (SP-1861) (2004). Konstandopoulos, A. G., Kostoglou, M., and Housiada, P. Spatial non-uniformities in diesel particulate trap regeneration. SAE Technical Paper No. 2001-01-0908 (SP-1582) (2001). Konstandopoulos, A. G., Kostoglou, M., Housiada, P., Vlachos, N., and Zarvalis, D. Multichannel simulation of soot oxidation in diesel particulate filters. SAE Technical Paper No. 2003-010839 (2003). Konstandopoulos, A. G., Kostoglou, M., Skaperdas, E., Papaioannou, E., Zarvalis, D., and Kladopoulou, E., Fundamental studies of diesel particulate filters: Transient loading, regeneration and aging. SAE Technical Paper No. 2000-01-1016 (SP-1497) (2000). Konstandopoulos, A. G., Lorentzou, S., and Kostoglou, M. Wall scale reaction models in diesel particulate filters. SAE Technical Paper No. 2007-1-1130 (2007). Konstandopoulos, A. G., Skaperdas, E., and Masoudi, M. Inertial contributions to the pressure drop of diesel particulate filters. SAE Technical Paper No. 2001-01-0909 (SP-1582) (2001). Konstandopoulos, A. G., Skaperdas, E., and Masoudi, M. Microstructural properties of soot deposits in diesel particulate traps. SAE Technical Paper No. 2002-01-1015 (SP-1673) (2002). Konstandopoulos, A. G., Skaperdas, E., Warren, J., and Allanson, R. Optimized filter design and selection criteria for continuously regenerating diesel particulate traps. SAE Technical Paper No. 1999-01-0468 (1999). Konstandopoulos, A. G., Vlachos, N., Housiada, P., and Kostoglou, M. Simulation of triangularcell-shaped, fibrous wall-flow filters. SAE Technical Paper No. 2003-01-0844 (SP-1755) (2003). Konstandopoulos, A. G., Vlachos, N., Stavropoulos, I., Skopa, S., Schumacher, U., and Woiki, D. Study of a sintered metal diesel particulate trap. SAE Technical Paper Offer No. 05FL-106, (submitted) (2005). Konstandopoulos, A. G., Zarvalis, D., Papaioannou, E., Vlachos, N. D., Boretto, G., Pidria, M. F., Faraldi, P., Piacenza, O., Prenninger, P., Cartus, T., Schreier, H., Brandstatter, W., Wassermayr, C., Lepperhof, G., Scholz, V., Luers, B., Schnitzler, J., Claussen, M.,

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Wollmann, A., Maly, M., Tsotridis, G., Vaglieco, B. M., Merola, S. S., Webster, D., Bergeal, D., Gorsmann, C., Obernosterer, H., Fino, D., Russo, N., Saracco, G., Specchia, V., Moral, N., D’Anna, A., D’Alessio, A., Zahoransky, R., Laile, E., Schmidt, S., and Ranalli, M. The diesel exhaust aftertreatment (DEXA) cluster: A systematic approach to diesel particulate emission control in Europe. SAE Technical Paper No. 2004-01-0694 (SP-1861) (2004). Kostoglou, M., Housiada, P., and Konstandopoulos, A. G. Chem. Eng. Sci. 58, 3273–3283 (2003). Lueders, H., Stommel, P., and Geckler, S. Diesel exhaust treatment-new approaches to ultra low emission diesel vehicles. SAE Paper No. 1999-01-0108 (1999). Muntean, M., Rector, D., Herling, D., Khaleel, M., and Lessor, D. Lattice-Boltzmann diesel particulate filter sub-grid modelling-a progress report. SAE Technical Paper No. 2003-010835 (2003). Nakatani, K., Hirota, S., Takeshima, S., Itoh, K., Tanaka, T., and Dohmae, K. Simultaneous PM and NOx reduction system for diesel engines. SAE Technical Paper No. 2002-01-0957 (2002). Ogyu, K., Ohno, K., Hong, S., and Komori, T. Ash storage capacity enhancement of diesel particulate filter. SAE Technical Paper No. 2004-01-0949 (2004). Olsson, L., Persson, H., Fridell, E., Skoglundh, M., and Andersson, B. J. Phys. Chem. B 105(29), 6895–6906 (2001); See also, Olsson, L., Westerberg, B., Persson, H., Fridell, E., Skoglundh, M., and Andersson, B. J. Phys. Chem. B 105, 10433–10439 (2001). Peters, B. Integrated 1d to 3d simulation workflow of exhaust aftertreatment devices. SAE Technical Paper No. 2004-01-1132 (2004). Raithby, G. Int. J. Heat Mass Transfer 14, 223–243 (1971). Rodriguez-Perez, D., Castillo, J. L., Antoranz, J. C., Konstandopoulos, A. G., and Vlachos, N. ‘‘Mixed ODE-MC Model for the Soot Cake Formation in a Square Section Filter’’. European Aerosol Conference EAC 2004, Budapest, Hungary, 2004, September 6–10. SAE International. Diesel exhaust emission control, SP-1860, ISBN 0-7680-1396-8 (2004). Skaperdas, E., and Konstandopoulos, A. G. ‘‘Prediction of Diesel Particulate Filter Loading Behavior for System Control Applications’’, CD AUTO 01, 3rd International Conference on Control and Diagnostics in Automotive Applications, Sestri-Levante, Italy, 2001, July 4–6. Tassopoulos, M. ‘‘Relationships between particle deposition mechanism, deposit microstructure and effective transport properties’’. PhD Thesis, Yale University, USA (1991). Tomadakis, M. M., and Sotirchos, S. V. AIChE J. 37, 1175 (1991a). Tomadakis, M. M., and Sotirchos, S. V. AIChE J. 37, 74 (1991b). Torquato, S., ‘‘Random Heterogeneous Materials: Microstructure and Macroscopic Properties’’. Springer, New York (2001). Vlachos, N., Housiada, P., Zarvalis, D., and Konstandopoulos, A. G., Transient filtration and pressure drop behavior of diesel particulate filters, in ‘‘Particle Loading and Kinetics of Filtration in Fibrous Filters’’ (M. J. Lehmann, and G. Kasper Eds.), pp. 13–26. University of Karlsruhe, Karlsruhe (2002). Young, D. M., Hickman, D. L., Bhatia, G., and Gunasekaran, N. Ash storage concept for diesel particulate filters. SAE Technical Paper No. 2004-01-0948 (2004). Zarvalis, D., Altiparmakis, C., Mei, F., and Konstandopoulos, A. G. ‘‘Evaluation of a Multi-layer Sintered Metal Fibrous Diesel Particulate Filter’’. European Aerosol Conference EAC 2003, Madrid, Spain, 2003, August 31–September 5.

Appendix. Microstructural Model of Soot Oxidation: The Effect of Catalyst In the two-layer model of a catalytic coating, soot particles oxidize when found within the ‘‘field of catalyst action’’. This is modeled with a parameter

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b denoting the fraction (0–1) of the soot surface in ‘‘contact’’ with the catalyst (meaning within its field of action) and in principle it depends on coating structure and amount of soot loading of the filter. In the present model the parameter b is taken as a constant although in principle can vary dynamically due to soot deposition conditions and/or flow or reaction induced restructuring of the soot. The layer can ‘‘store’’ a certain amount of soot until it is filled-up, which depends on coating structure and filtration velocity. The remaining soot particles form a ‘‘queue’’ on top of the filled-up catalyst-influenced layer, and this second layer can be oxidized only thermally. In the presence of a catalyst and with a finite selectivity for CO production, soot oxidation is described with the following global reactions, one for the thermal and one for the catalytic path (i.e. oxidation of soot by oxygen transferred from the catalyst by a redox and/or spill-over mechanism). Konstandopoulos and Kostoglou (1999b, 2000): th th C þ ð1  f th CO =2ÞO2 ! f CO CO þ ð1  f CO ÞCO2

(41)

cat cat C þ ð1  f cat CO =2ÞO2 ! f CO CO þ ð1  f CO ÞCO2

(42)

The local soot oxidation rate is F ox ¼ ½ð1  bÞK th þ bK cat ÞSrg rY

Mc M ox

(43)

The conservation equations for the different species across the soot layer are dY cat ¼ ½ð1  f th CO =2Þð1  bÞK th þ ð1  f CO =2ÞbK cat ÞSrY dx

(44)

dY CO M CO cat ¼ ½f th CO ð1  bÞK th þ f CO bK cat SrY dx M ox

(45)

dY CO2 M CO2 cat ¼ ½ð1  f th CO Þð1  bÞK th þ ð1  f CO ÞbK cat SrY dx M ox

(46)

uw

uw

uw

A total mass balance for O2 gives: Y ð0Þ  Y ðwÞ ¼

1 M ox M ox Y CO ðwÞ þ Y CO2 ðwÞ 2 M CO M CO2

(47)

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

The initial conditions for the above equations are: at x ¼ w : Y ¼ Y in ðtÞ;

Y co ¼ Y co2 ¼ 0

(48)

The deposit evolution equations according to the microstructural model introduced in Konstandopoulos and Kostoglou (2004) with parameters a and g are: @r @r ¼ aF ox  x_ @t @x Zx x_ ¼

ð1  aÞ

(49)

F ox dz r

(50)

0

S ¼ So ðcpw mw þ cps MÞ

 g ro r

(51)

dT Mc ¼ DH CO GY CO ðwÞ dt M CO þ DH CO2 GY CO2 ðwÞ

Mc þ Gcpg ðT in  TÞ M CO2

ð52Þ

where K th ¼ ko;th TeðE th =Rg TÞ

(53)

K cat ¼ ko;cat TeðE cat =Rg TÞ

(54)

f th CO ¼

f cat CO ¼

1 1 þ kf yy eE f =Rg T

(55)

1 0

1 þ k0f yy eE f =Rg T 0

The following dimensionless variables are introduced: T ¼ T=T o ;

Y ¼ Y =Y o ;

Y CO2 ¼ Y CO2 =Y o ; r ¼ r=ro ;

Y CO ¼ Y CO =Y o

G ¼ G=G o ; S ¼ S=S o ;

M ¼ M=M o

x ¼ x=wo

(56)

ADVANCES IN THE SCIENCE AND TECHNOLOGY OF DPF SIMULATION

w ¼ w=wo ;

K th ¼

K th ; Ko

K cat ¼

t¼

rg ¼ rg =rgo , K cat ; Ko

A¼

K o wo ro So uw

Mc T o S o rgo Y o M ox

C 1A ¼

G o DH CO M CO cpw mw T o K o So rgo M c

C 1B ¼

G o DH CO2 M CO2 cpw mw T o K o So rgo M c

C2 ¼

273

M ox G o cpg M c cpw mw KðT o ÞS o rgo Y o

C3 ¼

cps M o cpw mw

where To is the initial filter temperature, Yo the inlet oxygen mass fraction at t=0, Go the exhaust mass flux (with respect to filtration area) at t=0, Mo the initial soot mass per unit filtration area, rgo the exhaust density at t=0 (from the ideal gas law) and ro and So are reference values for the density and surface area of the deposit, respectively. Using the new variables, the Lagrangian form of the problem that must be solved is dY 1 cat 1g ¼ Y ½ð1  f th CO =2Þð1  bÞK th þ ð1  f CO =2ÞbK cat ÞAr dx GðtÞT dY CO 1 M CO th 1g ¼ ½f CO ð1  bÞK th þ f cat Y CO bK cat ÞAr dx GðtÞT M ox dY CO2 1 M CO2 cat 1g ¼ ½ð1  f th Y CO Þð1  bÞK th þ ð1  f CO ÞbK cat ÞAr dx GðtÞT M ox dr 1 ¼ a ½ð1  bÞK th þ bK cat r1g Y dt T

(57)

(58)

(59)

(60)

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ATHANASIOS G. KONSTANDOPOULOS ET AL.

dx ð1  aÞ ¼ dt T

Zx

½ð1  bÞK th þ bK cat rg Y dz

(61)

0

ð1 þ C 3 MÞ

dT ¼ GðtÞðC 1A Y CO ðwÞ þ C 1B Y CO2 ðwÞÞ þ C 2 GðtÞðT in ðtÞ  TÞ (62) dt

with initial and boundary conditions Y ¼ Y in ðtÞ

at x ¼ w

(63)

rðx; 0Þ ¼ ro ðxÞ

(64)

Tð0Þ ¼ 1

(65)

The above system is discretized across the soot layer leading to the following set of ordinary differential equations Y i ¼ exp½A

L 1 1X ððð1  f th CO ðY i Þ=2Þð1  bj ÞK th G T 2 j¼iþ1

1g þ ð1  f cat þ ðð1  f th CO ðY i Þ=2Þbj K cat Þrj CO ðY i1 Þ=2Þð1  bj1 ÞK th 1g þ ð1  f cat CO ðY i1 Þ=2Þbj1 K cat Þrj1 Þðxj  xj1 Þ

dri 1 ¼ a ðð1  bi ÞK th þ bi K cat Þri1g Y i dt T

ð66Þ

(67)

L dxi 1 X ¼  ð1  aÞ ððð1  bj ÞK th þ bj K cat Þrg j Yj dt 2T j¼iþ1

þ ðð1  bj1 ÞK th þ bj1 K cat Þrg j1 Y j1 Þðxj  xj1 Þ

Y CO;i ¼ A

ð68Þ

L 1 1X ½ððf th CO ðY i Þð1  bj ÞK th G T 2 j¼iþ1

1g þ f cat Y j þ ðf th CO ðY i Þbj K cat Þrj CO ðY i1 Þð1  bj1 ÞK th M CO 1g þ f cat CO ðY i1 Þbj1 K cat Þrj1 Y j1 Þðxj  xj1 Þ M ox

ð69Þ

ADVANCES IN THE SCIENCE AND TECHNOLOGY OF DPF SIMULATION

Y CO2 ;i ¼ A

275

L 1 1X ½ðððð1  f th CO ðY i ÞÞð1  bj ÞK th G T 2 j¼iþ1

1g þ ð1  f cat Y j þ ð1  f th CO ðY i ÞÞbj K cat Þrj CO ðY i1 ÞÞð1  bj1 ÞK th M CO2 1g þ ð1  f cat CO ðY i1 ÞÞbj1 K cat Þrj1 Y j1 Þðxj  xj1 Þ M ox

ð70Þ

where xi(0)=xio are the initial grid points. Each time the density in a grid point becomes zero, this grid point is eliminated and the ODE integrator is reinitialized.

(a)

(b)

1.0 Normalised Permeability (-)

0.9 0.8 0.7 Non-uniform coating Uniform coating

0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

0.2 0.1 0.3 Fraction of Pore Volume Filled (-)

0.4

(c) PLATE 7. Simulation of catalytic coating deposition at the same integral quantity of catalyst load. (a) Uniform coating and (b) non-uniform coating. The color code (blue to red) assists to visualize the local catalyst coating thickness from the filter surface. (c) Effect of coating distribution (uniform vs. non-uniform) on the DPF permeability (for Black and White version, see page 221).

(a)

Experimental Permeability, k (m2)

1E-11

8E-12

6E-12

4E-12

y = 1.02x 2E-12

R2 = 0.99

0 0 (b)

2E-12

4E-12

6E-12

8E-12

Simulated Permeability by LB, kLB

1E-11

(m2)

PLATE 8. (a) Velocity fields at different sections through the reconstructed filter wall (gray denotes the solid material, violet denotes lowest and red denotes highest velocity) and visualization of flow paths in the reconstructed filter wall and (b) comparison of experimental and simulated filter permeabilities (for Black and White version, see page 224).

Pressure drop (Pa)

2500 2000 1500 1000 500 0 time

(a)

(c)

(d)

0.00 g/m2

0.77 g/m2

0.00 g/m2

(b)

1.56 g/m2

0

1

2 3 4 6 7 5 Areal challenge mass (gr/m2)

2.27 g/m2

2.27 g/m2

2.81 g/m2

8

3.30 g/m2

3.30 g/m2

PLATE 9. Simulation of soot deposition on a filter wall. (a) Evolution of soot deposits (gray) in the wall (black is solid, white is pore space) and incipient cake formation; (b) pressure drop as function of challenge soot mass demonstrating the deep-bed to cake filtration transition; (c) visualization of soot deposition in an extruded ceramic (granular) filter wall and (d) development of soot deposits (black) and soot mass fraction in the wall (solid material is gray) to the onset of cake formation. Soot mass fraction scale is from 0 (violet) to the inflow value (red). In (d) the velocity on a section through the filter wall is shown, with overlay of the soot deposit shapes (for Black and White version, see page 235).

(a + ws) / 2

(a + ws) / 2

ws / 2

PLATE 10. Primary geometric unit for the effective conductivity of DPF (for Black and White version, see page 255).

PLATE 11. Temperature distribution computed over a DPF segment with a unit temperature difference between the top and the other three sides (for Black and White version, see page 256).

1/20

1/50

1/100

860 810 770 720 670 620 PLATE 12. Influence of the segment gluing material thermal conductivity to that of the segment (computed with the effective conductivity approach mentioned in the text) on the inter-segment heat transfer is shown (for Black and White version, see page 258).

r =R

r=0 z (Axis of symmetry) 1200 inlet temperature Probe 1 simulation Probe 2 simulation

temperature (C)

1000 800 600 400 200 0 0

50

100

150

200 250 time (sec)

300

350

400

450

1100 1050 1000 950 900 850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100

PLATE 13. Temporal evolution of temperature probes inserted in a regenerating DPF (bottom) along with the spatial distribution of filter temperature at 145 s, i.e. the moment of exothermic peak (top). Locations (r,z) in millimeter of temperature probes: Probe 1, (24.25,40) and Probe 2, (24.25,142) (for Black and White version, see page 259).

PLATE 14. Flow distribution at the entrance of a DPF with radially non-uniform soot distribution, resulting from incomplete regeneration (for Black and White version, see page 259).

(a) 4 gr

8 gr

velocity

soot load

1 gr

(b)

12000

3.5E-03

8 gr 7 gr 6 gr 5 gr 4 gr 3 gr 2 gr 1 gr

2.5E-03 2.0E-03 1.5E-03 1.0E-03 5.0E-04

8000 6000 4000 2000

0.0E+00 -1.00 -0.50

(c)

CFCFD solution update every 0.5 gr of DPF load

1000

3.0E-03 PRESSURE DROP (Pa)

channel loading (gr/channel)

4.0E-03

0.00

0.50

normalised position

1.00

0

0

2

6 4 FILTER LOAD (gr)

8

PLATE 15. (a) 3D simulation of a DPF placed downstream of a 451 bend in the exhaust. (b) Soot mass and velocity distribution at the inlet face at different soot loads of the filter. (c) Evolution of radial soot mass profile (along the dotted line) at different soot loads of the filter and resulting pressure drop (for Black and White version, see page 262).

t = 50s

t = 200s

Time

C t = 102s

t = 230s

t = 160s

t = 260s

PLATE 16. 3D–DPF simulation. Example of temperature field evolution in a 11.25  12 in DPF during regeneration (for Black and White version, see page 263).

-S-SiC-DPF : 5,66”x6” (2,47 liter) / 200cpsi

90000 km evaluation

85 80 -S-SiC-DPF : 5,66”x6” (4,12 liter) / 200cpsi 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

55 50 45 40 35 30 25 20 15 10 5 0

PLATE 17. Multichannel dynamic ash model. Simulation of additive ash accumulation at the end of the filter. Comparison with X-ray images of Blanchard et al. (2004) (for Black and White version, see page 264).

INDEX

A

F

Ash, 213–216, 230, 242, 245–248, 250–251, 261, 264 Asymmetric channel, 213, 242, 245–246 Automotive, 47–49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99

Fast SCR, 164–165, 176, 178–179, 181–185, 195, 198 Filter, 213–224, 226–235, 237–240, 242–244, 246, 250–251, 254, 256, 258–259, 261–267 Flow in porous media, 213, 217, 219, 228

H

C

Hydrocarbons (HC) adsorption, 104, 113, 131–134, 136, 140 142, 201 Hydrocarbons (HC) oxidation, 119, 130, 133–135, 139, 142, 151, 153–154

Cake, 223, 225–229, 231, 235, 237–239, 242, 245, 249 Catalyst, 213–214, 216, 219–221, 229, 234–235, 238–239, 241, 267 Catalyst modeling, 104, 110–111, 137, 198 CeO2–ZrO2 solid solution, 14, 17 CO oxidation, 112, 123, 133–134, 155 Combined exhaust gas aftertreatment systems, 103, 109 Computer simulation, 48–50, 97

I Inertial loss, 213, 242, 244 Isotopic exchange reaction, 15–17

K Kinetic modeling, 128, 142 Kinetics, 213, 234, 239, 242, 250

D Deep bed filtration, 228, 230 Deposit compaction, 213, 226–228 Deposit microstructure, 213, 219, 223 Diesel oxidation catalyst, 103–104, 108, 130–131, 199, 205 Diesel particulate filter, 49, 77, 93, 99

L Lattice Boltzmann, 220, 223 Lean exhaust gas aftertreatment, 103

M Modelling, 47–50, 53, 59–60, 63, 65–67, 76, 78, 84, 88–90, 93–94, 97–98 Monolith, 47–50, 56–58, 61–62, 70, 90, 94, 98, 99 Monolith modeling, 111, 134 Multichannel phenomena, 213, 257

E Effective conductivity, 213, 254–258 Entrainment, 213, 242, 246–248, 250 ExACT, 109, 123, 144, 151, 198, 201, 205 Exhaust aftertreatment, 48–49 Exhaust gas aftertreatment systems modeling, 109

277

278

INDEX

N NOx reduction, 108, 138, 142–143, 147–151, 155, 157, 201 NOx storage, 103–104, 108, 113, 137, 142–156, 159–163, 202, 205 NOx storage catalyst, 149, 154–155 NOx storage reduction catalyst, 2, 8 NOx trap, 47–48, 53, 77, 88–89, 97, 99 NO2 turnover, 213, 238–239, 241

O Optimization, 107–108, 128, 153, 201 Oxidation catalyst, 47–48, 76, 78, 86, 97, 99 Oxidation, 213, 234–235, 237–241, 261, 265–267 Oxygen mobility, 1–2, 15–16, 42 Oxygen storage and release, 1–2, 8, 16, 42

Soot, 213, 215–216, 223, 225–228, 230–231, 234–250, 258–263, 265–267 Steam reforming, 132, 150–151, 153–155, 202 Sulfur poisoning, 2, 33, 35, 39, 43, 44

T Three way catalyst, 3–5, 24, 48, 99 Transient catalytic kinetics, 124–126, 129, 132, 134, 149–151, 165–166, 172, 174–179, 181–183, 189–191, 198 Two layer model, 234, 236, 238, 239, 270

U Unit cell, 229, 257 Urea SCR, 124

V V2O5-WO3/TiO2 catalysts, 165

P Packing density, 223 Permeability, 217–218, 221, 223–224, 237, 265–266 Pressure drop, 215, 218, 226–229, 232, 235, 237, 261–264 PtRh/Ba/CeO2/Al2O3 catalyst, 146, 149, 154

W Wall heat transfer, 213, 242, 250 Water gas shift, 132, 148, 150–151, 153–155, 202

X XAFS, 13 XANES, 21–24

S SCR, 47, 61, 77–78, 83–86, 97, 99 Sintering of PGM, 1, 18, 43

Z Zeolite catalysts, 138