Process Control Engineering Edited by M. Polke
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Process Control Engineering Edited by M. Polke
0VCH Verlagsgesellschaft mbH, D-69451 Weinheim (Federal Republic of Germany), 1994 Distribution: VCH, P.O. Box 10 1161, D-69451 Weinheim (Federal Republic of Germany) Switzerland VCH, P.O. Box, CH-4020 Basel (Switzerland) United Kingdom and Ireland VCH (UK) Ltd., 8 Wellington Court, Cambridge CB11HZ (United Kingdom)
USA and Canada: VCH, 220 East 23rd Street, New York, NY 10010-4606 (USA) Japan: VCH, Eikow Building, 10-9 Hongo 1-chome, Bunkyo-ku, Tokyo 113 (Japan) ~~
ISBN 3-527-28689-6
Process Control Engineering Edited by M. Polke with the collaboration of
U. Epple and M. Heim and contributions by W. Ahrens, D. Baker, M. Bjorkmann, H. Drahten, U. Epple, M. Freytag, E. D. Gilles, K. Hartmann, M. Heim, R. Hotop, N. Ingendahl, N. Kuschnerus, R. Metz, W. Mutz, E. Nicklaus, W. Noerpel, U. Pallaske, G. Pinkowski, M. Polke, J. Raisch, G. Schmidt, K. H. Schmitt, H.-J. Schneider, G. U. Spohr, H. Steusloff, R.Vogelgesang, H. Wehlan
Weinheim - New York - Base1 - Cambridge - Tokyo
Prof. Dr. Martin Polke Rheinisch-Westfalische Technische Hochschule Aachen Lehrstuhl fur ProzeBleittechnik Turmstr. 46 52064 Aachen Federal Republic of Germany
This book was carefully produced. Nevertheless, authors, editor and publisher do not warrant the information contained therein to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate. Published jointly by VCH Verlagsgesellschaft,Weinheim (Federal Republic of Germany) VCH Publishers, New York, NY (USA) Editorial Directors: Stephen Hawkins, Philomena Ryan-Bugler and Karin Sora Production Manager : Peter Biel Cover Design: Phase Model of Production, Process Control System, and Operational Control of Process Plants.
Library of Congress Card No.: 94-061095 British Library Cataloguing-in-Publication Data: A catalogue record for this book is available from the British Library Die Deutsche Bibliothek
~
CIP-Einheitsaufnahme
Process control engineering/ ed. by M. Polke. With the collab of U. Epple and M. Heim and contributions by W. Ahrens . . . Weinheim; New York; Basel; Cambridge; Tokyo: VCH, 1994 ISBN 3-527-28689-6 NE: Polke, Martin [Hrsg.]; Epple, Ulrich
0VCH Verlagsgesellschaft mbH, D-69451 Weinheim (Federal Republic of Germany), 1994 Printed on acid-free and low chlorine paper All rights reserved (including those of translation into other languages). N o part of this book may be reproduced in any form -by photoprinting, microfilm, or any other means nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law. Cover illustration: Jurgen Wirth, D-63303 Dreieich Composition, printing and bookbinding: Graphischer Betrieb Konrad Triltsch, D-97070 Wiirzburg ~
Dedicated to my father Franz Polke, my teacher Prof. Dr. Fritz Stockmann, my mentor Dr. Otto Koch and the initiator of process control engineering Dr. Axel Lippert.
Preface
This book surveys the methods, tasks and tools of process control engineering. Its scope has been purposely made broad in order to permit an overall view of this subject. The book is intended both for interested nonspecialists who wish to become acquainted with the discipline of process control engineering and for process control engineers, who should find it helpful in identifying individual tasks and organizing them into a coh&ent whole. This objective has led the author to forego a detailed discussion of some subtopics; the interested reader can follow these up on the basis of the extensive bibliography. One problem in a consistent treatment of the content is that different areas have reached different levels of development. For example, the area of measurement and control technology can draw on a long tradition of scientific and practical research, and the reader can consult an establi’shed, didactically oriented specialist literature. In contrast, the situation in the field of information structures is quite different. Although some methodological approaches have been described schematically, the extension of these to process control engineering and their integration into an overall structure are relatively new lines of thought and rest on a few quite recent spezialist articles. The didactic treatment of this field on the basis of examplary problems, the testing of deductions in practical applications, and the construction of a broader scientific superstructure must await the coming years. A central concern of this treatment is to arrive at a consistent and comprehensive way of thinking about process control engineering and to show how the several specialities can be organically fitted into this total view. The volume is organized as follows: 0 The Introduction gives a history of process control engineering and shows that this discipline has grown as a logical consequence of the
development of measurement and control techniques and information technology. 0 Chapter 2, “Information Structures in Process Control Engineering,” describes the architectural principles used to define the Field. The first part deals with classification methods, and the second, with the application of such methods to examples of information structuring in process control engineering. 0 Chapter 3, “Knowledge about the Process,” gives an overview of the ways in which process knowledge is acquired, organized, and systematized. Examples of important disciplines covered are statistical methods of data reduction, mathematical process models, and the information content of flowsheets or recipes. 0 Chapter4, “From Process Knowledge to Process Control,” examines how knowledge about the process can be used for meaningful process control. Open and closed loop control techniques are introduced at this point, as are the various ways in which the operator can manage the process. 0 Chapter 5 deals with the fundamental question of acquiring information about the product and the process and, by means of appropriate taxonomies, brings together classical industrial measurement engineering and process analysis under the heading process sensor system technology. Future field communications systems are treated in terms of installation technology in Section 5.5, while an information-logistical treatment is presented in Section 8.6. 0 Chapter 6 describes the systems-engineering requirements for intervention in the process by means of actuators. Modern concepts of drive engineering and power supplies are presented. 0 Chapter 7 considers the high informationand communications-technology specifications that must be met by process control systems. Possible solutions are presented for current and future control systems.
VIII
Preface
0 Chapter 8 discusses modern information logistics and introduces methods and tools required for company-wide information integration. 0 Chapter9 is devoted to computer-aided methods. It describes the structure and functions of CAE systems used for process control engineering. 0 Chapter 10 deals with the design and construction of control systems. It considers the organizational aspects required for the realization of such a project and the phases of its realization. 0 Chapter 11, “Operation,” describes the activities that must be carried out during operation of a process control system. It deals with both maintenance of the system by the specialist engi-
neer and man-process communication by the plant operator. This chapter also explains how the process can be optimized by variation of process conditions during operation. 0 Chapter 12, surveys national and international organizations and activities involved in the standardization of process control engineering. 0 Finally, Chapter 13 deals with the integration of knowledge-based systems in process control engineering. I hope this original and comprehensive overview of modern process control engineering will be received with interest. Munich, Spring 1994
Giinther Schmidt
Biographical Notes
WOLFGANG AHRENS
Studied Electrical Engineering at TH Karlsruhe, Ph. D 1974. Since 1988, in Process Control Engineering Department at Bayer AG, responsible for CAE development in process control engineering.
DIETRICH BALZER
Studied Electrical Engineering at TI Leningrad, Ph. D 1969. Until 1975worked in a petrochemical plant in Schwindt. Habilitation at TH Leipzig 1976. 1975-1992 Head to Institut fur ProzeDrechentechnik, TH Leipzig. Now at Elpro AG, Berlin, Systems Engineering.
MICHAEL BJORKMANN
Studied Technical Physics at TU Helsinky, since 1982 in development and marketing of drive technology. Now Project Manager at ABB Stromberg Drives.
HASSODRATHEN
Studied Physics at Universitat Bonn, Ph. D 1975. Since 1976 Process Control Engineering Department at Bayer AG, now Head of Safety Department, Managing Director of NAMUR.
ULRICHEPPLE
Studied Physics at Universitat Stuttgart, Ph. D 1986 at Institut fur Systemdynamik und Regelungstechnik. 1986- 1990 in Process Control Engineering Department at Bayer AG, since 1991 freelance consultant and Managing Director of Gesellschaft fur ProzeDtechnik.
FREYTAG MICHAEL
Studied Electrical Engineering/Information Technology at RWTH Aachen. 1981- 1991 in Process Control Engineering Department at Bayer AG, Leverkusen, since 1991 Head of Process Control Engineering Department at Bayer AG, Antwerpen, N.V.
ERNST-DIETER GILLES
Studied Electrical Engineering at TH Darmstadt, Ph. D 1963 and Habilitation 1966 at TH Darmstadt. Since 1968 Director of Institut fur Systemdynamik und Regelungstechnik in Process Technology Faculty, Universitat Stuttgart.
KLAUSHARTMANN
Studied Physics at TH Hannover and TH Stuttgart, Ph. D at Max-Plank-Institut fur Metallforschung, Stuttgart. 1972-1977 at Erwin Sick Optoelektronik, Munchen. 1977-1985 at Bayer AG, Compur, Munich and Miles, USA, Process Control Engineering (management and plant design). 1985-1990 Head of Process Analysis Technology at Bayer AG, Leverkusen, 1990-1993 Director of Infrastructure Planning. Since 1993 Senior Vice President of Engineering, Miles, Pittsburgh, USA.
MICHAEL HEIM
Studied Energy Technology at RWTH Aachen and INSA Lyon. Graduated 1991. Now Assistant in Process Control Engineering Department at RWTH Aachen.
REINER HOTOP
Studied Physics, Ph. D at Institut fur Experimentelle Physik, Universitat Kiel. 1981-1993 in Process Analysis Technology Department at Bayer AG, Leverkusen. Now Head of PAT Department.
NORBERT INGENDAHL
Studied Mining Engineering at RWTH Aachen, graduated 1989. 1990-1991 at Institut fur Bergbaukunde 11, now Assistant in Process Control Engineering Department at RWTH Aachen.
NORBERT KUSCHERNLJS Studied Physics at Universitat Hamburg, Ph. D 1984 at Fakultat fur Chemie-Ingenieurwesen. 1985-1992 in Process Control Engineering Department at Bayer AG, since 1992 Head of Engineering Department at Bayer Ltd, Japan. RUDOLFMETZ
Studied Mathematics at TH Darmstadt, Ph. D 1975. 1978-1990 software development, 19801986 System Engineer at Ford, responsible for CAD databank development, since 1986 in Process Control Engineering Department at Bayer AG.
WOLFGANG MUTZ
Studied Electrical Engineering at Universitat Stuttgart. Planning and marketing of process control engineering installations at Bayer AG. Now Head of Process Control Engineering Department.
EBERHARD NICKLAUS
Studied Physics at Universitat Munster, Ph. D in Solid-state Physics 1976. 1976-1987 in Process Analysis Technology Department at Bayer AG. From 1987 diverse responsibilities at Bayer AG.
x
Biographical Notes
WOLFGANG NOERPEL
ULRICHPALLASKE
Studied Physics at Universitat Mainz, Ph. D in Molecular Physics 1974. Worked at Bayer AG in Biomedical Technology, Process Analysis Technology and Laboratory Control Technology for different Bayer plants. Now manager at PAT, Elberfeld.
Studied Mathematics and Physics in Koln and Freiburg, Ph. D 1969. Since 1969 at Bayer AG, responsible for mathematical modelling, process technology, and process control. After graduation, Development Engineer for hardware and software in Process Control EngiG~?NTER PINKOWSKI neering Department at Firma Krohne, Head of Systems Technology. Studied Physics at Universitat Wiirzburg, TH Darmstadt, Ph. D at TH Karlsruhe 1963. Since MARTIN POLKE 1964 in Engineering Department of Applied Physics at Bayer AG, 1971 Controller and 1975 Technical Head of Fiber Division. 1982- 1990 Head of Process Control Engineering Department. Honorary Professor at Universitat Stuttgart 1987. Since 1991 Head of Process Control Engineering, RWTH Aachen. Studied Control Engineering at Universitat Stuttgart, Ph. D 1991. Research in Department of JORGRAISCH Electrical and Computer Engineering (robust process control, decentralized control, design of hybrid control systems) at Toronto University, Canada. G ~ T H ESCHMIDT R Studied Electrical Engineering at TH Darmstadt, Ph.D 1966. Worked at Dornier AG, Friedrichshafen. Since 1972 Head of Control Technology at TU Miinchen, research area automation and robotics. Head of Process Control Engineering Department at Bayer AG, Krefeld-Uerdingen, until KARL-HEINZ SCHMITT retirement in 1993. HANS-JOSEF SCHNEIDER Studied Process/Control Technology at TH Darmstadt. Since 1966 at Bayer AG, technical support of process control engineering installations (inorganic chemicals and environmental protection), Head of Radiometry. Now responsible for process control engineering regulations for process control engineering planning (CAE) in Process Control Engineering Department. Studied Physics and Electronics at Universitat Bochum, Ph. D at Universitat Koln 1980. GERD-ULRICH SPOHR 1980-1992 Process Control Engineering Department at Bayer AG, Dormagen, planning and technical support of installations, Project Leader for CAE System Development, since 1992 Head of Automation Technology Department at Siemens AG, Koln. Studied Communications/High-Frequency Technology at TH Darmstadt and TU Munchen. HARTWIGSTEUSLOFF 1968 Fraunhofer-Institut fur Informations- und Datenverarbeitung (IITB). Development of Real Time Computer Systems for Automation Technology. 1977 Dissertation TH Karlsruhe. Since 1984 Head of IITB. 1987 Honorary Professor of Information Technology Faculty, Universitat Karlsruhe. Electromechanical and Electronic Development of Analog Computer Technology. ROLAND VOGELSGESANGStudied Physics at TH Karlsruhe, Ph. D 1969. Since 1970 various responsibilities at Bayer AG. HERBERT WEHLAN Studied Electrical Engineering at Universitat Stuttgart, Ph. D. Worked in Process Control Engineering Department at Bayer AG. Since 1989 Professor for Process Control Engineering in Process Technology Department at Universitat Stuttgart.
Contents
.....
1.
Introduction
2.
Information Structures in Process Control Engineering . . . . . . . . . . . . . . . . .
1
6.3. Electrical Drives in the Chemical Industry . . . . . . .
5
6.4. Electric Power Supply Systems . The Process Control System and 7. its Elements: Distributed Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Principles ..........................
2.1. Principles.. ........................
5
2.2. Architectural Principles for Information Structuring . . . . . . .........
9
2.3. Applications in Process Control Engineering ........................
24
Knowledge about the Process . . . . . . . . . 41 41 3.1. Principles 3.2. Analysis Methods for Process Quantities 45
3.
3.3. Process Models.. . . . . . . . . . . . . . . . . . . . 3.4.
3.5. 4.
50
58 Information ........................
60
From Process Knowledge to Process Control ...........................
73
4.1. Principles . . . . . . . . . . . . . . 4.2. Feedback Control . . . . . . . 4.3. Optimal Control ....................
87
4.4. Binary Control .....................
89
4.5. Operational Control of Process Plants . 93 5.
The Process Control System and its Elements: Process Sensor Systems
7S.
Design and Commissioning
*'
The Process Control System and its Elements: Information Logistics . . . . . . . 263
263 8.1. Principles .......................... 8.2. Functional Structures and Information Flow in Production Companies . . . . . . . . 263
8.3. Computer Communications Between and Within Control Levels 8.4. Computer Communications in Industrial Production;Standards . . . . . . . . . . . . . . . 8.5. MAP/TOP: Protocol Standards for Information Integration in Production Companies ......................... 8.6. Field Bus Systems . . . . . . . . . . . . . . . . . .
264 266
267 269
Quality Assurance: Conformance and Interoperability Tests . . . . . . . . . . . . . . . . 277 Methods and Tools for Protocol
. . . . . . . . . . . . . . 278
5.2. Process Sensor System Technology . . . . 124 5.3. Sensor Systems for Special Applications 140
8.9.
5.4. The Market for Sensors and Sensor Systems ...........................
9-
169
5.5. Field Installation and Cable Routing The Process Control System and its Elements: Process Actuator Systems . . . 185
6.1. Principles ..........................
217
7.2- System and Component Structure . . . . . 226 7.3. Process Control Operating System . . . . 241 7.4. General System Services
5.1. Principles .....................
6.
217
185
6.2. Actuator Systems for Material and Energy Streams . . . . . . . . . . . . . . 189
Production . . . . . Computer-Aided Methods
........... System Analysis . . . . . . . . . . . . . . 9.3. CAE System for Process Control Engineering ........................
284
9.4. Structure of a CAE System . . . . . . . . . . 285 9.5. Aids for Hardware Design . . . . . . . . . . . 286
XI1
Contents
9.6. Aids for Software Design . . . . . . . . . . . . 288 9.7. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . .
.
10
291
Design and Construction of Process Control Systems ....................
293
10.1. Principles . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2. Organizational Requirements .
293
. 295
10.3. Decision Phase .....................
304
11.3. Process Analysis and Process Optimization ....................... 11.4. Maintenance Strategies .
375 375
376
12.3. Technical and Scientific Bodies . . . . . . . . 382 12.4. Shows and Fairs .................... 388 13
10.5. Execution Phase
.......... 10.7. Process Control Rooms . . . . . . . . . . . . . . 11. Operation . . . . . .......... 11.1. Principles .......................... 11.2. Human-Process Communications .....
12.2. Standardization Bodies and Other Organizations Involved in Standardization ....................
.
10.4. Specifications . . 10.6. Quality Assurance
12. Standards. Committees. and Associations . . . . . . . . . . . . . . . . . . . . . . . 12.1. Principles . . . . . . . . . . . . . . . . . . . . . . . . . .
Integration of Knowledge-Based Systems in Process Control Engineering 391
324 328 335
13.3. Knowledge Engineering . . . . . . . . . . . . . . 403
335
14
338
14.1. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . .
413 428
358
14.2. Abbreviations . . . . . . . . . . . . . . . . . . . . . . 15 References . . . . . . . . . . . ......................
. .
Appendix ..........................
413
451
Process Control Engineering Edited by M. Polke
copyright 0VCH Verlagsgerellrchafr mbH.1994
1. Introduction
1. Introduction Since the invention of the steam engine in the 1700s, the world has gone through four long economic cycles, which were described by the Russian economist Nikolai D. Kondratieff (18921930) and are known as Kondratieff waves or cycles [1.1]. Each upward phase (identified as “prosperity” in Fig. 1.1) was borne by inventions such as the steam engine (WATT,1769), the mechanized 1784- 1786), the weaving loom (CARTWRIGHT, steam locomotive (STEPHENSON, 1814), the elec1866), the diesel trodynamic principle (SIEMENS, and Otto motors (1893-1897, 1876), and fundamental innovations in chemical process engineering such as the manufacture of basic chemicals. The world economy entered another long upward phase at the beginning of the 1980s. The development of the new basic innovation called “information technology”, characterized by the invention of the transistor [1.3] and the subsequent rapid development of miniaturization and laser technology, but also by results from the field of informatics such as object orientation [1.4] and Petri networks [IS], is described in depth in [1.6]. The economic boom identified with the new resource “information” [1.7] is a global phenomenon with heretofore unknown dynamic qualities. International business, national economies, and individual companies find in it growth and innovation potential that could scarcely have been imagined. PORTER describes the importance of information as an essential resource for a successful corporate strategy [1.8]: “The question today is not whether information technology has signijicant effects on a company’s Steam engine cotton
Railroad Steel
Electricity Chemistry
competitive situation, but merely when and how these effects will become apparent.. . . Anyone who does not react today will be compelled to follow a course that others have set.” The fifth Kondratieff cycle, however, is not only an imposing technical and economic upswing but carries along with it profound social, ecological, cultural, and intellectual changes and challenges. These connections, often revolutionary in scope, were pointed out by SCHUMPETER 11.91. The fifth Kondratieff cycle will render permanent changes in the way the individual thinks and acts and also into the strategies that corporations and national and international institutions will pursue. A technological advance will become a structural change [1.10]. Along with the classical production factorsenergy, labor, raw materials, and proven production technology-information has become not just an additional factor but probably the most important factor. CIM (computer-integrated manufacturing) and CIP (computer-integrated processing) are not merely slogans today [1.11]. Demands made by society, such as increased environmental awareness, better and more consistent product quality, improved delivery, and more economical management, necessitate production facilities and processes with greater flexibility and ease of operation. Strategy, that is, the management of the company as a whole, means identifying goals and achieving them. Because a company’s definition of goals must always be dynamic, it is important to inquire into what impact changes in the company’s environment will have. As trends in socioPetroleum Automobile
Information Knowledge
/
P
Oec Rep
1I
R
I I
1
I
2
1. Introduction
cultural, ecological, technological, domestic economic, international economic, and politicallegislative settings become stronger and change more rapidly, optimal access to information becomes a central requirement for a successful strategy (e.g., in marketing, sales, and production). But strategy also means making available the means for business success. An essential success factor is thus the exploitation of new information technologies based on engineering, physics, and information science. Information is an unlimited resource that can be managed only if it can be structured. In the economic, administrative, and technical spheres, those responsible for information management must optimize the utilization of information by coordinating all parties involved and by creating a technical infrastructure. The German term Prozessleittechnik (process control engineering) was coined at Bayer AG in 1980 as a working title covering the measurement, control, and electrical engineering groups [1.12]. The expression was chosen to denote the integral view of all those concerned with the management or control of production programs based on engineering processes. Since that time, a number of events have made it clear that classical or signal-oriented measurement and control technology is being supplanted by information-oriented process control engineering. Long discussions among experts regarding the terms “control” and “automation” came to an end when LAUBER stated a valuable dichotomy, placing humans at the center of the analysis [1.13]: The role of humans can be (1) to specify the framework and the sequence of the process, which then runs automatically, or (2) to gain the maximum information about the process and intervene in it directly, becoming the agent that ultimately controls it. This question is discussed in more depth in Section 4.3. The authors of this article have a clear preference for the more universal term “control” and make use of various levels or degrees of automation (see also [1.14]). “Essential impetus for development today is coming from information technology, which has become a significant tool in measurement and automation technology. Only information-oriented technology enables the industrial user to operate processes and plants in compliance with the criteria of flexibility, productivity, safety, and environmental protection. The integration of humans into the production process is of great im-
portance if the operator must, for example, perform control functions at the interfaces with the automation system and with the process” [1.15]. The national and international standardization organizations of greatest relevance to process control engineering, such as the International Electrotechnical Commission (IEC) with its Technical Committee TC 65 and the German Electrotechnical Commission (DKE) have also replaced the term “measurement and control technology” with the new complex of “control engineering” (see Chap. 9). The Standardization Working Group on Measurement and Control in the Chemical Industry (NAMUR) had already announced such a new orientation in September 1987 [1.11]. The strong influence of information technology on automation technology and process control engineering is visible not just in the area of devices and systems (especially process control systems; see Section 5.2), but in all aspects of design, construction, operation, maintenance, and so forth (procedures) [1.16]. BASF has made their training programs in operational practice available to the public [1.17]. Detailed procedural instructions can be found in STROHRMANN’S [1.181 course on process control engineering (serialized in atp since 1984). GRUHNet al. [1.19] and LAUBER [1.13] have published accounts of structuring practices for automation with an emphasis on process engineering and process computing automation technology, respectively.TOPER and BESCHhave also described principles [1.20]. The time now seems ripe for attempting to present an integrated, information-oriented exposition of this subject. The information structures employed in control engineering and the architectural principles that generate them are therefore discussed and explained with practical examples (Chap. 2). Next, the computational and mathematical methods used to acquire, reduce, model, and document process information are presented (Chap. 3). Information about the process is used for process control (Chap. 4) by applying isolated and integrated control concepts and object-oriented process-control strategies (e.g., recipe management). The control components of production plants, that is, the electrotechnical devices used for process control (sensor and actuator systems, process control systems including communication technology, process power supply, design of
1 . Introduction
control and power distribution rooms and cable runs), are next analyzed from the informationtechnology point of view (Chaps. 5 -8). Computer-aided techniques (C-methods) have proved an especially useful informationtechnological support for the engineering of process control systems. General aspects of such systems and detailed structures of data-modeloriented design aids now in use or under development are described in Chapter 9. The procedures used for the design, construction, and start up of process control facilities are set forth from a systems viewpoint and on a structural and sequential basis. Decision phases with the most important design document, (requirements and specifications) are clearly distinguished from execution phases. Quality assurance for the design, construction, and start up of process control systems is described in Chapter 10.
3
In the operation of production plants with process control systems, a change is taking place from signal-oriented man -machine communication to information-oriented, state-based man-process communication. This enables the plant operator to utilize knowledge about the process, formulated in an ergonomically correct way, to control the process. Continuous process analysis and process optimization take on new importance in this context. Strategies for maintaining or restoring the availability of process control functions are described in Chapter 11. The current status of standardization of process control techniques and systems in national and international bodies, societies, and institutions is presented in Chapter 12. Finally, the integration of knowledge-based systems and modern concepts of fuzzy logic and neural networks are discussed in Chapter 13.
Process Control Engineering Edited by M. Polke
copyright 0VCH Verlagsgerellrchafr mbH.1994
2.1. Principles
5
2. Information Structures in Process Control Engineering 2.1. Principles Information as a Corporate Resource. In principle, information is an unlimited resource that can be managed only if it is structured. It is the task of information management to optimize the utilization of information by insuring the efficient interplay of all parties involved and providing the technical infrastructure. The objective of information structuring is to reduce the degree of complexity by appropriate modeling (formation of data models and functional models) and thus create the prerequisites for information processing (input/output, storage, and analysis) by means of information systems. What has to be overcome, above all, is not the material but rather the inadequacy of the human intellect to deal with complexity. The importance of adopting suitable structuring aids is demonstrated by the way in which technical, administrative, management, and logistical data “circles”-none, as a rule, consistent with the others-frequently come together. Because they continue to be developed, application systems arise whose functionalities often overlap (Fig. 2.1) [2.1]. What is more, the demand for information depends very heavily on the level of automation present in individual production facilities. Production engineering systems often feature noncommunicating automation islands. In fact, all such information circles are necessary, from the technical and economic standpoint, in order for the corporation to accomplish its task unhindered by departmentalization. If information as a corporate resource is to be optimally deployed, it is essential to integrate information technology, and this can be done only when information concepts have been harmonized. Integration of Information/Information Models and of DatalData Models. Information integration is achieved by consolidating information across projects, leading to a mandatory corporate information model [2.2] -[2.5]. Such information models [2.6] are said to be “semantic” or “conceptual” because, at the user level, they reflect the definitions and interrelationships of concepts but not the realization of
Figure 2.1. Convergence of isolated data “circles” and automation islands [2.1]
information in data-processing (DP) terms or the selection of information for specific applications. In contrast to earlier management information system (MIS) implementations, the result is an extremely stable information model. Appropriate representation techniques yield data from information and data models from information models. Since 1970, theoretical publications on the form of such data models have been concerned chiefly with how the organizational problem of large data-base systems can be solved. For example, the mathematically grounded relational data model with its normal forms [2.7] was created first; the entity-relationship (ER) model, which stresses the semantic aspect of the data, came later [2.8]. In 1975, the ANSI/X3/SPARC DBMS Study Group introduced the “conceptual scheme”, a
6
2. Information Structures in Process Control Engineering
comprehensive, implementation-neutral structure description of corporate information. This conceptual scheme is of central importance for the understanding and management of information systems [2.2], [2.9]; see also the SPARTEN information and control systems (ISS-aa) developed at Bayer AG [2.10]. Further detailed exploration and discussion of the conceptual scheme has taken place in the IS0 Working Group with the same title. A detailed report is presented in [2.ll]. Work on these aspects has continued; some results have been summarized and various methods, procedures, and tools to aid in the creation of data models have been set forth [2.12][2.15]. Today, the state of the art is as follows: In the development of information systems, data properties relating to content, application logic, and organizational aspects are first examined in a “data structure analysis.” The results must be described in a semantic or conceptual information model before the data model (which depends on the form of presentation) and thence the data-base design can be derived. The database design must take account of requirements and restrictions having to do with data-processing hardware and software. Restrictions based on processing considerations are brought in at another level. The data-base design can thus be represented in a three-level model : 0 0
0
Conceptual level: The result is the semantic or conceptual information model Logical level: The result is the logical data model, which contains restrictions having to do with data-base hardware and software Physical level: The result is the physical data model, which makes allowance for processing restrictions
A conceptual information model is constructed with an eye not to isolated informationprocessing functions, but to the objects of corporate information processing that are common to all such functions. This is the strategic dimension of data modeling. Such conceptual corporate data models are now being created in many companies. As a corporation-wide, application-neutral, implementation-neutral structural description of corporate information, such a scheme also constitutes the common linguistic basis for communications between persons involved in DP management. Be-
cause it is independent of the allocation of data among the company’s computers, independent of physical data storage, and independent of the data-base systems employed, such a scheme can unify information systems based on a wide variety of hardware and software. Integration of Methods. In the first period of software engineering, the software life-cycle model was in the forefront of discussion. Software development environments were designed in accordance with this philosophy. Alternative philosophies are in use today, such as rapid prototyping and participative system development [2.16]. Both aim at the early involvement of end users, whose acceptance is the single decisive criterion for the success or failure of an application system. The appearance of data bases gave new impetus to the development of data modeling techniques. Because these are based on a variety of implementations, they are largely incompatible. For example, an elevator control system does not require a data-base design; an information system has no time-critical concurrent processes; and an expert system uses different knowledge representations from a procedural program. If the data model has a hierarchical, network, or relational structure, different design methods are used. An integrated methodology must generate both the data design and the functional design (Fig. 2.2). It has often been overlooked that every design level of this methodological concept deals with a different aspect of the problem. Thus, data and functional models are built up at the uppermost, conceptual level and then, in the further specification, broken down into a representation that can run on certain hardware. Integration of Representation Means. The basic prerequisite for the common storage of design data and the transfer of information between methods is an appropriate representation form. Today, graphical and textual representation means can be thought of as being diametrically opposed. Each, however, has its justification. Graphical means of representation (flowchart, function diagram, entity-relationship diagram, polling
2.1. Principles
7
r e l a t i o n systems)
application system
Figure 2.2. Methodological concept [2.17]
hierarchies, etc.) are a suitable idiom for use in the human-computer interface (Table 2.1). Textual means of representation (pseudocode, structured text, computer languages of various generations) yield expressions that are understandable chiefly to the computer. There must therefore be a way to translate between graphical and textual means of representation. The task remains to harmonize these means of representation with one another, obtaining a canonical set of description means that can be used to describe data structures and functional structures with regard to both static and dynamic properties. Integration of Tools. The most important tools for data integration are data-base systems and data dictionaries [2.4], [2.19]. In order that the data in a data base will represent a consistent system of facts, the permissible and necessary relationships between data are specified by means of integrity conditions and monitored and controlled in real time by a data-base management system. Data-base systems offer a range of concepts that support “information management.” In particular, these include concepts for modeling and manipulating data in an application-
oriented fashion (data models), for integrating various applications over the same data base without redundancy (data integration), for making an application neutral to changes in the logical and physical organization of the data (data independence), for insuring data consistency, multiuser synchronization, and data security, and for safeguarding the stored data against unauthorized access. A data-base system is said to be of hierarchical, network, or relational type, depending on the underlying data model. Hierarchical and network data-base systems have become established in commercial, administrative applications. Relational data-base systems are gaining ground in engineering applications, such as the support of design processes (in the design of mechanical parts, LSI circuits, process monitoring and control equipment, and software development) and process control. Distributed and object-oriented data-base systems have not yet become widely available from commercial sources. In contrast to data-base systems, a data dictionary is a system for storing and processing data definitions (data descriptions) for one or more software systems. In its simplest form, a data dictionary can be regarded as a well-organized notebook containing basic information
8
2. Information Structures in Process Control Engineering
Table 2.1. Methods and their points of emphasis [2.18] Method
Points of emphasis
Structured Analysis (SA) Representation of functions, data regions, interfaces, and data flows connecting them, in the form of hierarchically organizable nets Structured Design (SD)
Representation of modules and their interfaces in “Structured Charts” and module level model
SADT
Representation of functions, as well as data and control flows connecting them, in the form of hierarchically organizable nets Representation of objects and their quantifiable relations (1 : 1, 1:n, etc.) in the form of nets
Entity-Relationship Model Decision Tables Finite-State Automata
Representation showing under what combination of conditions certain actions can be performed, in the form of tables Representation of states and state transitions, and also of inputs initiating changes of state as well as outputs initiated upon changes of state, in the form of nets
Petri Nets
Representation of states and events in the form of nets whose behavior can be dynamically investigated with the aid of the Petri net rules
ISAC
Representation of information sets, as well as information flows and functional sequences, in the form of seven types of nets
Jackson Structured Programming (JSP)
Representation of the data structure of a problem, derivation of the program structure from this data structure
Jackson System Development (JSD)
Representation of objects and their actions in the form of trees, and of the connections between objects in the form of nets
MASCOT
Representation of processes (and devices) and their communication regions in nets Representation of processes and devices as well as their communication, in the form of nets
Functional nets HIP0
Tree-type breakdown of a system and definition of every part in terms of input and output data and required operations (steps)
about all the data elements stored in a computer for an application area [2.20]-[2.22]. These support not only a one-time, uniform, consistent documentation of information objects, but also the management of other objects used in the DP environment (such as jobs, projects, users, etc.) as well as their relationships. Data dictionary systems are themselves data-base systems that use a central data base to manage all information needed to describe, process, and use the objects of an application area, regardless of their internal organizational structure. Data dictionary systems also support data management for application systems and thus contribute to the efficient development, implementation, and description of software systems. A number of other tools have been devised in the course of the software engineering discussion. Each of these tools is suited to a certain class of problems; each either extends over multiple phases or is specific to one phase [2.6], [2.23], [2.24]. For example, editors are for the creation of graphics and texts for use in a variety of methods; compilers, linkers, debuggers, and so forth,
for the implementation phase; test tools, for the testing phase. Tools for retrospective documentation are becoming more and more important at present. Decades of software development have produced software systems that can scarcely be maintained any more (the “software crisis”). The documentation of these systems is often in a wretched state. Because of changes in methodology, it i s no longer sufficient to document such systems in the context of their own (usually obsolete) approach. What should be aimed for is to create new documentation for old systems, with an eye to future trends in software engineering. Retrospective documentation is a more and more pressing need in a management context, where decades’ worth of investment in software must be safeguarded. This does not necessarily mean that obsolete software systems must continue to be used in their existing form. The minimum requirement is that the information and technical knowledge contained in the system must be revealed and made accessible for new developments .
2.2. Architectural Principles for Information Structuring
The integration of tools means that documents can be passed along from phase to phase and from tool to tool. Integration of tools can succeed only if information, data, and methods have been integrated. This applies not just to all engineering activities during the lifetime of a technical device (see Chap. lo), but also to the design of monitoring and control equipment itself, especially of process control engineering systems (see Chap. 7).
2.2. Architectural Principles for Information Structuring Systems and Architectural Principles. Realworld systems and elements have internal structures. Along with Aristotle’s doctrine of categories r2.251, the Linnaean system-still a valid descriptive model employed in botany-provides an example [2.26]. Darwin’s ideas on the origin of species €2.271 also illustrate this structuring process. In the 1700s, “system” at first meant a collection of linked truths. The links had to be methodologically correct and completely deducible from an underlying principle. KANT’Sconcept of system stresses the theory of systematics. In his “Critique of Pure Reason,” he emphasizes the significance of architecture as the art of systems. Thus a system, traditionally, is a closed whole whose parts are interrelated, interact, and satisfy certain constraints. The parts, regarded as elementary, are called elements of the system. A system consists of elements with properties; the elements are structured and linked together by the relationships in accordance with a system principle. Systematization involves specifying system principles and devising system structures. Systems are characterized by the fact that they have properties that cannot always be accounted for by the properties of their elements. Thus simply collecting elements does not form a system. Instead, each system has a structure that is generated by the relationships among its elements. Systems can be subdivided into parts on as many levels as desired, whereby all parts that are not elements of the system are referred to as subsystems. System elements are the parts of a system whose further subdivision is impossible or undesired.
9
The sections that follow state principles for the structuring of information. They are called architectural principles because they can be identified at various points in the design process and in various representations of the information system, thus marking the architecture of the information system. The first principles discussed are general ones in the phases of design and implementation. Principles in the “definition of requirements” phase, quality assurance in parallel with other activities, and operation of a facility might be added here. These are also referred to as construction principles, since they are applied mainly in the construction of DP systems. This is demonstrated by the example of modern operator interfaces, where structuring of information is used to support human-process communication (see Section 112). Higher concepts of functional and data structuring also make use of the design principles discussed above but represent a more applicationoriented aspect of structuring. The focus is on the object-oriented formulation, which can be applied to both functions and substances [2.28] in an integrated methodological concept. It is the tool for the decomposition principle, with which complicated objects are broken down into their constituent parts. The next most important structuring principle is abstraction, which is a fundamental approach to concept formation. Adopted recently from expert-systems technology, abstraction is introduced as a construction principle of application systems through class-forming, conceptforming, complex-forming, and functional abstraction. Closely related to the principle of abstraction is that of inheritance [2.29]-[2.31]. The last section deals with transformation principles. Here the various models used for problem-solving are described, starting with procedurally oriented formulations (with their counterpart in functional design) and passing through logic-oriented formulations (as now employed by many expert systems) to state-oriented or event-oriented concepts (which come much closer to process-related DP problems). State-oriented and event-oriented formulations in turn are closely related to object-oriented ones. Objects are in certain states and communicate with one another via established protocols (see Chap. 8). The complexity of present-day systems increasingly calls for a holistic, network style of
10
2. Information Structures in Process Control Engineering
thinking [2.32], [2.33] if the basic laws governing systems, their dependences, and their interactions are to be understood. It will take a new kind of thinking to comprehend the dynamics of a continuous process, plan a monitoring and control system for it, erect and operate the system, employ higher-level information systems, and in the process not overlook human beings [2.34]. The Decomposition Principle. Design Principles. Structuring principles occur in all phases of system development. In the design phase, for example, the top-down and bottom-up principles are often encountered. In the top-down approach, a whole is broken down into parts step by step, whereby the complexity of the parts under consideration is systematically decreased (decomposition principle). In bottom-up design, single parts are assembled step by step and made into a whole. In practice, mixed forms are often encountered (Fig. 2.3). Bottom-up design comes into play when prefabricated elements are available from which the overall systems can be assembled. This principle
is often used in mathematical and statistical applications, where the elementary mathematical and statistical functions are known and can be made available in program libraries. New applications can be analyzed only by the top-down approach, though there is a trend toward reusable elements here too (see, e.g., Section 4.5). Implementation Principles. The implementation phase also exhibits principles that ultimately determine the structure and thus the understandability and maintainability of software systems. The more ordered the structures are, the better they lend themselves to verification, modification, and maintenance. The simplest ordering is linear. Iterations, recursions, and branches generate more complex structures (Fig. 2.4). Recursive data and functional structures are preferred today because of their mathematical compactness. It can be shown, however, that iterative and recursive structures are equally powerful and can be transformed to one another. Another type of structure is the directed tree with strict hierarchy or polyhierarchy, which ultimately becomes the general network if the rigorous requirement that every node has only a single input is dropped (Fig. 2.5). The last is es-
Top-down desig!
/Sub] , system
system
I
i
,
" I
i
Direction of development
1
Bottom-up desiqn
9 , r - 7 /Element1 TJ r\ I
1
system
t
Direction o f
Figure 2.3. Design principles
r
2.2. Architectural Principles for Information Structuring
t
Linear s t r u c t u r e
Iterative s t r u c t u r e
Linear s t r u c t u r e s with branches
Recursive s t r u c t u r e
Figure 2.4. Types of structure
pecially important in so-called master-slave structures. The slave should have only one master, since otherwise there can be conflicts in execution. Ring and star structures form a separate class. Principlesfor User Interface Design. The user gains access to the system through the user interface. A number of structuring principles apply to this interface, not all of which can be discussed here. Design principles such as the law of like form, the law of color, or the law of proximity (Fig. 2.6) should be employed in the layout of masks and printouts [2.35], [2.36]. Principles such as conformity to expectation in dialog with the system (also called “principle of minimum surprise”), consistency of dialog elements, and visualization of the application context also play an important part. Modern software ergonomics focuses on job design for people, a process that is heavily influenced by software [2.37], [2.38]. An important goal in software writing must be the design of complete activities. A complete activity has been defined as an activity that, to-
11
gether with simple execution functions, includes the following: 0 Preparatory functions : identification of goals, development of procedures 0 Organizational functions: agreement with other workers as to tasks 0 Monitoring functions: feedback to the worker on attainment of goals Incomplete activities are activities where there is essentially no possibility of independent goal identification and decisionmaking, no scope for individual ways of performing tasks, or no adequate feedback. Shortcomings in job design impair the motivational and learning potentials of the work process and affect the worker’s welfare and job satisfaction. Intimately linked with the completeness of activities is the degree to which the worker has opportunities to make decisions as to time and substance, that is, freedom of action. This freedom of action must permit creative users of an information system to devise an individualized procedure in accordance with their experience and working style. At the very least, defining freedom of action should not be part of the program author’s responsibility; it is a function of corporate management and personnel practices. Observed acceptance problems often stem from misjudgments in this area. A new type of human-computer interaction has come into being with the new class of workstations. This user interface has the new feature that it presents the user with graphical images of customary objects belonging to the office world. Such objects can be selected and then manipulated with the aid of a pointing device (mouse, light Pen). SHNEIDERMAN [2.39] introduces “direct manipulation” as a collective term for several new principles in the user interface. The most important principles are the following [2.40], [2.41]: 0 Permanent visibility of the object as an icon or pictogram 0 Quick, reversible, one-step user actions with immediate feedback 0 Replacement of complicated commands with physical actions (see also Section 11.2) Higher Concepts of Functional and Data Structuring. Historically, the functional structuring of application systems was emphasized in the early period of data processing. A complex function was successively broken down into partial functions until elementary functions realizable in
12
2. Information Structures in Process Control Engineering
I
'
TZ!?
I
Polyhierarchy
S t r i c t hierarchy
B Network
AHI Ring Figure 2.5. Types of tree and network structure
*
DP hardware and software were arrived at (flowcharts). Data were fitted into the functional structure but remained meager, as they had to be given in the programming languages known at the time. The structuring principle was oriented to the procedure employed in problem-solving. As a result, functional structuring led to process-oriented or program-oriented structures (Fig. 2.7). Even today, functional structuring is still the classical approach, because it leads directly to realizable, procedurally-oriented solutions. At Bayer, for example, all engineering tasks were modeled by the SADT method, somewhat as shown in Figure 9.1. [2.43], [2.44].
Star
Thus the modeling concept (functional structuring) and the implementation concept (procedural programming language) correspond. Data structuring is of secondary importance: What is interesting is the processing of the data, and data-base systems were not yet in use at this time. However, functional structuring has several weaknesses: 0
0
There are scarcely any objective criteria for breaking the function down into partial functions. Decomposition is often a matter of the developer's discretion. The resulting data structures unavoidably grow out of the functional structure and are
2.2. Architectural Principles for Information Structuring
function or an arbitrarily defined module may be used again-in contrast to mathematical and statistical program libraries, where reuse is straightforward. The complexity of such a DP system increases more than proportionally to the functionality.
Design l a w o f : -Proximity
0
O .
0 O.0 0 .
0
0
0 0 0 0 0
0
Design l a w s o f : -Proximity -Symmetry
0.0
0.0
0 0 .
OO.
0
0
. 0 . Design l a w s o f : -Proximity -Symmetry -Similarity
0
0
0
0 0 0 0 0 0
...
0.0 0 0
Figure 2.6. Simple design laws [2.35]
0
13
monolithically bound to it. Any change in the data structure impacts on the functional structure, and vice versa. Frequently, insufficient stress is laid on reusability, since it cannot be known how and in what connection an arbitrarily defined
In the early 1970s, greater value was accorded to data structuring. This was the period of developments such as the PASCAL programming language and the first hierarchical data-base systems. Data structuring became more important. Subsequent development is marked by attempts to shift the meaning of the data (the information) out of the programs and into the stored data, and to generate program systems in a manner directly opposed to the functional approach. Now the first step in system development is to design the data model on which the later program systems will work. One speaks of data-oriented or object-oriented structuring, since the analysis is focused on information objects (data structures) as representations of real or abstract objects. Objects are described and related to one another through their properties, also called attributes. Typical relationships are abstraction and mapping (correspondence) relationships [2.3]. The result of this approach to structuring is the semantic data model, which is designed independently of the functions defined on it [2.12], [2.15]. In his epistemological discussions, WENDT [2.13] classifies objects as concrete and abstract. Concrete objects include things and processes; abstract objects include concepts (see [2.28] and Fig. 2.8). After the existence of objects and the properties of objects, the relationships of objects are taken up third. A first type of object relationship is described by ordering, which is arrived at through a comparison of some property. Other important object relationships are part-whole relationships and instrumentality relationships. An instrumentality relationship is said to exist between a thing (e.g., a process facility, a process monitoring and control system) and a process (e.g., a continuous process, a control process) if the thing plays a part in the process. Inseparably connected to processes are states. States include the initial and final times of processes or subprocesses.
14
2. Information Structures in Process Control Engineering Develop software
Develop s o f t w a r e
Develop s o f t w a r e
Structure
Standards Guidelines Develop s o f t w a r e in system-neutral
L
Task statement
FUP/N Process c o n t r o l system t y p e
-
1
Transform t o ssystem-specific ystei representation
S t r u c t u r a l data-
Project data
7Transform 7
Hardware concept
1 1
Data on -Functional diagrams - S t r u c t u r a l diagrams -Variable l i s t s -Cross-reference l i s t s -Instruction l‘:-LS ists .I.._ I
Standards .Guidelines
I L c-i--i -Develop Develop s o f t w a r e -in system-specific -t fashion
L-
Rules
[ ~IIP/C u p / \s ) fF
.
Create documentation
(FUP/S)
D a t a - f l o w diagram by SAOT m e t h o d
system/stored-program c o n t r o l system lor program
Texts, variables
3
Object software
Manage data
Figure 2.7. Functional structuring for the example “develop software” [2.42]
If countability is associated with the concept of object, and measurability with the concept of property, then the concept associated with the concept of relationship is decidability [2.13]. Objects are counted, properties measured or described (see “Scales” in Chapter 3), and relationships are decided. Thus the three fundamental concepts-object, properties, and relationship-have been in-
troduced. These make it possible to structure experience, and thus they govern the world of human thought [2.13], [2.45], [2.46]. Concrete objects are the plant, the apparatus, the process monitoring and control system, the production process, the reaction process, the sensor-actuator process, the data-processing process, and so forth. This group of objects is also called “devices”; devices must be designed,
2.2. Architectural Principles for Injorination Structuring
(GzE)
(Architectural\ jprinciples
J
15
(GGz-s) legislation
processing
Figure 2.8. Structure of PCE propositions
built operated, maintained, and optimized, these activities being known as “procedures.” The outcomes of these procedures are represented as flowcharts, construction drawings, process monitoring and control station diagrams, and so forth. In the Cassirer scheme, both groups of objects are called substances. Functions that describe a task or activity (e.g., plant/apparatus coding; see Section 2.3) also have a representation in the object-oriented formulation (see Figs. 2.22, 2.24, and 3.26). Functions (i.e., tasks) are implemented through procedures (abstract objects) from devices (concrete objects). Figure 2.8 gives a large-scale survey of “object worlds.” For clarity in terminology, relationships are described not with the function concept usual in informatics, but with the epistemologically based relation concept (cf. entity relationship) [2.28]. The abstract representations are the objects that can be manipulated in the computer. They always relate to an object, which they describe from a certain abstract point of view. The object-oriented approach thus represents a “natural” modularization concept, since the objects can be extracted directly from the application. If the functions are likewise struc-
tured in an object-oriented manner, this constitutes a principle of functional structuring that is far less sensitive to modifications.
The Principle of Abstraction. The (provisionally) last step in development combines relational and object-oriented structuring. In the “abstract” data type, data structures and relational structures are merged into a whole. The relations enclose the data as if in a shell; the data can be manipulated only through the relations “guarding” them. The high modularity of these constructs suggeststhat there may be significant gains in the reusability and maintenance of software. This principle is referred to as data encapsulation or “information hiding” [2.31], [2.47]-[2.49]. The structuring principle is thus concerned with the structuring of data and relations, their (static) structural and (dynamic) sequential structures, and their interactions. Usually, the sensory or empirical view of things is said to be “concrete,” while any description in terms of concepts is said to be “abstract” (see also [2.13]). Abstraction means reducing complexity by neglecting all inessential properties or features. One also speaks of models.
16
2. Information Structures in Process Control Engineering Additive Endproduct
IDirsoiutionl Solvent
L
Separation1
I
solvent
I
Figure 2.9. Design steps in process engineering [2.50]
Models are always simplified pictures of the real objects (abstractions) as viewed from a certain aspect (see also Sections 3.1 and 3.3). In process engineering, flowsheets are models of a real facility in graphical form. They are standardized and represent the medium of communication for persons involved in the design, construction, and operation of plants (Fig. 2.9). Mathematical models in the form of systems of algebraic and differential equations depict partial aspects of the physicochemical and apparatus-specific parts of processes. Such models are used, for example, in model-aided measurement techniques for the control of processes, the detection of faults, and simulation (see Chaps. 3 and 4). A plant model is a representational picture of the plant that is much favored as an object of study by plant designers and operators.
The three-dimensional representation of plants in CAD systems is a way of getting the advantages of such models while using only DP resources. Along with mathematics, the field of informatics now offers methods with which complex engineering systems can be modeled, simulated, and optimized. It must not be overlooked that data models are always reductions from real systems. Models, no matter what kind or in what medium, represent a given system from various points of view. They emphasize essential properties and suppress inessential ones. Within these limitations, however, models are an indispensable part of any modern information technology. Class-Forming Abstraction. Object orientation means that once the objects have been identified, they are then described in terms of their
2.2. Architectural Principles for Information Structuring
tributes; this is equivalent to an abstraction (Fig. 2.10). Such attributes can be listed in a table (Fig. 2.1 1). One column in the table describes one characteristic property or attribute of the object. The table represents a “class of objects”; that is, a collection of objects having at least one attribute category in common. The attributes describing a class simultaneously identify the type of an object. If the same attribute categories are employed for the description of more than one object, these categories are said to be “generic.” A given object in the real world is called an instance of the class (Fig. 2.12). The concept of table, also called relation, leads to the relational data base, which utilizes the relation (= correspondence, dependence, etc.) as its organizing principle. The table as a data structure is purposely not given any further structure, so that it is possible to create a “relational algebra” that is mathematically well-defined. Any manipulation of the data base is thus broken down into set-theory operations. Apart from class-forming abstraction, the identification of objects is important. Each instance of a class must be identified by a unique name and/or a unique identification number. Without exception, every one of the classification schemes devised earlier for identification
t
Q!e9 m g - -
I
Abstraction
e 9
Abstraction
‘pzq pzq
-1
Figure 2.10. From things in the real world to abstract objects
properties or attributes. Each such property consists of a category and a value; these can be scaled either metrically or nonmetrically (see Section 3.1 and Fig. 3.1). The number of properties describing an object is not intrinsically restricted. The treatment is generally limited to the attributes relevant to a mode of examination or an application, or to characteristic at-
Name plant
Of
Identification
E/R diagram
‘Iant complex
Battery or works
I I I I
Capacity
Name o f plant
..
.
Identification Plant complex B a t t e r y or works Capacity
!
17
Utilization
Figure 2.11. The table as a form for describing object properties
utilization
18
-
2. Inforination Structures in Process Control Engineering Name o f plant
Building
Sulfuric acid unit A
4711
Plant complex
uric
Battery or works Brazil
Capacity
~
t/a
~
Utilization
~
85%o
~
o
Instances o f object class “plant“ Figure 2.12. Instances of a class of objects
purposes has failed, because objects cannot be unambiguously classified (see Concept-Forming Abstraction) and an excessively narrow decimal classification system often proved too restrictive. Classification systems were weak forms of data modeling at a time when data bases and semantic data models were not yet known. What is recommended today is an unsuggestive, system-free, numeric or alphanumeric code and the description of properties-including the membership of the objects in one or more classes (po1yhierarchy)-in a semantic data model. Another form of identification has been discussed in connection with direct manipulation. An object is identified by “pointing” at it. This principle of identification is extremely important for the ergonomics of user interfaces (cf. the icons first introduced by Apple in the Macintosh). A third option is to describe objects so that they can be found again. The relational data base draws its strength from this approach. Structured query language (SQL) represents an industry standard and makes the results independent of the data-base system. Concept-Forming Abstraction. Concept formation is an act of abstraction that leads to concepts and hierarchies of concepts. It is a generalizing process, in which particulars are disregarded. Unimportant properties of the object are left out; those remaining govern the class of the objects. Each abstraction rests on a certain objective of thought or speech, for example the objective of being able to think or speak about [2.45]: -
Things, regardless of engineering details Procedures, regardless of what principles or concepts apply to them
-
Factual statements, regardless of meaningpreserving transformations in the form of circumstances
According to FREGE [2.51], things that are already linguistically available are grouped, from the viewpoint of something they have in common, into a “concept,” which offers an invariant way of speaking or thinking about things already linguistically available. The formation of “abstract” concepts cannot take place in isolation from the intended purpose or objective. This may be a disadvantage if one wishes to do modeling in the most applicationindependent way possible at an early stage of system design. For example, a biologist might group dogs and cats together as domestic animals, place these in turn under mammals, and so forth, while a jurist might distinguish movable and immovable objects simply because movable objects can be stolen and such taking is a statutory offense. DARWIN, in “The Origin of Species,” had already discussed the difficulties involved in devising consistent taxonomies and hierarchies of concepts [2.27]. The concept arising from concept formation is not specified in advance but lies in the discretion of the data modeler. It is therefore quite possible for multiple taxonomies to exist in one and the same realm of objects. This can be illustrated by a simple example: If one abstracts from the manufacturer of certain bimetallic sensors, the concept or class used is that of manufacturer, while the concept introduced is that of bimetallic sensor (Fig. 2.13A; see also [2.52]). If one wishes to introduce the concept of expansion sensor along with the concept of bimetallic sensor, there is a further abstraction process using the principle of measurement as class (Fig. 2.1 3 B) and leading to the more abstract concept of temperature sensor.
2.2. Architectural Principles for Information Structuring
-
Manufacturer
Measurement principle
Manufacturer
-
-
Measurement principle
sensor
is, a
-
Manufacturer
19
sensor
I
sensor is. a
sensor 1s. a
I is, a
Figure 2.13. Taxonomies of sensor systems A) Abstraction according to manufacturer; B) Abstraction according to measurement principle; C ) Alternative taxonomy
Now it might occur to one to set forth the sensor program in closed form by manufacturer; now the taxonomy looks different again (Fig. 2.13C; see also Chap. 5). Complex-Forming Abstraction, Aggregation. In complex-forming abstraction, unlike objects are combined into a new object-concept. The process is also termed aggregation, because the new concept denotes either the static structure of one object or the relationship between objects. The object newly created by aggregation can also have new attributes that are made possible only by the new whole. A distinction can be made between this operation and agglomeration, a
simple collection of objects in which the attributes present merely change in value. Aggregation can also be represented in graphical form. Figure 2.14A shows the static structure of a sensor system. The concept “customer order” (Fig. 2.14B), on the other hand, is constructed from the concepts of customer, article, and time. The new customer order concept contains part of the information from each of the original concepts [2.14]. Quite early DP programs were able to handle aggregation in the form of parts lists. Functional Abstraction. A system can be modeled in greater or lesser detail and in a more
20
2. Information Structures in Process Control Engineering
processing
Customer
,*,&,,Connects
I
I
Figure 2.14. Examples of aggregation A) Structure of a sensor system [2.98]; B) Complex object: customer order
or less abstract fashion, depending on the objective. Different persons, with different tasks and areas of responsibility, take different views of the system. Systems analysts and system inodelers always describe a system as an excerpt of reality viewed from one or several of these standpoints. According to POLKE [2.34] (see Fig. 2.28, p. 29),
RASMUSSENintroduced systematic order into the more-or-less intuitively grounded models [2.53], describing a system (Fig. 2.15) as functional abstractions over multiple levels. The number of levels can differ from one system to another. depending on the type of system and the objective. Nor do all the levels have to be implemented.
/
/ Intensional description Production flow models, control systems, phase models plant manager
_/--
Viewpoint o f foreman
--. _.
q
i
I
i
i
o operator i n t of
-. -_.
Desired process state, objective, purpose
Abstract functions Causal structure, nominal functions, mass/energy/information flow, balances, topology, etc.
basis
General functions Standard functions and processes, control loops Physical functions Electrical, mechanical, chemical processes involving components and subsystems Physical description Spatial configuration, anatomy, material, form
Figure 2.15. RASMUSSEN’S scheme of viewpoints [2.53]
basis Causes o f malfunctions, current process s t a t e
/
2.2. Architectural Principles f o r Iiformation Structuring
The design levels of this model extend from intensional models at the uppermost level to extensional ones at the lowermost. Extensional models relate to the specific physical world, while higher-level models represent the purpose and objectives that are to be achieved with the system. Scales for the desired behavior must to be developed from the purpose of the system; in other words, criteria for errors and malfunctions can be formulated only against the background of the process objective. In certain operating states, for example, alarms may be meaningless; measurement signals may have different nominal and limiting values, depending on the operating mode; and so forth. The causes of errors or malfunctions arise from the physical world (bottom-up), while the criteria for defining errors can only be derived from the process goals (top-down). This approach to modeling brings order to the great variety of representations of a system. Above all, it demonstrates that no single representation is adequate; a different view of the system may make a different representation useful. Many technical and economic systems have been created under this architectural principle. Typical examples are computer architectures; architectures of communications equipment, the latter known as the ISO-OSI reference model (e.g.. [2.54]); and the level model of a corporation discussed in Section 2.3 [2.34], [2.55]. The structures of monitoring and control systems mentioned in DIN 19222 also fall under this principle (2.561.
Furnishing of Operation and Level data
Level r e l a t i o n s observation interfaces
Figure 2.16. Hierarchy of functions (level model)
21
The principle of functional abstraction is based on the following rules: 0
0
0
0
The levels must offer generally usable functions and relations; that is, they must represent a good abstraction. The levels must have clear interfaces and minimal upward and downward connection; they must obey the secrecy principle. They must completely implement the required functions; that is, they must be selfcontained, with the properties of locality and internal strength. A level “uses” the services of the level immediately below it. Jumping over levels is not permitted.
It follows that all functions requiring intensive data exchange with one another should be grouped on the same level (Fig. 2.16). The design process for engineering systems is also oriented to such a level model. For example, mechanical design begins with the functional structure, proceeds through the operative principle of design synthesis, and finishes with dimensional synthesis. The Transformation Principle. In chip design, the following levels are recognized: system design, logic design, circuit design, and layout design. The process makes use of detailing algorithms. Configurable detailing algorithms are not known for the corresponding steps in plant design based on flowsheets (Fig. 2.9). If the design process within a process monitoring, control, and automation system is to be
Guideline
Compressed
I
t
information
22
2. Information Structures in Process Coritrol Engineering
completely or partly automated, it must be clear above all how the representation form of one design level is to be transformed into the representation form of the next one. For the developer and designer of an automation system, such a concept supports creative design activities, design verification, project planning, and documentation. This transformation process in informatics is fully automated by problem-oriented languages, assemblers, and on to machine code. Programs that do these transformations are called compilers, assemblers. and so forth. Sequence-oriented description concepts and representations are required in order to describe the functions of information-processing operations. One speaks of a transformation principle because input data are transformed to output data and initial states to final states. The following description types are distinguished on the basis of how the operator is implemented : 0 0
0
Sequence-oriented descriptions Logic-oriented descriptions State-oriented descriptions
Sequence-Oriented Descriptions. Classically, information-manipulating processes are described in terms of individual, application-neutral elements. It can be shown that any routine or any procedure can be described in terms of the linguistic elements sequence, alternative, and iteration. In a procedural programming language, it would therefore be sufficient to make these three constructs available. Typical graphical descriptive media for procedural concepts include 0 0 0 0
Flowcharts Nassi-Shneiderman diagrams SADT diagrams Michael Jackson diagrams
Textual media include, among others, 0 0 0
Pseudocode Structured text Programming languages such as FORTRAN and PASCAL
The analogy between operators in mathematical physics and their importance in the description of state changes (including time-dependent changes) can only be mentioned here [2.57]. Logic-Oriented Descriptions. Logic-oriented concepts relate not so much to the “how” of the
process as to the “what.” Graphical media used for this purpose include 0 0
0
Combinational circuits Logic diagrams Decision tables
Textual media include 0 0 0
Fourth-generation languages Algebraic specifications Logic-oriented languages
State-Oriented Descriptions. The automation of technical systems often requires the control of processes that take place in a predominantly step-by-step (i.e., discrete) fashion. The complexity of a discretely controlled system becomes very high when there are processes parallel in the time domain (i.e., concurrent processes). If these processes use shared resources, there is always the risk of “deadlock.” The processes block one another in such a way that they cannot continue to operate without outside intervention. Such deadlocks cannot easily be foreseen. The need for an analysis and, possibly, a simulation of these systems is thus manifest (compare the typical situation of management in a multi-project environment [2.58]). Such problems can be described well in terms of mechanisms or automata. A finite-state automaton is a system having a specified number H of possible internal states zl,. . . , z,,. For each state zithere is a well-defined set of inputs for which the automaton changes to another state. An output can occur when such a transition takes place. Automata can be used to describe the time order of state transitions in technical systems. State graphs (Fig. 2.17), with circles for states of the system and arcs for transitions between states, are used to visualize these systems. In practical cases, however, the number of global states increases rapidly, so that the representation of the time order by state graphs becomes unwieldy. The Petri net, an extension of the state graph concept, offers a more elegant way to describe such systems. A Petri net is a directed graph whose nodes are of two types, places and transitions. Arcs, which can connect two nodes only if they are of different types, represent logical or causal relationships between nodes [2.38], [2.59]-[2.67]. Using a mode of analysis different from that of state graphs, the Petri net technique allows the
2.2. Arclzitectwal Principles f o r Inforniation Structuring
23
ated state graph is also called a reachability graph. It immediately shows whether there are markings of the Petri net that can be reached in just one way or cannot be reached, whether there are deadlocks, or whether the initial marking can be reached again (reversibility; see Fig. 2.18 D). Petri nets can be broken down in hierarchical fashion and so support top-down and bottom-up design. In the area of process control, Petri nets are usually represented by functional charts. The technique of functional charts makes it possible to portray directly the structure of a Petri net and offers linguistic elements for the specification of control commands to be issued at the individual steps (nodes of the net).
i;: 0;:
inputs outputs
@
P e t r i n e t with initial marking 2
Figure 2.17. State graph illustrating an automaton
description of systems that represent more extensive automata. Thus, the circles (places) represent partial states in the system and, therefore, conditions for the occurrence of certain events that can occur in the system and lead to state transitions. The partial state represented by a place is in effect if the place holds one (or more) marks or “tokens.” A transition is enabled, that is, it can occur or “fire,” when all its input positions (arcs leading to the transition) have tokens while all its output positions (arcs leading away from the transition) have none (Fig. 2.18A). When an enabled transition fires, tokens are removed from its input places and placed on its output places, which now may enable transitions in their turn. In larger nets, this rule means that several transitions may be enabled at once; this is known as concurrent execution (Fig. 2.1 8 B and C). A Petri net lends itself not just to graphical representation but also to mathematical description. On the basis of such a description, the dynamic behavior of the net can be studied by using computer-aided analytical tools. Interesting properties of the net include, for example, reachability and the presence of deadlocks: If each possible occupation of the Petri net with tokens (each “marking”) is interpreted as a possible global state of an automaton, the associ-
@
F i r s t subsequent marking
2
@
Second subsequent marking
2
A
@ Reachability graph
z,
0001
0100
0positi?!
0Transition
-Causal dependence O Token
z,
1: Position is occupied by a token 0 : Position is unoccupied Z;: Global s t a t e s Figure 2.18. Petri net with initial and subsequent markings and the associated reachability graph
24
2. Informution Structuies in Process Control Engineering
One graph that is especially easy to comprehend is the “phase model” of production, which is discussed in more detail in Section 2.3.
2.3. Applications in Process Control Engineering Object Worlds of Continuous Production Processes. If the system to be designed or operated (whether it is an apparatus, a plant, a control system, a software system, or some other system) with all its subsystems and components is placed at the focus of computer support, the monitoring and control of continuous processes can be broken down into plant engineering, process engineering, and process control engineering worlds. In order to describe the actual operation, these are supplemented by the world of process communications, logistics, and maintenance (Fig. 2.19) [2.68]. In an object-oriented approach, substances in these worlds can be defined as functions. They represent the uppermost level of a corporate data model within process control engineering [2.64]. Plant Engineering. Typical descriptions in the field of plant engineering take the form of data sheets (Fig. 2.20). The abstraction principles discussed in Section 2.2 can be seen in this data sheet: The glasslined stirred tank, as represented in Figure 2.21, consists of drive, clutch, stirrer, tank, stirrer
Process engineering
] P l a n t engineering1
Observation Service
1 Process communication 1
shaft, shaft seal, motor, and gearbox (complexforming abstraction, aggregation). Some of the objects are further specified; for example, the stirrer is identified as an impeller, dual-impeller, or anchor type, and the stirrer shaft seal is specified as a stuffing box or a rotating mechanical seal (concept-forming abstraction). All objects listed on the data sheet are described by a series of attributes and thus stand for a class of objects (class-forming abstraction). The “plant and apparatus coding” (PAC) scheme can be viewed as a first formulation and crystallization point for a corporate data model [2.69]. This coding system describes the static structure of a plant in terms of the required functionality (Fig. 2.22). It has analogies with the “power plant coding system” [2.70]. While most description systems do not separate the functions (tasks) of a plant (abstract) from the real technical plant (device), the PAC gives a rigorous description only of the function, up to the technical device and its functional components, and is therefore invariant to actual technical implementations (Figs. 2.22 and 3.26). Earlier DIN representations [2.71] could not employ the object orientation logically utilized in the implementation of the PAC [2.69]. For this reason, stress must be laid on the worldwide harmonization of concepts; this comment applies equally to all three of the “object worlds” under discussion here.
Production control
1-
1
inspection Maintenance
JMaintenanceI
Figure 2.19. Ohject worlds of continuous production processes [2.68]
Process/Plant
Face ring matt Recirculation
Figure 2.20. Data sheet for an enameled stirred tank
26
2. Information Structures in Process Control Engineering {Glass-lined <stirred tank
I
Consi;ts
\ r(Gear
boxk L
of
(MotarX
/
I
I
I
Oil b u f f e r chamber ring Leak-catching cup Number required Manufacturer Eternal cooling Material Type, size Drawing number
Figure 2.21. Data model of a stirred tank in the entity-relationship (ER) representation
Process Engineering. The following example of process engineering illustrates a taxonomy of unit operations (Fig. 2.23) [2.72]. The classification scheme first looks at the most important operations, such as separation, combination, and so forth. The next criterion applied is the state of aggregation of the starting material (see also [2.73]). (Battery
or works)
consists of
is p a r t o f
consists o f is p a r t o f
(PLant) I
I
consists
1-(
Of
consists o f
is either
is part of
t
I is p a r t o f
u (Technical /
device] \
and c o n t r o l s t a t i o n
Figure 2.22. Plant model [2.70]
The general production instructions for the making of a given product (Fig. 2.24) also arise from process engineering. While the first attempts to formalize production instructions were based on the classical structures of thinking in process engineering [2.74], more recent approaches start with the phase model of production derived from the transformation principle [2.75]. The configuration principle of modern process control systems builds on knowledge from object-oriented programming and, more recently, experience in modularization [2.76] -[2.78]. Figure 2.25 illustrates the correspondence of plant model and process model (see also Fig. 3.26). Process Control Engineering. To illustrate the relationship between process engineering and process control engineering, consider what happens in the transition from a generally valid recipe for making product A to a recipe for making productA in plantX. In a taxonomy of recipes, this is a concretization of the “plant-independent” basic recipe into a plant-dependent control recipe. New research by the ISA, particularly on the topic of batch control systems [2.79], and by NAMUR-AK 2.3 on requirements for recipe-mode operation [2.80] must be taken seriously as a basis for discussion. Figure 2.26 illustrates the principal recipe types [2.80]. If one assumes a self-contained recipe for the individual operating modes (see Fig. 4.48), the taxonomy of recipes shown in Figure227 results.
2.3. Applications in Process Control Engineering
27
Unit operations
ISepajai onj
Combination
I
is a1 Solid-liquid
Solid-solid separation
7 1
Solid-gas
Washing Hydroclassification Sieving
4
Screening
Figure 2.23. Taxonomy of unit operations (process engineering) (GDR standard [2.72])
Level Model of a Corporation. Structuring by hierarchical levels (functional abstraction) is encountered in a variety of forms (e.g., for the de-
(Reclpe) I
I
consists o f
(-) consists o f
is p a r t o f
I IS
part of
I
(-basic
function)
Figure 2.24. Structure of production instructions (process model, recipe) [2.38], [2.64]
scription of computer networks or data bases). A corporation can also be represented in this way. According to a suggestion made earlier [2.55], [2.81], if the abstraction principles stated above are applied to a corporation, four levels can be identified. By way of example, Figure 2.28 shows schematically a model of a company involved in the process industry. The four levels are the corporate management level, the production management level, the process control level, and the field level. The dispositive quality of the functions decreases from top to bottom, while the operative quality increases. In what follows, the corporate, production, and process control levels are discussed for the production division of a chemical industry corporation [2.34], [2.82]. Corporate Management Level. In a world where increasing competition demands rapid and often massive reactions in many markets, the corporate management level, where goal-setting and long-term planning are done, is becoming more and more important.
2. Information Structures in Process Control Engineering
28
Plant complex
Battery lworksl
Plant
Recipe
Process model
I,
I
Sub-recipe
Apparatus
Phase
Plant model
Basic
funrtion
Element o f basic function
Figure 2.25. Correspondence of process model and plant model
General production instructions w i t h o u t reference t o p l a n t , standard batch IS
imaged in General production instructions with reference t o p l a n t type, s t a n d a r d batch
is imaged in
General production
instructions with reference t o specific p l a n t and specific quantities
Figure 2.26. Types of recipe 12.801 Process for Plant
Operating mode
is a
I
is a
The key condition for the support of the corporate management level, once systems-engineering problems have been solved, is effective control of the lower levels, in particular the es-
tablishment of conceptual information structures. For this purpose, the information available on the subordinate levels must be compressed in a way appropriate to its use. A strongly department-oriented view is supplanted by a supra-departmental one; a country-specific view by an increasingly international and global one; and a focus on the individual industry or sector is supplanted by a view that takes in many industries. The increasing importance of effective product development management, for example, makes clear the need for a supra-departmental view. In order to avoid costs for dead-end development and delays in development, concrete requirement profiles should be derived from consumer needs at an early stage, the existence of competing products should be investigated, and the requirements of downstream processing industries should be taken into consideration (see also Section 11.3 and Figs. 11.38 ff.). This information should be derived with internal development capabilities. Corporate management should be able to examine the development process at any time. Product quality and environmental protection are other examples where new information aggregates, cutting across classical departmental lines and extending all the way up tc the corporate management level. are gaining in importance and must therefore be made available (see also Fig. 6 in [2.83]). Any company that wants to make intelligent decisions on a worldwide basis regarding, for example, research and production sites and markets to be entered, while optimizing its decisions in a supra-departmental fashion, must have consistent, up-to-date information covering all countries of interest. The result will be increased demands on internal accounting in the sense of comprehensive control, that is, a control instrument for corporate management. Highly diverse, historically evolved structures cannot meet these requirements.
2.3 . .Applications in Process Control Engineering Quality of t h e function
Function
Level
29
I
~m Corporate
11
Production
I
I
7
process groups,
1 pGqj I
3
i
control level
control, s a f e t y
Operative
Figure 2.28. Level model of a corporation [2.34]
Particularly in the chemical industry, the product orientation that comes about because of manufacturing conditions must be harmonized with the interests of the consumer market, which often have to do with more than one product. An integrated way of looking at problems can result in synergistic effects in purchasing management as well. A high degree of information transparency at a level higher than the industry or sector is therefore required. The contribution of information technology at the corporate management level was very limited in the past, because it was restricted to supporting activities directly related to performance or order filling. The technology was marked by an orientation toward single tasks. Data structures were devised in a task-specific manner, in the narrowest sense; data bases were hierarchically structured (see also [2.10]). Data communications between programs also featured a mainly hierarchical structure. The task-oriented application systems based on this technology naturally could make little contribution to supra-de-
partmental views. The core requirements of integration and flexibility could not be met. Personal computers were often made available to staff personnel on the management floors in an attempt to close this gap; most such computers were manually supplied with data from the functional systems and-not surprisingly-produced a wealth of incompatible data. More recently, however, information technology has reached a stage in which the logical requirements of integration and flexibility can increasingly be realized. This applies both to information storage and to information retrieval and interpretation. The first comprehensive concept was developed for all engineering divisions of Bayer in 1989-1992 [2.44], [2.84]. Requirements imposed on the lower levels are illustrated for the case of production in what follows. A supra-departmental view is selected; that is, the interfaces to other departments are examined (see also Chapter 8). Production Management Level. The discussion concerning the “factory of the future” has
30
2. Information Structures in Process Control Engineering
provided an outline of how the industrial production of the future will look. Although each expert’s picture of this factory is different in detail, there is a consensus as to the need for increasingly extensive automation (see Fig. 4.49) of production processes for reasons of quality assurance and optimal utilization of raw materials [2.82], [2.85]. Mass production is no longer a prerequisite for automation. The contemplated decentralized automation world, with the self-contained manufacturing islands already in existence, can be realized only if it is connected by unified information and communications systems. The objectives are to improve material flow, shorten storage times, increase the speed of transportation, and cut setup times when the product mix changes. These objectives are essentially the same no matter whether production is a manufacturing or a chemical process. Even so, there is a great difference between the implementations of these two types of systems. Typical for the manufacturing industry are situations in which one or a few end products are assembled from many individual parts. The assembly process is complicated but lends itself to a virtually complete computer analysis [2.86]. In process industries, such as the chemical industry, the situation is reversed: There is one starting material from which one product or several related products are made (see diagram F8 on p. 63 in [2.82]). This simple, branching flowsheet allows stage-to-stage planning; any change will affect the next stage downstream, but can still be made at any time. The reason is that the process and product properties in each process element are interrelated (see Fig. 2.23). The interrelations, in general, can be described only in a phenomenological way, so that theoretical relations cannot be applied directly. However, there are also many multiply branched flowsheets in which many end products are produced via several intermediates. These multi-stage batch or continuous production processes have the feature that the central event, the chemical reaction itself, is essentially a probabilistic occurrence and thus calculable only to a limited extent [2.86]. Such processes cannot be handled by production planning methods that are oriented toward parts lists. Thus the systems integrated in CIM cannot be immediately and directly extended to continuous-process production.
The general structure shown in Figure 2.16 is now implemented in a manner specific to the production management level in Figure 2.29 [2.87], [2.88]. Production specifications supplied by the corporate management level are the following: 0 0
0 0
Type of product Quantity of product Delivery schedule Special contract information (e.g., special packaging or formulation)
Detailing procedures are carried out with the aid of level-specific relations. Such procedures include adoption of production specifications, selection of basic recipes, comparison of material and apparatus requirements with inventories and free capacities, allotment to production units, assignment of apparatus-specific control recipes, and preparation of schedules. This detailing is often an iterative process that becomes necessary when the nominal/actual comparison in the evaluation algorithm signals disturbances in the production process or when preconditions for detailing are changed during the detailing process itself. This block of functions has a strongly dispositive character. Once the detailing and disposition process has been completed, the production control function communicates it to the lower process control level in the form of a control recipe (when. what, where, how). The process control level in turn reports the relevant process and product data upward for further compression, either while the process is running or after it has been completed. These downwardly and upwardly oriented relations include specific level data, which take the form of static inventories of apparatus and recipes. The updated execution plans, inventories, and actual product and process data are kept accessible in dynamic data bases. This type of data management naturally includes a suitable documentation function, which can generate both routine reports and notices of special requirements. Another relation, not shown in Figure2.29, deals with the optimization function. Points such as batch size, tooling time, startup and shutdown procedures, and stockpiles should be optimized. It is also important to modify the basic recipe and control recipe after the interpretation of process and product data (see Section 3.2) if product consequences reveal incompatibilities
2.3. Applications in Process Control Engineering [ C o r p o r a t e management level
-
I
Q u a n t i t y o f product and orders
31
Quantity/
[Production management l e v e l ]
Level data
)i
Apparatus Recipes
I-
Feedback r e p o r t s interpretation
Execution plans, inventories, product and I
Control
t c o n t r o l level
Figure 2.29. Production management level: structure and relationships
or if certain operating points turn out to be sensitive. In the near future, such a production management system will become a permanent component of all production operations in the process industries. These functions will be implemented in a decentralized manner in individual operations. Production management systems must be available in a configurable, off-the-shelf form, if only for economic reasons. The individual functions must therefore be implemented in the form of self-contained, comprehensible subsystems (Fig. 2.30). According to RIEMER, users can do the following on the basis of information that is kept accessible and up-to-date [2.89] (see also t2.871, [2.88]):
On the basis of sales and operating quantity plans and using inventories and orders, they can determine in advance the net demand for
products and feedstocks over all production stages (production planning). For this planned demand, on the basis of the instantaneous inventory, purchasing, and orderbook situation, they can ensure the timely procurement of all materials needed for fulfillment of the plan and monitor execution (demand disposition). They can keep their own production operations on target as to schedule, quantity, quality, and cost; allot materials and capacities; prepare working documents; continuously monitor production progress; and take prompt action when actual values deviate from nominal values (production control). They can accept specifications for production process control; support and monitor the process; and, by direct intervention as well as aggregation and structuring, prepare the continuously acquired production data for production control (process control; see Section 4.5).
32
2. Information Structures in Process Control Engineering
__-____---
[Corporate management l e v e l l
\contract execution
Contracts
I
1
r-------
I Sales planning] -_-_ L _ _
1
/Production management l e v e l ]
I
1
I
I
1
Information
Demand determination
dprocurement
I
Increases Warehouse management _________ Goods receiving and dispatching Warehouse space Inventory
;torage/removal rom storage
_ _ _ _ i__-_
A
-
Production management ________ Allocation Contract tracking Machine utilization * Yield Detailed material control
laboratories
I
Control procedures
Status reports
L
Status reports
[Warehouse control] [Prbcess c o n t r o l l e v e l )
Figure 2.30. Functions of a production management system [2.89]
0
0
0
0
They can better support the management of object (products, packaging, etc.) inventories (inventory keeping). They can automatically or manually check and plan inventories on the basis of customer, internal, and production orders (inventory disposition). They can have the materials at their disposal stored, removed from storage, commissioned, packaged, sealed, and prepared for shipping (warehouse management). They can store materials and remove them automatically and with optimal routing of warehouse vehicles (warehouse control).
0
0
They can monitor what is happening in the process, using the minimum number of clear signals and standard information, whether by ad hoc queries on the screen or as printout or graphical output (information and report management). They can acquire current data with the required level of detail, store the data, and make them available at various locations for the various functional divisions (corporate planning, controlling, accounting, procurement, duties and taxes, etc.) whether for information or for processing (interfaces).
2.3. Applications in Process Control Engineering
The example of the production management level shows how the analog of CIM in manufacturing operations is computer integrated processing (CIP) [2.90] in continuous-process operations. Clearly, information has now become a factor of production. Process Control Level. In the process control level, production orders (i.e., requirements on product, quantity, and delivery time) are transformed to realization processes. This change requires that a processing facility be available, the control recipe (i.e., the recipe adapted to the actual facility being used) has been determined, and all necessary resources are available (see Section 4.5). The production process is generally subdivided into a sequence of process elements each selfcontained in time. The structure of the process monitoring and control system must correspond to this horizontal breakdown of the process. Process control functions for plant sections must be performed in decentralized process control stations and must be suited to handling the information flow in parallel with the process [2.34], [2.91]. The performance of the production order then involves the allotment of partial control
recipes to plant sections and the phase-byphase execution of the recipes through the basic functions of process control engineering (see Figs. 2.24 and 2.37). Corresponding to the hierarchy of processes and subprocesses, the following functional complexes can be distinguished on the process controllevel(Fig.2.31;seealsoChaps.7and8)[2.92]: 0
0
Central functions for controlling the process itself (process operating computers, process control computers, process A1 computers) and for controlling the process monitoring and control system (process engineering computers for design and diagnostic service). The complex operations involved in diagnosing possible error states of a process control system are merely indicated here (see Chap. 7 and Sections 11.2 and 11.3). The analogy with medical diagnostics should also be mentioned [2.93]. Individual functions and coordinaton functions : the grouping of all process-level functions in a plant section (process section or process element) in a decentralized process control station (process-level components, PLC).
-I01
~
Production management - computer
N
I
-
--> W W
0
-
L + c
u 0
D
Process operating computer
v)
v)
W
U
L 0
a
I
I
33
System bus
I
Process-level components
0
0
0 0 0
Process-level Components
0
0 0
0
0
34 0
2. Information Structures in Process Control Engineering
System communications functions thought of as interface functions (system bus, field bus).
Phase Model of Production. An important aid for systems analysis of production processes is the phase model of production, which was adopted from software engineering [2.94] and belongs to the category of product nets. Other models lack any representation of product properties in between process elements, which is a prerequisite for constructable quality assurance and process reliability ; the phase model supplies this representation. As suggested by BUSING [2.95], the phase model is readily derived from the basic flowsheet by attaching a label for the products between the individual process elements (Fig. 2.32). Each rectangular box stands for a process element, and each circle for a product. The individual process elements can be regarded as operators that transform the product properties (see “Transformation Principle” in Section 2.2). This method facilitates, among other things, the representation of information flows, which are needed to control the entire process in such a way that it is always held in its instantaneous nominal state. The method also requires a clear picture of what information is (or should be) available in and downstream of each individual process element for process monitoring and quality assurance (see Section 11.3 and [2.96], [2.97]).
,Feed! Dissolution ~
,
polv;nt
1:dditiv;
Monitoring and control functions relating to certain product and/or process properties must be carried out in each process element. Product and process properties are the object classes of information about the process [2.98]. In Figure 2.33 product and process properties are listed for the example of one portion of a phase model. Every product or process property consists of a category and a value. Special attention should be paid to scaling (see Section 3.1, Fig. 3.1, and [2.99]). Process properties are the following: 0
0
0
State variables such as pressure, temperature, and concentration. The process can be described in terms of these or with derived state functions. Process parameters such as heat-transfer coefficient and catalyst activity. These identify the constraints under which the process runs. Process parameters are stationary or at least quasi-stationary quantities. Control variables. These characterize intervention in the process.
In theory, the information budget of an entire process can be completely described with an appropriate set of these quantities. In practice, however, it often happens that not all the desired state variables and control variables can be measured or specified. Other information is often employed as a substitute:
, solvent
Feed?
~
Size reduction Additive a f t e r size reduction
solution
t
T-l Separation
End p r o d u c t 1
?Additive
1 Solvent
Product
0
Separation
End product
Basic flowsheet (DIN 28004)
Figure 2.32. Development of the phase model from the basic flowsheet
Solvent
Phase model
2.3. Applications in Process Control Engineering
Oblect
Process property.
I
Inlet product Product
Energy
__requirement - - __ - _ _ -.
0.5 MPa T=fiml
Product p r o p e r t y Va(ue Categxy-
Categw!
Process
Pressure
..-
T_eflp_e_r_a_ture Speed
35
-Mass -_ ___.flow - ----r--a t e Viscosity E N?!_!e_-_ __ Concentration
I
1rnPa.s
4.5
Process (process element)
Outlet product Figure 2.33. Excerpt of the phase model with product and process properties
0 0
0 0
0 0
Setpoints are correlated with the desired control variables. Process indicators are empirically correlated with one or more state variables (these may also be, for example, product properties). Product properties are the following: Physical quantities Chemical quantities Technological properties Product indicators
The last two items, again, are stand-in values. Technological properties are determined in special tests when relevant product properties depend on physical quantities that are difficult to measure or when the relationship to physical quantities is not known. Product indicators are correlated with physical or chemical quantities. Process properties can be employed as product indicators. The importance of this interplay between product properties and process properties was pointed out as early as 1967 when RUMPF introduced the concept of “property functions” [2.100]. Process indicators [2.101] and product indicators are the forerunners of the results that are now obtained by model-aided measurement techniques [2.102]. Product properties and process properties can be represented as property profiles information stream accompanying the material stream in the phase-model [2.34] (see Fig. 2.34). As nominal values, they are an essential part of the production instructions (Section 4.5). As actual val-
ues, they are used in process monitoring (Section 11.2), process control (Section 4.5), process analysis (Sections 3.2 and 11.3), and quality assurance (see also [2.82], [2.103], [2.104]). Note that it is not just the values of the product properties that change from stage to stage; categories may also disappear and other new ones may appear. An understanding of this point is necessary in order to follow, for example, the suggestion of GILLES that quality be introduced as a state variable [2.96]. Figure 2.35 shows the property profile “quality” for an intermediate product, with its ranges of validity [2.105]. Figure 2.36 uses the same method to illustrate reliability. Process engineering is customarily concerned with the material flow (and the energy flow, although this is not illustrated here) in a production operation, whereas the task of process control engineering is to deal with the information flow. The crosslink between the material flow and the information flow is provided by sensors and actuators. Sensor technology (Chap. 5) acquires the information required for process monitoring and control. The function of actuator technology is to derive actions on the process from information extracted from the information stream (see Chap. 6). A phase model can be hierarchically refined in accordance with structuring principles. This has been done for the example of automating a wastewater treatment plant [2.106]. Figure 2.37 shows the detailing process for chlor-alkali electrolysis [2.75].
2. Information Structures in Process Control Engineering
36
Once the process analysis has been refined to the point that products cannot be broken down any further, a transition should be made from a product net to a causal model, either time-based or condition-based. Production process
Sensoractuator system
Material flow
This transition is accomplished by transforming the interpretation “product with property profile” to the logical proposition, “product present in sufficient quantity and adequate quality at time lo.’’
Process monitoring and c o n t r o l system
Information flow
Figure 2.34. Profiles for process and product properties [2.1]
Sensor-
Production
actuator
process
system
?aw materials
Product: 1 Property profile Requirement: quality Value
Process element 1 Intermediate product 1 Process element n-1
Material flow Information flow Figure 2.35. Material and information flows in the phase model of quality [2.104]
2.3. Applications in Process Control Engineering
This logical proposition can be supplemented by further ones, for example: 0 All resources needed for carrying out the process (plants, production specifications, moniProduction
toring and control system) are available and serviceable at time to All required energy sources are available in the proper quantity and quality at time to
Sensor-
fj
taw m a t e r i a l s o Process element 1
0
37
Material flow
1
Product: 1 Property profile Requirement s a f e t y
Information f l o w
Figure2.36. Material and information flows in the phase model of safety [2.104]
elements
H,,
--\
crude
0
Cooling I
Figure 2.37. Detailing of the phase model for the example of chlor-alkali electrolysis
38 0 0
2. Inforniation Structures in Process Control Engineering
All safety devices are operable at time to Approval for startup has been issued at time t0
With such a system of conditions applying to each minimal production unit, the way from product net to causal net is evident. In Figure 2.38. the transition is illustrated, starting with the phase model and ending with Petri nets, which in turn make it possible to present a control process in function charts complying with DIN 40719 [2.107]. The left-hand part of Figure 2.38 shows the phase model of a simple production process. Each box represents a process element and corresponds to a unit operation. On this level of the hierarchy. changes in product properties are in the foreground; the way in which the unit operations are implemented is not of interest yet. If the top-down design process is continued and the technical implementations of the unit operations
are specified (e.g., as shown in the diagram of Fig. 2.24, the middle part of Figure 2.38 shows the resulting detailed description. Each unit operation is now represented by its own Petri net relating to the setting of process properties. Basic functions are employed to establish the process properties. The production specification (or recipe) sets forth under what conditions which process property must be set to what value. Basic function elements are utilized in order to execute the basic functions. The resulting function chart describes the detailed solution in process control engineering terms (see also Section 4.5). The phase model of production is thus the appropriate tool for describing (Chapter 10) and analyzing (Section 11.3) production processes with regard to quality assurance (Figs. 2.332.35), process reliability requirements (Fig. 2.36), static structuring (Fig. 2.37), and dynamic structuring (Fig. 2.38).
2.3. Applications in Process Control Engineering
c 0 + + n 3 c L c rn
7
-
-2
-0 W
m v)
2 U m
0 c v)
.-
+ Y 2 t
.A-
u ._
m v)
m
c C W c
U
3
V 0 L
Q
Figure 2.38. Transformation from the phase model via Petri nets to the function chart
39
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
41
3.1. Principles
3. Knowledge about the Process 3.1. Principles An essential feature of modern process control engineering is that knowledge about the production process is utilized to the fullest. “Knowledge about the process” does not refer just to knowledge of the static and dynamic attributes or properties of the object classes of the production process (e.g., process or product properties [3.1]), as now represented in entity-relationship models (Chap. 2) and often taking the form of observations and measurements. Instead, this phrase denotes knowledge about how these attributes depend on one another. Because they pertain to the system “production process,” they will also be called “system quantities” in what follows. This knowledge about the interdependence of system quantities can exist in various forms (e.g., characteristic curves, correspondence tables, mathematical process models). The power of such records depends heavily on the type of system quantity. The type of quantity is established by the kind of scale used to measure it [3.2]. There are four types of scales: nominal, ordinal, interval, and proportional (Fig. 3.1). They differ in information content and interpretability. For example, the nominal scale permits only the relationship like/unlike (or equal/unequal) between two fixed values of a quantity; this applies to quantities such as tank numbers, production line numbers, and so forth. The ordinal scale contains the
further information that an order relation exists, as in sensory evaluations or quality levels. With these scales, items can be identified not only by a wide variety of possible symbols but also by numbers, but the intervals between such numbers have no empirical meaning: The scales represent only topological information. Metric scales, where the differences between numerical values are interpretable, are the domain of physical quantities. An example is temperature measured in degrees Celsius, which is an interval scale. If the ratios between the numerical values of a quantity have a physical meaning, as is the case for masses, absolute temperatures, and so forth, the scale is said to be a proportional or ratio scale. It is evident that the ways of representing knowledge depend on the type of scale applicable to the system quantities. Mathematical process models expressed in systems of differential equations can be employed only when the quantities lend themselves to a metric type of scale. Before dealing with ordering methods for system quantities and the structure of mathematical process models, first some key concepts of systems theory will be elucidated that are significant in a process control engineering (PCE) context. The notion of “system” presented here contains a deliberate balance between needed generality (to afford sufficient degrees of freedom for separating the overall system into subsystems) and certain properties that are absolutely necessary for arriving at a general system structure. This is a point of great importance in the writing and application of software based on modular princi-
Interpretable relations Scale type
Likelunlike
Ordering
Difference
X
Ordinal
X
X
Interval
X
X
X
Proportional
X
X
X
~~
Examples Tank no. Production no.
Nominal
Figure 3.1. Types of scales
Ratio
Quality classes Sensory evaluations Temperature Time (h)
X
(OC)
Temperature (K) mass, energy
42
3. Kmowledge about the Process
In recent decades, the specialist literature has included many contributions attempting to justify a general theory of systems [3.6]-[3.13]. The axiom-based treatments, like all axiomatizations. represent a formal abstraction of concrete experience. The basic systems-theoretical concepts will be discussed here only in so far as they are essential in a PCE context. In what follows, all system quantities are assumed to be “measurable.” that is, representable on metrical scales. The extension of this analysis to topological scaling, which has its own descriptive forms, leads to indefinite, conditional statements closely related to “fuzzy logic” and the methods of artificial intelligence (AI). They are not within the scope of this article (see also [3.14]). The scaling must, however, always be considered in the synthesis and analysis of production processes. If the behavior of a reactor, such as a cascade of stirred tanks, a tubular reactor, or the like, is examined the system quantities can be classified in several categories. For example, the following classes can be distinguished:
Lr+l Reaction (0
Figure 3.2. Phase model of a production process (excerpt)
ples. The system concept is closely related to the phase model of production processes (Fig. 3.2) [3.3], [3.4], in which each process step can be interpreted as a transformation (see Sections 2.2 and 2.3). Certain product properties that exist before the inlet to the process stage, often represented as an unordered set {Ei), or as the endpoint of a property vector in product space [3.5], are mapped onto the property vector existing after the process operation n with coordinates {Ei}n+1. In simplified form, property vectors are written as sin(inlet to the process stage) and s,,, (outlet from the stage), as shown in Figure 3.2. The transformation can then be written as: (3.1.0)
Inputs ui: Mass flow rate in, concentrations and temperature in the inlet stream, cooling/heating temperature, etc. outputs yi: Mass flow rate out, concentrations and temperature in the outlet stream, density of the outlet stream, etc. System parameters pi: Heat-transfer areas, heat-transfer coefficients, specific heats, enthalpies of reaction, kinetic constants, geometric quantities (pipe diameters and lengths), etc. In this classification, the inputs play the role of independent (environmental) variables acting on the system. They can be thought of as perturbations or, when purposely manipulated, as control variables. The outputs, on the other hand, are the means by which (at least in principle) the system acts on its environment (see Fig. 3.3). It may seem surprising that state variables have not been mentioned up to this point. In fact,
outputs yi
Inputs y isetpoints, perturbations)
Figure 3.3. Schematic of a system
3.1. Principles
there is an important class of systems in which only steady-state processes take place, and in principle these can be described without the use of state variables. For example, in a cascade of stirred tanks operating in the steady state, the outputs are uniquely determined by the inputs. The introduction of state variables xi is necessary only when investigating the time variations of systems. If, for example, the startup behavior of a reactor is to be studied it is not sufficient to know the time dependence u ( t ) ofthe inputs; the “internal state” of the system must be known at least at a certain “initial time” t o before the outputs y ( t ) can be predicted. This situation is reflected in the precise mathematical description when the system is formulated as a system of differential equations
x =f(x,u,t)
(3.1.1)
which can be solved uniquely only by specifying an initial state xo at an initial time t o . Formulation (3.1.1) suggests that we generally attempt to describe a system by finding a vector x that has a minimum number of components. In the description of a chemical reactor, for example, one often arrives at a state vector x having the “reaction coordinates” tiand the temperature T as components. All other quantities, such as concentrations, density, etc., are grouped in a vector z and can be stated as functions of x, u, and time t :
z
= g(x,u,t)
(3.1.2)
The output vector y can be defined in a similar way:
If a state vector x with the minimum number n of components is selected, n is said to be the number of degrees of freedom, or the order of the system. Systems with finite order are also referred to as having lumped (concentrated) parameters. Systems with infinite order are then said to have distributed state variables. The state variables in such systems are generally continuous functions of the position coordinates. The terms “system” and “process” are frequently used as synonyms. Here, however, “process” is defined as a one-parameter family of system states with the time t as the parameter.
43
Accordingly, processes are generally dynamic; that is, their states are truly time-dependent unless special precautions are taken. Even when the inputs of a system are held constant over time, the system state wilt be a function of time at least during a startup phase. A special case of extraordinary importance for many problems in practical process control is that of stationary processes, where the system states x ( t ) persist in a fixed state x, and thus become independent of time. The state x,is also said to be steady or stationary. The maintenance of specified steady states is one of the key tasks of process control. Steady states x, should be considered only in time-invariant systems, where the time does not appear explicitly on the right-hand side of system equations (3.1.1) to (3.1.3). Clearly, the steady states in systems of this kind depend on the (constant) inlet variables u selected, and since x, = 0, the stationary system relating to (3.1.1)-(3.1.3) is immediately obtained if (3.1.1) is replaced by (3.1.4) For practical purposes, it is important to have a knowledge of the system stability near a stationary state x,: If a process trajectory begins near state x,, does the trajectory move farther away from x, or return to x,? This raises the question of how the system reacts to small disturbances away from its stationary point x,. In order to handle this as well as some other questions, it is useful to have a concept of distance in relation to vectors. As is customary, the vector norm /I x 11 is used here; the distance between two vectors xI and x, is taken to be the norm of the difference: // x1 - xt 11. In what follows, the commonly encountered Euclidean norm (3.1.5) is used, where the ti( i = 1,2,. .., n) are the components of the vector x. Reconsidering the stability in the vicinity of a stationary state x,. We first recognize that only the state equations i =f(x,u) have any role to defines the stabilplay in this respect. LYAPUNOV ity of a stationary solution x,of j c = f ( x , ti) in the following way: 1) x,is stable if, for any sphere K , with arbitrary radius r about x,, there exists a number p > 0 such that the following statement holds for a
44
3. Knowledge about the Process
dynamics but responds to the time variations of u without inertia (time lag). The transition to a quasi-stationary system is a radical reduction of the system: The initial system (3.1.1) has n degrees of freedom, but the new system has no internal degrees of freedom. Consider, for example, an ideally mixed stirred tank containing an indicator substance whose timedependent concentration u ( t ) in the inlet fulfils z . liL(t)l l u ( t ) J (where z is the mean residence time in the tank). The outlet concentration y ( t ) then satisfies y ( t )= u ( t ) . The intrinsic dynamics of the tank, represented, for example, by its residence-time spectrum, is no longer identifiable. The quasi-stationary property of subsystems within a specified overall system is not just important for numerical treatment: It is one of the key concepts for explaining “synergetic” effects in nonlinear systems [3.18]. Finally, two terms whose understanding is basic to the mathematical solution of most control engineering problems: controllability and observability [3.13], [3.19] -[3.23]. If at time to a system is in a certain state x, and the inputs exhibit a time dependence u ( t ) such that the system is in a specified state x, at a later time t,, the system is said to be controllable from xo to xl.If any x, is controllable to any .xl, the system is said to be completely controllable. Consider, for example, a stirred reactor in which the reaction A B takes place, whereby the reaction is dependent on the temperature 0. Let the state vector be x = (O,[A],[B])T, and the inputs u, = heating/cooling temperature 0, and u2 = inlet concentration [AIi, of species A. Then any state x,, is controllable to any state xl provided the time interval from to to t, is sufficiently long. However, if u2 is not permitted as an input, then clearly xois not controllable to x1if [A], and [AIi, are smaller than [A], . The controllability concept is of crucial importance for the problem of feedback control (returning the state to the setpoints) and the related problem of pole specification. In the same way that the controllability links inputs u with state variables x, the observability relates the outputs y to the state variables. The state x is said to be observable at time t , if x(tl) can be determined from a knowledge of y ( t ) in the finite time interval to < t < t,. In the reaction system discussed above, for example, suppose the state variable 0 is simultaneously taken as a measured quantity y. Then the remaining state variables [A] and [B] can be calculated from the time variation of the temperature 0, and are
+
Figure 3.4. Lyapunov stability
process trajectory with initial value x(t,): x(t) lies in K , for all t 2 to provided that Ii x(to) - x, I1 < e (Fig. 3.4). 2) x, is asymptotically stable if x, is stable and there exists a number eo such that )I x(t,) - x, /I < eo implies the convergence of x(t) to x,; that is, Ilx(t) - xsl\+ O as t + 00. 3) x, is said to be unstable if x, is not stable. More on stability and related problems can be found in [3.15]-t3.171. The property of asymptotic stability says nothing about the rate at which process trajectories beginning near x,converge to x,. The notion of the relaxation time TRserves this purpose: If the estimate f-fo
llx(t) - XJ I ye-%
(3.1.6)
holds for all trajectories beginning sufficiently close to x,, then x, is exponentially asymptotically stable with relaxation time &. Systems with a globally valid relaxation time & (i.e., TRindependent of specially chosen inputs u) have a remarkable property: For sufficiently slow variations in the inputs u, they behave in a quasi-stationary manner. More precisely: If the rate of change with time u ( t ) of the input vector satisfies TR.lliL(t)ll < Ilu(t)l(,the time-variant state x(t) can generally be calculated from the formally stationary equation 0 =f(x(t), u( t) ) . The quasi-stationary system is purely algebraic (it no longer contains differential equations), and its dynamics depend solely on the time dependence of the inputs u ( t ) . The system thus has no intrinsic
--f
3.2 Analysis Methods for Process Quantities
hence observable, provided the enthalpy of reaction is nonzero. The observability concept is basic to the concept of the Luenberger observer in control engineering [3.13], [3.19], [3.24], [3.25].
3.2. Analysis Methods for Process Quantities Introduction. Suppose k process quantities (i,e., state variables, inputs, or outputs) are measured at a certain time. These observations can be expressed as a k-dimensional vector V(1,. . ., k). If there are many observations, they form a cluster of points in a k-dimensional vector space, which will be called observation space or property space in what follows. Mathematical methods of linear algebra and statistics now become applicable [3.5]. Important uses of these methods include data reduction and transformation. A data reduction can be performed if the point cluster lies, at last approximately, in an m-dimensional subspace of the k-space. A transformation changes the coordinate system; that is, it generates a more easily handled representation by replacing the measured process quantities with “parameters” calculated from them.
45
Statistical models are valid only in the region studied; extrapolation beyond this region is not permitted. Furthermore, such models hold only under the assumption that all independent variables influencing the dependent variables have been identified. Preparation of Process Properties. A statistical analysis can be broken down into the functions of acquisition, archiving, preparation, and interpretation (Fig. 3.5). Statistics proper is part of the interpretation function and will be discussed in depth in the next section. Acquisition and preparation, however, are vital for the practical use of statistical methods and should therefore be explained in further detail. Acquisition. Statistical research often entails a laborious data collection process. In plants equipped with process monitoring and control systems, the system acquires a large amount of process data for observation and for use in plant operation. Thus it is obviously desirable, to use process monitoring and control systems to acquire data for use in statistical studies. When this is done, archiving must be added to the functions of a typical system so that data collected at a given time can be accessed at a later time. What
I
Online measurements, descriptive data
A
data
\
Preparation -Selection,
interpolation
/
Selection o f
statistical methods
Figure 3.5. Acquisition, archiving, preparation, and interpretation of product and process data
46
3. Knowledge about the Process Data preparation
Matrix Matrix header data
Observation M I
Value M1
Value M2
Value
~3
Value
M4
Value M...
Variable 1. series 1 Each observation associated with one object (e.g.. charge, time)
is more, certain data on the configuration of the process control system must also be archived so that the archived process data can be properly interpreted even after a reconfiguration. Statistical software packages (e.g., SAS) can be used for the actual interpretation step; such packages for use on PCs offer good functionality at reasonable prices. Preparation (Formatting). The starting point for many statistical procedures is a matrix W in which the rows represent observations and the columns represent the observed features (Fig. 3.6). A typical requirement for a process monitoring and control system is that k process quantities be selected in a specified time interval from to to t,,, at a specified sampling rate. This would yield an rn x k matrix (i.e., a matrix having m rows and k columns), which can be input directly to statistical analysis. However, commercial software for process control systems does not to have this functional-
Value MN Variable N, series M
Figure 3.6. Data matrix for observations
ity. Nor does the simple selection of an m x k matrix satisfy all practical requirements, since professional presentation requires that descriptive data (name of measurement point, unit of measurement, etc.) be included. The extracted data must be output in a format that can be interpreted by commercial P C software. Because the result of data preparation is essentially a matrix, it makes sense to adopt a file format used in spreadsheet-type analysis. Software. The preparation (formatting) and statistical analysis of process properties is not a routine activity for the person who does it. This makes it all the more important for the software to have a simple user interface. The format of the measurement station list must offer an easy way of marking the stations chosen for data acquisition (Fig. 3.7) [3.26]. Since all the selected points are sampled at the same time intervals, which as a rule do not coincide with the rhythm of data acquisition, an interpolation type must be specified for each individual station. The interpolation
3.2. Analysis Methods f o r Process Quantities
47
Data preparation offline raw database Selection:
* Compression interval commssion interval. s
h l
Interpolation method M L M I V I I A S A NNXTR
S o u o n o m O D 0 O D D S O
O D D 0 0
D D O S O
Measurement point (analog, binary, analysis) Pressure reactor 1 Temperature reactor 1 Valve inlet reactor 1 Valve outlet reactor 1 Analysis 5/3 pH value
1
Standard table (including header data) Row: observation no. W variable
types include linear interpolation; constant incrementing of the measured value up to the next measurement; and mean, minimum, or maximum value (when several acquisition events fall into one data-preparation interval). Once the process information has been prepared, the data can be transferred to a statistical software package via a suitable interface. As already mentioned, the user expects that not only the measurements, but also the descriptive data, will be extracted from the process control system. The statistical package must then use these descriptive data as captions, axis labels, etc., without any intervention by the user. Many applications allow the user to define a catalog of statistical methods that can be employed. In such cases, a user interface with the following properties can then be implemented, either as part of the statistical software package or as an add-in: 0 0 0
0
Spontaneous selection of a variable as a dependent variable Selection of statistical methods by marking a list The possibility of depositing rules for conditioned or excluded statistical methods Dynamic specification of numbers of variables and observations and identification of descriptive data for statistical procedure
Figure 3.7. Interpolation of raw data to produce a standard table for subsequent analysis
If the statistical package employed does not have this functionality, the user interface can be implemented with additional software. A statistical procedure will then be written in which the numbers of variables and observations, names of dependent variables, and descriptive data are represented by placeholders. The steps in the procedure are defined as statistical methods. The add-on software uses selection rules to interpret user inputs, descriptive data, and files, replaces the placeholders, and ignores methods not selected. The procedure that results can now be handled directly by the statistical package. Statistical Methods. This survey briefly describes individual statistical methods and discusses their applicability to the interpretation of process information. Elementary Statistics. The examination of position parameters, such as mean, median, and mode, is meaningful especially when the process is expected to be stationary or pseudostationary (e.g., a constant flow rate in a continuous process). If the usual variability of the process has already been determined on the basis of large data sets, statistical quality control with control charts is possible. The purpose of control charts is to discriminate as accurately as possible between normal fluctuations of a value and unusual variations.
48
3. Knowledge about the Process
An important point when using a statistical test is to follow a definite methodology. This includes exact formulation of the hypothesis. so that the probabilities of Type I errors (rejection of a correct hypothesis) and Type I1 errors (acceptance of a false hypothesis) can be precisely studied and evaluated. In many cases it is useful to determine whether a classical distribution function can be assumed. The important tests used in practicethe F test (comparison of two variances) and the t test (comparison of two means)-assume a normal distribution of the population. Experimental Design. The concern of experimental design is to determine which of k potential factors x l , . . .,x k actually influence a dependent variable y. and to do so in the most efficient way by means of planned experiments. Two-level factorial experiments are important. First, a realistic minimum value (- level) and a realistic maximum value ( + level) are selected for each factor. Not only quantitative factors but also qualitative ones are possible; for example, + denotes product from plant A and - denotes product not from plant A. The complete design then consists of 2k experiments. It is obtained by taking all combinations of the - and + levels for the wi. The effects are the actions of the factors themselves, as well as the interactions of two or more factors. The level of an interaction is obtained by multiplying the levels of the individual factors. For example, if the factors wl, w 2 ,w gin an experiment have levels -, -, then in this test the effect w1,2.3has level +. After performing the experiments, the effects are calculated by taking the mean of all results from trials in which this effect had level + and subtracting the mean of all results from trials in which the effect was -. The literature gives formulas for deciding, on the basis of these results, whether an effect is significant. If the assumption is justified that interactions of more than two factors are not to be expected, then the complete factorial experimental design can be replaced by a fractional factorial design calling for fewer tests. Methods for generating partial factorial designs can be found in, for example, [3.27]. Software for creating and evaluating designs is available. The interpretation of two-level factorial experimental designs is a special case of analysis of variance, a technique that can be used with any
number of levels. The trick is to use two-level factors first to investigate which potential factors truly affect the experimental result y . The nature of this influence (e.g., whether it is linear) is investigated only after the merely notional factors have been identified and omitted. Principal Components. On economic grounds, it is often not possible to carry out a designed experiment, and one must be content with random process measurements from the plant. It may be difficult to gain any knowledge from such a collection of data. Principal-component analysis is a method of orienting and, in some cases, reducing high-dimensional observations. If the observations exist in k-dimensional space, principal-component analysis answers the following question: For any i, 0 < i < k, how well can the k-dimensional observations be described in a subspace U of dimension i (Fig. 3.8)? While a regression model minimizes only the squared distances in the direction of the dependent variable y , principal-component analysis minimizes the sum of the squared Euclidean distances between U and the individual observations. In this way, all the k process variables are
C>= 1
+,
Figure3.8. Effect of the shape of a point cluster on the principal components
3.2. Analysis Methods for Process Quantities
equivalent; none is identified as dependent. Scaling now becomes a problem, especially when a variety of physical units are present. A change of scaling means a numerical change in the Euclidean distance in the k-dimensional observation space, and thus changes the result of the principal-component analysis. In practice, all the process variables are usually scaled so as to have mean 0 and variance 1. When this is done, however, the scaling becomes experiment-dependent. It is also possible to determine a technical minimum and maximum for each process variable, then define the scaling to give these the values - 1 and + I . The principal-component analysis determines, for every i, 0 < i < k, the i-dimensional subspace Ui for which the sum of the squared Euclidean distances to the observations is a minimum. The procedure has the convenient mathematical property that these subspaces are all nested, that is, U , G U , G ... G U k - , . They can thus be stated as a sequence of k basis vectors; these are the principal components. The mathematical procedure yields an eigenvalue for each principal component; the sum of all the eigenvalues is k. If, for some i, the sum of the first i eigenvalues I , I 2 ...Ii is close to k, the point cluster of the observations can be regarded as i-dimensional to satisfactory accuracy. Often, ci = (1, + 1, + ... +li)/k is used as a parameter, known as the variance contribution of the first i principal components; this has a maximum value of 1. 'Time-Series Analysis. A typical problem leading to a statistical study of process variables is the estimation of dead times. The central concept is cross-correlation: The correlation coefficients between the time series x ( t ) and all time series y ( t + n T ) are calculated, where T is the sampling rate. If N denotes the value of n that has the largest correlation coefficient, then N T is the estimated dead time. This method has been extensively developed. Nonlinear relations between x ( t ) and y ( t ) can now be included, and processes for which the dead time is not a constant but depends on the nature of x ( t ) can be investigated.
+ +
Systems Engineering. A number of software packages for the statistical analysis and graphical presentation of data have become commercially established in recent years. The methods employed are complex and exist in a wide range of variations, and no practically useful function-
49
Statistical
== ==
data acquisition system
Analy;;; values, commands, etc. Figure 3.9. Problems in transferring inadequately organized data to statistical software
ality is left out. The publishers of most of this software offer their products for a variety of system platforms (MS-DOS, UNIX, etc.), and data can be shared with other widely used software products (database management programs, spreadsheets, etc.). Examples of established statistical packages are SAS and RS/I. These products are continually being upgraded as standards develop (e.g., X/Windows). While market conditions are generally good for statistical applications software, there are some problems having to do with inadequate management of process data (Fig. 3.9). In practice, statistical analysis includes the functions of data acquisition, archiving, and preparation; these have already been described. But no software standards have yet been adopted in this area, and there are no dominant products in the market. One step in the right direction is The Fix, a process data acquisition system published by Intellution for MS-DOS. The system supports the preparation of historical data in Data Interchange Format (DIF), which many spreadsheet programs, as well as the SAS and RS/1 statistics packages, can handle. Process monitoring and control systems published by the established firms, however, do not support the generation of a matrix (Fig. 3.10) in-
,,,= system
Matrix
e=
data a 5 acquisition system
=
Analysis values, commands, etc. Figure 3.10. The data matrix as interface between process control or data acquisition system and statistical software
. 50
3. Knowledge about the Process
Reality
Model=image o f reality from a particular viewpoint
Process
Mathematical
e, Numerical
P 8, I diagram
Chemical production plant
Flowsheet
1
I*
Figure 3.11. Model and reality
cluding descriptive headers in place of a series of measured values.
3.3. Process Models Introduction. The general concept “model” is very broad. Any means of analysis and synthesis that is used in learning about reality or in actively designing and fabricating human artifacts can be called a model (see also Section 2.2). Typically, models are simplified pictures from a particular viewpoint of the properties and functions of the real object on which they are based. As for the chemical plant models in Figure 3.11, it can be stated generally that a model reflects only a certain portion of the whole, from a certain aspect and for certain purposes. The example clearly shows the three main features of all models [3.28]: 1) Mapping. 2) Abbreviation: The model generally lacks many of the attributes of the original. In fact, this information loss is often an advantage because incidentals can be ignored in creating the model. One discards intellectual ballast so as to be able to focus better on the interesting aspect of the whole. These comments are
Modelinq errors: simplifications, theoretical short comings, inaccurate ]mode< parameters
engineering
Computer program
Computed result)
Rounding error
Figure 3.12. From process to computed result
closely related to the third model characteristic. 3) Pragmatism: The model represents the original, aiding in the acquisition of knowledge and permitting a limited range of mental or actual operations.
In contrast to models that represent physical images or simulations of particular originals, mathematical models are mental constructs that generally apply to an entire class of objects and situations. They thus fulfill a key prerequisite for being “scientific” (“Scientific theories are general laws” [3.29]). Mathematical models are formulated (as differential equations, etc.) in terms of variables that cannot enter into a concrete calculation until boundary or initial values and other model parameters have been established. It is in setting these values that the user applies the mathematical model to the “special case.” The mathematical model, as the image of an entire class of processes, constructed with the aid of the basic disciplines (chemistry, physics, process engineering, etc.), starts out with only a theoretical or prospective relationship to actual calculations (Fig. 3.12). Its use in computation
3.3. Process Models
depends on the existence of a solution algorithm; in general, a computer program is still needed. The usefulness of a mathematical model is thus determined not only by its consistency with the underlying real object, but also by the bounds set by numerical mathematics and computing capacity set. These limits are still relevant in practice today, because many of the processes found in the chemical industry are highly complicated from the standpoint of mathematical modeling (e.g., emulsion polymerization with its many associated phenomena). Advances in the calculability of mathematical models are essentially governed by advances in computer hardware. An example is the method of finite elements, used for the solution of partial differential equations. Although the fundamental idea was formulated by COURANT in the 1940s [3.30], this publication remained largely unknown, and finite elements were “rediscovered” by engineers in the 1950s. Only then did rapid progress occur in its numerical principles, and these advances accompanied the development of computer technology. Another example is adaptive control. The first publications on this method appeared in the 1950s [3.31], [3.32], but the impetus for further study came only when the development of fast process control computers made it practically feasible. The creation of a mathematical model thus means keeping track of numerical problems, the capacity of the computers to be used, and (especially when writing large programs) software engineering considerations. In particular, the three main sources of error (modeling errors, procedural errors, and rounding errors) must be treated as a compromise to minimize the total error. This topic has also been dealt with in [3.33], [3.34]. Mathematical Models: Survey and Structural Properties. This section treats mathematical models that describe the space time behavior of processes whose model variables and parameters are measured on at least an interval scale (see Section 3.1). The general mathematical description of such processes consists of coupled systems of partial differential equations. The models under consideration are those in which the system is described in terms of model variables classified as inputs ui, state variables xi, outputs y i, and model parameters pi (see Section 3.1). In creating and using mathematical models of process apparatus, it is particularly important ~
51
1. Physicochemical part (state relations) Enthalpy J=J(.x. T ) Density e=e(S, T ) Ideal gas law pV=vRT 2. Apparatus-specific part (process equations) 6w
-+div(; x) 6T = diffusion term + sources - sinks “Engineering formulas” Initial and boundary conditions Balance equations
Figure 3.13. Structure of mathematical process models
that the model nearly always consists of two parts, the physicochemical part and the apparatus-specific part (Fig. 3.13). The physicochemical component of the model includes relationships whose validity is totally independent of the particular apparatus in which the modeled operations take place. This self-consistency of the physical component insures that the model can be applied to a variety of apparatus; this feature is naturally a great advantage in the architecture of large applications software packages (material property databases, etc.). The fact that apparatus and facilities can be simulated with off-the-shelf engineering software, without regard to the wide range of variation in the details, is a consequence of the strong structural similarity of the essential apparatus-specific process equations. All spacetime processes taking place in continuous-flow equipment, for example, obey balance equations written for conserved quantities (e.g., energy and chemical species), in which one side always contains terms known from the continuity equation while the other side contains diffusive terms, sources, and sinks, which represent only the pure, non-apparatus-specific physics. As special cases, the continuity terms contain both purely spatial variability (steady continuous process) and purely temporal variability (batch process). “Stage models” are often used in simulation. Such models describe apparatus or parts of apparatus in terms of fictitious or real stages that have no spatial variability of the state quantities in a finite volume. The differential operator div(G . x) is then replaced by appropriate difference operators. In addition to the balance equations, the typical information includes initial and boundary conditions for the apparatus-specific equations. A number of software products now available make extensive use of these structural properties, both in their design and in the user interface. The conserved quantities are momentum (Navier ~
52
3. Knowledge about the Process Physicochemical part (state relations) Equilibrium relations y* =J(.x, T ) Boiling curve p = p ( . ~ ,T ) Ideal gas law pV=vRT Liquid density e=e(x. T ) Enthalpy of liquid J=J(r, T ) Heats of vaporization r = v ( T ) Reaction rates R=R(c, T )
Apparatus-specific part (process equations) Material balances ( n i . r i )= Z inputs-Z outputs Heat balances = C inputs -C outputs Separation effect Y - V ~ + 1of trays v*- J,i + l . 1
u:
Pressure balance Weir relation
ApL=gl (flow rates, densities, x, T, geometry)
Liquid flow rate= gz (liquid volume, geometry)
Figure 3.14. Mathematical model of distillation columns
Stokes equations), mass (continuity equation), and enthalpy (heat balance). Other conserved quantities, as well as sources and sinks, can generally be programmed by the user. The divergence term div(Ej. x) has uses that go far beyond describing convection in space. The balance equation can be applied to other pseudospatial propagation phenomena without change. Two examples: 1) Particle-size distribution “Space coordinate” = particle diameter Velocity v = rate of growth of particles Conserved quantity x = number of particles 2) Particle age distribution “Space coordinate” = particle age 7 Velocity v = dz/dt = 1 Conserved quantity x = number of particles
As a concrete example to show how the model equations are split, Figure 3.14 shows the mathematical model of a distillation column. For the physicochemical part, equilibrium relations are needed between the mole fractions in gas and liquid, the relation for the boiling point line (which interelates the pressure with the molar fraction of liquid and the temperature), the ideal gas law, and relations for the liquid density, liquid enthalpy, heat of vaporization, and (if appropriate) reaction rates. All these are purely physical state equations. Given this list of relationships, only an expert might suppose that someone is creating a mathematical model for a distillation column. The apparatus component of the model is required to reveal which apparatus is to be modeled. In the case of a distillation
column, this would comprise the material and heat balances for condenser, trays, bottom, and vaporizer; the separating effect of the trays (giving the departure from ideal equilibrium, described by, for example, the Murphree efficiency E ) ; the pressure balances for determining the pressure drop across individual trays (these use basic geometrical data such as perforation diameter, tray thickness, and active tray surface area); and finally a weir relation to give the downflowing liquid flow rate (again based on geometrical data such as weir height). Spatial discretization with stage models is a special case of a widespread practice (e.g., difference methods, method of finite domains, Galerkin method, with the important special case of finite elements), which meets practical requirements because partial differential equations can generally be solved only by discretizing to finite-dimensional substitute models. These are represented as systems of differential algebraic equations (DAE systems) of the form
A (x, z, u)x = f ( x , z, u)
(3.3.1)
0 = g(x,z,u)
(3.3.2)
The output equation (3.3.3) is also needed. The special structure of the left-hand side of (3.3.1), where the matrix A depends on x , z, and u, expresses the fact that nearly all process models of practical relevance are linear in the time derivatives i of the state variables s. For the numerical solution of DAE systems, see t3.351. [3.36]. In Section 3.1, the state vector x with minimum number n of components was introduced to describe the system, where n is equal to the number of degres of freedom in the system. Unfortunately, the mathematical modeling of a system based directly on the physical phenomena often leads to DAE systems (3.3.1),(3.3.2) in which the number of degrees of freedom is smaller than the number of components of vector x (DAE systems with index greater than 1). For example, in mathematical models of distillation columns, the boiling-line equation couples the pressure with the differential equation variables temperature and mole fractions. If the pressure is assumed to be constant, the number of degrees of freedom is thus reduced.
3.3. Process Models
The numercial handling of such systems with index greater than 1 is problematical because the solution contains time derivatives of the inputs u ( t ) up to order q - 1, where q is the index of the DAE system [3.37]-[3.39]. Thus DAE systems with index greater than 1 are mixed problems involving both integration and differentiation. Example: The equation k, = xo - x1 (stirred tank with mean residence time 7 = 1) with x1 = u ( t ) [with u ( t ) a specified function] has solution xo = u ( t ) ti(t).Here the number of degrees of freedom is actually zero; the index is 2. Design problems such as this example, where certain state or initial values are specified and certain inputs are to be calculated, generally have a high index. To transform a DAE system with index q 2 2 to an equivalent q = 1 system, index reduction methods have been devised 13,371, [3.39]. For large models, however, these are often time consuming since the transformations must be performed with closed formulas, and an expert system that will let the computer do the transformations is not available. If the system is discretized not only in space but also in time, by considering the system quantities only at specified (generally equally spaced) times ti = to iAt (i = 0, 1, 2,...), the time-continuous models (3.3.1)-(3.3.3) become time-disCrete models of the form
+
+
53
1) Stability analysis 2) Step response of the system 3) Controller design and tuning of controller parameters; simulation of genuinely dynamic processes such as batch operations 4) Startup and shutdown strategies in continuous equipment 5 ) Failure analyses Finally, dynamic models are increasingly used in identifying optimal process control (e.g., in time-optimized control of batch processes when safety and product quality are competing considerations). Besides these advanced simulation techniques, dynamic process models are used on-line in the form of observers, such as the Luenberger observer [3.24], [3.25] or the Kalman-Bucy filter [3.40], [3.41]. As discussed in Section 3.1, the notions of steady-state and quasi-steady-state process models come directly from Equations (3.3.1)-(3.3.3) if k is set equal to zero. Naturally, steady-state process models find use chiefly with steady continuous processes and in connection with problems such as
1) The design and optimization of plants and apparatus 2) The analysis of plant characteristics The state variables can in principle be eliminated from these models, so that a direct relationship appears between the outputs y and the inputs u: (3.3.7)
Time-discrete systems offer a number of advantages, including simpler numerical solution. Because of their inaccuracy (especially when the system behaves in a strongly nonlinear manner), their use is generally recommended only for online adaptation of the system parameters. The structural properties in most cases are invariant to the change from time-continuous to time-disCrete models, but there are changes in, for example, the concepts of observability and controllability [3.19]. Up to now we have discussed dynamic models that include both spatial and temporal variations of the system. These models are naturally of interest as aids where the time dependence is an essential feature of the problem. These situations include studies of system behavior by means of
Equation (3.3.7) is the simplest case of a transfer model, in which the value of the output vector y is obtained directly from that of the input vector LL by means of a calculation specification. Such a simple link between y and u cannot in principle hold in dynamic process models. For example, the transfer function between the concentrations u (input) and y (output) for an ideally mixed stirred tank with no reaction is given by (3.3.8) where 8 is the mean residence time. This formula represents the “steady-state’’solution of the dynamic model
54
3. Knowledge about the Process
1 6
X = -(u y=x
-
x)
(3.3.9) (3.3.10)
It is important, however, that in Equation (3.3.8) the value of y at time t does not, as in (3.3.7), depend solely on the value of the input u at time t, but on the time variation of u throughout the past. Transfer models for dynamic systems are functionals of the form (3.3.11) The second term h,(t,u(t)) in Equation (3.3.11)takes into account the possibility that the effect on y of time variations in u may be present not just in smoothed form, as in h, (discontinuous functions in u become continuous functions y ; continuous u become continuously differentiable y), but in unsmoothed form. Then h, describes the “feedthrough” of u to y. The importance of transfer-function models (Eq. 3.3.7 and 3.3.11) is that they make it possible to examine how the inputs u affect the outputs y without knowing the internal structure of the system (represented by the state variables xi and their dependences on u and on one another). This may be the only approach in, for example, complex systems (e.g., dynamics of drugs in human beings and animals) or in design calculations where only the inputloutput behavior of the apparatus is known at first and the detailed engineering has not yet been done (e.g., pre-simulations based on linear quantitative balances). Transfer-function models thus belong to a class of models whose structure is only slightly or not at all influenced by the “nature” of the system being modeled. These purely mathematical (statistical or regression) models therefore offer great freedom in the choice of model structure. The model parameters must always be obtained by fitting to measurements made in the underlying real system. Because such regression models are based on measurements (see Section 3.2) and no attempt is made to derive a predictive theory from them, the model cannot be applied or extrapolated beyond the range of the measured values and the specific apparatus on which the measurements were performed, in contrast to theoretical models. While pure regression models cannot be employed in the design phase, they have an important application in on-line simulation (see Section 11.3). Measurements on the run-
ning process make it possible to treat and adjust the model parameters as time-variant quantities, so this accurate knowledge of the model parameters permits the use of regression models with simple structure. When these “adaptive” process models are used, the control parameters can also be modified on-line to fit the current process situation. This technique is called “adaptive control.” Particularly important here are linear process models. In the state - space representation, they have the form
+ B(t) . u ( t ) y = C ( t ) .x + D ( t ) . ~ ( t ) X = A ( t ) .x
(3.3.12) (3.3.13)
The system parameters are thus the totality of components in the matrices A (system matrix). B (input or control matrix), C (observation matrix), and D (pass-through matrix), which completely characterize the model of Equations (3.3.12)and (3.3.13). For this model, the block-diagram visualization customary in publications on control engineering takes the form shown in Figure 3.15. The input xo to the integrator block symbolizes the effect of an initial condition x(to)= xo on the solution behavior. The transfer-function model associated with Equations (3.3.12) and (3.3.13) has the form y(t)=
f
J K ( t , z ) . u(z)dz + D ( t ) . ~
-*
( t )(3.3.14)
where the matrix K ( t , z ) depends on the time variables t and z.Equation (3.3.14) gives a mapping y = F ( u ) from the set of input functions u into the set of outputs y ; this mapping satisfies the superposition principle (3.3.15)
-Y
Figure 3.15. Block diagram of a linear process model
3.3. Process Models
(image of sum = sum of images) and
F ( a . u) = a . F ( u ) (image of a times u
(3.3.16) =a
times image of u).
Linear Process Models. The linear process models introduced above require some more discussion, since they are still very important in both systems analysis and the design and on-line modeling of control systems (despite advances in the calculation of nonlinear systems). Their importance undoubtedly stems from the fact that their simple structure can be completely understood from fundamentals and that many structural properties of nonlinear process models are conserved in linearization (approximation of nonlinear models by linear ones). What is more, the calculation of linear process models involves far fewer problems than that of nonlinear models. The use of linear models should always be considered when it is certain that the process trajectories in state space do not depart from the region in which linearization is valid. An example is control to maintain a stationary operating point by using an on-line linearized process model. Special importance attaches to the time-invariant linear models I = A . x + B-u(t)
(3.3.17)
y=C-x+D'u(t)
(3.3.18)
which are set by the constant (time-invariant) matrices A, B, C, and D. As an example of linearization about a stationary operating point, let us analyze the reaction A + B taking place in a stirred tank, using the heat balance. The nonlinear process model is described by the equations
a. = -1. a
B
E
1 a - s ., - T A P . a
Q
T = a ( ~-, T ) + p . s . e-Tn/T.a
(3.3.19)
+ y . (T, - T)
55
Consider given values of the inputs i, 'li,, and T,, and the associated stationary values ii and 'liof the state variable. If the deviations are defined as a = 5 + xl, T = T + x,, aE = ii, + u l , TE= TE+ u,, and TK= T, + u3. Linearization then yields the relations
If the temperature is known as an output (measurement) and we accordingly set y = x,, the linearized system has the system matrix
''
(3.3.23)
the input matrix (3.3.24)
observation matrix C = (0 l), and feed-through matrix D = (00 0). The stability (also of nonlinear systems) at a stationary state x , can be investigated with the system matrix A obtained by linearization about this state. Thus, for example, the system is asymptotically stable at x , if and only if all the eigenvalues iiof A have negative real parts. In the example described, the eigenvalues of the system matrix A have negative real parts if the conditions
(3.3.20) where a and aE are the concentrations of A in the tank and in the inlet stream; T is the reaction temperature, the inlet temperature, and T, the coolant temperature; Q is the mean residence time and S the collision factor; TAis the activation temperature of the reaction; and a, b, and y are further model parameters.
and
\
-
/
(3.3.26)
56
3. Knowledge about the Process
are fulfilled. In this case the stationariness G, T is asymptotic. Criteria of this type for the stability of linear systems can be generalized to arbitrary n-row system matrices A (e.g., the Hurwitz criterion) 13.161. The transfer-function model associated with a time-invariant linear model is y ( t )=
-m
K ( t - z). u(z)dz + D .u ( t )
(3.3.27)
The matrix K ( t ) in Equation (3.3.27),known as the impulse response matrix, is given by K ( t ) = C . eat. B
(3.3.28)
(For the definition of the matrix exponential function appearing in this formula, see [3.19].) If the system has no feed-through (ie., D = 0), and the idealized impulse function 6 ( t ) (delta function) is taken for u ( t ) , the result is
~ ( t=)
f -m
K ( t - 7 ) . 6(z)dz = K ( t )
(3.3.29)
K ( t ) itself is thus the response when a unit impulse 6 ( t ) is input to the system. Because transfer-function models are often obtained from measurements alone, Equation (3.3.28) cannot be used to determine K ( t ) ,since the matrices A, B, and C are unknown in these cases. Example: The residence-time distribution of a continuous-flow apparatus is w ( t ) , and w(t)At is the probability that a particle entering the apparatus at time t,, = 0 leaves it in the interval (t,t At). By the superposition principle, the following relation is then obtained between the inlet flow rate u and the outlet flow rate y of an indicator substance:
+
y(t)
S I
-m
defined by F ( s ) = 1e-”‘ . f ( t ) d t “1
(3.3.31)
n
assigns to the functionf(t), t 2 0, a function F ( s ) of the independent variable s. Here scan be taken as a complex variable; F ( s ) is then differentiable (holomorphic) at least in the convergence region of the improper integral in Equation (3.3.31).The convergence region is a half-plane Re(s) > 0, with boundary Re(s) = CT dependent on the function f(t). To make the mapping property of Equation (3.3.31) especially clear, the notation (3.3.32)
F(s) = L( f ( t ) )
is also used;f(t) is then called the “original function” and the Laplace transform F ( s ) is also called the “image function.” Of the characteristics of the Laplace transformation, here only three that are particularly important for application to linear process models are mentioned: 1) The transformation is linear. From Equation (3.3.31) it follows that ua,
..f,( t )+ a2 .f2(tN
= a1
. L(fl(t))+ a2
’
L(f2(f))
(3.3.33)
The application of L to linear models thus preserves linearity. 2) L . __ df(t) dt - s ‘ L ( f ( t ) )-- f (0)
(3.3.34)
3) If the convolution integral
ifl ( t -
.f 2 (z)dz
(3.3.35)
is abbreviated asfl ( t ) * f 2( t ) , then ~ (- t7 ) . u(7)dz
(3.3.30)
which is of the type of Equation (3.3.27) with K ( t )= w(t). Accordingly, w ( t ) can be found as the response to a unit impulse; this is a widely used method for residence-time distributions. When time-invariant linear process models are investigated, the use of integral transformations is often very productive. The Laplace transformation, which is particularly important, is discussed only briefly here. This transformation,
Equations (3.3.34) and (3.3.36), in particular, illustrate the simplifications that result from the use of the Laplace transform. Relatively complicated operations such as differentiation and convolution in the original domain turn into simple algebraic operations. Computations with the Laplace transform can be schematized as shown in Figure 3.1 6. Correspondence tables, showing the original and transformed functions corresponding to a nurn-
3.3. Process Models Original domain
Image domain I I
Algebraic with initial conditions I I
I I Direct solution I methods I c Inverse
I
Solution for X ( s )
I
for x ( f )
57
which offersa simple fink between the transforms U ( s )of the input and Y(s)of the output in terms of the transfer matrix G(s) = C . (s . I
-
A ) - ' B (I = identity matrix) (3.3.42)
Note that, as usual, we are considering the state that depends only on the temporal behavior of n ; that is, x ( 0 ) = 0 and Equation (3.3.41) holds only for inputs z r ( t ) = 0 for t < 0. Equation (3.3.41)in the transform domain corresponds to the relation
I
I
y [ t ) = j'K(t
I I
Figure 3.16. Application of the Laplace transformation
ber of elementary functions, are employed. Consider, for example, the linear model i=a.x+b.u
(3.3.37)
with initial condition x (0) = xo. By using Equation (3.3.34): s . X ( s ) - xo = a . X ( s )
+b
'
U(s)
(3.3.38)
From this the following purely algebraic relation between the transforms X(s) and U(s) is obtained
x
X ( s )= a s-a
+ bs -Ua( s ) '
(3.3.39)
~
Together with the correspondence L(e"') = l/(s - a) and the property (Eq. 3.3.36), transforming (3.3.39) to the original domain directly gives the solution formula x(t) = xo . eat
+
t
0
b . u(z)dz
(3.3.40)
For further details on the Laplace transformation, the reader should consult the specialist literature [3.42], [3.43]. The Laplace transformation can also be applied to vector-valued functions such as those appearing in the general model of Equations (3.3.17) and (3.3.18). Consider the case with no feed-through ( D = 0). Application of the Laplace transformation gives Y(s)= G(s) . U ( S )
- 7).
0
(3.3.41)
u(z)dT
[3.3.43)
in the original domain. which follows from Equation (3.3.27) under the stated conditions. Thus the role of the impulse function response matrix K ( t ) in the original domain is played by the transfer matrix in the transform domain. A function of great importance in practice is the frequency response G ( j w ) ,which is obtained by letting s take on imaginary values j w (w real) in Equation (3.3.42). The frequency response satisfies the fundamental relation y ( t ) = G(jw). ej"'
uo
(3.3.44)
which in the original domain gives a simple description of the response to periodic input functions u ( t )= ej''uo. Since the frequency response G(jw) contains all the information about the process, an obvious way to extract this information is to measure the response functions y ( t ) over an entire spectrum of input frequencies w and use Equation (3.3.44) to determine G(jw) from these. This "frequency response method" is also a highly sensitive procedure for stability analysis that does not require a knowledge of the system matrix A and its eigenvalues. The determination of the frequency response from measurements with the aid of periodic input functions also makes it possible to obtain the impulse response K ( t )more accurately than with the impulse method, even though the amount of measurement required is greater, since K ( t )is calculated from the frequency response G(jw) through the inverse Fourier transformation: 1 m K(t) = - G(jw)ej"'do 271 - m
(3.3.45)
58
3. Knowledge about the Process
This expression can be inverted in accordance with the Fourier integral formula: G ( j o )=
* -m
K(t)ej”‘dt
(3.3.46)
If the time dependence of the transfer function is replaced by a position dependence and, therefore, the temporal frequencies by spatial frequencies, the analogous operations lead to the contrast transfer function (optical transfer function) known from image transmission and photography. This function verifies the transformation principle described in Section 2.2 as well as the phase model derived from that principle in Section 2.3 [3.44].
3.4. Modeling The derivation of a mathematical model for a given purpose involves several problems. The objective governs
1) The type of model (dynamic, time-continuous, time-discrete, stationary, time-variant, etc.) 2) The structure of the model (theoretical, statistical, transfer-function, linear, etc.) The restrictions mentioned in Section 3.3, regarding the computability of the model and computing time problems, must also be taken into account. It is often not possible to give an a priori answer to the question of which physical processes should be incorporated into the model and which can be neglected. The addition of a further physical phenomenon to the model may render the former numerical method of solving the model equations useless and may lead to a large increase in total computing time. In such cases, attempts are made to use a simplified version of the model. Ultimately, however, the power of the model depends on the comparison of computed results with experimental data. The modeling process often becomes an iterative procedure in which calculation and experimentation alternate. It is not always the case that successive, improved versions of the model, when compared with the experimental data, will converge to the final model. Occasionally, new measurements are needed for deciding whether to accept or reject certain model assumptions. These problems suggest a generalization of the problem statement: Given some competing
models M I , M , , ..., describing a certain system, to find quantitative decision criteria for identifying the “best” model at minimal testing cost. A number of publications on this model discrimination problem have appeared [3.45] [3.47]: in the well-known Box-Hill method [3.48], an entropy value is assigned to each model based on its agreement with previous experimental results, and the next test point is chosen so as to maximize the entropy difference between models. Section 3.3 identified discretization as an essential step in obtaining finite-dimensional equivalent models of the type defined in Equations (3.3.1)-(3.3.3). This implies, in general, the problem of choosing the correct state variables in the vector x. Usually there is a wide range of options, and this transformational freedom should be utilized in mathematical models in order to exhaust certain optimization possibilities. The following main objectives can be stated: ~
0
0 0
Models as small as possible Short computing times Model structures as simple as possible
These goals are interrelated and they also compete with the requirement of a minimum model accuracy. Known examples in which suitable coordinate transformations are used to give simpler model structures include the diagonalization of linear systems, with a diagonal matrix A as system matrix, and the change to dimensionless notation, which indeed often works wonders. The requirement of small but efficient computational models must often be invoked, especially for on-line use. The model reduction or order reduction technique described here involves the replacement of a mathematical model by a smaller model. There are essentially two approaches 0
0
The use of special physicotechnical properties of the system under consideration yields a reduced model that is often highly efficient The second approach consists of purely mathematically formal methods for obtaining reduced process models
In both procedures, a key role is played by the coordinate transformation concept. An attempt is made to position the new coordinate system so that the fewest possible coordinates (state variables) are needed and so that these reflect the truly relevant degrees of freedom in the
59
3.4. Modeling
system. The technique involves projecting onto the state-space subspace that is essential for the processes taking place in the system. The idea is closely related to the principal-component analysis method used in Section 3.2 to spot relationships in existing data. A well-known example of such a projection is the expansion in specified functions in the RitzGalerkin method. In this technique, eigenfunctions of the operator or so-called functions with compact support are employed in a finite-elements procedure. Other examples are Fourier series expansion; the method of moments used to solve systems of equations arising in, for example polymerization kinetics; and the use of symmetry properties generally. Mention should also be made of similarity theory [3.49]-[3.51], in which quantities relevant to the problem are determined, a dimensional analysis is performed, and the resulting set of dimensionless quantities is smaller in number than the dimensioned starting quantities. Another example of order reduction is statistical mechanics, in which the ca. state variables that describe the ensemble of molecules in the subject system are projected onto a few new quantities, such as temperature, pressure, and enthalpy. In the examples examined up to this point, either the transformations were physically motivated or plausible trial functions, series expansions, or the like were applied, and the coefficients in the expansions were taken as the new state variables. A number of methods have been devised [3.52]-[3.60] which attempt to eliminate this element of arbitrariness. In a certain class of transformations, these techniques identify the one for which a certain figure-of-merit functional is minimized. Figure 3.17 illustrates the method schematically. Any reducible system has the following property: Every solution trajectory, after quickly passing through an initial branch, merges into a one-dimensional curve. This system therefore has just one relevant degree of freedom, even though it is two-dimensional. The discrepancy between the number of degrees of freedom n and the number of relevant degrees of freedom r is large for most large systems and represents the key to their reducibility. The appropriate coordinate transformation must be found so that z1 will be the relevant state variable in the new coordinate system (as shown in Fig. 3.17) while z2 plays a secondary role and can be neglected. If a certain
x2
A
Zl
Figure 3.17. Schematic diagram of model reduction
set M of representative solutions x ( t ) and associated solutions x* ( t )of the reduced system is chosen and the merit function
[3.58] is examined, then for specified r the reduced system is defined such that J takes on a minimum value Jmin,whereby m
Emin =
100 J m i n / C
J II x(t) /I 'dt
(3.4.2)
is a measure of the mean relative error of the solutions x*( t )of the reduced system. Figure 3.18 shows as a function of the dimension of the reduced system r, where the original mathematical model was that of a distillation column with 33 trays and three components. The number n of
6
7 8
9
I
r=
0.337 0.195 0.124 0.0730 0.0527
number of degrees o f freedom of reduced system
cmi,=mean relative error o f solutions of reduced system
I
Figure 3.18. Model reduction for a distillatioil tower
60
3. Kfiowledge about the Process
differential equations is 68. The set of all response functions to step inputs was selected as the representative ensemble of solutions. The error cmin decreases with increasing r, becoming zero for r = 68, where the unreduced original model is reached. Even for r as small as 4 the error is only ca. 0.5%. Thus the problem requires a reduced model with only four differential equations instead of 68.
3.5. Management and Utilization of Information Introduction. As early as 1986, the Association of Large Power Plant Operators (Verband der Grosskraftwerksbetreiber) carried out a thorough discussion of changes in process control engineering and the demands imposed on the management of engineering documentation [3.61]. With increasing complexity of plants and processes, the number of monitoring and control devices has increased out of proportion. The
main reason has been the growing level of process automation. Rationalization has had little to do with this development; a far more important point is that stringent requirements on product quality not only call for increased information about the process but also demand more powerful process modeling aimed at better reproducibility and direct control of product quality. In addition, a keener awareness of plant safety has led to extensive safety analyses, which in turn have given rise to practices that frequently involve monitoring and control equipment. These growing demands can easily bring about a documentation conflict between designers and builders of process plants, on the one hand, and operators and maintenance personnel, on the other. The management of data on control devices must enable the use of the documentation right through from concept, through design, execution planning, construction, and operation of the plant. The benefits of such an information system are not limited to rationalization; consistent documentation also simplifies the exchange
Condensate
Figure 3.19. Example of a piping and instrumentation diagram (DIN 28004, Part 1) [3.62] B = belt conveyor; C = column; H = heat exchanger; M = motor; P = pump; S = separator; T = vessel, tank; V = valves; Z = size reduction; FRC = flow registration control; LRC = level registration control; TRC = temperature registration control
3.5. Management and Utilization of Information
of experience between operators and builders of plants. Many discussions have contributed to the new edition of DIN 40719, Part 2, (“Circuit Documentation: Labeling of Apparatus, Signals, and Documents”). The basic ideas can be extended in large part to the documentation of plant monitoring and control systems. Forms of Organization. The classical organization of control engineering documents is functional (see Chap. 2). Documents are created in order to accomplish a certain task. The instrumentation of “something” is set forth in, for example, the piping and instrumentation diagram
Manual OFF I
Manual ON
ml
61
(Fig. 3.19). “Any” control system can be described by functional diagrams (Fig. 3.20) and schematic diagrams (Fig. 3.21) or program flowcharts. Documents for the installation of “any” process control device include schematics as well as terminal diagrams. This type of documentation does fulfill its intended purpose, but it leaves the user virtually unable to form a comprehensive picture of the properties of the control system. Indeed, the user can hardly get an idea of the system from any other viewpoint than that of documents prepared in the past (see Section 2.2). As a consequence, whenever a new question arises, it is often necessary to create new supplementary
D
30“
0
w I
Manual CLOSE Manual OPEN
IU
Shutdown program 2
v
Final gas valve
OPEN
Manual OFF Manual
I -
1.2
ON
Shutdown program 1
Reactor temp.
Reactor temp.
-
Y Start
Restart
O
Waiting
I
Gas valves
Figure 3.20. Example of a control system function diagram (DIN 40719, Part 6 )
62
3. Knowledge about the Process Spare feed-in
1
Spare switch
@
IPSO
r"" See sheet 2 circuit 1
Figure 3.21. Example of an electrical schematic (DIN 40719, Part 3)
A783 TE1
3
-81
-ul
@
-u2
-u5
@
-N1
&
I
L
-p1
L
-P2
Figure 3.22. A process control device (control station) from the design viewpoint: station (loop) diagram
3.5. Management and Utilization of Information No. 1
Control station KACP Functions checked I St A+ V105BA20 P2
Reason for check Doc.reqd.? Plant safety Y
63
Reqd. condition Shut down
Object-related instructions: Written permission, station setup data sheet, station diagram required Inspection: Check pressure measurement with I S
+A+
Report
1. Perform visual inspection OK
Are the elements of the process control station fully labeled, in good mechanical condition, and clean?
OK after repair
Not OK
2. Connect mA test generator in place of transducer. 3. Check measuring range of readout at three points; note actual values. Nominal values:
Readout
Tolerance
4.0 mA
0.2 bar
f 0.02 bar
Second value
12.0 mA
0.6 bar
& 0.02 bar
Third value
20.0 mA
1.0 bar
f 0.02 bar
First value
Test generator
Actual values bar
~
bar
~
bar
~
OK
OK after repair
Not OK
Does visual indication “PO2 max” occur in A639 W 1 panel 7?
OK
OK after repair
Not OK
Does acoustic indication occur in A 639 W 1?
OK
OK after repair
Not OK
OK
OK after repair
Not OK
OK
OK after repair
Not OK
Are the actual readout values within tolerance?
4. Check maximum limit indication. Limit: 0.90 bar
Tolerance:
& 0.02 bar
When limit is exceeded, following indications must occur:
5 Check maximum limit response.
Limit: 0.95 bar
Tolerance :
f 0.02 bar
When limit is exceeded, following responses must occur: Does valve P2
- Y1
open?
Does pump P27 shut off?
6. Restore to initial condition.
Remarks:
Date:
Inspector’s name:
[7
Not inspected
Figure 3.23. A process control device (control station) from the maintenance viewpoint: partial job plan
64
3. Knowledge about the Process
Labeling block 2 =
documents even though these may contain scarcely any new information. One remedy for this documentation bottleneck is object-oriented management of data on monitoring and control devices. Documents for a given task are then representations of object-related information, that is, projections from certain viewpoints (Figs. 3.22 and 3.23). Implementation of this concept requires, above all, the devising of a consistent data model for hardware objects (see Section 2.2). It has proved desirable not to model the process control system in isola-
Higher-order (system)
Labeling block 2 + Labeling block 2 -
Figure 3.24. Devices (DIN 40719, Part 2)
I
Technical function
I
Mfr. order no. Mfr. parts list
7638 1278
Inspection sheet 476
Figure 3.25. Information blocks associated with process plants
3.5. Management and Utilization of Information
tion but to see it as a component of the process plant. The initial step must be to understand which data blocks are the fundamental ones. DIN 40719, Part 2 defines the most important data blocks for electrical apparatus (Fig. 3.24). This is not adequate for process plants, since it does not include the purpose of the facility: the process. Incorporating the process into the information structure becomes more important for process control engineering, the more use is made of higher process control functions such as recipe procedures and process models for process control (see Section 4.5). It has proved desirable to separate detailed description of the engineering properties of a single instrument (substance) from information about its technical function (see also Section 2.3, p. 24). This makes it possible to manage hardware improvements by replacing devices in the plant with others that perform the same function. All these aspects lead to an information structure for process plants and their process control systems as shown in Figure3.25. When an object-oriented model is created, these information blocks must be described in terms of interlinked individual objects. Fig-
65
ure3.26 presents a simplified example in which the “entity-relationship model” described in Chapter 2 was used. When such a model has been set up, it is possible to file all information by object. For example, data on a process control station is filed only under that station. Data on a higher-order plant subunit are filed only under that subunit. They are automatically relevant to the lower-order control stations. Data on an apparatus o r instrument type are filed only under that apparatus or instrument type. They are automatically relevant to the associated process control stations. Application. After such a data model has been created (provided it is complete), documents can be generated at any time and tailored to any objective. It is at least equally important that given information is managed by object, not in various documents. A computerized revision service can then be set up easily, so that information retrieved at any time will be internally consistent. Flowsheets are now prepared for a plant (plant unit) or subunit in accordance with this data model (Fig. 3.27).
Figure 3.26. Simplified data model for information blocks relating to process plants
66
3. Knowledge about the Process
EG I
Y i
I
’I
Figure 3.27. Piping and instrumentation diagram of a plant subunit
A functional diagram may refer to a certain subunit (Fig. 3.28) or to a certain control station (it is then known as a station or loop diagram; Fig. 3.29, p. 68). This object-oriented management of engineering data makes it possible to work with the same documentation at every stage of plant activity and to update the documentation step by step. A glance at the phases of plant activity (Fig. 3.30, p. 69) shows the feasible and necessary level of detail in the documentation at each stage (see also Chap. 7). Structuring of the processes in terms of unit operations (DIN 28004) is possible and necessary even at the basic research stage. Plants can already be subdivided into units as documented in the basic flowsheet (Fig. 3.31, p. 69). The as-
signment of unit operations to plant subunits, which is so important for higher-level automation tasks, can take place at this stage. The phase model derived from experience in software engineering (discussed in detail in Section 2.3) is suitable for this purpose. Quality assurance, in particular, calls for a systematic representation of product and process requirement profiles, since the needed data acquisition measures as well as sensor and actor systems must be established from this standpoint (Fig. 2.32, p. 70). In this connection, the reader is referred to Japanese Industrial Standard JlS Z 8206-1982, “Graphical Symbols for Process Charts.” This goes beyond DIN 28 004 by specifying which quantity and quality checks must be performed on product streams. Recent practice is
3.5. Management and Utilization of Information HCI filling No malfunction
67
Malfunction Open phase LO1 reactor overfilled
Figure 3.28. Function chart of a plant subunit
to generate process control flowsheets and checklists as tools for quality assurance; these can also serve as evidence that the quality assurance system itself (Fig. 3.32, p. 70) conforms to the standard. The most important process control equipment is selected in the engineering design stage. Documentation relating to each control device, such as a control station, can be created here. Of special importance in the approval planning (regulatory engineering) phase is the safety analysis, which establishes particular safety objectives and practices. It is important that all information used in this analysis refer to definite, documented engineering hardware (Fig. 3.33, p. 70), since only then can the engineering of protective measures be assessed before commissioning and during plant operation. At the stage of execution planning, all engineering details are fixed. The number of implementation options is unlimited in principle, but in any real enterprise the factors of economics and practicability call for the selection of proce-
dures as well as instruments and systems with which, or from which, the requisite functions can be developed. These choices then lead to design and installation standards, but also to equipment and system standards. As discussed in Chapter 2 and at the beginning of this section, these decisions are also documented in the object-oriented framework (Fig. 3.34, p. 71). Examples of activities during execution planning are the preparation of control station diagrams for process control devices and the specification of requirements on engineering properties. This information is filed and managed in the process control station. Realizable hardware units, for example, are handled as instrument types derived from the instrument standard, while the functional elements are assigned to control devices (Fig. 3.35 p. 71). This has the great advantage that a distinction can be made between requirement and implementation and that, when instruments differing in type are interchanged, only the assignment must be changed. Manual updating of engineering details becomes unnec-
68
3. Knowledge about the Process
21
11
[:
& 11
25
Pos. 4
Pos. 6
Pos. 9
Figure 3.29. Station (loop) diagram
essary. Similarly, the basic functions of the process monitoring and control systems are assigned to the control devices. In addition to a more rational configuration, this offers the advantage of greater reliability with regard to the consistency of engineering data (see Section 4.3). Similarly, individual devices can be assigned by technical function if necessary. This would be the case, for example, if custom instruments were used instead of off-the-shelf devices, if the instruments were subject to individual inspection, or if
maintenance cost records were to be kept for an individual unit. Such assignments are important items of information for installation and maintenance. When necessary, they must be indicated locally. Maintenance and inspection work is classified by technical function (Fig. 3.36, p. 72). The performance of such work and the results of inspections are documented by technical function. The analysis and repair of defects are done chiefly in reference to the technical function. In the course of
IBasic research1 Specifications [Preliminary design
I
Proposed solution
I
& 1
Costing
Engineering documentation
Licence application safety certification
]Procurement
Cost estimate
1
Economic assets Delivery in accordance with documentation Installation and commissioning Plant ready for production Operation and maintenance Feed:
co
Figure 3.30. Phases of plant activity
CI,
CA1 Production o f substance A
HCI
BEY
Substance A HCI ODB
f&w Preconcentration
ODB
BAY
of substance A
Figure 3.31. Basic flowsheet of a plant TA9 On-battery tank storage
Filling BAY
3. Knowledge about
70
the
Process
Feeds
8
I
Recycle feed A
Chemical reaction
Feed A/feed B Suspension storage Distillation
+
Wastewater
0 latioi
T
Cooling
0Reaction step
+
+
A
silo
Packaging
8
Release testing Screening
o Material
stream
D Product storage
silo
0 auality
Packaging
0 Quantity control
control
Figure 3.32. Process monitoring flowsheet of a vulcanization accelerator (cf. 31s 28206)
_____
V -A -G i0/27/aa . __-__ a Page: Plant: ___---____Date: -----__Version:
Safety analysis Hazard discussion Facility identification
L
I
_ _ _ _ I
(Internal Standard 9060)
I
I
I
I
Deviation from nominal condition:
Figure 3.33. Example of a form used for plant safety analysis The information from this form must be correlated with the corresponding device (shaded blocks)
I
3.5. Management and Utilization of Information
(Functions
71
(PAC))
*Generic ordering Figure 3.34. Structure of decisions relating to process control stations
A t Location LED0 LIE EL BR cdnn!ct/on! : belo,,,
Controller Controller for unit pressure (compact) Eckardt Compact P400/P600
Jei
1 1 1 1 1 1
.q
Instrurnenl standard p 4 1st edition
Edition
justrnent station
Figure 3.35. Assignment of instrument types to technical function for the example of a process control station
72
3. Knowledge about the Process
FS-IT Maintenance
Plant 22
Plant subdiv. FSDL
Plant unit v40
Inspection plan Job specification Mechanical
Subunit ws33
Area: Dralon
622001
Building: PLanning date:
15.04.89
Eng.-No.:
Order no.:
075350
Function group:
432
Test no.:
Registration no. 24 *
Inconvenience bonus:
I Component I
I
Operation: Spinninq Location no.: 108
Carried out by:
Operations :
PA 1
Replace GLRD. adjust coupling
PA 4
Check tightness of motor can, adjust coupling
RM 1
Remove NaOH feed and clean nozzles
VE 1
Inspectheplace V-belts
F8 1
Clean mist collector
WS 31
Use pressure cleaner t o remove biological and other deposits; perform visual leak check
examination, the engineering details on the assigned information can be considered; technical defects can be localized through the assignment of installation locations. Such an information model makes possible an integral analysis of process facilities and, ultimately, of an entire company. The details of the model must be tailored to the technical circum-
stances. The storage and management of information in relation to engineering objects is crucial for its consistent use throughout the economic life of the plant. Only in this way is it possible to generate documents, at any time and at comparatively low cost, permitting instant analysis from any desired viewpoint.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
4.1. Pr incijdes
4. From Process Knowledge to Process Control 4.1. Principles Industrial processes, in contrast to natural processes, are artificially designed, consciously initiated, and purposely controlled so as to serve a certain end. It is not enough to understand an industrial process and be able to describe its internal dynamics in terms of a model; if the process is to take place at all, some form of external control is needed. This external control includes all functions required for coordinating and controlling the process. The structure, form, and implementation of these process control functions are the subject of the chapter that follows. The sections on feedback control and optimal control set forth the most important design methods from control theory. Section 4.4 explains the concept of binary control in a variety of applications. In Section 4.5 it is shown how the operational control hierarchy can be structured for a plant as a whole. Process control functions play a central role in operational process control. For better understanding, these can be explained briefly before going on to the rest of the chapter. Figure 4.1 shows schematically how process control functions work. The same diagram applies to all control functions. Each control function, on the basis of its own logic, seeks to bring the actual properties of a system into accord with the specified nominal properties. Each control function thus embodies certain assumptions about the behavior of the system. Using this knowledge, a control function can issue control commands in such a way as to influence the system in the desired fashion.
11
In process control, the control variables and manipulated variables correspond to variable process or product properties. Manipulated variables either impinge directly on actuators or play the role of control variables, acting on lower-level control functions. In this way, process control can be built up in a hierarchical manner. If we consider, for example, the adjustment of the pH in a reactor by the addition of acid, we obtain the hierarchy illustrated in Figure 4.2. The task of the production action is to make a product having certain properties at the discharge from the process section. For this purpose, one of the manipulated variables available to it for controlling the process is the pH in the vessel. The metering device seeks to establish the specified time dependence of pH in the vessel; it therefore passes on the acid flow rate to the lower-level flow controller as a setpoint. The flow controller, in turn, sets the required flow rate by specifying a certain valve position. The valve position controller exerts a corresponding control pressure to insure that the valve comes into the required position.
Control variables w1-wm
Pump control
-ed variables
Figure 4.1. Schematic diagram of process control functions
73
Valve control
74
4. From Process Knowledge to Process Control
In most cases, a process control function has only one control variable; in general, however, m control variables and n manipulated variables must be allowed for. The control concept can be customized for any process control function. The methods of control engineering are therefore applicable to process control functions at all levels in the hierarchy. Process control functions can be classified in four groups by the concept employed:
1) 2) 3) 4)
Timed process control functions Event-controlled feedback functions Continuously controlled functions Process control functions (optimized with respect to some quality criterion)
The selection of a concept depends less on the type of process control function and its position in the hierarchy than on the quality requirements, the anticipated severity of disturbances, the possibility of directly or indirectly measuring certain process properties, and (for the fourth group) the availability of suitable process models. All the concepts are equally capable of reproducing a specified process if it is assumed that there are no disturbances. Only when malfunctions or variations affect the course of the process will the four groups differ in their capabilities. In practice, the coitzepts are combined as needed. In what follows, the various concepts are illustrated for the case of a pH control. The task for the process control function is to regulate the addition of acid so as to keep the pH in a reactor at the proper value for the process.
1) Timed process control function: Acid is admitted to the reactor at a fixed, specified flow rate q(t). If there are no disturbances, the desired pH value is established in the reactor. The actual pH value is not fed back. 2) Event-controlled feedback function: Acid is admitted to the reactor at a fixed, specified flow rate q(t) until a certain specified pH has been reached; the flow is then cut off. If the process takes place without disturbances, the pH in the reactor follows the desired trajectory. If there are disturbances, the actual pH agrees with the desired pH, at least at the end point. The pH must be measured at least once.
3) Continuously controlled function: The admission of acid is controlled so that the pH in the reactor follows a fixed, specified control trajectory. Control actions compensate for upsets. Continuous pH measurement is required.
4) Optimal process control function: The control trajectory pH(t) is not specified in fixed form but is determined on the basis of the instantaneous process state and a quality criterion. If the fixed trajectory of concept 3 is assumed optimal in the sense of the quality criterion, functions 3 and 4 initially result in the same control trajectory. No difference is seen until a disturbance causes the actual value to depart from the nominal trajectory. Under concept 3. the control system attempts to force the process back onto the old trajectory; under concept 4, the process is guided along a new trajectory that is optimal when the actual situation is taken into account.
4.2. Feedback Control Introduction. This section explains the concepts of feedback control and feedforward control. It discusses the distinction between the two as well as what process properties and what process requirements make a feedback structure necessary. The drawbacks to a feedback structure are also elucidated. Any process is characterized by the action of the inputs on the outputs (Fig. 4.3). Inputs are classified as manipulated variables and disturbances. The former make it possible to intervene in the process, while the latter are undesired environmental effects that act on the process (see Chap. 3). Process outputs are quantities that are to be influenced in an appropriate way. Desired values for the outputs are called setpoints. Timevarying setpoints are called command or reference variables. The objective of any control system is to interact with the process via the manipulated variDisturbances
Manipulated variables
q p r o c e s s ]---outputs
Figure 4.3. Schematic diagram of a process
4 2 . Feedback Control
ables so as to insure stability and good performance. The second point means that the outputs should react in a desired fashion to a change in the setpoints (reference variables) or the disturbances. In particular, they should react sufficiently fast, and the steady-state difference between setpoints and outputs should be sufficiently small. Feedforward control can be diagrammed as an open loop (Fig. 4.4). On the basis of externally specified setpoints and measured disturbances, the controller generates manipulated variables as process inputs. No check is made to determine whether the selected manipulated variables actually have the desired effect on the outputs. With this structure the objectives cited can be achieved only if
All disturbances to the process can be measured The effets of control actions on the outputs are known with sufficient precision, so that the mathematical process model underlying the controller design reflects the main characteristics of the real process The uncontrolled process is stable (an unstable process cannot be stabilized by feedforward control) In contrast, good performance can often be achieved with feedback control (Fig. 4.9, even if the above requirements are not met. Some of the
Disturbances
Controiler
1
Process
+-
outputs
Setpoints1 command variables
outputs to be influenced are measured and compared with their setpoint values. The differences between these values are transmitted to the controller, which uses them in calculating the required control actions. The drawbacks of a feedback control structure are that feeding back the outputs 0
0 0
Introduces additional measurement errors May (if the controller is inappropriately designed) make a stable process unstable Fails to react to disturbances until after they have already influenced the outputs
In view of the last point. feedback and feedforward control concepts are often combined. An example is a feedback structure with an additional disturbance input to the controller (Fig. 4.6), in which measurable disturbances are partly compensated by feedforward control but in general they cannot be completely suppressed because of shortcomings in the mathematical process model. Feedback control is used in an attempt to diminish the “residual effects” of these disturbances, as well as the effects of unmeasurable disturbances on the outputs. The difficult aspect of such “mixed” concepts is generally the design of the feedback component. Therefore, the description in what follows will be restricted to this component. Detailed discussions of feedforward and feedback control concepts, along with analyses of their advantages and disadvantages, can be found in [4.1], [4.2]. Characterization of Desired Control Loop Properties. Before discussing methods of control system design, it is necessary to explain some concepts required for characterizing properties of a control loop. This section is limited to linear process models (see Section 3.3) and controllers. This restriction is justified in most cases because engineering processes often behave in an approximately lin-
Figure 4.4. Feedforward control
Setpoints/ command
Disturbances
Disturbances
I
command variables
Measurement errors
Figure 4.5. Feedback control
75
u Measurement errors
Figure 4.6. Feedback configuration with additional feedforward control of disturbances
76
4. From Process Knowledge to Process Control
ear fashion near the operating point and therefore can be handled by a linear controller. The deviation of the process behavior from the linearity of the model equation can then be treated as part of the model uncertainty, which must be taken into account in controller design anyway. It is also assumed that process model and controller are time invariant. The frequency response is then an ideal tool for investigating control loop properties. Frequency Response. A linear, time-invariant system can be described by its frequency response G(jw) (see Chap. 3). For a single-variable system (i.e., a system having one input and one output), G ( j w ) is a complex-valued scalar, which can be graphically represented in either of two ways. First, it can be plotted in the complex plane for all (positive real) frequencies w ; this gives the Nyquist locus or polar plot of the frequency response. Second, the complex variable G ( j w ) can be split into modulus (magnitude) IG(jw)l and phase arg[G(jw)] by polar decomposition, G(jw)
(4.2.1)
= ( G ( j w )(&rg[G(.@’)l
and each of these real quantities plotted separately as a function of w . If logarithmib scales are used for frequency and modulus and a linear scale for phase, the result is the Bode plot of the frequency response. The modulus scale is usually multiplied by 20 and the unit of modulus is then the decibel: \G(jw)ldB= 20 log IG(jw)l. Figures 4.7 and 4.8 show the polar plot and the Bode plot of the frequency response for a second-order lag element.
I
-2
Real
0
I
2
Figure4.7. Nyquist locus or polar plot of frequency response
The frequency response has a straightforward physical explanation: If an asymptotically stable system characterized by G ( j o ) is excited by a harmonic oscillation with frequency w and amplitude z2 and the transients are allowed to decay, the output of the system oscillates harmonically at the same frequency, but with a phase shift cp and a different amplitude j (Fig. 4.9). The ratio of the output and input signal amplitudes (gain) and the phase shift between the two signals are described by the modulus and phase of G (jw), respectively, and can thus be read directly from the Bode plot:
The frequency response is analyzed in detail in control engineering textbooks, for example, [4.1]-[4.3]. The requirements imposed on the closed control loop can be quantified with the aid of the frequency responses G(jw) of the process and K ( j o ) of the controller. Stability. The Nyquist criterion represents a necessary and sufficient condition for the stability of a closed control loop [4.1], [4.4]. It requires that,the Nyquist locus of the open-loop frequency response Q ( j w ) : = G ( j w ) K(jw)
(4.2.4)
encircles the “critical point” (- 1,O) mi2 times counterclockwise as the frequency w goes from 0 to co,where m is the number of unstable poles of the open loop. Because the Nyquist locus of the example in Figure 4.10 encircles the critical point 1/2 times, the associated closed loop is stable if and only if either G(jw) or K ( j w )(but not both) has one unstable pole. Response to Reference Inputs and Disturbances; Noise Suppression. To determine whether a control loop exhibits good response to reference inputs and disturbances and has the ability to suppress measurements noise, it is necessary to examine how all quantities acting on the control loop from the outside (Fig. 4.11) influence the control error. The error e ( j o ) is defined by e(jw):= r ( j o ) - y ( j w )
(4.2.5)
4.2. Feedback Control
77
1z 2 : ~ \ t L ~ - - , , ‘0 ai
+!-100
-20
c
m
2
-40 10-1
100’ Frequency
Figure 4.8. Bode plot
-
g-200 lo-’
10’
102
100 Frequency
-
102
10’
Hence
.
Figure 4.9. Harmonic oscillations at the input and output of a linear time-invariant system
T(j4
The quantity S ( j w ) is called the sensitivity function, because it gives the sensitivity of the control error to a disturbance acting on the output and to the reference variable. T ( j w ) is known as the complementary sensitivity function. The objective is to keep IS(jw)l and 1 T(jw)l small, and thus to achieve a small gain between quantities acting on the control loop from outside and the control error. Unfortunately, this is generally impossible, since for any w
P k ; ) c
-0.5
-
-1.0 -2.0
.-m
2
-1.6
-1.2
Real
-
-0.8
-0.4
Figure 4.10. Example of Nyquist stability criterion
LzkDisturbance d
Reference variable r
Klju
It
G(ju 1
Controlled variable Y
Measurement errors q
Figure 4.11. Linear feedback loop
. -301
/ Frequency
-
f -30
S(jw)
(4.2.7)
This “fundamental dilemma” of control engineering, embodied in Equation (4.2.7), is often alleviated because in most casses reference variables and disturbances contain mainly low-frequency components and hence vary comparatively slowly with time, while measurement noise commonly occurs at high frequencies. It is then sufficient to make IS(jw)l small at low frequencies and I T ( j w )I small at high frequencies. Figure 4.12 shows typical plots of the sensitivity and
1
Figure 4.12. Typical shapes of 1Sg’w)l and I Tg’w)I
+ T ( j w )= 1
\
Frequency
-
18
4 . From Process Knowledge to Process Control
complementary sensitivity functions. The bandwidth w, of the closed loop is the frequency above which
1 T(jw)l < 0.708 or I T(jw)IdB< - 3
(4.2.8)
It “separates” the frequency range in which the closed loop has good response to reference inputs and disturbances from the frequency range in which measurement noise is suppressed. Establishing the desired bandwidth is thus one of the most important decisions in any controller design problem. Robustness. The discussion up to this point has had to do with how to insure desired properties for the “nominal” control loop (i.e., the system consisting of process model and controller). The objective is to transfer these properties to the real control loop (the system consisting of the real process and the controller). In this context, robustness means that the properties of the nominal control loop should be robust with respect to errors in the model. Robustness can be investigated only if an upper boundary can be given for the size of the model error. Often an upper bond for the relative model error is estimated by assuming that the real process behaves in a linear and time-invariant fashion near the operating point under consideration. The (unknown) frequency response of the process is represented by G,(jw). If the known frequency response of the model is G (j w), the relative model error is then defined by (4.2.9) Suppose the modulus of the relative model error is bounded by a known real-valued and positive function p ( j o ) : IA(jw)l < P ( j 0 )
(4.2.10)
Figure 4.1 3 shows a typical curve of p ( j w ) , which grows rapidly with increasing frequency.
:
1 d
-50 5
-100 10-2
-
10-1 100 Frequency
Figure 4.13. Typical error bound
10’
102
If G, and G have the same number of unstable poles, it is easy to show [4.5] that the stability of the control loop is robust to all model errors permitted by Equations (4.2.9) and (4.2.10) if and only if the following holds for the nominal complementary sensitivity function (4.2.11 ) To achieve good performance of the real loop the nominal sensitivity function must usually be made sufficiently small at low frequencies while the nominal complementary sensitivity function must be sufficiently small in the frequency range where the model uncertainty is large (see Fig. 4.13) [4.6]. Open-Loop Shaping. The Nyquist criterion makes it possible to guarantee closed-loop stability by appropriate manipulation of the openloop frequency response Q ( j w ) . The sensitivity function and the complementary sensitivity function are used to guarantee the other controlloop properties stated. Since both quantities depend on Q in a simple way (see Eq. 4.1.6), all the desired properties of the control loop can be achieved by suitable “shaping” of the open-loop frequency response Q (jo). The term “open-loop shaping” is often used in this context. Good response to reference inputs and disturbances thus requires that
at low frequencies, whereas suppression of measurement noise requires that IQ(j4I<< 1
(4.2.13)
at high frequencies. In the region near the gain crossover frequency w d , however, 1 Q ( j w )I must not roll off too rapidly. (The gain crossover frequency is the frequency at which I Q ( j w ) l intersects the 0 dB line.) Otherwise, disturbances, reference variables, and measurement noise can produce large control errors in this frequency range. Moreover, rapid gain roll-off near the crossover frequency threatens closed-loop stability. This is a direct consequence of the Bode gain-phase relation [4.1], [4.7]. The ideal would be a 20 dB per decade roll-off for 1 Q (jo) I in this frequency range. If this is the case, the gain crossover frequency wd is a good approximation to the bandwidth obof the closed loop.
79
4.2. Feedback Control
Multivarinble Systems. The discussion to this point has related only to systems with one input and one output. Most industrial processes, however, have multiple manipulated variables and multiple outputs to be suitably influenced. Each of the manipulated variables will, in general, affect several or even all of the controlled variables. The process is then said to be a multivariable (multi-input, multi-output) system, and its dynamics (assuming linearity and time-invariance) are described by the frequency response matrix
Most of what has been discussed up to now can be extended to multivariable systems with no difficulty. The multivariable Nyquist criterion is a necessary and sufficient condition for the stability of the closed loop. It requires that the frequency-dependent eigenvalues of the matrix Q ( j w ) = G(jw)K(jw) (the“characteristicloci”), taken together, encircle the “critical point” m/2 times [4.8], [4.9]. To assess the other control-loop properties, the concept of gain must be generalized [4.10]. In the single-variable case, the gain of an asymptotically stable system corresponds to the modulus of the frequency response. The situation is somewhat more complicated in multivariable systems. Such a system has a gain that depends on the direction of the input vector. The gains of most interest are the minimum and maximum (frequency-dependent) gains. These are characterized by the minimal and maximal singular values of the frequency response matrix: (4.2.15)
(4.2.16) Here 11.. . // denotes the Euclidean vector norm (see Chap. 3), while Amin and ,Im,, are the smallest and largest eigenvalues. With these gains, the requirements derived for single-variable systems can readily be extended to the multivariable case [4.5]:
6 [ S ( j w ) ]<< 1 or g[Q(jw)] >> 1 for w << wb
(4.2.17) a[T(jw)] << 1 or C[Q(jw)] << 1 for w >> wb (4.2.18) The sensitivity matrix S and the complementary sensitivity matrix T are defined similarly to the scalar variables in the single-variable case : S : = ( I + GK)-’ and T := ( I + GK)-’GK. If the requirements on the various controlled variables differ sharply, frequency-dependent weighting matrices for S , T, and Q can be used to handle the situation. Controller Design Methods. This section surveys the most important methods of controller design. Classical Control Engineering. The methods of classical control engineering continue to be dominant in many areas. In general, these methods assume a linear, time-invariant, single-variable process model. If the requirements on the controlled system are not too stringent, however, these methods can still be used in many cases even where such models give a highly erroneous description of the real process. The most important design method in classical control engineering is the frequency response method [4.1], [4.3], [4.11]. It has the objective of shaping the open-loop frequency response Q(jw) in accordance with what was said in the preceding section. For this purpose compensators 1 +jwT, (4.2.19) K ( j w )= k 1 +jw& ~
are connected in series with the process. The compensator increases the phase if TI > T, (lead compensator) and decreases it if T, > Tl (lag compensator); see Figures 4.14 and 4.15. A special case of the lag compensator for k = ET, and T, -+ co is the proportional-plus-integral (PI) element. Because of the particular axis scaling, the Bode diagram of a series system can be obtained by simply adding the amplitude and phase curves of the individual elements. It is thus easy to see what kind of compensating network is required to modify the curves in the desired way. For example, Figure 4.8 shows the frequency response of a process model; the bandwidth of the closed loop is required to be wb = 1. In this case a PI element with frequency response K(jw) = (1 O.ljw)/jwcan be used as
+
80
4 . From Process Knowledge to Process Control
Frequency
;-2:w
-
Frequency
Figure 4.14. Bode plot of a lead compensator
t
m -0 a- -20
E-40
-0
.z -30 +
m
2
-40 10-4
-
10-1
Frequency
---
VI
102
-60
-80
10‘4
Figure 4.15. Bode plot of a lag compensator
Frequency
-
Figure 4.16. Bode plot for Q (jw) in the example
the compensator. The resulting amplitude and phase plots for the open loop are shown in Figure 4.16. Clearly, Q (jw)satisfies requirements of Equation(4.2.12) at low frequencies and of Equation (4.2.13) at high frequencies. The polar can be used to check whether the plot of Q (jo) Nyquist criterion is also satisfied. Figure 4.17 shows the curve near the origin. The quantities 4, (“phase margin”) and a, (“gain margin”) give the “distance” at which the polar plot of the frequency response passes the critical point (i.e., how “close” the loop is to instability). The gain and phase margins can also be read directly from the Bode plot in Figure 4.16. The following can serve as a rule of thumb for the use of the frequency-response method: a, > 2.5 or larldB >8
lo-’ Frequenty
-
Frequency
102
-
Real
-
Real
----
Figure 4.17. Polar plot for Q (jo) in the example
and br > 30“. Because the gain and phase margins in this example are sufficiently large, a further compensation of Q ( j w ) can be dispensed with.
4.2. Feedback Control
Another widely used technique is the root-locus method [4.1], [4.4], [4.11]. This design procedure starts not with the frequency response, but with the transfer function of the plant (process) model. The location of poles and zeroes of this transfer function indicates how the poles, and thus the dynamics of the closed loop, vary as a function of the controller parameters and structure. This method is accordingly suitable for insuring the stability of the control loop. On the other hand, it generally proves quite difficult to insure other desired properties of the control loop. Another disadvantage of this design technique it that it cannot be applied to process modes with time delays. In process engineering, a controller structure (PID) is often specified and the parameters are obtained by experimenting on the process and evaluating the Ziegler-Nichols rules [4.4], [4.11]. When such a procedure is used in practice, there is no need for an explicit process model; the theoretical justification, however, presumes the existence of linear, time-invariant single-variable models. Discarding one or more of the three model assumptions in classical control engineering leads to the other three (overlapping) “classes” of control engineering (Fig. 4.18). Multivariable Control. Of the three model assumptions in classical control theory, the one furthest from reality is usually that the process can be described in terms of several independent single-variable models. Ignoring the links between various manipulated and controlled variables can lead to major problems: input controllers that work against one another (wasting energy) and instability of the controlled process may result. Design methods that take account of
81
the links and thus the multivariable structure of the control loop exist in both the frequency and the time domains. Frequency domain methods begin with a transfer or frequency response matrix as the plant model; time-domain methods are based on state-space models (see Chap. 3). The frequency-domain methods can be classified in two groups : generalizations of the classical frequency-response method, in which an attempt is made to manipulate the open-loop frequency response matrix Q ( j o ) in an appropriate way (open-loop shaping), and approaches that seek to influence directly, or optimize, the closed-loop frequency response matrices (closed-loop shaping). Table 4.1 lists the most commonly used linear, time-invariant multivariable design met ods. Some of these techniques are fully described in textbooks of multivariable control engineering [4.9], [4.12]-[4.15]. The LQG (linear quadratic Gaussian) method is based on a linear, time-invariant statespace model (see Chap. 3) of the control loop:
+ Bu(t)+ ”(t)
(4.2.20)
+ v(t)
(4.2.21)
i ( t ) =Ax(t)
y ( t )= C x ( t )
where w ( t ) and u ( t )are error terms representing, respectively, disturbances and measurement noise acting on the plant. They are modeled as white (Gaussian) random processes with a mean of zero [4.16], [4.17]. Their spectral densities P and R provide a measure of the “strength” of these random processes. They tell how dependable the deterministic components of the models are. If the goal is to keep the plant as close as possible to the operating point x = 0, subject to a restriction on the use of control energy, then it Table 4.1. Multivariable design method Time-domain
Hierarchical and decentralized control
1
LMultivariable control
Figure 4.18. Classification of methods in control theory
Frequency domain Direct Nyquist array method Inverse Nyquist array method Characteristic Open-loop locus method shaping RFN method LQG/LTR method
LQG method Pole assignment
H, Minimization H, Minimization
IMC method
Closed-loop shaping
82
4. From Process Knowledge to Process Control
is desirable to seek a control strategy that will minimize the cost function u’Mu)dl
1
(4.2.22)
The (positive semidefinite) matrix L penalizes deviations of the state variables from zero, and the (positive definite) matrix M penalizes the control energy. The symbol E represents the expected value. This problem can be solved straightforwardly. The separation principle states that the problem can be approached in two independent steps: 0 First, ignore the fact that the state is not measurable and suppose it is exactly known. The optimal control strategy is then a constant state feedback: ~ ( t=) - K,x(t)
(4.2.23)
Here K, can be uniquely calculated from the model matrices A and B and the weight matrices L and M . 0 In the second step, determine an optimal (in the sense of minimum error variance) estimate P for the unmeasurable state vector x.This is done by the use of a Kalman-Bucy filter [4.16], [4.18] (Fig. 4.19). The Kalman-Bucy filter includes the deterministic part of the plant model and thus attempts to simulate the behavior of the state variables. What is more, however, the model also receives measurement information y , which is used for correcting the estimate (unless it agrees with the measurement j predicted by the model internal to the filter). The filter matrix Kf that specifies this correction is uniquely determined by the matrices A and C of the deterministic model component and the spectral densities P and R of the stochastic component. The optimal control strategy is now to replace the true state x in Equation (4.2.23) by the estimate 2. The derivation and details can be found in, for example, [4.19], [4.20]. Despite its theoretical elegance, the use of the LQG method in’p;actice commonly presents problems. In chemical engi-
Figure 4.19. Kalman- Bucy filter
neering, state-space models rarely have a simple physical interpretation. Generally, one has to be content with simple frequency-domain models (transfer function matrix, frequency response matrix) derived from the input/output behavior of the real process. While these state-space models can be determined from such input-output models their state variables then often fail to correspond to physically relevant quantities. This fact makes it difficult to determine a suitable weighting matrix L. Finding the spectral densities (especially for the “system noise” w) may well prove to be a matter of luck as well. In such cases, the elements of P and L offer a large number of design parameters whose effects on the desired control-loop behavior are difficult to assess. One way of avoiding this problem is the LQG/LTR (loop transfer recovery) design method [4.15], [4.21], in which the design parameters P,R , L and A4 of the LQG design are divorced from any physical meaning and selected such that the open-loop frequency response matrix Q ( j w ) satisfies the requirements stated in the preceding section. For this reason, the LQG/ LTR design technique is often classified as a frequency-domain method. A further time-domain method is pole assignment [4.4], [4.12]. It has the objective of specifying the poles and thus the dynamics of the closed loop by means of appropriate state feedback. If the state is not measurable, it is estimated by a Luenberger observer. The Luenberger observer resembles the Kalman-Bucy filter in that it is an algorithm for reconstructing the system state and in that it contains a model of the plant. It differs from the filter only in the way the correction matrix Kf is computed. In the case of the observer, this is done by specifying the dynamics of the equation for the estimation error. There is again a kind of separation principle in pole assignment: the observer matrix Kfand the stateestimate matrix K, can be designed independently. The use of this technique may also create problems if the state variables have no direct physical meaning. Furthermore, establishment of the control-loop dynamics does not guarantee satisfactory loop behavior. The characteristic locus curve method [4.22] seeks to meet the requirements stated in the preceding section by manipulating the eigenvalues of the open-loop frequency response matrix Q ( j w ) . These eigenvalues A Q i ( j w ) are called “characteristic frequency responses.” If the cor-
4.2. Feedback Control
responding eigenvectors are collected in the matrix V ( j w ) ,the following holds Q(j4=
V ( j w ) diag ( A Q i ( j w )V) - l ( j w )
(4.2.24)
Spectral decompositions for the sensitivity matrix and the complementary sensitivity matrix are then given by
The eigenvalues of S and T thus depend in a very simple way on the A,, ( j w ) . Although the gains of a matrix cannot be deduced from the absolute values of its eigenvalues, these do serve as a good indication of the gains in many cases. Therefore, small IAQi(jw)Iat high frequencies and large lAQi(ja)lat low frequencies are required. The crossover frequencies for the characteristic frequency responses are selected to correspond to the desired closed-loop bandwidth. What is more, the multivariable Nyquist criterion makes it possible to say how often the locus curves of AQi(jw)(the “characteristic locus curves”) must enclose the critical point (- 1,O) for the (nominal) control loop to be stable. Once the characteristic frequency responses have been chosen, the behavior of O[S(jw)] and O[T(jw)] must be examined to ensure that Equations (4.2.17) and (4.2.18) are satisfied. Now a way must be found to modify the characteristic loci in the desired way. The ideal method is to use a controller frequency response matrix having the same eigenvectors as the plant frequency response matrix. The following would then hold: Q ( j w )= V diag ( L G i ( j w )V-’ }
G(.iw) V diag { L K i ( j w )V-’ }
(4.2.27)
83
In this case, the /ZGi ( j w ) could be treated as single-variable systems, and suitable single-variable controllers LK, ( j w ) could be designed by the classical frequency-response method. Unfortunately, this is rarely possible, because the elements of Vare generally nonrational in j w , and the resulting controller matrix thus cannot be realized. One therefore has to be content with approximating the ideal controller by approximating the complex matrices V ( j w i )with real matrices for selected frequencies wi. A shortcoming of the characteristic locus method is that the I /ZQi ( j w ) I provide only an indication of the actual gains of Q ( j w ) .In the RFN (reversed-frame normalizing) method [4.23], attempts are made to bring the absolute values of the eigenvalues and the gains (singular values) of Q ( j w ) into coincidence. If this is successful (approximately), all the conditions stated in the preceding section can be met through manipulation of these quantities. A drawback of the characteristic locus and RFNmethods is that the characteristic frequency responses cannot be associated with individual plant input and output variables. There is accordingly no systematic way of making different controlled variables display different transient responses or different steady state accuracy. Such difficulties are less pronounced (or nonexistent) in the methods discussed below. The direct Nyquist array method [4.8] breaks the design process into two steps: 1) First, an attempt is made to approximately diagonalize the plant model by means of a simple-and, if possible, constant-compensator K,. This means that off-diagonal elements of the frequency-response matrix should be small in magnitude compared to the diagonal elements. In particular, if the elements of the compensated p x p matrix Q, = GK, satisfy
C I Qcki( j w ) I < I Qcii(jw)l P
(4.2.28)
k=l,k#i
for i = 1, . . ., p and 0 2 w < co,then Q,(jw) is said to be (column-)diagonal-dominant. The diagonal-dominance of a frequency-response matrix can easily be checked graphically. The polar plots of the diagonal elements Q,,, ( j w ) are overlaid with circles whose radii are given by the left-hand side of Equation (4.2.28). The bands formed by these circles are called Gershgorin bands. If Equation (4.2.28) is satisfied, these bands do not cover the origin of the complex plane. A number of methods have been ad-
4. From Process Knowledge to Process Control
84
vanced for obtaining narrow Gershgorin bands [4.8], [4.12], [4.15]. 2) In the second step, the small off-diagonal elements of Q, are ignored. Single-variable controllers K , ( J w )are designed for the principal diagonal elements of the compensated plant model Q,; thus the open-loop frequency-response matrix is obtained:
ized by Ostrowski bands, which lie in the interior of the Gershgorin bands. The method has the disadvantage that it entails the use of inverse frequency responses. Furthermore, the technique can be applied only to systems in which the number of manipulated variables equals the number of controlled variables. A design method that has been gaining popularity in the process engineering field is the IMC (internal model control) technique [4.24]. In its simplest form, this method assumes a stable plant. The controller then contains a plant model in parallel with the actual plant (Fig. 4.21). The requirements on the closed control loop are easily fulfilled with this structure, because
Q ( j w ) = G ( j w ) K , ( j w )diag { K i ( j w ) } (4.2.29) P
Q, ( j w )
This operation alters the form of the Gershgorin bands but leaves their width unchanged, since both the center and the radius of each Gershgorin circle are multiplied by the same factor. The Gershgorin bands of Q ( j w ) make it possible to assess the stability of the closed loop, since they contain the characteristic loci curves. If the bands do not cover the critical point (- 1,O), they determine how many times thecharacteristic locus curves encircle this point. Accordingly, the K i ( j w )must be chosen such that the Gershgorin bands of Q do not cover the point (- 1,O) and, collectively, they encircle this pbint counterclockwise mi2 times. Figure 4.20 shows an example form = 0. To perform a classical single-variable design for the i-th controller component, the corresponding frequency-response element of the compensated plant model also must be known for the case where the remaining control loops are already closed. Because small off-diagonal elements are still present, this frequency response will not coincide with Qc,<.Its polar plot, however, lies in the interior of the Gershgorin band associated with Q,,;. This is a further reason for the importance of narrow Gershgorin bands. Experienced users generally prefer the inverse Nyquist array method [4.8], which has the advantage that the relationship between yi( j w ) and ui( j w ) becomes sharper when the other control loops are closed. This relationship is local-
? -0.4 -0.8
-0.5
(4.2.30)
A promising approach to the design of multivariable controllers is H , minimization [4.25]. [4.26]. The discussion in the preceding section showed what kind of behavior should be sought for the maximum singular values of the sensitivity matrix and the complementary sensitivity matrix. In addition, the matrix K ( j w )S ( j w ) can be r---------
I
Controller
L_ _ _ _ __---_I Figure 4.21. IMC structure
? -0.4
.-mt
E
1) The closed loop is stable if and only if the controller component r ( j w ) is stable 2) The sensitivity matrix and the complementary sensitivity matrix depend on r in a very simple way:
0
Real
0.5
1.0
-0.8
Figure 4.20. Gershgorin bands for a 2 x 2 system
-0.5
0
Real
0.5
1.0
I
85
4.2. Feedback Control
included in the analysis. This matrix indicates how disturbances in the control loop affect the manipulated variables (see Fig. 4.1 1). Typically, s [ K ( j w )S ( j w ) ]is required to be bounded for all frequencies. This is intended to prevent the manipulated variables being saturated when disturbances occur. To avoid high-frequency oscillation of actuators, a further requirement is that C[K(jw)S ( j w ) Jroll off at high o.For S, T, and KS to have small gains. each in its own frequency range, distinct frequency-dependent weighting matrices W,,W,, and W,, are assigned to them. A controller is then sought that stabilizes the control loop and minimizes
(4.2.31)
11 . . .I\a is known as the Ha norm. Algorithms for solving this problem are complex and computationally intensive. For the user, however, this point is usually of as little importance as the nonuniqueness of the solution. The design engineer need only select the weighting matrices. As a rule, they are chosen to be diagonal matrices with stable, minimum-phase elements. Low-pass elements are commonly used for weighting S, so that small 0[S] is obtained at low frequencies. Similarly, high-pass elements are used for weighting T and KS. This method has the considerable advantage that the elements of the weighting matrices can be directly assigned to distinct manipulated and controlled variables. Their effect on closed-loop behavior is thus easy to assess. The selection of weights is illustrated on p. 86. Decentralized and Hierarchical Control. When the known multivariable methods are used, difficulties arise if the process to be controlled has many coupled inputs and outputs. Such large-scale systems should be broken down into subproblems. This can be done heuristically (and the subproblems defined, for example, as physical parts of the plant) or by means of formal techniques (e.g., the block relative-gain array [4.27]). A (multivariable) controller is assigned to each subsystem; in the design of this controller, however, links between the subsystems cannot be completely neglected. If the con-
trollers operate independently of one another, this is known as decentralized control; if they are coordinated at a higher level, the control scheme is said to be hierarchical (Fig. 4.22) [4.28]-[4.32]. Most known hierarchical and decentralized methods belong to the time domain. With regard to their use in practice, essentially the same problems arise as in the LQG or pole assignment methods. The development of frequency-domain decentralized and hierarchical methods promises to be helpful. Adaptive and Nonlinear Control. Linear control methods reach their limits in processes having no operating point or a continuously variable operating point (batch processes). Conceptually, the simplest way of dealing with problems involving an operating point that varies slowly over time is to use adaptive control [4.33], [4.34]. Adaptive controllers continuously modify themselves to match the process being controlled. In general, only the simplest class of adaptive methods are employed in practice: selftuning controllers. An indirect variant of such a controller is made up of a three-level algorithm: on-line estimation of parameters of a process model with specified (usually linear) structure ; calculation of parameters for a controller structure, also specified; and action of the resulting controller on the process (Fig. 4.23). In this way, Coord. algorithm Controllers
Recursive parameter estimation
Caltulation o f controller parameter Controller
& Process
v
Figure 4.23. Self-tuning controller (indirect)
86
4 . From Process Knowledge to Process Control
the entire (nonlinear) control problem is broken down into an estimation problem and a straightforward, generally linear, control problem. Adaptive control structures can therefore be treated as special nonlinear control structures. If the operating point of the process changes very rapidly or a linear description is altogether impossible, explicitly nonlinear methods must be considered. However, these methods, such as exact linearizaton [4.35], are restricted to a very small class of nonlinear systems. Exceptions are semi-heuristic methods such as nonlinear versions of MPC (model predictive control) [4.36]. Design Aids. Most modern design techniques are so complex that they could not be employed without the help of suitable CACSD (computeraided control systems design) packages. A continuously updated survey of available software is the ELCS (Extended List of Control Software) [4.37]. A further summary can be found in [4.38].
Design Example. This section shows how the classical frequency-response method and H , minimization can be used in the design of controllers for a pilot-plant distillation column with 40 trays (Fig. 4.24). The device separates a mixture of methanol and propanol. The control objective is to keep the concentrations constant in the bottoms and distillate as the feed rate or feed concentrations change. As is common in binary separations, the concentrations and temperature on an individual tray are related in a unique way. Instead of the concentrations, which are difficult to measure, the temperatures can therefore be controlled. Simulation studies based on a detailed nonlinear model (using the DIVA simulation system [4.39]) have shown that the temperatures on the 14th and 28th trays are most sensitive to disturbances in the feed. These two
Reboiler -QE
Figure 4.24. Distillation column V, = vapor stream; L = reflux stream; D = distillate; QE= heat input rate
temperatures are therefore chosen as controlled variables. The manipulated variables are the heat input QEand the reflux ratio E = L/ V,. The controller design is based on a simple linear model obtained by evaluating step responses:
r
1220 100 1 6.ljo + 1 (1.2jo + 1) (l.5jw + 324 1.6jo+1
- - - - - -
GW )
(4.2.32)
Here A denotes deviations of the indicated quantities from the desired stationary operating condition. The time constants of the model are given in hours. The most important specification for linear controller design is the closed-loop bandwidth, which should be close to 40 h-'. In the classical frequency-response method, the off-diagonal elements are neglected and only the diagonal elements of G are considered. Figure 4.25 shows the magnitudes for these elements. To suppress steady state errors PI controllers are used. Their time constants are selected such that the magnitude of both elements rolls off at a rate of ca. 20 dB per decade over the whole frequency range. The controller gains are set so that the two magnitude curves coincide over a broad frequency range and the bandwidth of the closed multivariable control loop takes on the desired value. The last point is verified by computing the singular values of the complementary sensitivity matrix (Fig. 4.26). In the H , minimization method, weight matrices are selected for S , T, and K S . Diagonal matrices with minimum-phase elements are employed. Figure 4.27 shows the amplitude characteristics of the elements of W, (low-pass) and W, (high-pass). All elements are chosen such that their gains intersect the 0 dB line at 40 h- ', because closed-loop bandwidth should lie close to this frequency. Small constants (0.01) are chosen as diagonal elements of WKs,since in this example no problems with actuator saturation or high-frequency actuator oscillation are expected. The H , cost function is completely defined in this way. It is minimized by a controller of order eight, but this can be reduced to order 5 by model reduction techniques. Figure 4.28 gives plots of the singular values o f S and T.
4.3. Optimal Control
Both controllers were tested in a pilot plant. It was found that the controller obtained by minimization of H , met all practical requirements, while the classical PI controller required on-line tuning of the gains. Details and comparisons with other multivariable design methods can be found in [4.40].
Frequency
-
4.3. Optimal Control Introduction. Before something can be optimized, the term “optimal” must be defined. In quantitative form, this is done by establishing a performance index (performance criterion, optimization criterion).
Frequency
Figure 4.25. Magnitudes of the diagonal elements
. Frequency-
-
Frequency
-
Figure 4.26. Singular values of the sensitivity matrix and complementary sensitivity matrix
5:E
-50
Frequency
-
-100
10-1
+ m
-60 10-2
-
10-1 100 Frequency
101
lo2
Frequency
Figure 4.27. Magnitudes of the weight elements
’+
87
-6 102
-8 10-2
10-1 100 Frequency
-
101
105
102
Figure 4.28. Singular values of the sensitivity matrix and complementary sensitivity matrix
88
4 . From Process Knowledge to Process Control
The optimization of a dynamic system means formulating the dynamic system behavior in an optimal way under the selected criterion. The system is characterized by the time-dependent processes by which it goes from a given initial state x (I,) to a final state x ( f e ) . The optiniization of a controlled dynamic system generally begins with the system already specified; only the control scheme is varied to achieve the optimal system behavior. If the control structure is fixed, so that only its parameters can be varied, we speak of parametric optimization. For example, the Ziegler-Nichols rules (see Section 4.3) yield optimal controller parameters for PI or PID controllers and a class of systems. If, on the other hand, the control scheme-including its structure-can be freely chosen, this is referred to as structural optimization. In the case of parametric optimization, the numerical values of the variable parameters are assigned a numerical rating based on the performance index; this rating is to be maximized or minimized through the variation and is thus a function of the parameters. In structural optimization, the entire time dependence of the control function u ( t ) in the time interval /, < t < t , is varied. The performance index assigns a numerical rating to this function and is therefore a functional (quality functional, cost functional) [4.41].
Usually, the term “optimal control” is understood as referring to the results of a structural optimization, and this meaning will apply in the discussion that follows. Optimization Problem. The starting point is a mathematical description of the system in state space (see Section 3.3) in the form of the system equation:
What is now sought is the control function n ( t ) that yields the minimum value of the performance index J for the process by which the system goes from the initial state x ( t , ) = x , to a final state x ( t , ) = x,. This is the optimal control function zi* (t).Solving the system equation with given x , and u* ( t )gives the optimal state- space trajectory x* ( t ) associated with u * ( $ The final time t, may be fixed or subject to choice, and similarly for the final state x, or its components. Quantities that can be freely selected are available for variation; that is, they can be incorporated in the performance index.
Along with specifications of this kind, there are often constraints: State or control variables cannot, or must not. go outside certain value ranges at any time during the transition. For example. a value can be at most completely open or completely closed, the concentration of a species cannot take on values outside the range from zero to unity. and a reactor temperature must not go above a certain upper boundary or below a certain lower boundary. The problem thus comprises 1) The system equation 3) The initial state so 3) The performance index J 4) Possible specifications o f t , and s, 5) Possible constraints on x and ti Open- and Closed-Loop Optimal Control. For a given initial state .yo, the solution of the optimization problem yields an optimal control function u * ( f ) . It does not. however, say anything about how the control function can be technically implemented. There are two approaches to doing so: In open-loop optimal control, the system state .Y is measured once at time r, and {I*([)is determined from the result. For the remainder of the time interval < t 5 t,, ~i*(/) is presented at the control inputs of the system; no further account is taken of the system state. The control function is thus a function of time only from time to onwards; it can be generated, for example, by a computer, which merely plays the role of a function generator. Such an open-loop structure has, however. the disadvantage that the system follows the optimal trajectory x * ( t ) only if no disturbances occur (see Section 4.1). For this reason, open-loop optimal control is often replaced by closed-loop optimal control. The optimal controller continuously generates the control function based on the current system state. If there are no disturbances. the controller holds the system on the optimal trajectory, thus behaving in the same way as the open-loop optimal controller. If disturbances occur, resulting in departures of the system from the optimal trajectory, the controller will initiate corrections so as to offset these departures while the transition process is still under way. Peyformnnce Indices. A general form of performance index, which includes many special cases of importance in engineering [4.4], is the functional
J
= h ( x ( t , ) ,t,)
+ S f , ( x ( f ) .i r ( f ) , r ) d r I(,
(4.3.2)
4.4. Binarj Control
The first term in this Bolza performance index evaluates the system behavior at the end of the transition process, while the integral term evaluates the entire process. This is known as a Mayer performance index iffo = 0, and as a Lagrange index for h = 0. The following cases occur under the Lagrange performance index: 0 0
0
u’(t)Mu(t), the energy-optimal performance index j o = xr(t)Lx(t), the ISE performance index (performance index of the integral squared error type) f b = 1, the time-optimal performance index fo =
The diagonal elements of the matrices M and L evaluate the squares of the control and state variables. respectively. All the other matrix elements are often simply set equal to zero. Methods. This section lists the most important methods and concepts, with references to the literature. The classical method of minimizing functionals is calculus of variations f4.41, [4.42], [4.43]. This technique is suitable for solving optimization problems without constraints on the control and state variables. An important special case, the closed-loop optimal control of linear systems [4.19], [4.20], [4.44] with quadratic performance index J = x’(t,)Sx(t,) + \
+ u’(t)Mu(t))
dt
leads to the so-called Riccati controller. Systems with constraints on the control variables can be optimized with the Pontryagin maximum principle [4.45]-[4.47]. Such constraints arise, in particular, in time-optimal control problems where the solution is expected to yield control functions that alternate between the upper and lower extreme values of the control variables. Feldbaum’s theorem states, for a given class of systems, how many such alternations are required at a maximum. Dynamic programming [4.48] - [4.50] is a general optimization method that can be used for, among other purposes, the optimization of dynamic systems. It offers the possibility of utilizing the constraints not only on the control variables but also on the state variables. An essential foundation of dynamic programming is Bellman’s principle of optimality, which says that any final portion of an optimal trajectory is
89
itself an optimal trajectory. This principle implies the basic procedure: The optimal trajectory and the associated open-loop optimal control scheme are determined in successive backward steps, beginning with the final state x,. A thematically organized literature survey of the optimization topics discussed here, as well as others, can be found in [4.4]; see also [4.51][4.56].
4.4. Binary Control Principles. Classical control theory is concerned chiefly with “analog” signals, which are discrete as to time and value. The mathematical description of the process (“plant”) and of the automation device (“controller”) employs differential equations as well as transformations (Fourier, Laplace). Industrial control practice, on the other hand, deals with all devices used to influence plants and machinery in a purposeful way. Traditionally, however, the emphasis is on working with binary signals (e.g., in relay and contactor controls) in open-loop structures [4.57]. Analog control loops occur only as low-level elements. Boolean algebra and its adaptation to hardware (switching algebra) are employed for the mathematical description of binary control [4.58]. These contrasting approaches have historical roots: Analog thinking relates to early work on the automation of continuous operations such as those practiced in the process industries, while binary thinking grows out of attempts to automate piecewise operations such as those used in the production industries. Process automation as now practiced in industry involves the convergence of both approaches. This applies to processes being automated and to the automation equipment itself, but not to the mathematical apparatus. Operations. Operations that have long been automated by open-loop control methods are now being operated with closed-loop methods as well; an example is the motion programs of industrial robots. Process engineering, a classical domain of closed-loop control, now makes increasing use of open-loop control, both for the automated startup and shutdown of continuous processes and for automated batch production. At the same time, a trend toward higher-level optimization of the entire production process,
90
4. From Process Knowledge to Process Control
with the goals of improved efficiency and flexibility, is apparent in all areas of industry. The result is generally coupling of subprocesses in the sense of material streams, energy, and information. Process automation thus calls for a holistic examination of continuous and discrete subprocesses, analog and binary process variables, and continuous and sequential operations. (This section does not deal with a third class, object-related operations [4.59], in which individual, identifiable objects pass through the process. As far as process engineering is concerned, this concept figures chiefly in high-level production automation; examples of “objects” are charges and production campaigns. See also Chap. 2 and Section 4.5.) Automation Equipment. Just as continuous and discrete subprocesses have been linked into “hybrid“ processes, automation equipment has also undergone a merger with classical measurement and control devices (Fig. 4.29A) becoming components of integrated automation systems (Fig. 4.29 B). This development has, however, been driven less by user demand for integrated process automation than by technological advances in instrumentation and control. Mechanical, pneumatic, hydraulic, and electromechanical devices have gradually had to give way to microprocessor-based electronics. Hence the step from consistent instrumentation and con-
@ Structure:
trol devices to integrated process automation systems was obvious. The adoption of these systems by users has been aided by the appearance of two variants that use software to mimic the operator interface of classical instruments and controls (Fig. 4.29 B) :
1 ) The programmable logic controller (PLC) for users oriented toward open-loop control in the production industries. Programming may be performed graphically with ladder diagrams. which are similar to the schematic diagrams of relay-based control systems. 2) The process control system (PCS) for users oriented toward closed-loop control in the process industries. The PCS interface can, for example, imitate conventional control stations with live flow diagrams and controller front panels (see Section 5.2). The combination of first-generation PCS and PLC into integrated “hybrid” process automation systems has led to a second generation of process control systems [4.60]. Mathetnatical Mefhods. A hybrid system consisting of a hybrid process and a hybrid automation system can be simplified by splitting it into a continuous, “analog” part and a discrete, “binary” part (Fig. 4.30). These subsystems have common structural properties: They contain internal feedback paths and must be treated as dynamic systems with memory elements. The mathematical methods applied to the two classes of systems have not yet converged [4.61], mainly because the theoretical techniques are at far different stages of development. For continuous systems (and also for the closely related sampling systems), control theory
Analog
m
1
Structure:
I 1 - 1I
Signals:
Binary Analog
, (Without feedback \I
I
I
1
With feedback
programmable logic controller A (integrated au;omation
I
(PLC)
system)
process control system (Pcs)
I 1 I I
Figure 4.29. Application areas of automation systems A) Classical automation devices; B) Modern automation systems
---------_--_----
Analog signals: 1 I
system II I
7
I Binary
I signals:
Threshold signals system Switching signals
_--__-__-___
L
I T---J
I
System boundary Figure 4.30. Hybrid system Input and output signals connect the system to surrounding systems (e.g., systems on higher and lower levels: operator interfaces)
91
4.4. Binary Control
offers a polished, complete, and proven mathematical toolkit. For the modeling, analysis, and synthesis of dynamic discrete event systems, in contrast, there is only a rudimentary selection of diverse methods, including switching algebra [4.58], Petri nets [4.62], Boolean differential calculus [4.63], and theory of automata [4.64].
Terminology. The foundation of binary control is switching algebra [4.58], which works on two-valued or logical variables. Its axioms establish the elementary logical operations conjunction (AND), disjunction (OR), and negation. Figure 4.31 shows schematic symbols for the hardware implementations of these operations. Other basic operations are derived from the elementary ones: NAND, NOR. equivalence. antivalence (exclusive O R or EXOR operation), and implication. Combining the basic operations generates switching functions, the implementations of which are called combinatorial circuits or logic controllers. The rules of switching algebra permit the mathematical analysis of networks. The Quine-McCluskey method can be used for the minimization of given logic functions [4.65]. If a combinatorial circuit contains internal feedback paths, memory elements (flipflops) can arise (Fig. 4.32). In this way, a sequential circuit or sequential controller is obtained. There are two classes of flipflops: level-operated and edge-
@
Figure 4.31. Schematic symbols for the elementary logical operations AND, OR, and negation, according to IEC 117-15 A, B inputs; Y output
1
Y
0
I
a 4
21
1 h
I
I
Figure 4.32. RS flipflop A) Schematic symbol; B) Construction from elementary logical operations S set input; R reset input (with priority over S)
triggered. In level-operated devices, the output can change its state as soon as one input state changes; in edge-triggered circuits, the output can change its state only when a certain (e.g., positive) edge is presented at a certain input, called the clock input. If, along with the logic functions, a sequential circuit contains only edge-triggered flipflops whose clock inputs are all driven by a central system clock, it is said to be a synchronous sequential circuit (since all the memory states can change only in synchrony with the edge of the clock pulse). Otherwise, the sequential circuit is said to be asynchronous. A systematic design procedure exists for synchronous sequential circuits. The starting point is a graphical description of the problem, called the state diagram [4.66]. The technique does not, however, insure a minimal realization of the sequential circuit. State diagrams are also unsuitable for the analysis of complex circuits, since a sequential circuit with n memory elements can have up to 2” states. Besides the combinatorial and memory elements, there is a third type of basic functional element: timers, which shortens, lengthens, or delays binary impulse signals. They are of very minor importance in the theoretical analysis of binary control systems but are frequently used in practice. The program of a control system means the establishment of all signal-processing functions.
i---lY=A+B
5
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4. From Process Knowledge to Process Control
This can take either of two forms. In a wiredprogram controller, the functions are established by hardware connection of the individual functional elements. If the connections can be altered in a simple way, the controller is said to be reprogrammable; otherwise it is called fixed-programmed. In a stored-program controller (or programmable logic controller, PLC), both the functional elements and their connections are simulated in software on a digital computer. The control program is thus part of the computer program. The program for a RAM-programmed controller is stored in read-write memory (random-access memory, RAM); that for a programmable controller with exchangeable memory is stored in read-only memory (ROM). A PLC includes a digital computer and is thus essentially a synchronous processor. It can, however, be used to simulate combinatorial circuits, asynchronous and synchronous sequential circuits, timers, and also analog signal-processing elements such as controllers, or any combination of these, to a very good approximation. It accordingly represents the most flexible and advanced way of solving all control problems, and it is the most cost-effective technique for all complex control problems. A number of text-oriented and graphics-oriented languages are available for the specification of control tasks and the programming of PLCs [4.58]: Structured text (ST; IEC 1131/3) is particularly suitable for writing implementationneutral specifications. The instruction list (IL; DIN 19239. VDI 2880) is a low-level assembler-like PLC programming language (i.e., similar to machine language). With the ladder diagram (LD; DIN 19239, VDI 2880), simulations of relay-based circuit diagrams are employed for PLC programming. This is a graphical language. The function block diagram (FBD; DIN 19 239, DIN 40 719, IEC 117-15 ) technique, another graphical language, uses elements similar to the circuit symbols for logic gates and memory elements in digital schematics (see Fig. 4.33). If individual logic and memory elements in the logic diagram are combined into macros (“actions” and “steps”; see Fig. 4.33). the result is a sequential function chart (SFC).
I
-
I
Figure 4.33. Linear step sequence Macro representation: sequential function chart (left): Explicit representation (right) 1, 2,3 steps; A, B, C transitions; X, Y, Z actions
The sequential function chart (DIN 40719/6, VDI 2880) is especially suitable for sequential controls where the individual control goes through states or steps not in an arbitrary sequence but (at least section-wise) in a fixed, linear sequence. This condition does usually hold in practice. Besides these standardized languages, there are many manufacturer-specific languages, partially constructed from elements of the standardized languages (e.g., Siemens STEP-5). Sequential function charts include not only sections of linear step sequences, but also branches. A distinction is made between OR (alternative) branches and AND (parallel) branches. In the latter. concurrent control sequences take place in the controller; that is, sequences that proceed in parallel and independently of one another until they are again synchronized to one another at the end of the AND branch. Such controls cannot be usefully diagrammed and analyzed with state diagrams; a much better tool for this purpose is offered by Petri nets [4.62]. The draft of DIN 40719/60 for sequential function charts, in contrast to the old version of DIN 40 719/6, includes some important restrictions on branching options to permit Petri net modeling of concurrent controls. Both Petri nets and sequential function charts are suitable for the specification, simula-
4.5. Operatiorial Control of Process Plants
tion. analysis, programming, operator interface, and documentation of controllers. They are particularly useful as support for hierarchical control concepts, which are often important in practice. Classification of Control Systems. The common language of automation technology includes many control terms, most of which are defined in DIN 19237 and many of which were mentioned in the preceding section (seep. 91). In what follows, these terms are listed under different classification criteria. Signal-oriented classification : analog, digital, binary control Function-oriented classification: combinatorial, sequential control Structure-oriented classification : asynchronous, synchronous control Technology-oriented classification: wired-program control, storedprogram (programmable) control: (wired-program control :) fixed-programmed, reprogrammable; (stored-program control :) RAM-programmable, exchangeable memory; relay, contactor, electronic control; mechanical, hydraulic, pneumatic, fluidic control, etc. Hierarchy-oriented classification : individual, group, coordinating control Application-oriented classification: interlock control, sequence control; burner, scale, transport, recipe control, etc. Another concept is mode control, which refers to a control level. higher than the signalprocessing part of a sequential control, in which an operating mode is selected. VDIjVDE 3683 and DIN 19237 define the following modes (among others) : automatic, semiautomatic, single step, step setting, manual. In hierarchical control concepts, the operating modes available may differ from one level of the hierarchy to another and must be coordinated in a suitable way.
4.5. Operational Control of Process Plants Requirements on the operational control of process plants have grown much more stringent in recent years. The reasons include increasing flexibility in production, shortages in personnel
93
and plant resources, shorter product throughput times, production structures with crosslinks due to energy and product coupling, quality assurance standards, and the greater and greater number of maintenance operations that have to be performed at regular intervals. Optimized production programs and maintenance practices must be integrated into a plant control concept against the background of environmental and occupational safety and health requirements and the reliability standards that they imply. A number of approaches have been devised and used with success for specific problems. BROMBACHER [4.67] and NIESE[4.68], [4.69] used the term “process logistics” to describe proposals they developed for managing product streams given the coupling of process elements in normal operation, but also in transient and exceptional situations. Under the leadership of NAMUR, “recipe” control concepts were developed for batch processes; these have been implemented as standard products by control system manufacturers [4.70]. RIEMER has described concepts for production control, which have been turned into tools [4.71]. The same holds for special functions at the plant complex control level [4.72]. A closer analysis reveals that these concepts do accomplish their specific goals but are not consistent with one another in the sense of operational process control. In future, accordingly, a broader approach must be taken to the operational control of process plants. The action and resource model discussed here is one such expanded approach to describing the operational control of a process plant. The model is constructed in an object-oriented way. A wide variety of requirements, relating to recipe-based process control, logistical product management, maintenance, and other areas, can be reflected in the model. The model includes a general formalism for resource allocation and control. Automated tools support the application of the formalism. In the context of the level model, the action model represents a logical standard interface between the production or plant complex level and the process control level. Introduction. The fundamental task of operational process control is to coordinate and, with the aid of plant resources, carry out various tasks
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4 . From Process Knowledge to Process Control
stemming from production requirements, inplant product logistics, or technical support and plant maintenance. The action and resource model provides a formalism for systematically finding and implementing solutions. The action model is based on the idea that every task arising in operational plant control can be completely accomplished by performing individual actions. These actions are performed through orders on the available resources. The process control problem is thus broken down into three parts:
1) Mapping of tasks into individual actions 2) Planning and scheduling of these actions 3) Control and monitoring of the performance of the actions Figure 4.34 illustrates the basic structure of the model. Some important concepts figuring in the model are explained first. These should be understood as working concepts and are used here only in a very restricted sense. Functional Units. A functional unit is, quite generally, a unit aggregated from various functional elements and, as a whole, accomplishing a self-contained task. Functional elements are selected such that they can be directly implemented with the help of existing plant equipment. Thus they are not fictitious functional objects but concretely represent the capability of a group of process devices. A plant complex includes a variety Requirements
Actions required
Actions scheduled
of functional units, such as plant sections, maintenance foreman groups, auto transport services, energy sources. and so forth. In general, functional units are organized in a hierarchical manner (see Chap. 2). Maintenance groups, for example, are organized as follows: Maintenance Maintenance Maintenance Maintenance
technician gang foreman group support group
Process-oriented functional units might be organized as follows: Individual functional unit Group functional unit Plant section unit Plant unit Plant complex The internal structure of these units is discussed in depth below. Resources. In the concept discussed here “resources” means “that which can perform services in the plant.” Resources correspond to functional units that do not functionally overlap and which together represent the capability of the plant. Resources so defined are “intelligent” units to which the plant can pass on orders either disectly or through plant actions. Resources are part of the permanent equipment of a plant complex; they are present whether they are processing an order or not. The sum of the resources Actions being performed
Resources
El Production
devices
Product logistics/ resources
Define
Schedule
Activate
Perform PLant control
Figure 4.34. Actions as a means of plant control
I
4.5. Operational Control of Process Plants
reflects the functional capability of a plant complex. Several special rules apply to resources : Self-sufficiency: Each resource has the capability of managing itself and independently carrying out orders. For the real performance of its task, each resource has its own apparatus and resources to which it has unrestricted access. Each resource can thus be planned, scheduled, and worked independently of all other resources. 0
Control interface: Each resource has a control interface and an allocation mechanism associated with the interface. A resource may be needed for a variety of plant actions. At any time, however, only one action is allocated to a resource by means of orders. The planning and scheduling of resource utilization is accomplished by higher-level functions. Independently of such planning and scheduling, however, each resource has an allocation mechanism, which locally protects it against improperly being assigned more than one order. 0
No parallel processing of orders: A specific feature of this model is that each resource can process only one order at any time. Accordingly, there is no concurrent working of orders within a resource. This provision is crucial in determining the overall structure of operational process control. 0
This “flatness” and the ban on parallel processing of orders are an important advantage in making operational control easy to understand and simple to implement, but they represent a severe restriction on structuring. Functional units, as substantial objects, can be aggregated in accordance with the structural principle, The rules for resource formation now require that one level of aggregation be selected and permanently identified as the resource level used for operational process control. Only the functional units on this level are competent to perform operational process control services. If, for example, the maintenance foreman group is identified as the resource for the maintenance hierarchy, the operational process control system can then issue orders only to the foreman group. The issuance of an order to a gang or to a support group would not be permitted. In this example, the rules would demand that a foreman group be processing only one order at any time. This is,
95
however, generally not so in practice. Accordingly, a foreman group is not the proper resource for operational process control. The resources identified should be those on a lower level of aggregation. Gangs, in general, process only one order at any time; hence gangs meet the criteria for a resource and would be a suitable choice as schedulable units for operational control of the maintenance area. It is important to stress that this restriction to one level of aggregation relates soiely to operational process control. All the levels of aggregation are still of interest from the standpoint of other organizational concerns. Actions. The plant complex can accomplish its task by issuing orders directly to its resources. Such a manual procedure is, however, expensive for the plant complex and also hard to track. The action model offers a more convenient solution. An action is a self-contained control unit that permits the automatic performance of an entire functionally coherent block of tasks. Typical actions in this sense might be “Pump X liters from tank A to tank B,” “Do the scheduled maintenance on emergency trip system X,” or “Produce X tons of A in accordance with recipe B.” For each action there is a performance specification, which sets forth how the action is to be carried out. Any action may require more than one resource for its performance. Actions can be combined and grouped at the plant complex control level. At the interface to the lower-order process control system, however, each action is essentially a self-sufficient, independent, self-contained object. The model says nothing about the complexity of an individual action. Both extremely complex actions and elementary actions can be defined and carried out simultaneously in a system. Actions need not be free of redundancy as to their functionality. For example, an action such as “transfer X liters from rail transport to tank A” may well have the same elements as the action “Pump X liters from tank B to tank A,” Regardless of the nature of the task or the type of resources employed to accomplish it, a common scheme is used for performing and managing actions. In this way, they form an object class with well-defined properties. In the hierarchy, actions form a standard interface between requirements on plant management
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4 . From Process Knowledge to Process Control
and operational performance. This formal decoupling is an essential goal of the action concept, but it should not be taken to imply that task and execution can be analyzed and organized independently of one another. If the structures of resources and those of tasks are not consistent, the result will be a poorly organized package of actions that cannot be efficiently carried out. Thus it makes no sense, for example, to define special plant engineering resources simply for purposes of process control if such resources cannot be utilized to perform maintenance actions or contribute to in-plant product logistics. Central importance attaches to the development of a resource structure that is matched (in substance and form) to the internal concerns of various task areas. Requirements on Operational Plant Control. Operational plant control must meet requirements arising from many areas. These are broken down by the concerns of the areas where they originate. The next section presents examples of “Requirements from Production Planning,” “Requirements from In-Plant Product Logistics,” and “Requirements from Plant Support” in order to show how actions can be derived from requirements.
I
I
Requirements f k m Production Plnnning. From production planning, the plant complex receives orders to produce certain products, along with product-related specifications such as product name, nominal quantity, nominal quality, and nominal deadline. In order to accompiish this production, a number of actions must be initiated and carried to completion. These actions can be analyzed in two ways: 0
0
An operational breakdown of the production process into steps corresponding to the subdivisions of the process A purely quantitative breakdown of the production process into lots making up the desired (nominal) quantity
Operational breakdown of the production process For structuring purposes, a production process can be broken down by using the phase model (see Chap. 2, Figs. 2.32-2.37). Figure 4.35 shows an example for a process A. At the left, the entire process is shown as a closed block with inlet products IP1 to IP3 and outlet products OP1 and OP2. The guiding principle in breaking down the process is that the operations belonging to a process can be separated by simply dividing up the material system by product 0
P I
I
P
IP1
I
React
AZP2
Break down
Mix ZP 4
OP 1 Figure 4.35. Breakdown of a process in the phase model
IP 3
-
React
?,PI Overall process A
I
IP2
0OP2
4.5. Operational Control of Process Plants
and dividing up each operation into phases by time. If this is possible, individual, self-contained subprocesses corresponding to the phases of the phase model are obtained. Each subprocess is characterized by the performance of certain operations. In the example shown, these operations are reaction Al, separation A2, reaction A3, mixing A4, and reaction A5. The products at the beginning and end of a subprocess have well-defined properties. In the example, these products include the inlet and outlet products of the overall process plus intermediate products ZP1 to ZP4. The subprocesses are coupled to one another via these products. Depending on the complexity of the overall process, the same scheme can be re-applied to break down the subprocesses. This method reaches its limit when operations cannot be further separated either by a product-related subdivision of the material system or by a time subdivision into phases. Production specifications are instructions for the execution of a production in a plant. In other words, they are performance specifications for production actions. They extend beyond general process descriptions, stating precise conditions to be maintained and states to be established in order to carry out a certain process in a plant. These conditions include, for example, properties of products that enter the process. The starting material present in the plant at the beginning of a process should also be regarded as a product occurring in the process. An important initial condition is the state of the plant at the beginning of the process (see also Section 2.3). Initial condition refers not only to state variables in the narrow sense, such as temperature or control state, but also to general properties such as “degree of contamination,” “functional reserve,” “modification effected,” and so on. These conditions govern the need for tooling or cleaning orders. The fundamental part of the production specification, however, describes how certain process properties must be, controlled in the course of the process so that the desired overall process can be carried out. This also includes the establishment of certain intermediate and end product properties t4.731. Each production specification is based on a model of a reproducible process. By analogy to the breakdown of a process, a production specification can also be divided into
97
parts (see Fig. 2.24). It should be noted that every portion of the specification, by virtue of the analysis, contains supplementary conditions on the inlet product (feed) properties and on the states of plant sections at the beginning of the subprocess. When such a breakdown is carried out, a production specification becomes a number of subspecifications, each of which describes the execution of a subprocess in a plant section (see Fig. 2.25). It is evident that the analysis of processes and production specifications has a direct effect on the structure of action objects and the way they are managed. This can be illustrated by two extreme situations as shown in Figure 4.36. In Example 1, the process has not been broken into parts. In other words, the action takes in all partial production steps including intermediate product transport. The overall production process, including the time sequence, is described by a single production specification and, in its entirety, corresponds to a single action. In Example 2, the overall process is divided into subprocesses, for each of which there is a partial production specification independent of the other subprocesses. An independently schedulable action is assigned to each subprocess. Temporal coordination is achieved through the availability of intermediate products and the initial conditions for the individual subprocesses. Coordination comes under the planning and scheduling functions of the production or plant complex control level. What level of “granularity” is appropriate for a given production process will depend on the requirements and constraints imposed. The action concept permits arbitrarily broad or granular actions and does not place any restriction on the depth of the breakdown. Quantitative breakdown of the production process If the nominal product quantity is too large to be produced in one step in a closed material system, the overall production can be subdivided into individual batches. A number of batches can be combined into a lot, and a number of lots into a campaign. The NAMUR definition states that a lot is “a delimitable and identifiable quantity of product having a uniform quality. In the case of a batch production process, a lot may include one or more batches” [4.70].
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4. From Process Knowledge to Process Control
I v 2-2
Example 2 simple action Example 1 complex action Figure 4.36. Complex and simple actions Z = prerequisites strictly state-oriented; A = prerequisites also sequence-oriented
Again, the action concept allows any degree of aggregation. In general, actions will relate to the production of one batch; the combination into lots is a planning and scheduling task. In the extreme case, however, a complete lot can be defined as a single action. Requirements from In-Plant Product Logistics. In-plant product logistics refers to the control of product (material) flow within the plant complex. This activity supplies inlet products (feeds) to the “producing” plant sections and removes the end products and byproducts. The tasks of in-plant product logistics also include the control of internal buffers, tank storage, bunkers, high-bay warehouses, and devices used for storage and reclaiming. This area of responsibility may also extend to the control of tank cars, rail cars, and other mobile storage located onbattery. Depending on the application, product-logistics activities may take place in the context of production actions or may be independently planned and scheduled tasks executed by separate actions. Such actions might then be: “Transfer by pumping,” “Convey from tank X to tank Y,” “Transfer from tank car unloading station A to tank X,” or the like.
These actions are required to be highly flexible as to scheduling. Often the orders are not issued until shortly before performance, or rescheduling may be necessary at the last minute (e.g., tank cars are in the wrong sequence on the track, operators at the tank car terminal wish to carry out order A before order B, etc.). This required flexibility in the planning of logistic actions and the requirement that noncentral (local) entities must have access to scheduling (e.g., from the receiving station) must be taken into account in the design of the scheduling system. For higher-level product management, the granular logistic elements are combined into more complex units. For example, starting products may be delivered to the plant complex, whereby each delivery may consist of a number of tank cars. Within the plant complex, not the receipt of the delivery as a whole but rather the discharge of an individual tank car is generally regarded as a single action. Again, however, the action concept is open and permits modification to suit the organizational setup of the plant. (For example, a complete coal train is unloaded in a different way from a delivery made up of separately marshalled cars.) Requirementsjrorn Plant Support. Plant support, in order to accomplish its tasks, must periodically carry out plant inspection, maintenance,
4.5. Operational Control of Process Plants and repair operations. These can also be defined and performed as actions, for example: “Calibrate probe X,” “Drain separator sump,” “Do scheduled maintenance on pump A,” “Inspect overcharging protection B,” and so forth. If a maintenance action renders plant equipment unavailable for production while the action is being performed, the maintenance action must be included in the planning and scheduling system like any production action. The action concept permits a systematic treatment of maintenance actions in resource allocation. What is more, it allows plant-wide monitoring of the progress of planned maintenance. Every maintenance operation defined as an action in the system monitors its own correct performance. If unplanned events occur, the progress status of the action should reflect them. If necessary, an explicit notification should be provided to plant management or the planning and scheduling system (see Fig. 2.19). Maintenance operations can be broken down in two ways: (1) by the functions or functional units maintained and (2) by expected frequency, for example, on a time or wear basis. Ultimately, these analyses lead to individual activity elements. Job planning combines these elements into packages meaningful from the engineering, functional, and management standpoints, and the packages are treated as maintenance actions for planning, scheduling, and administrative purposes. How this grouping of elements takes place in a particular case will depend in the maintenance concept practiced at the plant complex (see Section 11.4). Analysis of Plant Equipment and Definition of Resource Structure. The plant complex has technical equipment and personnel resources at its disposal to perform its tasks. This section describes how the apparatus and personnel resources can be broken down and how they can be combined into technologically meaningful functional units. On the basis of the system of functional units created in this way, a suitable resource structure can then be established. This description is for the example of plant engineering functional units but can be extended to other areas as well. Breakdown of Plant Equipment. The purpose of analyzing equipment and apparatus available
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in the plant complex is to identify clearly delimited, nonoverlapping elements. Elements belonging to various devices can then be assembled into meaningful functional units. The process is illustrated for the combination of plant equipment elements and control system elements into plant engineering functional units. a
Breakdown of Process Equipment
Production takes place in process equipment and facilities where the product is conveyed, stored, and transformed. A process plant or facility ultimately consists of valves, pumps, pipes, and so on. Between the plant as a whole and these single elements, however, a number of useful analytical levels can also be found. An important level is the plant section. From some points of view, the plant section is actually more important than the plant. Plant sections are typically delimited in such a way that further subdivision of the material system present in them is impossible or needless. Thus exactly one subprocess can run in a self-sufficient way within every plant section. Here “exactly” means that one, but only one, subprocess can take place in any plant section at a given time. A plant section has inlets where products enter it and outlets where products leave it. These inlets and outlets serve to connect plant sections to one another. The topological structure implies certain rules for product streams between plant sections. Figure 4.37 shows the most important basic ways of connecting plant sections. In the train, a product moves through one plant section after another (stage by stage). If there are several trains, a product moves through only one of the trains. In present-day chemical plants, especially plants where several different products are made, these structures are no longer found in pure form. Instead, the plant sections are connected by a complicated network of piping. Figure4.38 gives an example of such a complex structure. A plant section consists of the components exclusively assigned to it, such as valves, pumps, sensors, piping connections, and so on. Within a plant section these components can be assembled into groups. In general, a group has the property that it effects a certain flow of matter or energy in the plant section. Figure 4.39 shows a reactor with its groups. Each group consists of the components exclusively assigned to it.
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4. From Process Knowledge to Process Control
Single-stage multitrain plant
t
Multistage single-train plant
Multistage multitrain plant
Figure 4.37. Basic connection patterns of plant sections
1' Reactor
I
L
Figure 4.38. Real network of connections between plant sections
Groups are coupled to one another by transfer points for matter or energy, called connections. 0
Breakdown of Process Control
The goal of process control is to bring about certain process properties. A certain control logic is required for this purpose. The basic structure of this control logic is described by a control model. The basis for the control model employed here is a hierarchical control model. As Figure4.40 shows, a control unit receives orders, commands, and setpoints from a higher-level unit. It executes the order by, in turn, specifying orders, control values, control parameters, and
commands to lower-order control units. How it does this is an aspect of its internal logic. The methods of feedforward and feedback control can be used to derive a suitable internal logic. Grouping of Functional Units. The purpose of forming functional groups is to combine plant equipment in various areas in such a way that it together accomplishes a partial task of the plant. Workable modules for accomplishing the overall task of the plant are obtained in this manner. This is illustrated by an example of the grouping of plant units and control units. 0 Plant (equipment) units and control units can be combined into functional units. The model
4.5. Operational Control of Process Plants
provides that one control unit is assigned to each plant unit. At the lowest level, for example, individual control elements such as valves, pumps, electrical heating devices, and so forth, are grouped with individual control units to form “individual functional units.” Such an individual functional unit for the blocking and release of a product stream is illustrated in Figure 4.41.
Catalyst metering
Each individual functional unit permits a certain process property to be set (see also Section 6.2). The second level comprises group functional units. Figure 4.42 shows schematically how a group functional unit is built up. A group functional unit consists of a group of individual functional units exclusively assigned to it, together with a group control unit. which controls and
Recirculation/discharge
Figure 4.39. Groups of a reactor
-
-
Orders, commands, control values f r o m higher-level control unit
ders, commands, control values lower-level c o n t r o l units
OP 1 Figure 4.40. Hierarchical control model
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Vacuum
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4 . From Process Knowledge to Process Control Control interface Automatic Manual
valve
Block/release product stream in pipeline
neering are excluded by the group functional unit. If several effects useful from the process engineering standpoint can be brought about with a group of individual functional units, the group control unit supports each of these effects with its own control structure. The group functional unit is said to have alternative or selectable “modes.” The functionality of the group functional unit is implemented when the group control unit issues specifications to its assigned individual control units in accordance with the control model. Figure 4.43 illustrates the structure of a group control unit. Note that the group functional units discussed here have also been described under the name “basic functions” [4.74], [4.75]. Because this term leads to repeated misunderstandings, however, and is still under discussion in NAMUR [4.70], it is not employed here. The third level is that of plant section functional units. A plant section functional unit consists of the group functional units of the plant section and a plant section control unit. The plant section control unit reflects all the process control capabilities of the plant section. Typically, group functional units permit an arbitrary number of meaningful program options. Which
, I
1
Ind\ividual functional unit
Interface t o process/ product system
Figure 4.41. Structure of an individual functional unit
coordinates the activities of the individual functional units. The overall goal of a group functional unit is to combine a group of individual functional units from the control standpoint in such a way that together they act on the process in a manner that is meaningful from the process engineering viewpoint. Individual effects that do not add up to an effect meaningful in terms of process engi-
Group control unit “inlet metering“
Group control unit “inlet meter in g”
control unit
control unit
control unit
I
Individual functional unit ”Valve on/off“
Individual functional unit “tonveying On/Off”
Figure 4.42. Structure of a group functional unit
Individual functional unit “Flow-rate control”
4.5. Operational Control of Process Plants
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Higher-Level control unit
by .ator
Selectable inlet values Inlet parameters
Stal
-
Interlock signals Actual values
Control interface Lower-level control unit
option is needed in a particular case is set forth in the performance order for the individual process. In this way, when issuing an order to the plant section control unit, it is necessary to provide not only control values, control parameters, and commands, but also portions of the performance specification.
To sum up: Each level offers certain possibilities for functional intervention in the process; that is, each level can carry out certain process control orders. Furthermore, each level completely reflects the process control capability of the plant. The higher the level, the more complex the task. In the simplest case, the order is to open or close a valve or to set a certain flow rate. Every functional unit contains control units that carry out specific tasks. A control unit receives orders and/or control values from a control unit on the next higher level and, in turn, issues orders and/or control values as setpoints to control units on the next lower level. The number of levels in the hierarchy can be selected on
Figure 4.43. Structure of a group control unit
technological grounds. Criteria might be, for example, the size and complexity of the process plant or facility. For the reference model under consideration here, a two-level structure below the plant section is chosen. Determination of a Suitable Resource Structure. Establishing a suitable resource structure means selecting an appropriate set from the many functional units for operational plant control. The following criteria must be taken into account: 0
0
The set chosen must fully cover the plant properties relevant to operational plant control. The functional units in the chosen set must not overlap. No resource may contain devices belonging to another resource. The structure of the resources must have a modularity suitable to the requirements.
In what follows, possible resource structures are discussed for the example of plant functional units.
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The criteria of completeness and freedom from overlaps are satisfied at the levels of individual functional units, group functional units, and plant section functional units. Accordingly, the level most suitable for analysis can be selected. Maximum flexibility is gained by selecting individual functional units as resources, though at the cost of involved performance specifications. The fact that there are group control functions, which can coordinate the individual functional units independently of the action being carried out, is not included in the treatment. The coordination of individual functional units must be explicitly described in each concrete case by means of instructions contained in the performance specifications for the action. If group functional units are chosen as resources, virtually all the order-neutral conditions and program steps are contained in the internal logic of the resources. Simple and powerful performance specifications result. Group functional units are an appropriate choice. In the model under consideration, group functional units are identified as plant engineering resources. In this way, every order involved in operational process control references one group functional unit. Orders to individual functional groups are no longer possible. Plant sections are not known to the operational plant control system, and there are no plant section control units. Figure 4.44 shows the control hierarchy that results from identifying group functional units as resources. In some process control concepts in use at present, plant section functional units instead of
Plant functional
functional unit
functional unit Figure 4.44. Control hierarchy
group functional units are the resources for allocation, planning, and scheduling. This notion is suggested by the rule: “At any time, only one subprocess can take place in a plant section.” In practice, however, this approach results in some difficulties and disadvantages. Such problems arise, for example, in managing the interconnection of plant sections. Other difficulties come about when actions in the areas of in-plant product logistics or plant support are considered; here it is a hindrance to assume that one, and only one, action can be performed in a plant section at any time. For example, given certain constraints it is quite possible that production, calibration, and maintenance actions can be carried out simultaneously in a plant section. A component-oriented control structure leads to further difficulties as well. In multifunction facilities, plant sections often carry out many different subprocesses. The needed control specifications can no longer all be established as “modes” embodying all the capabilities of the plant. They must be loaded into the plant section control systems before the subprocesses are performed. This procedure of loading program sections into existing control units entails serious conceptual problems. On the whole, there is no advantage in the functional coupling of group functional units to form plant section functional units that can be allocated. The reference model accordingly looks directly to group functional units as the allocatable plant engineering resources. Regardless of the resource structure, the plant section naturally plays a major, if not the
4.5. Operational Control of Process Plants
central, role as the smallest unit of the plant engineering world. Consequences of the Choice of Resources. The selection of a functional unit as resource not only implies additional formal requirements on the functional unit itself, but also has a strong effect on the semantics of the performance specifications for actions and on the overall structure of the plant control system. These consequences must be considered when making the choice. Additional Formal Requirements. Resources in this model are self-sufficient, largely self-managing, and self-protecting objects. All resources must exhibit certain standard properties. In this way, the issuance of an order to a resource is standardized and is formally independent of the type of resource. An order to a maintenance gang does not differ formally from the order “heat” given to a group functional unit. The formal provisions include the following standard properties of a resource :
Standard order interface Each resource must have an interface through which it can receive orders, setpoint values, control parameters, and so forth. The interface is standardized and is, formally, the same for all resources. It is dedicated to communications with the allocating customer (e.g., action X, action Y, manual intervention). 0
Standard status interface Each resource must have an interface at which it makes certain (generally standardized) significant states available as information for other units. This interface provides information to all interest units and is held available by the resource as a read-only interface. It is formally standardized and is the same for all resources. The value ranges of the state variables “availability state,” “type of allocation,” and “operating state” are consistent for all types of resources. 0
Allocation logic Each resource protects itself against multiple allocations. The allocation logic uses a simple principle: The first to allocate an available resource is the customer. A customer must release the resource in order to make it available to other potential customers. Prioritization and the identification of optimal allocation sequences are in the planning and scheduling domain ; these operations are not supported by the resource itself. 0
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This simple structure appears to be especially appropriate for process engineering, where briefly interrupting one production process to make the resource used for it available for another task is generally not desirable. Self-management and self-organized activity Each resource is a functional unit that manages itself and operates in a manner internally independent of an allocating customer. Even unallocated resources carry out their control tasks and are continuously active and operational. Resources are responsible for the correct functioning of the equipment subordinate to them and the management of their resources. They are intelligent alarm and report generators. 0
Self-sufficient protective logic Each resource has protective logic to prevent its creating a forbidden condition. This protective logic operates under its own rules, independently of the order system, and insures priority local protection. 0
Semantics of Performance Specifications.
Once the resources have been chosen, the reference elements are identified for the formulation of actions. The selection of resources thus influences the way of thinking and communicating during operation. The importance of this fact is commonly underestimated when specialists analyze their individual areas. The linguistic consequences can be illustrated for plant engineering resources. Three examples of plant engineering resources are considered. If the input/output channel is chosen as the resource, the performance specification will contain elements such as “set output BA43.” “read input AE65,” and so forth. But then both the EMR/process control system engineer who writes an automation function and the chemist who formulates a recipe must ultimately do so on this linguistic level. It is obvious that this “channel level” is unsuitable as a medium of description. If individual functional units are selected as resources, the requirements can be formulated in engineering language. The performance specifications now contain elements such as “pump 34 on,” “valve 56 closed,” “setpoint for controller 36 = 50 “C,” and the like. Accordingly, this level permits an engineering formulation and includes device-specific details. But the formulation depends on the specific instrumentation present in the plant.
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4 . From Process Knowledge to Process Control
Finally, if the group functional units are chosen as resources, the instrumentation-related details become internal to the resources. Group functional units represent the functionality of plant (equipment) units. Now the performance specifications will contain elements such as “heat to 40°C” (vessel heating), “meter in 100 L at 1 L/s” (inlet line), “mix vessel contents” (stirrer), and “transfer up to a maximum of 300 L” (inlet line). In order to avoid terminological confusion, Figure 4.45 shows the situation schematically. The focus is on the “performance order,” which corresponds to a concrete instruction to a group functional unit to perform a certain procedure. Every group functional unit has the capability of doing certain procedures. On the other side are the possible performance orders, that is, the linguistic elements from which the performance specifications are assembled. Group functional units are a convenient, meaningful, and workable level. Choosing this level permits both the formulation of performance specifications for automatic actions and the manual control of actions by issuing single performance orders at a technologically understandable, not excessively complicated level. Actions. Each action serves to accomplish a self-contained partial task and, within the plant complex, is a separate, identifiable object. How an action is to be carried out is set forth in its performance specification. The system responsible for the performance of an action is identified in planning and scheduling. This performing system must know the performance specification or
must be given it. For example, the processing of a production action can be transferred to the plant operator or the process control system. If the action is assigned to the process control system, this system independently performs the action in accordance with the performance specification stored in the system. If the action is assigned to the plant operator, the operator handles the action by manually issuing performance orders in accordance with the performance specification contained in the operating manual. Processing includes issuing performance orders, controlling the allocated resource, collecting archival data, generating the proper reports, giving the ready signal, and monitoring correct operation. Each action is generated, planned, and scheduled by the plant planning entity and is transferred to a performance system where it is actively processed. It is then deleted from the performance system and the planning and scheduling system, remaining only in the archive system. Figure 4.46 shows the states of an action and the possible transitions. Lot planning, tank storage planning, inaintenance planning, and other activities generate requirements for the performance of certain actions in certain spans of time. In order to schedule the performance of these actions, all actions planned up to a certain time “horizon” are reported to the in-plant planning and scheduling entity. An action thus becomes an object in the planning and scheduling system. which maintains a list of all actions generated. It makes available an operator interface, with
Action requires a virtual unit t o oerform a oerformance order Allocation o f resources t o virtual units
A e r f ormance order
the performance order
Figure 4.45. From performance specification to implementation
Resources
4.5. Operational Control of Process Plants
Start Being processed
Done, waiting
for deletion
Archived
Figure 4.46. Operating states of an action
whose aid further actions can be generated and existing actions can be rescheduled, modified, and deleted if necessary. The planning and scheduling system supports planning by means of prospective calculations, then uses the list of actions to be perfomed together with the current status to arrive at resource allocations, determine the contents of product storage units, and predict the future states of the actions. For this purpose, there is a simulation specification for each type of action. With the help of this specification, which treats each action as a planning and scheduling unit, it is possible to forecase when which plant resource will be needed, and to say when and where substances are to be received and discharged (and in what quantities). On the basis of such a forecast, the action plan can easily be checked for critical resource allocations or critical states of product storage units. During production, this forecast must take account of the actions now running and those loaded. In other words, the planning and scheduling system must always know the present status of all actions being processed.
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Scheduled actions are initially pure scheduling objects in the planning and scheduling system. At this point, they have no access to resources. To be performed, actions must first be loaded. When an action is loaded, a performance system is ordered to perform it; this system receives all the required functions. If there is no instruction to the contrary, the performance system then automatically carries out the action. An important property of the loading operation is that pre-loading is possible, meaning that an action can be loaded into the performance system long before its scheduled performance. In this way, the requirements on the availability of the planning and scheduling system are eased. An adequate reserve of actions can always be kept loaded in the performance systems. For example, all production actions to be performed automatically in the next 24 h can be loaded into the process control system; the maintenance group is given a list of all maintenance actions to be carried out for the week; the plant operator receives a list of all production actions to be performed manually during the weekend. A performance system of special interest for process control engineering is the process control system. The loading and automatic execution of actions might be thought of in this way: When an action is loaded, a control unit is created in the process control system and initialized. The control unit is a self-contained object with the ability to verify conditions and to perform its task automatically. It is no longer dependent on the intervention of higher levels but still can, if necessary, be influenced by higher levels. The loading of an action can be initiated either manually by the plant operator or automatically by the planning and scheduling system. Once loaded, the action considered as a control unit goes into an initialized condition: “loaded, waiting for start.” It has no effect on process control and no access to resources, and it can be deleted or reset at any time without having exerted any influence on the system. The processing of the action begins when the control unit is started; during this operation, the control unit is given access to the needed resources. It gains such access through allocation of group functional units and the construction of the control links required for this purpose. Two concepts should be distinguished here. In the simpler one, all links that the action will need at any time during processing are made at the outset. In a somewhat more complicated concept, only those
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4. From Process Knowledge to Process Control
links called for during the initial phase of processing are present at any time; links that are no longer needed are dynamically broken while the action is still being processed. Once an action is complete, all links created for it are abolished. While an action is being processed. the action control unit acts like a normal, permanent control unit (Fig. 4.47). Once an action has left its initialized state and has been started, it cannot return to the initialized state. The action has been launched, cannot be called back, and must be processed all the way to a correct or incorrect termination. Every action, once started, is stored in an archival system; it cannot be cancelled without entry in the archive. All stored information about the progress of the action and all stored information about group and individual functional units Waiting f o r s t a r t
In processing
working under its control can be accessed by virtue of this entry in the archival system. The form of storage depends on the implementation. It is evident that the action model described here is especially suitable for batch processes, where a one-to-one mapping of actions to production actions is possible. The steps in an action (see the state model in Fig. 4.46) largely correspond to the steps in a segment of a batch production process. This is not the case in continuous processes. which are characterized by the continuous operation of process steps rather than by steps with definite terminations. The stress is on managing and controlling the modes and transitions between modes as shown in Figure 4.48. Nonetheless, continuous processes can be described in terms of the action model. One simple Finished
Control unit "action"
Group control units Individual control units
Figure 4.47. The action as control unit
Continue a f t e r
abort Figure 4.48. Operating modes of a segment of a continuous process [4.67]
4.5. Operational Control of Process Plants
option is to “discretize” the continuous process into small increments delimited in time and having definite production objectives. Automatic Performance of Actions. The automatic performance of actions means implementing the functions required to complete the action by using automatically functioning software or hardware modules. This can be achieved, for example, by implementation in a process monitoring and control system, a maintenance system, or another system for carrying out actions. The reference model has a structure appropriate for this purpose: Resources and the actions themselves are built up in object-oriented fashion, in two senses: first, that each resource or action contains its own control unit and thus acts as a self-contained unit in the system; second, that the resource or action has properties that can be derived from a type concept. Control units occupy places in a control hierarchy. Each control unit is connected to the next higher-levelcontrol unit and the next lower-level control units through well-defined control interfaces. In addition, each control unit has interfaces through which it can exchange information with all other units. In process control systems, control units can be implemented as modules. This is a common and a standardized practice in process control engineering; for example, the IEC standardized languages (WG65A) are based on an object-oriented modular approach. Example of a Modular Solution. The following section shows how the action and resource model can be implemented in a modular form in a process control system. The modular model has the following properties :
Support of the type concept The type concept is a special manifestation of a one-level abstraction hierarchy. Each module is of a certain type. The type defines all methods, the description of connectors (input/output interface module), and the description of internal variables. Further, the type includes certain typespecific variable values. The entity, as a management unit, contains not only its name but also its specific data set and the reference to its type. Accordingly, the entity contains no methods or variable descriptions, only parameters. In contrast to the individual device approach, in which each compact controller contains the type information (e.g., control algorithm), in process control systems the type in0
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formation can be contained only once, in one component. Entities then contain only the module state information and module-specific parameters. Under this concept, the type information must already be present in the target system before the creation of an entity. Loading of type information is a separate step from instantiation of the individual module. Data exchange through connectors and links According to this concept [4.76], data exchange between modules must obey the following principle: Every module offers certain connectors to serve as interfaces to the external world. Two matching connectors of two modules are connected to each other via a link. Once the link is in existence, data can be exchanged. The construction of the link itself is a separate process from data exchange. Links are independent management objects in the system and can be created and again destroyed while production is under way. 0
Closure of modules Each module as a whole is a unit that can be manipulated. Modules are created by simple instantiation commands. The prerequisite is that the proper type must be present. Once modules have been created, they can be incorporated in the processing by an activation command. They bring along all the prerequisites for their function. Modules can be created at any time, and also deleted again, while production is under way. Once modules have been deleted, no fragments remain in the system. A particularly simple implementation of the action concept is obtained if all functions required for the performance of the action are contained in the performance specifications (and thus in the type description). The individual action contains not specific functions but only parameters for the control of the performance specification. In this way, there is a strict division between the creation and loading of performance specifications, on the one hand, and the creation and processing of individual actions, on the other (Fig. 4.49). This strict separation is not a practical drawback, since in general the creation of performance specifications takes place separately from the scheduling and operational performance of the actions. The various tasks are often accomplished on different levels : Performance specifications are created and managed in a “recipe” system, while action management and initiation take place in a scheduling system. 0
1
I
1
I I Defined performance specification
I ;! I I I I
1 1
I Automatic assignment by SS functions
II 1I __________________________________J! IL ________________ e.g., production specification
1 I
i
;
I I
I
I
1
Control unit “action” (instance of action)
!
1
I I
in process control system
I
i
J
j
4
Referenced
Loaded performance specifications (action type)
Performance system here: PCS
Figure 4.49. Separate loading of performance specification and action (performance system here = process control system)
An important performance feature of a process control system is how convenient it is to create and load a new performance specification, modify an existing performance specification, or generate the associated simulation specifications, original data sets, and parameter masks for actions being created. Another important point, however, is that it is possible to attribute all required manipulations in the performance system to operations formulated in the modular concept. Thus no loading, writing, or coupling steps on the modular system are necessary. In this way, the action concept can be logically implemented in a modular system.
Automation. A very important property of the model is that automatically performed actions and actions manually performed by the plant operator can be handled concurrently in a process control system. It is thus possible to build up to a higher level of automation in a step-by-step fashion. When actions can be automatically completed, a system acquires the degree of automation that Polke has called “logistical mode” (see Section 11.I) [4.73]. Summary and Outlook. The action and resource model attempts to describe a general structure of operational plant control. Impor-
tant features of the model are the resource structure, the standardization of the order interface, and the introduction of “actions” as self-contained control and management objects. This contribution has taken the example of plant engineering resources in order to show how such a structure can be created and implemented in a suitable way. The concept suggested is. however, a technology-free general mechanism. It is strictly limited to describing the operational performance of actions; thus it contains no specifications on how the planning and scheduling task is to be accomplished or on how recipes are to be generated, and it provides no rules as to how resources are to be selected and organized. Special models are required for these purposes. By way of example, a resource model for plant engineering resources is described and its consequences explored. A plant complex must carry out its tasks while fulfilling a variety of requirements and satisfying a number of constraints. “Plant-wide” decisions are needed on a daily basis. The action and resource model enables the plant to formulate its operational decisions in a consistent way and implement them in a systematic manner. The results are not just a simplification of scheduling, but also an improvement in the quality of planning and performance of in-plant actions. The documentation of plant activities is part of the
4.5. Operational Control of Process Plants
concept and can be synthesized in a systematic way. The action and resource model is logically object-oriented. It can be extended directly to a corresponding modular system. With regard to usability, this model has the great advantage that technical devices and personnel can both be used in constructing resources and performance systems. Plants can
11 1
thus be controlled in a consistent way, regardless of their instantaneous degree of automation. A special task for the future is to integrate existing process control concepts for production control, plant management, recipe-based process control. and production logistics into the general methodology of the action and resource model, thus arriving at a comprehensive solution for the operational control of process plants.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
5.1. Principles
5. The Process Control System and Its Elements: Process Sensor Systems 5.1. Principles Sensor, Sensor Technology, and Sensor System: Classification and Standards. Modern process control engineering, with a distributed structure of information processing based on microelectronic devices, has greatly expanded the potential of the information economy for the control, monitoring, optimization, and reliable conduct of processes [5.13. If this increasing potential of information processing is to be utilized fully, the amount of information acquired directly or indirectly as to the status of the process must also be augmented. A relative bottleneck exists here, because the development of sensor technology lags behind that of information processing [5.1]. There are a number of reasons for this. Technical functional units in the immediate vicinity of the process are always much more expensive and complicated than those in a protected environment such as the control room. Furthermore, the continued use of conventional, internationally standardized signals together with proven transducer technology stands is hindering further development. Finally, information processing is often regarded as being much more important than information acquisition. Decentralized process automation is beginning to extend distributed “intelligence” based on the use of microelectronics to the point where measurements are acquired. Therefore, the meanings of “sensor element,” “sensor,” “sensor technology,” “sensor system,” and “direct signal processing” have become blurred today. For example, consider the “critical” link in the action chain associated with measurement. In the current language of process and production engineering, the functional element that embodies the interaction between the material system and the instrumentation is referred to as a “sensor” [5.2]. Here, “sensor” no longer means the same thing as it commonly does in the electronics industry, where it is restricted to the proportional transformation of nonelectrical input quantities to electrical output signals [5.3], [5.4]. The applicable standard (DIN 19 226) is therefore in need of revision. A useful starting point for a mean-
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ingful definition of the term “sensor” is offered by Sheet 3, “Apparatus Terms,” of VDIjVDE Guideline 2600, “Metrology” [5.5]. This document employs “pickup” or “primary element” for the (entire) device that accepts the quantity for measurement at its input and emits a corresponding measurement signal at its output. The “sensor” proper, on the other hand, is the part of the pickup that directly acquires the quantity for measurement and reacts in some sensitive fashion to the quantity. Even this definition, however, is appropriate only for a relatively small fraction of the sensors currently utilized for measuring material quantities. The definition does apply, for example, to semiconductors and piezoelectric sensors; it is difficult to apply to IR photometers and paramagnetic oxygen sensors, because here the interaction between the material system and the instrument does not take place in the sensor, as VDIjVDE 2600 presumes, but via an energy field serving as an auxiliary quantity [5.6]. The present terminology and standards, such as the definition in VDIjVDE 2600, are thus insufficient and require further discussion. An unambiguous resolution of terminology in this area is essential in the long term. Suggestions for refinement of the terminology have been made in [5.2], [5.4], [5.6], [5.7]. Here, it is proposed to separate information acquisition in and on the process from information acquisition in the laboratory and, as applicable, in the pilot plant. Both classical “process measurement” [5.8], [5.9] and “process analytical techniques” [5.10]must fulfill fundamentally different requirements from the comparable instruments in the laboratory. The shift from information acquisition in the plant laboratory and “process analysis rooms” to acquisition in the production process itself justifies the information-oriented term “process sensor system technology” as opposed to “laboratory measurement technology.” Process Models and SensorSystems. The concept of “process model” has been known theoretically for more than 20 years; it encompasses “black box,” mathematical, physical, and other types of models. Especially important in connection with measurement technology are adaptive process models, also known as observer models. Hundreds of books and tens of thousands of other publications have appeared on “process
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5. The Process Control System und Its Elements: Process Sensor Systems
models” (see also Chapter 3), “process identification,” and “process simulation.” There are academic departments concerned with these topics, and many congresses and symposia on them are held every year. Although the theory is advanced, there is a wide gap between theory and practice [5.1], [5.1 I]. According to PERNEand POLKE[5.12], the process model is ultimately a mapping between product properties and process properties. Problems of product variation or product development could thus be attributed to a process variation if a suitable process model were available. Identifying such a variation would provide a starting point for “optimization” under a wide range of criteria (see Section 11.3). This problem can be handled systematically and-most importantly-optimized only if a representative mapping or transformation (i.e., the process model) can be found between the process space and the product space (see Fig. 11.49). But why use a model instead of the real production process? Simply because the real process is generally much too complex and opaque to permit direct operations based on physical and chemical relationships and algorithmic descriptions of these (see Chapter 3). However, this chapter is less concerned with the representability of a model in relation to the real process and the desired product qualitites, and more interested in the reaction on sensor elements and sensor system technology. Material and
It is important, above all, that questions of sensor technology not be separated from the topics of process models, process properties. and product properties. Material Flow, Energy Flow, Information Flow, and Sensor System. As Figure 5.1 shows, there is a prallelism and correlation between material-energy flow and information flow. The link between an event or state in the process and the information system is effected by the sensor system. Accordingly, the sensor system has to carry out two basic tasks. First, the input quantity resulting from an event or state must be sensed and transformed into a signal quantity suitable for processing. Second, the signal quantity must be set in relation to its cause so that interpretation can extract usable information (see also Section 5.2). The sensor itself must perform the first task, while the second is in the realm of information processing. Some thought should also be given to the possibility of augmenting the information by serial, parallel, or associative linking of single items, whereby the result may be of far greater relevance than the sum of the individual pieces of information. Typical examples are the temperature profile, the stress tensor, the density field, and the concentration gradient. This multi-sensor approach is widely used in the field of biological sensors. A typical example for the capabilities of an array of identical sen-
Product properties
energy f l o w Product
Process
Product
Actuator system Figure 5.1. Position of sensor systems relative to material, energy, and information streams
5.1. Principles
sors is image recognition by the eye. A further advantage of arrays becomes apparent in biological sensors but has been little utilized in industrial sensors: While the individucal biological sensor is markedly inferior in performance to many industrial sensors, a system with intelligent preprocessing of signals achieves an amazing degree of flexibility in dealing with unforeseen events. Naturally, this flexibility is more vital to biological systems than to industrial ones, where the service loads can be established more precisely. Nonetheless. the gain in flexibility through multi-sensor technology is of interest for industrial systems as well [5.13]. What holds for any economic system also applies to the information economy [5.14]: One can use only what one has. The first step, accordingly, must be information acquisition. It is reasonable to limit the information acquired to that which is important and actually necessary. There are two reasons for such a restriction: to avoid an information flood and to hold the cost of instrumentation within acceptable bounds. Both experience and mathematical and physical methods can be used as guides for limiting the information flow. Because it is often difficult to identify the relevant properties, that is, the properties that adequately describe the process or the product, more easily measurable properties (indicators) are often resorted to. Process optimization requires better penetration into the process, and this in turn means finding ways to make relevant properties-i.e., those that can serve as significant process indicators-more measurable. HESSE,in his editorial in the “Sensors 1992” special issue [5.15], used the following terms to describe the shift in emphasis in the development of sensor systems: “At the beginning of the 1980s, the problems were to systemize technical terminology, to discover effects and technologies that could be exploited in practice, and to find out how mediumsized companies could establish themselves in this field. Later, in the mid-I980s, publications on practical sensors utilizing a wide range of technical principles became dominant. These were still in the foreground in the early 1990s, but systems aspects are now being examined along with applications and on an equal footing. In fact, systems concepts should have been considered right from the start. These aspects aid in the development of strategic insights and objects, since they describe the overall high-level
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solution. The introduction of process control engineering in the chemical industry can serve as a model: Sensors are dictated by the system itself and can thus be developed under explicit constraints.” For these reasons, some concepts having to do with the use of sensor (system) technology in process control engineering are discussed first. From Phase Model of Production to Process Information Model (see Chapter 2). A key aid in describing and analyzing processes is the phase model of production. This object-oriented semantic tool originated in software engineering. The outcome, a directed bipartite graph, belongs first to the category of product nets (see Chapter 2). The phase model of production consists of two object classes, “product” and “process element.” Individual products and process elements are described in terms of attributes. The attributes are the object classes of information about a process; they are defined by category and value (Fig. 5.2 [5.16]). The value is made up of a numerical value and a unit. Whenever metric scaling is possible (see Chapter 3), the units employed should comply with U.I.P. 20 (1978) [5.17] and I S 0 31/0 (“General Principles Concerning Qualities, Units and Symbols”) and DIN 1313 [5.18]. Higher-level requirements such as “product quality,” “process safety,” and so forth are imposed on the production process. Objective: Product Quality. Product quality refers to a subset of the actual properties of a product, which in terms of category and value form an intersection with the nominal properties demanded by the customer (Fig. 5.3). The development objective is to maximize this intersection within reasonable tolerances. Ojective: Process Safety and Environmental Safety. Safety tasks such as process safety and environmental safety can be accomplished with the resources of process engineering and process control engineering. This can be achieved by means of technical or organizational actions [5.20] or a combination thereof (see DIN V 19250 [5.21]). This procedure makes it possible to find alternative solutions that are equivalent from the safety standpoint, so that the most economical solution can be applied in implementation [5.22]. In the new NAMUR Recommendation, the required process control safeguards are matched to the risk arising from a process
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5 . The Process Control System and Its Elements: Process Sensor Systems Process attribute Value
1
Trans formation principle
Product attribute
Category
/Process
h
Rotation
Process
(process element) Product
Figure5.2. Assignment of attributes in the phase model of production [5.16]
Safety
Figure 5.3. Product quality [5.19]
Limiting risk
i
Danger
---- I low
Risk
-
high
Figure 5.4. Safety, risk, and danger [5.24]
facility or the absence of such process control safeguards. This risk assessment is necessarily qualitative and must be carried out case by case. Other procedures that lead to a comparable level of safety should, however, not be ruled out. The protective objective in the context of this recommendation is to prevent injury to persons as well as major environmental harm and damage to property. In the case of major damage to property, damage assessed as serious danger (e.g., according to [5.23]) should be distinguished from damage evaluated as affecting the company’s interests. Some important concepts should be defined here (see Fig. 5.4), without an in-depth examination of the extensive safety literature [5.22], [5.24]-[5.26] or a workshop on Safety in Chemical Production: Danger: A danger is a situation in which the risk exceeds the limiting risk. Limiting risk: The limiting risk is the highest still-acceptable risk of a certain technical process or state. In general, the limiting risk cannot be quantified; it is described indirectly or in terms of safety considerations.
Risk: The risk associated with a particular technical process or state is described in terms of probabilities. Two factors are included: the expected frequency of occurrence of an event leading to damage and the extent of damage expected when the event does occur. Safeguards are actions taken to reduce risk by lowering the frequency of occurrence, the extent of damage, or both. Safety is a situation in which the risk is lower than the limiting risk. Availability: The availability of a technical device is the probability that the proper functionality is present. It follows that. with regard to the safety-related availability of process control devices, solutions for holding the risk less than the limiting risk should be evaluated from the economic standpoint only if they are equivalent from the safety standpoint. In contrast, it is useful and necessary to analyze other availability characteristics to determine the economic consequences of malfuncI
5.1. Principles
tions. This statement applies to the logistical availability of equipment that would impair the ability to deliver (just-in-time, just-in-case) and also to the process availability of production equipment that would impact directly on product quality or plant condition (e.g., production stoppages, market losses, repair costs). The unambiguous description of proper functioning, that is, the complete set of all nominal attributes needed for such a description, leads to the information model of quality (Fig. 2.35, p. 36) or the information model of safety (Fig. 2.36, p. 37). The histograms illustrated show, for each product object or process object, all attributes necessary for its description, along with the nominal values and their tolerances (“good,” “defective, acceptable,” and “defective, unacceptable” ranges), which may be scaled either metrically or topologically (see Chapter 3). This information must be statically and dynamically defined at the maximum level of detail [5.16]. What is more, the spatial and temporal representativeness of the attribute values must be taken into account, especially for ex-line sensor systems (Figs. 5.20 and 5.21, p. 129). Product and Process Properties. The attributes used to describe the objects of the production process are process properties and product properties. The term process properties includes the following: 0
0
0
State variables, such as pressure, temperature, and concentration, with which the process can be described either directly or via derived state functions. Process parameters, such as heat-transfer coefficient and catalyst activity, which characterize the constraints under which the process takes place. Process parameters are steady or at least quasi-steady quantities. Controlled variables, such as motor speed and setpoints, which characterize actions on the process.
In theory, the process can be described completely if a suitable set of these quantities is available. In practice, however, it often happens that not all the desired state variables and controlled variables can be measured or specified. Substitute information often must be resorted to :
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Setpoints, such as stirrer speed, are correlated with the controlled variables or represent residual categories. Process indicators [5.27] are empirically correlated with one or more state variables (and may also be product properties; see Section 11.3). These represent substitute information. Controlled variables and setpoints characterize the part of the process state that is imposed from outside. It is the task of the actuator system to convert the specification, provided by the information flow, to an action on the process, The remaining process properties characterize information that is to be acquired from the process with the aid of sensor technology. Product properties are the following: 0
0 0 0
Physical quantities Chemical quantities Technological properties [5.27] Product indicators (see above), which are substitute information
Technological properties are determined by special inspection procedures when relevant product properties depend on physical quantities that can be determined only with great difficulty, or if the relationship with the physical quantities is currently unknown. Product indicators are empirically correlated with physical and chemical quantities, but process properties can also be employed as product indicators. All product properties characterize information that can be acquired by using sensors. “Technological” and “Physical” Test Procedures. Some fundamental problems in establishing material properties are illustrated for the case of rubbers [5.27]. Technological Test Methods. The testing and characerization of chemical substances is generally limited to assessment based on technological tests or application-oriented model tests, which are single-point measurements which have limited predictive power and do not take account of the functional relationships actually present. Technological tests do not directly lead to conclusions about the physical and chemical properties of chemical substances. This means that such data alone are insufficient as a basis for the optimization of chemical production processes. The essence of technological testing can be illustrated by a simple diagram (Fig. 5.5). A certain material Mj under a certain load X, exhibits a technological behavior Z,j.
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5. The Process Control System and Its Elements: Process Sensor Systems
ii Load X i
1 Figure 5.5. Technological inspection [5.27]
When the test load (generally complex) is specified in terms of the arbitrary design of a testing machine, the actual loading often cannot be expressed physically. For example, the Mooney tester [5.28]-[5.30] has an inconsistent shear rate and inhomogeneous stress and temperature distributions in the steady state. Furthermore, the variation of temperature with time during the measurement varies in an uncontrolled way as a function of the specimen used. The single-point measurement performed, namely the torque after 4 min, describes only an integral response of the material relative to the testchamber geometry as the specimen is subjected to a load that is neither homogeneous nor steady. The predictive power of this measurement is thus limited to comparison of materials under these conditions. In general, no certain conclusion can be drawn as to the processing qualities of the rubber tested in this way, since the shear loads that occur in industrial processing machinery are several orders of magnitude higher. Physical Test Methods. A material can be better characterized in terms of its physical properties. These quantities are obtained by applying simple loads and measuring the relationships between load and response. Such simple loads include linear, uniaxial stress, laminar flow at constant shear rate, and a steady homogeneous temperature distribution in the specimen. Complicated loading modes such as occur in practice can generally be synthesized by superimposing simple loading states. The material properties Mij are material-dependent quantities described a functional relation: zij
=f (Mij,xij)
describes the behavior of many elastic solids. Here, the stress z is the load (X,= T), while the deformation or strain y represents the material behavior or response (Zi,= y). A material constant, the shear modulus C = z/y,interrelates these two quantities and characterizes the mechanical properties of the material (M,, = C). Figure 5.5 shows that the technological behavior is governed by the material and the loading. The task of a phenomenological theory is to define the formalism that describes the observed relations (Eq. 5.1.1) and the properties M,, of the material (Fig. 5.6). Idealized models of substances and bodies play a major role here, for example the ideally elastic (Hookean) solid, the Newtonian fluid, and the Maxwell and Voigt bodies (Fig. 5.7). Deviations from the behavior of the idealized body are handled by restricting the range of validity (Hooke range) or by introducing correcting relations. For example, the viscosity of a general non-Newtonian fluid is described by T = qdyidt
where T is the shear stress, dyldt is the shear rate. and the viscosity is treated as a function of the shear rate dyidt,
The phenomenological theory describes the physical properties of a material by interrelating the load state and the behavior of the material, whereas polymer physics seeks to devise a theory that correlates the physical properties of a material Mijwith the chemical composition and structure of the material or a structure model (e.g.,
Q Load X i
V
(5.1.1)
For example, Hooke’s law
z=Gy
(5.1.3)
behavior Z;;
(5.1.2)
Figure 5.6. Physical inspection [5.27]
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5.1. Principles
7 lTl
t
Chemical composition
Load X i
Structu-Hooke r=G g
I ?
I
b I
behavior 2;;1
,
Figure 5.8. Physical inspection and structural theory [5.27]
Voigt
r-G g + r ]
f
Qaxwetl g= ( r/O + ( ?/ r])
Figure 5.7. Rheological model [5.27]
Kuhn coil model [5.31], Pechold kink theory [5.32]); see Figure 5.8. If this were achieved, there would be a basis for the planned development of particular polymers. Among the most important properties of elastomers is the viscoelastic behavior. There is still no self-contained phenomenological theory to describe the entire mechanical deformation behavior, including fracture. For small deformations, relaxation spectroscopy [5.32] can be used for complete determination of the viscoelastic behavior of polymeric materials. Under harmonic loads, the complex shear modulus G *( w , T) is measured as a function of frequency w and temperature T. The results can be used to calculate the relaxation time spectrum H(zR,T ) (zR = relaxation time), which is closely related to the structure. Using the Boltzmann superposition principle allows the material behavior under complicated, nonharmonic loads to be calculated. These methods fail at large deformations, where the relation between stress and strain becomes nonlinear. The Boltzmann principle, which states that the effect of a composite load can be treated as the sum of the effects of its composite simple loads, does not apply in such cases. Fundamental findings and results on the material properties of multiphase chemical substances, including these application-relevant ranges, can be found in [5.33]-15.353.
The question arises to what extent the mechanical behavior of elastomers can be completely characterized despite the lack of a unified nonlinear theory of viscoelasticity. NINOMIYA and FURUTA[5.36] proposed that in general four functions are sufficient to characterize the deformation behavior : 0
0 0 0
The relaxation time spectrum H(z,), which characterizes linear viscoelastic behavior at small strains The stress-strain curve z (y), which characterizes the nonlinearity The fracture strain cB( T ) ,which characterizes fracture behavior The shift factor aT ( T ) ,which characterizes the temperature dependence [5.37]
Such a description of a polymer, however, requires substantial efforts. To determine the relaxation time spectrum and shift factor, the complex modulus must be measured over a wide range of temperatures and frequencies. Moreover, extensive calculations must be carried out. The difference between technological and physical test methods can be summed up as follows: Technological tests investigate the behavior of the material in an application-oriented model test, generally involving a single-point measurement under complex loading conditions. In contrast, physical tests are concerned with the fundamental behavior of the material. Simple and easily quantifiable loading modes are selected and then varied such that the fields of properties are determined in multidimensional product spaces (see Fig. 11.45). Despite the obvious advantages of the physical analysis, technological tests will continue to be important because of their lower cost.
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5. The Process Control Systenz and Its Elewtents: Process Sensor Systems
A crucial point in selecting a method is the purpose of testing. Three aims must be considered : 1) Product characterization in general, in the sense of quality assurance (e.g., DIN I S 0 9001 [5.38]) 2) Acceptance testing and production control 3) Prediction of the practical behavior of a material The objective of product characterization is to achieve at the most complete possible description of product properties in terms of instrument-neutral physical quantities, to determine the key parameters that affect the product properties, and to quantify functional relationships. Accordingly, product characterization encompasses the area of physical testing. The demands on instrumentation and the amount of work required for interpretation are so great that these methods are generally only justified for new or as yet unknown products. This is the only way to carry out a reliable, exact comparison between materials. As a technique for routine inspection, for example in acceptance testing and production control, sampling inspection of material properties is generally adequate. Sampling techniques (representativeness over space and time) are applied to both the quantity of material and the selection of loading mode combinations. The principles of statistical quality control should be observed [5.39]. The creation of correct, optimal experimental designs, however, presupposes a comprehensive knowledge of material properties. The requirement on acceptance testing procedures is that they can be performed by the vendor at one location and the buyer at another, using different instruments. The parties must agree on the test conditions (establishing the controlling parameters); this is one of the key tasks of international standardization (e.g., ISO). In contrast to product characterization, single-point measurements may be relevant here as well. In production control, various batches of a product are subjected to spot checks to verify that they have identical physical properties. The important prerequisite is good reproducibility of measurements on a given instrument. It may be desirable to choose complex test conditions in the interest of quick, effective monitoring [5.40].
There are two possible approaches to forecasting the mechanical and technological behavior of a material in practice. First, tests can be performed under the process conditions and loading modes encountered in practice; this step should be preceded by a thorough analysis of the process (see Section 11.3 with Figs. 11.36 and 11.37). Second, in principle it should be possible, given suitable results from the determination of the physical material properties, to calculate in advance how the material will behave under the required loading conditions. The second method, however, often runs into the difficulty already mentioned: the lack of applicable theories. For this reason, the first approach (simulation of practical conditions) is becoming the more important. By analogy with this example of mechanical product properties, the electromagnetic product properties defined by Maxwell’s equations D
(5.1.5)
= EE
L=pH
(5.1.6)
I = oE
(5.1.7)
can be examined. The first product properties determined here (generic class “application features”) are due to molecular motions and are measured as collective phenomena. For example, suitable spectroscopic or chromatographic methods make it possible to resolve the required product reactions over a variable frequency and temperature field: E = E ( T ,w
, . . .)
(5.1.8)
Again, the limit of the linear relations is found by considering the variation of the field quantity: s=s(7:CO,E)
(5.1.9)
The product properties are in general complex: E*
= s,
+ is,
(5.1.10)
Model-Aided Measurement Procedures for Determining Product and Process Properties. From the connection between process model and information demand, it is possible to identify ways and means of overcoming the limitations of sensor technology by utilizing the present capabilities of information processing (see also [5.1]).
5.1. Principles
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(Real-time I linformation flow t
Material and
Product
Process
+JB-
- m-
Sensor system, a c t u a t o r system
Interpretation o f measurements t o g e t h e r with a priori knowledge o f process behavior
Product and
Model-aided measurement
Information-processing system
Figure 5.9. Model-aided measurement procedure
The broad potential of model-aided methods, which has been under-exploited in process engineering, should be mentioned first (see Fig. 5.9). In the context of intelligent sensor systems, this procedure makes it possible to reconstruct unmeasurable process and product properties from measurable quantities by interpreting a priori knowledge about the process in the form of a mathematical model. Ideally, the entire process state can be derived from the available measurements in this way. By integrating a priori knowledge into the measurement process, the predictive power of measured quantities is markedly enhanced in both scope and reliability. The prerequisite, however, is a sufficiently accurate mathematical model of the process under consideration [5.41]-[5.45]. Especially in chemical process engineering, however, mathematical modeling presents some difficulties. Many processes, because of their complex nature, do not lend themselves to accurate mathematical description. Process facilities often include subprocesses that are not subject to the classical mathematical model-building procedure. In such cases, for example where the product properties and their relationships can be described only in qualitative terms, new ways of incorporating a priori knowledge about the process into the measurement must be sought. The methods of applied information processing can be expected to reveal new solution approaches. Under some circumstances, verbally formulated algorithms and heuristic methods can lead on to
a qualitative description of the process. The use of knowledge-based methods is more and more attractive, for example the inference methods employed in expert systems [5.46] -[5.48] and fuzzy-logic methods [5.49]-[5.51]. Interesting tasks arise here for future research. An initial approach was the project “Technical Expert Systems for Data Interpretation, Diagnostics, and Process Control (Tex-I),” in which several participants (Siemens, Interatom, Bayer, and others) attempted to adapt expert systems techniques to process control. Other projects in this context are SENROB and NERES [5.52]. It is to be hoped, however, that the practical benefits of model theories will be studied and utilized more intensively than in the past. However, casual use of modern or simply fashionable modeling methods to make up for shortage of process knowledge must be avoided [5.1]. Figure 5.10 outlines a model-aided measurement technique for the case where a mathematical process model is available. The mathematical model is set up in parallel with the process and run (simulated) in real time (for example on a microcomputer). The known controlled variables acting on the process are supplied to the model as inputs. The class of model-aided techniques includes the Kalman filter and the Luenberger observer. These are similar in their fundamental structure, differing in that the Ralman filter is designed on a stochastic basis while Luenberger observers are designed by deterministic methods. The structure cited here applies to both continuous and batch processes.
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5. The Process Control System and Its Elements: Process Sensor Systems
Material and
Product and process states
Sensor system, Information processing actuator system system Figure 5.10. Schematic diagram of model-aided measurement procedure
In general, the properties of starting materials are interpreted as inputs, since they fix the starting conditions. They are therefore fed to the model as inputs, together with the controlled variables acting on the process actuators. It is tempting to use the model to reconstruct the unmeasurable part of the process state in view of all knowledge about the inputs. A variety of problems arise, however. Even if an exact model exists, the simulated process state may coincide with reality only when the initial conditions selected for the model are identical with those of the process. But the initial conditions of the unmeasurable state components are generally also unknown. Further, it must be assumed that unavoidable modeling errors and perturbations acting on the process will lead to increasing divergence between the actual and simulated state variables. Forcing convergence between the model and the process, regardless of the selected initial conditions, perturbations, and error effects, means taking corrective action in the model. This can be done by comparing the actual measured process and end-product properties with the same properties calculated from the simulated process state with sensor models. If the resultant difference signals are then amplified in a suitably designed correction element, the correction can be made in the process model itself. The chief problem is to design the correction element in such a way that the behavior of the model will converge to that of the real process as rapidly as possible. Figure 5.11 shows the structure of a modelaided measurement method in a simpler form.
Here, the product properties and the controlled variables of the process have been combined into a single multidimensional input vector. Similarly, the output vector includes all the measured properties of the process and of the end products. The block diagram clearly shows that the process model, via the measured output variables, is made to track the real process in the manner of a servo follower. The correction element, acting as a controller (closed-loop controller), compensates for differences in behavior between the real process and the model resulting from perturbations, modeling errors, and incorrect initial conditions. In the case of the Kalman filter, it is generally assumed that both process noise and measurement noise act on the plant [5.41], 15.531. Under the assumption of certain properties for this stochastic perturbing signal, the correction element is designed such as to minimize the expected value of a quadratic function of the difference between the actual and estimated process states. In the design of a Kalman filter, the a priori knowledge thus includes not only the process and sensor models but also models of the two stochastic processes. In the case of a Luenberger observer, in contrast, the design process is purely deterministic [5.42], [5.44]. For this concept of estimating or reconstructing the process state to work in a satisfactory manner, it must be insured that the process can be observed by means of the measurable quantities. In other words, all components of the process state manifest themselves in various ways in the time variation of the measured process and product properties. Criteria are available for in-
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5.1. Principles Materia\‘ and
Reconstructed product and process states
-
Reconstructed product properties (end product) Process properties Sensor system, actuator system
Information processing system
Figure 5.11. Structure of a model-aided measurement procedure
vestigating the observability of a process [5.41], [5.42]. Examples of applications in process engineering can be found in, for example, [5.54]. The discussion to this point has related to the determination of process and product properties in general; measurement and test methods employed in the laboratory, pilot plant, and industrial plant must meet some additional requirements. This chapter is exclusively concerned with measurement in the plant itself, or in an environment close to the process or similar to it. In the past, this subject was referred to as process metrology, or, when analytical questions were being emphasized, process analysis. The dividing line between these two fields is fluid, and often they are inseparable. It is therefore proposed that only the distinction between process-level (local) and remote sensor system technology be preserved, so that process sensor system technology becomes an appendage of the well-known field of laboratory metrology [5.55] in which special requirements have to be met. The computer processing of information from the sensor system changes the profile of requirements. In manual operation, measured values are commonly interpreted and processed by an expert plant operator. In this form of information processing the fault tolerance and error rate depend on the plant operator’s level of experience and knowledge. Computer processing imposes the following new requirements on the sensor system: Reliability at an acceptable maintenance cost
Functionality even in extraordinary process states (startup, shutdown, disturbances) Accuracy [5.56] and dependability These requirements result from advances in microelectronics that make computerized information processing a possibility. Microelectronics is also an important tool for the realization of these requirements. After optimization of the instrumentation and installation, the design of intelligent sensors with the ability to report faults and initiate maintenance procedures automatically is in many cases the most economical way to satisfy the requirement of reliability. The requirement of functionality even in extraordinary process states implies not only robustness but also a thorough understanding of what happens on in the process. Especially in sensor systems used to measure quantities that depend on the composition of the measured stream, many independent variabIes must be handled, and up to now it was only rarely the case that these were known for all possible process states. Measurement (from the physicist’s standpoint) becomes metrology (from the eingineer’s). It is not enough that a method works in principle; it is essential that it always functions effectively 15.571. The use of computers for information processing, however, makes it possible to acquire information more economically than before. In signal-oriented process control, the motto was : “Acquire information item by item, in parallel, and always,” whereas information-oriented pro-
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5. The Process Control System and Its Elements: Process Sensor Systems
cess control engineering follows the principle: “Acquire information in the smallest possible amount, but as much as necessary, and only then when it is to be used” I5.581. The advantage, especially in batch processes, is obvious. Large savings are possible in the wear-out reserve (see Section 11.4 and Figs. 11.61, 11.62) and thus in maintenance costs if, for example, a pH probe in a reaction vessel is brought into contact with the product only when the pH must be measured and otherwise is held in a protected standby condition (see Section 5.3 and [5.59]). The new requirement profile and the new principle of information acquisition have influenced not only instrumentation but also the interface between the sensor and information-processing systems (Fig. 5.1). Information coupling, for example to a process control system, is no longer limited to the transmission of a measured value. Sensor status messages can also be included (e.g., reports that a unit has failed or malfunctioned or that a maintenance procedure is under way), and it should also be possible for the sensor system to receive information from the process control system (e.g., an instruction to hold itself available; see Figs. 5.14-5.23). The same holds for actuator systems (see Section 6.1). The definition of the extended interface between the sensor and information-processing systems should also include the signal levels and types and the signal contents. Besides the change in emphasis as a result of computerized information processing, new measurement functions are being assigned to sensor technology (as they have been throughout the development of this technology). When existing processes, such as polymerizations, are automated and optimized, the need for information is reconsidered and new constraints applied, with the consequence that new measurement tasks are created or an old task is accomplished. The introduction of novel processes (e.g., biotechnology) involves new problems for sensor technology [5.60], [5.61] (see Section 5.3). The requirements that must be met in the further development of sensor technology for production processes fall into two independent classes (Fig. 5.12) ; The changed profile of requirements and the need for higher availability in computerized information processing mean that existing sensor systems must be enhanced. An example is the development of the magnetic induction flowme-
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Figure 5.12. Continuing development of sensor system technology
ter. In 20 years, the power consumption-a key factor in internal heat generation, service life, overall dimensions, etc. -has been reduced by a factor of 20 (Fig. 5.13) [5.62]. New problems require the development of new methods. Again, the realization of technical systems, often based on well-known methods of physics, is facilitated by the new capabilities of microelectronics and modern materials. Examples are systems using the Coriolis force to determine mass flow rate and others using the radar principle for level measurement.
5.2. Process Sensor System Technology Information Flow in Sensor Systems. Section 3.5 showed that it is desirable to separate the precise description of the technical properties of an individual instrument from information about its technical function (Fig. 3.26). This makes it possible to maintain required functionality even when instruments are replaced. Thus it is advantageous to apply the same formalism to information transformation (from the measured property of the material stream to the output signal) that was employed in structuring the material flow in production processes [5.7]. This leads to the idea of information flow in the sensor system, whereby a sharp distinction is made between the form taken by the desired information after each transformation step (corresponding to the product elements in the production process) and the metrological process steps required for information transformation (corresponding to the process elements of production).
5.2. Process Sensor System Technology
t
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Figure 5.13. Development of the magnetic induction flowmeter (MID) illustrated by power consumption over time [5.62] a) Introduction of switched d.c. field; b) Introduction of compact devices; c) Introduction of microprocessor control
As an example of a simple sensor system, Figure 5.14 illustrates the measurement of temperature with a jacketed thermocouple. The measured property E of the stream is coupled to an intermediate medium and thus transferred to the latter. The intermediate medium with property Z now interacts with the sensor element, which transforms Z into an electrical output signal S. The information flow between the process and a simple sensor system is illustrated, in abstract form, by Figure 5.15 [5.10], [5.63]. It is usually necessary to amplify and condition the electrical output signal of the sensor element (Fig. 5.16): The electrical output signal of the sensor element S“ is amplified to give a signal S’. This amplified signal S’ is linearized, mapped onto a unit interval, and made available as a signal S at the sensor system output. The term integrated sensor refers to a system in which the functions of coupling, interaction with sensor element, and signal conditioning are integrated in a single component. If signal processing is included, the term intelligent sensor is used, especially when signal processing involves complex calculations and range matching steps [5.64]. In a simple sensor system with compensation (Fig. 5.17), an additional sensor is needed to determine the parameter whose conditioned signal is incorporated into the signal processing. Auxiliary energy is generally required for this purpose; in abstract form, Figure 5.15 is elaborated to Figure 5.18. The energy needed for signal conditioning and signal processing is supplied to the sensor system by a power supply device that
Property of process stream
Coupling t o intermediate medium
Property o f intermediate medium
Interaction-with sensor element
I I
I Electrical output signal o f sensor element Figure 5.14. Information flow in sensor systems: Temperature measurement with a jacketed thermocouple
must be analyzed independently as far back as the design stage. Sensor systems in production processes are often highly complex in structure. Figure 5.19 shows the conventional representation, for the example of a gas analyzer [5.10]. Here, the measurement proper is preceded by the functions “sampling,” “sample transport,” and “sample preparation.” Sampling has the task of acquiring a representative sample of the stream to be measured. The sample must be transported without alteration or falsification to the analyzer, which performs the actual measurement. This stage of sample transport often involves conveying the sample over a long distance. The “sample preparation” function involves adapting the properties of the process stream at the sampling point to conditions com-
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5 . The Process Control System and lts Elements: Process Sensor Systems
Signal
-0
Figure 5.15. Information flow in a simple sensor system [5.10]
patible with the measuring instrument itself. The measured property of the stream -for example, the concentrations of certain components in the stream-must naturally be preserved in a welldefined way. The planning and execution of sampling, sample transport, and sample preparation are strongly dependent on the properties of the stream material, the analytical objective, and the measuring method employed. This strong application dependence is the reason that sensor systems for determining composition-dependent quantities are complex in structure and must be serviced by specialists. Along with the sensor system modules just described, Figure 5.20 also shows the functions “delivery” and “disposal.” The disposal step matches the properties of the sample stream at the analyzer output to the permissible conditions at the point of return to the stream. The delivery function modifies the auxiliary energies and substances such that they can be utilized in the sensor system. Both these functions must be included in the diagram of the entire system because the correctness of the measurement also depends on them and because they are potential sources of error for the sensor system as a whole. On grounds of economy, it may be desirable to withdraw samples at various points in the process and measure them with a single analyzer. The functions of sampling, preparation, and transport are then preceded by a measuring point selection step (Fig. 5.21). In combination with signal processing for each sampling point, this function correlates the analyzer output signals (S, to S,) with the properties to be determined (El to Em).Conversely, several analyzers
can determine a variety of values on a single sample by accessing the same sample preparation. As in the case of the production process, monitoring and control systems must also be available for complex sensor systems. All complex sensor systems include for this purpose auxiliary devices such as displays, pressure gauges, flowmeters, or valves for the admission of calibration substances. In more abstract terms : the background functions grouped together as “functional check, control of maintenance procedures, and setting of parameters” must be supported as a minimum [5.63]. These functions are performed manually in conventional sensor systems. At intervals specified by the maintenance strategy. the system is inspected and serviced (e.g., calibrated) by maintenance personnel. However, analogous to the production process, this routine activity is increasingly being automated (Fig. 5.21) and turned over to a dedicated computer system that monitors all the basic functions of the system, controls procedures, and executes corrective actions. For this, the dedicated computer requires system-internal sensors and actuators. The computer furnishes status information about the sensor system and can execute incoming instructions, for example, to come into action in an intermittent mode (on-request or on-event). The outcome is a general structure for complex sensor systems. The information blocks K and S are the basis of the protocols for the future field bus. “Intelligence” and Sensor Systems. In an intelligent sensor system, the functions discussed above are integrated or are supported in such a
P r o p e r t y o f process s t r e a m
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5.2. Process Sensor System Technology Properties of process s t r e a m
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Conditioned signal
Output signal
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Figure 5.17. Information flow in sensor systems: sensor system with compensation (e.g., pH measurement with temperature compensation)
Output signal
--_-__ - Integrated
-_
sensor Intelligent sensor
Figure 5.16. Information flow in sensor systems: temperature measurement with jacketed thermocouple and signal processing
way that it can be regarded as a sensor system with communication capabilities. In Figure 5.22, these links are provided by a field bus, whose function could equally well be carried out by the use of smart technology based on the HART protocol. In what follows, the functions of sensor systems are described as classical or intelligent. Classical functions include: 0
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Sensing functions, i.e., transformation of physical and chemical effects into a signal suitable for downstream processing Signal conditioning (e.g., amplification) Signal output (part of the signal processing, such as A/D conversion, takes place in the local or process-level components)
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Intelligent functions include: 0 0 0 0 0
Signal processing Interconnecting measurements to expand information Auxiliary functions for one-line metrology Automatic execution of maintenance functions Failure strategies (see Fig. 5.16)
Figure 5.22 illustrates classical and intelligent sensor systems and their incorporation in higher-order information-processing systems. The use of a field multiplexer for the connection to the process monitoring and control system bus, which is already a practicable transition approach (even in explosive environments); see Section 5.5. The intelligent functions of a sensor system can be employed in a five-point program: -
Making all the needed information available In a form suited to the application At the proper time Ready for downstream processing Even in extraordinary process states
The goal of this development is to devise autonomous field-level devices that will automatically perform functions such as the maintenance
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A
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Figure 5.18. Information flow in a sensor system with auxiliary energy and signal processing [5.10]
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Preparation
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Figure 5.19. Functional groups of a process analyzer (gas analyzer) [5.10]
functions of DIN 31 051 [5.66] discussed above. Intelligent process pH sensor systems can already carry out functions such as electrode cleaning, zeroing, calibration, flushing, and so forth, either “on-event’’ or “on-request.’’ Communicationwith Process Sensor Systems. Consideration of the functional scope of complex sensor systems leads to the question of how the sensor system can appropriately communicate with information-processing systems. The output signal categories M (messages), B (instructions), and S (signals) are the information blocks involved in communications (K) with
process control and automation systems. A straight point-to-point connection with communication in one direction does not meet modern requirements. The need for bidirectional, open communications structures, and messaging services is essentially derived from the functions discussed above. More detailed information can be obtained from the literature on field buses I5.671, [5.68]. The connection of a classical sensor system to a local component (LC) is a unidirectional link employing a uniform signal such as 0-20 or 420 mA (Fig. 5.22). All that is transmitted is the measurement signal or a report of a device failure. Signal processing, that is, conversion to a value with a physical unit, takes place in the LC. Maintenance tasks must be performed locally in the sensor system. According to DIN 31 051 [5.66], these include inspection to evaluate the actual status, maintenance to preserve the nominal status, and repairs to restore the sensor system to nominal status. General discussion of these points can be found in Section 11.4. If a dedicated computer performs the function monitoring of the intelligent measuring system and carries out simple maintenance procedures, using messages to report on both activities, the process sensor system acquires, functionally, a second interface (in addition to information delivery to the control room via the “process port”), which mediates information exchange with the engineering team via the “engineering port.” This functional distinction is illustrated in Figure 5.23. Given the wide range of methods employed in process sensor system technology and the
5.2. Process Sensor System Technology
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for information exchange between the field level and the process monitoring and control system: “As little as possible-as much as necessary” [5.58]. This requirement governs the functional sensor interfaces shown in Figure 5.23 and is discussed further in Figure 5.24 in application to the process port. In relation to local (process-level) functions, the sensor system has only one task: making the measured value available in proper form. If the sensor system ceases to respond as specified because of an upset of malfunction, this fact should be reported in a group message “Failure” (Mi),
broad spectrum of application-specific hardware configurations, the only way to achieve systemwide reliability and effective maintenance is by unifying the two interfaces, process port and engineering port. A flexible dedicated computer system, matched to the structure of complex process sensor systems, can organize the required information exchange from and to the sensor system. In this way, expensive custom solutions can be avoided and replaced by acceptable, standard solutions. Information Exchange with Local Functional Units. According to SCHMITT the following holds
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Figure 5.20. Information flow in a sensor system with sampling and other auxiliary functions (disposal, delivery, etc.) [5.65]
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Figure 5.21. Information flow in a sensor system with sampling, auxiliary functions, and integiated dedicated computer [5.65]
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5. The Process Control System and Its Elements: Process Sensor Systems
signals may appear inomeiitarily that are correct in content but have nothing to d o with the process and product properties being measured. This state is indicated by the status message “function monitoring” (M?). Because of the absolute priority of the information measurement signal, it may be necessary to permit such nonavailability due to internal programs in the sensor system only when they can be tolerated by
which is to be forwarded directly to the display and control component of the control system (see Chapter 7). Any still-pending measurements are then rejected as unusable. This assessment of sensor information presupposes that the sensor system monitors its own internal technical functions. If internal sensor function monitoring is initiated automatically or manually, measurement
IEngineering(
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”Server“ “CLient“ Figure 5.23. Functional breakdown of the sensor interface K (communications; see Fig. 5.21) into “process port’’ and “engineering port” [5.10]
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Figure 5.24. Information exchange between sensor system and local process control system component (LC) [5.10]
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the control system. An instruction “release” (B,) from the process control system to the sensor system is a suitable way of handling this situation; for example, the instruction can inhibit or enable the calibration procedure. The power and low cost of modern microprocessor boards make it possible to use them locally to optimize internal operations in sensor systems without having to shift such functions to central systems. In this way, straightforward system structures can be created. For the sake of manageability, information exchange with process-level functional units of the process monitoring system should be restricted to the measurement signal plus a few status messages and instructions. The handling instructions and procedures associated with each type of status message or instruction should be defined as a strateg y in the process control system (for status messages) or in the sensor system (for instructions). Information Exchange with Engineering Functional Units.The maintenance functions have the task of keeping up reliability and insuring availability. Routine inspection and function monitoring jobs should be automated and turned over to the sensor system itself whenever technically possible at reasonable cost. When service is needed or when a malfunction occurs, the engineering group is requested. From a functional standpoint, access to the sensor system and necessary information exchange take place over a separate interface, labeled “engineering port” in Figure 5.23. If a process sensor system is fully equipped with self-monitoring functions and devices to maintain the functionality of both the instrument proper and the sample preparation part, the volume of information produced is difficult to manage without structuring. Engineering be-
@ @
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interactive via DCC Fault analysis (failure) Requirement analysis (maintenance) Design Retrospective documentation
Figure 5.25. Structuring of the interface to engineering [5.10] DCC = display and control component; EN = Engineering
comes inefficient and personnel find themselves overloaded if the separate functional concept and distinct operator interface of each instrument type and each manufacturer have to be kept in view. The capabilities of future field-level communications are discussed here, based on analyses of information structuring for process analyzers [5.10].
For efficiency in engineering, it is proposed that information exchange (see Fig. 5.25) be accomplished in three functionally distinct information “planes” differing in complexity. A status summary is needed for the instrument proper; this includes a group message “failure” when the measuring device is no longer reliably
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5 . The Process Control System and Its Elements: Process Sensor Systems
available; a status message “function monitoring” when the sensor system is occupied with itself as a result of a manual intervention or automatic program; and the display “measurement” so that the engineer can make an on-site assessment. If the sensor system is equipped with selfmonitoring functions that can detect a failure (for example due to drift in the analyzer or exhaustion of the test gas supply), the status message “maintenance required” should also be displayed. The inspection, maintenance, and repair of process sensor systems need not always involve specialists. It appears useful to be able to distinguish whether a given malfunction can be remedied by simply replenishing auxiliary substances or replacing an electronic component, or whether the device can be put in service again only after a specialist works on the sample handling system or the physical part of the instrument. For this purpose, the first engineering (EN) plane includes simple, binary diagnostic indicators. The “failure” message may, for example, be broken down into just three categories: M,, if there are problems with the delivery of auxiliary supplies or energy; MI, if there are problems in the measuring section, including sample preparation; and MI, if a failure occurs in the electronics. For general maintenance, simple instructions such as manual initiation of a function check should be integrated into this EN plane. This initial diagnostic feature requires communications with the sensor system above and beyond a simple status indication. These communications are carried on through a display and control component near the sensor level. The display and control component can take the form of a keypad with display integrated into the sensor system; an engineering computer some way off; or a portable unit such as the handheld monitor shown in Figure 5.22, which is plugged into the sensor. As to what technical and functional capabilities exist and how they can be exploited, see [5.67], r5.691. Detailed diagnosis of the causes of malfunctions, which can only be performed by process sensor specialists, should be carried out in a second engineering plane, via one of the functional units. This kind of diagnostics includes singlefault analysis upon a system failure, as well as detailed need analysis upon a maintenance request. The design and commissioning of complex
process sensor systems often include configuration and parametrization. These tasks are also performed by specialist personnel and thus form part of the second EN plane. For the manageability of process sensor systems, it is important that the system data be kept updated and that retrospective documentation be maintained. When such a ternary structure is used iii the EN interface, standardization across vendors and instruments appears possible at least for the status summary and the first EN plane [5.70]. For efficient maintenance, it would also be desirable if information exchange through the second plane could be structured in a consistent way and mediated by operator interfaces as similar as possible. The “sensor-level display and control component” function is increasingly being implemented with a portable computer (laptop PC). If a standardized operating system and graphics environment are employed, vendor- and devicespecific parametrization and diagnostic routines can be loaded and run in software. For process analyzer technology at least, such a consistent engineering tool offers substantially greater benefits than a variety of individual solutions with their associated library of manuals (see also [5.711). The communication ports of field-level devices should generally be implemented on the basis of the object-oriented model [5.72] defined by the MMS (Manufacturing Message Specification; see Chapter 8 and [5.73]. [5.74]). The field device plays the role of server and performs the services requested by the client; see Figure 5.26. Linked with the cited background functions and their use for process control, maintenance, and design (configuration) tasks is the introduction of general, standardized descriptive languages for field-level devices; these are called Device Description Languages (DDLs). The objective of manufacturer-neutral field-level functions is that the field device to be operated should be addressable without any special knowledge of the physical device. even if devices are modified or replaced by hitherto unknown devices. Standardization efforts toward a universal device description are currently focused on creating a proposal for the ISA SP 50 international field bus standard [5.67], [5.75]. Furthermore, it is not sufficient to solve the problems of field-level communications without taking up questions of what user interfaces are
5.2. Process Sensor System Technology Probe method
Status Position. medium Position, calibration
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All objects logically or functionally belonging together Variable Inumeric, symbolic,...l
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(Fig. 5.27). From the categories and attributes of the process property (here the wastewater pH), the property can be methodically assigned to a suitable sensor system. A CAE tool for this purpose, accessing a source database for sensor-actuator systems, is discussed below. Figure5.28 shows an example of how to structure a taxonomy of sensors for process engineering. The user is guided by generic attributes: application features and design features. For the wastewater pH example mentioned above, a sensor system can thus be defined that has the application features on-line measurement, determination of a collective property, measurement in the product stream, and so forth. In general, the requisite instrumentation cost can thus be determined from the information demand obtained from the phase model. Sensor or actuator systems are selected on the basis of the category of process and product properties, with the aid of a taxonomy of such systems (navigation, hypermedia, etc.). Manufacturers still offer monogramlike selection aids [5.77], which will certainly be replaced soon by expert-systems tools [5.150].
Service: s t a r t , s t o p,...
Figure 5.26. MMS communications objects
appropriate for sensor systems and how these interfaces can be standardized [5.71], [5.9]. The application connected above the communications services must be dealt with at the same time. On practical grounds, a variety of devices having the same function must not have unlike operator interfaces [5.67], [5.76]. Moreover, the rapid growth in information demand and the need for auxiliary energy in process sensor systems-which may well exceed the permissible limits in intrinsically safe systems-have made it essential to consider separating the information and energy channels (see Section 5.5, Chapter 8, and [5.68]). Determination of Instrumentation Cost. Given the information economy of a process modeled with the phase model (see Figs. 5.1 and 2.35), the instrumentation outlay can be determined at any level of detail (see Chapter 2). Consider, for example, the process step of neutralization in a wastewater treatment process. Among the quantities to be determined are the pH and the inlet flow rate of the wastewater
Taxonomy of Sensor Systems. The classical listing in book form, arranged by apparatus features, is no longer adequate for presenting an up-to-date survey of application options for modern sensor systems. Nevertheless, such proven classics as Profos [5.78], Strohrmann [5.79], Hengstenberg [5.80], or the Kohlrausch [5.81] are indispensable aids. The journals TM, atp, and MPA have presented thorough surveys of problems and current solutions (see also [5.9], [5.82]-[5.84]). However, in general, encyclopedic treatment must be replaced by structuring. A priority task for structuring available sensor systems in a way relevant to process control engineering is to emphasize those features that actually bear on potential applications. As discussed previously, these features cannot be identified without a knowledge of process conditions met with in practice; that is, an apparatus-oriented analysis alone is insufficient. Instead, the conceptualizations introduced to structure information characterizing a production process lead to a powerful technique. In future, this approach may serve as a basis for developing powerful CAE tools for the systematic instrumentation of new plants. Figure 2.8 shows that, if the distinction between function and substance-or function,
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5 . The Process Control System and Its Elements: Process Sensor Systems
function-generating procedures, and devices (Fig. 3.26)-is taken into account, then the world of sensor systems can first be represented in terms of the tasks meaningful in certain industries; see Table 5.1 [5.85] and the lower row of Figure 5.29. The E-R (entity-relationship) modeling technique described in Chapter 2 is employed, at
various levels of hierarchical detail, for this purpose. Such an ordering rule, as explained in Chapter 2, is known as a “taxonomy” (Greek: taxis = tree). Generic Clnss: Application Fentirres. The generic class of application features or task features, defined from the functional viewpoint, becomes the upperniost level in this context.
Figure 5.27. Determination of instrumentation costs
Figure 5.28. Taxonomy of process sensor systems: System properties required for determination of pH and BOD,
5.2. Process Sensor System Technology
The following are employed as classification criteria: 0 0 0
The attributes of the object classes of technical processes The degree of indirectness with which the measurement signal is determined The ratio of relaxation times between the acquisition of the measurement signal and the variations of the property of the stream being measured (see Section 5.1)
Table 5.1. Letter symbols for process control point functions [5.85] Letter Group 1 : measured values and symbol other input quantities As first letter
A C D E
F I
L P
Q R T
As additional
letter)
letter
alarm density all electric variables flow rate
Group 2: processing (as
difference ratio
level pressure or vacuum quality, e.g.. integrate analysis, conor totalize centration, conductivity nuclear radiation temverature
controlling sensing element indicating low test point connection integrating or summating recording transmitting
0
135
Specification of the components of the substance measured
Under these criteria, sensor systems are now classified according to whether they determine product properties or process properties, whether they operate on-line or off-line, whether they are employed in-line or ex-line, whether they can determine collective or specific properties, and whether they are usable in homogeneous or heterogeneous phases. These generic features are explained briefly below: - On-line: A continuous correlation is possible between information provided and properties of the process or product. The essential condition for on-line measurement is that the relaxation time of changes in the process or product properties must be markedly longer than the time required to acquire, convert, and deliver the information [7R(property) 9 7R(sensor)] (see also Section 5.1). - Off-line : Discontinuous measurement ; determination of sampling intervals requires knowledge of process characteristics (e.g., kinetics). This pair of attributes describes the problems the (process) sensor system must handle in terins of communications. -
In-line: The systems used to prepare information about the process or product properties
Generic class: application feature
/
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\
\ Generic class: design feature
(GiLzG)(Oesigntype)(m)(Material)@(-] /I\ /I\ A A\ /I\ I
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Figure 5.29. Taxonomy of process sensor systems: Breakdown of sensor functions
A
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Sensor function
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S. The Process Control System and Its Elements: Process Sensor Systems
interact directly with the product stream (or a bypass stream). - Ex-line: The required sampling operation (extractive) makes it necessary to take into account additional changes in the properties of the stream substance. Questions of positional and temporal representativeness are extremely important here. This pair describes the problems the system must deal with in terms of metrology. - Collective:
~
The quantity measured can provide information about the quality of the stream as a whole (e.g., density or flow rate), its reactions (e.g., pH, conductivity), and the relationships between its properties (e.g., relative concentration). Specific: The “manifestation of a property” determined makes it possible to gain qualitative and quantitative information about individual components of the stream; such techniques include analytical methods such as spectroscopy and chromatography.
This pair thus states clearly whether the chosen sensor system yields information about atomic/molecular, mechanical, and electromagnetic mechanisms. -
-
Homogeneous: The sensor system can acquire information only in homogeneous phases (e.g., flow-rate measurement with a mass-flow anemometer). Heterogeneous: The sensor system permits the determination of the measured quantity even in heterogeneous states of aggregation, such as dispersions (e.g., flow-rate measurement by the Coriolis principle).
This pair accordingly requires an unambiguous description of the sensor system functionality with regard to its possible use in multi-phase environments. Generic Class: Design Features. The next level consists of a further class of features which basically summarize information about the design of the sensor systems under consideration. Sensor systems are classified by communications capability, design type, utilities required, material of construction, range of measurement, and manufacturer. Individual features can be further structured. The dependent lines in Figures 5.29 and 5.30 illustrate this point. In principle, the depth of structuring is unlimited.
To illustrate characteristic features o f importance in field-level devices, the safety requirements 011 process sensor systems are discussed at this point. If “safety’% defined as a risk less than the limiting risk t5.241, as in Section 5.1, safetyrelevant properties of a process sensor system can be divided into two classes with regard to possible sources of danger: 0 0
Properties related to insuring the availability and reliability of the sensor system itself Properties relating to reducing or preventing danger interactions of the sensor system with the system environment
These contributions to safety are correlated with possible technical safeguards in Figure 5.31. The potential dangers due to interactions with the environment are divided into those that require protection o f the sensor system against the environment, and those requiring (possibly indirect) protection of the environment against the effects of the system. As Figure 5.31 shows, present-day principles, guidelines, and standards on insuring the safety of process control hardware can be classified in the same way: From a safety standpoint, enhanced availability and reliability gained through fault detection, fault reporting, fault compensation, and automatic calibration figure in the tendency toward increased functionality in field systems through integration o f information-processing components, as discussed in this section. An initial and crucial requirement on the safety features of a process sensor system will be the ability of the system to verify and, if appropriate, report its complete functionality. The protection of live or moving parts of an electrical device against foreign solid bodies or liquids is required by all national and international standards, for example. [5.86].Because they are represented by the letter code IP (International Protection), these provisions are also referred to as IP protection types. A topic still under vigorous discussion, in contrast, is how to insure electromagnetic (EM) compatibility. In German and European standards, electromagnetic compatibility is defiued as the ability of an electrical device to function satisfactorily in its environment without unduly influencing this environment [5.87], [5.88]. In Figure 5.3 1. measures to insure electromagnetic compatibility are divided into safeguards against EM interference induced in the device (static dis-
5.2. Process Sensor System Technology
137
Figure 5.30. Taxonomy of process sensor systems: breakdown of sensor principles for the example of temperature measurement
charges, electromagnetic fields, and fast transients) [5.89] and safeguards against EM emissions into the environment, in particular human exposure, due to fields generated by the device or system [5.90]. Another type of protection incorporated into the field-level device is explosion protection [5.91]. The avoidance of flammable materials and explosive mixtures are usually the primary safeguards against explosions [5.79], [5.92]. With regard to requirements on field-level systems, these practices are of relevance to design and material selection. Primary explosion safeguards must also be considered when analyzing the use of sensor systems, especially the use of process control resources for plant security [5.22]. This area of applications includes all process control hardware employed for monitoring, protection, and damage limitation [5.93]; see also Section 10.4. Secondary explosion protection in field-level process cotnrol systems includes all measures taken to prevent ignition of explosive substances by preventing energy transfer. The regulation governing electrical devices in explosion hazard
areas distinguishes seven types of protection. Among these, intrinsic safety [5.94] sets rigorous criteria on integrated circuits in modern sensor systems (see Section 5.5). For redundant and fail-safe design, see Section 10.4. Lightning protection practices are generally based on suppressor circuits [5.95]. The lowermost level is concerned with the physical, chemical, and other principles on which sensors operate. The commonly used nomenclature is set forth in DIN 19227. Figures 5.29 and 5.30 show a selection of possible sensor principles; all others are indicated by the dependent lines. As in the case of the design features, further structuring is possible. The foregoing application-oriented description can serve as a basis for a conceptual, objectoriented classification of sensor systems, differing sharply from the device-oriented and method-oriented structures, as listed below. Methods of measuring “classical” quantities: Temperature Resistance thermometer Thermocouple Radiative pyrometer
138
5. The Process Control System and Its Elements: Process Sensor Systems
Fully from environment
Fault detection
Calibration
enclosure
emissions
Fault r e p o r t protection Fault ;ompensation
Electrohagnetic interference
Figure 5.31. Safety requirements on process sensor systems
Pressure Elastic elements with capacitive, inductive, piezoresistive or piezoelectric signal conversion Flow rate Magnetic-inductive Differential Level Hydrostatic Electrical and electronic Radiometric pressure
Nonspecific methods: Density Radiometric Other Viscometry Thermal conductivity Optical scattering and extinction Acoustic
Group-specific methods: Electrical conductivity Flame ionization Photoionization Methods based on group-specific auxiliary reaction Calorimetry
Specific methods: Paramagnetism Photometry Absorption (UV, VIS, NIR, IR) Diffuse reflectance Gas chromatography Liquid chromatography Mass spectrometry Electrochemical methods Potentiometry Amperometry Titration
Analytical methods based on auxiliary reaction Chemoluminescence Absorption in aerosols or liquids Special moisture measuring techniques
The concept presented here makes it possible to create flexible CAE tools, which will gain increasing importance in the design of process monitoring and control systems. For example, interactive and iterative equipment selection by the design engineer is aided by computer-generated querying of the features introduced here [5.96]. There is no need of once-for-all solutions or a priori optimization criteria. Hypermedia concepts can serve as navigation or explanatory aids [5.97]. Determination of Communications Costs. Field-level communications systems (i.e., field bus systems) will be essential components of a networked system. The design of future field bus systems will be based on the information econo my derived from the phase model (Fig. 5.32). A suitable field bus system will be chosen on the basis of a taxonomy, here the taxonomy of field bus systems. Typical message lengths for sensors and acuators, as well as maximum cycle times derived from the process and product properties, are balanced in order to arrive at the communications costs for the system as a whole. The number of field bus systems and the number of segments in these systems are set on this basis. The result is a
5.2. Process Sensor System Technology
139
Figure 5.32. Determination of communications costs
picture of a CAE tool for the information-oriented design of communications systems [5.98]. The advantages of information modeling dictated by the process are again manifest here. A proposal for a taxonomy of field bus systems is shown in Figure 5.33 [5.98]. The generic attributes are architectural features and the communications structure. Monomaster systems have a pure master/slave structure with strictly hierarchical vertical communications between planes. Multimaster or peer-to-peer systems, in contrast, permit horizontal communications within a plane. For the wastewater treatment example of Figure 5.27, both monomaster structures within a plant section and multimaster structures for communications between plant sections are possible. Communications between local components and sensors (e.g., pH and flow sensors) within a plant section might be based on the PROFIBUS or the DIN measuring bus, while communications between plant sections and with higher-level central functions might be implemented on the basis of PROFIBUS.
General Requirements on Process Sensor Systems. A complete introduction to process sensor system technology would also cover selectivities, detection thresholds, time dependences, sensitivities, stabilities, and so forth, all qualities that have to be defined for each sensor system [5.63],
[5.99]. These are technical specifications that differ from one measurement task to another because of differences in service conditions, products, perturbations, and so forth. The profile of requirements on sensor systems is strongly influenced by the downstream computer processing of the information : - In-line and ex-line sensor systems must not disturb the process. - It is essential to separate the energy channel from the information channel. - Communications capabilities and system capabilities must be insured. - Electromagnetic compatibility is also required; in some cases, external constraints dictate “intrinsic safety” as well [5.7]. See the generic class “Design Features.” - Special conditions in chemical production (corrosion, abrasion, etc.) must be taken into account when choosing the sensor principle and construction material. - One special requirement for in-line sensor systems is that they must remain functional even under extraordinary process conditions (e.g., upon a change in the state of aggregation).
Economic Aspects. From the standpoint of the process control engineer who designs, commissions, and maintains a sensor system, meeting the stated requirements is closely linked to
140
5. The Process Control System and Its Elements: Process Sensor Systems
Figure 5.33. Taxonomy of field bus systems [5.98]
the lifetime costs of the sensor system. The term “cost of ownership” expresses the fact that the key criteria include not only the technical specifications and the resulting investment costs, but also longer-term aspects of sensor system use, such as training and maintenance costs. As discussed in Section 2.1, a holistic approach [5.100] must be taken from the outset. In general, however, the main stress should be on the need for a process control station and a process sensor system. Total costs for a process monitoring and control system are largely determined by this sum. What is decisive is not the initial cost of devices and systems; the field installation costs make up a respectable fraction of total costs (see Section 10.2 and Fig. 10.12). Finally, the costs of control system components depends in part on the number of process control stations. Often a fear of safety aspects and regulations leads to an exessive number of process control stations being set without a careful and critical analysis of the necessary equipment availability (Section 10.4). Inadequate knowledge about the process at the time when costs are determined can, on the
other hand, result in too few process control stations or incorrect equipment selection. This problem often cannot be corrected until commissioning, by which time costs are very high. The departures from the original design may give nonspecialists the impression of “overengineering,” especially if process control engineering resources must be used to correct solutions that are nonoptimal to poor from the process engineering standpoint. Overengineering due to ignorance is the most common cause of this phenomenon. It is therefore essential that some tool such as the phase model of Figures 2.35 and 2.36 be employed at the bidding specification stage, in order to take full account of all information needed for the production process.
5.3. Sensor Systems for Special Applications In accordance with HESSE’S suggestion [5.15] to put systems concepts at the beginning, Section 5.1 deals with systems integration of sensors or sensor systems, while Section 5.2 discusses
5.3. Sensor Systems for Special Applications
process sensor systems from the standpoint of internal information flows and the taxonomies derived therefrom. Nevertheless, it is necessary to descend from the lofty Olympus of informatics to the lowlands of the reality that must be dealt with at a pragmatic level. Sensor systems behave very differeqtly in the laboratory and the plant (see Section 5.2). The tasks they must perform, and therefore the requirements they have to meet, vary from one application to another. Sensor Systems in Manufacturing [5.101]. For example, a sensor system in a manufacturing industry must provide an industrial robot with information about its surroundings that are relevant to the operation but not predictable. The robot must be able to react to this information in such a way that the desired result is achieved or maintained automatically. This is why a variety of sensors are employed to determine physical quantities and states, transform them into (generally) electrical signals, process them, and interpret them. As a rule, information transmitted to the robot influences
Figure 5.34. Taxonomy of sensors for manufacturing
0 0
0
141
The position and orientation of the robot’s manipulator or tool The speed at which it moves The execution of the program
In rough terms, sensors of interest for the field of industrial robots can be classified into 0 0
Tactile sensors Noncontact sensors
Another principle of classification is the complexity of the information acquired, allowing sensors to be grouped into 0 0
0 0
Sensors that transmit binary signals Sensors that transmit one-dimensional measurements (forces in a preferred direction, distances) Sensors that transmit multidimensional measurements (forces of arbitrary orientation) Sensors capable of pattern recognition (workpiece identification)
Figure 5.34 shows a taxonomy based on these criteria. Under each of the categories cited, a number of functional principles can be employed, depending on the prevailing boundary conditions (Fig. 5.35).
142
5. The Process Control System and Its Elements: Process Sensor Systems
Figure 5.35. Distance measuring systems for industrial robots [5.101]
The linking of robots to image-processing, pattern recognizing sensor systems and to tactile, multidimensional force and moment sensors is an especially urgent problem for future robotics applications. Important future applications that can be exploited only with the aid of powerful sensors for industrial robots include 0 0 0 0
The machining of workpieces Assembly Emptying of containers Continuous joining operations such as welding and seam cementing
The emptying of containers is a typical application for imaging sensors, which have the task of identifying workpieces in the container, measuring their orientations and positions, and providing the industrial robot with the information required to grasp the workpieces correctly. The areas of machining and assembly are important applications for tactile sensors. One task that illustrates significant problems and requirements in connection with industrial robots is machining with tolerances specific to the tool, the workpiece, and the robot. If the tolerances and deviations exceed a certain value, it is no longer possible to achieve sat-
isfactory machining results within a series of workpieces. An optimally designed, and therefore workpiece-variable, control of the machining process is only possible if the most important toleranced quantities are determined by sensors and the process is automatically optimized. SCHWEIZER and WARNECKE distinguish two groups of variables: technological variables oriented to the machining process, such as power consumption and cutting forces, and geometrical variables such as tool wear, geometrical and clamping tolerances, and surface quality. The fundamental problem in such tasks lies in the high data rate and the mathematically demanding simulation process. Adaptation to a variety of tasks also complicates this application, which most commonly involves highly dedicated sensor systems. Artificial sensors cannot offer the capabilities and flexibility of human senses, but they can reliably perform single functions in manufacturing. A survey of current approaches in the field of quality assurance through production metrology can be found in [5.102]. In assessing future sensor technology, it is useful to compare biological and technical sensor systems [5.13].
5.3. Sensor Systems for Special Applications
In many areas technical sensors can be made substantially more powerful than biological ones. Furthermore, the spectrum of technical sensors is broader; that is, there are technical sensors for a wider variety of physical variables than nature has provided for among biological sensors. For example, technical sensors exist that can detect high- and low-frequency electromagnetic waves, electrical currents, and electric field strengths. With regard to preprocessing by the sensor system, however, nature is far superior to artifice. Technical sensor systems at present are designed chiefly to detect events in preplanned technical processes. Accordingly, they can be more highly specialized for the expected events. Thus the knowledge that a foreseeable process event has taken place often comes solely from the fact that the sensor has responded. Scarcely any preprocessing is needed; the sensor “speaks for itself.” By the same token, however, the system loses flexibility with respect to unexpected events. And this flexibility is particularly important for biological sensor systems, under some circumstances more important than high performance of individual sensors. Biological systems are designed to be more universal, while technical systems are more specialized. This notion is expressed in the term dedicated systems. Most biological sensor systems are more “intelligently” constructed than artificial systems: Preprocessing takes place in amazingly powerful processing systems, with the result that vastly many individual items of information acquired by many identical sensors are reduced to summary information in a process of “super-sign” formation. It is hardly possible to imagine the demand imposed on the processing system, not just by “concept-forming’’ logic but also by the coordination of the many sensors in arrays. In the eye, for example, the sensor array of the retina comprises millions of cone and rod cells. Nature again and again offers a very impressive demonstration to technology, showing how flexibly such array systems can react to unforeseen processes of many kinds. The array principle is now being increasingly used in technical systems, specifically where sensors and information-processing systems, given the proper degree of miniaturization and economy, can be integrated to form “intelligent” systems. This incipient symbiosis between electronic sensor technology and the microcomputer is opening up new dimensions for an innovative potential that has scarcely been tapped before [5.13].
143
Sensor Systems in Medical Technology [5.103]. Sensor systems used in medical diagnosis and monitoring must meet different requirements. The physician, in examining a patient, used to be dependent on what the patient and his relatives said, the physician’s own experience, and above all his senses. Today, medical technology, developing in parallel with general technology, offers valuable help to the physician. The range of his sensory perceptions, in particular, has been hugely expanded by technical resources, sensors (i.e., technical sensory organs) playing an essential role. Milestones in this development include the discovery of Xrays by ROENTGEN in 1896 and the recording of a electrocardiogram by EINTHOVEN in 1902. Procedures based on these techniques remain the cornerstones of clinical and practical diagnosis today. The principal objective in the use of medical instruments is to support the physician in administering the optimal treatment to the patient. Important narrower objectives in diagnosis and monitoring are to acquire reliable diagnostic information, place the lightest possible burden on the patient, and hold the user’s costs and efforts within reasonable limits. Three areas of medical technology are especially dependent on sensors : imaging techniques [5.104], physiological measurements [5.105], [5.106], and laboratory analytical techniques. In imaging and physiological measurement, the sensor is closely linked to the patient (Fig. 5.36); application aids or transmission media are also employed in many cases. Only sensors and sensor systems with electrical output signals offer the possibility of instrumental processing, representation, and storage of information. A wide range of methods, drawn from a wide range of sciences, are available for the conditioning and interpretation of these signals; see, for example, the approach to dynamic entropy analysis in [5.107], [5.108]. Nonelectrical sensors that are still of major practical importance include X ray film and mercury thermometers and manometers. The quality of the signals delivered by the sensor has a substantial influence on the value of the technical system as a whole. Often, no additional useful information can be derived from a poor output signal, no matter how extensive the post-processing. This consideration alone means that the effort of developing and maintaining sensors must be continued along with the work of applying them.
144
5 . The Process Control System and Its Elements: Process Sensor Systems
I I I 4
-Signal flow, ----- Reaction,
Therapy unit
Physician, nurse
Patient
action intervention
Figure 5.36. Diagnosis and therapy with medical devices (5.1031
Zmaging Techniques. Medical imaging techniques can reveal information that is not directly accessible to the human eye, either because of the nature and frequency of the radiation or because of the geometry of the body (Table 5.2). Except for thermography, these are active procedures; that is, the measurement is based on energy introduced into the body in the form of radiation or radiation-emitting substances. Additional contrast-enhancing substances are often applied as well. Crucial advances with regard to exposure time and image quality in some techniques have been achieved by using arrays (with many detectors working in parallel) instead of discrete detectors. Nuclear medicine employs as many as 60 photomultipliers; X-ray computer tomography (CT), over 2000 detectors; and ultrasonic examination methods, up to 200 piezoelements. More recently, nuclear magnetic resonance (NMR) tomography has become an established technique. It has the advantage of using strong magnetic and high-frequency fields instead of ionizing radiation, so that the patient avoids the risks associated with radiation exposure. Significant results can be anticipated from positron emission tomography (PET), which can be regarded as a symbiosis of nuclear medicine and computer tomography. This technique permits not only the three-dimensional determination of blood circulation in the brain, but also quantitative studies of metabolism. Together with single-photon emission computer tomogra-
Table 5.2. Sensors for imaging procedures in medical technology [5.103] Imaging procedure
Sensor
Radiation
Nuclear medicine
scintillation crystal (NaI) + photomultiplier
11-rays (radioisotope sources)
Classical x-ray (transmission image)
film + intensifier film (CaWO,) image intensifier storage film (Ba halides)
X rays
X rays X-ray computerized discrete scintillators (Csl) tomography, NMR tomography, gas detectors (Xe) semiconductors PET, SPECT (Ge, CdTe) Endoscopy
fiber optics
visible light
Thermography
thermofilm (liquid crystal) bolometer IR vidicon
infrared light (passive)
Nuclear spin tomography
HF antenna
high-frequency pulses, magnetic fields
Ultrasound: B display (realtime, compound) Transmission camera Transmission CT Doppler
piezoceramic piezofilm
acoustic waves (MHz)
5.3. Sensor Systems for Special Applications
phy (SPECT), this procedure raises the hope that diseases in the future may be detected long before anatomical lesions or other symptoms develop i5.1091, [5.110]. The images produced by nonelectrical sensor systems, such as X-ray film (with intensifying film) or fiber-optic endoscopy, can be electronically scanned for further processing. Ultrasonic imaging presents a difficulty not encountered with other imaging techniques: the coupling of the sensor to the body. The contact must be made through a couplant having acoustic properties (impedance) similar to those of water or tissue; serious disturbances result from air inclusions. Physiological Measurements. The task of physiological metrology is to determine time-dependent states and functions of individual organs and organ systems in the body. In contrast to imaging techniques, the procedures in physiological metrology are mostly passive; that is, the signals acquired are chiefly mechanical and electrical signals inherent in the body. Quantitative results are needed in most cases. As a consequence, instruments used for temperature and pressure measurement in a medical setting must be calibrated against standards. Parallels to process sensor system technology, with regard to nearly all application-oriented generic attributes (see Fig. 5.30), abound in this field. In Table 5.3, important and characteristic applications are listed by groups of organ and the quantities measured. In addition, the relevant sensors and sensor principles are indicated. Further, the table indicates in what setting the techniques are used: D stands for clinical and practical diagnosis, M for monitoring (chiefly intensive monitoring), L for long-term diagnosis or monitoring under everyday conditions [5.111]. If a procedure can also be invasive, that is, if the sensor or parts of the sensor system are introduced into the body for the duration of the measurement or the body must be opened for the measurement, this is indicated by the letter I. Often information can be obtained only by invasive measurement; the decision is then based not only on the balance of benefits and risks for the patient, but also the cost of the sensor, which is influenced by more stringent requirements on quality and miniaturization.
145
In general, the following points are taken in-
to consideration in the development and selec-
tion of sensors: Essential precondition : a satisfactory functional principle Reliable application with the least possible expenditure of time and materials for routine uses Compliance with national and international standards of technical safety Compatibility of materials, especially for internal and long-term use Resistance to a wide range of mechanical loads and chemical attack (contact agents, body substances, cleaning, disinfection, sterilization) Transportability and adequate shelf life under variable ambient conditions Economic mass production with reproducible properties Robust sensors of simple design, which require no involved preparation or post-treatment, are advantageous; the increasing adoption of one-time articles is a direct consequence. 0 Electrodes for Surface Potentials: One of the most frequently performed physiological measurements is studying the electrical activity of organs by measuring electric potential differences at the surface of the body. The sensor system used to measure these low voltages (EKG amplitude ca. 1 mV; EEG amplitude ca. 50 pV) includes the electrodes and the electrolyte (each electrode plus electrolyte constitutes a half-cell) together with attachment agents, power cords, and connectors. The criterion for choosing the electrodes and electrolyte is a well-defined, stable electrode potential with a low noise level. The time required to reach equilibrium after mechanical or electrical disturbances should be in the range of seconds, and the effect of imposed d.c. or a.c. currents should be slight (time-varying overvoltages, corrosion). Electrolytes and adhesives should be skincompatible; the electrolyte, as contact agent, must also lower the skin’s electrical impedance at the point of the measurement. For applications in which the required EKG quality is not too high, robust metal electrodes are frequently used. Where the requirements are more stringent or the conditions are more difficult (stress and long-term EKG, intensive care), the Ag-AgCI electrode is the only type em-
146
5. The Process Control System and Its Elements: Process Sensor Systems
ployed. It is also the preferred electrode in readyto-use one-time electrodes precharged with electrolyte. 0 Sensors in Intensive Care: The principal intensive care sensor applications are in surgery and neurosurgery, cardiology, internal medicine, obstetrics/gynecology, and pediatrics. Typically the patient is connected to various diagnostic and therapeutic systems (e.g., infusion, respira-
tion, dialysis) and also continuously attended. The sensors or probes attached to the body must not interfere with care and therapeutic measures and, conversely, must be affected as little as possible by these measures; sensors, instruments, and accessories must be as simple and reliable as possible. A variety of designs are available for EKG, blood pressure, and temperature measurement in all fields:
Table 5.3. Physiological quantities and sensors listed by organ system [5.103] Gastrointestinal, urogenital
Quantity
Sensor/ principle
Heart, circulatory
Respiratory
Force, acceleration
piezoresistive, piezoelectric, optical
DM ballistic cardiogram
D M respiratory mechanics, monitoring of apnea
DMLI blood pressure
D M respiratory pressure, resistance
DMI bladder pressure, uterine contraction pressure
optical, thermal, electromagnetic, ultrasound
DMI blood flow, pulse, stroke volume
DM respiratory flow, volume
DI urine flow, volume
microphone
D heart sounds, Korotkoff s sounds
D auscultation
D intestinal sounds, fetal heartbeat
Temperature
thermal (NTC)
DML integral quantity: body-core, skin temperature
Electric potential, impedance
electrodes
DMLI EKG, rheogram
Magnetic properties
SQUID
D myocardiogram
Optical
photodetector
Gases
electrochemical (polarography, potentiometry), spectroscopic
Pressure
Flow rate
Sound
Ions Organic substances
1
I
1
am gr:: D silicosis
Brain, Skin, nervous system, connective sense organs D tremor
I
it,”EKG, EMG
DMI intracranial pressure, intraocular pressure
hearing
1
DMI COz, PH oximetry
DM COz
0 2 ,
alcohol acetone
D eye
1
FMG
lDL
D
DMI
DI PH
dialysis
DI glucose enzvmes
DMI EEG
D D iron in the liver MEG D amniotic fluid
0 2 ,
DML motoric, weight
o,, co,;
tissue, transcutaneous
I
* D = diagnostic use; M = monitoring; L = long-term diagnosis or monitoring; I = invasive
ID1
5.3. Sensor Systems for Special Applications
Ag-AgCl adhesive electrodes for adults and children, as well as for operations - Blood pressure measurement by intermittent noninvasive means (cuff) or by continuous invasive means (catheter) with external pressure transducer - Temperature sensors for body core or skin temperature -
In addition, specific procedures and transducers are used in certain special areas, for example: Photoelectric monitoring of the peripheral pulse Monitoring of respiration in adults (thermistor) and in infants (rheographic, noncontact methods) Cerebral pressure Fetal EKG and contractions in obstetrics Monitoring of oxygen supply: CO, in respired air (by infrared absorption); transcutaneous measurement of 0, and CO,, chiefly in newborns Sensors and their application must meet higher standards when used with infants, since all the bodily dimensions are much smaller than in adults and both risks and interactions must be more strictly avoided. Invasive Measurement of Chemical Quantities : Except for CO, measurement in the respiration gas and transcutaneous measurement, the continuous measurement of substances involved in metabolism and other substances in the body has not yet become routine in intensive care medicine. Development of sensors for these purposes, especially in combination with catheters or for use as implants, has been going on for a long time ;techniques known from the analytical laboratory, such as spectroscopy, polarography, and potentiometry, are in the foreground. The use of specific membranes, electrolytes, and enzymes makes it possible to modify electrochemical sensors (oxygen and pH electrodes) for the measurement of other substances, such as glucose, CO,, or ions. The miniaturization of sensors and the mechanical and electrical connection of sensors to a catheter are tasks that have already been accomplished or are at least feasible in principle. Applications involving contact with blood have still not been satisfactorily solved, because in its properties and its interaction with materials, blood is probably more complex than any other 0
147
fluid encountered in technology. The deposition of fibrin and other proteins in the sensor pocket has two consequences: an increased risk of thrombosis for the patient, and disruption of the primary contact surface for the measurement. The routine adoption of new catheter sensors is also difficult because the station personnel must possess additional knowledge and capabilities, for example, in the operation and calibration of the instruments and in the assessment of the measurements. Requirements as to the reliability and accuracy of catheter sensors are far surpassed by the requirements imposed on implantable sensors and sensors for controlled therapeutic procedures. Therapeutic systems controlled with chemical sensors are a topic of major interest for the future. Promising applications for intensive care are respiration, dialysis, and balancing, for example the regulation of the water, electrolyte, and energy economy of the burn victims. Implantable devices for the delivery of medication, such as insulin delivery controlled by a glucose sensor, are a key area for long-term therapy. The transition from the laboratory analyzer, still routinely used for this purpose, to catheter sensors has not yet been completed, even though the use of integrated circuits has already led to major advances in the direction of miniaturized and multiple sensors. The next step-to implantable sensors with long-term stability-may well be equally large. A contribution toward solving this problem may be anticipated from biosensors, in which integrated electronic structures are built and operated in direct contact with organic materials. Sensor applications range from imaging systems to the invasive measurement of metabolic variables, with cardiological diagnostics and intensive care as points of emphasis. Metrological methods and application techniques merit special attention, as does the management of the patient-sensor interface. A comprehensive literature survey can be found in [5.102]. Information on research projects in the methods and applications of microsystems technology can be found in [5.112], which contains a detailed bibliography, and [5.1 131. Sensor Systems in Automobiles [5.114]. Automobiles today must satisfy increasingly stringent requirements for pollution control, economy,
148
5. The Process Control System and Its Elements: Process Sensor Systems
safety, and comfort. These standards can be met only with the help of electronics. Sensors are the essential link between physical, chemical, and technical processes in the automobile on the one hand and electronics on the other. Sensors in an automobile must offer high reliability and accuracy in a harsh environment, together with small size and low cost. Costs for transmitters as used in process technology, where quantities are correspondingly small, range from a few hundred to a few thousand Deutschmarks (DM); in contrast, the maximum long-term cost for many types of automobile sensors must be < 10 DM. Classification of Sensors in Automobiles. The sensors needed in automobiles can be classified by use, characteristic curve, output signal type, and interpretation.
By Use: Functional sensors in drive-train control systems and in subsystems including the steering, braking, and body electronics. Sensors for monitoring and diagnostics, for example in the cooling, braking, lighting, fuel, and other systems. Sensors for safety and security purposes, for example in passenger protection, locking, and anti-theft systems. Sensors for information, for example in the measurement of fuel consumption and outside temperature, or in travel route monitoring [S.1151. By Characteristic Curve: Which of the following characteristics is preferred will depend on the sensor application : -
~
-
-
Continuous, linear: Preferred for systems that perform continuous measurements with a wide control or display range. Uniform sensitivity is desired. Continuous, nonlinear: Preferred for systems that perform continuous measurements with a narrow control or display range. High sensitivity over a limited range is desired. Discontinuous, limiting value: Used for state-dependent “program variation” in complex systems, for example to enrich the mixture when the throttle valve is full open and to monitor the critical limits in the automobile. Discontinuous, stepped: Suitable for monitoring components subject to wear where re-
placement must be planned ahead of time, such as brake lining replacement before a vacation trip. By Output Signal and Interpretation: Voltage or current analog Amplitude analog Frequency analog Period analog Pulse-length analog Digital Voltage and amplitude analog signals must be submitted to anaiog/digital (A/D) Eonversion before they can be processed in digital systems. Recently, however, inexpensive A/D converter circuits have appeared on the market in integrated form or partially integrated on a chip with the microprocessor. Integrated commuting circuits, known as multiplexers, make it possible to use these converters in a quasi-simultaneous manner for several pickups. Time-varying signals such as frequency, period, and pulse-length analog signals have the advantage that they are not very sensitive to noise on transmission paths and they are easily digitized at the point of reception. The prerequisite, however, is that an appropriate converter circuit is located at the transmitter, because measured variables are not generally in this signal form. What is more, clocks and counters must be provided at the point where the information is to be processed. Sensors with digital output signals have not been considered because of costs. The downward trend in price for integrated logic circuits, in particular microprocessors, has led to reconsideration of such new sensor configurations, in which microprocessors are included in the sensors. The resulting “smart sensors” offer marked advantages with regard to compensation of undesired variables as well as equalization; in some cases, A/D conversion can be carried out by the sensor itself. Code security procedures can insure reliable signal transmission, even in a noisy environment. Requirements on Sensors
Environmental Protection: Sensors used in automobiles operate in a hostile environment, being exposed to heat, cold, moisture, dirt, water spray, salt, electrical interference, mechanical vibrations, and mechanical shock. These factors make the measurement tasks immensely more
5.3. Sensor Systemsf o r Special Applications
difficult and represent a serious threat to sensor reliability and service life (Table 5.4). The most severe conditions are experienced by sensors mounted directly on the engine. A temperature swing from - 40 "C to 150 "C and even around 800°C in the exhaust gas stream, as well as 100 g accelerations. are common environmental conditions here, but components installed in the engine compartment also experience harsh conditions (Table 5.5). Whenever possible, sensors are therefore removed from the zones of most severe stress and installed at more favorable locations. For example, pressure sensors used to obtain the motor load signal are usually connected to the intake pipe by means of a hose.
+
0 Reliability and Service Life: Reliability requirements for sensors on board an automobile are in all cases extremely high, but can be ranked as follows: safety functions, mobility functions, diagnostic and monitoring functions, and information. In addition to the needed reliability, there is often a further requirement that a sensor malfunction must also be detected so that the subsystem can switch over to emergency mode and permit at least a limited "limp home" function. Frequently this point has to be considered early in the sensor development process. As to service life, sensors must also fit in with the trend toward longer-lasting automobiles. Expected values are more than ten years or > 3000 h service. 0 Accuracy : Although accuracy requirements in many instances are as high as those in process plants, this accuracy has to be attained at much lower cost. This can only be achieved by mass production in a highly automated manufacturing process. Because the required accuracy is generally the main factor governing selection of sensor principle as well as cost, it is important to take great care in determining the acceptable error. Excessive requirements have a superproportional influence on cost. Accuracies of 2- 3 YOare needed for many applications.
0 Space Requirement and Weight: The trend toward smaller and lighter automobiles, dictated chiefly by fuel economy, means that components have to be made small; accordingly, there is often little space available for the installation of sensors. Lightweight, space-saving sensor designs are therefore preferred. Small sensors, furtheiinore, have the advantage that they experi-
149
Table 5.4. Environmental influences on sensors and typical effects [5.114] Environmental influence
Typical effects
Temperature/tempera ture change Atmospheric humidity (with dew and fog) Precipitation (rain. snow; ice, frost) Pressure (air, water) Sand, dust Sunlight Aggressive substances (NaC1, SO,, H,S) Mildew Salt water and sea mist (road deicing salt, coastal areas) Sulfur dioxide (industrial regions) Ammonia, perspiration
aging, embrittlement, fatigue, cracking stray currents, surface and contact corrosion electrolysis, swelling
Fuel and fuel vapors Brake fluid Battery acid Motor oil
function stray currents ageing, cracking surface and contact corrosion stray currents electrolysis, contact and surface corrosion surface corrosion creep due to stray currents, loss on insulating resistance surface corrosion, swelling surface corrosion, swelling surface and contact corrosion swelline
Table 5.5. Service conditions of sensors installed in the engine compartment [5.114] Mechanical Vibrational acceleration
(potholed road) chiefly stochastic chiefly low-frequency (max. 10-30 Hz) effective (time-average) value: 5-25 m/sZ max. peak acceleration: 50-80 mjs'
Clirnntic normal 120°C; extreme 140°C Temperature Max. humidity 38"C/95% R.H. 24-96 h (DIN 50021). depending on Salt fog packing density splash to flooding Water Dust and sand density up to max. 5 g/m3 gasoline/oil vapors, alkaline cleansers Chemical mildews and molds Biolonical
ence smaller accelerations due to vibration and impact and thus are less prone to malfunction. Quantities Measured in an Automobile. Despite the wide range of electrical and electronic systems used in automobiles, the number of physical quantities measured by sensors is limited. The most frequently used sensors are those measuring distance, angle, rotation speed, pressure, and temperature.
150
5. The Process Control System and Its Elements: Process Sensor Systems
Less common but still important are sensors for flow rate, acceleration, force and torque, partial pressure, and humidity. Even though the list of quantities is not long, a considerable variety of sensor types are einployed because of the diversity of media (streams) measured, measurement ranges, required accuracy, and environmental standards. Two examples are given below: 0 Engine Electronics for Gasoline Engines: Fuel injection and ignition must be controlled jointly and matched to each other. In other words, every operating condition must be identified and serve as input to the process of fuel metering and ignition timing. This results in following improvements : increased fuel economy, increased engine power per unit of displacement, and reduction of pollutant levels in the exhaust. The motor-electronics system needs sensors for engine speed, crankshaft position, air flow rate, air and engine temperatures, and intake pipe pressure (load signal). To insure compliance with stringent exhaust quality regulations, the system is augmented by a 1 control with a A probe. Other possible functions of the electronics include automatic transmission control. idle speed control, knock prevention, and exhaust recycling.
0 Electronic Control of Diesel Engines: Diesel engines have traditionally featured mechanical control, but stricter air pollution and noise emission standards as well as demand have led to greater use of electronics. Figure 5.37 shows the process properties involved in optimizing a diesel engine along with the required sensors. Apart from the parameters listed above (e.g., rotation speed, temperature, pressure, and flow rate), quantities such as the beginning of injection at the nozzle and accelerator pedal position are measured. The example of the sensor detecting the start of injection illustrates how harsh some of the sensor service conditions are. This pickup is directly integrated into the nozzle holder and is thus in the immediate vicinity of the conlbustion chamber.
Measuring Principles of Autoniotive Sensors. The technical implementation of sensors for automotive service can be based on a wide range of physical effects (Table 5.6). Coating techniques are used in temperature sensors (e.g., Ni, Pt), strain gages (e.g., Ta), and rotation speed sensors (e.g., Permalloy). The constant-resistance heated-wire sensor is used to measure air flow rates. The requisite long-term stability is imparted to the platinum
Figure 5.37. Process properties for optimization of a diesel engine [5.114]
5.3. Sensor Systems .for Special Applications Table 5.6. Technologies for automotive sensors [5.114] Quantity measured
Sensor principle and sensors
Distancejangle
short-circuit ring or disk potentiometer galvanomagnetic effects (Hall, magnetoresistive) piezoresistive semiconductor pressure pickups diaphragm unit with Hall, thin-film strain gage, or capacitive pickoff thick-film pressure pickup thin-film metallic sensors semiconductor (Si) sensors heated wire or film propeller (fuel) circulating-ball sensor springjmass system with strain-gage (cast film, thin-film) pickoff piezoelectric sensor piezoresistive semicanductor sensor magnetoelastic sensor (torductor) eddy-current sensor optical sensor 0, concentration probe with ZrO, solid electrolvte
Rotation speed Pressure
Temperature Flow rate Acceleration
Torque Oxygen concentration
wire by special surface treatment plus regular “refining.” Torque sensors would be useful especially in generating a load signal for electronically controlled automatic transmissions. The torque on the main shaft might be measured, for example. Because the shaft cannot be split, its elastic deformation can be exploited for this purpose. The eddy-current damping principle has proved suitable here. Two metal cylinders with lengthwise slots are slipped one over the other in the axial direction, with a clearance of about 10 cm, and rigidly attached to the shaft. When a torque is applied to the shaft, the cylinders rotate relative to one another. A high-frequency coil placed axially over the cylinder housings experiences unequal degrees of damping as a result. Continuous acceleration sensors (accelerometers) are used for prompt collision detection so that passive passenger-restraint systems such as an airbag or belt tightener can be triggered. Spring-and-mass systems are a proven type of accelerometer; they are mechanically damped by being operated in silicone oil. A triangular leaf spring, preloaded in one direction, bears a mass at its free end. The inertia of the mass causes flexural loading of the spring in a roughly uniform manner, so that the spring is deflected ac-
151
cordingly. The flexural stress is detected by strain gages, which can also be thin-film devices. In order to meet exhaust regulations and minimize pollutant emissions, the engine should be supplied with an air/fuel mixture as close as possible to stoichiometric (1= I), so as to insure proper functioning of the downstream catalysts. Exhaust probes or 1probes permit accurate measurement of the oxygen content and regulation of the 1ratio. These probes have a characteristic curve such that even small departures from 1= 1 results in a very marked change in the voltage signal. For engines operated over a wider 1 range, partial-pressure probes with continuous characteristics are in development. The situation in the automotive sensor field can be summed up by saying that much has been achieved already but much development is still needed to reach further goals with regard to economy, safety, and exhaust cleanness with the aid of complex system concepts and electronics. Above all, ways must be found of resolving conflicts between the objectives of performance and cost. One approach involves sensor technologies that offer major economies when put into mass production. The clear favorites at present are semiconductor and thin-film technologies. However, functional packaging, contacts, electronic interpretation, etc. account for a large fraction of sensor costs and largely determine performance and reliability. For these reasons, the industry must not simply further develop but must also invest in the development of these incidental technologies. Sensor technologies of the same type, strongly oriented to specific requirements, are encountered in rail-mounted vehicles, ships, and aircraft. These application areas cannot be discussed further here. Sensor Systems in Solid-state Process Engineering [5.116], [5.117]. Solid-state processes offer a further example of the relationships between product properties and process properties (as described in Chapter 3 and Section 5.1), especially with regard to quality assurance standards. The properties of disperse solids are influenced by the chemical composition and the physical state of the product (property function). For development, manufacturing, and use, it is necessary to know how the product state affects the quality attributes. Because quality attributes are determined on the end product, the characterization of the disperse state must be utilized in con-
152
5. The Process Control System and Its Elements: Process Sensor Systems
trolling the process. An important principle of quality assurance is that quality must be definable and thus inspectable. Metrology thus occupies a central place in quality assurance. The practices involved in quality assurance are oriented to the following steps, each of which contains specific problems for disperse products : 0 0
0
All desired quality attributes are set forth in the profile of requirements The process must be structured Process control variables must be determined; property functions and modeling are particularly important here
Solid-state Processes. A solid-state process is a sequence of process elements put together in various combinations so that a solid with certain properties is created as the end product. A typical process sequence [5.118], [5.119] is shown in Figure 5.38 in terms of the phase model. As shown in Chapter 2, the phase model contains not only process elements but also the respective product states, and thus takes account of the way the product state changes in the course of the process. After a chemical reaction, the solid is formed by crystallization or precipitation. It is separated
from the liquid phase in a mechanical separation stage, such as filtration, then washed. and finally dried in a thermal operation. A confectioning step may follow, in which the product is put into the form offered for sale. Confectioning may include the unit operations of size reduction, agglomeration, mixing, or fractionation. The end product must ultimately be conveyed, stockpiled, packaged, or metered. The product state changes after each process element: from solution via suspension and filter cake to powder or granular form (depending on the drying or confectioning step). An important feature of the solid-state process is that the product properties do not depend just on the chemical composition but also depend heavily on the disperse properties of the product. RUMPFtreats this dependence of the technological properties of a product on the physical properties by introducing the property function [5.120] : Technological property = f (physical property) Qi = . f ( E p i ) E i C h = , , const. Technological properties Q i
Processing Qi, Filterability Formability Agglomeration behavior
sion
(Separation] Filter
material
Figure 5.38. A typical solid-state process: process elements and product states [5.116]
Physical properties EpL Dispersity properties E,, Size Shape Porosity
Application Qi, Activity Action Instantaneous property Property functions apply to the sequence of process steps brocessing properties) and also to the end properties, that is, the quality of the product (application properties). In filtration, for example, the fineness of the product is crucial; in the case of an automobile, the color properties depend in part on how finely divided the pigments are. The product state is not only crucial to the end quality but must also be considered for each process element in solid-state processes. Not only does the product state change in each process element, the product state itself significantly influences the process element. A knowledge of how the quality attributes depend on the product state is essential for the development, manufacture, and use of products. Structuring of the pro-
5.3. Sensor Sj’stemsfor Special Applicutions
cess into units (unit operations) or meaningful areas is necessary if hold points with intermediate goal values are to be defined. These intermediate targets must be checked with appropriate methods of measurement, as a means of early process monitoring. The state of the end product is not simply established in the last process element; every process element throughout the process contributes to it. For this reason, close tolerances must be maintained even in the intermediate stages if the subsequent stages are to remain in the correctable range. The following steps must be considered from a quality assurance standpoint: 0 0 0
0
Establishing the quality levels in the profile of requirements Structuring the process and identifying the process control variables Insuring the reproducibility of the process Optimizing the process
nation of pigments. The first substantial problems arise in the testing of tinting strength, since this measurement cannot be performed on a dry pigment. In practice, an entire paint factory must be “simulated” before this test can be performed; in other words, a procedure similar to that in the paint factory must be followed on a laboratory scale (Fig. 5.39). The test preparation must be done in the same medium, in the same technical facility, and with the same energy expenditure as would be used by the paint manufacturer; the paint must be applied by the same technique; and the same instrument must be employed for color measurement. All these requirements for pigment quality inspection cannot, of course, be satisfied when Process step
Characterization o f product s t a t e Particle size distribution (PSD) of pigments Molecular mass o f resin
The various types of product quality target values are listed below for the example of pigment manufacturing: Application properties Color strength Color coordinates Purity
Homogeneity PSD
Processing properties Pourability Freeness from dust Dispersion hardness Gloss Interfacial properties Rheological properties
Rheological properties PSD
Stability
Safety Low content of side products Easy to dispose of Nontoxic Incapable of dust explosion Efforts must be made to achieve a number of quality attributes, and these target values relate not just to the final quality (characterized by the application properties) but also to processing properties (see Fig. 11.38), environmental aspects, and safety considerations. All these quality attributes must be monitored (i.e., measured). Two examples indicate how difficult this is. Suppose that a paint, for example an auto body paint, is to be manufactured from a combi-
153
Rheological properties Course material content Application Running
Color Appearance Dye markings Figure 5.39. Process for manufacturing a paint, from the pigment to the painted surface [5.116]
154
5. The Process Control Svstem and Its Elements: Process Sensor Systeins
the pigment is to be used in paint for several users. Instead, from the test in one system, in one apparatus, and so forth (representative testing), deductions must be made about the behavior in other types of apparatus. The development of tinting strength in two unlike dispersion machines illustrate the potential problems of such deductions. In Figure 5.40, the tinting strength is plotted as a function of dispersion time. Unequal energies are applied in different dispersion machines, for example in a laboratory apparatus and the full-scale system. As a rule, tinting strength is determined after one or at most two dispersion times. The result is that the quality attribute “tinting strength” varies widely when the dispersion times and energies cannot be matched, or unambiguously correlated, with those of the user. This example shows what difficulties arise in quality inspection even when there is just one final target value. A process is designed and optimized for the final target values of a product, and the aim of quality assurance is to maintain these final target values. A standardized testing method leaves open questions of product quality for any individual case. Every user, however, wants a suitable pigment for its own formulation, where “formulation” or “recipe” covers both the paint system (including additives) and the apparatus and operating conditions. Property Function. An important, necessary, but not sufficient step in quality assurance is quality control. As a rule, quality attributes are measured only on the end product. Thus quality can be controlled but not assured. Assurance, in the sense of “controlling,” requires some way of influencing the process, and doing so at the earliest possible process stages. This in turn means
that the process must be structured into single steps or meaningful subdivisions and that intermediate goals must be defined or established. These intermediate goals are suitable for early process control. The phase model (Fig. 5.38) and the property functions become especially important for quality assurance. Only when the relationships between quality attributes and physical quantities (property functions) are known is it possible to identify an appropriate process control variable. If the disperse state can be measured and controlled at the point where it is generated or changed, intervention i n the process steps can be implemented at the proper time and thus quality can be controlled. In other words. a knowledge of the property functions is a prerequisite for the assurance of the quality of disperse products. In the example of colored pigments discussed above, some of the property functions are well known. Figure 5.41 shows the target value tinting strength as a function of particle size. For two or more distinct target quantities, a compromise is necessary. The desired particle size should already be established at the crystallization stage. The property functions must be known if crystallization is to be optimized with respect to
$:bl 9
$:f r
I
,
I
I
I
I
I
I
I
I
I
40
: ._ E o L
, ,
20
.&
+
0
0.1
0.2 0.3 0.4 Particle size, prn
t
I
I
0.5
I
0.7
L
+ 07 c L (Y
+ VI
m
C .+
0.05
.-c I-
I
tl
Dispersion time
-
j t2
Figure 5.40. Tinting strength development for two dispersion energies [5.116]
Particle size, prn
-
Figure 5.41. Property functions for a colored pigment [5.116]
5.3. Sensor Systemsfor Special Applications
the application properties. Each subsequent process step changes the particle size and the state of agglomeration [5.121]; that is, the generation of particles by crystallization should be controlled and changes later in the process should be avoided or monitored. The dispersity values based on the property functions are important control variables and thus make it possible to implement early process control. To determine the property functions, both quantities must be known and measurable, namely the physical quantity (disperseness) and the technological quantity (application or processing quantity). The problems that arise in the measurement of the latter are discussed in Section 5.1. The measurement of dispersity is the focus of particle-size measurement techniques. Some aspects of particle-size measurement will be discussed briefly below. Examples of particle properties with corresponding test methods are listed below: Particle size (distribution), agglomerate size (distribution)
diverse methods
Specific surface area
BET method
Flow behavior
shear test
Bulk density
determination of volume and mass
Compressibility
determination of volume and mass, force-distance diagram on compression
Redispersibility
transmission measurements PSD measuremens for various degrees of dispersion
Wetting, porosity
liquid absorption, capillary pressure
Generally, the particle size (primary particles up to agglomerates) and the particle size distribution are of fundamental importance for solidstate process engineering. A number of methods are available for their measurement. These have been described in many publications and analyzed with respect to time, costs, and problems [5.122], [5.123]. The trend is toward measurement techniques and instruments that utilize the optical properties of disperse systems as a tool for characterization. Instead of going further into individual measurement methods, the problems linked with the use of these methods is discussed here [5.124]; see Figure 5.18. As a rule, a sample must be taken
155
from the process. (The special features of sampling of powders and granular materials have been thoroughly treated [5.125].) The sample must be transported to the measurement location and prepared so that the constraints imposed by the measurement technique can be complied with. Preparation may mean filtering or diluting the sample, cooling or heating it, dispersing it, or adding other substances to it. These measures, which can bring about a change in the product, transform the process state into the measurement state. Moreover, the time between sampling and result is often much too long. A measurement that does not become available until hours or even days after sampling is not useful for process control. Thus the objective can only be on-line measurement [5.65] to obtain information on the product or process state quickly. Sample preparation must be automated so that the measurement state can be correlated with the process state in the most reproducible manner possible. Ultimately, however, in-line measurement (see “Generic Class : Application Features,” p. 134) must be employed to generate the required information about the process state. Two examples of new in-line methods of importance to solid-state processes are presented briefly. Laser scanners [5.126]: The particles are scanned with a laser and a chord-length distribution is obtained. The probe can be immersed in any vessel whose contents in are in sufficient motion (0.01 I v 5 1 m/s). The particle-size measurement range is 11000 pm. Photon correlation spectroscopy [5.127]: In this laser technique, particle mobility is measured on the basis of Brownian motion. The Einstein relation links the stochastic motion with the particle size via the diffusion coefficient. This method is in successful use, for example for process characterization in vitamin production.
A comprehensive exposition of particle and particle-system characterization can be found in [5.117], which treats this topic within the framework of sensor system technology. Particle measurement, which extends far beyond the boundaries of particle-size analysis, is completely discussed as to status, trend, and demand in [5.128], which builds on ideas similar to those that underlie quality assurance in solid-state process engineering [5.129].
156
5. The Process Control System and Its Elements: Process Sensor Sj'stenu
Sensor Systems for Environmental Protection. The prevention or reduction of environmental pollution caused by industry, commerce, agriculture, households, etc. requires not only technical measures but often also measurements, whereby process sensor systems (see Section 5.2) are used to provide the required information about the properties of the relevant product or process element, about water and air pollution caused, and also about noise pollution. To enable quick reaction in the sense of environmental responsibility, and also as mandated by environmental legislation, the required information must be available on-line (see Section 5.2). For this purpose it is especially important that information about the product properties be obtained in undistorted form. This can be achieved by measurements on the relevant product itself, performed in-line, that is, in the actual product stream. This is not a simple matter with many present-day sensor systems. The special features of sampling (in comparison with solids sampling as discussed elsewhere in this section), with the aim of insuring spatial and temporal representativeness, are discussed further on. Figure 5.42 shows, in highly simplified form, the tasks of process sensor systems in environmental protection. These systems acquire information about the product and process properties, measure workplace pollutant levels for occupational safety and health reasons, and
monitor wastewater, solid and gaseous emissions in stack gases. and noise emissions for environmental protection purposes [5.130].
Flue-Gas Monitoring Sensors. By way of example, some of the requirements for flue-gas monitoring are examined for a coal-fired plant. 0 Power-Plant Flue-Gas Monitoring: The German regulations on power station emissions require continuous measurements of the mass concentrations of particulates, CO. halogen compounds, NO,, and SOz, and also of the volume concentration of 0, (Fig. 5.43). All instruments used must be tested for suitability; proper installation of measuring devices must be verified by services designated by the authorities; calibrations must be carried out every five years (plants with thermal power under 300 MW) or every three years (plants over 300 MW). In addition, unannounced functional tests are performed yearly. The measured values (instantaneous values) are interpreted by -
Pursuant to the regulation, the emission liinits are deemed to be complied with if
........................................................................
:
I
Control systems information processing
I
Taking half-hour averages Taking daily averages Converting the averages to reference oxygen contents Classification of 0,-referred averages and storage in the form of frequency distributions
I
I
i
...... Production process Wastewater
+---rEpl
A
-
Figure 5.42. Process sensor systems (S) in environmental protection [5.130] A = actuator system: EP = environmental protection; OHS = occupational health and safety; PS = plant safety
5.3. Setisor Systeins for Special Applications Emission measurement equipment
-
Flue gas t o stack (173000m3/hl
system
co,: co: 0,:
0-20 vol% 0-0.1 vol% 0-3 VOI% 0-10 V O l %
Turbine Figure5.43. Example of a fired facility [5.130]
Figure 5.44. Viewpoints, presentation, and interaction [5.131]
157
All daily averages are < 1.0 EL (EL = emission limit) 97 % of all half-hour averages are 1.2 EL 100 YOof all half-hour averages are < 2.0 EL
A cross-reference to the concepts of humanprocess communication (Section 11.2) is relevant here. If compliance with the emission limits is also to be monitored by the operating personnel on behalf of the operator, the levels should be represented by means of suitable presentation and interaction forms. An example of such a form is a cumulative frequency chart updated on-line (see Fig. 5.44) [5.131]. 0 Sludge Incinerator: Sludge from biological wastewater treatment is dewatered in filter presses and is then forwarded to a furnace for combustion. Such an incineration system includes a hot (“dry”) stack gas scrub, which is subject to HCl and SO, controls (Fig. 5.45). The emission measurement hardware is designed to comply with TA Luft. Emissions (concentrations) are referred to wet gas in the measurement stream, so that the content of water vapor must be determined each time. This is done directly on the basis of the measured 0, levels in the wet gas (OZw)and the dry gas (OZD). The measurements are analyzed by averaging, referring to 0, , and classification. Instruments
158
5. The Process Control System and Its Elements: Process Sensor Systems
EF
to
HHA
Figure 5.45. Diagram of analytical instrumentation installed in a sludge incinerator plant [ S . 1301 EF, electrostatic filtration; S, stack-gas scrubbing; HHA, half-hour average
are calibrated by a service holding an official permit. General Emission and Impact Monitoring of Gaseous Pollutants: Gaseous pollutants represent a special danger from the environmental and occupational safety and health standpoints. Formerly, this problem could be handled only with expensive suction-type gas collecting systems, which delivered the mixture of pollutants to product-identifying sensor systems. Examples of such systems were spectroscopes, chromatographs, surface-sensitive metal oxides t5.1321 and semiconductors, as well as physicochemical flame ionization detectors [5.133] and ZGSM gas detectors [5.134]. As described in Section 5.2 [5.7], [5.10], the sensor system proper then includes sample preparation, measurement, and application. As early as 1985, WARNCKE [5.3] reported on optically integrating absorption measurements over long lines in the open atmosphere. The measurement of pollutant components in the infrared by this remote analysis technique is best performed with lasers. Rotating sensor systems place a “detection cone” around the object where emissions are expected to occur (Fig. 5.46). By way of example, Figure 5.41 shows emission monitoring at a chlorine plant with an “analytical eye,” an ultraviolet instrument using a conventional light source. The special innovation here is that the interference due to sunlight is compensated by using a pulsed light source.
Detector A
0
Without /Detector
With
A
Detector A
-c m
.-m LA
m
f-
v,
f-
Figure 5.46. Continuous plant emission monitoring with rotating laser backscatter photometers [5.3]
The source intensity during the pulse is much greater than that of the interfering light. Accordingly, in the detection range the aperture can be made small enough to prevent presaturation of the detector by the interfering light. If necessary, measurements can be performed at two reference frequencies to compensate for light scattering by particles. This procedure amounts to setting up an optical enclosure around the whole production plant. Despite the use of modern lock-in amplifiers, the cross-sensitivity and relatively high cost of the apparatus has kept these developments from gaining wide adoption.
5.3. Sensor Systems.for Special Applications
R2
M
R1
AFigure 5.47. Optical monitoring of chlorine emissions [5.3] a) Flash lamp; b) Lens; c) Telescope; d) Beam splitter; e) Detector; 0 Evaluation
The current status of this process sensor system technology is surveyed by [5.135]. Wastewater Monitoring Monitoring of Combined Sewer Water: Much of the energy of reaction generated in a process is typically transferred to water (usually withdrawn from a stream) in heat exchangers. Along with storm sewer water, this cooling water is discharged directly into the receiving water through a combined sewer. Contamination can occur when rain falls on roofs, streets, and other areas containing pollutants; in the ordinary drainage lines; or through leaks in the heat exchangers. 0
0 Process Wastewater Monitoring: All other water used in the plant, including scrub liquor from final cleanup stages, must be conveyed through the now-standard system (including mechanical, chemical, and biological treatment processes) in order to comply with legislative requirements. Without going into detail on national and international laws and regulations governing wastewater, or on the legal consequences of a violation [5.136] (see Chapter 12), Figure 5.48 illustrates some aspects of the situation in Germany. The new philosophy of international environmental protection declares the protection of natural waters from pollution to be a high good, regardless of whether the polluting wastewaters
159
originate in households, public facilities, or industry. Accordingly, the availability of treatment functions in treatment plants is declared to be beyond the scope of economic discussion. RAABand WACHTER-BUCHNER state in particular [5.137]: “In order to comply with the legislative mandate, precautions commensurate with specific potential hazards must be taken. These include, first, technical precautions that reliably prevent contamination of the wastewater or the occurrence of severe pollutant burdens in the first place. An effective complement to these primary technical measures is continuous analytical monitoring of wastewater streams for substances foreign to water. In the chemical industry. many such substances are carbon compounds, which often means organic contaminants. The parameter TOC (total organic carbon), which states the total content of organic carbon in mg C per liter of water, has proved to be a highly suitable quantity to measure for this purpose. Water monitoring is not, however, limited to the detection of undesired organic contaminants in wastewaters. Increasingly, it is also employed in process monitoring. Along with environmental protection applications, TOC measurement can also be found in production monitoring and control. A company has control of its production only if it also knows the makeup of its wastewater. A topic of recent discussion is the continuous measurement of TOC burdens in wastewater streams (balancing), which is a prerequisite for source apportionment of wastewater streams within a production complex. Environmental protection regulations and production monitoring needs dictate the measurement mission. If it is taken seriously, this means the continuous determination of TOC. Only if wastewater streams are subject to continuous, on-line monitoring as near as possible to a potential point of entry of water pollutants can inadvertent contaminants be detected promptly and countermeasures put quickly in train. The monitoring of process wastes also requires continuous surveillance, whether to prevent overloading of the treatment plant or to promptly detect improper states in the plant. There is a definite trend toward process TOC measurement, that is, continuous 24-h determination of TOC in the wastewater. From the metrological point of view, this is not a simple task; from the instrumentation standpoint, it requires substantial expenditure.
160
5. The Process Control System and Its Elements: Process Sensor Systems
According to DIN 38409 Part 3 [5.138], TOC is the sum of organically fixed carbon in dissolved and undissolved organic compounds. The DOC (dissolved organic carbon), is the sum of carbon contained in dissolved organic compounds. Other terms in common use are TC and TIC. TC (total carbon) equals the sum of organically and inorganically fixed carbon in dissolved and undissolved compounds. The inorganic component of the TC is called TIC (total inorganic carbon). Each of these is expressed as a concentration in milligrams of carbon per liter (Fig. 5.49 [5.137]). The basis for all methods used to determine organically fixed carbon in water is the oxidation of carbon to carbon dioxide. This can be accomplished in a thermal process (combustion), by wet chemistry (with suitable oxidizing agents), and/or by irradiation with ultraviolet light. The last method is restricted to solids-free samples.
Y W 'I I J I t
t
AbfG
t
t
The carbon dioxide resulting from oxidation is either determined directly or reduced with hydrogen and determined indirectly as methane. The determination can be carried out in the liquid or gas phase. Methods suitable for use in the liquid phase are photometry and conductometry. The most common technique, however, is to determine carbon dioxide in the gas phase by IR spectroscopy. For the TOC measurement methods in ordinary use, the individual process steps are summarized in Table 5.7 [5.137]. together with the starting and end products of each phase transformation, up to the acquisition of the measurement. Depending on the physical locations of the sensor systems and the way they are tied into the sewerage (process discharge, sewer junction, plant discharge, treatment plant), the requirements on the following perforniance features vary widely:
t
t
t
(L-AbwAGI (LWHGI (Ind.-Einl.V.1 lA[to[-V.l ( S A B V I IAbf.Nachw.V.1 I T A A b f a l l l
J
I
t
Production
t
t
Figure 5.48. German wastewater legislation and regulations [5.136] AbwAG: Wastewater Levy Act; WHG: Water Resources Management Act; AbfG: Waste Disposal Act; L-AbwAG: State Wastewater Levy Legislation; LWHG: State Water Resources Policy Legislation; 1nd.-Ein1.V.: Regulation on Indirect Dischargers; Altol-V. : Waste Oil Regulation; SABV : Regulation on the Definition of Hazardous Waste; Abf.Nachw.V. : Waste and Residual Materials Monitoring Regulation: TA Abfall: Technical Directive on the Disposal of Hazardous Waste; Chem.G.: Chemicals Act; BImSchG: Federal Pollution Control Act; Gefahrst.V.: Hazardous Materials Regulation; St0rf.V. : Regulation on Disruptions of Operations
mg C per Liter in
TC ( t o t a l carbon)
organic compounds
inorganic compounds
dissolved undissolved
dissolved undissolve
0
0
TIC ( t o t a l inorganic carbon)
TOC ( t o t a l organic carbon)
0
DOC (dissolved organic carbon)
0
0
0
0
0
0
5.3. Sensor Systems for Speciul Applications
161
Table 5.7. Steps in conveiitional TOC measurement Process step
Starting/intermediate product
Phase transition
I
organically/inorganicallyfixed carbon (in water)
separation inorganic/orpanic species
CO, (inorganic) volatile organic carbon compounds (in gas phase) organic carbon (in liquid phase)
organic carbon (in liquid + gas phases)
oxidation thermal chemical photochemical (UV)
H,O reaction coinCO, poiients (in gas phase)
+
+
+
+
+
I11
CO, HZO reaction components (gas phase)
CO, (in gas phase) measurement gas preparation (e.g., drying) CO, (in liquid phase) absorption of CO, (in liquid reagent) CH, (in gas phase) reaction (hydrogenation of CO,)
IV Analysis
CO, (in gas phase) CO, (in liquid phase) CH, (in gas phase)
selective IR absorption (photometry) color reactionjconductivity change (phototnetry/conductometry) combustion (flame-ionization detector)
Selectivity Detection threshold - Time behavior - Availability -
Besides the carbon contents TC, TIC, TOC, and DOC, water quality is also determined by other product properties [5.136]: Physical and chemical product properties Temperature Conductivity pH, redox potential Turbidity Contaminants from process COD PO, Salt Ammonium Nitrate Phosphate Heavy metals Biological parameters BOD, Saprobic index Toxicity The methodology of wastewater monitoring is shown in Figure 5.50 [5.136]. On-line instrumentation is available for the determination of most product properties. Such fully automated devices take water samples, analyze them, and output measurements in computer-readable form in a continuous manner (or
TOC (mg C/L)
quasi-continuously in the case of cyclical analytical methods); see, for example, [5.139]. The spectrum of on-line measuring instruments that come under consideration ranges from robust devices proven over many years in process monitoring service to complicated and expensive special-purpose apparatus such as TOC analyzers (see above) and toximeters. Similarly broad is the range of performance features and costs for installation and maintenance of the measurement systems. Representativeness in space and time merits special attention, since the products present in a plant wastewater system generally change from time to time, they are poorly mixed (in laminar flow), and if the pipes do not run full they do not reflect the actual product qualities. Furthermore, it must be ensured that no chemical reactions take place downstream of the sensor system that might lead to other pollutants that then remain undetected. The use of analytical instruments for wastewater analysis must always be examined from the standpoint of explosion hazard. If there is no usable foreknowledge of the potential pollutants, measurement techniques of universal applicability must be employed; examples are TOC determination (see above) and flame-ionization detectors with phase changers. The apparatus is more expensive and cannot always be installed in an explosion-proof manner; the selection of physical location may also be severely limited.
162
-
5. The Process Control Systein a i d Its Elernrnts: Process Seiisor Sj!sterns
e
W a s t e w a t e r monitoring
,
I
I
( A n a l y s i s f o r specific p o l l u t a n t s ]
Water p u r i t y monitoring
Inorganic contamination Dissolved acidic or alkaline contamination
A D i s s o l v e d volatiles. colorless Phase changer + FIO or IR]
colorless
I
m
Ion-sensitive electrode Titration
colorless
P y r o l y s i s + FID/ID
E x t r a c t i o n + UV/IR
Ion-sensitive electrode
colored
Titration
Extinction
Auxiliary r e a c t i o n + photometry colored
I Extinction
Phase changer + p y r o l y s i s or + ion-sensitive electrode
Figure 5.50. Methodology of wastewater quality monitoring [5.136]
If instrumentation is present at the process discharge, expansion is possible by relocation aimed at bringing internal substreams under continuous monitoring. In this way the source of wastewater upsets may be localized [5.130]. Sensor Systems for Noise Pollution Control. The well-known instrumental methods of noise measurement cannot be treated in detail here, but for the sake of completeness a few remarks are offered from an applications standpoint. The metrological tasks of noise pollution control in the environmental protection context include the following: Noise level measurements in the vicinity for the determination and monitoring of noise con-
tributed by the plant and by the surroundings, in particular by traffic. Emission measurements to determine the level of noise radiated from individual pieces of equipment (acoustic power) and prepare an emission register (acoustic power levels of individual sources). Calculation of impacts from emissions (noise radiation), with allowance for propagation damping between source and test point, to prepare an impact register (acoustic pressure level in the vicinity). Comparison of measured and calculated impacts (levels) to validate the level mesurement technique or the propagation damping law used in the calculation 15.1301.
5.3. Sensor Svsternsfor Special Applications
In the framework of the occupational safety and health effort, these tasks are extended to the workplace proper. The large volume of interpretations require sensor and computer systems that are matched from an information-technology standpoint. Sensor Systems in Polymer Production. In principle, the same problems arise in establishing the product properties throughout polymer production as are discussed in Section 5.1 [5.27], in Section 11.3 (process analysis and optimization aspects), and specifically in this section (see p. 152) [5.116], [5.128]. Like all production processes, polymerization can be represented in terms of the phase model (see Chapter 2 and Fig. 5.51). Determination of Relevant Product Properties throughout the Process. The first central problem in detennining the product properties is that the properties established or required in the end product (elements of the requirement profile) cannot generally be represented after all the prior steps in the process (see Fig. 2.34); for example, even a change in state of aggregation makes it necessary to examine more than just one category of these product properties. Determination of Relationships between Process and Product Properties. The second group of problems arises because the relationships between the properties required in the end product or technological application (requirement profile), the actual product properties (qualification profile), and the properties that exist in the prior stages of the production process are not directly known. RUMPFfirst discussed this question from the standpoint of experience with solid-state processes (see above), coining the term “property function” for these relations [5.120]. Later, ECKERet al. applied terms such as “process indicators” to similar solution approaches [5.27]. Here, as discussed in Section 5.1, the relationship between “technological” [5.7], [5.12], [5.27] and “physical” product properties was formulated. In acrylonitrile polymerization, for example, a viscosity quantity is often used as a control variable (K value) [5.140]-[5.142]. Relaxation Time of Product Properties. The third set of problems is due to the fact that important product properties cannot be determined on-line (see Eq. 5.2-1) because the measurement process has a relaxation time z~~~~~
Material flow
163
Information flow
Mono rne r
-Product key (describes property profile)
Polymerization
-Recipe
Polymer
-Product
Extrude
-Recipe
Semifinished product
---Product
Post-treatment
-Recipe
Commercial product -Product
key
key
key
Figure 5.51. Material and information flows for production of a chemical material [5.12]
(required for information acquisition) that is too long or fails to satisfy Equation 5.2-1. In-line Measurements. Another important aspect is that information acquisition, in its sensor-linked stage (see Fig. 5.21), often involves not the product itself (in-line, see Section 5.2) but rather an extract, usually prepared after being obtained by appropriate extraction of the product from the production events (ex-line). HOTOPand KONIG have readdressed this problem with application to polymer processes [5.143]. Complex Shear Moduhis. The product properties (chiefly the mechanical-technological ones) can be described by the complex shear modulus G* (see Section 5.1). This “phenomenological” quantity, which statically and dynamically describes the reaction of a product when a mechanical field acts on it, affords a key with which the mechanical behavior can be traced back to elementary molecular motions, provided there is an adequate theoretical framework. KUHNp.31J and PECHOLD [5.32] carried out useet al. ful preliminary work, which MORBIKER [5.33]-[5.35] has developed to the stage ofpractical application.
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5. The Process Control System and Its Elements: Process Sensor System
Molecular-Mass Distribution. The macroscopic mechanical behavior of polymers is essentially governed by the chain-length distribution or molecular-mass distribution and by the degree of cross-linking. A variety of HPLC methods are used here (Fig. 5.52 [5.144]). Only when, as discussed above, there is a stable relationship between the molecular properties of the “process product” (in-line) and extracts rendered “measurable” through a series of differential-solubility operations (ex-line) can the conclusion stated above be justified (see also [5.145]). In any case, a specific relation and a specific model must be sought for each application, so that process control consistent with quality assurance can be implemented. From a modern information-oriented standpoint, all such efforts lead to measurement techniques associated with process indicators [5.27] or supported by models [5.53]. A comprehensive bibliography can be found in [5.11]. Purity Guarantee. Similar aspects must be considered for the second collection of tasks, the guarantee of chemical purity for intermediate and end products. It is beyond the scope and aim of this article to describe material-identifying spectroscopic and chromatographic methods (see [5.55] for more details). An illustration of these points is shown in Table 5.8 [5.143].
Sensor Systems in Biotechnical Process Engineering. In the preface to [5.60] SCHUGERL states that the most important difference between biotechnology and its predecessor, fermentation
technology, is that fermentation technology is an assemblage of hard-to-quantify empirical relations, while biotechnology is an assemblage of relations that are still empirical but can be represented in quantitative form. The foundation has been laid for biotechnology to arrive at a point where it can make quantitative predictions with the aid of general laws. The same holds for both microorganisms and cell lines. Previously, production strains were always selected by tedious screening and then laboriously improved by induced mutation and screening. Today, these strains are increasingly being modified to order by using genetic engineering. Protein design makes possible the purposeful local modification of proteins through appropriate genetic modification of the microorganisms or cells. Metabolite design allows the metabolic pathways detrimental to production to be eliminated and desired pathways to be reinforced. Analytical techniques play a key role in the industrial exploitation of the full biological potential of the microorganisms and cells obtained in this way. These techniques make possible a precise study of the cell physiology and allow quantitative determination of the regulation of the metabolism. Furthermore. the optimal process conditions for growth and for product formation can be brought about and maintained by means of process analysis. The same rules apply here as for the other fields discussed in this section, though at a high level of abstraction ; see the discussions of solidstate process engineering and polymer production engineering above, as well as Figures 5.1 and 5.2. Sensor systems commonly used in biotechnical process engineering [5.61] are as follows:
HPLC High-performance liquid chromatography
/
Column chromatography
1 I
\\
Thin Layer chromatography
‘I
/\
Low-pressure chromatography Solid-phase extraction
Normal-pressure
High-pressure chromatography
Paper chromatography Ring chromatography
Figure 5.52. Scheme of HPLC methods [5.144]
High-pressure
5.3. Sensor Systemsf o r Special Applications
165
Table 5.8. On-line measurement tasks in polymer processes [5.143] Measurement task
On-line analytical methods Conventional
New
Mixture. purity, and trace analysis Additives Side components Residual monomer Polymer End groups
density, refractometry, photometry, titrimetry, GC, photometry after color reaction
spectrometry, liquid chromatography
Molecular mass determination Average molecular masses Molecular-mass distribution Chemical constitution as function of molecular mass
viscosity, light scattering
gel chromatography
Characterization of polymer nielt/coniniercial product Additive content Rheological properties Branching, cross-linking Chemical composition Residual solvent content
rheonietry, photometry
spectrometry
On-line and in situ fluorimetry NMR spectroscopy ESR spectroscopy X ray structure analysis Chromatography Mass spectrometry Electrophoresis Optical and acoustic microscopy Flow cytoinetry Flow injection analysis Bioelectrodes Biosensors Biocalorimeter Bio-FETs Piezoelectric biosensors Optodes Antibody/antigen systems Along with the classical methods, which are known at least in general principle in chemical process engineering, a new class of sensors are now appearing: biosensors, which are used especially to mesure specific biological product properties (see Fig. 5.28). In these sensors, only specific biological reactions yield a sensory effect. As a rule, however, parallel, serial, or associative linking of information is used [5.3]. A typical example is given by fermenter analysis (see Fig. 5.53): The following specific items of information are available: pressure, flow rate, and oxygen content of inlet air; oxygen, carbon dioxide, and nitrogen content of outlet air; pH and
Product properties o f inlet air: 02, PO,. PCO,
Process properties of fermentation:
T
F, P. T. PW PO,, PCO,.
Product properties o f discharge air: 02. PO,, ~ “ 3 2 . N2
Figure 5.53. Measurements in fermentation [5.3]
turbidity of the solution; and contents of oxygen and carbon dioxide dissolved in the solution. A calculation relating the oxygen and carbon dioxide concentrations to the air throughput (Fig. 5.54) gives the respiratory quotient as the ratio of oxygen uptake rate to carbon dioxide evolution rate. Together with a knowledge of the mass-transfer coefficients from the gas phase via the liquid phase to the cell, this makes possible an optimization of the air throughput at a higher level of assessment [5.3]. As shown by WARNCKE [5.3] and HOTOP and KONIG[5.143], the greatest difficulty in biological product property determination lies in sampling and sample preparation, which must be done case by case. Without the aid of robotics in sampling and sample preparation and without information-oriented model support, sensor systems in biotechnical process engineering would be inadequate.
166
5 . The Process Control System and Its Elements: Process Sensor Systems
tt
I RCl I
It
c PO, Po;
PO, Po; P
i=
I
r CO, evolution - lr a t e pco, pco; P
F
F-
-rpm Figure 5.54. Gain in information due to coupling [5.3]
Additional constraints include foreign influences and the disposal of sample material after the measurement (see Fig. 5.20). Biology itself, however, must increasingly become the guide in the development of biosensors. Multisensors, sensor arrays, and the processing of many simple signals in a manner similar to the neuronal structure of organisms should provide the solution to many problems [5.13]. Interesting results in the identification of process trends have been obtained by ADLER[5.146], who used fuzzy clustering methods, which WINTERSTEIN has tested in practice by applying a multivariate sensor system to wastewater monitoring [5.147]. Outlook. In order to control a production process, it is necessary to look at information on various levels (Fig. 5.55). Sensor and actuator systems link the process with the information processing. Process monitoring and control systems take on the function of information processing and human- process communication. The information orientation of process control engineering means that proven measurement techniques must be developed toward a sensor system technology. The necessity of providing interpretable information to the process monitoring and control system at any time means a change in the requirement profile for sensor systems of the future: 0 Reliability at reasonable maintenance cost 0 Functionality even in extraordinary process states (e.g., startup, shutdown, upsets) 0 Adequate accuracy and predictive power 0 Easy adaptation to production variation [5.148]
Self-diagnosis and self-maintenance are important qualitites of future sensor systems. The information structure of a universal sensor system is illustrated in Figure 5.21. Increasingly, manufacturers face the challenge of designing modular, engineered sensor systems. Design, together with easy installation and maintenance, represent untapped potentials for improved efficiency. Of course, there are not yet any adequately tested sensor systems for polymer and biotechnical production, for the solid and suspension states of aggregation, and for many areas of environmental technology. Essentially, however, it is more important to bring the classical (physical, chemical, and technological) measurement techniques up to the new standard. HAUPTMANN calls for the following among other points [5.149]: “In order to develop sensors and bring them to market at low prices, some fundamental requirements must be met: 0
0
0
Precise knowledge of the market and of international development projects as a basis for innovation Concepts. strategies, and visions of future sensor markets and technologies as a basis for long-term success Strategic partnership and cooperation with research institutes, universities, users, and manufacturers”
There is a clear trend away from the laboratory towards the process environment [5.3], [5.135]. Already, under the auspices of ISA, IFAC, VDI/VDE-GMA, and other organizations, nu-
5.3. Sensor Systemsfor Special Applications
167
Figure 5.55. Sensor systems in the information chain from material/energy flow to hunian -process communication
merous forums are available worldwide for the discussion of sensors, sensor systems, technologies, and applications. These discussions, often academically conducted, are brought down to earth by applications-oriented conferences such as GVC, the Eisenhuttentag (Foundry Conference), and NAMUR, which deal with the harsh conditions of everyday service (see above). With increasing success, attempts are being made to bring structure into this unbounded variety by using new methods of applied informatics [5.150] and to find routes for effective application through hypermedia (see Fig. 5.30). Nevertheless, the need to set down in-depth knowledge in encyclopedias [5.151] will still exist, so that this knowledge can be kept up-to-date (with a view to the taxonomies cited above) through supplementation or modification of the assignment of object attributes. Special sensor technologies are continually being reported on by technical and scientific journals such as TM, IFAC, ISA, and atp. These include : 0 0
0
Optical sensors (including fiber optics) Chemical sensors, such as - Liquid electrolyte sensors - Solid electrolyte sensors - Mass-sensitive sensors - Optochemical sensors - Calorimetric sensors [5.149] Surface-active metal oxide and semiconductor sensors
0
Biological sensors
Four general trends can be identified:
Advances in Existing Sensors. The development of existing sensors and the creation of sensor systems continues. Multisensor systems based on commercial sensors, coupled with new signal recognition and signal processing techniques, will open up new application areas. An important aspect is the modeling of the sensor with regard to the effect measured as well as parasitic effects. Pattern recognition in forms ranging from classical to fuzzy logic will promote this development. Digital Sensors. The resonance sensor is an important step toward digital sensors. This class includes micromechanical resonant sensors as well as sensors based on piezoelectric materials. Analog sensors can be replaced by resonant detection mechanisms with no change in sensor configuration. The coupling of resonant structures with fiber-optic excitation, detection, and signal transmission will increase. The development of entirely novel sensor principles will largely remain an exception.
New or Modijkd Sensor Materials. Material development is currently a major theme in sensor development. An important role is played by polymers, ceramics, composites, alloys, biomaterials, modified glass-fiber materials, 111-V semiconductors, and so forth. Silicon, however, will
168
5. The Process Control Systeni and Its Elements: Process Sensor Systems
remain dominant and combinations of silicon with other materials will be increasingly used. Microsystems. Microsystems technology is having a lasting effect on the field of sensors. A microsystem is a spatial integration of sensor, signal processing, data processing, and actuator. This technology combines methods from microelectronics, micromechanics, microoptics, and information technology. It even makes use of techniques from acoustics, hydraulics, chemistry, and biology. It includes the design, fabrication, and application of technical systems that integrate a variety of functions. The functional units of a microsystem interact closely with one another, and their geometrical dimensions, dictated by functional considerations, are typically in the range from millimeters down to micrometers. An essential criterion for the design of microsystems is optimization of the overall function. This is advantageous from the standpoint of technological effort, because it is not always necessary to optimize the subfunctions per se. But because the interaction between the parameters has the nature of a function itself, the function of a microsystem is more than the sum of the functions of the subcomponents (see also [5.112], [5.152]). The principle that functionality can be enormously increased by a greater integration density while system reliability and economy are maintained provides the basis and motivation for future microsystems technology. The integration of electrical and nonelectrical subfunctions on a chip or substrate enhances the performance of microsystems and is therefore particularly interesting. The requirements that must be met when integrating sensor and electronics, as well as the actuator whenever possible, are, however, in part contradictory. The manufacture of future microsystems will require not only suitable principles but also highperformance materials, technologies, and design systems. New assembly and connection techniques will play a crucial role, as will quality assurance. Present-day solutions, technological approaches, and principles of microelectronics, materials research, integrated optics, process engineering, production engineering, chemistry, biology, and medicine must all be examined with regard to their systems capability and integratability [5.153]. The development of sensor systems will, however, be influenced by two other trends:
Transition from Batch to Continuous Processes. As production is changed over from batch to continuous processes, information acquisition cycle times become shorter. Direct coupling or integration of self-contained, autonomous sensor-actuator systems (e.g., for metering pumps, variable-speed and variable-torque motors, and so on) will probably be a necessity (see Chapter 6). Automation. The problems cited in the preceding sections show how important robot-aided sensor systems will be for sampling, sample preparation, disposal of auxiliary substances, and maintenance actions. For example, Figure 5.56 shows a fully automated pH sensor system with remotely calibrated probe [5.59]. Figure 5.57 shows the sequence of automatic functions in a phase model. Information utilization leads development in a completely different direction. “Model-supported measurement methods,” which have now become well established, should be mentioned first [5.11], [5.45]. The connection between process model and sensor system implies the ways and means for remedying fundamental shortcomings in measured-value acquisition with the present capabilities of information processing. Given these procedures, unmeasurable process and product properties can be reconstructed from easily measurable quantities through interpretation of a priori knowledge, represented in a mathematical model, about what happens in the process. Furthermore, experience in petroleum exploration with properly positioned sensors has led to the use of tomographic methods so that, ultimately, spatial representativeness can be achieved on the basis of a one-point (local) measurement. Modern methods of human-process communication (see Section 11.2) will make it easier to develop temporal single-point measurements by using characteristic curves in product and process configuration spaces (Fig. 5.58). Sensor and actuator systems, as the link between material flow, energy flow, and information flow, will be designed in future such that they relieve the load on the central processing units of the process control system or actually become subsystems of it. Developers and users, particularly in the area of sensor-actuator systems, will need to grow beyond the historic borders of their field and
5.4. The Market for Sensors and Sensor Sysfenzs Measurinq system 71(X)
9-p
m
169
Unical 79(XJ Auxili energ
Compressed air, 4-7 bar
Selector switch,
e.g., f o r
Pt
2-6 bar
Liquid
- - - ---
--
- and reference electrode
Figure 5.56. Example of an automated pH sensor system with remotely calibrated probe [5.59]
apply a keen and critical sense of analogy to the problem of transferring insights from other experiential worlds into process control engineering.
5.4. The Market for Sensors and Sensor Systems Extensive market surveys [5.154], [5.155], shows and congresses [5.153], [5.156], [5.157] and seminars held by technical societies and manufacturers all come to one conclusion: The already large sensor market (world volume in 1988: ca. DM 24 x lo9) wil expand even further,
reaching ca. DM 45 x lo9 in 1995 and > D M 65 x lo9 by the year 2000 [5.158]. Figure 5.59 shows a regional breakdown of the expected boom. The current recession is not expected to alter this trend in a major way. According to the Prognos figures, process engineering and plant construction are the largest customers of sensor and senosr system manufacturers at present (DM 9 x lo9 in 1988). It will be these users that largely motivate and support the market growth; the absolute increase in sales to these customers will be exceeded only by applications in automaking, the relative increase by applications in building and safety engineering as well as in medical technology (Fig. 5.60).
5. The Process Control System und Irs Elements: Process Sensor Systems
170
Sensor in medium, process t e m p e r a t u r e R e t r a c t electrode, isolate, equalize t e m p e r a t u r e Electrode in chamber Flush Cleaned e l e c t r o d e Calibration 1 Zero adjusted
d %
Calibration 2 SLope a d j u s t e d Flush Cleaned e l e c t r o d e ready f o r use Advance e l e c t r o d e Sensor in operating position
Figure 5.57. Subprocesses in calibration of a pH sensor
If the forecasts are accurate, process engineering and plant construction will account for roughly one-third of sensor sales in 1995 (DM 14.7 x lo9).This development will be motivated by requirements for increased product quality and plant availability, and also by more stringent environmental and occupational safety standards. The Prognos study cited above also shows that plant and process engineering are the dominant users of sensor technology on a region-byregion basis too (Western Europe, United States, Japan) and will probably remain so. For the sensors used in plant design and construction, Figure 5.61 presents a summmary of application areas, while Figure 5.62 gives a similar breakdown for those used by process plant
operators. Notable is the dominance of the petrochemical industry, which is accounted for by the high degree of automation in plants now on-stream. Figures 5.63 and 5.64 show the sensor market for plant construction and process engineering divided according to the physical quantities measured. Clearly the process properties identified as “classical” in Section 5.2- temperature, pressure, flow rate. and level-account for the highest expenditures, while the properties grouped under “chemical quantities” represent a less important market for the process analysis technology discussed in Section 5.2. It is interesting to do a cost analysis by the quantities themselves. A comparison of Figures 5.64 and 5.65, the latter showing the distribution of process control stations by quantity measured in medium-sized chemical process plants [5.69], suggests that temperature and level can be measured much more cheaply than pressure and flow rate. This is supported by a market survey [5.159] that compares the numbers and values of standard sensor transmitters sold in the US market (Fig. 5.66). The reason for this disproportion certainly lies in the use of many relatively simple and robust principles for measuring temperature (for example, with the Pt 100 resistance sensor) and level; with regard to the markedly higher figures for pressure and flow, a detailed comparison with other conventional “classical” technology (e.g., orifice plates) makes it clear that manufacture prices here are motivated by market policies. An intimate knowledge of the market reveals that modern “contactless” methods of temperature and flow measurement, which require costly signal processing arrangements, are being offered at dumping prices in comparison with conventional “mechanical” methods. A more detailed analysis can be done for sensor systems used in flow rate measurement. According to present figures, these represent costintensive process control stations (see also Figs. 5.64, 5.65, and 5.66, in which the third to sixth transmitter categories can be employed in flow measurement). Newer developments in this field make clear the advantages of intelligent field systems with remote diagnostic and maintenance capabilities. The additional functionalities of these systems, which in conventional technology are rather demanding in maintenance terms, should pay off in the long run by lowering maintenance costs and
5.5. Field Installation and Cable Routing
171
Figure 5.58. From process signal to configuration space (process and product property space)
Figure 5.59. Sensor market broken down by region and year up to 2000 [5.158]
reducing energy losses (e.g., diminished pressure losses). There is an increasingly pronounced trend toward methods such as vortex shedding, magnetic induction, and Coriolis methods (direct determination of mass flow rate) and away from the older methods of differential pressure and rotameter-type instruments (see Figs. 5.67 and 5.68). Modern trends in flow measurement are reported in [5.62], [5.160], [5.161]. All forecasts on applications in plant and process engineering agree on the expected development thrusts and market opportunities: biosensors and microsensor technology [5.152], [5.1621.
5.5. Field Installation and Cable Routing Introduction. Field installation and cable routing denotes the connections and structural work that must be performed to connect the field
instrument to the data-processing system in the switch room and thus insure the flow of information between the field level and the process control level. Modern installation technology is characterized by the use of an individual line from the sensor system to the switch room where the information-processing system (e.g., process control system) is located. Here, “sensor system” means the sensor with sensing element plus the metrology and instrumentation required for data acquisition [5.4]. It is not important whether the sensor system is made up of one unit (compact device) or several parts (e.g., the sensor plus a 19“ rack-mount transducer in the switch room). In the compact unit, sensing element and transducer are conbined in a single instrument. Compact devices have some major advantages. As to functionality, there is a clear division between sensor system and data-processing unit. The sensor system generally performs the measurement, while all information processing takes place in the control system or stored-program
172
5. The Process Control System and Its Elements: Process Sensor Systems
Figure 5.60. World sensor use broken down by application [5.158]
Figure 5.61. Use of sensor systems in plant design and construction, broken down by industry [5.158]
controllers. The incorporation of microprocessor technology into sensor systems has turned them into “intelligent” systems, even if they show no outward sign of this new capability. The potential of intelligent sensor systems can be realized only through bidirectional communication comprising the transmission of all com-
Figure 5.62. Use of sensor systems in process engineering, broken down by industry [5.158]
mands and information. This is also the only way to take advantage of the wide range of possible functional expansions of sensor systems. Field bus technology will not only allow bidirectional communication but also simplify field installation and reduce costs. The cables needed for field installation are placed along the cable routes provided for them. Cable routing must comply with structural and
5.5. Field Installation and Cable Routing
113
Figure 5.63. Western Europeati sensor market in plant and process engineering, broken down by quantity measured [5.158]
Figure 5.64. World sensor market in plant and proccss engineering, broken down by quantity measured [5.158]
field bus and field multiplexer continue to be discussed, new systems and components today arc generally installed by proven conventional methods. Figure 5.69 shows five variants of present-day installation practice in Gcrmany [5.67]. In variants (a), (b), and (c) the transducer is located in the switch room, whereas in (d) and (e) it is in the field. On going from (a) to (e), the amount of functionality incorporated in the field-level devicc increases : Figure 5.65. Number of process monitoring and control stations in chemical process plants, broken down by quantity measured [5.69]
safety requirements; important points include fire and mechanical protection of the cables. Other practical requirements can be found in the section on cable routing (see p. 182). Field Installation. Qpes of Installation. Even though the prospects for field installations with
0
Variant (a): The sensor element (e.g., a resistance thermometer) contains no electronics. The transducer is located in the switch room and is connected to the sensor by a two- to four-wire cable. If the cable has three or four conductors, the effect of the line resistance can be compensated. Becausc there are no electronics in thc field and no auxiliary energy is supplied to the sensor, the signals depend only on the sensor and are said to be sensor-
174
5. The Process Control System and Its Elements: Process Sensor Systems
specific. The transducer in this variant is normally installed in a 19" rack. Variant (b): The field-level sensor element (e.g., a pressure sensor) has a two-wire power supply cable and uses a second two-wire cable to transmit a signal (0-20 mV or 4-20 mV) back to the transducer, which is usually located in a 19" rack. The signals to the transducer are manufacturer-specific, since the sensor signals are altered, but no intelligence resides in the field instrument. Variant (c): The sensor element in this variant has eiectrical preprocessing. In physical terms, the transducer is now present in the field. The manufacturer-specific signal, modulated onto the two-wire cable, is not yet finally pro-
Figure 5.66. Numbers and values of transmitters sold in the U.S. market, 1990 [5.159]
Figure 5.67. Flow rate and mass flow rate sensors broken down by type (estimated 1988 [5.158])
Figure 5.68. Western European sales of flow sensors in 1990, broken down by sensor type [5.62]
Display and operating level
I
' I
Terminal board
Switch raom
Field
x
Sensor
Variant.
a
Sensor Sensor
+
preprocessing
b
L
+Cornpact d
Nt
transducerse
f
4
Figure 5.69. Development of field installation PLS = Process control system; SPS = Stored-program controller; PC = Personal computer; U, = Auxiliary energy: MUS = Transducer power supply; FV = Field distributor
5.5. Field Installation and Cable Routing
0
0
cessed ; for example, linearization and damping have not been performed, and the output has not been converted to a 0-20 mA or 4-20 mA signal. These functions take place in the 1 9 rack-mount transducer, whose power supply also supplies the sensor element through the same two-wire cable. Variant (d): The transducer is located at the measurement station and is combined with the sensor element to form an overall system (compact instrument). A 1 9 rack-mount transducer power supply powers the compact instrument, furnishing 4 mA. The transducer sends the measurement information to the power supply unit as a 0-20 mA or 4-20 mA signal, over the same two-wire cable. The transducer generates this information by varying its internal resistance. Variant (e): This variant also employs a compact device. Because of the physical principle used, instruments in this variant are supplied with a relatively large amount of auxiliary energy, so that a separate power supply line is necessary. The output signal corresponding to the measured value lies in the range 0-20 mA or 4-20 mA and can be input directly to postprocessing or used for displays in either the switch room or the control room. Hence no other instrument is needed in the switch room.
All these variants have in common that the information-bearing signals from the field instruments are led to a field distributor or distributor box, to which other field-level instruments are also connected. The cable employed for connecting the field instrument to the field distributor is generally a twisted pair or four wires in two twisted pairs, with 0.5 mm2 shielded conductors. Data lines and cables are described in DIN VDE 0800-0899. Consider, for example, the 32-pole field distributor. From it, a 32-wire master cable usually runs to the terminal board in the switch room. Partial allocation is also possible, provided this is allowed by the allocation of master cable signal loops to the interface plane and no future change in the signal loops is permitted. The master-cable specifications could be as follows: 32-wire (16 x 2 x 0.5 mm’); i.e., 16 twisted pairs, not shielded painvise but having a common shield. In addition to the 19” interface plane, when required, a connection to a second terminal board is also possible. The second terminal
175
board provides the connection to, for example, the process-level components of the process control system. This connection can be made with a master cable having, for example, the following specifications: 32-wire (16 x 2 x 0.5 mm’); i.e., 16 twisted pairs, not shielded pairwise but having a common shield. Field Installation in Explosion Hazard Areas. Special regulations apply to the installation and operation of equipment in explosion hazard areas. These requirements have not been harmonized on a worldwide basis, so that some discrepancies arise. The discussion that follows applies to Europe. 0
Definitions and Terms (VDE 0165): The basis for assessing the scope of the requirements to be imposed is a division of explosion hazard areas into zones by the probability of occurrence of hazardous atmospheres. The gases and vapors present in the plant are classified into explosion groups, while the instruments and other devices to be installed are divided into temperature classes. Each temperature class specifies the maximum surface temperature permitted for the device. - Zones: Zone 0 includes regions in which a hazardous explosive atmosphere is present at all times or over prolonged periods. Zone 1 includes regions in which hazardous explosive atmosphere is expected to form only rarely and for short periods. Zone 2 includes regions in which hazardous explosive atmospheres are expected to occur occasionally. - Explosion Groups: Explosive gases and vapors can be classified into explosion groups according to the minimum ignition temperature of their mixtures with air. The groups are designated IIA, IIB, and IIC; gases and vapors in group IIC are most easily ignited. Instruments and devices must be approved for the appropriate explosion group. - Temperature Classes: Instruments and devices for installation in explosion hazard areas are classified into temperature classes corresponding to the maximum surface temperature. The classes range from TI (max. 450°C) to T6 (max. 85T).
0
Cables: Cables and wiring for circuits that are not intrinsically safe must be selected so that they can withstand the anticipated mechanical, chemical, and thermal stresses. The selection must comply with one of the following
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5. The Process Control System and Its Elements: Process Sensor Systems
standards: DIN VDE 0298 Part 1, 3, or DIN VDE 0891 Part 1. 5. 6.
0
0
On mechanical grounds, wire cross sections should not be smaller than certain values (VDE 0165). Conductors should generally be copper or, under certain conditions, aluminum. Placement of Cables and Wires: Cable and wiring penetrations to areas free of explosion hazard must be sealed adequately tightly; possible methods include sand seals, approved elastomers, and mortar seals. Cables and wires should have appropriate protection at points where there are especially severe threats due to thermal, mechanical, or chemical attack. One such form of protection is to place the cables and wires inside pipes. This should be done only with approved piping systems; case connections and transitions to cables and wires must also be approved. Once the installation for the explosion hazard area has been completed, it must be inspected by electrical specialists for compliance with ElexV $ 12. A further inspection for compliance with the accident prevention regulations of UVV 4 0 5 must also be performed by an appropriately licensed person. Equipotential Bonding : In explosion hazard areas, equipotential bonding is required to prevent static discharges that might cause ignition. A good foundation earth is incorporated when the plant is first constructed, and the structural steel of the building and, for example, the switch room floor structure are grounded to this in star fashion. The interface cabinets and terminal board cabinets, as well as all other units, are grounded to this floor structure, along the shortest path. The interface cabinets contain the transducer power suppiles and other devices. Between two active devices, such as motors, located not more than 2.5 m apart, the measured resistance should not be greater than 0.2 Q. The measured resistance between an active device and a passive object such‘aspiping, located a maximum of 2.5 m apart, must not exceed 1 Q.
An electrical specialist must verify compliance with the equipotential bonding requirements (UVV 4 $5) visually and by measurement. These measurements must be recorded in an inspection record. Systems with Intrinsically Safe Circuits. In the “intrinsic safety” type of explosion protec-
tion, electrical energy and surface temperature are limited such that sparking- and thermal effects that could lead to ignition of certain atmospheres cannot occur. In other words, not even a short circuit can cause sparking. This type of protection exists in all countries that accept the IEC standards, and also in the United States and Canada, which have not yet adopted them. Only insulated wires (insulation test voltage 500 V a.c.) may be used in intrinsically safe circuits. Several intrinsically safe circuits may be combined in one cable. Cables and wires of intrinsically safe circuits must be labeled or color coded. Sheaths or jackets are color-coded in light blue. Intrinsically safe circuits and circuits without intrinsic safety must not be laid together in cables, wiring, conduits, or conductor bundles. In cable ducts, insulated spacers must be inserted or wires with certain kinds of sheath or jacket must be selected. When installing an intrinsically safe circuit with just one apparatus, care should be taken that the maximum permissible inductance and capacitance for the circuit, including cables and wiring, are not exceeded. These permissible values are found on the nameplate of the apparatus. Wires must be treated as lumped capacitances. Intrinsically safe circuits from the field are generally led to interface planes located in the switch room, which provide the separation from circuits without intrinsic safety. Possible implementations include approved safety barriers and transducer power supplies. The transducer power supply limits the maximum power available on the intrinsically safe side and also supplies a maximum of 4 mA and, say, 15 V to the transducer. The sensor system connected to this device thus has ca. 60 mW available to supply all the electronics. Comparison of Circuits with and without Intrinsic Safety. The preceding section said that a transducer drawing its power via the 0-20 mA or 4-20 mA signal has ca. 60 mW at its disposal. This implies that only relatively low-power sensor systems can be supplied in this way; however, such sensors make up well over half of all sensor systems. There are, however, other sensor systems that cannot now be operated at such low power levels. The trend is for marginal systems to be supplied with low power at the expense of reliability, accuracy, and/or stability over time. It should be considered whether these drawbacks
5.5. Field Installation and Cable Routing
outweigh the advantages. Such a decision must be made for each application. The advantages of intrinsically safe sensor systems and their circuits are easy handling and low cable costs, since only two wires per unit are needed. Intrinsically safe instruments and circuits can also be worked on while in service without having to cut off the power. When nonintrinsically safe apparatus is used, there must be a separate power supply for the sensor system. That is, separate signal and energy channels must be present (see also Sections 5.1 and 5.21, so that at least four wires are required. If the power supply voltage is, for example, 24 V d.c., the supply and signal lines can be put in one cable. This arrangement has the further advantage that the terminal board plane and interface plane can be omitted. If the two approaches are compared, the intrinsically safe variant has the clear advantage as to handling. while the other offers significant cost benefits. Futhermore, when using intrinsically safe sensor systems that are barely able to function with the power available, some attention must be paid to reliability, accuracy, and time behavior. Field Installation in the United States. Installation techniques in the United States are quite different from those in Europe. In most cases, the cables are placed in conduits. 0
0
Definitions and Terms: Classes are defined as follows: Class I relates to gases and vapors; Class 11, to dusts; and Class 111, to fibers or fiber dusts. Each class is further subdivided into Division 1 and Division 2. The discussion that follows concerns only Class I areas. - Division 1 is characterized by the permanent, occasional, or periodic occurrence of hazardous concentrations under normal conditions. - Division 2 is characterized by the fact that hazardous concentrations can occur only under accident or malfunction conditions. Cabling in Class I, Division 1: The only types of cabling permitted in these areas are cables in conduits and mineral-powder-insulated cables of type MI with the end closures in stressrelieving grips. Only materials approved for these areas may be used; for example, conduits and fittings must satisfy U S . codes, some of which differ from the DIN standards. Conduits are inserted directly into the pressure-tight housing. The conduit system is
177
completely “flameproof,” so that a spark originating in the conduit cannot escape to the outside. Cabling in Class I, Division 2: Apart from conduits, NEC Article 501-4 also permits sealed track channels or cables or wires of types MI, MC, ALS, CS, TC, or SNM with approved end fittings to be installed in stressrelieving grips. Normal wiring is also permitted for circuits that, under normal circumstances, do not liberate enough energy on a wire break, short circuit, or ground fault to ignite an explosive atmosphere. Housings, fittings, and connectors generally must not be explosion-protected. Installation Practice in the United States: The classification of wiring in the United States is similar to variants (d) + (e) in Figure 5.73, that is, with a field distributor and master cable to the switch room. Even when cabling without conduit is permitted, common practice is to place all cables in conduits. This has historical reasons: When installation practice was codified in the United States, intrinsic safety did not yet exist, so all cables were run in conduits; in Division 1, the conduits also had to be pressure-tight. Sensor systems, as well as field distributors, were used in pressure-tight designs (as they still are). This type of installation, designed by piping engineers, was reflected in the standards. Changes, even when they appear to make sense, take place very slowly. By now, intrinsic safety does exist in the United States, but its availability has had little influence on installation practice. The insurance industry actually promotes the retention of this practice. Plants are usually covered by “electrical insurance,” written by companies that approve the installation method and generally expect conduits. Comparison of European and American Field Installation. 0
Disadvantages of Conduit Use in the United States: The use ofconduits increases the space requirement for the installation, since the routing has to allow for bend radii and offsets. The cost of installation is much higher, not only because of the placement of the conduits themselves but also because of the difficulty of pulling the cables through the conduits, their inflexibility, and the clumsy and quite heavy pressure-tight distributor boxes.
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5 . The Process Control System and Its Elements: Process Sensor Systems
Additional gas barriers must be placed in the conduits to prevent the movement of gases and vapors and the propagation of flames from one part of the electrical system to another. In the long term, problems arise from condensation and the pitting that it causes, as well as from the higher transfer resistances due to corrosion. This is important because the conduit system is also used for equipotential bonding. Condensation is controlled by drains through which the liquid can be removed. Advantages of Conduit Use: The conduits afford mechanical protection to the cables. As electromagnetic compatibility becomes more and more a factor in plant design, conduits offer much better shielding. Especially for digital data transmission, it is very difficult to separate signals from noise, since they occur in part in the same band of frequencies.
Trends in Field Installation. Cost Anal-vsisfor Present and Future Installations. An important question in the evaluation of new installation techniques is that of cost. New methods must not only provide technical benefits but should also be, at least, no more expensive. One study has found that future installation practice can make a significant contribution to cost reduction [5.163]. The comparative cost analysis is based on four cabling concepts in a representative plant. The plant consisted of 400 process control stations. For the first calculation, these were assumed to consist of 240 analog and 160 binary stations. The second calculation was based on 300 binary and 100 analog stations. For the variants with field bus, it was designated that fewer than half the process control stations could be connected to one field bus. All design, installation, shop, and material costs were included, from the individual line to the field instrument up to and including the input/output cards of the process-level components. The results are shown in Figure 5.70. Variant I : conventional, parallel cabling. Cost = 100% (basis for comparison). Variant 11: field multiplexer hardware used at the field level, so that only a few cables (field buses) had to be run from the field to the switch room. The result shows that there are marked
savings. Cost = 82% (74% for the plant with the larger fraction of binary stations). Variant 111: field bus (intrinsically safe; transmission rate 31.25 kbaud) for the field instruments that can be directly connected to a field bus. The remainder of the field instruments are connected to a field multiplexer. Cost = 73 % (77 Yo). Variant IV: field buses from variant I11 compressed into one field bus with higher data rate (not intrinsically safe, 1.5 Mbaud). Cost = 69 % (78 %). When the analysis of “present-day” instrumentation was extended by distinguishing between separate versions and compact devices, it appeared that future trends in instrumentation would see only the compact instruments still in use, with the intelligent transducer generally forming a single unit with the sensor element. Under severe ambient conditions, it often makes sense to install the transducer with its complement of electronics near the sensor element. Because it is not desirable to connect a 19” transducer on a field bus, this alternative will largely disappear in future. Field Installations with a Field Multiplexer. The field multiplexer solution is illustrated in Figure 5.69, variant (f), and Figure 5.70. A mixed field installation with field multiplexer and field bus is also possible. The field multiplexer must be approved for service in the explosion hazard area (zone 1). It provides data compression so that the total cost of cabling to the switch room, the required terminal board, the 19” rack for the transducers and their power supplies, and the necessary plant documentation are all substantially reduced in comparison with conventional installation practice. Such a field multiplexer can be obtained in a range of sizes. The user must decide the number of channels connected to it above which partial or complete redundancy is to be employed. In the case of complete redundancy, a second field multiplexer is connected in parallel. The line from the field bus directly to the input/output cards of the field-level components in the switch room should not be run together with the existing field bus. The IjO interface module can also be designed with redundancy. One possibility for the individual lines from field devices to the field multiplexer is an unshielded 2 x 0.5 mm2 twisted pair. The type of field bus cable depends on the run length and the data transmission rate. A shielded 2 x 0.5 mmz
5.5. Field Installation and Cable Routing
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Figure 5.70. Cost comparison for field installation variants [5.163] EV = Final distributor; FM = Field multiplexer; U, = Auxiliary energy; BM = Bus multiplexer
twisted pair may be needed for simple applications, a coax cable for more difficult applications. The field multiplexer itself requires external power (e.g., 220 V a.c.). Field Installation with a Field Bus (see Chapter 8). 0
Principles: Figure 5.73, variant (g), shows a possible field installation with field bus. This is a simple example for the direct connection of intelligent field instruments to the field bus. For use in process situations, especially in explosion hazard areas, the field bus must be of the intrinsic-safety type, so that the disconnection and connection of a bus subscriber is possible even while the system is operating. The bus can accommodate a combination of transducers powered from the bus and transducers
with external power, as shown in variants (d) + (e). Important is that the bus interface of the sensor system can always be connected to the Ex"?' (explosion protected, intrinsically safe) bus. The field bus cabling concepts illustrated in Figures 5.69 and 5.70 all relate to a field bus employing copper wire. The field bus can also be realized in the form of an optical fiber cable or a radio link. Radio transmission plays a role in widely dispersed or poorly accessible plants and is not further considered here. Although the "Physical Layer" is now included in the IEC Draft International Standard, optical fiber has not yet been adopted as a physical data transmission medium. Nevertheless, optical fiber technology offers a serious alternative to copper wire.
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5. The Process Control System and Its Elements: Process Sensor Systems
Comparison of Copper Wire and Optical Fiber: A field bus installation that seems appropriate for process engineering is that shown in Figure 5.70. The design in question has parallel individual lines from field instrument to field distributor. The several lines are combined into a two-wire line in the field distributor, and the two-wire line is then led to the process-level components. The field distributor is a purely mechanical component. In contrast, the optical fiber system has as its distributor an active element that forwards the signals after appropriate conditioning. The advantage of optical fiber technology is that the information is unaffected by electromagnetic interference. The separate power supply to the instruments must, however, satisfy the requirements of electromagnetic compatibility. For data transmission via copper wires, it can be expected that proper shielding or the use of special cables will still make it possible to comply with electromagnetic compatibility standards such as those of NAMUR. The price of such a cable is, however, much higher than that of a normal bus line. Very high data rates can be realized with optical waveguide techniques (5 Mbaud). However, such a rate is not generally needed for the connection of sensor and actuator systems through the field bus. The number of field instruments that can be connected to the international field bus (now in the definition phase) now appears to be 32. For a copper bus line intended for service in the explosion hazard area and also designed to power the instruments connected to it, the number of instruments is smaller, just nine at present. With optical fiber technology, 32 instruments can be connected to a field bus, even in explosion hazard areas. It depends on the application whether it is desirable to connect this many devices to one IjO card in the process-level component for the sake of redundancy. Present-day I/O cards can accomodate eight channels, roughly the same as the number of instruments that can be connected in explosion hazard areas when copper wire is employed. One advantage of the copper bus line-that the field instruments can also be powered from the bus line-becomes a drawback in many cases. In accord with the current trend, an attempt is sometimes made to use the bus to supply instruments that could vield more accurate. more
reliable, and/or quicker results if more power were available. This dilemma of the instrument designer is often resolved with optical fiber technology, since each field instrument then requires a separate power supply. Consider, for example, a plant with 200 field devices to be connected to the field bus. A calculation gives the following numbers of lines to be run : Field bus with copper cable: Number of buses: 23 (9 instruments per field bus) Number of separate power supply lines: ca. 20 (10% of 200) Field bus with optical fiber cable: Number of buses: 7 (32 instruments per field bus) Number of separate power supply lines: 200 (possibly implemented with a field distributor) Bus Cable: The definition of a single bus cable for all conceivable applications is unlikely, since it would be too expensive for simple applications. The Draft International Standard for the Physical Layer of the international field bus proposal identifies four cable types for the slow (31.25 kbaud) field bus (ISA/ SP.50-1992-236P) - Type A cable: Shielded twisted pair with following specifications : - z ,= 1 0 0 0 & 20% - Maximum damping = 3 dB/km - Maximum capacitance = 2 nF/km (wire-shield) - Maximum resistance = 22 Q/km (per wire) - Type B cable: Twisted pair in a master cable with a common shield and certain specifications - Type C cable: Twisted pair, possibly in a master cable, but not shielded, with certain specifications - Type D cable: Simple (not twisted) pair in a master cable, again with established specifications; the worst of the variants Field Installation of Safety Circuits: How safety circuits will be handled in future installation practice can be examined on an application-specific basis. Simple binary operations, such as “if limit switch trips, cut off feed,” should be distinguished from more complex
5.5. Field Installation and Cable Routing
PTB Tests: A study by PTB has shown that simple descriptive features make it possible to connect instruments made by different manufacturers to an Ex“?’ field bus complying with EN 50020 (Design Standard for Intrinsic Safety) with no special system certification. A variety of cable types were tested for a range of cable lengths and bus supply voltages [5.67].
conditions for triggering a safety circuit. Such a condition might be a mathematical operation on multiple binary and analog inputs. The simple binary operation will continue to be wired separately and realized individually by appropriate modules. It may be that an “autonomous” unit in the field multiplexer, powered only by the system, can perform this binary operation, so that the cable need not be run to the switch room. For a more comprehensive safety assessment, it is conceivable that either a hardwired unit or a stored-program controller could be used. Field Bus in Explosion Hazard Areas. The situation is analyzed for copper wire as the data transmission medium. A field bus for service in explosion hazard areas must comply with the explosion protection class “intrinsic safety,” so that an instrument can be connected or disconnected while the system is in operation. For lowpower sensor systems, such as those usually employed for pressure and temperature, it should be possible to use the bus line for both information and power supply to the instruments. Even externally powered sensor systems must at least carry out the bus connection in the “intrinsic safety” class, so that these can be connected to the Ex‘Y field bus together with instruments not requiring external power.
Figure 5.71 illustrates the concept underlying the PTB tests. Only “one active device,” the bus power supply, is connected to the field bus. The other devices are “passive” in that they cannot inject power into the bus. In this way, only one device can put power on the line, even in case of a malfunction. The passive devices have low maximum external capacitances and inductances (Ci < 5 n F and Li < 10 pH) and minimum input voltages of 9 V. It was further required that each instrument connected must draw a base current of at least 10 mA. The cable length and number of nodes can be optimized by varying the power supply voltage and line length. The concept is applicable to a variety of field buses (Profibus, international field bus, etc.). The most important finding was that over the wide range of parameters tested, and for various cable types and lengths and various bus power supply voltages, the probability of ignition was not increased by the length of the field bus, the
Field area (“Ex”)
Switch room Bus power supply
Field
instrument
Field instrument
Field instrument
181
Manual control unit
Figure 5.71. Base concept of the PTB study on field bus use in explosion hazard areas Ic = base current
5. The Process Control System and Its Elements: Process Sensor Systems
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of heightened fire hazard and danger zones as defined in Elex V and VbF at the stage of site and building layout [5.164]. The overall concept for the main cable and piping routes must be defined at an early point in time and incorporated into the construction plans. Whenever possible, cables should be located only in areas of slight danger. Accessibility and ease of maintenance must always be kept in mind; this includes later expansions. The spread of fire through lining penetrations into fire hazard areas must be restricted by suitable covers. When cables and pipes are routed together. the possibility of a harmful interaction through thermal, chemical, and mechanical attack must be borne in mind. Cable and pipe routes should therefore be kept separate when possible. When this is not possible and an adequate standoff to hot plant apparatus cannot be maintained, heat shielding must also be provided. Where areas of severe fire hazard cannot be avoided and the functioning of the process control system must
number of bus connections, or the number of instruments connected to the bus. These results are illustrated in Figures 5.72 (category ib, groupo IIC, RL = 44 Q/km) and 5.73 (category ib, group IIB). For a realistic bus power supply voltage of 15 V, the maximum number of nodes is nine for group IIC and 27 for group IIB. The maximum cable length is 787 m for group IIC, and 310 m for group IIB. The bus power supply voltage or conductor cross section can be varied in order to adapt to a wide range of applications. Important observations from the user’s standpoint can be found in [5.68]. Cable Routing. The sensors, actuators, and so forth contained in a plant are usually connected to the process control system by cables. Cable routes are the “streets” along which the cables are laid. The project manager, in collaboration with fire services and operators, must identify zones
-Line Length ---_Number
9
11
o f nodes
13 15 17 Supply voltage U,, V-
19
21
Figure 5.72. Results of the PTB field bus study (category ib, group IIC) Line Length o f nodes
----Number
Supply voltage U,, V
-
Figure 5.73. Results of the PTB field bus study (category ib, group IIB)
5.5. Field Installation and Cable Routing
be insured under all conditions, a fire-resistant lining or cladding or the flooding or spraying of cable routes may be necessary. The hanger design must be suitable to the load. Lining may necessitate cooling of the affected section (forced ventilation). Ventilation systems must not extend over the boundaries of fire compartments, so that the spread of fires into other compartments will be hindered. Reliable extraction of cooling air (fumes and smoke in the case of a cable fire) must be insured. Chemical attack must be handled by appropriate selection of construction materials, coating, or jacketing. The number and location of process control rooms must be selected to yield shorter and more secure cable routes (see Section 10.7). These rooms should be in areas of little danger. Process control rooms and principal cable routes should always be configured so that fires in the rooms and localized fires in plant areas will affect only a portion of the plant (decentralization). The insertion of cables into process control rooms should take place outside the explosion hazard area whenever possible. Massing of cable routes near such insertions should be avoided (subdivision). If power, control, and data cables are run together, the thermal load on the cable route is diminished; there is no objection to this arrangement provided electromagnetic compatibility is insured. High-voltage cables (> 1 kV), however, should be run by themselves. Dedicated cable routes may also be needed for emergency power supply and safety system cables, whose functioning must be guaranteed for as long as possible during upsets. Bringing cables from outside to inside to supply a floor or platform is generally safer than bringing them from a central cable tray inside the building. When cables are run on exterior walls or in open recesses indoors, any potential threat from their surroundings should be considered. Working platforms connected by fixed ladders should be built on exterior walls around the height of wall penetrations. In outdoor installations, cables can also be routed on the outside and, if necessary, should be provided with protection against thermal effects. The cable route must be acccessible to firefighters in case of fire. “Cable walls” must permit access to areas located behind them. Exposed cables must not be run in staircases or on walkways that serve as escape routes.
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Placement of cables in underfloor ducts should always be avoided. It creates the danger that aggressive fluids will penetrate the cables or that explosive gas-air mixtures will form and spread without hindrance. In cases where the use of underfloor ducts cannot be avoided, possible sources of danger should be eliminated by appropriate precautions, for example by sand filling or ventilation. When sand filling is chosen, cables approved for underground placement and having adequate thermal ratings should be selected. If fire protection considerations mandate the placement of cables in a cable pit or vault, this must be of fire-resistant design. Suitable access must be provided for both firefighting and the operation of vent and exhaust valves. The chimney effect must be taken into account where cable fires can occur in cable pits. If necessary, the proper design precautions should be taken, such as fire partitions between floors and section-bysection removal of smoke. Compartmentalization should also be considered in horizontal cable routes. Cable Routing inside Process Control Rooms. The design of cabling and wiring inside process control rooms should allow for some key constraints (see also Section 10.7): 0
0
0
0
0
0
The penetration to the production facility must be gastight and safe with respect to fire hazards. For cables and wires that are to be led into cabinets or cubicles from overhead, or brought from overhead to terminal boards or mounting racks, cable trays should be provided below the ceiling. For cables and wires that are to be led into cabinets, cubicles, or racks from beneath, a double floor at least 20 to 30 cm high must be provided (Fig. 10.49). Cables and wires can be laid free on the floor. For larger cable cross sections, in particular, care must be taken that sufficiently large bending radii are allowed. On grounds of electromagnetic interference, cables and wires for power and those for data should be spatially separated. Massing of cables and lines should be avoided. Design must make adequate allowance for retrofit capabilities.
For high power density, continuous air cooling may be needed. The placement of product and energy piping (except process control system piping) in cable pits is always to be avoided.
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In areas where there i s a danger of dust explosions, design or organizational practices must insure that accumulations of dust are avoided. Cable Penetration to the Switch Room. Cables running between the field and the switch room must be led into the switch room through a cable bushing in the wall. On grounds of fire protection, a bushing that meets the required fire protection class, such as F90, is selected. The fire department establishes what fire protection class is required. Not only the cable bushing but also the wall and the door to the switch room must
comply with this fire protection class. In a lowcost method of penetration, holes of the desired diameter (e.g., 150 nim) are bored at appropriate points in the wall. The holes are half filled with the cables to be led through them. A fireproofing compound is then injected into the spaces between cables. The remainder of the opening is sealed with adequately close-fitting, flexible plugs inserted from both sides (the plugs should foam up in case of fire). In future, 3-D CAD tools will be important aids in three-dimensional cable routing and piping design.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
6.1. Principles
6. The Process Control System and Its Elements: Process Actuator Systems 6.1. Principles Chapters 2 and 5 have pointed out the necessity of adopting suitable structuring practices to gain an overview of the wide variety of hardware implementations. The need for currency and completeness of description is complicated by the restriction to essentials (for the sake of clarity) and by the fact that technical progress continues after the manuscript of any publication is submitted. For these reasons, an attempt is made here (as in Chap. 5 ) to achieve some consistency in description, from the information point of view. This can easily be done, in object-oriented terms, by appropriate assignment of attributes. The viewpoint of the modeler is shaped by the objective, as in any modeling process, and if the objective changes then this viewpoint may (or must) change. Information-oriented process control engineering uses the term “actuator systems” for all technical systems that enable intervention in the process properties, and ultimately the product properties (see Fig. 6.1). Regardless of the hardware implementation of the system under consideration, this choice of terminology has the advantage that the analysis focuses on the way in which the technical process is influenced or acted on, without necessarily being tied to implemen-
185
tation-dependent nomenclature such as controller, positioner, servo drive, and so forth. Furthermore, the degree of automation, the availability of energy, and the complexity of the actuator system remain initially unaffected by the choice of terms. If an actuator system is analyzed from this viewpoint, a minimum of three components can always be distinguished (Fig. 6.2). First there is a functional unit that receives and interprets a control command (actuating command), represented by the control signal (actuating signal) from a higher level. The signal may originate in local components (Chap. 7) of a process control system or in programmable controllers. This functional unit of the actuator system is labeled as “control signal processing” in Figure 6.2. An energy-furnishing component, controlled by the output signal from control signal processing, can then be identified. In its simplest form, this component (called “servo drive” in Fig. 6.2) must act as an energy store or else utilize energy supplied via the control signal, passing on this energy in accordance with the control signal. Finally, the third functional unit is the actuator proper, often known as the “final control element.” This component is in physical contact with the process properties [6.1]. The components listed here are found in related forms in the classical control devices (control valve, shutoff valve, ball cock, etc.). The classical terms control element, control drive, and control signal processing indicate the unambiguously defined direction of action; they
Figure 6.1. Actuator systems: analogy with the phase model of production
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6. The Process Control System and Its Elements: Process Actuator Systems
do not imply feedback (confirmation of execution or of effect achieved) as to the intervention in the process. This type of analysis further reveals the problem of separating information flow from energy flow in such simple actuator systems. For example, the energy transmitted by the servo drive to the final control element can simultaneously be treated as a control signal, but one whose action on the process properties is determined by quantities specific to the system and apparatus. It is clear that the resulting dependence of process interventions on unknown effects, such as turbulence, aging, and the like, does not promote goaloriented process control. Figure 6.3 shows an actuator system in which an auxiliary energy supply has been added. This configuration makes it possible-at least in principle-to separate information flow from energy flow, to decouple the actuator and the drive from
the signal processing, and to achieve flexibility in design. The recent trend toward “intelligent” actuator systems is illustrated in Figure 6.4. Here an integrated (dedicated) computer takes care of function monitoring, function maintenance as required, control of the process intervention, and other services relevant to design or maintenance purposes [6.2]. Communication with the dedicated computer takes place via a display and control panel, a handheld terminal, a PC, or a display and control component [6.3]. In the long term, communications requirements stemming from additional functionalities will make a field bus solution essential. The shift of intelligence into the field level permits additional functionalities in the field (local) components. Because the real effect of an actuator operation on the process or product properties is of far greater interest for process
Figure 6.2. Structure of a simple actuator system
Figure 6.3. Structure of an actuator system with auxiliary energy
187
6.1. Principles
Function monitoring Function maintenance Control Operator functions
A
v
Control device local c o n t r o l
Communica tions
Integrated display/ c o n t r o l panel
A
A
@
( i J
4
-
I
Auxiliary energy
Figure 6.4. Structure of an “intelligent” actuator system
control than information such as the actuator position, there is a trend toward coupling of information directly at the local level. The speed control of a pump as a function of the actual flow rate, as determined with an integrated sensor, is an example of fast control loops on the local level. In order to survey and classify actuator systems, it is necessary first to define an appropriate viewpoint. In what follows, some criteria are described, leading to the classification presented here. A breakdown of actuator systems meaningful in the automation context might be based on the degree of automation of process interventions (manual operation, automatic operation), the type of auxiliary energy (electrical, pneumatic, hydraulic, etc.), or even hardware criteria. With regard to information-oriented process control engineering, however, a different type of classification appears more desirable. Actuator systems are analyzed from the standpoint of possible actions on material and energy streams. An attempt to set up such a classification is described here. The action of an actuator system can be characterized in terms of the possible change in process properties. The manifestation of a property category can be described as changing “continuously” or “discontinuously.” The taxonomy that follows therefore uses the criterion of a continuous versus a discontinuous control range. For example, a mass stream can be controlled in continuous fashion by a control valve or discontinuously by a shutoff valve.
In continuous actuator systems, a further division into proportional and integrating types is possible. Similarly, discontinuous systems can bring about defined absolute changes or incremental changes in the process properties. An additional feature of actuator systems is important from the standpoint of safety. A system may have an undefined position on failure, where the actuator remains in its most recent position in case of an interruption in the signal or energy flow, or it may have a well-defined safe position (fail-safe). An intermediate group comprises actuator systems in which the drive has a memory characteristic that takes the undefined failure position of the actuator over to a well-defined rest position. For certain processes, this represents an ideal solution from the safety standpoint. Figure 6.5 shows a taxonomy of actuator systems based on the criteria discussed above as well as the energy flows to be supplied. Because the application and design features here are generic properties, they apply to all the actuator systems discussed here. Design features-ranging from physical form, material of construction, and control range to communications, safety, and manufacturer-are listed in a second generic class. Actuator systems are first classified by objective, that is, actuator systems for material streams are distinguished from those for electrical energy streams. For each group, systems that effect a quantitative change in the streams are next separated
188
6. The Process Control System and Its Elements: Process Actuator System
manufacturing and process engineering these two classes are often regarded as independent groups. The category of transforming systems includes not only the turbine and the burner, but also the pump. This is admissible since the primary use of a pump is to increase the pressure of a material stream; thus the pump, as an actuator system, alters a qualitative property of the material stream. Other examples of material transforming systems can easily be classified; for example, a stir-
from those that essentially bring about a qualitative change (most often defined in energetic terms). The first type, acting quantitatively on the streams, are described as “controlling” systems in Figures 6.5-6.7, while the second type is described as “transforming” systems. If we consider first actuator systems for material streams (Fig. 6.6), the group of controlling systems include “distribution” systems such as screw conveyors, metering apparatus, etc., and “throttling” systems such as control valves, shutoff valves and gates, cocks, and so forth. In (Actuator
systems)
Generic class: application f e a t u r e s
Continuous c o n t r o l range
Discontinuous c o n t r o l range
A
Proportional characteristic
Integrating characteristic
Generic class (communicat,ons)
A
A
Undefined position on f a i l u r e
Failsafe
design f e a t u r e s
/[\/I\
Systems controlling electrical energy
Figure 6.5. Taxonomy of actuator systems for material and energy streams
Figure 6.6. Taxonomy of actuator systems for material streams
Systems t r a n s f o r m i n g electrical energy
6.2. Actuator Systemsfor Material and Energy Streams
rer plus drive in a stirred tank reactor, or of a roller, a press, or a tubular reactor. Figure 6.7, based on the same classification principle, shows a taxonomy for actuator systems working on electrical energy streams. The quantity controlling systems include relays, thyristors, transistors, and gate-turnoff (GTO) devices (see also Section 6.3); the energy transforming systems include generators, lighting, motors, heating. etc. A more detailed breakdown of energy controlling systems can be found in Section 6.2, which also distinguishes between switch and positioner on the basis of whether the control range is continuous or discontinuous. Development trends in actuator systems can be briefly described as follows: Development from the actuator to the actuator system with integrated intelligenceand the incorporation of lower-level control functions Expanded communications capability with field multiplexer or field bus [6.4] Improved service life, especially for actuators, due to novel materials, designs [6.5], and automatic maintenance techniques [6.6] Increased use of electronic frequency converters for motor speed control [6.7] The trend toward intelligent actuator systems is possible because of advances in microelectronics. For example, a valve position controller with an integrated 16-bit microprocessor is now available [6.8]. Self-parametrization makes it much easier to adapt the device to the drive and the valve; the internal control loops of the actuator are more powerful; and the adaptability to a
189
range of service conditions has led to a reduction in the number of position controller types needed. The augmented functionality of actuator systems cannot be fully utilized unless there is a suitable facility for communication with higher levels in the process control system. Parametrization, function monitoring, and maintenance require special information, which will be made available by future field bus and communications concepts (see also [6.9] and Chap. 8). Section 6.3 deals with the trend toward variable-speed drives. It is seen that improved control behavior (linear control characteristic), reduced energy consumption (especially under part-load conditions), motor power and motor losses restricted to the design values, and optimal matching to the process machinery used offer substantial potential for economic and technical optimization.
6.2. Actuator Systems for Material and Energy Streams Introduction. Together with sensor systems, actuator systems link material, energy, and information streams in processing and manufacturing systems. The control path with sensor system, controller, and actuator system forms a closed control loop. Changes, whether in perturbing values, setpoints, controlled variables, or other quantities, manifest themselves in the system and also in the signals transmitted to and by these devices. With the aid of actuator systems, the material or energy stream is set in the coiitrol loop as a function of the system deviation. The
Actuator svstems for \ j e ( e c t r i c a 1 energy streams)
Systems controlling electrical energy
Figure 6.7. Taxonomy of actuator systems for electrical energy streams
190
6. The Process Control System and Its Elements: Process Actuator Systems
action also depends on the characteristics of the control path, the sensor system, and the controller with its control parameters influencing its dynamics. With regard to the dynamics, the control element can be included in the control pathway and the servo drive (actuator drive) can be included in the control device. In the open loop case, the intervention in the process is effected through a control command, which takes the form of a control action or a manual action. The control pathway need not carry feedback. In actuator systems, the control command (the output of the controller) must be tranformed to a control signal (Fig. 6.2). Servo drives in the form of power amplifiers are needed for this purpose. These may be pneumatic, electric, or hydraulic, but hydraulic drives are rarely used in process engineering. Because of their wellknown advantages in manufacturing and robotics, however, hydraulic power amplifiers are preferred in these fields. They are robust drives whose high power density results in good control of complex motions, accurate positioning under load, and wide speed ranges. Problems, however, such as poor damping, marked nonlinearity, and load-dependent eigenfrequencies, should be mentioned. Continuous and switching control elements are distinguished, just as control elements with memory must be distinguished from those with a well-defined rest position. Single action modes or combinations may be required, depending on process or safety requirements (see also Fig. 6.5). Continuous control elements effect a steady or gradual change in mass or energy flow within the design range, while switching elements act in a step-by-step manner. In process control engineering, there is often only a minor difference between the two types. Control elements of continuous or switching type can be designed by modification of globe and plug valves, cocks, shutoff valves, and the like. The required characteristic determines whether the required accuracy of positioning or control can be achieved under given pressure conditions. The characteristic associated with the system (inherent characteristic) can be matched to the operating characteristic. With regard to accuracy of positioning, a corresponding resolution of the control signal must be ensured. Control elements such as slide valves or solenoid-actuated valves have a design that makes them unsuitable for continuous control and positioning.
Control elements with electric motor drives have memory and remain in the last position adopted if the auxiliary energy goes off or the control signal fails. Ordinarily, control elements take up a well-defined position on a failure of auxiliary energy or control signals. For material streams, this is generally full open or full closed, and for energy streams, full on or full off. In safety devices, this position is called the “safety position.” Control elements such as gate valves or stop cocks with double-acting pneumatic drives can be made to exhibit memory by the addition of blocking valves; the use of return springs or pneumatic buffers can result in their exhibiting a well-defined rest position. The size of the pneumatic buffer depends on the number of switching operations and the quality of sealing. The advantage over energy storage in springs is that several switching operations can be performed after the occurrence of a fault. The term “control” is explained in DIN 19222 [6.10]. Control is the changing of mass, energy, or information flows by means of control elements. Standardized definitions of the terms actuator, actuator system, control element, controller, and control machine have not yet been adopted. They are used at present even though the demarcations between them are often not clearly defined.
Examples of Actuator Systems; Delimitation. Controllers or final control elements are also referred to as actuators or actuator systems, in analogy to sensors or sensor systems. Ordinarily, “sensor” means an element that performs a detection or measurement function. If this element is equipped with signal processing and other modules that permit further options, the result is a “sensor system.” The analogy to “actuator” and “actuator system” can be made complete only if the “actuator” is defined exclusively as the part that comes in contact with the product. Accordingly, the actuator is the final control element or control machine itself, that is, the throttling organ, the pump, the compressor, the blower, the resistance coil (for heating), or the fluorescent tube or incandescent lamp (for lighting). The corresponding actuator drives would be (for controllers of material streams) the diaphragm drive, the piston drive, the rotary drive, the motor, the motor with converter when speed control is required, or the switch or power controller for heating for lighting. The last two items apply to the case of controllers for energy
6.2. Actuator Spterns,for Material and Energy Streams
streams. In common usage, however, the terms actuator, actuator system, controller, and final control element are often employed as synonyms, generally for the complete device. Generally, the actuator system (Fig. 6.2) consists of a final control element (control machine), a servo drive, and a control device. Figure 6.8 shows the setup. The final control element is moved by the servo drive in order to change the flow of material and energy. The servo drive is driven externally with the control signal from a control device. With further devices, parametrization, configuration. status, feedback, and other signals can also be transmitted. I f these terms are applied to two well-known cases-the control valve and the variable-speed pump drive-the consistent representations in Figures 6.9 and 6.10 are obtained. Figure 6.9 shows a controller made up of a control element in the form of a valve (fitting) with a throttling organ, a pneumatic drive, and a control device. The control device is a positioner, which processes the control signal (as setpoint) to the position of the drive (as actual value) in the course of a closed-loop control operation. Along with the control section, the positioner includes a power section, where the control signal is amplified and which is to provide the desired positioning of the control element with the required quality and dynamics. The terms control element, actuator, and fitting are often used synonymously.
Y 1
191
The variable-speed pump drive is a typical interconnection of a control element in the form of a pump, a control drive in the form of a motor with power section integrated into the frequency converter, and a control device in the form of an Auxiliary energy (hydraulic, 6 b a r ] (energy channel]
I
Control signal (information channel)
I1
Controlled auxiliary energy 10-6 b a r )
Control unit (pneumatic controller) Feedback
Servo drive (hydraulicmechanicall
Mass stream element (valve]
Figure 6.9. Actuator system for mass streams: structure of a control valve Auxiliary energy (electrical, 50 Hz) (energy channel]
Auxiliary energy
and s t a t u s
Control signal
c l
l
Control unit (frequency converter)
Controlled auxiliary energy (0-100 HZ)
Feedback
Servo drive (electromechanicall
Servo drive
____
Final control element
Masdenergy
Figure 6.8. Structure of an actuator system
-
Mass/energy
Mass stream -L
0
-
-
Final c o n t r o l
192
6. The Process Control System and Its Eletnents: Process Actuator System;\
information-processing component. The control signal is fed to the control device, which monitors, reports, limits, diagnoses, tests, generates error messages. archives, establishes a connection as needed, communicates, and so forth; it can also be parametrized and configured (see also Fig. 6.4). Section 6.3 goes into more detail on variable-speed drives with d.c. and a x . motors. The simplest case of control of an energy stream is the making and breaking of a circuit to a consumer. Figure 6.1 1, which is based on Figure 6.8 and analogous to Figures 6.9 and 6.10, shows an actuator system whose control device receives the sensor system signals of interest to the operator, and appropriately switches auxiliary energy to the control drive (coil) in order to activate the control element (switch). The terms can also be extended to a positioner in a heating system (Fig. 6.12). This is a continuously adjustable heating device, whereby the control signal is generally a unified current signal (0/4-20 mA). The control device, with monitoring, testing, reporting, communications, and display functions among others, drives the control drive o r power section. The energy stream to the control element o r to the heater coil is adjustable from the control room or locally. It can be set automatically o r by hand-depending
on what devices are connected in circuit with it-anywhere in a design range specified by the designer. Points to consider in the design of a controller for a material stream include dynamics, operating point, positioning forces, operating range, characteristic, safety position. operability, service under explosion hazard conditions, and sealing. Design steps include the determination of the nominal diameter (DN), the rated pressure (PN). the construction materials for components in contact with the product, the type of sealing. the positioning forces, the noise emission. the possibility of cavitation, and the matching of the inherent characteristic to the operating and control drive characteristic. In the case of the variable-speed pump drive or a control machine. special consideration should be given to the motor startup characteristics, the bypass circuit, the power rating of the motor and the converter, the motor heating, the torque ratios, the proper matching of the actuator (in the form of R pump) to the motor, the cooling of the converter, the use of tested and certified devices for service i n Zone 1, and so forth. In this connection, a number of regulations, specifications, technical guidelines, and so on must be complied with. A process control station data sheet is filled out in which the product, device, process, and design
Auxiliary energy (electrical) (energy channel)
I
Sensor system signals (information channel)
A
Controlled auxiliary energy
(information channel)
Control unit
Control unit
1
Controlled auxiliary energy
Feedback
Feedback
V
device) Clutch (electrical, mechanical)
Clutch (electrical, mechanical) Final c o n t r o l
from mains
-
I
-
1 -
contacts)
To consumer
c
f r o m mains
Final c o n t r o l system (variable resistor,
# IGBT) +- TO consumer
._ _.-.
6.2. Actuator Systems ,for Material and Energy Streams
data are entered along with data on safety, availability, quality assurance, etc. With the station data sheet and the requirements from the piping and instrumentation diagrams or the bid specifications, the appropriate design is prepared and the process control system station plan is generated. The procedure is illustrated for a control valve (Fig. 6.13); see also the generic classes in Figure 6.5. The control valve consists of the valve itself plus a lever or plunger drive, depending on the motion of the throttling organ. The valve can be realized in the form of various devices, such as a globe, peristaltic, or plug valve. The design specification differs from one type of throttling organ to another. The configuration or the way the devices are installed in a process plant can also vary. The best installation position depends on the product, the heating medium, the sealing system, the flange design, and so forth. The design of the lever drive can be based on equipment offered by several manufacturers. As a rule, however, only a single design specification is considered. The same holds for the case of a plunger drive. The devices with their designs, the layouts, the selection, the installation of these devices have been fully described elsewhere. There are also many DIN, IEC, and VDMA standards. In many cases, calculation and design programs are offered with the respective data sheets. Examples of commercially available programs can be found in [6.10]. The path of the control signal via the control signal processing section of the control device, the control drive, and the final control element itself, where it is transformed to a position value, is shown in the middle part of Fig. 6.4. The control element alters a process value in a process element. Generally, one measured quantity is provided to the control device from the process. This quantity is needed as a control or monitoring quantity for the controller or actuator sys(.Control
193
tem. Feedback to the process control system or to local components takes the form of messages. In the control device, the actuator computer, together with its communications devices, is shown in Figure 6.4. This device performs function monitoring in the form of self-tests or externally initiated diagnostic tests. The result of these tests is directly displayed locally in error messages, error displays, status messages, and so forth, or (in future technology) will be transmitted over a field bus to a remote location. An integrated display/control panel is always included in complex actuator systems for local parametrization and configuration. Human-machine communication takes place via this interface. Taxonomiesof Actuator Systems for Material and Energy Streams (see Figs. 6.5 to 6.7). In what follows, an attempt is made to devise taxonomies of various actuator systems under various criteria [6.11]. The entity-relationship model is employed, converted directly to file structures (see Chap. 2 and 16.121). Controllers (actuator systems) are first classified in terms of their tasks. Controllers for material streams and those for energy streams are grouped separately (Fig. 6.14). At the next level of detail, controllers for energy streams are broken down into switches (e.g.. Fig. 6.4) and positioners (Fig. 6.5). Figure 6.15 shows the classification by technical and functional criteria. Each actuator system consists of a control element, a drive, a control device, and energy storage, if required. The control element (actuator) may consist, for example, of a valve with a throttling organ, a pump with case and impeller, the inductive coils of an induction furnace, a halogen lamp, the mechanical contacts of a power contactor, or the power thyristors or transistors of a positioncr. The drives (power section) are listed in a similar way. In more complex actuator systems, the con-
valve)
Figure 6.13. Descriptive structure of a control valve for design and installation
194
6. The Process Conirol Sysieni and Its Elements: Process Actuator Systems
f o r mass/material
Figure 6.14. Taxonomy of actuator systems for mass and energy streams
(Actuator
system)
Energy storage Servo drive Valve Heating Lighting Opener Closer
Changer
Hub drive -Piston drive -Motor Positioner Cqil
I I
Controller
Spring -Air chamber -Battery -Emergency power supply
Feedback Display Interface Machine-apparatus Human-apparatus CManual olr:;;[ unit
t
SPS
Figure 6.15. Taxonomy of actuator systems for mass streams according to functional criteria
trol element and drive are based on a control device (information-processing component). This component, in the form of an information or signal-processing section, processes, conditions, and transmits the signals to the actuator system and also from the actuator system to and from higher-level devices. When safety and availability requirements are more stringent, energy storage is required. If the auxiliary energy fails, this stored energy brings the control element to a desired safe posi-
tion via the drive. Examples are compression and tension springs, which release energy on demand and test the “emergency case” upon every switching operation. A pneumatic buffer can be used as energy store; from which energy stored in compressed air can be withdrawn for a certain time and with which repeated switching operations can be carried out. In other applications, batteries and backup power supplies maintain safe operation. Battery-backed systems can be kept operable without interruption; with emer-
6.2. Actuator Systems for Material and Energy Streams
Another possible classification, in accordance with the terms of DIN IEC 534, Part 1, is illustrated in Figure 6.17. Mention should be made of the many and varied process connections, sealing systems, fitting types, throttling organ configurations, noise suppression options, drive design possibilities, and control device types. The control element can also be a controller without auxiliary energy but employing spring or weight loading to insure the admission of a
gency generators, on the other hand, interruptions must be reckoned with from the outset. It is often sufficient that the motors continue to run after a short interruption. Final control elements for material streams are classified as shown in Figure 6.16. The control element may be a control valve, whereby the function of the throttling organ is carried out by translational or rotational motion. The throttle bodies are said to be straightline (translational) o r rotational. {Final \unit
control\ /)
or auxiliary
I&]$(
\Weighted
Translational
i
Hub valve Gate valve Constricted tube valve Magnetic valve
Rotational
195
t
Compressor Ventilator Calander
Rotary -[Gear Seeberger
Stopcock Plug and b a l l valve Flap valve
Figure 6.16. Taxonomy of final control elements for mass streams
\ (stem>
Figure 6.17. Taxonomy of a control valve [DIN IEC, Part 11
6 . The Process Control System and Its Elements: Process Actuator Systems
196
For this reason, variable-speed motors are increasingly used to drive control machines (see Section 6.3 and [6.14]). Drives. Each actuator system has a power section or drive system (Fig. 6.18), which must be matched to thecontrol element or actuator. In devices with throttling organs, translational and rotational control drives are distinguished. Motors can be classified into d.c., multiphase (Fig. 6.19), compressed-air, and other types. Depending on the network form, power, required dynamics and quality ofcontrol, field and later maintenance conditions, and so forth, the designer selects an asynchronous, a.c., synchronous, or d.c. motor. Attributes of these
certain quantity (setpoint value) of material per unit time. Large numbers of such devices are used not only in process engineering, but also in heating and air conditioning as well as other branches of industry. Control machines are becoming increasingly important with the development of modern power electronics, because they can be operated much more efficiently in variable speed mode than controllers with throttling organs. In systems with throttling devices, energy must first be introduced into the material stream, and then part of the energy dissipated by throttling [6.13]. Cavitation can result, with outgassing, phasechange effects, and noise as the consequences.
.
(+)(Rotatianal) Hub drive
Piston drive Scr,ew drive I
\ (F)
\ LRotary piston P a r t - t u r n actuator I
I
I
1
d s . motor
T hr ee-phase mot or
Compressed-air motor
Figure 6.18. Descriptive structure of an actuator or power section
-Pole-changing motor -Slip-ring motor
Figure 6.19. Taxonomy of servo drives for the example “motor”
1-
co n ve r te r U- c on v e r t er Control tr a n sfo r m e r PWM-converter Control potentiometer Power controler Transistor T h yr i sto r
t
6.2. Actuator Systems for Material and Energy Streams
I
Poled r e l a y Latching r e l a y i n t e r n a l time-delay r e l a y Reed-relay Time-delay r e l a y Current-impulse r e l a y
-Device with operating method Induction cup c o n t a c t Core magnet contact Clapper-armature contact Toggle contact -Device w i t h s p a r k suppression method Oil contact SF, contact Vacuum contact
i
197
Motor p r o t e c t i n g s w i t c h Bimetallic r e l a y Power circuit breaker Automatic circuit b r e a k e r Thermal motor p r o t e c t i o n -Fuse switch disconnector -Disconnecting s w i t c h
Figure 6.20. Taxonomy of discontinuous actuator systems for energy streams
objects include power, rated voltage, speed, number of pole pairs, motor and explosion protection, case shape and size, type of protective enclosure, weight, ventilation, position of terminal box, manufacturer, supplier, model number, and stock number. Devices for controlling energy streams can be broken down into switching devices and devices continuously controlling the energy stream (Fig. 6.14). Typical representatives of switching devices or switches are relays, contactors, disconnectors, power circuit breakers, and currentlimiting circuit breakers. Each of these consists of a drive (operating mechanism) and a mechanical system, which in the simplest case (e.g., in a low-voltage, high-breaking-capacity breaker) is operated by hand. More complex devices employ a magnet coil or, in the medium- or high-voltage range, a motor drive. Switching functional elements are normally-open, normally-closed, and changeover switches. Figure 6.20 illustrates this taxonomy, but only for the mechanical switch side; electronically operated devices with corresponding functions can be classified similarly. In the continuously operating devices, the energy stream or power can be set within a certain range, so that matching to a certain operating point within a stated range is possible (see also the schematic diagram in Fig. 6.12). Positioners or position controllers can be used to set the voltage, current, frequency, and other values [6.13], [6.15]. Rectifier technology includes devices to transform or control electrical energy by the use of rectifier valves. The fraction of electrical energy switched, controlled, and trans-
G=4 Positioner
d.c. c o n v e r t e r Indirect c o n v e r t e r PAM c o n v e r t e r U converter PWM c o n v e r t e r U converter I converter
t
d.c. c o n v e r t e r d.c. chopper
Direct c o n v e r t e r a.c. power c o n t r o l l e r (ACPCJ ACPC f o r lighting ACPC f o r heating
t
Figure 6.21. Taxonomy of continuous actuator systems for energy streams
formed in the power electronics will increase steadily with growing requirements on the controllability and transformation of electrical energy. Applications for power electronics include industrial drives, electric heating, and energy generation and distribution. The basic functions of positioners can be classified as shown in Figure 6.21 (see also Section 6.3). The basic functions are rectification, inversion, a.c. conversion, and d.c. conversion. In rectifiers and inverters, which are not regarded as actuator systems, the direction of energy flow is specified. In a.c. converters and d.c. converters, the direction of energy flow can generally be reversed. Variable rectifiers make it possible to control the output voltage and frequency and
198
6. The Process Control System and Its Elements: Process Actuator Systems
devices can be broken down into final control element, control device, and power section (control drive). The entire equipment is also called a control machine in accordance with IEC 514. All centrifugal, turbine, gear. and plunger pumps. as well as special-purpose pumps. are included in the category of machines for handling gases. liquefied gases, and liquids. The control drive consists of a power section and the motor. The power section and the control device are commonly integrated, except for the transformer of the rectifier. Control Device. The control device has two functions: to match the actuator system to various equipment connected before it and to the mode of operation, and to match it to the final control element and the drive itself. Figure 6.23 shows a possible classification. In more complex actuator systems, control devices are made up of devices such as controllers and monitors with their respective memories, actuator buses, display/communications/ feedback devices, accessories, integrated display and control panels, and so on. It can be expected
thus the energy flow. A possible classification of rectifiers by direction of energy flow, into unidirectional and bidireactional rectifiers, would also be conceivable where the energy flow can be reversed. The a.c. and d.c. controls shown in Figure 6.21 should be emphasized; these can be used to control heating and lighting and also in power systems. Further, the various link converters used for motor speed control should be mentioned. There are various pulse-amplitude-modulated frequency converters, pulse-width-modulated frequency converters, and link converters. These devices are commonly referred to as motor controls, heater controls, and lighting controls (see Figs. 6.15 and 6.25). Their attributes as individual objects are power, voltage, current, driving signal, manufacturer, accessories, limiting, torque limiting, type of starting torque enhancement, power and torque transducers, and braking unit. In general, without going into the individual technical differences between frequency converters, a classification such as that shown in Figure 6.22 can be employed. As shown above, the (Actuating
machine)
(section) I/U c o n t r o l
Rectifier control Power i n v e r t e r coni Watchdog unit
Rectifier Intermediate circuit Smoothing unit Converter t r a n s f o r m e r
Llnterface
Figure 6.22. Descriptive structure of an actuating machine
/ (GGiG) Two-position c o t r o l l e r Three-position controller Continuous controller Positioner
Open-loop CMaonetic valve Signal processing Watchdog unit
Figure 6.23. Structure of a controlling element
Human-machine interface LMachine-device digital Human-device
6.2. Actuator S y s t e m .for Muteriul and Energy Streams
that the control device will go through major changes in the future. The control device should make possible matching to the connector in conventional or smart technology (see Section 5.3 and Chap. 8) or in bidirectional field bus technology. The type of central electronic controls common up to now, with expensive cabling between the sensor and actuator systems and extensive setup operations during commissioning. will be modified for the sake of more easily understood, less expensive, distributed signal processing. For the user, such a device will become a “plug-and-play” unit in spite of its complexity. Not yet fully achieved are networking capabilities and a consistent, manufacturer-neutral, menu-based operator interface as desired by both operator and designer. Futhermore, major efforts are needed in order to bring about a unified field-level communications technology based on internationally accepted standards. Such technology will permit interchangeability of hardware across manufacturers. These interfaces must satisfy increasingly stringent requirements, partly because of the trend to “rationalization” and the desire for a central service. In the near future, this area will become a focus of the activities of manufacturers, users, and developers (see also Section 5.3 and Chap. 8). The control section, as a module of the control device described above is needed for the human-device interface (Fig. 6.24). In the simplest case, the actuator system can be set with a hand-
w
7
(Human-device i n t e r f a c e (HOI)
-Display -Feedback Min/max Open/closed -Position -Wheel -Lever
199
wheel, or via a manual control signal from the supervisory device of the controller or from the process control system. In automatic mode, positioning takes place after signal processing with specified setpoint values; this function is performed automatically. Figure 6.24 illustrates the possibilities of manual and automatic operation. Thus, there are two interfaces: a human-device in manual mode, and machine-device in automatic mode. In manual operation, interventions can be accomplished by 0 0
0 0
0 0 0
A local control station, in the simplest case a handwheel, lever, or the like A display and control panel, integrated into the actuator system, on which the system can be set A control panel at the supervisory device, for manual and automatic operation A screen mask on the display and control component where manual control signals can be set or nominal values and tolerances can be specified In automatic mode, interventions take place Between the final control element and the field controller Between the control element and the individual controller in the control room Between the control element and the controller component in the process control system, stored-program controller, or PC
200
6. The Process Control Systerii and Its Elements: Process Actuator Systems
Outlook. The object-oriented representation in data processing has the following advantages for the user: Objects are presented in an intuitive, structured, transparent form, and searches (e.g., for a certain device) are carried out in prompted mode. Because devices in this representation are merely sketched. further detailing is necessary a t the lower levels if installation, shop, and maintenance workers are to find individual repair o r replacement parts with given specifications and given relationships to other devices. Similar requirements arise in the design of a process monitoring and control system, where the specification document dictates the selection of devices; their identification by, say, a CAE plot symbol in a process monitoring and control functional chart; o r their representation in a positional world with lower- and higher-level devices. On the basis of Figure 6.25, this point is illustrated for the example of a device object tree [6.16]. In the object-oriented representation, the devices required for a process control system are described by groups. For example, they can be broken down into structural elements of equal rank : sensor system, actuator system, auxiliary device, information-processing components, electrical, control air system, infrastructure, and installation materials. Sensor systems are classified by physical quantities; actuator systems, by the criteria discussed above; auxiliary equipment, into input and output devices, switches, and protective device; information-processing components, into controllers, supervisory devices, software modules, stored-program controllers, and process control systems with their components, such as central unit, local components, display and control components, and so forth. Note that a “pure” or textbook-type classification is not possible in many cases, and for practical reasons a compromise must be found, especially in the case of actuator systems. Here, continuous control devices are listed under the structural element “actuator system,” except for solenoid valves and switching-type control valves, while switching devices for energy streams are classified exclusively as “auxiliary equipment.” Naturally, a practical worker seeking a controller without auxiliary energy will look among the other controllers under “information-processing components,” and if in need o f a reducing station will look under “control air system.” Under this object-oriented approach,
once a certain device has been found, design specifications, device configuration and installation standards, and even manufacturing standards (if needed) can be found in a further level having the same structure and the same nomenclature of structural elements. After a device has been selected, designed, and configured, the respective activities can be carried out in CAE applications modules, such as process control station data sheet, process control station plan, procurement, and so forth. Such a system has the further advantages that innovations in hardware and installation can be adopted better and more quickly and that workers in these fields obtain design and manufacturing standards in easily understood form. What is more, changes in standards and directives can be complied with in a better and more economical way (see Chap. 9). Finally, two more developments should be mentioned : 0 Workers in automation, information, and microsystems engineering are energetically pursuing: ~
~
Piezoelectric actuators [6.17] Shape-memory actuators Magnetoelastic actuators Micromechanical actuators Actuators using electrorheologic and magnetorheologic liquids Precision positioning with electromagnetic actuators
Information on these developments can be found in [6.18]. 0
0
0
The trend in the chemical industry toward smaller-sized batch processes is generating a demand for much faster metering systems, which can only be realized by direct coupling between metering element and flowmeter (see Chap. 5 ) [6.19]. In addition to a number of other advantages (energy savings, decrease shear gradient at almost closed position of the control valveimportant for biotechnological and polymeric materials-smooth start-up, etc. [6.20]), by means of r.p.m controlled drives, pumps with variable performance can adopt the functions of classical control valves [6.21], [6.22]. The coupling of sensor and actuator systems in self-sufficient control loops at the field level allows the amount of communication with and the computing load on the process con-
6.2. Actuator Systems for Material and Energy Streams
[Sensor
1
)Actuator]
[A diaries]
Information processing components
Electrotechnical plant
Control air 1 ant
Infrastructure
I
201
Installation material
1
I
Switching plant
E
Reduting
Control room
F
L
I I P
s t a t ion
Flap
a
[Batteries]
II R
ransducer
1
coupled
delay
drive witching nit,
1
rotection nit ectifier
Posit ioner ds. motor
Terminal block
Charging unit
I
Magnetic valve
positioner
I
Power inverter/ rectifier
I
f: Power supply
I
free of interruptions
Fuse
switch disconnector
Positioner as. motor Lighting positioner Heating positioner
Overload protection
I
Thermal potor protection
I
lothers1
l u g 1
socket
Figure 6.25. Device object tree for process control systems at the field level
202
0
6. The Process Control Systeni and Its Elements: Process Actuator Sysietns
trol system to be decreased. Expecially in the area of building automation, interesting application are already available at reasonable cost. In the field of sensor/actuator systems in particular it will be necessary for developers and users to move away from the traditionally narrow viewpoint and, by means of analogy, apply insights from other areas to process control engineering 16.211.
500 <SkVA 5-50kVA 250kVA
t
400 *300 0
0
-
g200 rn > 10 0
6.3. Electrical Drives in the Chemical Industry Development of Electric Drives. Electric drives have long been used in large quantities in various applications in industry. The majority of the drives are not speed controlled in any way. The first major application for variable speed drives was in traction, applications, especially in locomotives and trams, and later also in metallurgy. Variable speed drives subsequently found their way into a large number of applications in general industry. The general trend today is from fixed-speed to variable-speed drives. Typical applications for modern variable speed drives are, for example, in the field of traction, the main drives for TGV and ICE trains, main propulsion drives for ships, and a large number of processoriented applications in the pulp and paper, chemical, and metallurgical industries. Other typical application areas cover manufacturing industry, energy production, and water treatment. Most motors in use today are not speed controlled. About 95 % of all motors sold today are asynchronous motors, of which 95 % run at fixed speed, connected directly to the mains. In future, the number of asynchronous motors driven by frequency converters is expected to grow rapidly, at ca. 15 %/a, while the growth rate for all variable speed drives is ca. 7 %/a. The estimated world market for variable speed drives was ca. $3.3 x lo9 in 1991, ofwhich 55 % is ax. and 45 % d.c., a trend that will intensify in the future. The ax. market (Fig. 6.26) is concentrated to Europe, the United States, and Japan. The rest of the world accounts for ca. 1 0 % of the total market. The market split into the various power ranges reflects the differing structures of the industry and the price of energy in the different areas. In terms of numbers, the majority of drives (ca. 90 %) are small (< 5 kVA),
Europe
United States
Japan
Others
Figure 6.26. World market for variable-speed a.c. drives in 1991
ca. 8 % are in the range of 5-50 kVA, and only 2 % exceed 50 kVA. The number of motors controlled by variable speed drives in Europe is about 5 % for 5 kVA, 9 % in the range 5 50 kVA, and 16% for >50 kVA. The larger the motor is, the more likely it will be driven by a frequency converter. Process demands at higher powers often require variable speed drives, and the realized energy savings are economically more important. The basis for this development is the a.c. motor, which has a simple, robust, cost-effective, reliable, and rugged design that is relatively easy to modify for use in Ex-areas. Developments in power and control electronics have made drives more technically advanced and cost effective. The price of an a.c. drive has decreased by about 10 %/a during the last ten years, while the price for d.c. drives has shown a decrease of ca. 4 %/a. The total cost of a drive (drive electronics and motor) is about equal for a.c. and d.c. although a.c. can be slightly more expensive in the middle power ranges. The total life cycle costs for an ax. drive are in general lower than those of a d.c. drive. The German chemical industry has a large number of motors in use (Fig. 6.27), one estimate [6.18] is ca. 500000 units. The average power is 7-13 kW, the minimum power is 120 W, and the maximum power is 6800 kW. About 80% of all drives are smaller than 10 kW. Only about 10% of all motors have variable speed; in two-thirds of the cases, this is achieved by pole changing. The large proportion of fixed speed motors indicates that other control methods than variable
6.3. Electrical Drives in the Chemical Industry
tation (turn-off) circuit is required. This increases the size and the complexity of a thyristor drive and also limits the maximum switching frequency achievable. Thyristor PWM drives normally switch at ca. 400-600 Hz and the motor is thus fairly loud with a motor loadability of ca. 85 % at 50 Hz. The dU/di is ca. 500 V/s. Thyristors are now used only in high power drives. The next major power semiconductor is the G T O (gate turn-off thyristor). As the name implies. it is a thyristor that can be turned off as well as on. This is achieved by careful design of the semiconductor layers and device geometry. As the device can be turned off, no additional commutation circuitry is needed and the size of the drive thus decreases. The G T O is fairly sensitive to a correct handling at turn-off and is today used in high-power drives (200 kW) and at high voltages ( > 1 kV). An advantage of GTOs is that they are latching devices, like thyristors, and thus require no control power when they are in a stable state (on or off). In the case of short-circuits in the output, they pose problems, as they d o not inherently limit the current and are difficult to turn off in this state. The switching frequency and dU/di are comparable to thyristors. In lower power drives today, the main power component used is the bipolar transistor. With this component powers of u p to 200 kW can be achieved, and up to 600 kW with several transistors in parallel. The device consists of two Darlington-connected transistors. By controlling its base drive the device can be made to turn on and off. Transistors require a continuous base drive in order to conduct, i.e., they d o not latch. In the case of shorts they are more forgiving than GTOs but they require a stiff base drive to turn them off. The simpler turn-off requirements simplify the control circuitry and allows drives to be smaller and more efficient. With Darlingtons, switching frequencies of up to 4 kHz are achieved with a dUldt of ca. 1-2 kV/s. The higher switching frequency increases the motor loadability to ca. 90 % at 50 Hz and reduces motor noise. In newer ranges of frequency converters, the power semiconductors are IGBTs (insulated gate base transistors). These devices are a combination of voltage-controlled field effect transistors (FETs) and bipolar transistors, connected so that the control is like the voltage control of a FET and the power side is of the low-drop bipolar type. These components inherently limit the current and they are thus more forgiving and easier
203
to handle in short-circuit situations than transistors. As they are voltage controlled, not current controlled like bipolar Darlingtons, the control circuit is simplified and reduced in volume. There is also a significant reduction in the rating of the auxiliary power supply, as IGBTs require less control power than transistors. The IGBT allows switching frequencies of up to 20 kHz which increases the motor loadability to 100% at 50 Hz and gives an attainable dU/dt of 5 kV/s. New components are being developed by power semiconductor manufacturers, for example, MCTs (MOS controlled thyristor) and FETh (field effect controlled thyristor), hybrids between conventional thyristors and FETs, with voltage-controlled switching and a very low state loss, which will reduce the size of future drives. Frequency Converters. Frequency converters are divided into two main types, depending on their construction. One is the current source inverter (CSI) and the other the voltage source (PWM). Current source inverters contain a controlled thyristor bridge connected to the supply and a second one connected to the motor. The two bridges are connected by a large inductance. The current to the motor is controlled by the input bridge, and the frequency is controlled by the output bridge. The motor voltage then adjusts itself to the actual current and frequency. In older types, the output bridge consists of thyristors, which means that the motor is a part of the commutating (turn-off) circuit of the thyristors and thus every inverter and motor forms a pair. In newer drives the thyristors are replaced by GTOs or bipolar transistors, which eliminates this coupling. Due to the basic construction with a controlled input bridge, CSIs are suitable for single, stand-alone four-quadrant operation. As they contain a large choke, however, they are usually bulky and the drive may be noisy. The most common type of drive today is the voltage source inverter. It consists of a diode input rectifier, which generates a constant intermediate circuit voltage. This intermediate circuit consists of a choke and a large capacitor. At lower powers the choke is often omitted. This causes the harmonic content of the supply current in the main to increase, so for installations requiring smooth mains current drives with an intermediate circuit, chokes are recommended. The rectified and smoothed voltage is supplied to the motor via an output stage, which controls the voltage and the frequency of the
204
6. The Process Control System and Its Elements: Process Actuator Systems Pumps 31%
Variable speed drives are used mainly to improve process control [6.23]. By controlling the speed of the prime mover, the material flow can be better controlled; variable-speed pumps can handle logistics and the dosing of materials with low losses and low machine strain, thus improving process control and reducing mechanical wear. Variable speed drives are also used to replace control valves in polymer and biotechnical processes. The soft start feature of variable speed drives eliminates pressure shocks in pipes when motors are started and they also significantly reduce the thermal and mechanical strain on the motors at start, which usually permits more economic dimensioning of the total system. The supply dimensioning is eased by the lack of high starting currents and compensating capacitors. The direct drive energy savings, especially in centrifugal machines, are also of importance when deciding the type of drive required. A 40 % saving for variable-speed a.c. drives compared to fixed speed solutions is not uncommon. Variable speed drives also make less noise, as machines are used at optimum speed, rather than at full speed at all times.
Others 33%
I Screw
Extruders
Conveyors 11% Mixers 16%
YIIF
F i l t e r s 8%
Centrifuoes 8%
S e p a r a t o r s 2%
Mills 2%
Figure 6.27. Areas of use of variable-speed drives in the German chemical industry
speed are used, usually control valves, bypass control, or mechanical variators. Most of the drives in the chemical industry are used in the main process, although many drives are used in auxiliary processes in energy production and raw and wastewater treatment. The use of variable speed drives has increased markedly during the last few years. Most of the applications are pumps and fans, but several other applications are also quite common [6.231.
Development of A.C. Drive Technology. Power Semiconductors. The development of power semiconductors is summarized in Table 6.1. The first commercially successful power semiconductor was the thyristor, developed in the early 1960s. The thyristor is a four-layer device that can be turned on (changed into a conducting state from a nonconducting one) by the application of a gate pulse. Because it works by positive feedback, to turn it off, the current through it must be decreased to zero, after which it regains its blocking, nonconducting state. If the applied voltage across the thyristor does not naturally reverse sign (as happens for components connected to the mains supply). a commu-
Table 6.1. Main electrical data of various power semiconductors Thyristor
GTO
~ ~ _ _ _ _ _ _ _
Voltage, V Current, A Frequency, Hz Power, kW Chip size, cm2 Year Processor/ Memory
6 000 5 000 400 104 80 1960 256 bit
4 500 4000 4 00 1o4 80 1911 8080/85 4 kbit
Bipolar transistor
IGBT
FET
MCT
FETh
1600 1200 2 000 103 > 10 1985 8086/186 64 kbit
1600 400 20 000 103 > 10 1991 8096/486 1 Mbit
1 000 10 100000 10’
3 500 50 5000 102 2 1993 80586 16 Mbit
4 500 50 10000 102 5 1995
5 1980190 1 Mbit
64 Mbit
6.3. Electrical Drives in the Chemical Industry
waveform applied to the motor. The most commonly used components in this stage today are bipolar transistors at lower powers and GTOs at higher powers. The bipolar transistors are being replaced by IGBTs. The waveform applied to the motor consists of a pulse-train with a sineweighted duty cycle (pulse-width modulated, PWM). The inductance of the motor filters the current resulting from this pulse-train to near-sinusoidal shape. The effective voltage applied is normally increased in direct proportion to the output frequency to generate a constant flux in the motor. This is possible until the mains voltage is reached (i.e., up to 50 Hz) and with the latest modulation techniques up to about 60 Hz. At higher frequencies the voltage remains constant, and the flux diminishes in inversely proportion to the output frequency. The maximum speeds that can be reached with standard squirrel cage motors are of the order of 40-120 Hz; higher speeds require special motors. The normal PWM inverter described above is used in about 90% of all cases. It is a simple, straightforward approach to variable speed control, requiring no special motors or any sort of feedback. As it is an open loop drive, its dynamics and precision are not of the highest degree. It is, however, sufficient for pumps, fans, extruders, conveyors, and simple machines. For higher performance, vector-controlled drives are used. In these drives, a feedback signal is required from the rotor position. This information enables the controller to precisely calculate the torque on the motor shaft and also indicates the exact position of the shaft. The information is then fed to a PWM output stage, which controls the current and frequency in the desired manner. With this drive, full load torque at zero speeds and precise position control can be achieved. The drive always requires an encoder on the motor. Typical applications are synchronized lines, paper-machine drives, rolling-mill drives, and all applications where the torque must be precisely controlled. Power semiconductor development has had a major impact on the size of drives. The average volume per power has decreased to '16 in ten years. Today the decisive factor determining the size of a drive is its thermal design. Modern power semiconductors allow a junction temperature of up to 15O"C, corresponding to a case temperature of ca. 100"C. The case temperature is limited to about 8 0 ° C sometimes up to 100 "C.
205
Modern control electronics use 16 bit processors with designs for 32 bit processors or for signal processors in development. This trend of incorporating more computational power in the drive will continue, so that the possibilities for control offered by modern drives will increase. The requisite discrete logic is commonly realized in an ASIC (application specific IC). These are monolithic ICs built using standardized logic cells, so that a defined, application-specific function can be realized. One ASIC can replace several hundred conventional TTL circuits with large savings in space and powering. Lately, technologies have become available that allow a similar approach to analog circuit design. These developments have also increased the reliability of drives. The MTBF (mean time between failure) for drives in 1983 was ca. 40000 h, while in 1993 it was ca. 250000 h. The number of parts necessary to build an 1 1 kW frequency converter has decreased from 2500 to 500, a trend that will surely continue. The basis for this lies in modern component technology, both for logic components and for power components, and in the better possibilities for simulating and measuring complicated and fast processes. For critical applications, redundant drives are still recommended, especially in cases where there is a risk of injury to humans, where the process flow is critical and must be controlled at all times. An aid to achieving high process reliability is the use of switchgear converters, which allow easy changing of drives and of system configuration. Combined with a fully digital commissioning and down-loading of various drive parameters, this allows an extremely rapid change of drives, simply by changing a cassette. An external sign of quality control and an internal tool is a certification according to I S 0 9000, Parts 1-3. This standard defines the requirements regarding standardized working methods and documentation for a company or profit center. Quality is also achieved by standardization of methods in development, production, and testing. Standardization allows a high repetition rate when using various components, either software or hardware. This allows a short development time for new products without endangering product quality. As an example of standardization, consider solving EMC problems (electromagnetic compatibility), that is the problems occurring when devices have to work in an electrically noisy envi-
206
6. The Process Control Svstem and Its Elements: Process Actuator Systems
ronment. The main standards in use are the various DINjVDE and CISPR standards. Generally speaking, two types of interference occur: 0 0
Low frequency: frequencies up to ca. 5 kHz and earth currents High frequency: frequencies from ca. 50 kHz to 30 M H z
Low-frequency interference can be divided into periodic interference (caused by nonlinear components such as diodes and thyristors) and aperiodic interference (caused by supply disturbances). High-frequency disturbance is caused by switching phenomena in power switches. The various interferences can influence other devices nearby, either through radiation o r conductance. Generally, conducted interference is more problematical. A standard solution to high- and low-frequency interference is adding filters to the input of the device. At high powers this is usually difficult, bulky, and expensive, especially as the highand low-frequency ranges require different features. I t is also possible to separate the power supply to interfering devices from all other supplies, or to use a d.c. distribution system, in which 12-pulse circuits for supplying the d.c. power distribution minimizes the 5th and 7th harmonic current in the supply. This d.c. power is then supplied to independent output stages in order to drive motors. Aperiodic disturbances include supply failures, which can last from milliseconds to several seconds. Drives usually use the kinetic energy in the driven load as a supply when the supply fails and then accelerate to the set speed when the supply returns. The possible time the drive can survive depends on the type of load and its kinetic energy. Usually there are also other questions of synchronism: can the drives run when the total process is otherwise down? To eliminate undesirable influences from the power stage on the control electronics, fiber optics are used. This allows a clean separation of the control circuits, which can be standardized irrespective of rated power, and the power stage, which can be separately optimized and standardized. This technique also allows customized interfaces to be developed for standardized, massproduced drives. In future, it will be possible to eliminate a significant number of sources of high- and lowfrequency interference by using so-called resonance circuits. For example, with single-phase
inputs, a completely sinusoidal supply current can be achieved. A further development allows switching of the power semiconductors in a zeropower state, which reduces disturbances. Drives as System Elements. A drive is regarded as an actuator from the systems engineering standpoint. It is connected directly to the process and to a field bus. Normally, only commands and status information are exchanged through the bus system, but in special cases the entire commissioning and the parameter setting must be performed associated through it. This can be done either from a remote panel connected to the field bus or from the engineering workstation functioning as bus master. The goal is to move the detailed process control to the sensors and actuators exchanging information through the field bus. To reach this goal intensive work is being done to standardize the field-bus protocols. A modern a.c. drive (Fig. 6.28) is functionally divided into the actual motor control and the interface with the external world, with control and setting possibilities. This concept uses at least two microprocessors (one for each function). The drive is self-contained, and no external power is necessary. It is in many cases possible to process data locally to control the drive. Such a drive will form a yield unit (Fig. 6.29), decreasing the load on the process controllers and on the control system. This is especially important in fast processes (for example, in dosing applications), so that the central control is not burdened with all details of the process. The drive is then closely connected to the process through local measurement and feedback, making it possible to implement local control of, for example, volume and heat flow. The bus system transmits only commands and status information from the various field units; details are handled locally.
I l l P r o c e s s o r ___ S Processor Control B, Supervision
Status Commands
~
___
Ill
~
~~
Figure 6.28. Information structure of a modern drive
-
6.3. Elrctrical Drives bi the Cliernical Industrj) Contra\ system r e f e r e n c e value Ireciw)
41
w I
Sensor
I n t e g r a t e d motor Dosing u n i t s High-speed compressors
Field unit Direct c o n t r o l
lo/
M(t)
Q(t)
Clean division between energy and information
Superdivision o f M(n, / ) = s t a t u s dependent maintenance
Figure 6.29. Field unit
The planning of a drive system is identical to the general planning of control systems. The basis for the planning is a requirement specification drawn up by the user, describing the function of the drive and its behavior in special cases such as mains failures. This must be carefully thought through and defined, as experience shows that most of the subsequent problems can be avoided here. Based on the requirement, an engineering specification is written, describing with which devices and with which technologies the desired functions will be realized. Here, the economic restrictions of the various projects must be taken into account. This work is usually facilitated by standardization within a plant or company. It is also recommended that a test specification be written for the possible delivery tests, preferably in cooperation with the chosen supplier. When dimensioning and specifying a drive, the following features must be taken into account: voltage, power, torque requirement as a function of speed, speed range, control signals, braking capability. cabling recommendations, and EMC aspects. The behavior in special cases (short circuits, overloads, underloads, earth faults, etc.) must also be considered. The desired motor features must also be defined (construction, EX class, loadability, etc.). Drives are considered actuators in modern CAE systems and they are treated as standardized objects with certain, defined features [6.24]. When a drive has been specified and built, an acceptance or delivery test is often required, especially in the case of high-power drives. The goal of a delivery test is to establish that the drive
207
fulfils all the requirements defined in the requirement specification. It is important to specify in detail how the various features must be measured to ensure that the specifications are fulfilled. At lower powers. a type test certificate is usually sufficient. The supplier must be able to test the behavior of the drive at high break-away torques or at mains failures. A test proves that the drive works as specified and allows a first, coarse setting of parameters to be done. It is usually not possible to optimize the drive; this must be done under true working conditions on site. This optimization is made easier by digital technology, which also allows data transfer and trouble shooting over telephone lines. A multichannel recorder integrated into the drive also facilitates trouble shooting. For trend analysis, the drive itself stores and displays certain key data as a function of time. This can be done by a process control system, but it is simpler if the drive makes it itself and transmits only the results to the control system. The analysis of the trends makes it easier to judge when maintenance is needed, and avoids being bound to a fixed time interval. One example of this is the calculation of when bearings need to be lubricated. In this case, the drive calculates the lubrication interval and indicates when it is necessary to lubricate the bearings. This time depends on the speed at which the motor is running and can be appreciably longer than the time calculated from the nominal speed of the motor. Trends. Two directions can be distinguished in the development of drives. On the one hand motors, and on the other hand power electronics and information processing electronics are continually being developed. In addition, a development in the mechanical construction of electric drives can be traced. New motor ranges better suited to the use of converters are being developed. Standard motors are designed for mains use, as 95% of motors are connected directly to the mains. Use with a converter makes different demands on the motor. FEM methods help when new, converter optimized designs are being simulated and built. They also aid in developing new high-speed motor concepts by optimizing the rotor slots for inverter use. The new slot shape reduces the losses for a 100 kW motor from 4 kW to 0.8 kW. This motor is designed for a compressor drive with a speed of 10000 rpm.
208
6. Tlze Process Control System and Its Elements: Process Actuator Systems
The main problems of high-speed drives are mechanical and concern bearings, lubrication, and life time. Conventional lubrication is sufficient up to about 16000 rpm above which it is necessary to use oil fog lubrication, which increases cost. The high speeds also allow better material utilization. The high speed motor allows a higher power for the same motor size, or a smaller motor at the same power rating. In high-speed applications, gears are completely eliminated leading to lower investments, less maintenance, and better total efficiency. The development of new magnets makes it possible to manufacture motors with permanent magnet rotos. The materials development has been rapid, making it possible to obtain realistic performance at a reasonable cost. The cost for neodynium magnets is today about 300 $/kg, allowing the development and sale of special motors. These motors have high efficiency, a power factor equal to unity, and a simple positioning drive without feedback can be realized, as the motor has no slip. Powers up to 100 kW have been reached under laboratory conditions. Research in this sector is concentrated on production methods, as it is difficult to work permanent magnets. A motor of this type is also well suited to integral motor applications. In these motors, the motor and the drive are mechanically and thermally integrated, making it possible to talk about integrated field units consisting of motor, power electronics, and information processing electronics. In the inverter, several areas are being developed. The development of microprocessors for the consumption and automotive industries makes powerful and cost efficient microprocessors available for use in drives as well. Semiconductor manufacturing technology also allows the design and manufacture of large ASIC circuits to replace discreteJogic. The high processing power available makes it possible to realize complex argorithms, for example, the so-called flux control of a s . motors. The magnetic vectors in the motor are calculated based on the position of the switches in the output power stage and on measurements of the currents and voltages. This requires a very powerful processor if it is to be performed in real time. This control method gives a standard a.c. motor dynamics superior to those of a d.c. motor.
The powerful processors make it possible to use adaptive control in motor control as well as in a possible controller built into a field unit. It is possible to optimize the motor control as a function of load. These adaptive methods make commissioning easier and are also a step in mnking drives more user-friendly. Other trends are control algorithms that take into account the mechanical features of the shaft between motor and the driven machine. With an elastic or possibly an electroviscous clutch between motor and machine it is possible to reduce the impact of torque shocks in the load on the motor, but control of the load is more difficult. An algorithm of this type allows the use of smaller motors. Applications can be found in rolling mills and in long and heavy conveyor drives. To reduce the size of the inverter and to modularize it, several approaches are being pursued. One is the so-called matrix inverter [6.26], which connects a three-phase motor directly to a three-phase supply and allows the motor speed to be controlled. The motor voltage does not correspond to the full mains voltage and thus a special motor is needed. One difficulty with this concept is that switches able to conduct and block in both directions are needed, and such components are not yet available in monolithic form. Semiconductor manufacturers are developing these components. As the matrix inverter does not contain any bulky components, intermediate circuit chokes, or and capacitors, it is a good candidate for integrated solutions. Another solution for standardizing drives is d.c. distribution (Fig. 6.30) [6.25]. Instead of having one rectifier bridge, choke, and capacitor bank in each drive, the separate drives are supplied with a d.c. voltage from a common source. This larger unit contains one rectifier, choke, and capacitor bank. It can be smaller than the combined power of the individual units. as it is often possible to use the regenerative power from braking drives to supply driving ones. Typically, considerable savings are achieved, for example, in centrifuge applications, where by braking one centrifuge the energy can be used to accelerate others. The mains rectifier must only supply the energy lost. At higher powers it is also possible to use multiple-pulse connections to reduce the strain on the supply. Galvanic, periodic, and aperiodic disturbances are also easier to control.
6.4. Electrical Power Supply Systems
209
Three-phase supply
Single drives
a.c. drives with d.c. supply
Figure 6.30. d.c. distribution
A further step in modularization is the combination of power switches and their controlling circuits in one package. Modules are today available containing power switches and their drivers and protection circuits [6.26]. Such modules reduce the size of the inverter and its reliability increases as the number of separate components decreases. This trend towards higher integration has also reached the chip level. Chips will soon be available with several functions integrated. This is called SMART power. Further in the future lies chips integrating power switches and total control of the drive. There are no theoretical difficulties in integrating the matrix inverter onto one chip but many practical difficulties. The limits of what can be done today lie at one power switch and its immediate drivers and protection circuits at a voltage of 600 V. These chips are very expensive, as volumes are small, the production process complex, and yields low. The developments in the fields of power electronics, information electronics, silicon technology, and mechanics will combine in the form of integrated devices, where most of the necessary functions are integrated. The devices will integrate all necessary sensors, actuators, controls, and protections in a single mechanical unit. The energy savings made possible by using variable speed drives, not only in the drive itself but through the total energy supply chain can be quite substantial. When a control valve is replaced by a variable speed drive, the efficiency of the total system is significantly increased, especially at low volume flows. The total efficiency can increase from 11.5% to 23 %, an increase of 100%. These savings reduce environmental pollution and require smaller investments, as the available energy is used more efficiently.
6.4. Electric Power Supply Systems Principles. A useful way to visualize the structure of electric power supply systems in industrial plants is provided by the level model (Fig. 6.31). This scheme assigns a central role to the process power supply system, which connects actuators and sensors on the “consumer” level with power plants via medium-voltage distribution units on the “transport” level. The process power supply system is tailored to the needs of each plant and must satisfy requirements under the following three headings [6.27]: 0 Adequate capacity under normal conditions. The specific requirements are as follows: Design for operation under rated conditions - Availability of electric power at any desired location - Flexible delivery of electric power under various service conditions - Voltages suitable for various consumer interfaces ~
0 Availability requirements fall into three groups, each with its own set of objectives: Availability from the safety standpoint (occupational safety and health, environmental protection) Availability from the process engineering standpoint (product quality, safety of equipment) Availability from the logistics standpoint (capacity of vendors, production costs)
In engineering terms, this means: Limited effects on the process when power disruptions occur - No interference of one electrical consumer with another -
210
6. The Process Control System und Its Elements: Process Actuator Systenis Imported power
In-plant generation
Procureme and generation level .- - - - - - -
Transport level
- - - - - - -.
Oistributio level
- - -_--- Consumer level
I I
El i l @ @
1
Figure 6.31. Level model of power supply -
Limited effects on the power supply system in case of operation upsets (e.g., fires).
0 Safety of electrical equipment. The specific requirements are as follows: No danger to personnel - No environmental hazards ~
These three sets of requirements completely describe the profile of requirements for the process power supply system. This section describes the essential concepts employed to meet the stated requirements [6.27], [6.28]. By way of illustration, the process power supply system for a wastewater treatment plant is described in [6.29]. Implementationof Normal Power Supply. The basis for supplying electric power for the nominal operation of a plant is established in the design phase. Here, the three-phase switchgear and control gear is dimensioned in view of the predicted requirements of consumers; utilization and demand coefficients are employed for this purpose. The numbers and sizes of power trans-
formers needed are also determined at this point. Direct-current power supply systems are designed by a similar method, with the closed-circuit current and estimated component peak loads as starting points. Under normal operating conditions, the entire power supply system of a plant is secured by splitting up the power demand among a number of sources, using in-plant generating capacity and imported power. The relationship between in-plant and imported power depends on a number of factors, including the size of the plant and seasonal variations in steam demand. In the chemical industry, the principle of cogeneration (combined heat and power generation) is used in creating the optimal power generation system. The fact that power to run the plant comes from two sources does not affect the design of electrical equipment inside the plant. The production process is embedded in a complex, twodimensional network of cables that covers the entire battery area, connects supply to consurnp-
6.4. Electrical Power Supply Systems
tion, protects the process systems. and insures any redundancy that may be required. Cable networks in industrial plants may be radial or meshed. The radial design is more important today. The meshed network, which offers the advantage of two-way supply, is declining in importance, in part because problems can arise in dealing with the short-circuit power when existing facilities are expanded. For special cases, distributed generation with diesel-generator sets or battery-backed converters serves to deliver reliable power. This approach should be used only when the solution for the normal case is not technically feasible or when the economics are very unfavorable. Direct current systems are generally designed in distributed form within the plant unit or section. In future, the increasing number of variable-speed drives may also lead to other structures, such as d.c. networks. Cable and installation costs can be controlled if the location of switch rooms and battery rooms in the central plant buildings is tailored to the process and/or such facilities are distributed in accordance with demand concentration. Modern switchgear technology can react quickly to service changes by virtue of 0 0 0
Modular switchgear design Busbar distributors, which are especially suitable for laboratories and workshops Worksite distribution boards for installation jobs
Process power supply systems in normal service are designed for standard voltages. Commonly used voltages are as follows: Data acquisition and processing Lighting 1 Wall receptacles Low-voltage, low-power motors J Low-voltage motors, general High-voltage motors
}
24 V d.c. 2301400 V a.c. 500/690 V a.c. 5 kV, 6 kV, or 10 kV a.c.
Special situations may require special voltages, such as 25 kV or 30 kV for electrolysis processes.
Implementation of High Availability (Conceptual Solution). Before the adoption of measures to enhance process power supply systems, two points must be considered:
0
0
21 1
The availability of power supply systems in general is normally higher than 99 %. Outage time per year, as a rule, is in the range of minutes. This holds for every point in the network and for each consumer. The cost of upgrading, with the aim of approaching even closer to 100% availability, increases with rising power level, increasing number of hierarchical levels in the system, and decreasing outage length. It is not, however, possible to achieve 100 % availability.
The profile of requirements for the process power supply system, as set forth in the bidding specifications, is met by standard and special practices. Standard practices include the following: 0
0
0 0
Use of network architecture typical of industrial plants, with multiple pathways at the supply and generation levels and complex network structure at the transport and distribution level (Fig. 6.31) Use of the IT network Use of modular switchgear Installation of A B machinery, with one machine always on stream and the other on standby ~
Special practices include: 0 0 0 0
Inclusion of a standby power supply system Upgrading of d.c. power supply system to a protected d.c. power supply system Splitting up both switchgear and cable installations into two subsystems Compliance with interference immunity requirements
In that follows, practices of a special nature are discussed in more depth; among this group of practices, only those involved in standby power supply and protected d.c. power supply are covered. Two types of standby power are important for process power supply systems in industrial plants (Fig. 6.32): 0 0
Standby power from a second, independent network Standby power from a standby generating system (diversity of power sources)
Standby power supply from a second, independent network is implemented by means of a reserve, backup, standby, or emergency ring feeder (Fig. 6.31), continuously supplied via automatic voltage detectors.
21 2
6 . The Process Control System and Its Elements: Process Actuator Systems
In general, when standby power supply systems are employed, the bidding specifications must list consumers by permissible outage time and assign them to classes of low-voltage switchgear. Figure 6.33 shows a classifiction that has proved acceptable as a first rough scheme. Figures 6.34 and 6.35 identify types of lowvoltage switchgear and the classes to which they are assigned. Class 1 consumers must be further broken down for in-depth analysis. Protected d.c. power supply systems (Fig. 6.36) feature 100% reserve capacity because of
0 0
The dual feeder arrangement Additional backup of each feeder in case of line outage
This redundancy must be extended to the data transmission and processing system, that is, to the cards in the subracks if possible. Implementation of Safety. The terms safety, protection, and danger are interlinked by the concept of risk. Safety practices are intended to insure that the limiting risk is not exceeded. Such practices are mandated by legislation. regulaLow-voltage switchgear
Feed t o standby power
I
Classes
emergency generating
Design variants Normal feed Standby feed
Class 1/ class 2
I
l
l
I
l
I
l
Possible outage times t o consumers on failure o f normal power supply Number of feeders
Class 1
[Lass 2
Class 3
0-15s
Normal feed
Devices actuated on failure o f normal power supply Backup system continuously on line and ready Backup system automatically switched on line
>I5 s
A t least until restoration of normal power
I
I
Figure 6.34. Classes and variants of low-voltage switchgear
Figure 6.32. Standby power supply system Classes o f low-voltage switchgear
Distribution center
1
Figure 6.33. Classes of low-voltage switchgear, possible outage times, and details of implementation
6.4. Electrical Power Supply Systems Incoming supply 1
Incoming supply 2
Design variants
Classes
Normal feed Class class 2
21 3
Two alternative feed points for standby operation
Transformer distributionIT0) Distribution
I
Spec ally p r o t e c t e d nerworlc
(VDE 0108) I
_
Feeder 2
Feeder 1
A
B
&& && A
B
A
. .
Plant, office, outdoor installation
B
Figure 6.35. Classes and design variants of low-voltage switchgear in plant with A and B machines
Q
Q I
p, processing
Figure 6.36. Protected d.c. power supply system
tion, engineering codes, and plant-specific directives issued by operators. In the case of power supply equipment or power supply systems in general, “safety” refers to two independent objectives: 0 0
Protection against electric shock (personnel protection). Protection against failure of the electric power supply system, which is ultimately a question of “reliability” (safety of equipment, protection of product and plant). This kind of
S t r i p lighting units
’oorn 2
Figure 6.37.Lighting system fed from the specially protected network
protection is achieved by means of the measures employed for high availability. Apart from functionality, protection against electric shock has been the second dominant theme in power supplies since the start of electrical engineering [6.30]. Notable among the many standards now in existence is the extensive VDE code, which is continually updated. The practices listed below serve as examples of how personnel protection is achieved in process power supply systems for the chemical industry: Increased use of 30 mA ground-fault circuit interrupters. This switch cuts off the flow of fault currents that could cause fibrillation. 0 Installation of devices safe against touch by the hand. 0 Application of the accident prevention specification VBG 4,“Electrical Installations and Equipment.” 0 Wider use of safety switches on drives. 0 Application of power supply options originally intended for escape route lighting to overall production power supply systems (Fig. 6.37) [6.31]. Environmental considerations apply chiefly to process engineering. In general, these require0
214
6. The Process Control System and Its Elements: Process Actuator Systems
ments do no impact on the process power supply system, which comes into the picture only when “reliable” power is required for devices affording protection or limiting damage to process control systems [6.32]. It must be verified whether a standby power supply system or a proteted d.c. system is adequate. One type of such a “secure” power supply system is a high-availability power supply system. High-Availability Process Power Supply System for a Wastewater Treatment Plant (Example). If a plant unit does not have its own waste disposal facilities, a central plant with biological treatment stages is of fundamental importance for maintaining production in a chemical plant. Such a treatment plant requires a high-availability power supply system, which must comprise all the levels of the power supply scheme and extend all the way down to the process control components in the field as well as control rooms [6.291. The quantity structure of the plant as built is as follows: 0 Distributed process control with dual host computer system 0 1800 process control stations 0 530 uncontrolled 3-phase drives with total power consumption of 10 MW 0 70 controlled electrical drives with total power consumption of 3.5 MW The following voltages are available (Fig. 6.38): 220 and 110 kV High voltage: Medium voltage: 25, 10, and 5 kV 500 V for motors; Low voltage: 380/220 V for lighting, wall receptacles, computers, and hand tools; 24 V for measurement and control apparatus ComDarison with the level model (Fig. 6.31) shows the relationships to be as follows: The high-voltage network corresponds to the import and generation level; the medium-voltage network, to the transport level; the low-voltage network, to the distribution level. The dual-bus structure illustrated in Figure 6.38, with the consumers separated into two subsystems, insures that the failure of one unit (e.g., a transformer o r feeder) affects only some of the consumers a t levels lower in the hierarchy. The failure of both system would, however, lead
to shutdown of all consumers. However, this must be avoided for particularly important consumers. In such cases, further standby power supply practices come into play: 0 Use of diesel generators 0 Use of battery-backed uninterruptible power supplies 0 Use of protected d.c. power supplies Figures 6.39 to 6.41 show these backup arrangements for the low-voltage (500, 220, and 24 V) systems in the wastewater treatment plant. 220 k V / 110 k V
High-voltage network
25 k V / 10 k V / 5 k V
Medium-voltage network
__ - __ ____
______ 500 V
I
Low-voltage network
380/220 V
24v
I
t
Measurement and c o n t r o l devices
Figure 6.38. Voltage levels in a chemical plant
Standby
Standby Standby Buffering Neutralization Tower biology
Preclarification Sludge t r e a t m e n t 2nd biological treatment stage
Figure 6.39.500 V low-voltage supply system in a wastewater treatment plant
6.4. ElecIrical Power Supply S-vstems
Changeover distribution\-----? 1
1
I
1
I
l
l
Battery
cu
j T ;I 2
-
L-
F
Uninterruptible power supply
cu L
230 V
500V
r---1
I
-
8
500 V
215
I
I
-_1
230 V
t
as Process control computer
Process control station
Printer for e r r o r messages
Figure 6.40.230 V low-voltage supply system in a wastewater treatment plant
t
d.c.
t
Buffered voltage
Figure 6.41. 24 V low-voltage supply system in a wastewater treatment plant
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
7.1. Principles
7. The Process Control System and Its Elements: Distributed Control Systems 7.1. Principles The task of the process instrumentation and control hardware is to assist plant management in the control of its plants and in the control and monitoring of its production processes [7.1]. This chapter discusses the structure and function of process instrumentation and control hardware. The structure is discussed on the basis of a reference model corresponding to the fundamental structures of current systems. In a general sense, process instrumentation and control hardware comprise all devices between the sensors and actuators and the operator controlling the process (see Fig. 7.1 and 11.4). The process instrumentation and control hardware must perform a number of distinct functions. Figure 7.2, for example, shows a metering device. The required functions can be classified as follows: 0
0
0
~
0
Controlling the process Automation functions that permit the selfcontained control of plant units and process steps. Example: - If action “metering on,” then meter from B 1 to B2 x liters at q L/s. Enhancing the safety and reliability of the process M a t e r i a l and energy f l o w
Product
Process
Product
Information flow
,
Functions prior in time, independent of control function specifications, that intervene in the process in order to enhance its safety and reliability. Because of high required availabilities and special performance provisions, these functions are often implemented in a separate “protective level.” Example: If L 2 > L2max, pump K 1 is disabled. Providing displays Display functions that report plant and process status to the operator in a concentrated and easily understood form. Example: - Display level L 1. Generating messages Message and alarm functions that explicitly notify the operator of certain events and provide information to the central message and alarm processing system. Example: If L 2 exceeds L2max, transmit a message. (Class: “Alarm”; text: “L2max alarm”) Archiving Archiving functions to store records of events and status logs. Examples: ~
0
flByk Figure 7.2. Example: metering system
c
I
@$=$jU t4al-4 I I
I L
Sensor system, Actuator system
I
+ Information
processing system
+Human-process communications system
= Process control system Figure 7.1. Process monitoring and control system
217
Human
21 8 -
7 . The Process Control System and Its Elemenis: Distributed Control System
Archive all reports.
- Archive level L 1 every 10 s. 0
Generating and interpreting reports Recording and evaluating (interpreting) functions that retrieve, evaluate, and print out archival and current data when a certain condition or command occurs. Example: - Log the last metering operation performed (starting time, ending time, quantity, events).
If all functions to be performed by the instrumentation and control hardware are arranged in accord with the functionally abstract “level” model of the process control functions (see Section 2.2 and Fig. 2.16), some basic conclusions can be drawn as to the automation of this system. The lower levels of the hierarchy contain many parallel functions with high intervention frequencies. These functions are chiefly suitable for automatic processing by process control devices. At higher levels, the intervention frequency generally decreases, but certain functions (e.g., load recipes) require the transfer of large amounts of data. The automation of these functions generally proceeds from bottom to top, in keeping with the hierarchical breakdown of process control functions. Different plant sections can differ in degree of automation. The degree of automation can be divided into four levels [7.2] (Table 1 1 . 1 ) . Each degree includes not only the automation of the respective process control functions but also the automation of the associated safety/reliability functions. A safety- or reliability-enhancing function is assigned to the level at which it intervenes in the instruction branch. A safety function “if pump temperature r permissible maximum --+turn pump off’ is assigned to the individual function level; a safety function “if coolant temperature > permissible maximum -+ turn column off” is assigned to the plant section level. The functions that a specifit process instrumentation and control system must perform can be set down in a requirements list. In principle, a project specification consists of a list of such required individual functions. Some of these functions, as shown in Figure 7.3 [7.3], are carried out by the plant operator, the rest by the process control system. The term “automatic” refers to a function that is performed
low
high
Degree o f autornationFigure 7.3. Degree of automation
by the process control system without external action. The degree of automation is the fraction of all functions that can be represented by automatic functions. Nevertheless, the overall scope of the control task must be considered regardless of the degree of automation. Each function that does not take place automatically must be performed by the plant operator. By changing the operating mode, the instantaneous degree of automation can be made lower than the maximum degree of plant automation provided for in the design (see Fig. 7.4). When examining the properties of a process monitoring and control system, a distinction must be made between the external functional viewpoint and the internal structural viewpoint. The external functional view regards the finally configured process instrumentation and control system as a “black box” having certain functional qualities. The internal structural viewpoint regards how the system is structured and how the individual functions are implemented. From the process control standpoint, the internal structure of the process instrumentation and control hardware is often of secondary interest. The required automation functions, safety and reliability functions, archiving functions. operator and monitoring interfaces, and so forth can be implemented under a wide variety of system concepts. The differences, however, manifest themselves in important supplemental properties. The plant operator, for example, might see differences in system reaction times, the way in which data surges are controlled. the failure characteristics of components. or the central coordination of workstations. Important from the plant management viewpoint, then. are additional properties that cannot be implemented simply as individual functions but result from the struc-
7.1. Principles Order handing Automatic mode
-
Manual
Logistical mode Manual
Computer mode
Manual mode
Manual mode
----ll
Manual
$’ $
’
’I
Manual
Automatic
Automatic
Automatic
Automatic
I
Manual
Automatic
Figure 7.4. Auto/manual selection
ture of the process instrumentation and control system as a whole. The same applies even more so to the planning and operation of a monitoring and control system. Such additional properties of importance include the following: 0 0
0 0 0
Transparency of structure Ease of operation Structure with few defects Capability of modular expansion High availability of individual functions
These can be realized only if they are incorporated in the control system design, i.e., in the internal structure, right from the start. Historical Development. Today, the phrase “modern” process control systems refers more precisely to distributed or decentralized systems. The world of process control engineering before the development of the type of system discussed here (i.e.. before ca. 1975) featured two extreme structures. both of which still exist.
219
In the totally parallel structure, each control task (control action, display, recording, etc.) is performed by a discrete device or combination of discrete devices. An example is the compact controller used in the implementation of a control loop. The parallel system terminates in the control room with a great number of instruments, buttons, and lights. It is left to the designer to ensure that the operator can scan the panels without becoming overstressed in critical situations (see Section 8.2). The decentralized structure means that a plant so equipped has a fairly high availability, since the failure of a discrete device generally affects only the control task directly assigned to it. The solution of complex control problems, however, requires considerable interlinking of the individual process control stations (see Fig. 3.26). With discrete devices, this networking can be implemented only through explicit interconnection of devices. Subsequent changes of task are therefore expensive because the system must be rewired, and the flexibility of such a plant is low. For more complex applications, there was the totally centralized structure implemented by the process control computer. This structure, which features the maximum degree of centralization on a single device, has all the process monitoring and control functions concentrated in one device, which is intended to alleviate the disadvantages of discrete-device technology. All control tasks are carried out by a central computer with interfaces to the process. Individual process control stations are linked together by software, so that the scheme can easily be modified, for example, if the recipe is changed. The resulting flexibility is partly offset by the lower availability of the plant. A failure of the central computer affects the entire process. Therefore, this structure has generally been used only in conjunction with special practices such as hardware redundancy or subordinate backup systems, which have lead to higher costs for such plants. Nevertheless, the centralized structure in this form still plays a role in the landscape of system structures [7.4]. Modern decentralized process control systems now fall between these two extreme structures (Fig. 7.5). The search for an optimal structure has led the manufacturers of these systems to solutions that differ in the degree of decentralizati on.
220
7. The Process Control System and Its Elenzenis: Distributed Control Systems
Discrete-device technology
m I
Central process c o n t r 01 comput er
I
Process
Communications s y s t e m
I
Process
I
0 ecen t r alized process. control system
Figure 7.5. Decentralized process monitoring and control system compared with central process control computer and discrete-device structures
In simplified terms, a decentralized process control system arises through the logical subdivision of a centralized system. This approach is useful because only the use of a process control computer provides the conditions for implementing process monitoring and control functions through digital computer technology. The development of microprocessors has made the desired or necessary computing power economically viable. Thus, another important prerequisite for the creation of decentralized process monitoring and control systems was fulfilled [7.5]. Such a system consists of individual computer components, each of which exhibits the properties of a process control computer to a limited extent. These components are linked together by a communication system. As a whole, they have the same capacity as a large central process control computer. However, the decentralized structure leads to higher availability than a centralized design. The development of process instrumentation and control systems has followed three distinct lines: 0 0
0
Decentralized stored-program controller systems Integrated decentralized process monitoring and control systems Intelligent central systems with dumb peripherals
Most process monitoring and control systems can be placed fairly unambiguously in one of these classes. Differences in historical development of the three groups have led to variations in characteristic features such as size of decentralized components, structure of system communication, type of internal processing, integration of operator control and monitoring into the overall concept, and type of design. The Decentralized Stored-Program Controller (SPC) System. The starting point for SPC systems was the individual SPC (or programmable controller), which is a self-contained unit performing a particular automation task. The emphasis was on the solution of binary networking and sequencing problems. SPCs were conceived as an alternative to relay controllers. which were becoming increasingly complex, costly, and inflexible (as to wiring). For this purpose, the SPCs were for optimized fast execution of binary logic functions, the integration of control and monitoring functions initially playing a very minor role. Elements such as buttons. switches, displays, and lights were employed to operate the SPC and observe its functions. These were connected to the SPC via conventional field-level inputs and outputs. Outwardly, the SPC thus behaved as a wired discrete control device. The development of SPC systems has led to three major new capabilities: Binary-word processing for handling integer and real variables Connection to serial bus systems for data exchange with other SPC components Support of display-aided operator control and monitoring systems Compatibility requirements have dictated consistency of internal structure in the development of SPC-aided systems. Present-day process monitoring and control systems based on SPCs thus show traces of their original functions. SPCs have gained wide acceptance. Other apparatus frequently contains self-sufficient automation components based on them and is sold in pre-assembled package form. Software creation and design are performed by the apparatus manufacturer, so that special in-house control system know-how can be exploited and protected. From the plant engineering standpoint, manufacturing automation has been and still is the
7.1. principles
domain of SPCs. An early development was the linking of SPCs and bus systems to form large networks. The purpose of these communications systems was to permit the sharing of data, especially control data and status messages, between SPCs and between the central control computers and the SPCs. A typical feature is simple variable sharing, in which the communication event and all shared variables must be individually designed. An adequate integrated operating and monitoring structure with appropriate systemsupported accesses has not yet become available (see Section 11.2). SPCs are also being used increasingly for plant automation in the field of process engineering. The starting points are often logistical side areas such as formulating lines, packaging lines, and high-bay warehouses. Starting from these applications, SPCs are spreading into cheniistryrelated areas. Especially in small and mediumsized plants, SPCs are often preferred to decentralized process monitoring and control systems because of their lower hardware costs. Although such SPC-based solutions are often more expensive than complete distributed control systems when a total cost analysis including engineering costs for design, maintenance, and technical support is carried out, the low cost of adopting them may make them more attractive to plant management when investment funds are being sought. CRT-aided operator control and monitoring features are available today for SPC systems (see Section 11.2). Generally intended for use at individual workstations with limited quantity structure, these devices offer good functionality. Problems arise in larger systems with coordinated multi-station operation, increased availability requirements, and central system services (e.g., network clock synchronization). The separate design of SPC automation functions, communications functions, and operator control and monitoring functions remains an unsolved problem. The lack of certain central functions that would be present in a process control operating system has proved especially serious in SPC-based systems. Integrated Decentralized Process Control System. The development of integrated decentralized control systems began with the control of large continuous process plants. In contrast to SPCs, the initial motivation was not so much the need to increase the degree of automation as the
221
wish to improve functionality at the individualfunction level by making design and installation simpler and cheaper and by making operation and monitoring more straightforward. The starting point came when discrete-device technology ran into its quantitative limits. In this approach, each individual function is implemented in a separate, individual device; every motor has its own start button, its own run light, and its own contactor control. Figure 7.6 shows a control panel with discrete devices, which are wired with electrical signal lines on the back. The increasing number of individual functions and the presence of up to 50 control panels in central control rooms meant that an immense collection of controls and indicators had to be put together, creating space problems and also difficulties in comprehensibility. The primary task of the plant operator was to maintain a steady watch over the continuous process. To read the instruments, the operator had to walk along meters of panel bays. The main goal of the decentralized process control system was to replace this discrete technology with a system combining the advantages of distributed structure and easy operation with the advantages of centralized data management and easily understood, display-supported operation and monitoring. Figure 7.7 illustrates the component structure employed in decentralized process control systems. The distributed components contain the automation functions for the individual plant areas. They are organized in parallel with the process. The central components contain operator control, monitoring, archiving, evaluation, and central coordination functions [7.6]-[7.8]. From the very first, decentralized process control systems were characterized by ease of design and effectiveness of communications between process-level components and central control and monitoring components. Distributed systems have the following features: highavailability redundant bus systems, components that function reliably, low error rate in component design, and good technical support at the component level. An important property of process control systems is the functional-module approach, in which software modules are handled in the same way as discrete devices. Functional modules can be inserted, switched on, removed, and connected to other functional modules while the plant is on stream. These properties result in simpler and
222
7. The Process Control Sj~stemand Its Elements: Distribured Control Syslems
new technology. This was an important reason for the successful introduction of decentralized process control systems over a broad front. The successful introduction of distributed process control systems led to a demand for a higher degree of process automation. Concepts that have come to the fore in recent years have led to the integration of higher-order control functions, up to the level of recipe control and plant management. into the structures of distributed process control systems (see Chap. 2).
Figure 7.6. Discrete-device technology Front and back of a control panel
hence cheaper design as well as high system reliability. The operator interface of functional modules is designed to correspond to the discrete-device operator interface. Because of the similarity in operating properties, operating and maintenance personnel quickly learned to work with the
Intelligent Centrril Spterns wiih L~nintelligrrii Peripherals. The basis for this line of system development is the technology of process control computers, which became available for industrial service in the 1960s. that is, before stored-program systems and decentralized process control systems. Their introduction was greated with a certain technological euphoria, and due to the lack of alternatives this approach was adopted for the full range of plant automation, operation, and monitoring tasks. However, this concept was unsuccessful because of availability problems (due to the centralized structure), the fault susceptibility of the computers then in use, and the fact that both the hardware and the applications software could be supported and serviced only by specialists. Today, the only situations in which this concept is used are those where high-quality functions are required sporadically and access to installed monitoring and control systems is not possible. An example is the case of miniplants and pilot plants, where availability requirements may not be high but it is necessary to test higherperformance control algorithms, implement model-based measuring techniques, or evaluate particular data. Furthermore, in research-level activities, personnel are often able to operate a process control computer themselves. On the whole, however, this technology is declining in importance. It is increasingly common to use small, decentralized process monitoring and control systems o r SPC-based systems for the control and operation of pilot plants. Special “research” functions can be implemented and tested on networks of flexible process control computers.
Structural Models. A process monitoring and control system is a complex pattern made up of many object classes; it can be structured in a wide variety of ways. As a result. a system de-
7.1. Principles Central control r o o m 2
223
Central control room 1
v Remote component 1
Local component C Plant section BA4 Plant section R A 2 Plant section V A 6
Plant Plant Plant Plant
section section section section
B A l Plant section RA9 RA3 RA5 RA7
Figure 7.7. Component structure of a distributed process control system
scription is difficult to understand and is always incomplete. Individual features can be explained only from a certain viewpoint, with other aspects being ignored. Nonetheless-and this is precisely the art of structural analysis-all aspects must be equally kept in view. To provide a framework for the properties briefly discussed in the sections that follow, the most important structural models and the most important ways of looking at a process control system are described briefly. Builditzg-Block Model. The building-block model represents the structure of a system unit in terms of disjoint elements and connections (Fig. 7.8). This model is generally applicable; it can be employed, for example, to describe the structure of mechanical, electrical, and functional system units. The building-block model plays an important role in the structuring of process control systems. The building-block model has some properties that set it apart from general system decomposition and aggregation schemes (see Section 2.2, Fig. 2.1): Nesting principle: Each element used in building up a system unit is completely and exclusively allocated to this system unit. It is, so to speak, “built into” the system unit and is no longer available outside the system unit. 0 External interfaces: Each element and each system unit exhibits its properties at certain well-defined external interfaces (e.g., electrical terminals, functional module inputs/outputs). 0
System
m B u i l d j n g blocks Uisjoint subsystems System elements __ Internal relationships ...- - ..External properties
Figure 7.8. The building-block model
External connections are possible only through these interfaces, which can in turn relate it to various worlds. A compact controller, for example, has terminals as its electrical interfaces, displays and buttons as its human-machine interface, and bore holes for mechanical mounting. Implementation background: Buildingblock models describe realizable system structures. Hence it is not meaningful to define fictitious (dummy) elements with no realistic
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7. The Process Control System and Its Elements: Distributed Control Systems
background and thus to built up fictitious systems. Instead, the objective must be to identify elements such that they are realizable but also represent manipulable units in the context of the overall structure. Building-block models are an effective aid to describing the structure of system units of all types. In process control systems, they are wellsuited to describing the structure of the hardware, describing the structure of the logical components, and describing the structure of the system and applications software. Types,Roles, and Functional Units. When one speaks of a system unit or a building block, one must bear in mind that the unit referred to is a self-contained object that can be examined from various standpoints: role (function), functional unit (device; see Fig. 7.9), type of role. and type of functional unit. When a control system manufacturer refers to its control system, it means the functional types offered. The design of a control structure means interconnecting functional units. These units correspond to roles that are selected from a list of well-defined types of roles. Ultimately, what is installed are functional units. This distinction is just as important for design and commissioning as it is for the overall understanding of the system. The relationships are shown schematically in Figure 7.9. Functional units are the concrete devices, components, or software modules that actually perform a function. The type of functional unit describes the essential qualities of a functional unit. Manufacturers' catalogs list, for example, the types of functional units available. If a catalog lists a PREG compact controller, then one can order x
Type o f functional unit
j I
Compact controller P - R E G / from company A I Cat. no. 3-12-5786/3 {
-
units of this type. The manufacturer will then delivers functional units of this type. The type description of functional units goes beyond the purely functional description, taking in electrical properties. overall dimensions, weigths, prices, etc. The functional properties are a subset of the type properties of the real functional units. Roles are fictitious functional units. An example of a role is tlow controller F 1 in plant section B 12. This role can be defined-and used in building up the role system-independent of which functional unit (i.e.. which device or software module) will later fulfill the role. The model employed here specifies that each role is of a certain type. The type defines all the essential properties of a role. Obviously, any number of roles of the same type can be defined in a plant. Role systems are systems built up from discrete roles. In structure, role systems correspond to the building-block model. A functional role system is illustrated in Figure 7.10. This role system describes exclusively functional properties. It can be implemented with a wide variety of technologies involving a wide range of functinonal unit types, for example with software modules or in discrete devices. There are also more concrete formulations of role systems. For example, the control panel shown in Figure 7.6 can be thougth of as a concrete instance of the functional role system illustrated in Figure 7.10. In this role system, the roles are established much more fully. The description of each role includes not just functional properties but also electrical and constructional properties. These definitions can be made concrete if only functional units of a certain type are considered for its implementation. Trend F1
Type of r o l e PI0 controller R
-----------------------+--------------------
Functional unit
I 1
Role Type R
GW L 1
?!L2?y B12F1
Figure 7.9. Roles and functional units
+gq
I
Figure 7.10. The system of roles
I
Section 7.5. gives a detailed account of how role systems are built up, concretized, and implemented. Hierarchical Structural Models. The object worlds of a process monitoring and control system can be arranged in hierarchically related functional levels. Each functional level represents a level of abstraction on which certain functional elements are available and certain tasks (or functions) can be carried out (see Section 2.2). The modeling of a process control system involves not just one hierarchical structure, but hierarchical structures based on various criteria. 0 Operational levels: The hierarchical structure of the operational levels includes interfaces for
-
-
The control system manufacturer who develops the system The project engineer who configures the process control system for the specific application The plant operator who ultimately guides the process
Figure 7.11 illustrates such a structure. The lowest level is the base system level, which contains units and functionalities provided by the control system manufacturer, that is, not specially developed for the control system. These might include PCs; workstations; operating systems such as DOS, UNIX, or VMS; database management systems such as dBase, Informix, or Oracle; and all the hardware not specially designed for the control system. The second level contains the process control operating system. This level makes up the functionality of a manufacturer's control system. The user receives its units and functionalities when the control system is delivered. The third level is the process control level. It is custom-designed and is available to plant management for active control of the process. It satisfies all requirements on the control system as set forth in the project specifications. In the model just described, the various levels are regarded as dense. This means, for example, that the designer does not need to access any level other than the process control operating system level (such as the base system). The operating system can include any required elements of the base system in its functional scope. Thus the levels of functional abstraction correspond to self-contained working platforms. An
I
7.1. Principles
225
Operator a n d monitoring l e v e l
I
Process c o n t r o l operating system
Base system
Figure 7.11. Operational levels
important criterion for assessing a process control system is the functionality of the process control operating system level. Functional elements not available on this level must be programmed by the designer as required (see Figs. 2.15 and 2.16). 0 Virtual machines: The execution functions necessary for the accomplishment of the tasks imposed can also be arranged in a hierarchical model by analogy to the operational levels. During its execution a function makes use of functions of the lower levels. The result is a system of virtual machines, each of which (as shown in Fig. 7.12) offers the higher-level functions an interface with certain services [7.9]. The hierarchical structure of execution functions is crucial for the internal implementation of program structures as well as the startup/restart behavior of the system. At this point it becomes clear that the hierarchical structures illustrated are not to be viewed simply as fictitious structures but as concrete conceptual and real structure-generating programs. This is true of all the organizational schemes presented here. According to a bottom-up principle, the functions on one level can be implemented with functional elements made available by the next lower level. In this way, more complex functional elements can be created and, in turn, made available for use by higher-level functions.
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7. The Process Control System and Its Elements: Distributed Conlrol Systems
-----__ - - _ _- _
_ - -_ -
_ - -_ _ - - -
Production c o n t r o l running Measures a c t i v e in i n t e r v e n t i o n ; disposition s y s t e m r u n n i n g
- _- _- - _ _- -
--_
__---- -- _----
Group f u n c t i o n l e v e l r u n n i n g Group c o n t r o l s a c t i v e in i n t e r v e n t i o n
- _- - _ _- ~
- _- - _
__---
_ _ -_- _ - -
Individual f u n c t i o n l e v e l r u n n i n g Individual t o n t r o l s a c t i v e in i n t e r v e n t i o n
- _- - _ _- _ --_
-
_ - - -_ _ - - -
Process control operating system running Executive s y s t e m , module-to-module communication, messaqe operator control, . . - qeneration, .
- _- - - _ - - _- _- -
I
/
__---
_ - -_ _ _ - -
Base s y s t e m r u n n i n g Base o p e r a t i n g s y s t e m , databases, communications u p t o L a y e r 7
- - _- - _ _
- - _ _- _
I
I
_--_ - -_ _ _ - - -
Hardware operating
Figure 7.12. Virtual machines in a process control system
3) Communications protocols : Communication between two applications can also be represented by a level model. Each protocol level corresponds to a “virtual medium” having certain properties, which the next higher protocol level can utilize in implementing its functions. A wellknown scheme is the OSI reference model [7.10], which consits of seven layers and the overlying user functions and user data (see Fig. 8.3): -
7 6 5 4 3 2 1
Application layer Presentation layer Session layer Transport layer Network layer Data link layer Physical layer
The structure of the communications layers (levels) is discussed in Section 7.2 for the example of systems communications and then described in general and comprehensive terms in Section 8.4. 4) Process control: Process control tasks can be arranged into a hierarchical control model.
In broad terms, the control model can be broken down into the following hierarchical levels : process control level, production management level, and corporate management level (see Section 2.3., Figs. 2.30 and 2.31). The process control level can be divided into the following sublevels in accordance with the model of Section 4.5: - Action level - Group control level - Individual control level - Channel/signal level Viewpoints. In the structural analysis of a process control system, the diverse viewpoints of the teams involved must be taken into account. For example, the following aspects are among those that must be considered: - Development - Sales - Plant management - Design - Plant technical support and maintenance - Device management Each group represented by a viewpoint has an interest only in certain objects with certain properties. It does not make sense, however, to develop an isolated model from each individual viewpoint, covering only the objects of interest. Instead, it should be possible to relate all viewpoints to a single building-block model of a process monitoring and control system. What is needed are balanced model structures that will yield a useful mode of organization for all groups involved.
7.2. System and Component Structure Overview. A decentralized process control system consists of individual components connected to one another through a system bus. The division of labor among the components varies in organization from system to system. One general distinction is between process-level or local components and remote components. Any component that has an IjO interface to the field is regarded as local, while any component that has no IjO interface to the field is considered remote. Figure 7.7 illustrates a typical functional structure. Process-level components are generally organized in the same way as the plant. Each plant section is permanently assigned to a local com-
7.2. System and Componeni Structure
221
/[71 System bus
Figure 7.13. Organization of local Components in parallel with plant organization
ponent which controls and monitors the plant sections assigned to it. The component is autarkic (self-sufficient) and viable in the sense that it can perform its control and monitoring tasks independently. The result is a typical decentralized structure for process control systems as shown in Figure 7.13. Remote components embody functions that are not directly a part of process-level control and monitoring. Examples of such functions are central operator functions, archiving functions, evaluation and interpretation functions, logging functions, production control functions, recipe management functions, and also engineering functions and diagnostic functions [7.6]. This division of labor can be modified from case to case. Many systems today still have “entry-level” versions in which design functions, operator and monitoring functions, and archiving functions, for example, are integrated into the local components. Such a local component can be used alone, with no remote components. The introduction of economical remote components based on PC solutions has led to a perceptible decline in the use of such discrete component systems. I n future, local components will perform only control and monitoring tasks, and even entry-level versions will feature a separate, small remote component.
Local components. A local component consists of a central unit and a peripheral unit, which in turn consists of peripheral o r signal modules (Fig. 7.14)
I
I
I Process INPUT
Signal modules Internal reoresentation
I
Central unit o f local component
Processing
Process interface OUTPUT
Signal modules
Z
Q
E
Contacts
interface
32
>z N
-3 V
Figure 7.14. Coupling of field signals by signal modules
Peripheral (signal) modules differ in functional form and can accordingly be placed in four categories:
1) Intelligent discrete control modules, which completely match the functionality of an individual control unit 2) Straight interface modules, which serve only as signal converters
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7 . The Process Control System and Its Elements: Distributed Control SJstems
3) Subsystem connection modules, with which subsystems can be linked 4) Field bus modules, which connect intelligent actuators and sensors through a field bus Intelligent peripheral modules include, for example, controller modules, metering modules, and control modules specificto motors and other devices. The functionality of the individual control level as a whole can thus be realized by separate, independent modules. This added decentrality does, however, have tradeoffs both in cost and in space requirements. If intelligent modules are not absolutely necessary for reasons having to do with the scanning rate or special availability requirements, they are not used at present; instead, their functions are implemented in software in the central unit of the local component. The function of the peripheral unit is thus reduced to simply the input and output of signals, which can be achieved in compact fashion by using multichannel interface modules. For example, input modules are needed in order to feed and read two-wire transducers; to process the signals from NAMUR initiators, strain gauges, and mechanical contacts; to acquire and linearize thermocouple and Pt 100 signals; to handle multirange frequency inputs; and to process O(4)-20 mA, 0-10 V, 0-24 V, BCD, and other input signals from units. The corresponding output modules provide the following functions: output of current/voltage signals, possibly into standard loads (up to 24V); power outputs for solenoid-actuated valves; floating outputs; pulsed outputs for integrating positioning drives; and so forth. In process control engineering, it is important that terminals always be floating or isolated. Inputs for intrinsically safe measuring circuits are a desirable option. The conversion of measurements and control information to electrical signals can be relocated into local hardware by using field multiplexers. These devices still provide conversions to and from standard electrical signals, but in this case the operation takes place in the field rather than in the control room. Subsystem coupling modules allow the connection of intelligent subsystems such as scale controls, converters, process analyzers, press controls, filter controls, and other machinespecific controls. In most cases, subsystem coupling functions support serial point-to-point connections, but there are also bus-type struc-
tures with the process control system as permanent master. Field buses provide logical connections of actuators and sensors with no conversion to static electrical signals. Numerous proposals have been made for defining and standardizing field buses and marketing them as standard systems. Up to the present, it has not been resolved which solutions will finally prevail in the marketplace. Figure 7.15 illustrates some important field bus systems currently under discussion [7.11]. In addition to national criteria and corporate tactical decisions, differences in functional requirements also contribute to difficulties in the standardization of field buses. The central unir of a local brocess-level) component generally consists of a special microprocessor, ca. 1-4 MB RAM, and a connection to the system communications. Additional modules are available in many systems for implementing a local operator control and monitoring function or to provide a background memory for archiving purposes. It is important that the local component should have a process control operating system and be integrated-with its object and communications structureinto the overall system. This means that the system functions of the local components permit, for example, the support of central design and the provision of server services for the operator control, monitoring, archiving, alarm, and message generation functions. Remote Components. Figure 7.16 illustrates the tasks (functions) to be performed by a process control system independent of how the system is implemented [7.12]. Two basic structures exist at present: 0
0
First-generation control systems have no information model that applies throughout the system. The individual components are specially designed and optimized for the tasks they implement. Thus. operation, computer, and engineering components generally have different system architectures and can neither be configured and maintained in a uniform manner, nor controlled through a consistent user interface. Second-generation process control systems have an information model consistent throughout the system. Data processing management (operating system, data handling, communications, operator interface) for all
Figure 7.15. Classification of field bus systems
Q Field b u s
N N \D
h,
u
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7. The Process Control Svstetn and Its Elements: Distributed Control Systems
management computer
Process Al computer
I
.
Process control com t e r Process operating
Process engineering
S v s t e m bus
component
2
-
Produc Productt
component
3
Field bus
-
Produc P r o d u ctt
Product
Figure 7.16. Ideal structure of a process/production control system
remote components is implemented in the most uniform way possible (Fig. 7.17). The advantage of this concept is that each function can, in principle, be performed in any component. The allotment of required functions to components in a specific case is carried out with regard to the following criteria: decentrality, uniform processor loading, minimal data exchange, and realization of certain redundancies. The operator interface is uniform across the system. Any function in any remote component can be accessed from any terminal. Thus, from a
given terminal, one can configure a component x in the system, use an operator function to perform a certain action in a component y, view the archive stored in component z, and effect an input to the production control system located in component z. Which interventions are possible in a particular case is governed solely by access authorization. The extent to which the humanmachine interface is consistent between packages will depend on the state of development of the individual system packages. The need for a consistent concept, however, requires a consistent systems approach from the outset. In small
7.2. System and Componenr Structure Functional p a c k e t s ( d i s t r i b u t e d i n s t a l l a t i o n ) OP o p e r a t o r f u n c t i o n s EN engineering f u n c t i o n s PR logging / r e p o r t i n g f u n c t i o n s Workstation or
PC
231
Terminals
’ Remote
f
I
I
I
I
I
I
component
Figure 7.17. Process control system with continuous operating system platform for distributed use
plants, this concept has the important advantage that a modest hardware platform can make available the entire system functionality; thus the hardware governs primarily the realizable quantity structures (quantitative) rather than the functions that can be performed (qualitative). If the requirements are increased, the power of the system can be adapted to the new tasks by the modular addition of hardware. The presence of a consistent information or data model is, however, an essential prerequisite (see Chap. 2). System Bus (see also Chap. 8). The system bus is the nerve center of the process control system. The following services, among others, are performed via the system bus: Continuous delivery of data from local and remote components to the monitoring functions Transmission of operator commands, acknowledgments, and instructions from central process and production control functions Provision of required data to the central archive Delivery of messages and alarms to the message receiving services Object manipulation and object information to assist central engineering Loading of actions (production specifications, execution specifications) Internal update functions when individual components go out and are restarted Temporal synchronization The system bus of a process control must have the following technical qualities:
High availability Insensitivity to electrical and electromagnetic interference Deterministic behavior, even when under load Open architecture (compliance with international and de facto standards) Hardware and software capable of adding or deleting subscribers while bus is in operation The following features are possible ways to meet these requirements: Use of optical fibers for data transmission. Optical-fiber technology permits high transfer rates even in large-scale networks. Electromagnetic interference and potential problems in the bus system are physically excluded. Transparent redundancy of medium. The required high availability is possible only if the transport medium is redundant in design. Its redundancy must be transparent; that is, higher communication levels must not be affected by a switch from a pathway to the redundant pathway. Use of token-passing protocols. The use of the token method for transfers means that every subscriber can send important messages within a fixed length of time, even when the bus is heavily loaded. Currently there are no standard system buses having these implementation features in the middle- (ca. 1 Mbit/s) and high-performance (ca. 10 Mbit/s) ranges. It is therefore no surprise that all system buses now available, while they do use standards for certain levels, still represent manufacturer-specific solutions on the whole.
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7 . The Process Control System and Its Elements: Distributed Control Systems
Unfortunately this commercially important range of medium and high transfer rates is not currently experiencing any development that could lead to a standard solution with the properties needed in a process control system. It appears that there is still some time to wait for an “open” system bus for use in process control engineering. The only hope at the moment is F D D I in the ultrahigh-performance field. FDDI has some prospect of acceptance as a general standard and has all the technical qualities expected in a system bus for process control systems. It is currently handicapped by its very high cost and by the space and power requirements of the interface groups to handle the high transfer rate (100 Mbit/s). Nonetheless, this approach is going to be a focus of attention in process control engineering circles. The following comment should, however, be made on the evaluation of data-transfer rates. If the transfer requirements imposed on a system bus in a process control system for controlling the process are considered, it can be seen that a gross transfer rate of 1 Mbit/s is sufficient for such tasks, even in larger networks. The net transfer rate strongly depends on a choice of individual transmission services and the network data distribution model.
Process Operator A S 500
OP i C )
Bottlenecks can occur, for example, when archives are updating one another, when static image information is being transmitted over the bus, or if an unsuitable data model has been chosen. A typical example of a transfer-intensive data model is the centralized display of a11 local component status information at remote cornponent locations. In the assessment of a bus system, therefore, the gross transfer rate is less important than a well-thought-out model of data management and data sharing on the network. Component Structure of Commercial Process Control Systems. Figures are intended to explain the component structures of several process control systems. The selection is meant to provide examples and does not reflect either market position o r technical ranking. In Figures 7.18-7.31, the notation used for the components complies with the manufacturers’ product designations.
The following symbols are used for functional packages:
AR Archiving functions CO Control functions EN Engineering functions GA Gateway functions
lGA
B a t c h Control S t a t i o n
Engineering S t a t i o n
Master OP (81 B a t c h 200 AR
Remote components
Local components
Gate 230
+Master OP (81 Piece 200 C O EN iS,Ll
Process S t a t i o n Figure 7.18. Functional structure, ABB Master
a EN
(L)
M a s t e r Bus 300
7.2. System and Component Structure
Engineering S t a t i o n
A S 500
Process Operator Station
OP [C)
AS 500
EN [C)
AR
PR
Remote components
EN (C) CD I
troller Local components
EN IS,L) OP (5)
233
Compact S t a t i o n
-
Turbo node
OP K,L,S) CO AR PR EN 6 , C I CD
communications network)
Process Station Figure 7.19. Functional structure, ABB MOD 300
Process Operator Station
Remote component 5
Viewstar 750
OP [C) AR
PR
EN CD
t -
GA Engineering S t a t i o n ModiCADE
EN [C)
is)
packages are separated into client and server portions and a letter (C, S, or L) is appended in parentheses : (C) Acts in the system as client of the function (S) Acts in the system as server of the function (L) Function is implemented locally with no involvement of system communications
The codes C and S are used only for components that support the function on the system side, that is, when communications need not be separately programmed.
Local components
Process S t a t i o n
Figure 7.20. Functional structure, AEG Geamatics P
I0 OP PR CD
Process input/output functions Operator functions Protocol (reporting/logging) functions Coordination functions
Note that some functions relate to not a single component but several. In such cases, the
Examples : Engineering Functions. The engineering component in which design activities are carried out acts as a client in the system. The target system designed by the central engineering function acts as a server in the system. The engineering station would thus have (C) appended to its symbol, while the target systems designed by the central engineering functions via system communications would have (S) appended. Locally configurable components would have an (L). Operator Functions. Functions available troughout the system provide for operator control, monitoring, and the transmission of messages and alarms. With these functions, information is shared between individual applications in the target system and the operator (via the hu-
234
7 . The Process Control System and Its Elements: Distributed Control Systenis
01s Remote components
OP (Cl AR PR EN (51
EN ( C l
EWS
System
Network interface I
Controtway
Local components
Process Station Figure 7.21. Functional structure, Bailey INFI 90
Process Operator Station Engineering S t a t i o n
lLS 520 Remote components
OP (C) AR PR EN (C.L
7 Trend Station
Logging S t a t i o n Computer l n t e r f ace Unit
I I
I
I S y s t e m bus
Local components
Figure 7.22. Functional structure, Eckardt PLS 80 E
7.2. System and Component Structure
man -machine interface). The display and operator components act as clients, while the target component in which the operation takes place or in which a status is interrogated is regarded as the server. If one examines the structures in detail (see Figs. 7.18-7.31), one sees that most of the proProcess Operator Station Data Engineering S t a t i o n Management S t a t i o n
Remote components
EN (L)
EN (C.S,Ll
I
I
System bus
Local components
Process S t a t i o n Figure 7.23. Functional structure, Fischer & Porter DCI System SIX Process Operator Station
IPROVUE
235
cess control systems illustrated offer extensive systems solutions for the handling of local components. The major shortcomings are in communications between remote components. Server properties are largely lacking in these remote components. In other words, remote components often cannot be centrally designed (configured), and the normal operator control and monitoring functions d o not make it possible to operate and monitor applications in other remote components. Redundancy. The following concepts can be applied to redundant design: 0 Necessity of redundancy (see also Section 10.4) The availability of the process control system depends on the frequency and duration of failures of its components. In general, when the process control system or a portion of it fails, the production plant or the respective plant section also fails. If an especially high availability is required of a plant section, or if the failure probability of this plant section must be particularly low, the local components controlling it must be designed with redundancy. However, this is an exceptional case. In many situations, it is acceptable for individual plant sections to fail. Production can be briefly interrupted, or plant sections redundant from the production engineering standpoint can
Data Management S t a t i o n Engineering S t a t i o n
Remote components
~~=Tj* PROVOXplus D a t a bus
Local components
Interface
Process Station
Process Station
Figure 7.24. Functional structure, Fisher Controls PROVOXplus
236
7. The Process Control System and Its Elements: Distributed Control Systems
be switched over. The decentralized structure of the control system means that the failure of one local component affects only one of the redundant plant sections, provided the allocation has been chosen correctly. Here, the process control system need not exhibit additional redundancy.
AP
Remote components
-
OP (5) AR PR EN I0 CD
In the case of continuous processes that run nonstop for 3-5 years, as is common in some industries, scheduled maintenance of the monitoring and control hardware becomes a problem. The performance of scheduled maintenance generally involves removing modules, while inspec-
OP 10
WP
GA
EN IS)
IN1 15
OP IS) Local components
EN IS)
Process Station
Figure 7.25. Functional structure, Foxboro IAS
LS Remote component- S
EN I C J
-
LS EN (S)
CD
S e r i a l S y s t e m bus
OP IS) Local components
EN lS,LJ
Process Station
Figure 7.26. Functional structure, H & B Contronic P
7.2. System and Component Structure Enoineerino S t a t i o n
Process Operator Station
A m l i c a t i o n Module
237
H i s t o r y Module
Remote components
0
LCN
N e t w o r k I n t e r f a c e Modul (Bus I n t e r f a c e Unit)
UCN
OP IS)
Local components
EN IL)
Logic Manager Process Station
Process Station
Figure 7.27. Functional structure, Honeywell TDC 3000
GA
Station Engineering S t a t i o n O p e r a t o r OP IC) ’ Station AR PR Remote Components EN 1C.L)
Control File Local components
SCI Host Interface
OP I S )
EN IS)
Figure 7.28. Functional structure. Rosemount System 3
tion entails removing entire system portions from active service. Redundancy is essential if this i s to be done. Dispensing with integrated operator and monitoring functions in the decentralized local
components makes it necessary to have a redundant bus system with redundant implementation of operator functions in the remote components. Back-up operation with local intervention at the local components is generally no longer possible. a Modular redundancy concept In the modular concept, the decision whether to make a unit redundant or not is made separately for each unit in a system. Three points favor modular redundancy: - Redundancy is only desirable for well-defined, self-contained units - Modularization creates redundancy nodes, which separate the individual redundant units from one another and increase the availability of the system - Redundancy is expensive and it is therefore sensible to employ redundant design only where necessary for a specific application Figure 7.32 illustrates a modular redundancy concept for the system bus and local (processlevel) components. This is an instance of transparent redundancy of the medium, which means that the individual data-transfer and processing units implement redundancy in such a way that higher-level system and user functions do not see it logically.
238
7. The Process Control System and Its Elements: Distributed Conrrol Sysiems
mately realized in the structure of the user functions. 0 Quality of changeover to redundant units The redundant layout of units means that if a unit A fails, it is possible to change over to another unit B which continues to implement the required functions.
The situation is different in the area of remote components (i.e., on the "client side"), where standard components (workstations, industrial PCs) are employed in a nonredundant system structure. Redundant design is aided by special functions in the process control operating system and general system services and i s ulti-
OS 265 Remote components
Local components
rl
OP (0 AR PR EN (S,Ll
~
PROGRAF
EN ((1
OS 520
EN (S,Ll
KOPlX
OP (C) AR PR EN ( L l -CD
AR PR EN IL)
CS 215
r-T 55-155U
OP IS, EN (L)
= Process Station
Process Station
Figure 7.29. Functional structure, Siemens Teleperm M Process Operator Station WEStation Remote components
OP ( C i
Engineering S t a t i o n W E S t a t i o n EN (C)
VAX/p-VAX
Data Manaoement S t a t i o n WEStation
t
AR EN ( 5 )
.'I :
W e s t n e t II D a t a Highway
EN (5)
Local components
EN (Sl
Process Station Process Station Figure 7.30. Functional structure, Westinghouse Controlmatic WDPF I1
'r'
I
t
SPS
7.2. System and Component Structure
239
104
Process Operator
Station
Remote comoonents
HF Bus
Local components
Process S t a t i o n
Figure 7.31. Functional structure, Yokogawa Centum XL Redundant modules
Redundancy nodes
S y s t e m bus Interface Backplane bus Central u n i t Cabinet bus Discrete c o n t r o l module
8 8 0 0 0
Figure 7.32. Modular redundancy concept
An ideal changeover is as follows: -
-
-
-
A malfunction in A is correctly detected as soon as it occurs, and the changeover takes place before a single datum in A is altered Prior to the changeover, B is in exactly the same state as A The changeover takes place with no time delay The changeover is “bumpless”
This is, of course, an idealization. In real systems, a number of problems arise: - Detection of the Malfunction. A malfunction can be detected by self-diagnosis in A itself or by
external monitoring. External monitoring systems are commonly based on the comparator principle. In a 1-out-of-2 redundant setup, however, localizing the malfunction is a problem. If the two units do not agree, which is wrong, A or B? Another problem is the time between the occurrence of a malfunction and its detection. Has A improperly changed its state in this interval as a result of the malfunction? Synchronization of B. B must begin working at the point where A quits. The question is how B, at an acceptable cost, can track A so as to hit the starting point as exactly as possible. The designer must then ask: How is the synchronization to be done? If A and B are clock-synchronized, or if there are synchronization points in the software, what deviations in the instantaneous states are tolerated and what deviations are unacceptable? Are data transfers in progress terminated and restarted, or is control passed “on the fly”? And so forth. Changeover. When the changeover takes place, all communications links of unit A are broken and replaced by links of unit B. The following problems arise at this point: How is the logical changeover accomplished? How long does B take to come into sync? How long will it take before all partners of A/B have dropped the old connections and made the new ones? Is the changeover automatic or manual? Does unit B remain on permanent standby, or ~
~
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7. The Process Control System and Its Elements: Distributed Control S-vstems
must it be turned on or even fetched from storage? - Restart. Once the malfunction has been corrected, how is the automatic restart of A (up to hot standby) supported without impairing the function of B? The implementation of a modular redundant process control system is essential if the stated requirements are to be met. A good redundancy concept is the mark of distinction in a process control system. The questions stated under “Quality of Changeover to Redundant Unit” are not intended to lead the designer to a particular solution but rather to indicate the complexity of the problem and afford some warning against obvious but half-baked solutions. Electrical and Mechanical Design. Process monitoring and control systems must function reliably, feature robust construction, and be adaptable and modifiable even while in service. Special importance therefore attaches to the mechanical and electrical design of these systems. Mechanical Design. Local components are generally rack-mounted inside steel cabinets. Central modules are located in one plug-in unit; peripheral modules, either along with the central modules or in additional plug-in units. A module can be a simple card or an enclosed rack-mount device. The technique used to connect signal lines leading to the field varies from system to system; plugs on the front and backside are common, as is direct wiring to terminal posts or screw terminals in a special terminal bay. An important mechanical design question is whether the cabinet must be accessible from one side or both sides. Practice has shown that access to the back of the plug-in units is necessary. This can be achieved by leaving a walkway at the back or, when this is not possible, by using a swing-out cage. Remote components must also be considered in the course of mechanical design. Because these are generally off-the-shelf hardware, they must be mounted in an appropriately stable and secure way, either in control room panels or in properly equipped cabinets in the process control rooms (see also Sections 5.3 and 10.7). Electrical design includes the overall electrical behavior of the process control system components. Important aspects of electrical design are power supply, isolation and earthing, protective enclosures and safety requirements, electro-
magnetic compatibility, and response to external conditions such as climatic, seismic, and chemical effects (see Section 6.4). 0 Power supply. The process control system is responsible not only for controlling the plant but also for monitoring it. It is desirable to provide a highly reliable uninterruptible power supply for the process control system; this makes it possible at least to acquire and record information about the process and plant status even when power to the processing facility is interrupted. I t is precisely these exceptional situations that make the process control system so vital as an information system (see Section 6.4). 0 Isolation; earthing concept. A process control system communicates with a number of partners, some of them from different manufacturers. This is particularly true of local (process-level) components as well as the subsystem and field areas. It must be possible to adapt the control system to the local grounding concept. The goal is thus a control system “open” from the grounding standpoint. This requirement will be fulfilled if each input and output in the process monitoring and control system is electrically isolated. The earthing concept determines which potentials are connected i n a specific case (see Section 5.3). 0 Protective enclosures and safety requirements. Process control systems are electrical systems and must satisfy regulations on shock-hazard protection and protection against the ingress of solid bodies and water. These regulations are based on IEC 529 (protection against foreign bodies and water) and IEC 536 (shock-hazard protection). In process control systems it is particularly important that the following sections be spatially separated from one another: low voltage; functional extra-low voltage with safe separation (PELV as specified in DIN 100, Part 410); and intrinsically safe circuits. 0 Electromagnetic compatibility. Process control systems are operated in an electrically harsh environment. In many situations today, these systems are located next to static converters and other power electronics, in the same room or even cabinet to cabinet. Operating personnel use handheld radios when the cabinets are open, and the rebranching field cables can pick up interference. Therefore, electromagnetic compatibility is a special concern in process control systems. Electromagnetic com-
7.3. Process Control Operating System
patibility is a measure of resistance to noise resulting from ~
-
Static discharges (IEC 801-2) Electromagnetic fields (IEC 801-3) Bursts [IEC 65(Co)39] Hybrid voltage transients [IEC 801-5 (Draft)]
Other guidelines, recommendations, and standards have been issued by NAMUR and DIN; see, for example, [7.13]-[7.15]. For each device, the severity of the effect can be measured and classified. Four classes can be defined : 1) No impairment of operation or function 2) Momentary impairment of operation or function, self-correcting 3) Momentary impairment of operation or function in which restoration of service requires resetting or operator intervention 4) Permanent loss of function resulting from damage to the device (or its components) In general, it is expected that the maximum effects under moderately severe testing (IEC Severity 3) will fall into Class 2. 0 Other conditions. This heading takes in all other environmental effects that might lead to impairment of the functioning of the process control system. They include, among others : -
Temperature and humidity Seismic stress Chemical attack (e.g., acidic gases)
Environmental conditions are defined and classified in IEC 721 ;test methods are set forth in IEC 68.
7.3. Process Control Operating System A process monitoring and control system is a distributed system in which modular control structures are managed and executed in the most efficient way possible. This applies in particular to the local (process-level) area. The process control system should make it easy for the user to work with a modular system (as illustrated in Fig. 7.10, p. 224) in a similar way to a system based on discrete devices. For example, it should be possible to install or remove a module without interfering with the operation of other modules, and to make or break a connection or modify a parameter value
241
while the system is in operation. Another important point is reliability. For example, the making of a connection should not inadvertently cause internal interference with a modular algorithm, while the manipulation of a module should not result in the accidental change of a parameter value in another module. These requirements can be satisfied only if the process control system has an operating system with appropriate object manipulation and object-oriented programming capabilities; the operating system should also support the creation of modular software structures. Three approaches have been taken to implementing these requirements. In the first approach, the entire “modular intelligence” is concentrated in the design system. The local components responsible for control system operation do not have a modular structure. The design system makes a translator run, generating a transparent code from the objects managed, and loads this code into the local components for execution. The advantage of this technique is that the requirements on local components are modest; the local components can be structured easily and thus economically. On the whole, however, this concept has not yielded any satisfactory solutions. The main reason is that changing a single object requires retranslation and reloading of the entire program. Such a system is extremely resistant to changes. When partial translation or partial loading is desirable, the design system must have a detailed knowledge of, and partial authority over, the data organization of the local components. The cost of developing such a design system is high and cannot be controlled, especially when the local components are further developed. For these reasons, all successful SPC (storedprogram controller) systems are decentralized process control systems that include management structures for objects in the local components themselves. It is necessary to distinguish between the “procedure-oriented” modular approach used in SPC systems and the “object-oriented” modular approach used in distributed process control systems. Figure 7.33 illustrates the difference. In the procedure-oriented modular technology, data modules and procedure modules are the objects. Distinct procedure modules communicate through data modules. The procedures describe certain data elements of the data modules, which
242
7 . The Process Control System and Its Elernenls: Distributed Control Sysrerns P r o c e d u r e - o r i e n t e d modular technology
O b j e c t - o r i e n t e d modular technology
Elements: D a t a modules, p r o c e d u r e modules
Elements: Functional modules
Data
Data
Data
Access t o d a t a by p r o c e d u r e modules is allowed in principle
Access t o d a t a only via communication connectors; m e t h o d - p r o t e c t e d
Figure 7.33. Procedure-oriented and object-oriented modular technologies
in turn are read by their procedures. The connection does not exist as an explicit object. In this method, the system does not afford protection of access to the data areas; that is, all data can be read by any procedure, and also written if the procedure is write-enabled. The right-hand part of Figure 7.33 shows the object-oriented modular technology, where data and procedures (here also called methods) are combined in a single module that can be handled only as a whole. Data sharing takes place via connections between the module connectors. Direct writing and reading of internal module data is not possible at all. The only way to access these internal data is locally, through the methods inherent to each module. It is important to maintain the distinction between these two technologies. In practice, many misunderstandings arise when the term “modular technology” is used. In the SPC world,
W
H& Project team
Muller
procedures are also referred to as “functional modules” and data as “data modules.” The forms of office organization shown in Figure 7.34 illustrate the properties and applications of the procedure-oriented and object-oriented approaches. On the left, Miiller and Schulz work together in a project team. They sit across the table from each other, in a room containing a cabinet with a shared set of tiles. This arrangement corresponds to a procedure orientation. Muller and Schulz correspond to the procedures; the set of files, to the jointly available and processed data. This form of organization has the advantage that every worker has quick, direct access to the common data. The setup does, however, require intensive communication and agreement between the two team members: each must know exactly what the other is doing and what data are being changed. While this organizational form is feasible for small. easily over-
I \/I1 \/I1 \ /
Schulz
P r o c e d u r e - o r i e n t e d technology
O b j e c t - o r i e n t e d technology
Figure 7.34. Examples of procedure-oriented and object-oriented setups
7.3. Process Control Operating system
seen units, in larger and more complex structures it is no longer possible. More complex structures require local data safeguards. Such a form of organization is shown on the right in Figure 7.34. Maier has his own set of documents, to which only he has access. No other team member can enter his office to view a file or enter anything in a file; anyone wishing to obtain such information or make such changes must ask Maier to provide information or make an entry. Maier himself decides whether and how to respond to this request. Maier and his data form a unit that, as a whole, performs certain tasks. This form of organization corresponds to an object orientation. If the parallel is drawn to process control systems, it becomes clear that the traditional approach in SPC-based systems-which historically arose from small automation systems with fast access requirements-is procedure orientation. Decentralized process control systems, on the other hand, were designed for large, complex networks from the very beginning, and in these systems the object-oriented modular approach predominates. The remainder of this section will describe the basic properties of a process control operating system supporting an object-oriented modular system. Software Modules. One characteristic of a modular system is that a module-with all its parts, data records, connectors, and methods-is treated as a unit. In production service, from the communications standpoint, the module can be identified by a single reference. Such an object-oriented modular concept can be implemented in either of two ways: by realizing the modules as flat, completely autonomous objects, or by realizing them as entities belonging to a class hierarchy. Methods using discrete devices correspond to the first approach. Each compact controller has its own data storage, its own interfaces, and its own logic for implementing the PID algorithm. In a software-type concept, which follows the second approach, the modules are realized as instances of module types; this corresponds to a special, one-level abstraction scheme. When it is realized in the functional system, each module is therefore split into two objects, a type object and an instance object. The type object contains the processing methods, a description of the data structure, and a description of
243
the connector structure. The instance object (entity) contains the module name. the individual status information, and a reference to the type object. This functional structure implies two properties that are significant for module handling in process control systems: 0
Type objects and entities can be created and loaded separately More than one entity can be defined for one type object
Figure 7.35 shows realizations of three controller modules. All three use the common type object “PID controller” ; modules TA 12 L 1, TA14F1. and TA3T2 are three entities. The creation and loading of types is an operation that generally calls for profound system knowledge and can be performed only by certain persons. In many process control systems, the user cannot create new module types with their own data structure, connector structure, and methods. The user must make do with the set of types provided by the manufacturer. In contrast, it is very easy to work with instances under the software-type concept. Because all data structures, connector structures, and methods of a module are defined when the type is created, the instances need only be generated as data records, parametrized, and inserted in the processing sequence. These are simple operations, and in virtually every process control system they are supported by the operating system of the local components, even during production operation. The software-type concept has proved effective for the handling, management, and sequential organization of the modular system in process control systems. In a variety of components, the concept has been embodied and optimized for the requirements of a real-time system with many objects to be processed concurrently. Certain constraints relative to a general class hierarchy are unavoidable. They include the following: 0
The functional system supports only one level of abstraction (entity-type). Type data are present in multiple copies and are thus not free of redundancy. The type data for a module are available locally, as loaded type objects, with respect to every processor.
244
7. The Process Control System and Its Elements: Distributed Control Systems
I
Type c o n t r o l l e r
I
-Methods -Data structure -Connector s t r u c t u r e
*
Type o b j e c t
Module TA14F1 Type: c o n t r o l l e r
Type: c o n t r o l l e r
P7 aM rameterdstates
Pararneters/states
Entity
Entity
c;l
*
Module T A 3 T 2
~
Type: c o n t r o l l e r Pararneters/states
Entity
Figure 7.35. Software implementation of three modules of the type “controller” 0
Each instance has a direct reference to the type object of its logical environment and knows what information it will find where.
As far as the external function of a module in the modular network is concerned, it makes no difference whether the module is realized as an autonomous object or as an instance of a type. In the software-type concept, the instances provide the external representation of the module with all of its properties.
Communications Network. A major task of the process control operating system is to manage and dynamically perform communications in the network of modules. Each module has connectors serving as external interfaces, through which it exchanges information with its surroundings. All communications in the modular network can be depicted in terms of individual communications links, each between one module input and one module output. From the applications standpoint, there are two basic forms of information exchange: the “get” relation and the “send” relation. Figure 7.36 illustrates a get relation. B is interested in the present state of A and reads this state from A without feedback. Such a relation corresponds, in discrete technology, to acquiring a signal from an output terminal. A given output A can be read by various inputs.
Fi=E
Figure7*36.Get re’ationsh’p
In this kind of relation, only the recipient is interested in exchanging information. The recipient needs a state that another unit is furnishing as an output, and needs it as quickly as possible. Regardless of the concrete form of this transfer-a network may well contain sending and event-driven system services-the relation as seen from the user’s viewpoint corresponds to a get relation. Relations of this type lead to low-error-rate structures that are easy to reconstruct in dynamic fashion. The fact of access without feedback means that get connections can be made without further safeguards. Within the component, a get connection corresponds to a read access of an output variable and, accordingly, can be implemented efficiently. Many process control systems permit the treatment of get connections as objects in the local components, or at least get connections internal to a component. Their management and general implementation are taken care of by the operating system.
7.3. Process Control Operating System
I
\
b
\
245
I
Figure 7.37. Send relationship
Figure 7.37 shows a send relation. Here one application has a functional interest in communicating something to another application. This would be the case, for example, when higher-level units transmit orders to lower-level units; where monitoring units have the task of automatically generating messages and alarms; or where operator control units wish to send a new control value to an application. The attribute send here does not refer to the type of transmission; it represents the fact that the initiative and interest in data exchange is primarily on the part of the sender. A send relation is characterized by the following features: The application behind connector C knows under what conditions C is to send what information to whom and activates connector C accordingly. The message from C leads to a write access in D: that is. the state of D changes. The associated service in must therefore have a protective mechanism that receives the message, checks it, and performs the write only after successful checking. The protective mechanism checks both sender and content. To avoid unpredictable response times, the server service responsible for checking the message and processing it on receipt must be initiated as soon as the message arrives.
Figure 7.38. Instruction input connector
connector-to-connector$
Figure 7.39. Communications as a svstem service I
I
By way of example, Figure 7.38 shows an instruction input connector. When a message containing a new instruction arrives, the availability of the connector is first checked. If the connector is available, it is then occupied. The message is next checked for formal correctness (type of message, variable types, etc.). If the result is positive, the message is correct. This does not mean that the instruction can also be accepted by the module in terms of its content. The receiving service therefore has a link to a part of the module method specially allotted to the connector. This checks whether the content of the instruction is permissible from the application viewpoint. If the result is positive, the instruction is finally accepted and written as a new effective instruction. The sender of the instruc-
tion is notified of the acceptance, or the reasons for nonacceptance, of the instruction. Most process control operating systems also permit unprotected writing into effective module input areas. Some users make intensive use of this capability, transforming the process control system into a quasi-procedure-oriented system. This is not, however, in keeping with “good proand should be avoided -gramming- practice” whenever possible. A process control operating system must have the goal, as illustrated in Figure 7.39, of managing all communications relations between connectors in the modular network as objects and embodying their implementation, as seen from the user’s viewpoint, in a system service. Many process control systems already do this within a local component, at least for relations internal to the component. The critical relations, however, are those between connectors belonging to modules implemented in different components. Here the user must explicitly design (configure) the communication in many cases. For example, it may be necessary to integrate and wire certain LAN communications modules. The message orientation and the strict formalization
246
7. The Process Control System and Its Elements: Distributed Control Systenis
Component 1
I
Component 2
Figure 7.40. Connector-to-connector communications via the LAN
mean, however, that the connector model will later enable the operating system to manage and execute connector-to-connector relations over the LAN. As a consequence of the special two-level module + connector object structure and the client/server character of the relations defined, it is possible to make efficient use of M M I services (see Section 8.5) for imaging. Open communications (see Fig. 7.40) can thus become a basic feature integrated into a communications network extending over many components. Connector-to-connector communications can thus be organized as a network-wide message exchange system. A crucial point is that the simple local relations, with their efficient execution methods, are incorporated as an integral part of this overall system. If, as shown in Figure 7.41, two applications are located in the same component, the process control operating system can implement connector-to-connector communications with optimized local access functions. This technique can greatly increase the processing power of the execution system. At the process (local) level, “get” relations within a component are the most frequent type of communications and are crucial for the loading of the communications network. If all these relations were executed through a general message exchange system, the capacities of the functional units would be far too small. This level of performance can only be achieved by effective use of internal pointers and direct reading, both organized by the operating system. Addressing. The objects and name spaces contained in a process control system depend on the organizational viewpoint and scheme. It is necessary, for example, to distinguish between
h
A
Component X
Figure 7.41. Coininunications internal to a component
the hardware viewpoint (component, module, unit), the access viewpoint (bus segment, subscriber, module, unit, connector, variable). the open communications viewpoint (VMD, domain, variable), the programming viewpoint (unit, connector), and the engineering viewpoints relating to the structure of the process plant, the breakdown of the process to be performed, or the structure of the production sequence. Each way of analyzing the system has its own hierarchical name space, in terms of which the individual objects in the system so analyzed can be addressed. There are many mappings and assignments among the objects i n various system breakdowns. and hence many object management structures and addressing options. These are not presented in detail here. A question that is of interest, however, is addressing for LAN communications in the modular network. Most process control operating systems have not integrated communication via LAN into their module communications. and, therefore, either special communications functions must be installed (READ, WRITE, SEND, RECEIVE,. .,) or communications must be structured with direct, unprotected read or write access to the module variables.
7.3. Process Control Operating system S e r v e r connector
PCE s e r v e r
L A N communication
PCE Client
247
Client connector
Unit number
Variable number
Figure 7.42. Example of connector addressing via LAN with names
Figure 7.42 shows one possible addressing scheme in the modular approach. The scheme is shaped by three aspects, which must be mapped into one another. Addressing in the client is done from the programming viewpoint. The client connector wishes to make a connection to a certain server connector. The server connector is identified by its name and the name of the module (unit). The process control operating system (0s) recognizes that this unit is located in another VMD and maps the requirements onto an MMI function. The unit is thus mapped into an MMI domain and the connector into an MMI variable in 1 :1 fashion. In the target VMD, the process control 0s performs the MMI server functions and exchanges data with the server connector. The 0s uses its internal access addressing scheme, for example the module number, unit number, connector number, and individual variable numbers. This is just one possibility. There are others, such as access through abbreviated addresses. The important point for effective open communications is that modules (units) and connectors can be mapped 1: 1 into addressable communications abjects.
Execution Properties. An important task of the process control operating system is to control the sequence of module processing. In general, a module consists of a main method and various connector-specific methods. The main method is started by a call to the module by the executive system. In process control systems, calls typically come in cycles at fixed time intervals. Each module can be allotted
such a fixed cycle for itself. Cyclic method calling is independent of whether or not changes of state have taken place at the input connectors. Cycles of between 1 and 4 seconds are typical in process engineering. Connector-specific methods are permanently allocated to the input connectors. They are started in acyclic mode when messages arrive at the respective input connectors. Asynchronous Operation. The use of fixed cycles generates deterministic sequences between certain modules. Even so, the modular network as a whole must be regarded as a network of modules in asynchronous operation. The processing of a module can be interrupted, for example, by the processing of a higher-priority cycle sequence or by connector-specific methods started in acyclic mode. Furthermore, if module processing is shared among several processors, cycles of equal priority also run asynchronously. Asynchronous operation results in data consistency problems: 0 0
While one module is running, the outputs of other modules may change While one module is running, processing of another module may be interrupted, so that its outputs represent results from different cycles
These problems are not generally relevant. The output states requested must be as current as possible. Inconsistencies are not a difficulty for the processing algorithms. Applications requirements, for example in module type creation, may call for appropriate measures to insure consistency in certain cases.
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7 . The Process Control System and Its Ek'merits: Distributed Conrrol Systems
The efficiency-oriented concept of process control operating systems manifests itself again here : avoidance of unnecessary management overhead. Synchronization methods are available to insure the consistency of output and input data. However, they impose a substantial burden on the executive system and markedly impair the performance of the local components. Most process control operating systems purposely dispense with system measures to impose consistency. Note that all the devices in a discrete control system also function in asynchronous mode without consistency-insuring measures.
7.4. General System Services A control system is expected to offer, as an integral feature, certain general system services for production operations. These should be implemented as firmware in the process control operating system (0s). The pertinent services include the following: 0
0 0 0
Communications and organization services for operation, monitoring, the generation of messages and alarms, archiving, and report creation Time management and network synchronization services Monitoring and diagnostic services for process control hardware Services supporting the changeover between redundant units and the matching of their states
Integration of these services into the process control 0s by the manufacturer is necessary because only in this way can they be implemented in a meaningful way and performed with the necessary effectiveness. Unfortunately, there are currently no standards governing the functionalities of these system services; the available, manufacturerspecific solutions differ all the way back to their principles. The user must consider the structure of these manufacturer-specific system services and fit the applications software to it. A manufacturer-neutral design procedure is therefore possible only within narrow limits. In principle, this design approach is restricted to the configuration of certain processing functions with standard languages such as FUP, module language, and so forth.
This part of the configuration job often makes up only 40% of the whole. The remaining configuration work must deal with configuring and parametrizing manufacturer-specific system services. This section will describe in detail the tasks and contents of the system services. Each service is treated as an individual functional module. Monitoring (see also Section 11.3). Today. processes are generally monitored by using color graphics displays. The interface can be based on standard solutions from the PC or workstation realm. The monitoring services provide the monitor interface with the appropriate video and status information. One monitoring service includes transmission of the state value from its source to the monitoring system. maintaining an internal state map (state or status image) in the monitoring system, and displaying the state value at the interface. Figure 7.43 shows schematically a monitoring service. The monitoring system always displays the current state values. If a state is dynamically changing, it is important to display its most recent value. Past values and timestamps are not of interest. On arrival, each new value overwrites the old value on the screen. All monitoring interfaces are implemented by the monitoring system. The same holds for the display of archival data, messages, records, and so forth.
Figure 7.43. Schematic diagram of a monitoring service S = state; SD = state display; SIM = state image; STS = state transmission server: DCC = display and control component; LOC = local component
7.4. General System Services
When the message log is displayed, for example, the message system is the source of the states displayed. The timestamp of a message is a state of this system and is retrieved and displayed by the monitoring system in the same way as any other state. State Displays. A state can be displayed in a variety of ways, and standard interfaces offer all the possibilities. It is therefore a fundamental question of human -machine or human-process communication design how a state is represented in each particular case. Figure 7.44 presents examples of ways of depicting a binary state. Often, a binary state is represented by a color change in a text field or by the presence of a mark. In flowsheets, the process state that the binary state represents is indicated symbolically. For example, a curved arrow or a color applied to the valve symbol can be used to indicate whether a valve is open. Figure 7.45 shows several representations for an integer or floating-point value. Here, standard systems offer a wide range of easily implemented display options (see Figs. 11.13 to 112 1 ) . Maintenance of State Image. The various ways of maintaining the state image in the monitoring system give rise to basic differences in process control system data models. Important goals in managing and maintaining the state image (map) in the monitoring system include the following: 0
Keeping the displayed states updated: The current value of a source state should be displayed within one second if possible (or at least within two seconds in exceptional cases). 0
1
0 0
+
0
0
0
0
Rapid preparation of a new image with the proper state values: When a new image is selected, the image together with all updated states should be constructed within 2 (within 4 in exceptional cases). Minimizing the data transfer cost: In most situations, the state sources are not located in the same display and control component as the monitoring function itself. The required states must then be transferred via the bus. In large systems in particular, or when the depth of detail is great, measures must be taken to reduce the amount of data transferred. Minimizing the cost of preparing the state information in the source system: Some effort must be expended in the source system to prepare the state data and organize the data transfer. This extra work must be considered from the outset when calculating the load on the source system. Problems arise, in particular, when several display and control components are used, each with its monitoring interface and all accessing states of a relatively small local component, or when a changedriven data transfer service is implemented. Modular organization: The state image maintenance procedure must be set up in such a way that new images can easily be inserted in a monitoring system and an additional display and control component, with its own monitoring system, can be connected to the system communications at any time.
The simplest way to organize and maintain the state image for monitoring is by using a 100% image in the display and control components (DCCs). All states that might ever be re26.4
As binary numeral 011
0
A s symbolic r e p r e s e n t a t i o n o f a lamp
By color change
<<
BY alteration of a f l o w s h e e t symbol
+-@Figure 7.44. Representations of binary states
As f i x e d - p o i n t number
0.264E2
As mark
@
249
1m -
A s exponential
100 AS l e n g t h o f a bar
u AS
posirion o f a pointer
A s fill l e v e l o f a symbol (fill Level o f a v e s s e l ) A s color o r shading code ( f u r n a n c e casing t e m p e r a t u r e ) Figure 7.45. Representations of a floating-point value
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7. The Process Control System and Its Elements: Distributed Conirol S y s t e m
quired for monitoring are held in every DCC at all times. This solution, which is practicable in small one-station systems, leads to performance problems in large multistation setups. If the possibility of insetting detailed state information is not to be lost, data transfer must be optimized, for example by selective or change-driven transmission. In selective transmission (Fig. 7.46), only those states that are currently displayed are updated in the state image. The data-transfer load thus depends on the scope of the images currently displayed, not on the scope of all images configured. The image can be carried to any depth, down to the smallest details, without any change in the preparation and transmission load. Selecting a new image means a change in the set of states to be updated. This must also be reported to the source systems and new arrangements made there. The process control operating systems of the components involved must therefore include an effective service for this purpose. Process control systems with the option of selective state images can generally be upgraded to large multi-workstation installations without difficulty. The second possibility is change-driven transmission. In normal operation, many states undergo changes infrequently o r not at all. This leads to the possibility of transmitting only when a state has changed significantly. The number of data to be transmitted under normal conditions is drastically decreased. The detection of state
\States\ screen
\States screen
\
C S t a t e image is u p d a t e d +State
image is n o t u p d a t e d
Figure 7.46. Selective state display SD = state depiction; DCC = display and control component; SIM = state image
changes in the source system presents a problem. Any change of state in a binary value is significant, but in the case of an integer or floatingpoint value, a change is only significant if it exceeds a certain set distance from the old balue. Detecting deviations leads to a perceptible load on the source system. A further drawback to change-driven transmission is that load fluctuittions are difficult to calculate. Large numbers of switching operations take place in the plant precisely under critical conditions, so that there are simultaneous changes in nearly all process parameters. A change-driven system must be designed for these extreme peak loads. Operator control refers to the specification of values by an operator via the system’s own human -machine interface. The following units are involved :
The operator, who communicates with the operator control system through the input device. The operator control system, which handles the selection and execution of instructions. The instruction acceptor, which checks the value entered. If the result is positive, the value is accepted and furnished as a new effective value to the device addressed. Each operator console has its own operator control system, which functions independently of the others. The topology of the operator control system is shown in Figure 7.47. An operator intervention is generally distinguished from a design intervention. The plant operator operates while the design engineer designs. The distinction is that an operator control action always communicates with an instruction acceptor (as an existing entity). Design (configuration) functions define, delete, and manipulate all possible types of objects. Linguistic usage aside, the actions of the designer can also be understood as “operating.” Design functions are initiated and, on the basis of the operator instructions, cause appropriate object manipulations to be performed. The sequence of events in a typical operator action is as follows: Selecrion. The operator action is generally initiated from an operator window. which provides the operator with all information needed for operation, such as the current value of the variable to be manipulated, local enabling of operator control, control limits to be maintained.
7.4. General System Services
251
depending on the type of input device, the type of value, and the control philosophy employed. Some possibilities are as follows: 0 0 0 0 0
Figure 7.47. Structure of the operator control system OIN = operator input; OCO = operator control; IAC = instruction acceptor; I>CC = display and control component ; LOC = local component
or process states relevant for operation. The structure of the window and the information furnished in it are governed by monitoring functions. An operator window should be opcnable regardless of operator authorization. The variables that can be manipulated by the opcrator are indicated in the window (e.g., shading as in a pull-down menu system). The first step in the actual operator action takes place when the opcrator selects a variable or variable group for manipulation. The opcrator control system chccks whether the selected variable is manipulable and whether the operator is permitted to manipulate it. Several options are available for the nature and syntax of the selection. It can be effected, for example, by cntering a command on the keyboard, or by clicking on a symbol marking the variable. Once a selection has been made, the operator control system is in instruction mode. Now the operator can identify and manipulate one of the selected variables or can terminate the control action. In general, concurrent selections of an instruction acceptor are permitted. The instruction acceptor does not know whether it is selected or not in an operator control system. The instruction acceptor is not occupied by being selected. The same variable may be concurrently selected for manipulation in several operator control systems. Input. The control value is input in a specified input syntax. A wide variety of possibilities exist.
Input the value on the keyboard and press ENTER Click on the appropriate symbol to change a binary value Simultaneously press the command key and the release key (two-hand operation) Press an UP or DOWN arrow key to increase or decrease an analog value Move a slider
A11 these actions ultimately constitute a control instruction in the operator control system. Once the input is complete, the instruction is sent directly to the instruction acceptor concerned. Control Instruction. The operator control system transmits the control instruction to the instruction acceptor, which may be located in the same component as the operator control system or in another component of the system. If the operator control system does not succeed in properly transmitting the instruction to the instruction acceptor, the manipulation is considered not possible or rejected. If the instruction is properly transmitted to the instruction acceptor, the acceptor begins to process its methods. First it chccks whether the manipulation is permitted from the standpoint of the application. During the processing of these acceptor methods, no other control instruction can be transmitted to the acceptor. Although such a collision is extremely unlikely, the process control OS must exclude this possibility. Processing of Instruction Acceptor Methods. Data security in a process monitoring and control system requires that the instruction acceptor have its own methods, which it uses to check whether it will accept a control instruction or not. System solutions in which the effective value in the instruction acceptor is dircctly overwritten by a control action should be avoided. In the context of the conncctor model, an instruction acceptor is an input connector of a write connection. The events at the instruction acceptor can be described as follows in this model: The connector consists of two parts, a system unit and an application unit. The control instruction is first received by the system unit, which checks whether the connector is enabled and whether the instruction is formally correct. The system unit then starts the application-specific method component of the instruction acceptor.
252
7. The Process Control System and Its Elements: Distributed Control Sjstetm
In this section, customized for the application. a check is made to determine whether the specificd value is or is not permitted on the basis of the instantaneous state of the application. If the valuc is acceptable, it is taken over as thc new effcctive value; if not, thc old effcctive value is retained. The execution of these steps is recorded in a mcssagc stating that a manipulation was attcmpted and whether it succeeded or failed. Reaction on the Part o$the Process. The operator control function described here is strictly limitcd to thc transmission of thc control instruction. The consequences of such an instruction havc nothing to d o with the control function itself. Oncc the control instruction has bcen cntered in thc instruction acceptor. the control action is terminated. The process consequences of the control instruction can be tracked by, for cxamplc, the monitoring system. An important point for thc opcration of a process control system is that thc operator always be notified of the acceptance or nonacccptance of a manipulation. A control instruction must therefore be processed and passed on to the instruction acceptor immediately after its input. Proccssing in the component that houses the acceptor must be interrupted so that the checking methods of the instruction acceptor can be processed. The total time between the end of input and thc report from the acccptor must be no more than 2 s, or 4 s in the worst case. In order to maintain this timing, many systems simply acknowledge the input directly from the operator control system, only supplying it to the instruction acceptor later. Such solutions should always be rejected. Thcy give the operator a false picture of the facts, and if difficulties arise, they rcsult in a loss of confidence in the correctness of the status display. Message Handling. "Messages", in gcneral terms, arc explicit reports on important evcnts. Not every change of state in the systcm leads to a message. Among the purposes served by messages arc the following: 0 TO inform the plant operator about evcnts in the process TO provide the maintenance personnel with information about malfunctions and changes of operating status in the plant 0 TO furnish the production manager with key data on the production process 0 To documcnt the process in the plant
Thc following rcstrictions apply: 0
0
0
Mcssages d o not serve to replace rccords of status, switching logs, and detailed cvent reports needcd for cvcnt analysis. Thc recipients of messages are always special message system units. Messages do not havc the task of notifying othcr applications that an event has occurrcd so that automatic control proccsscs can be initiated. The mcssage functions do not update the state image in the display and control components during startup or operation.
Thc functional unit in which a messagc is gcneratcd is referred to as the "message source". The inclusion of a mcssage sourcc establishes that a certain cvent is so significant that a mcssage is to bc transmitted when it occurs. Two distinct concepts are cmployed : dccentralizcd (distributed) and central message generation. Decentralized M C S S U ~Cherrrtion. C In the dccentralized message gencration. the messagc sources are built into thc lowermost functional level at which the significance of a signal can be determined. Message sourccs can bc intcgratcd directly into the proccss functional units. Exaniples includc the following: 0
0
0
Malfunction of a motor interlock: Interlock malfunctions are generally classified and reported as significant events. A malfunction of the interlock is detected in the motor control and can be reported directly from this unit. In othcr words, the functional unit "motor" acts automatically and independcntly to gcnerate an appropriatc mcssage. Channel failure in a module: Channel failures arc always reported as control hardware malfunctions. An intclligcnt module can indcpendently gencratc a message if such a malfunction occurs. Arrival at a mark in a rccipc sequencc: Whcn a significant mark is rcachcd in a rccipc operation (e.g.. "preliminary rcaction completed"), the recipe control unit can automatically generate an operational message.
Under a conccpt of this typc. mcssagcs arc created in all local functional units as well as in various modules and components. The messages must first be conveyed securely to the rcspective message rcceivcrs. In thc distributed conccptand this is an important advantage- message transportation can be effected by system services.
7.4. GeneruI Syslern Services
Figure 7.48. Structure of the message system in a system with decentralized message generation DCC = display and control component; LO<: = local component; MAR = message archive; ME1 = message interprevation; MRC = message receiver (ccntral unit); M K X = message rccciver (auxiliary unit): MS = message source; MTS = message transmission receiver
Figure 7.48 illustrates one configuration. Messages are always generated in message sources and locally delivered to a message transmission server. Each logical unit has its own local message transmission server. The task of this device is to collect the messages from a component, hold them in interim (buffer) storage. and transmit them by secure means to designated message receiving and archiving units. Centrulized Messuge Generation. A concept based on centralized message generation is shown in Figure 7.49. Messages are generated exclusively in the central component, where message generation, message archiving, and archive interpretation are generally combined. The centralized concept is based on a different information model from the decentralized one. In the distributed concept, not all events must be reported to the ccntral units via system communications. For example, a change of state of the control valve of a two-position controller is a normal operational event of secondary importance. This switching operation need not be reported and documented as an event. In the decentralized concept, reporting of the state (instantaneous valve position) for representation in an image is handled by services independent of message transmission. The centralized concept links state and event reporting. The central component usually maintains a 100% image of all required states for this
253
Figure 7.49. Structure of the message system in a system with c e n t r a l i d message generation DCC = display and control component; LOC = local component; MAR - message archive; ME1 = message interpretation; M G = message generation; MS = message source
purpose. When an event occurs in a central component, the state image is updated to reflect the state affected by the event. A check is run in parallel fashion to determine whether the event is one that must be reported. One problem in the centralized message concept is the management of information between multiple central stations. For every message, there must be no more than one message source in the entire system. The aeknowledgement status is managed at this message source. Regardless of where it takes place, every acknowledgement must lead to resetting of the acknowledgement request, so that the acknowledgement is central for the entire system. In this respect, many control systems with centralized message generation (in particular SPCbased systems) d o not have a clean structure. They d o not support the sharing of message functions among central components and d o not offer the possibility of central acknowledgement. Conten/ of u Message. The following information must be contained in a message: Who sent the message? Each message contains the identification of the message source and of the process unit in which the event being reported took place. When was the message sent? Every message contains a timestamp giving the time when the event reported took place. What was reported? Every message contains a precise description of its meaning. This de-
254
0
0
7 . The Process Control System and Its Elements: Distribirted Control Systems
scription can be a plain text or a pointer to a plain text. How important is the mcssage? Each inessagc has a certain significance class. for example alarms, warnings, out of tolerance, sequential cvcnt report, and so forth. What change of state occurred? A message is sent whenever a changc of state occurs in the mcssage source. In gcneral, a message contains the state in effect after the changc and the nature of thc change. Types of changes include, for example, “incoming”, “outgoing”. “pulse”. “acknowledged”, and so forth.
Along with the message source ID, the timestamp, the state, and the type of state change must be entered as instantaneous valucs at the moment when the message is created. Other information can be appended to the message later. The key here is the messagc sourcc ID. Message Trunsmission und Messuge Distrihution. The task of the message transmission server is to receive, buffer, distribute, and transmit messages to the proper receiving and interpretation units (see Fig. 7.48). Message transmission scrvers must therefore insure that no message gets lost, even during a flurry of messages. Message distribution can be dcscribed by the following general picture: The message receiving unit logs on to the message transmission servers every time a message is received.
May 28, 1991
Therc is one “main” message receiving unit. Other “secondary” message recciving units. for certain mcssage groups. can log on to the message transmission servers in p a r a k l . If the main message rccciving unit fails. the message transmission servcr attempts to buffer messagcs until a replacement unit has logged on as the new main receiving unit. Message Receiving arid Archive Units. The task of a message receiving unit is to receive incoming messages and cnter them in thc loggcdo n archive. The most important archivc is the sequential cvent archivc. All incoming messages are chronologically entered into the sequential event archive. Figure 7.50 shows a scquential event display. in this case an excerpt from the last messages entered in the archive. From the sequential event archive. messages can be grouped under a wide variety of criteria, for example sorted by charges, plant sections, significance classes, timc intervals, and so forth. or by combinations of such criteria (see Fig. 3.26). Besides the sequential event archivc. there is usually a list containing all message sources that have generated current messages. Figure 7.51 illustrates such a list. A rnessagc source appears on this list (it is said to be “signaled”) if either thc event-generating bit is still set (value still out of range) or acknowledgement has not taken place yet.
Sequential event display, Group V13RAh 16:5L:05
Time
Unit
Text
Class
9 4 1 23 9 4 1 33 9 43 22 9 43 3 1 9 4138 9 4 1 46 9 4 6 65 9Z7Z1 9 47 53 14 34 23 16 33 06 16 53 53
V13RA4T3 V13RALT3 V13RAZ LZ V13RA4LZ V13RALT3 V13RA4T3 V13RALL2 V13RA4F6 V13RALF6 V13RALKl V13RALF7 V13RA4S1
temperature high temperature high level high level high temperature high temperature high level high flow low flow Low interlock malfunction invatid measurement please confirm recipe data
A B W
Figure 7.50. Sequential cvent display
B
A
B W A B A A
I
incoming acknowledge incoming acknowledge out going acknowledge out going incoming acknowledge incoming incoming incoming
7.4. General Systern Services
M a y 2 8 , 1991
255
S t a t e , Group V13RAL 16.54:05
Time
Unit
Text
94322 94741 14 34 23 16 33 06 16 53 53
V13RALT3 V13RA4F6 V13RA4K1 V13RALF7 V13RALS1
l e v e l high flow low interlock malfunction invalid measurement please confirm recipe d a t a
Figure 7.51. List of mcssagc-originating sources
Archiving. If processes are to be studied in retrospect, certain cvents and thc time variations of certain states in thc system must be archived. The resulting data can then be accessed by interpretive functions. The outcome of an interpretation can be output in the form of, for example, plots or lists. In modern systcms, a strict distinction is observed bctwccn the data stored in thc archive, the interpretive function per se, and the form and representation of the printout. While data storage in the archive is structured in keeping with considerations internal to the system, the user can define what the interpretive functions are to do and in what form the reports are to bc output. Archiving presents a numbcr of problems that can be solved only with the aid of special system services. Examples are: 0 0 0
0
To append on-line process data quickly and reliably To carry out informational functions in parallel with the continuous addition of new data To safeguard the data automatically To implement the archiving functions in redundant fashion (see Section 3.2)
Time Synchronization.Time (clock) synchronization is an essential system function in a distributed process control system. If events are to be listed in time order, the messages must be timestamped as near as possible to their place of
origin. This may bc in a local component or may be in a modulc where, for example, a control hardware malfunction is detected. A sequence of two events in two distinct system units can be correctly detected only if the clocks in the two units “tell the same time”. Synchronization insures that the time discrepancy ovcr all clocks in the system will fall within a narrow time interval. A typical value is 10 ms; specially equipped systems achieve discrepancies of < 2 ms. The time is absolutely specified by a central system clock. Additionally. radio clocks are employed as systcm clocks, with the advantage that time changes (e.g., from standard to daylightsaving time) are performed automatically. Process Control Hardware Diagnostics. The diagnostic functions monitor the opcration of the control hardwarc and include tools for analyzing the state of the monitoring and control equipment. System diagnostic functions fall into three groups. The first group includes control hardwure monitoring funcrions, which are integral constituents of the individual control system modules and functional units. These functions run automatically and continuously. The result of each check is written into the disturbance status of the unit concerned and can be read from there. If the trouble status of a unit changes, the unit
256
7 . The Process Control Sysiem cmd Its Elements: Disiribuied Control Systems
automatically sends a hardware message. Hardware messages get special treatment in many systems, although this is not really necessary. That a message is a hardware message can easily be determined, either directly by cxamining the message source I D or indirectly by means of a special flag in the message. In this way, control system hardware messages can be treated in the same way as process messages. Typical hardware messages include: Failure of an I/O channel (alarm) Failure of a redundant bus connection (warning) The second group takes in diugnostic functions of interest to the designer or to the on-site technician. These have to d o with special system analyses that are initiated by the operator only as needed. The functions are supported. in many cases, by external diagnostic equipment. Examples from this group are functions for analyzing the following (and comparable functions): 0 0
0 0 0
The current configuration Memory utilization and processor load Run-time measurements
The third group consists of diagnostic funciions used only by specialists at the tnanufacturingfirrn which permit detailed analysis of system behavior.
7.5. Design and Commissioning Before a plant is operated, it must be designed and commissioned. The engineering work necessary for this purpose is a key item in the total cost of a process control system. The distribution of the work varies widely. In a decentralized process control system with a wcll-designed 0s and the proper dcsign and commissioning support, pure system procurement costs are relatively high but design and commissioning costs are low. In a “cheap” system (most SPC-based systems fall into this category), the hardware is favorably priced but design and commissioning costs are substantial. The discussion in this section is based on thc structural model of design and commissioning shown in Figure 7.52.
Design. The design process begins with the technological task statement. In practice. however, all that is usually available is a piping and instrumentation diagram. Informative docu-
mentation o n the functional relationships is either nonexistent or, for the project team at least. difficult to access. Functional data are often found scattered throughout the process development reports or in descriptions provided by the equipment fitter. Accordingly, one of the most important tasks for the project team is first to gather together all such information, develop the functions, and describe them in a formalized way in the project specification (see Chaps. 2 and 10). This formal summary may well take into account the structure of the later implementation. For process control, this mcans that the specification takes the form of not a list of requirements, but a role system with certain functional properties. This role system can be refined stepwise and ultimately can be employed as the basis for the implementation. Thus the process control specification can be expanded later, thus becoming the documentation of the process control system. An important advantage of this approach is the systematic procedure. Each function discussed in project conferences can be directly incorporated into the role system as a property. As a result, efficient design work with few errors is possible, and the operational aspect of thc procedure is implcrnentation-oriented. In what follows. thc design steps required in such a proccdure are presented in detail in terms of the structural model of Figure 7.52. Definition oJ’ Teclinologicul Module Tbpcs Required. The definition of a suitable collection of module types generally precedes the design work. Industry and corporate standards apply in many cases. The GMA is now working to define a standard set of module types. and the IEC has recently taken up this project. The following criteria must be considered in establishing the module types : 0 Manageablc functional scope. Functional modules should be neither too small nor too large in terms of functional scopc. Small modules pennit very flexiblc structuring of functionality and thus can be uscd very generally. The disadvantage is the largc numbcr of small modules and connections required, rcsulting in increased dcsign and object managcmcnt costs. The error rate incrcases, and the efficiency of processing Fdls off drastically whcn the system is later mapped onto the functional system. Another drawback is the low degrcc of functional standardization that inevitably rewlta from the clement types defined.
7.5. Design and Commissioning
! Construct technological role system
-- - - _ - ~
257
Technological
role system
Construct
Functional
role system
Programming,
i
Install. make ready, start
* c
0 Functional s y s t e m
Figure 7.52. Structural model of system design and commissioning
Large modules permit fairly standardized structuring with a few modules. Large modules can be efficiently proccssed in the later mapping in the functional system. The disadvantages of largc modules are the cost of defining them, their functional rigidity, and the possibility of an excessively broad spectrum of models. If large modules arc to bc used, a consensus must be found as to their functionality. Because thcse modules embody technological standards, this is
a much costlier operation than for generic small modulcs. To cover all technological cases, either the modules must be made vcry extensivc, with many selectable and parametrizable options, or a great number of types must be defined for fairly similar technological tasks. (What functions must a motor control module contain, and how many distinct motor control modules are desirable?) 0 Feasibility of using functional module types.
258
7. 7he Process Control $,stern and Its Elements: Distributed Control Systenis
It makes little sense to define technological module types such that thcir later mapping onto functional module types is difficult or impossible. It is therefore essential to check. while fixing the technological module types, how they can be realized in various systems. Configurution OJ the Technological Role Sysrem. From the spcctrum of types, the required modules arc identified. instantiated, connected, and parametrized. The result is a technological role system in modular form. In accordance with a structure such as the hierarchical structure of Scction 4.5, this role system is broken down into individual function, group function, plant coordination, action, and other levels. This technological role system comprehensively and formally reflects all requirements derived from the technological task statement. Thc documentation of the technological role system can be regarded as a formalized specification. In addition, it directly serves as the basic documentation for further design work specific to the process control system. The technological role system itself should be formulated in a manner largely independent of the control system (see also Chaps. 2 and 10). Definition of Funcrional Module Types. Along with the firmware or special-purpose software, most manufacturers of process control systems supply a collection of functional module types. If the user remains with these prefabricated types, the complicated procedure of creating customer software module types can often be dispensed with. Indeed, many process control systems d o not allow user programming of software modules. Thus it is chiefly the manufacturer that defines the functional module types. The user must manage with these manufacturer-specific collections of types. Only in special cases can a competent user define and program new sets of types; such a user will attempt whenever possible to create software module types that cxhibit a 1 : 1 correspondence with the functionalities of the technological module types used. Otherwise, the manufacturer must attempt to satisfy user requirements. Configuration of Ihe Functional Role System. The functions embodied in the technological role system are transformed into software structures specific to the control system. In this process, the structures of thc technological role system are preserved in unchanged form as far as possible. A frequently used aid is the creation of"typica1s."
Typicals are pre-assembled structure5 madc up of functional module types. They are not types but structurcs having spccial naming conventions and which can be easily copied in the design interpace. Typicals can readily be put together by the user. If typicals are creatcd that functionally correspond to thc technological module types, the functional role system can cabily be configured with the aid of these typicals. The configuration of the functional role system falls into two parts: 0
0
Mapping the technological structure onto the functional structure specific to thc control system Establishing the locations of individual functional units
"Location" here refcrs to thc logical location of a unit. For a modulc, for example, this is the logical card slot; for a software module, it is the processor board in which the module is run. Oncc thc network of software modules has becn defined. it is thereforc necessary to establish which modules will be run on which processor boards. The component and module structure of the process monitoring and control system must be known, at least in outline. before this step can be done. The result is the finally configured functional system. In a design systcm, functional units arc handled as objects; they can be represented and documented in accordance with certain criteria and be loaded directly into the functional unit for execution. Commissioning (see also Sections 10.5 and 10.6). Commissioning includes all actions rcquired to bring a system or system unit from an initial state to an active working state. A single functional unit, a group of functional units, a whole system component, or a complete system can be commissioned. In defining what will be regarded as the initial state, one commonly takes an organizational (management) view, defining the initial state to be whatever state the commissioner finds when the operation begins. In a process control system. for example, the initial state is the system fully installed but not yet energized. When a compact device is purchased. commissioning starts with unpacking. Certain simple installation tasks may thus be included in commissioning. From a systems engineering standpoint, commissioning is the last step involved in mak-
7.4. Design and Commissioning
ing the functional system available for service. The functional systcm includes all subsystems needed for active implementation of production operation. Avoilahility of a Functional Unit. The functional system is the system that actively realizes production operation. Its units fulfill roles whose interplay results in thc desired system functionality. The scparation of rolc systems and functional systems is of central importance for an understanding of commissioning. A functional rolc system is set up in such a way that for every role there is a functional unit that plays this role. For a functional unit to carry out a role. it must be allocated to this role and must be made available at the defined location of the role. This part of the operation is called “installation”. Figure 7.53 illustrates the availability states of a functional unit along with the transitions between thcse statcs. Not present: There is no suitable functional unit at the location. For example, the card is not plugged in or the software module is not loaded. Install: Installation means making a functional unit available; for example, assembling a cabinet rack, plugging in a card, or loading a software module. There are two key events in installation: when the functional unit is physically present at the location, and when the functional unit has been configured for the task it is to perform. The order of thesc events is not significant here and can be determined case by case. For example, in installing a card, the jumpers are placed first and the
card is then plugged in. whereas for installing a software module entity, the entity is created first and only then are values assigned for the parameters. 0
0
0
0
0
Present: The functional unit is said to be present after installation; i.e., it is finally configured for its role and is available at the location designated for the role. Make ready: The state of the object after installation, for example of a newly loaded software module, is generally undefined. A special function called “make ready” must be carried out in ordcr to bring the object to a well-defined ”rcady” statc adapted to the instantaneous system state. Ready: The availability state “ready” means that the unit is not only present but also ready to start. In other words. at this instant all conditions exist for connecting the unit actively into the network. The “ready” state plays an important role specifically in distributed process control systems. Here the situation often arises where certain units have already been activated and begun carrying out their tasks. A further unit is now to be connectcd into this running structure. Naturally, such a connection must not bc made except under certain conditions. The “ready” state indicates that these synchronization conditions exist at this moment and the unit can be connected into active operation. Start: From the “ready” state, a functional unit can bc started. It then goes into the “operating” state. Operating: In this statc, the unit is effective in the functional systcm as an active element. It is performing the role intended for it.
”Running”
Figure 7.53. Availability states of a functional unit
259
260
7. The Proccm Conrrol Sjsrem und Its Elements: 1)istrihuied Conrrol Svstems
A typical situation in a distributed process control system is where the functional systems arc opcrating in certain rcgions and not in others (see Fig. 7.54). The process control system is purposely designed so that functional units can be handlcd in a “granular” fashion. If a functional unit is not opcrating, no role that is performed by this unit can be realixd. In the example shown, modulcs C and E are not opcrating; modules A, B, D, and F are operating. To the cxtent allowed by disrupted communications relations I, 11, 111. and IV, the operating modules are fulfilling the role intended for them. Hierurcliy of the Functionul System. The functional system of a control system should be regarded as having a multilevel structure. Each level satisfies the role requirerncnts of the next highcr level. The structuring and implementation of a functional system are always carried from the bottom up, starting from the physical hardware units. This strict bottom-up principle underlies all commissioning and startup operations. Figure 7.55 illustrates a typical functional system structure for control system hardware. Each functional unit can operate only when all functional units are operating in the lower levels on which it is built. If a functional unit fails, no functional unit in any higher level that requires the failed unit for the cxccution of its role will remain in the “operating” availability state.
Hardware level: The hardware level furnishes devices, modules, displays, cables, and so forth as functional units. The lowest level of an industrial functional system is always the physical level. Without a physical basis, no industrial functional system can opcrate. S y s t e m elements c u r r e n t l y n o t availahle
Base system level: The basc system level provides the basc operating system. the required databascs. and othcr standard tools (c.g.. standard communications tools) as functional units. The control system functions arc built up as actively available units from thcsc standard tools. Process control level: The process control operating system level includes all functional units necdcd for the rnanagcment and executive control of technological functional units. Examples from this class are systcni functions such as module processing. modulc-to-module communications. message generation, operator control. display. archiving. and so forth. Application level : The application level implements the process control functions on the
1 Application
1 I ( o b j e ct management syste m , executive syste m , c o n t r o l s e r v e r fu n cti o n s)
I
Uses
Base s y s t e m (base o p e r a ti n g syste m , d a t a bases)
Uses
Hardware
Figure 7.54. Functional system with scveral unavailable units
Figwe 7.55. IIierarchicdl structure of the functional system
7.5. Design and Commissioning
basis of a functioning process control operating systcm. If process control is structurcd in a hicrarchical fashion, the application level itsclf can be regarded as a hierarchical system of process control functions, onc built on another. Commissioning of the process control system based
261
on operating softwarc modules is a special aspect that will not be discussed here. From the standpoint of thc process monitoring and control hardwarc. the application level consists of a flat network of intcrcommunicating modules.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
8.2. Functional Structures and Information Flow in Production Companies
8. The Process Control System and Its Elements: Information Logistics 8.1. Principles The need for information integration in production companies is generally acknowledged today, but this effort is subject to two kinds of limitations. The first are due to historical circumstances surrounding the introduction of computer-aided information processing (“island“ solutions); the second have to d o with the hcterogencity of the computer systems employed, which in turn stemmed from functional requirements. The starting point for solving the associated problems is the derivation of the requisite integration functions along with their scopc; the process is aided by proper intcrface definitions and standards. Two worlds are involved here: the world of production engineering in the definition of what functions are needed, and the world of information and data processing in the implementation of these functions. Informatics is called on to create suitable methods and tools for the second range ofproblems. A11 experience to date shows that optimal solutions demand close cooperation between the two fields; this necessity represents a special challenge because of, among other factors, difficulties in understanding functional rcquiremcnts and differences in terminology. The new field of “information logistics” will contribute to an integrated view of methods and tools for information distribution and process-
ing in production plants. By analogy with the logistics of materials, energy, or personnel, information logistics combines methods that are concerned with the modeling, distribution, storage. and retrieval of information. The new field must identify the interrelationships of these individual disciplines with regard to the tasks of information processing in production and, with the aid of tools, must put them to work in optimizing information-logistic systems. The methodological edifice of information logistics is not yet complete (8.11, (8.21. but separate disciplines d o already exist in distinct form. One such is distribution- the “communication” of information within the company- which is treated in depth in this chapter.
8.2. Functional Structures and Information Flow in Production Companies For a better understanding of what requirements manufacturing information systems must meet [8.3], [8.4], it is desirable to work out models of information flow in the company. If such information flows in production companies arc examined more closely. an information hierarchy such as the one shown in Figure 8.1 is revealed [8.5].The subdivision of production companies into various control and management lcvels arises from the way corporations are organized, and so the details will differ from one industry to another [8.6]. A consistent feature, however, is that the lowest level of the structure includes many singlc items of information about
C o r p o r a t e management Level _ _ _ _ - _ - - _ - _ _ - _
Production management l e v e l
Process control level
Production areas Figure 8.1. 1,cvels of control in production
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8. Tire Process Control System and I t s EIements: Injbrmution I,ogistics
processes, while this information is more and more strongly condensed in the higher levels (all the way up to corporate management). Conversely, the objectives of the higher levels of management and control give rise to items of information about production steps o r stages at the lower levels. Figure 8.1 shows that these processes can cxtend to various levels and take a variety of forms. Again. structural differences between industries and companies are a factor. The subdivision into corporate, plant, and process levels (l:ig. 8.1) is specific to the process industries, because here it is often the case that independent facilities on the process control level are supplied with production specifications (e.g.. recipes) directly from the production managcment level. In othcr industries. such as iron and steel, the products move in sequence through a number of plants or units where the raw material is upgraded, step by step, and ultimately converted to a salable product. This production structure is advantageously described by introducing an extra or intermediate level of management or control. This intermediate level breaks the gencra1 instructions from the production management level down into detailed instructions for the individual production units. This structure implies that information flows both vertically (between control levels) and horizontally (within each control level). Both the transport and the storage of information reflect these information streams. Information logistics has to supply methods for the modeling and optimization of communications and information storage systems. In general, the various levels employ disparate computer systems for information storage and processing. Information logistics, however, demands suitable high-capacity information pathways within and between the levels of control. It should be pointed out that information logistics is not just one of the tasks involved in information integration for production companies; it is a methodological aid to the planning and implementation of a corporate information utilization policy. Data communications and storage facilities d o not become information communications and storage facilities until the meaning and utilization of the (digital) data have been established. At first, information integration across the control levels of Figure 8.1 was thought of chiefly as a problem in manufacturing engineering. One indication of this is the wide adoption of
the term CIM (computer-integratcd manufxturing). Nevertheless, proccss engineering requires the same kind of information intcgration and has a functional structure wholly congruent with Figure 8.1. The term CIP (computer-integrated proccssing) is secn more and more often in this connection, showing that information integration is increasingly rcgardcd as vital in both branches of production. It is therefore suggestcd that CIP be taken to mean computer-integrated production; this term expresses the Fact that the same methodological aids will be applicable, and must be available, for information integration in all kinds of production operations. An analysis of the steps involved in manufacturing further shows that there are not only the discrete, piecc-by-piece o p erations but also completely continuous “processlike” operations. If information integration bccomes a way for methods of manufacturing engineering and proccss engineering to come together, this will be in accord with the situation in the plant. The integrative techniques of information logistics can then be put to optimal use.
8.3. Computer Communications Between and Within Control Levels The need for information communication between the levels of Figure 8.1, as well as the requirements of information logistics, demand that appropriate aids be made available in the production company. As Figure 8.2 shows, the implcmentation of computer-aided information communication calls for a hierarchy of computer networks 18.51. The information-handling facilities at each level of control must meet the following two requirements: 0
There must be dedicated computers at each level. Information proccssing for more than one level can be combined in one computer, but the unavoidable increase in system complexity will result in a loss of flexibility, efficiency, and maintainability.
0
The communications facilities at each level must be tailored to the particular needs of the level. The communications equipment must also be able to connect heterogeneous computer systems togethcr when a level uses coniputers of different gencrations, types, and capabilities because of spccial requirements, procurement strategies, or historical circumstances.
8.3. Computer Communications Between and Within Control L ~ v e l s
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c u
-a
Sensores/Actuators
Figare 8.2. Networks for computcr integration
The networking conccpt illustrated in Figure8.2 is one approach to meeting these demands. The following points may be important in individual cases: The communications facilities must be designed and implemented with attcntion to the necessary time and consistency conditions. The effort of determining and modeling such conditions is justified by the optimal economics and flexibility of the communications systcm.
The communications system must be faulttolerant as well as safe and secure with respect to intentional and inadvertent falsification of information. The redundancy and coding procedures employed for this purpose should be selected to fit each application [8.7]. Because production facilities are continually being changed, information logistics must be correspondingly flexible. Well-defined and, when possible, standardized interfaces and communi-
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8. The Process Control Sptem and Its Elernenis: Injbrmution Logistics
cations rulcs (protocols) arc nccded in order to meet this requirement economically. especially in heterogeneous systems. Information integration across all lcvels of management and control is possible only if all information is in digital form. However, information integration does not mean just data communications bctwecn one computer and another. Information logistics, in particular the transport and storage of digital data, must be supplemented by conventions on the meaning (scmantics) of the digital data. The producer and consumer, who may be in different areas of activity, must interpret the digital data in the same way; only then d o the data become information. Thus, when disparate computcr systems arc formed into an information network, it is particularly important that protocols be defined and standardized for semantic transition arcas between applications systems. Formal methods of syntactical and semantic notation, based on experience and rcsults from informatics, should be fully utilized for this purposc.
8.4. Computer Communications in Industrial Production; Standards Standards already exist for the implementation of computcr networks. These are discussed from a technical standpoint in what follows. The equally important economic aspects of various approaches call for an exact knowledge of each situation and its constraints. Computer-integrated production does not exist as an off-the-shelf solution, but the cxistence of standard protocols, however, makes it casier to handk specific applications problems.
Communications at the ProcessControl Level. Plant computers and the technical process often communicate (Fig. 8.2) through intelligent subsystems, for example stored-program control systems (SPS) or field-level multiplcxers (MUX). Other direct methods of communication include digital input/digital output (DIDO) and standard serial and parallel buses (SCSI, IEEE 488, RS 232/422/485). Field buses, which permit scrial data transfer under field conditions, are especially important and arc treated in depth in Scction 8.6. Local Area Networks (LANs) at Higher Control Levels. Local networks have become vital in connecting computers with near real-time
functions. such as plant unit. plant. and controlroom computers, at the process and plant control levels. These setups allow both protccted communication bctwecn two partners and unprotected simultaneous communication with more than one partner in thc network (broadcasting). Physically, these nctworks are availablc based on either copper wire or optical fiber. The standard for levels 1 and 2 is IEEE 802, which has now becn adopted by I S 0 as well. Line acccss protocols arc CSMAICD and tokcn-passing. The CSMAjCD protocol ( I S 0 IS 8802,!3) has found wide acceptance in oflice systems, and a number of industrial products are on the market. This protocol is easy to implcment. but the possibility of collisions means that transmission times are not well-dcfincd. Its applicability undcr real-time conditions is therefore controvcrsial. In the token-ring protocol ( I S 0 IS 8802,’4), pcrmission to transmit is linked with possession of the “tokcn,” a special data structure sent in the form of a telegram. There is no possibility of colliding transmissions. Certain participants can be assigned priorities for pcrmission to transmit through special token distribution mechanisms, so that cven worst-case communications times are guaranteed. As a rcsult, the token-ring protocol is particularly suitable for real-time applications. On the other hand, special fault-tolcrance methods should be noted, such as the replacement of a lost token, which can markedly increase the administrative effort involved in running the token protocol compared with the CSMAjCD protocol.
Open Networks (WAX =Wide Area Networks). At the production and corporate levels of control, computers must communicate over distanccs longer than the fcw kilometcrs to which local area networks are rcstricted. Wide arca networks (WAN) can link two communications partners over any distance, worldwide. In contrast to the broadcast mode of I A N s , conncctions between nodes in open networks must bc explicitly opened and closed. A variety of transmission media are availablc: all kinds of copper wires, wireless links (radio, satellite, infrared), and optical fibers. Open networks are characterized by the possibility of adding new participants while the network is in operation; thcrefore, communications standards arc especially important in this area. I S 0 is making its contribution in the well-known OSI model [8.8].Both LANs
8.5. M A PITOP: Protocol Stundurds for Inf)rmution Integration in Production Companies
and WANs can be employed at all lcvels shown in Figure 8.1 ; special applications requirements may make thc use of an open network desirable even at the process level of control. Communication through Private Branch Exchanges (PBXs). The private branch exchange (PBX) has been a common installation in all kinds of companies for decades. For computer communications. the PBX has many of the properties of an open network [8.9]; for example. it sets up a point-to-point connection between two participants. A point to consider is the nonnegligible, and unpredictable. time needed to cstablish and drop conncctions, which are thus only partially suitable for real-time applications. An important development is the transmission of digital data in parallel with telephone traffic, which is still partly analog but will be increasingly digital in future. With equipment now on the market, thc Integrated Services Digital Network (ISDN) is limited to 64 kbitjs for the transmission of digital data. The PBX is therefore more suited to the transmission of digital data in parallel with speech at present, and isthus interesting chiefly at the corporate and production lcvels of control. The introduction of faster call-processing devices (with processing times in the millisecond range) will allow the PBX to penetrate local area network applications in the medium term.
8.5. MAPITOP: Protocol Standards for Information Integration in Production Companies Experience with thc IS0 standard protocols for data communications between computers [8.9] has shown that the standards are not adequate for true information communications. U p to the bcginning of the 1980s, level 7 of the OSI model (the applications level) included only fairly general functions (file transfer, virtual terminal, etc.) with no reference to the meaning (scmantics) of the transferred data. What was defined was thus tools with which the user could construct an information integration. While this was a significant advance for computer communications applications, with their initially commercial orientation, the broad functionality and thus the semantic variety of the digital information that must bc handled when
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computers are integrated into production meant that further standardization was needed on level 7 of the OSI model (see Fig. 8.3). This situation has led, since 1980, to proposals for MAP (Manufacturing Automation Protocols [8.10]) and TOP (Technical and Office Protocols). These proposals originated with users of automation and process control systems. MAP was developed by General Motors for purposes of manufacturing automation and. TOP, by Boeing as an approach to automating thc engineering office. MAP and T O P together provide for comprehensive information communications in industrial companies, from the administrative and resource allocation aspect through design and development (CAD) to computer-aided production and quality control (CAQ). Both protocol environments constitute a key tool for CIP (Computer-Integrated Production). The impetus for MAP and TOP came from users of industrial automation and process control systems. It is in the user’s interest to select from the widest possiblc range of options the optimal solution to each problem while insuring the integrability of the new features. This is the alternative to the “single-source” solution often put together by one computer manufacturer. The intercsts of the suppliers and users of automation and process control systems are in conflict here: Complete standardization of interfaces for a wide variety of information-processing devices in a production company will breach the protective walls around computer networks, which have commonly been supplied by one vendor. On the other hand, this standardization trend expands the market for each vendor. The prerequisites arc precise and strictly consistent standardization of protocols and a user with sufficient know-how and capacity to cope with the intcroperabihty problems. Objectives and States of MAP/TOP. The full definition and application of the MAP/TOP protocol hierarchy should guarantee direct communication between computers, control units. and their programming systems, even when these are supplied by different manufacturers. The objectives of MAP/TOP take in the following points: The protocols should allow for the special properties and requirements of local plant and oflice networks. Further, they should be as compatible as possible with the respective
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8. The I’rocess Conirol System und Iis Elements: Information I.ogisrics
:ommunications standards
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Figure 8.3. MhP/TOP protocol hierarchy
open network protocols, so that communications between the two types of networks can be simplified (Fig. 8.2). In contrast to the I S 0 protocols for open networks, with the few generally applicable functions of applications level 7, the MAP/TOP protocol hierarchy for level 7 includes special applications functions tailored for manufacturing and office automation (e.g., MMS = Manufacturing Message Specification and MHS = Message Handling Specification). In support of these ambitious objectives, the MAP/TOP definition includes quality-assurance measures such as conformity tests, which insure and certify the ability of protocol products from different manufacturers to communicate with one another. Thus the objectives of MAP/TOP go beyond those of the I S 0 open network protocols, taking on the difficult task of identifying and standardizing applications functions in the manufacturing and office area at level 7. This makes it clear why the development of MAP/TOP, especially the applications protocols, is still in flux and will not be completed in the foreseeable future.
Figure 8.3 illustrates the MAP protocol environment as now available. At levels 1 and 2, the LAN protocols discussed in Section 8.3 are standardized. Levels 2 to 6 contain protocol definitions equivalent to WAN protocols (the samc for M A P and TOP). The use of these protocol standards at levels 1 to 6 insures data communications between applications functions in production and office. On applications level 7, three protocol types arc distinguished. The first corresponds to the I S 0 protocols for WANs; these are essentially I S 0 standard applications protocols such as file transfer. The second protocol type covers applicationspecific functions. Examples arc the MMS protocol family in MAP and the MHS protocol system in TOP (Fig. 8.3). A third protocol type on level 7 deals with the functions of network administration; examples are Network Management (NM) and Directory Service (DS). These protocols also offer functions for handling errors as well as fault-tolerance practices. Figure 8.3 shows the intention of the ESPRIT CNMA (Communication Network
8.6. Field Bus Lyvstems
for Manufacturing Applications) project. a Europcan undertaking that supports MAP and TOP in a similar way [8.11]. While the M A P specification focuses on manufacturing operations, a similar problem is evidcnt in process opcrations. Proposals for a PMS (Processing Mcssagc Service) are thercforc coming from a variety of sources. Its functions will correspond to the communications requirements of computcrs in the processing industrics. Generally, therc is the problem of linking various functional environments to the basic MMS standard. Figure 8.4shows the "Companion Standards" concept that has now bcen chosen. A standardized MMS corc includes gencrally usable concepts and structural spccifications for applications standards that will serve as the basis for a variety of Companion Standards relating to technical specialties. This procedure allows the step-by-step construction and expansion of the applications function standards on the basis of user experience.
8.6. Field Bus Systems Basis and Objectives of Field Bus Systems. Communication at the field level links field
/
devices with control-level computers. The deviccs in question include sensor systcms that locally acquirc complex information about thc production process; actuator systcms that influence material and energy flows; and programmable (stored-program) controllers that perform local control and optimization tasks. The same holds, with suitable adaptations, for both processing and manufacturing operations. Distribution, as a basic property of presentday control and instrumentation, extends to information processing at the field level and. to an increasing degrec, blurs thc distinction between sensors and actuators, on the one hand, and control computers on the other. This means that powerful communications systems meeting a variety of requirements arc also needed at the field level. Examples include direct communication between intelligent sensors and actuators in local control loops, as well as communications links used to transmit product data between plant control systems and control computers. Today, most signal transmission at thc field level is by field devices individually wired to the control computers. The standard 0-20 mA and 4 -20 mA signals are dominant in process engineering and have become standards in chemical engincering. In manufacturing, sensors and actu-
Companion s t a n d a r d s Numerical c o n t r o l
Building b l o c k s (module) Operations Communications s y n t a x
ISO-case/ACSE
Figure 8.4. Appkdtion-oriented Companion Standards
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8. The Process Control System and Its Elernents: InJ'ormution Logistics
atom on machines are also singly and directly linked to the cell computer or to programmable controllers. The variety of information involved in modern production processes sets limits to this form of communication with regard to plannability. maintainability, immunity to electromagnetic interference, and economy. Field bus systems should overcome these limitations: 0
0
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The star configuration of individual lines is replaced by a digital bus known as the field bus. It is cabled to the various field devices and transmits digitized data via digital interfaces. Such a bus connects some tens of field devices among themsclves and to a plant computer. If the devices are close together, the cost of cabling is substantially less than for individual cabling (Fig. 8.5). If field devices arc assumed to bc evenly distributed over a specified area, the savings in cabling costs can be estimated from Figure 8.6. The growing intelligence of field devices calls for the transmission not only of process measurements and control information but also of digital status and control data between field devices and control computers. The additional cabling that used to bc required for this purpose can be omitted when the field bus is employed. The conversion of analog signals to digital locally, that is, at the connection to the field bus, prevents interference to the signal on route to the control computer. The redundancy of digital codes also makes it possible to reconstruct erroneous data. The standardization of field bus protocols permits standard information interfaces between field devices and a wide variety of control computers, so that open communication results at the field level. This is not possible at present with the wide range of information transmission media at the field level (Fig. 8.6). Diverse media can be used for digital data transmission (e.g., copper twisted pair, coaxial cable, optical fiber, radio waves), so that economic and technical optimization is possible (transmission rate, noise immunity, energy cxpenditure/intrinsic safety. electrical isolation, etc.).
These objectives and potential features make up the basis for field bus standards now being
devised. The many degrees of freedom in the specification and implementation of field buses lead to a varicty of approaches and have worked against the desired uniformity in field-level information interfaces.
Field Bus Systems in Development. In recent years, teams in several countries have made a start toward specifying and implementing field bus systems. The properties of an architecture for all these projects is described first. The various approaches to implementing this architecture employed in the several projects are then discussed. Gencwl Field Rus Architecture. A field bus system is a local network having a restricted number of nodes, which communicate exclusivcly among themselves or with a control computer. There is accordingly no need for the services of I S 0 levels 3 to 5. Presentation level services arc also unnecessary in general form, since they can be performed in special form by the services of level 7 or 2. For open communication bctwcen field bus nodes, however, the services of level 7 must be specified. A general field bus architecture thus includes I S 0 levels 1 (physical layer), 2 (link layer), and 7 (application layer). This reduced architecture must bc supplemented by network management services as well as application-specific services on level 7, such as those already introduced as Companion Standards [8.12] in MAP/TOP. The American standards group ISA SP 50 has defined two performance classes (H1 and H2) for thepliysicd luyer. The scope of H1 is the replacement of existing analog standard intcrfaces (0-20 mA and 4--20 mA); the H1 specification must pcrmit adequately F a t and errorproof transmission of process, measurement. and control signals. Accordingly, the H1 specification is largely governed by process engineering requirements. The H2 specification is to extend the H 1 concept to applications with higher performance demand. This includes the fast transmission of large data sets, as is needed for the loading of machining programs in manufacturing enginccring. H2 is therefore specified for manufacturing. IEC TC65C has created specifications for a field bus standard for use in industrial control and instrumentation. In addition to the physical layer performance classes already cited, it dcscribes other properties: the topology (linear bus or tree), the transmission medium (twisted pair
8.6. Field Bus Systems
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Figure 8.5. Transformation from star-configured individual cabling to field bus structures
or, later, optical fiber or radio link), and finally the number of nodes on cach field bus segment (32). For performance class H1, a data rate of 31.25 kbit/s and a maximum cable length of 1900 m are set forth. Performance class I-I2 has a data rate of 1 Mbit/s and cable lengths of up to 750 m. The intrinsic safety of the physical layer is handled by limiting the electric power that can be
carried on the bus. The H1 specification allows enough power to run at least five field devices, at least three of which can operate in a hazardous environment. The H2 specification provides for at least three field devices to be supplied on the bus and permits AC transmission over the bus lines. Intrinsic safety is not explicitly considered in the
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8. The Process Control System and Its Elements: Informution I.ogistics 3000 Measurement and a c t u a t o r signals, s t a t i s t i c a l l y d i s t r i b u t e d
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H2 specification, since it is of no importance as a property of manufacturing systems. At present, power supply to field devices, even with auxiliary energy sources, is not a function of field bus systems; over the long term, however, separating the information channel from the (auxiliary) energy channel in process operations is a goal to be sought [8.13]. The specification of the link layer in field bus designs corresponds to the ISOjOSI Data Link Layer (DLL). In this way, the two sublayers LLC (Logic Link Control) and MAC (Medium Access Control) are established. For the application area of field bus systems in general, the link layer must provide adequate efficiency (real-time environment) and also support access priorities as well as point-to-point connection or simultaneous connection of one node with several or all other nodes (multicast, broadcast). The essential novelty of the field bus
concept as described, however, lies in the procedures by which nodes access the common data transmission medium (MAC). While the French FIP proposal [8.14] features a centrally organized mechanism in the sense of bus allocation, other proposals (PROFIBUS [8.15]) utilize a distributed access mechanism based on token passing as set forth in IEEE 802.4. These differences exist because F I P assumes better real-time properties for the central bus control. PROFIBUS uses standard microelectronics for the efficient performance of the more complex token-passing protocol, which allows more flexible parametriAing of the bus allocation for individual nodes [8.16].
The applicafion layer must furnish application functions without regard to the protocols of the lower layers. These functions must insure the exchange of information in a distributed system and with adequate eficiency. Because applica-
8.6. Field Bits Systenu.
tions in industry, whether processing or manufacturing, are subject to strong timing conditions. efficiency is particularly important. This holds for cyclic information transmission as well as the detection of spontaneous events. While the functionality of the protocol services on the application layer can be specified independently of the lower protocol layers, their performance under real-time conditions is crucially dependent on the efficiency of all protocol layers. The application layer for field bus systems must ofrer cyclic and noncyclic services. Cyclic services are, for example, the reading and writing of process variables or parameters as well as access to information on the validity and consistcncy of such variables. Noncyclic services include the sending of reports based on an event, the sending of management information, and the initialization or configuration of subsystems during startup of tcchnical processes. A variety of approaches can be taken to the implementation of such services. The essential difference lies in the relationship between the information-generating and information-consuming processes. For example, client/server models as well as producer/consumer models can be used [8.17]. Requirements on the field bus for applications in process engineering have been and thoroughly described by P ~ L E G E 18.181 R DRAWEX [8.19]. Already in MAP and TOP it was seen that the specification of complete function sets for all conceivable applications in industrial production is not possible in a uniform standard. On the basis of an MMS core standard (Manufacturing Message Specification, I S 0 Is 9506), special function sets have therefore been standardized for various application areas; these are called cornpunion sfanhrd.7 (8.121. A similar procedure is also provided for the standardization of field bus systems. Companion standards define function blocks. such as those for the control of processes (analog and digital input/output, PID controllers, etc.) or for the utilization of production facilities (configuration, object description, maintenance, startup, etc.). The companion standards for field bus systems will resemble those for MAP wherever appropriate in terms of functionality. BUSIKChas described the importancc of MAP for process control engineering [8.201. In the ISOjOSI model, the network management functions supplement the transmission protocol functions and communicate with them. As
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in any case where intelligent subsystems are nctworked, network management is also required for a field bus system. Of the five management areas of the ISO/OSI model, the following are pertinent to field bus systems: 0 0 0
Performance management Configuration management Fault management
The management functions for a field bus system are implemented in a network management station. Its functions and services include system initialization, the loading of programs and parameters, the starting and stopping of the communications system, the detection and localization of faults, and the reconfiguration of the field bus system in reaction to faults identified. Furthermore, the management station monitors the performance and loading of individual communications channels, identifies bottlenecks, and generates planning data for network upgrading. Such a network management component appcars in all the field bus system now in development. Incorporation of Field Bus Systems into the Informution Hierarchy. As shown in Figures 8.5 and 8.6, field bus systems connect intelligent field devices with one another and with plant computers. They must support information transport at the field level but are also incorporated, through the plant computers, into the overall information pyramid. Since field bus systems are used in both processing and manufacturing, their real-time performance and message formats are subject to a variety of requirements. Within the field level, real-time requirements are critical when information transmission over a field bus is within a control loop. Depending on the type of process being controlled, guaranteed transmission times in the range of a few milliseconds are needed; the messages to be transmitted are, however, short. The situation is similar for field bus systems in manufacturing cells. Longer messages and less critical real-time conditions occur when information is exchanged bctween CAD/CAM systems and manufacturing cells. The guaranteeable response time over a field bus depends on the types of services implemented. When field bus systems are integrated into thc information pyramid, it must be kept in mind that levels3 to 6 d o not exist or are emulated
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8. The Process Control System und I t s Eli,miwts: Injbrmution Lqistics
Alarms (length < 2 bytes, transmission in a few ms) Overall field bus transmission rate > 250 kbit!s Process dimensions: Tens of meters up to roughly 2 km; distnncc to control room between 50 m and 3 km: total bus cable length between 75 m and 10 km Ambient conditions: High temperatures ( > 80°C). humidity, dust, vibration, electromagnetic interference; the transmission medium must be resistant to oil. steam. and ultraviolet; insulation must not contain halogens Safety: Intrinsic safety is an essential property; safcty classes IP 65, NEC Class 1 Division 2 Conformance to protocol specifications Certification of Conformance in order to insure connectability, interoperability, and interchangeability of field devices on thc field bus is regarded as important
only to the extent necessary for direct field bus operation. This means that a special field bus node must offer the necessary network functions and also must have the requisite information about the communications partners at higher levels of the information pyramid. As in reduced MAP architectures. there must be a gateway that, on the one hand, is fully networkable and. on the other, can execute the field bus protocols (Fig. 8.7). Constraints Arising from Applications. The ESPRIT CNMA project (Communication Network for Manufacturing Applications) carried out a survey to find out what operational constraints would apply to a field bus standard in the fields of processing and manufacturing engineering: Processing:
Field devices to be connected: Programmable controllers, sensor and actuator systems. minicomputers, field multiplexers, and power supplies for simple sensors and actuators
M m ufuciuring : Field devices to be connected: Programmable controllers. numerical controllers, robot controllers; intelligent sensor and actuator systems are of minor importance
Information to be transmitted: Cyclic (length I4 bytes, transmission time between 100 ms and 2 s)
1 Process control level
Field level gateway
r
I
I
Field b u s
1
I
F i e l d bus i n t e r f a c e
F i e l d bus Router
I
Local n e t lMAP/TOPl
Figure8.7. Linking of ficld bus and MAP LAN
r I
I
Full MAP/TOP
Field level
I
8.6. Field Bus Sysrrrns
Information: Cyclic data (length 1 64 bytes. transmission time between 10 ms and 1 min) Alarms (length 2 8 bytes, response time ca. 1 s) Production data (length 2 -256 bytes, time interval between 10 s and 8 h) Process dimensions: The diameter of manufacturing applications is ca. 10 80 m ; usually thecontrol room is nearby; bus cable length between 5 m and 50 m Ambient conditions: Severe electromagnetic interference. wide fluctuations in power supply voltage, static discharges Safety : N o special requirements on safety or intrinsic safety of bus system Protocol conformance: Certification of field bus products for conformance to protocol is regarded as important These survey results are reflected in different ways in existing field bus standards and those in preparation. The discussion that follows will deal with the most important properties of two field bus definitions; these properties typify the current situation in the establishment of field bus standards. As early as 1989, POLKE pointed out the needed separation into energy and information channels and questioned the requirement of “intrinsic safety” against the background of modern sensor and actuator systems [8.13].
PROFIBUS (Process Field Bus). PROFIBUS is a joint industry/scicnce project in Germany, supported by the Federal Ministry of Research and Technology (BMFT), which prepared a field bus definition that is now a German standard (DIN 19245). The PROFIBUS user organization (PNO) is charged to maintain and further develop the PROFI BUS standard. PROFI BUS corresponds to the architectural principles discussed above and was conceived for use in both process and manufacturing industries. Its essential properties include the use of the token-passing access procedure on layer 2 and the Field Bus Message Specification (FMS) including network management over all layers. FMS [8.21] is derived from IS0 IS9506 (MMS = Manufacturing Message Specification) so that it can communicate with MAP applications in a simple manner and make use of experience gained in the development of MMS.
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A Lower Layer Interface (level 7a) replaces the functions of I S 0 layers 3-6 needed for field bus systems. A conformity test is included in the PROFI BUS definition. At the physical layer, PROFIRUS defines a twisted pair cable with a maximum length of 12OOm (without repeaters) or 4800m (with repeaters). If repeaters are d,the bus topology can be straight bus, tree, or star. The transmission rate can be betwccn 9.6 and 500 kbit/s. The number of nodes can be up to 122, of which a maximum of 32 can be master stations. Redundancy, intrinsic safety, and transmission of auxiliary energy comply with the specifications of ISA SP 50. Layer 2 of PROFIRUS (MAC) includes two protocols. the distributed token-passing protocol (IEEE 802.4 or IEC IS 88204) and the centralized access protocol. In this way, communication among a number of master stations, or between one or more master stations and the substations (slave stations), is possible. A special characteristic of the PROFIRUS definition is the use of standard processors for the protocol implementation, with a crucial effect on the efficiency of data transmission. On the basis of this efliciency, PROFIBUS at present is only conditionally suitable for service as a signal path between closed control loops with critical real-time conditions. FIP (Factory Instrumentation Protocol). The F I P protocol definition has been in preparation since 1985, with support from the French ministry of higher education and research ministry. Its purpose is information transmission in a manufacturing setting. The FIP bus consortium includes more than 70 cornpanics located in scvera1 European countries. There are at present three French standards for layers 1, 2, and 7. Other French standards for network management, MAP/MMS-compatible functions at layer 7, and optical fibers on the physical layer are in preparation [8.14]. The physical level allows for a maximum of 256 nodes. Three transmission rates are provided on the twisted-pair cable (31.25 kbit/s, 1 Mbit/s, 2.5 Mbit/s). The cable can be up to 3 km long at 1 Mbit/s. For optical fiber systems, the maximum length between two node connections is 1600 m (glass fiber) or 125 m (plastic fiber). On layer 2. there is a centralized access control procedure (MAC), which supports both cyclic data transfer and the transmission of aperiodic data or longer messages.
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8. The Process Control Systern ond Its I3ements: Information Logistics
The FIP definition, based on centralized bus control. assumes that data can also be transmitted within closed control loops. To achieve the required efficiency, protocols are implemented in specific ASICs differing in functionality. The FULLFIP ASlCs include the currently standardized functions of layer 7, layer 2, and (in part) layer 1. This approach supports the connection of simple and inexpensive field devices (simple sensors) and minimizes the energy requirements of the interfaces. The decision to use special ASICs means, however, that they must be continuously modified to keep up with changing technology and standards. Given that international standardization of field buses has not yet stabilized, this point may be a problem. The Route to Open Communication at the Field Level. Opcn communication at thc field level means a consistent, internationally standardi x d protocol environment allowing field devices and plant computers made by a wide range of manufacturers to be linked to one another. This is particularly important at the field level, because a division of labor bctween manufacturers is natural and unavoidable in the area of field devices. Open communication has not yet penetrated into the production control and process control levels, but this fact cannot conceal the fundamentally different situation at the field level. Accordingly, manufacturers of field devices have not failed to come up with suggestions to remedy the lack of unity in the world of field bus definitions. For example, the EUREKA field bus project has the goal of evaluating present field bus concepts and thus promoting the standardization of device interfaces to field buses. Another approach is embodied in the ISP project (Interoperable Systems Project), an international initiative by leading manufacturers of instrumentation and automation hardware wishing to create and standardize a field bus definition optimized for industrial applications on the basis of experience with existing field bus definitions. The international standardization of field bus systems is proceeding in IEC TC65/SC65C, which is collaborating with the American standards body ISA. The ISA SP50 working group is creating a field bus specification for use in international standardization. The H1 and H2 performance classes defined by ISA SPSO have already been mentioned. In 1989. ISASPSO
attempted to harmonize the discrepancies between the access control methods (layer 2, MAC) in various field bus definitions. N o final result has becn achieved. but there is a trend toward “distributed” access control as set forth in IEEE802.4 o r ISOIS88024 and used i n PROFIBUS. The actual linking functions on layer 2 (LLC) have also becn specified; the objective is to unify the most important mediation functions in all field bus proposals while simplifying these functions and thus improving their efficiency. On the whole, there is a trend toward the harmonization of field bus proposals. although the effect of existing national standards and international industrial cooperation on the likelihood of an international standard being accepted must not be underestimated. The European Commission supported (under ESPRIT) the FICIM project (1-ieldbus Integration into CIM) in order to promote the harmonization efforts discussed. From November 1990 to November 1992. the properties and interoperability of the two Europcan field bus specifications (PROFIBUS and FIP) were tested in a pilot installation. The project had the participation of industrial companies as well as NAMUR, the Standards Working Group for Measurement and Control Engineering in the German Chemical Industry. The application functions and application standards were to support and insure the integration of information from these two field bus systems into a CIM system, independently of the lower field bus layers. The pilot phase of this project showed that such an integration goal is indeed achievable. The pilot Facility is therefore being used for further studies of the interoperability of field bus systems. The certification of field bus implementations for conformance to the standardized protocols is regarded as important in all field bus projects. Specifications and implementations of conformance test systems for all layers now exist for the FIP bus and the PROFIBUS. The Upper Tester, that is, the part of the test system above the protocol layer bcing tested, presents a special difficulty here. Because it is expected that field bus protocol implementations will be optimized for medium-sized and small intelligent field systems, the formerly usual (in MAP) configuration of the Upper Tester on the field bus device under test is no longer possible. The standardized conformance test for such devices thus includes an “Upper Tester Agent” (UTA) on the field bus device; through the field bus. this communicates
8.7. Qualiry Assurance: Conformance and Interoperability Tesrs
with the actual Upper Tester on the separate test system. The wide range of efforts to unify standardization work now in progress. and also to insure conformance of field bus components to the standards, suggests that open communication at the field level can be realized despite the variety of approaches now used. Manufacturers and users in the field of industrial technology should provide intensive support to this development work, since it is an important prerequisite for managing the complexity of future industrial control systems. Again, a meaningful solution will be obtained only when the information model providing a semantic description of field-level problems has been adopted (see Chap. 2 and Scction 5.1).
8:7. Quality Assurance: Conformance and Interoperability Tests When computer communications equipment made by different manufacturers is used, the user must be able to rely on correct implementation of the protocols. The current state of the art does not allow a formal specification and thus an automatic verification of complex protocols, and so protocol products have to be tested with a standardized set of test cases. These “scenarios” define status data, parameters, and function calls, with whose aid all relevant protocol functions and protocol states can be tested as completely as possible or necessary. A protocol product that passes these tests is said to “conform” to the protocol specifications. If these specifications are taken to be complete and correct, a conformant protocol product will be able to communicate with any other corresponding protocol product. In a full MAP/TOP or field bus protocol implementation, all protocols at the various levels must be tested. Figure 8.8 shows the general procedure for conformance tests [8.22]. The protocol being tested (“implementation under test”) is enclosed by a test frame consisting of an upper tester (UT) and a lower tester (LT). While the LT responds to the test protocol in a well-defined way and detects the reactions of the protocol, the UT interacts with the protocol as if it were a protocol product on the next higher level. The UT is assumed to function without errors; this supposition is justifiable only because
277
Upper t e s t e r
Test Coordination
Implementation under t e s t IIUTI
Figure 8.8. Principle of conformance testing [8.22] ASP = Abstract Service Primitive
the UT merely provides certain interface conditions (ASP = Abstract Service Primitive) and nccd not exhibit the full functionality of a protocol. A test coordinator makes certain that the test scenarios set forth in the conformity test are run, causes the responses of the specimen to be stored, and interprets the tcst results. Interpretation consists of comparing the reactions of the specimen with a predefined set of expected reactions. The conformance of the protocol product or the presence of implementation errors can be deduced from this comparison. The fact that protocol products have passed the conformance tcst does not necessarily guarantee their interoperability. Like any other test, the conformance test is incomplete; what is more, it cannot be more complete and correct than a possibly incomplete and inconsistent protocol specification. This uncertainty can be reduced only by additional interoperability tests. An interoperability test studies the actual intcroperabihty of protocol products, that is, their real ability to interact in open networks. Intcroperability depends on conformance to protocols but also on the matching of parameters that can be adjusted when the protocols are run. In this context, the term profile means an applicationspecific selection of parameters and functions of a protocol in accordance with a standard. Depending on the ranges of variation of such parameters, different profiles may conform to the standard but not be interoperable. The problems associated with the range of options in protile definition are not discussed further here.
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8. The Process (’onrrol Sysrem nnd Its Elements: Information Imgisrics
A final key aspect of the serviceability of protocol products is efliciency and performance. Even given correct and conformant protocol implementation. interoperability can be significantly impaired by, for example. wide differences in timing between protocol products. In future, accordingly, standardized interoperability and performance tests will be defined. An interoperability test system must cstablish a connection between the protocol components being tcsted and monitor thc resulting flow of data and information. The use of expanded lower testers (cooperating with a communications observer) is conceivable. The interoperability tester compiles the results from the lower tester and the communications observer. While conformance tests are done against a (possibly partly formal) protocol specification, the interoperability test deals with communications aspects that cannot be so well spccified. It is thus much harder to standardize this kind of test. The user of communications protocols, however, sees interoperability as the proper acceptance criterion for protocol products; this is why work is under way to standardize these tests.
8.8. Methods and Tools for Protocol Specification ?’he preceding sections have made it clear how crucial the degree of formalization of protocol specifications is for the correct implementation of protocol standards. This section describes some procedures and aids now being used in the specification and implementation of protocols and in test systems. The protocols in question are those in the context of the ISO-OSI reference model [8.8]. These protocols [8.8] define services that can be described by the functions invoked and the data exchanged. Services are composed of service elements such as establishing and dropping connections, transferring data, and exchanging acknowledgements. Such service elements are deemed indivisible sequences of events and must be implemented in a proper way with regard to fault tolerance and consistency requirements. They are neutral with respect to the data or function calls mediated by them. Service elements are based on “service primitives,” that is, functions such as “request,” “response,” “indicate.” or “confirm.” In prcsent-
day protocol specifications. these service priniitives and thus thc servicc clemcnts built up from them are commonly described in natural language. In this way. a formal specification of the functional (procedural) part of protocols is not usually stated, notwithstanding the fact that thcse functional (procedural) parts of protocols represent finite automata. The form of description as a finite automaton is. however, used only in exceptional cases and for subfunctions. One important reason for this situation is thc complexity of the automata, whose many and varied options make them hard and inconvenient to describe in the form of states and changes of state. Protocol Data Units (PDUs) exchanged with the aid of the service primitives, on the other hand, are now specified formally. A “language“ of abstract syntactical notation is available for this purpose; IS0 has standardized it under IS 8824, and it has become known under the name ”ASN.1” (Abstract Syntax Notation Onc) [8.23]. Advantage has also been taken of cxperience gained in the informatics field with various higher-level programming aids and their data structure description methods. At present, ASN.1 is used chiefly to define PDUs in protocols at the applications level of the I S 0 model [8.24]. As Figure 8.9 shows, applications programs or applications entities exchange information by means of PDUs. The A S N . l languagc makes it possible to formulate these PDUs in the abstract manner Standardized by IS 8824. It should be ensured that the meaning (semantics) of the I’DUs in a given local applications syntax and in the abstract ASN.1 syntax remains unchanged. Thus, semantics-preserving transformations between the syntax of any local language and the abstract ASN.1 syntax must be defined. The transmission of these PDUs via a communications system requires a concrete syntax. called the transfer syntax, that is understood by both communications partners. The transfer syntax must be defined at levcl6 of thc IS0 model. Accordingly. IS0 has standardized a “concrete transfer syntax” under IS 8825 (8.231. just as the abstract syntax is Standardized under IS 8824. Thus, for PDUs actually to be transferrable between two applications entities, thc abstract PDU definitions notated in A S N . l must be translated from the real local syntax of each applications program into the IS 8825 transfer syntax. This assignment of a transfer syntax to
8.9. Steps Toward Compurer-lntegrared Producrion
Local ( l a n g u a g e A
Level I
219
Local /language B
Level 6
Representation case A
Representation c a s e B
Figure 8.9. Representation context [8.26]
an abstract syntax is called “representation context.” It is significant that the representation context currently standardized by I S 0 for ASN.l is not a unique standard valid for all time. Other representation contexts especially suitable for certain applications areas can be defined later on; this is particularly important in connection with the companion standards discussed in Section 8.4. Thus, in order that one applicationsspecific representation context can be rcplaced by another for communications betwecn applications program, every communications process begins with the negotiation of the proper representation context. One example might be an exchange of communications of applications program systems between partners in the realm of process control engineering and partners in the realm of offce communications. The difference in encoding conventions (e.g., measured data or C A D data) means that the representation context must be adapted. There is already an alternative to the representation context ASN. 1/Basic Encoding Rules, in the form of the “default context,” which is used whencver no special representation context has been negotiated. This is the case in particular when a connection is being established. The default context, however, contains neither an abstract syntax notation nor a concrete transfer syntax. The unstructurcd bit stream bctwcen the two applications programs must be directly interpreted by the programs. Because the PDUs of important MAP applications protocols, espccially the MMS (Manufacturing Message Specification) services. have been specified in ASN.1, it is an obvious step to employ the same representation context for
specifying the Conformance tests for MMS. As Figure 8.10 shows, the conformance tests for the protocols MMS, Network Management (NM), and Directory Service (DS) are based on a set of ASN.l tools with whose aid the test parameters and dynamic tcst cases for these three protocol groups have been formulated and processed. The system of tools for conformity tcsts, as shown in Figure 8.10, were devised by the Institute for Information and Data Processing of the Fraunhofer Society. Though not completed at the time, they were used successfully in the conformity test at IENE ’88 (International Enterprise Networking Event) in Baltimore, June 1988. It is cxpccted that the rcprcsentation context based on ASN.l is sufficicntly powerful to cover other Companion Standards as well. The standardization of PDUs, at least for the protocols named, is thus formalized to the extent that conformance tests can be derived from the actual protocol definitions in a direct manner, without substantial recourse to natural-language definitions.
8.9. Steps Toward Computer-Integrated Production Consistcnt information integration in production cornpanics is the objective of the computer communication utilities derived and discussed in this chapter in the context of information logistics. While very important, they are by no means the only tools available for reaching this objective. Computer-Integratcd Production (CIP) can be achieved only through collaboration between disciplines: production engineering (manufacturing cngineering, control
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8. The Process Control System urid Its Elements: lnj~rmaiionLogistics SPAG-CCT
Test system architecture
Source PDU T e s t schedules
grammars
Operator
controller
@-i
Test parameters
Test control manager
D e f aul t s
I
Limits
ASN.1 T o o l box
I
i
I
I I
I J-
I
I I
O b j e c t POU grammar
\
-
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O f f line
Session Transport Network
1
Figure 8.10. Implemented architecture of conformance testing
engineering, logistics, production planning, etc.), communications engineering (digital data transmission), and informatics (systems engincering, hardware and software technology). This integration of methods, in which information logistics is merely one part of the needed mcthodological edifice, is only just starting to gain support from products on the market and certainly cannot be achieved through standardized communications protocols alone. The custom CIP solution for a given plant often constitutes an analysis and modification of internal structures and modes of working. The translation of such an internal reorientation t o the use of computer communications techniques must take place in a continuously
changing environment. Thus, MAP Versions 2.1 and 3.0 are not compatible; an early involvement in this protocol world gives rise t o valuable cxpcrience, but also to a continuing necd for new investment in more-advanced protocol products. Even within General Motors. initial cxperience with MAP was problematical. But it would be wrong to think that these unavoidablc startup difficulties in a very complex field rcprcsent a failure of information integration. Outcomes of this experience have includcd more protocols on the applications level (or on all protocol levels at once), such as Network Management or Directory Service. In this way, gaps in the MAPjTOP definition are closed and the use of computer communications is facilitated.
8.9. Steps i7)i)H'urdComputer-Integrated Production
The same applies to the present status of'field bus standardization. Again, international differences in the view of applications requirements and product line definition must bc ironed out in such a way that a single set of international standards can be devised in a usable form. The reaction of industrial firms to the very protracted standardization process shows once more that industrial users and manufacturer of communications systems are interested in open systems of information logistics. All this development of tools will, however, be fruitless if users of CIP do not acquire the knowledge and training they will need to understand the total system. Along with education within the corporation, occupational training has a role to play. At the same time. there is a
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need for competent, neutral sources of advice. It is precisely small and medium-sized companies that lack the personnel resources to keep up with the rapid development of information integration and turn it to their own use, even though this assimilation is urgently needed to insure the future of the company's production and products. Regional sources of advice (chambers of commerce and industry) as well as research institutions cooperating with industry are increasingly available. The path to computer-integrated production can be descerned today. It will lead, with much difficulty and many setbacks, to an improvement in the competitiveness and market position of those companies that have the commitment, ereativitiy, and perseverance to follow this path.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
9.2. System Ana/wis
9. Computer-Aided Methods 9.1. Principles Coming years will see greater and greater importance attached to the use of computer-aided methods and computerized tools for the design, erection, opcration. and maintenance of proccssing facilities. The steadily increasing number and complexity of industrial plants and the requirement for complete, current documentation will generate a volume of work that could scarcely be handled without computer support. At present, however, the ratio of computer-aided tools to demand is still fairly small. As the needed computer-aided engineering (CAE) systems are developed and sclected, it is crucial that the engineering disciplines involved must be treated not as individual. independent working groups but as the intermcshed system they really make up, in which the results achieved in one discipline influence the work of the others. This does not mean that one “universal” tool has to be dcvised for all engineers. The model of a well-filled and organized “toolbox” appears to promise better success. Such a box contains a special tool for every job. The tools, however, are designed so that they all share consistent interfaces and thus can be combined with one another at will. Any problem that arises can thus be solved in optimal fashion; if the objective is changed, the “toolbox” can be adapted to the new situation through the modification of an existing tool or the addition of a new one. This book does not deal with computeraidcd design tools for plant engineering (e.g., INTERGRAPH).
9.2. System Analysis The background for system selection must include the formulation of overall objectives and possible interfaces. The overall objective is broken down into partial objectives, which in turn are refined further into tasks. This procedure is continued until the individual tasks are manageable in their complexity and the functions necded for accomplishing them can be described along with the rcsulting data flow patterns. To make this task analysis easier for the user and permit an unambiguous interpretation of the results. a variety of methods have been de-
283
vised for displaying the process in graphical form with defined symbols. The best-known methods are structured analysis and design technique (SADT) [9.11 and structured analysis (SA) [9.2]. Either method can be carried out manually with pencil and paper, but increasingly they are embodied in software packages that make it possible to run these analytical tools on PCs and graphics workstations. In what follows, the SADT method is briefly illustrated in a simplified example. Thc essential elements of the SADT diagram are labeled boxes and arrows. Boxes symbolize activities (functions), while arrows stand for data and information and thus characterize the interfaces between boxes. The following rules also apply: 0
0
0
Input information always enters a box from the left, is transformed into output information in the box, and leaves the box on the right. Controlling information influences this transformation and may also be used per sc in producing the output. Such information always approaches a box from above. The function of a box may be supported by mechanisms, which are optionally reprcsented as arrows entering from below.
A model generated by SADT is a structured breakdown of a problem, represented as a hierarchically ordered sequence of diagrams. The topmost level of the hierarchy is a diagram with a single “black box” representing the total system, showing its connections to the outside. On the next level, this black box is broken down into a number of boxes; the same procedure is continued down the levels. Figure 9.1 shows, in simplified form, the top two levels of an SADT model representing the engineering functions involved in the design, erection, and operation of a process plant. The upper part of the figure shows the black box representing the overall system, with interfaces to the outside world. In thc bottom part, the overall problem is broken down into the following subproblems: -
Design the process Design the plant Erect the plant Operate the plant
In the graphical presentation, the functions (boxcs) are normally arranged on the diagonal
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9. Computer-Aided Methods
Experience
from top left to bottom right. The order of functions is governed by the required flow of information and data, shown as arrows between the boxes. Feedback of information may also occur, in which case the proccdure becomes interative. Interfaces to the outside world are also shown in greater detail. Now each of the functions is further broken down in diagrams and documented. Obviously, complex problems yield many diagrams, all partially interdependent. Hence, if changes are made, these relationships must be taken into account and the appropriate corrections performed. The use of such a method thus calls for a considerable amount of administrative effort; on large projects, this is difficult to accomplish without suitable computer support. However, the implementation of a software project is virtually impossible without a prior system analysis. In parallel to the design of the function model just described, it is necessary to define the overall system data model that applies to all functions. Only in this way can proper sharing of informa-
___-P l a n t model
1
tion among functions and consistent data management be insured. As a rule, the data model is set up as an entity-relationship diagram with data dictionary. Programs for this design task are also on the market; in many cases they are already combined with SA or SADT tools to make up software development systcms (see also Chap. 2).
9.3. CAE System for Process Control Engineering Within the overall system, the individual CAE tools used in the several engineering disciplines must have compatible interfaces. In addition each tool should have the optimal functional scope for the objectives of the field in which it is used. The special requirements on such a CAE system for use in process control engineering are therefore discussed in what follows. A CAE system for process control engineering must support both the design and the later
9.4. Structure of a C A E System
operation of the process measurcment and control system, including maintenance (Section 11.4). This latter aspect is particularly important here. Over a span of years, the design changes in an onstream plant due to detail improvements or process changes can easily loom as large as the original dcsign. At the same time, the documentation must meet stringent standards of currency and consistency, since these changes generally must be performed while the plant is on stream or during brief shutdowns (see Section 3.5). The CAE system must therefore be able to handle data having a variable and complcx structure and to afford assistance in the processing of such data. Process measurement and control functions are implemented in hardware and software. In practicc, it has been found that a subdivision of the task into hardware design and software design is useful, and in fact these sub-tasks can cven be carried out by different persons in the case of large projects. Both areas, of course, have access to a jointly defined interface, but are otherwise not temporally linked and can therefore work independently of one other. Thc hardware data to be managed in the CAE system can be broken down into three classes: 0
0
0
Station data: all information describing the task and the functional design of a process control station. As a rule, these are documented in station data sheets and station diagrams (see Figs. 10.31 - 10.34). Device data contain the exact technical specifications of the devices employed. These serve as the basis for later procurement. Position data describc the configuration of the devices in the Pacility and contain the infrastructure created by the master cable. Typical forms of documentation are setup plans, terminal allocations, and cablc plans.
The data describing the software havc a different structure. Generally, modern process control systems have modular software. The modules are linked to one another and arranged into various levels of a hierarchy, depending on the task. The lowermost level is made up of modules that implement the functional design of a single process control station (Section 4.4). Higher-level modules coordinate the work of lower-level ones. Each module is composed of functional building blocks, which are the smallest software
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elements of a process control system. The most important documentation forms for software are function charts, logic diagrams, and element StrUctUrdl plans. All these data groups are closely linked; together, they represent all the information about the process control equipment. In what follows, they are treated together under the term “project data.” As a rule, these project data are managed by a database system (Chap. 10).
9.4. Structure of a CAE System The ideal structure of a CAE systcm as shown in Figure 9.2 reflects the constraints discussed above. The heart of the system is a relational database, which contains the computer model of the planned equipment. All manipulations of the database take place interactively; depending on the task, graphical interfaces may be used. At the start of the design work, basic information for the project is acquired from the other departments involved. The most important source is the process control tender specifications, which set forth requirements on the proPCE specifications
Plant database
Requirements
Apparatus, v e s s e l , pipe data
Figore 9.2, Structure of a CAE system for process control engineering
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9 . Compurer-Aided Methods
cess control devices to be implemented as well as their interconnections. Information is also required from the plant database, which supplies technical data on the plant sections affected as well as ambient conditions where the process control device is to be installed. On this basis, the designer begins with the detail design of process control devices. This procedure is interactive and employs a graphical user interface. The system offers a number of tools that can, for example, prepare plans, enter specification data. define cables, or copy entire plans or cabinet terminal allocations. These manipulations gradually build up an internal model of the process control equipment in the database. Every interaction on the screen brings about a corresponding modification in the database. This internal (computer) model also serves as the basis for all documentation. The various documents are merely the results of various inquiries formulated to the same database under various criteria. In this way it is insured that any subsequent changes will automatically be incorporated into the next round of documentation. A CAE system with this level of complexity may consist of a number of largely independent modules, all of which communicate with one another through the common database. One possible subdivision into task areas is as follows: 0 0 0
0
Hardware design Software design Procurement Installation
These are largely independent as far as processing is concerned and have access to one another only through a few well-defined interfaces. Accordingly, independent solutions are now on the market for certain of these task areas. When such products are employed, however, special attention should be focused on the possibility of later integration into an overall system. Because process control devices cannot be designed and operated in isolation from the plant as a whole, smooth communication should be insured between this process and other departments, such as plant design, process engineering, purchasing, and maintenance [9.3]. The only way to be certain of this is to define a common data structure within the company: the company data model. Without such a data model, the danger exists that terms and information might be differently interpreted in the several
systems, so that consistent data management would be impossible. A crucial problem for the acceptance and efficiency of a CAE system is whether and to what extent the system user interface is adapted to the user’s methods. Typical users impose varying. and in part inconsistent, requirements. One user is the design engineer, who quickly becomes an “expert” through daily work with the system in a varicty of modes, can operate it “in his sleep.” and therefore wishes to get results quickly, with the fewest possible manipulations. and without having t o read help messages. In contrast, there is the operating engineer, who is confronted with the system only occasionally and briefly, for example during process upsets and when design changes are being worked out. The system should prompt this user in an intuitive way, with extra help functions, to avoid a prolonged learning process and the study of manuals. Because the drawing is the most comprehensible “language” in convcntional design work too. it is an obvious step to design the entire user interface and system tools for largely graphical operation. In this way. the user’s previous working methods are largely preserved, so that learning the system can be accelerated. The use of hierarchically structured menu trees can drastically simplify the operation of choosing from a large number of devices or symbols. If the menus and the underlying database are cleverly designed, progressive narrowing of the field quickly leads to certain individuals without the use of lengthy catalogs and lists. This technique is widely used in modern systems [9.4].
9.5. Aids for Hardware Design A number of CAE systems for process control hardware design have appeared on the market. They take two distinct approaches to implementing the task. One is to combine independent standard programs for database applications and 2-D graphics, using suitable auxiliary programs to link and supplement the commercial software. The other approach leads to an integrated system. in which the graphical tool is directly linked to the database and each is independent of the other. Both solution approaches will be described in what follows, and their advantages and disadvantages will be discussed.
9.5. Aid7 for Hardwure Design Alphanumeric processing
I
I
Graphic processing
Standards
Standards
Typical
287
system
Alphanumeric project data
I
transfer
I
I
I
system
Graphic project data
Figure 9.3. Structure of a combined CAE system
Combined CAE System. A combined system includes a free-standing, independent software package for alphanumeric tasks and one for graphical tasks, each package having its own data management features (Fig. 9.3). Basic information contained in the alphanumeric package includes specifications on standard devices (process control equipment data; Figs. 2.9 and 10.31). Typical configurations, with information on combinations of devices for stated purposes, are also included as standards. In parallel, the graphical program package must have the respective symbols for the individual devices as well as typical plans for devicc combinations. It has to be explicitly verified that the typical configuration and the typical plan are logically consistent, since this forms the basis for subsequent automated processing (see also Figs. 10.32-10.34 and 3.22). The first step in design is to input the process control stations, in the form of a station list, into the alphanumeric software and assign a typical configuration to each station on the basis of the task statement. These data are supplemented by structural information, such as the terminal allocations of cabinets and racks and the master cable connections. The devices used may also be specified more closely. Once the station and configuration data have been prepared, they can be exported to the graphical side with the aid on an
auxiliary program. The respectivc plans are now prepared. Infrastructure information, such as terminal codcs and the like, is entered into prepared text placeholders. The information managed in the alphanumeric module can thus be output as documentation along with the graphics. There are a number of advantages to this approach. The use of common standard programs makes it possible to assemble such a system at relatively low cost. Because the programs d o not have to run in parallel, the system can be and usually is executed on a PC with the MS-DOS operating system. The system as a whole is highly modular in structure, and the user can generally expand it with additional functions. These advantages are partially offset by several drawbacks. The use of several software packages from different publishers means that the user must be familiar with the diverse operating concepts of the programs. Because configurations and plans are generated in different programs, there is a danger of input errors, which can lead to inconsistencies. A combined CAE system thus has its strengths in the design areas, where a high degree of standardization is possible. If subsequent changes not complying with the standard are made, however, corrections must be made in
288
9. Computer-Aided Methods
both the alphanumeric and graphical componcnts of the documentation. Integrated CAE System. The integratcd solution features a direct link betwccn the graphics package and the databasc. Graphical manipulations thus influencc thc databasc contents immediately, while alphanurncric inputs influcnce the graphical rcprcsentation on the basis of certain rules. This close connectedncss both insures the consistency of the data and makes it possible to carry out pkausibility checks. Interactivc operation builds up an intcrnal model of the proccss control equipment, which serves as thc basis for all interpretations. All documentation, whcther in graphical or alphanumeric form. is derived from this model (Fig. 9.4). Thc close coupling of graphics and database system usually requires the use of a multitasking operating system. The graphics packages employed arc specially developed software with special provision for interaction with the database. UNIX graphics workstations servc as the hardware component. The advantages of the integrated approach lie in the consistency of the data. In case of subsequent changes, corrections are essentially nccded at just one place. Because of their more complex structure, integrated systcms are necessarily more expensive than combined oncs. What is more, high-performance hardware is required if an acceptable response time is to be achievcd [9.4]- [9.7].
Standards
Device data
Alphanumeric
P r o j e c t data Figure 9.4. Structure of an intcgrated CAE system
Over the long term, thc intcgrated CAE system is thc better solution. Its present cost disadvantages arc bcing offsct by technical devclopments in the workstation market.
9.6. Aids for Software Design Hierarchicdl Organization of Software. Thc principles of functional organization and progrcssivc refinement have been tcsted in practice for the description of complex relationships (see Chap. 2). A good example of such a procedurc are the dcsign steps involved in the development of a complex intcgrated circuit such as a CPU. First, the required functions are reprcscnted in the form of modules at a fairly abstract levcl. and thc requisite interactions between these modulcs are described. Next, the intcrnal structures of thc individual modules are established; thc same principle is applied recursively in this step. The objectivc is thus narrowed down to thc level of single transistor functions. In the subsequent fabrication operations, the complex functioning of the circuit is achievcd through the combination and interconnection of many vcry simple subfunctions. A similar procedure can be applied to the preparation of software for process control systems. The overall task is broken down into subtasks with well-defined intcrfaces (sce Section 5.2). By narrowing down the description, one ultimatcly reaches the lowermost level of abstraction, which in thc case of process control systems is formed by the components and logic functions available. Thc softwarc for a systcm is finally written by appropriatcly combining thcsc basic dements (Fig. 9.5). If there is so much similarity in thc procedures, the question arises whether parts of the highly advanced commercial CAE tools for circuit dcsign might not be usable in modified form for process control system softwarc dcvelopment. In what follows, the minimum requircments on such a tool are stated briefly. Graphical Generation of Function Charts and Structural Diagrams. The building blocks or logic functions of the process control systcm, as furnished by the publishers. form the basis of the applications software. The stated task is accomplished by combining thesc, in suitablc form. to makc structures. The most important aid in dcsign and subsequent maintcnancc is the func-
9.6. Aids for Software Design
289
Figure 9.5. Progressive refinement of a software structure
tion chart or structural diagram (Figs. 3.20 and 10.35). It shows graphically all the components used, with their interconnections and paramcters. The creation of a graphical tool for generating these diagrams is crucial. The clement symbols are selectcd from a library and placed on the Screen interactively. The connections between elements are gcnerated by constructing lincs between the element terminals. Parameter values can also be interactively assigned to specific elcment terminals. The logical content of' these diagrams must be determined by the system and
automatically translated into a sequence of programming instructions for the process control system. Plausibility checks can bc pcrformcd to climinate programming errors directly at the source. This procedure simultaneously insures consistency between the program and the documentation (sce Section 10.6). Thc CAE tool must support thc method of succcssive rcfinement described above. The user must be able to generate abstract modules and their connections in a plan without needing to know their internal structurcs. Internal structurc
290
9. Compuier-Aided Meiliods
can be rcprcscntcd in a latcr diagram; the system automatically takes care of cross-rcfcrencing. In this way, complex structures can be detailed step by step, down to the component level. During code generation, the system must recognize and take into account information about the entire hierarchy of diagrams. Another important point is the usc of “typic a l ~ . ”These are multicomponent structures that can be uscd without alteration in many diagrams. Constructing a good library of typicals can drastically reduce the required design effort. At thc same time, it leads to standardized software, since a fixed combination of elements can always be used for like functions. Such a capability must therefore be implemented in a CAE tool (see also Sections 4.4 and 5.7). Apart from building blocks defined specifically for each process control system, there are system-neutral descriptive forms for logic and sequential control systems. The appearance and interpretation of these are specified in I S 0 and DIN standards. It should therefore be possiblc to program controls with the aids described above, using the standard symbols. The translation to the language of a particular process control system is another function of the CAE tool.
Construction of Screen Diagrams. Like the preparation of component-oriented software, the programming of the user interface for a process control system is a particularly critical point (see Section 11.2). At present, this reprcscnts a major design effort, requiring support by CAE tools. Graphical plant diagrams uscd in interactive mode have come into widespread use in the user interfaces of process control systems. The graphical design of such images should be performed with tools whose quality and ease of use are consistent with those of commercial C A D systems. The definition of dynamic displays or controls might be done by marking the position and assigning the variable name. Diagrams constructed in this way should be usable for all components of the process control system without case-to-case modifications. Documentation of Existing Facilities. A special problem arises when existing plants must subsequently be documented (see Section 3.5 and Chap. 10). Often, the current program no longer corresponds with the original documentation because changes have bcen carried out commissioning or maintenance. All process measure-
ment and control systems can document the current program structure in the form of cornponcnt and configuration lists. This form of documentation is, however, difficult to understand, and in practice it cannot be employed for troubleshooting. As a rulc. thcrcforc, thcrc is no altcrnativc to changing or regenerating function charts and structural diagrams on the basis of the current lists, but this procedure is time-consuming and expensive. Users are therefore demanding automatic graphical documentation of the current state of a process control system. Synthetic diagram generation on the basis of information present in the control system constitutes a basic problem. Because the system contains no information about thc original form of the diagram, the component and terminal positions nccdcd for the reprcscntation must bc determined by using algorithms such as “information flow top to bottom” and “minimize feedback paths.” The consequence i s that a synthetic diagram with given contcnt may have a different graphical organization from a hand-drawn diagram. Furthermore, even small changes may alter the positions of somc components relative to the old version because of differences in evaluation in the placement operation. This property may interfere with the immcdiatc rc-idcntification of a circuit. Nevertheless, retrospectivc graphical docurnentation can insure complete consistency between the documentation and the current program. For this reason, users are willing to tolerate the disadvantages of retrospective documentation until a better alternative becomes available.
System-Neutral Programming. In recent years, various manufacturers of process control systems have developed CAE tools, each tailored to a particular system. Although such a package provides effective support to the user. it also poses a new problem. Large firms and large engineering offices. which usually prepare designs for a variety of control systems, would have to employ a large number of diverse, system-specific CAE systems in order to simplify software writing. Because these systems are mutually incompatible, porting existing application software to a new system ultimately means doing a complete redesign. Users have therefore welcomed the efforts of the IEC to devise a neutral, standardized programming language for process control systems. With such a language, system-independent and thus portable technology applications could
9.7. Outlook
be developed. IJsing such packages, thc user could eventually perform tasks in a problem-oricnted way regardless of which CAE systcrn was in USC.
9.7. Outlook The development of CAE systems has been strongly influenced by rapid advances in computing hardware and software. If users are not to find themselves isolated in a few years and rcstricted to possibly obsolete computer systcms, modcrn CAE systems must be made independent of both hardware and software. This objcctivc can be achieved only by means of international standards. In the operating systems area. UNIX Sys. V or OSF can servc as a standard. Most publishers have now adopted the MIT X-Windows Systcm as the basis for the graphical user interface. A number of competing tools are currently available for the actual design of the user interface, but OSF MOTIF is emcrging as the favoritc. A
291
CAE system developed with these tools can thus bc regarded as having a good future. One particularly critical point is the data management problem. Because plant design data gencrally must bc kept for 20 to 30 years, stringent requirements apply to the dependability and development potential of the database program publisher. At the same time, cxotic data structures should bc avoided so that data can, at least in principlc, be imported into a different database system. In the context of workstation networking. thc potential of dccentralizcd data storage should be investigatcd. Graphical workstations, connected to one another and to service computers by local networks, will become the standard hardware. The simultancous rapid rise of computer performance and decreasing hardware costs will allow use of high-pcrformance solutions. The high requirement for storage space will be partly answcred by advances in rcwritable optomagnetic discs, which offcr high data security and longterm stability.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
10.1. Principles
10. Design and Construction
of Process Control Systems
10.
. Principles
The previous chapters have described the functional and hardware elcments of process control engineering. This chapter deals with the design and construction of process control facilities. It is seldom that process control problcms arise in isolation. As a rule, they are embedded in an overall problem. This may concern an investment project in the field of chemical plant construction; process control engineering then represents one component of the problem, while other disciplines such as machinery and appdrdtus, building construction, and piping are also involved. In the design and construction of plants, a distinction is madc between new construction and extension or reconstruction. Extension and reconstruction activities are generally more difficult to plan than new construction projects. since interfaces to existing plants have to be taken into account and the existing infrastructure and engineering must be modified. The overall extent of the work usually only becomes apparent when planning is already under way. A complicating factor is that economics often dictates the incorporation of changes while the plant is on stream or during shutdowns. Because the scheduling and duration of shutdowns is governed chiefly by operational constraints, however, planning of schedules and resource utilization must be done with special care (Section 10.2). New construction projects based on proccsses already in use within the company are generally simpler. The handling of proccsses new to the company ( i t . , created in process development or purchased from a licensor) is more dificult. Scaleup problems can involve substantial risks. Figure 10.1 gives a model for the implementation of chemical plant construction projects. The main blocks represent 0
0 0
Project management Project planning and execution Quality assurance
The design and construction of plants is carried out in the framework of projects. Project
293
management (Section 10.2) accompanies project activities. coordinates the work, and monitors compliancc with directives. In Germany, the usual approach project planning and execution is based on the regulation governing architects' and engineers' fees [lO.l]. It is useful to illustrate the procedure in the form of a phase model, which breaks tasks or functions down into phases and steps and shows their outcomes (Fig. 10.1). Note that the phase model represents an ideal. In actuality, the scveral phases merge continuously and may even overlap. Feedback and iteration between phases are also common. Interfaces between disciplines further complicate the process. The phases can be grouped into two large phases on the basis of the problems to be solved; these are called the decision phase and the execution phase [10.2]. The decision phase (Section 10.3) involves the conceptual work. The task is formulated here; it can be thought of as the mission to be performed in the course of the project. The written description of the problem is called the performance specifications (Section 10.4); it forms an essential component of the decision phase. The performance specifications give rise to the "binding concept,'' embodied in the specifications, which describes how and with what resources the stated task is to be performed (Section 10.4). The concept has long-term consequences, since it influences the economics of the plant throughout its service life. Identifying the concept is therefore accorded special importance. This step requires great care and is supported by analyses of alternative solutions as well as an examination of the overall project. One task may admit of several solutions, among which the optimal one for the company (from both economic and technical standpoints) is to be selected. The selection criteria may include 0
0 0 0
0
Attainment of targets Fulfillment of economic criteria (Section 10.4) Compliance with regulations (Section 10.6) Availability of plant User-friendliness Use of proven techniques
The total analysis of all disciplines involved in an investment helps to prevent the overall pro-
294
-
10. Design ond Construction of Prociw Control Systems
-
Q Contract
-
Bidding specifications
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Designs Procurement
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Finished plant
DActivity/function Figure 10.1. Phase model for chemical plant construction projccts
ject being harmed by suboptimization of individual disciplines. The binding concept is the basis for corporate management’s decision t o proceed further. The execution phase (Section 10.5) is oriented toward workmanship. Using results derived in the decision phase, it creates a functional plant. When the chemical plant design and construction process is divided into decision and execution phases, with the stress on holistic analysis and conceptualization, the result is that the
problem is handled in a fundamentally different way from what used to be common (see also Chaps. 2 and 8). In the past, planning for certain functional specialties, including process control enginecring, often caused delays in other parts of the process, such as plant design. The tasks of the “trailing” discipline were somctimes formulated from the standpoint of the “leading” one. A holistic view of the task could scarcely come about in this way. If such a view was formed in the course of the project. any correc-
295
10.2. Organizational Requirements
tions arrived at through such an analysis, no matter how logical and necessary. were not carried out, simply because the planning processes in the several disciplines had reached such very discrepant stages by that time that revision of existing plans by the methods then in use could not be justified in terms ofcost and lost time. The systematic use of data-model-oriented planning in plant construction, such as the Intergraph plant design system, makes it possible to implement changes such as those described in Chapter 9. In the past, compromise solutions replaced logical, holistic solutions; suboptimization of individual disciplines was the rule. The complexity and divcrsity of techniques now in use. together with the resulting interdepcndence, have led to continuous interaction between disciplines, virtually barring such an approach. Only the analysis of the project in its totality, coupled with the conceptualizing approach and an intensive quality-assurance effort (Section 10.6) going along with the execution of the task, can satisfy the requirements imposed on the economics, safety, and environmental compatibility of engineering solutions today.
10.2. Organizational Requirements The organizational concept in the execution of projects is called project management. The term “project” refers to the performance of individual tasks bounded in time and having clear objectives; these tasks may further be characterized by features such as interorganizational components, many involved divisions, high level of complexity, and possibly heightened risk [ 10.31. Various types of project exist, including 0 0 0 0 0
Construction projects Plant projects OrganiLational (management) projects Research and development (R & D) projects Software projects
In general a project is the complete temporal framework of conditions of all substances for the realization of tasks represented by functions [10.4]. “Substances” are suitable procedures, systems, and elements (Figs. 2.9 and 3.26) and are also called “resources”; Figure 10.2 illustrates this relationship.
Q Constraints
Problem
Procedures
I
Functions
II
1I
Devices, design documents, standards. substances Project duration
tstart
Plant
1
I Plant
I -/
c
Time
tend
Figure 10.2. From problem to plant
In order to achicve the project objective, these resources must be procured, combined, coordinated. and utilized. Project management is responsible for these functions.
Organization. The organizational setup of project management differs from the ‘‘line’’ organization generally found in companies. Organizational Forms. A variety of organizational forms are seen in project management. The three most important are briefly described below [10.5]. An across-lines form. also called “matrix” project organization, has proved especially successful (Fig. 10.3). The customary line organization is retained but is enlarged by a further dimension project management. Certain aspects of the project, such as technical responsibility, remain under line jurisdiction; others, such as project responsibility, are transferred to project management. This form of organization offers the advantage that personnel resources are optimally utilized, since employees remain in the project only as long as they are needed. As a result, specialist knowledge and special experience can be more easily brought in as well as transferred to subsequent projects. These advantages are partly offset by some drawbracks, in particular jurisdictional conflicts between line and project management and the relatively great effort req ui red to demarcate responsi bi li ty.
296
10. &sign
and Construction o/’ Process Control .yysterns
Figure 10.3. “Matrix” pro-
jcct organiration [lO.Sl
Corporate
Ip+J~I*l Planning/design I I
----
I I
I
I I
The advantages, however, outweigh the disadvantages. The matrix form of project organization has therefore found a wide variety of applications. It has been broadly accepted, in particular, by the chemical industry. In “straight” project organization (Fig. 10.4), a separate, independent project line is created in addition to the main lines; project management has its own organization partly similar to those of the main lines. Project responsibility and technical jurisdiction are combined at a single point. Quick reactions are possible when difficulties arix in the planning and execution of the project. Against these advanpages stand difficulties in recruiting personnel at the start of the project
I
Figure 10.4. “Stright” prqicct organiialion [lO.S]
and in keeping these people occupied after its completion. Straight project organization is mainly used in large, long-term projccts. In “influence” project organbation (Fig. 10.5), project management is simply a coordination instrument, without the authority to issue orders. It tracks progress with respect to schedules and costs and, if necessary, suggests actions t o the respective line jurisdictions. Project management has no responsibility for the project: it is rcsponsible only for providing timely information to the line jurisdictions. This arrangement does not favor successful cooperation across lincs. When difficulties arise. reactions usually come slowly and delays result.
10.2. Organizational Requirements
___ _--
I Engineering Production
This form of project organization is usually practiced on smaller projects and projects involving several companies. Orgnnizational Structure. For project management with matrix organization, especially in the chemical industry, a threefold division of responsibility has proved logical and useful (Fig. 10.6): 0
0 0
Project management committee Project manager Project engineer
The project managment committee is the actual supervisory and decision-making body. It includes the decision makers of the crucial lines involved in the project, i.e., those issuing the order for it and those executing it. The project management committee formulates global mission targets. It monitors the complete and onschedule attainment of these targets, makes the needed decisions when targets come into con-
flict, and causes these decisions to be implemented in the lines. The committee insures the availability of material and personnel resources. The project manager is responsible for structuring the project. His duties include the definition of sub-tasks in accordance with the project structure. Principal activities are the planning and oversight of schedules, costs, materials and other resources used, and progress. In addition, the project manager sets up and implements the reporting system. The project engineers support the project manager by providing organizational and technical guidance and oversight to the project teams, which are detailed by the lines to perform sub-tasks within the project. The makeup of project management depends on the size and importance of the project (Fig. 10.7). The “size” of a project can be defined in a variety of ways; for investment projects, the lcvel of investment is the key criterion. In large projects, the project management committee is made up of delegated decision makers from top management. For small to medium sized projects, on the other hand, decision makers from lower strata of the hierarchy form the committee. For large projects, a head-offce project manager is designated; this person always performs management functions. Smaller projects d o not require so much expense for the project management function. and a number of alternatives exist here. One manager can manage several projects at once, or one manager can focus on a single project but add specialist functions to management ones. The manager then becomes a participant in the project. The same holds for the project engineers: They have 100% of their time
a
I
Project management commit tee
I
Project manager
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Plant design project engineer 1
project team
297
1
1: 4-
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Construction project engineer I
project team
project team
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Figure 10.6. Organizational structure of project management
298
10. Design ond C'onsiruciion of Process Control Sysiems
Project management commit tee
I
Project manager
Project engineer
~
Oecisionmakers from top levels in the hierarchy [top management)
Key projects, p r o j e c t s of strategic importance
0
f o r one project: with pure management function
~~
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Medium-sized projects
Decisionmakers from middle levels o f the hierarchy
Small p r o j e c t s
Decisionmakers from lower levels of the hierarchy
0
0
~~~~~~
0
~~
f o r one project: with pure control and monitoring functions
~~~~~~
f o r several projects: with pure management function or f o r one project: with management and technical specialist function
0
0
f o r several projects: with pure control and monitoring functions f o r one project: with pure control and monitoring functions
Figure 10.7. Project management organization as a function of project s i x and importance
Project management commit tee 0 0 0
0
0
Monitoring and control Formulation of global t a r g e t s Resolution of t a r g e t conflicts Implementation of decisions in the lines Securing of material and personnel resources
Project manager 0
0 0
0 0
0
0
Project structuring Definition o f sub-tasks Schedule planning and monitoring Cost planning and monitoring Capacity planning and monitoring Progress planning and monitoring Reporting system
Project engineer 0
Technical and organizational control and monitoring of project teams
Figure 10.8. Tasks of project managemcnt
assigned to large projects, a lower percentage for smaller projects.
Tasks. Figurc 10.8 summarizcs the functions of the project management committee, project managcr, and project engineer. Major management activities include project structuring as well as cost, schedule, and resource management. Performing thesc functions requires close coopcration between all the activities involved in executing the project. Project Structuring (see also Chap. 2 and Sections 3.5 and 4.3). The nature, scope, and complexity of a project dictate the manner and depth of structuring. This activity rcsults in structural charts and can be based on a variety of approaches, with a corresponding variety of structural charts as outcomes 110.61.
Thc object-oriented structural chart brcaks down the matcrial and immaterial objccts to bc created by the project into components in hicrarchical fashion. Figurc 10.9 shows a portion of such a structural chart lor the erection of a chcmical plant. The function-oriented structural chart shows the breakdown of project activities. The structural chart of technical scope of performance shows what intcrmediatc and final outcomes are to be generated by thc project. All documents to be produced, such as rcports. plans, drawings, lists, descriptions, certificates, contracts, and so forth are included, regardless of whethcr thcy will still be nceded aftcr thc end of the project. The last two types of structural chart arc seldom madc up by themselvcs; a combincd form is usual.
299
f0.2. Orgunizationuf Rrquirrments
0
0
0
Plant
Building
Piping
Control devices
Sensors,
actuators
Control systems
Control room outfitting
I Functions
Electrical equipment
The main goal of project structuring is to determine the total volume of work neccssary for the success of‘ the project. In this way. the preconditions for further project planning are fulfilled, since project structural charts influence, among other activities, the planning of schedules, costs, and resource utilization [10.6]. The transparency achieved through project structuring makes is possible to identify key tasks. It becomes easier to coordinate project teams and delegate their sub-tasks. Economic, technical, and also organizational risks, such as jurisdictional conflicts between project management and lincs, can be delimited. Project structural charts are a prercquisite for effective project monitoring. Cost management refers to all cost-related activities in the course of a projcct, such as costing, planning, monitoring, and billing. Cost management is of central importance for a projcct. It must cover in detail Procurement costs for machinery, apparatus, buildings, process control equipment and systems. infrastructure, and other materials Erection and installation costs Engineering costs due to planning, design, and administrative activities Start-up costs The mecialist litcrature offers a number of methods for determining procurement costs in chemical plant construction [10.7]. The accuracy of the results depends on the method used, the depth of knowledge about the project, and other factors.
I Protection
Process
I
System control
INF 0
It is beyond the scope of this chapter to describe all methods in detail. Just two, widely used in plant construction, merit discussion. The first can be called the “method of multiplication (or ratio) Pictors.” The simplest application of the technique requires a rough, objectoriented breakdown of the overall costs into, say, the categories: -
--
Machinery and apparatus Buildings Piping Process control equipment and systems Miscellaneous
The machinery and apparatus needed for the realization of a process are the first things to become known during an investment project. They can be calculated from offers or empirical figures and adjusted for inflation and market trends. The principle of thc method is that the costs of all other categories involved in the investment can be determined by using ratio factors based on the costs of apparatus and machinery (Fig. 10.10). The person practicing the method needs experience to obtain usable results, since the factors include scope-dependent ranges. For example, oftice structures must be costed differently from industrial structurcs, and larger factors should be used for new “green-field” plant investments than for extensions based on existing buildings. The assessment of piping costs depends on, among other things, the complexity of the piping structure and the material of construc-
300
10. Design and Construction ($Process Control Systems
Total investment Apparatus, machinery Buildings Piping Process measurement and control Miscellaneous
n
Total costs = ( 1 + IfiI.S,, i=l
f a c t o r cpl = f k t r u c t u r e type, plant type, ...I Factor cp2 f(comp1exity. materials of construction, ...I Factor cp, = f(functiona1 scope, required availability, s a f e t y aspects, ...I Factor cp,, = f l ...1
So=t o t a l machinery and apparatus cost 5;= t o t a l cost of category i cpi = s/so
Figure 10.10. Cost estimation by the multiplication factor method
tion. Influences on the ratio factors for process control equipment include functional x o p c , required availability, and safety aspects. Experience has shown that the method of ratio factors makes it possible to estimate costs with an accuracy of ca. +20%. The second cost-cstimation technique, is “unit-cost estimation.” Like the ratio-factor method, it is based on an object structure, but a more detailed one. The simplest case for cost estimation is the procurement of “substances” (see Fig. 2.9), provided the quantity structure is known accurately enough (see also Fig. 10.24). To every object at a structural level, the technique assigns a price referred to an objectspecific unit (per item, unit area, etc.). The total price is obtained by multiplying this unit price by the quantity. For example. a possible cost structure for process measurement and control is shown in Figure 10.1 1. This method is suited both to coarse and fine estimates, since cost estimates can be obtained for virtually any degree of subdivision. The accuracy of estimation increases with the level of detail. At the same time, however, more effort is required because the detailed breakdown calls for thorough knowledge, which can be acquired only gradually and after the project has been started. The amount of effort can be reduced if the in-depth analysis is restricted to cost-relevant items whilc only a rough estimate is done for items of less relevance to the cost. This proccdure does not result in any marked loss of accuracy. Cost-relevant items are those that make a large contribution to the total cost, for example by virtue of large quantities and/or high unit prices. Incorrectly determined unit prices have less effect on the estimated cost than does an incor-
rect quantity structure. Experience indicates that this is where most errors occur. Multiplication factors and unit prices are obtained by evaluating the invoices for a number of investment projects already carried out, extracting the cost structures, and then taking averages. Today, installation is rarely carried out by in-house departments, and is mostly performed by contractors. Installation can be paid for in any of three ways [10.8]: by expenses, by unit price, or by fixed price. When charges are based on expenses. only the pricc per hour of work is cstablished; the fee is then calculated from the number of hours worked. This form of payment requires careful. continuous ovcrsight. When the unit price basis is used, a price is agreed on for cach unit of performance. The total charge is the sum of these unit prices. This form of payment is suitable especially when changes and additions to the cost category are accepted. This billing system makes these particularly easy to handle. The amount of bookkceping effort involved, however. is not inconsiderable. The fixed price system calls for an exact description of the work to be done, largely completed plans, and installation conditions free of disruptions. The administrative cost is low. Changes in required installation may, however. lead to substantial cost overruns. Engineering costs include all costs arising from -
-
Planning design activities Contract administration Supervision of installation work Start-up
10.2. Organizational Requirements Level in cost structure 1
301
3
o t
ement and control
0 .-K
2
Control rooms
Measurement and control system IDM/sys tern1
I !
.e m 3
5 ; IU A U
m
0 K .c
observation
1,
.
m
.-E
.+ m
Vendor A lDM/componentl
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Vendor 6 IDM/cornponentl
UI
c W .L
‘I I
n
Central operating IDM/CPU unitl
Controller card IDM/card unitl
In Germany, for example, engineering costs are largely set by the regulation governing architects’ and engineers’ fees [10.1]. Design costs are generally estimated on the basis of unit prices for stated functions. These activities can, however, vary in difliculty, and this Fact is reflccted in variable ratio factors. Some planning and design work, for example in the areas offeasibility study and project management, will be roughly equal in volume for any design project. This weighs more heavily on small projects than on large ones, so that small projccts are more costly than large ones. Contract administration and installation supervision are calculated on a percentage basis; commissioning is calculated in accordance with the man-hours required. By way of example, Figure 10.12 shows the cost structure of an investment for erecting a green-field chemical plant.
r
.J= m
Figure 10.11. Example of a cost structure for unit-cost estimation
Cost estimates are made in parallel with the planning and design phase of a project. They become more accurate as the planning progresses. The results of cost estimation form the basis for a profitability analysis and become part of the application for approval of the investment. Upon approval, the estimated costs become the binding cost framework for the project. Overruns are permitted only in a few justifiable (and exceptional) cases. Adherence to the cost schedule requires continuous monitoring of costs over the project; an accounting of costs must be presented at the end of the project.
Schedule Management. In simple cases, scheduling can be carried out by using bar charts (Fig. 10.13, top), which show the bcginning and duration of each activity as well as the earliest
302
10. Design ond Construction of Process Control .Yy.siems Machinery and apparatus
25 /o' Complete facility (procurement and installation]
Building 20 %
I
Piping 15 7 0 Miscellaneous 15o/'
Process measurement and control 2 5 %
i
Delivered equipment
50 o/'
Process control s y s t e Process measurement and control
Installation
25 y o
Engineering
25 y o
Pure planning/design
50 Yo
I
Hardware
Software
Engineering f o r process measurement and control
Order-placing activities 57 0
Project management 15 /o' Installation 'supervision and commissioning 30 '/o
and latest occurrence of procedures along with buffer times. Such charts can be prepared at various levels of detail; if they arc properly maintained, they provide a good overview of the schedule situation at any time. However, their critical disadvantage is that they d o not show the interdependences between activities. For projects having a complex sequential structure, scheduling cannot be planned or overseen on this basis. Network charts can be of use in these cases [10.9]. In network charting, activities and relationships are stored in network files and graphically
Figure 10.12. Cost structure of a chemical plant investmcnt
plotted, for example as shown in Figure 10.13, bottom. This technique makes it possible to determine not only the earliest possible but also the latest acceptable inception points for activities. Furthermore, if buffer times are analy7ed. "critical paths" can be identified. A critical path is a path through the network that has no slack time. Projects may contain more than one critical path, and these require especially close supervision during project execution. Schedule changes may open up new critical paths, but their inlluence on the overall schedule situation can bc cal-
10.2. Orgunizational Requirements
303
‘W
Installation, field installation, motors
F==tk-
Software t e s t Software simulation of control system in testing center Functional t e s t s of individual devices
Software simulation of control system in plant Water r u n Commissioning
0
0
F
c
culated in advance, allowing prompt reaction to such changes. Network methods offer the project manager an aid in focusing on essentials in schedule supervision. The manager thus becomes free to perform other management tasks. The charts used for this purpose must be continually updated. Because of the work involved in updating and the complexity that is sometimes associated with network charts, expecially in large projects, it is recommended that specially trained personnel be assigned to this job. A hierarchical procedure is also applied to network charts, where each decision maker is furnished with the requisite information structure. Petri networks (see Chap. 2) are the basis for all types of network charts. Resource Management. Resource management (resource allocation) is an extension of schedule management (10.61, whereby “resources” arc personnel, machinery, funds, etc. In general, schedules are drawn up as if the needed resources were available in adequate
G Figure 10.13. Bar chart (Gantt chart) and network chart
quantity at any time; however, only rarely is this actually so. Generally, in-house and outside resources are limited. Resource shortfalls or the complete unavailability of resources will result in delays if, for example, certain activities must be carried out later or not at all, or if activities that should be performed in parallel and have been so scheduled, have to be carried out in sequence instead. Such imponderables in scheduling can be avoided if scheduling and resource planning are combined. There are two approaches: resourceconstrained allocation and resource leveling. Resource-constrained allocation is based on what resources are available. Deviations from the original schedule are allowed, but the project duration must be made as short as possible. In resource leveling, the specified completion date is the top priority and additional resources are procured to the extent required to achieve it. In practice. an intermediate course is generally followed. The resource “personnel” is often a cause of special concern. In boom times, for example, a
304
10. Design and Construcrion of Process Control Systems
Project A
resources
The problem of rcsource management becomes more complicated if several projects differing in structure must be planned at thc sanic time, as often occurs in the chemical industry. The problem can often be solved only by assigning priorities to the projects (Fig. 10.14). Project activities arc supported in a variety of ways by computer-aided management tools. These have bcen reviewed in [10.10] and [10.1I]. Recently. the Microsoft project managcmcnt system MS Projcct [lo.131 appears to be finding acceptance in multiproject management situations.
Project A
resources
Figure 10.14. Rcsource managcnienl
10.3. Decision Phase
resource levcling approach is questionable because not enough of the specialists needed for execution of a project may be available. One way out of this conflict, though it is seldom considered, is to reduce the originally stated scope of work. The prerequisites for doing so are that the quality of thc work will not suffer and that retrofitting will be possible later on at no significant extra cost.
The fine structure of the decision phase can bc seen in Figure 10.1. The purpose of the feasibility survey is to elucidate and develop the statcment of tasks in closc collaboration with the customer. Figure 10.15 shows possible points covered by a feasibility study for a chemical plant construction project. The economic aspects are just as important as the chemical and process principles. Thcse are supplemented by substance data and constraints.
0
Economic aspects - Market i n t e r e s t - Contemplated production level - Nature o f investment p r o j e c t New plant Expansion - Intended site - Intended capital investment - Economics or profitability
o Substance properties f o r feeds,
intermediates, byproducts. and end products - Physical data - Toxicological data - Ecological data - Engineering data - Safety characteristics
* *
0
0
0
Chemical principles - Reaction equations - Mole r a t i o s - Reaction stages - Enthalpies o f reaction Process principles - Structure - Product and heat streams - Process conditions - Mode o f operation Batch/continuous Single-component/multicomponent
*
Constraints - Legislation and internal requirements Environmental protection Plant s a f e t y Occupational s a f e t y - Patents - Local conditions
*
* *
0
Description o f plant Stricture S t a t e of preservation Utilization Availability Operation and monitoring Engineering functions Safety requirements Infrastructure
Figure 10.15. Possible topics covered by feasibility study
10.4. Specifications
A detailed description of the plant complctes the picturc of the project. An additional analysis of weak points will reveal any flaws and thus contribute to making thc task statement more concrete and precise (see Chapters 2-4). This stage results in the performance specifications, which set forth the solution-invariant description of the problem to bc solved (rcquiremcnts). Its structure and content, in particular as it relates to plant safcty, is discussed in more detail in Section 10.4. In the preliminary design, a number of alternative solutions can be offered to the stated problem; from these, one is to bc chosen as optimal for the company from both economic and technical standpoints (see Section 10.1). whereby a cost/benefit analysis may be useful. Any optimization potential in the selected solution can then be revealed by sensitivity analysis, which investigates the influence of important, highly sensitive variables on the process by means of, for example: 0 0 0
Mathematical models Application of empirical and theoretical knowledge Plausibility analysis
The optimization potential thus found can be realizcd in the design study. The result is a binding concept for both process and plant. The binding concept is described in the specifications, whose structure and content are discussed further in Section 10.4, with particular regard to plant safety and economics. The decision phase ends when the documents required for obtaining regulatory approval have been prepared and costing has been done (Section 10.2). Errors in project planning and execution can arise from a wide variety of causes. The more serious are these problems and the later they are discoverd, they higher is the cost of correcting them.
10.4. Specifications Bidding Specifications. The specifications describe the task in structured form, mainly from the user’s viewpoint, and state what effort must be brought to bear [10.2], IlO.131. The bidding specifications can be regarded as the basis for a contract betweeen the party designing and constructing the plant and the later
I
305
Bidding specifications
I
General section Particulars of process and plant engineering I
.= L
Particulars of piping
I
Particulars o f ... ~~~
Figure 10.16. Breakdown of bidding spccilications
plant operator. They are the basis of all further planning and design. Structure and Content. The bidding specifications are broken down in parallel with the topics in the feasibility study (Fig. 10.15). They are broken down into a general section relating to all components and a specific section for each component (Fig. 10.16). The general section includes important information applicable to all components. Possible topics in the general section of the bidding specifications are: 1) General information on the project 2) Current position and shortcomings 3) Objective 4) Structure of the process and plant 5) Safety and availability requirements 6) Miscellaneous conditions to be considered 7) Framework concept
A vital part of the general section of the bidding
specifications is the “framework concept,” which describes the real environment of the plant as well as expected expansions. This section, in particular, describes interfaces to other plants (not just for process measurement and control). The component sections of the specifications discuss these points only briefly, or may simply refer to the general section. Instead, these sections state the points of importance for the execution of the particular tasks. Possible contents of the section on process measurement and control are:
1) Typical requirements for process control station functions 2) Special control and monitoring functions
306
10. Design and Construction of Process Control Systems ~~
Corpor a t e management ~~
m Strategic planning
m Logistical systems
Contract performance Maintenance Operations planning Procurement
m Information systems 0
0
Sales and marketing Sales Partners Sales planning Production Production planning (overall) Capacity planning Investment planning Quality assurance Internal cost accounting Personnel management
.
Plant management
Administration and production logis tics Production planning (detailed) Plant section operations planning Personnel planning Production flow monitoring Batch approval Schedule monitoring
Process management Higher-level functions: Management of processes and plants Recipe-mode operation 0
Process logistics Startup and shutdown Load and product changing B u f f e r strategies
Economic tasks Cost functions Cost analyses Statistics tasks Recipe handling Assessment o f process and product properties Machine changeovers Process engineering procedures Quality assurance
Process operation Monitoring Intervention Documentation
Disturbance handling
I Engineering
Field-level functions: Measurement, control. s a f e t y Determination of process properties 0
0
Determination o f product properties Setpoint (closed-loop) control Operational chart (open-loop) control
0
Reporting o f deviations Shutdown
0
Operation and monitoring
Figure 10.17. Functions broken down by level of the production model [10.14]
3) Process control functions 4) Humaniprocess communications 5 ) Off-line interpretation functions 6) Infrastructure; room and building equipment 7) Communications hardware 8) Process analysis 9) Safety requirements 10) Availability requirements 1I ) Quality assurance 12) Miscellaneous An important item in the bidding specifications is the description of functions used to control the process. The particulars of process measurement and control are described (Fig. 10.17, column 3) [10.14]. “Field-level” functions for the performance of control and safety tasks are distinguished from “higher-level” functions involved in the management of the process and the plant. The
later include, for example. process operation. proccss logistics, and recipe-mode operation (Chaps. 2 and 4). These functions in turn are interfaced to other higher-level functions involved in the management of a works/plant complex or corporate management (Fig. 10.17, columns 1 and 2) [ 10.141. The breakdown of functions is based on the level model of production (Fig. 10.18) [10.15] (see also Chap. 2). The totality of functions to be performed and their interfaces to one another defines the type and size of the process control systems. For example, Figure 10.19 shows a system that handles the functions of all levels of the production model (see also Section 5.2). The holistic view and conceptual procedure require prompt acquisition of information about necessary and desired functions for the later operation of the plant. Particulars of various pro-
10.4. Specifications Functions
1-
Levels
management
Plant/subplant management
Sensor/actuator functions
Figure 10.18. Level model of production [10.14]
cess management tasks are treated in depth in Section 4.3. The rules stated in Chapter 2 apply in a special way to the description of recipe-mode operation [10.13], [10.16] (see Fig. 10.20), [10.17], [ 10.181. As proposed earlier [ 10.131, the bidding specifications are now prepared in checklist form [ 10.191. Process engineering functions may also include room and building functions (heating, climate control, ventilation, lighting, etc.) as well as communications (e.g., telephone).
Specifications. The specifications give a detailed description of the nominal condition, that is, how and with the requirements of the bidding specifications are to be realized. The specifications set forth the solution-variant realization. These documents are binding on the contractor (in-house planning and design dcpartment or outside firm). The specifications form the basis for the contract, especially when the planning and design work is contracted out [10.2]. Structure and Content. The specifications are organized similarly to the bidding specifications
301
and consist of a general section and componcntspecific sections. The process-engineering aspects governed by the specifications are illustrated in Figure 10.21. For the sake of comparison, provisions for the process and for the plant are listed in parallel [ 10.81. [ 10.201, [ 10.211. Provisions on process and plant are first briefly cxplained (left column of Fig. 10.21). The structuring of the process and of the plant is the same as set forth in the bidding specifications (see also “Phase Model of Production” in Chapter 2, as well as Figs. 10.22-10.24). The breakdown is taken over into the specifications unless changes have bcen made; otherwise, it is modified to accord with the new requirements. The breakdown of the process is independent of whether it is already known or new within the company. Even known processes must be restructured since a new investment always entails adaptation to the state of the art, and adaptation can strongly affect the proccss structure. According to [10.22], the process can be broken down into process stages and unit operations. Some concepts in [10.22] must be modified, because there is no guarantee of completeness, uniqueness, or freedom from conflicts. “Process stages” are generally self-contained portions of a process. Unit operations are the smallest steps in a process and correspond to process elements. Any unit Operation converts inlet products with particular properties to outlet products with different product properties. As was discussed in Chapter 2, product properties are those qualities that characterize the product, such as physical and chemical values, engineering properties, and product indicators [2.102], [10.23]. A unit operation can be specified as shown in, for example. Figure 2.23. It can be specified, for example, that a separation is to be carried out by distillation. Such a statement of the method of performance also establishes the basic form of the trajectories that describe the transformation of inlet products with their properties into outlet products with theirs. The properties that characterize this transformation process are the process properties. Figure 10.22 shows a unit operation with the inlet and outlet products, their product properties, and the process properties. The product and process properties must be set forth in the specifications for the process. As Figure 10.23 shows,
308
10. Design and Construcrion of Process Control System.,
management
-
-
Production control computer
L - 4
I I
Process control computer
> W W
0
+ L
c " 0
control System bus
In ll
"
0)
L 0
a
II
Field-level components I
I
1
d.lmI
-.-.-
-.-.- Field bus
Plant section
11
W > W
=._ al
L
the acceptable ranges of variation (tolerances) should also be given if possible. In this way, a profile of requirements on the overall process is obtained; this should cover all operating modes (startup, shutdown, load change, etc.; see Fig. 10.24).This description, coupled with actual values, is the basis for a quality information system (QIS). The profile of requirements is the essential condition for creating a reproducible opcration 110.21. The overall sensor and actuator system is later defined and quantified on the basis of this profile. Further more, possible ciritcal states of product properties, which might affect the availability of the sensor- actuator system, can be identified in this way (plugging due to solid/ liquid phase separation, etc.). Material and thermal balances indicate ways of insuring the supply of substances and energy to the process and the removal of substances and
Figure 10.19. Schematic diagram of a control system [ 10.141
energy from it. They also give the consumption rates per unit amount of product, which are required for the profitability calculation at the end of the decision phase (Section 10.4). Once the process structure has been complcted. the plant structure is broken down by assigning unit operations to appropriate plant sections. A plant section is a functional unit ofa plant that can be opcrated independcntly (corresponding to a unit opcration). Examples of plant sections in chemical process plants are absorbers, chcmical reactors, crystallizcrs, tank storage facilitics, and so forth. The totality of all plant sections required for the performance of a process is the plant. Technical equipment in a plant section includes both devices involved in the process, such as vessels, machinery, apparatus, piping, and fittings, and process measurement and control units [10.24]. The detailed description of a
10.4. Specifications
plant section is based on these elements (see Fig. 2.22). Safety and economic considerations result in a variety of requirements being imposed on the Recipe: Complete specificatlon f o r manufacture of a product
@--...
b+ :::::;;:;; ::.::::::: ::::.,.... ::y::::; :::::::;::
:::::::::
Partial recipe: Controls one process operation in one plant section Example: s t i r r e d tank Phase: Controls self-contained steps in the partial recipe Examples: reaction, filling, neutralization, etc. Unit operation: Controls one unit operation within a phase Examples: metering. temperature control, stirring
1Unit operation element:
Controls individual process control stations Examples: s t i r r e r motor, valve, pump motor, etc. Plant section
Figure 10.20. Example of recipe control [10.16]
I
Process - Structure - Material and thermal balances - Unit operations - Product and process properties
o Plant
-
0
Structure Plant sections Safety concept
0
-
Construction concept Provisional site designation
Field-level and associated devices - Type of station and interconnection - Types of devices - S a f e t y and availability measures
0
Systems hardware and s o f t w a r e - Level of automation - System s t r u c t u r e - Safety and availability measures - Operation and monitoring - Software specifications
0
Infrastructure and building equipment
Infrastructure
o Miscellaneous
objects of a plant, such as plant sections and technical equipment. Such requirements must be taken into account in planning and design, construction. and operation. In the lifetime of a plant, a large quantity of data is generated, used, interchanged, and if necessary modified. If such data and other information is to be exchanged without losses, each item of information must be uniquely associated with the plant object to which it relates. This can only be achieved if the objects are uniquely labeled 110.251. Plants and apparatus can be identified in various ways. For example, each can be assigned an unstructured designat ion. In a complex system such as a chemical plant, it is better to structure the system in a hierarchical fashion. and then have the system of identification codes reflect this structure. In this way, an informative and easy-to-use identification of plant objects is obtained [10.26], [10.27]. An engineering object can be “named” in many ways; a few examples are: by purpose, by technical function, by location, or by device type. If all such features were included at once in the object identification, an unwieldy code would result. For this reason, just a single attribute is employed. It is desirable to choose a feature that is completcly and permanently identifies the object (e.g., the technical function
Process control specifications
Process/plant specifications o
309
- Process control rooms
-
Power supply
- Safety and availability measures
I
Rough to:
estimation
Figure 10.21. Structure and content of specifications
3 10
10. Design and Consrruction Product
Product
OJ Process
Ch r r o l Systems
properties o f product B Flow rate Density Viscosity Temperature Concentration o f impurity component A Reactivity
Process properties Pressure Throughput Concentrations
I
Product1 Product Product1
Product properties of product E Flow r a t e Temperature Concentration
Figure 10.22. Extract from a phase model
Product
Attribute
Nominal value
-Flow r a t e -Density -Viscosity -Temperature -Concentration o f impurity A
15 k g / h t 0.5 kg/h 1030 kg/m2 ?: 0.5 kg/m2 10 000 rnPa-s
9-l
a‘
+
25 O C ? 5 O C
510 ppm
Figure 10.23. Example of a list of product properties
[2.69]).This can bc illustrated for a temperature measurement and control station. Suppose the purpose of the station is to maintain a constant temperature in a certain unit operation, say mixing. The technical function is to measure, record, indicate, and control (TIRC) at a certain point in the plant. a Pt 100 instrument being used as the measuring sensor. If this sensor is now replaced by a thermocouple or if the plant is changed over to a differcnt process with different unit operations (e.g..
reaction instead of mixing), the station still retains its tcchnical function. The coding of technical objects is a planning/ design task. I t is carried out as early as possible, but at the latest when the specifications are prepared. Today this is donc in entity relationship (E/R) models, which are generated in a consistent way throughout the company. On the basis of the safety provisions in the bidding specifications. the safety concept is derived from the operating conditions together with the potential hazard presented to persons and the environment by substances involved in thc process. A construction concept and a provisional identification of the site round off the process and plant concept. The overall concept is docurncnted in phasc modcls plus the overall and process flowsheets discussed in Chapter 2, which contain fundamental and supplemental information. The process measurement and control conccpt follows from the process and plant structure, the profile of requirements on thc process, and the task statemcnt contained in the bidding specifications. The specifications set forth the types of process measurement and control stations and, roughly, the way in which they are interconnected. The process and plant control functions performed by the control system determine thc cxtent to which the plant is automated (see Section 4.3). If functions on the plant control level are also to be implemented, a production control computer must be provided. The interfaces between functions at different levels must be described and specified as exactly as possible (sce Chap. 2). The number of field-level componcnts and thcir assignment to plant sections is based on the process structure, the required functionalities, and the safety and availability requirements. The operation and monitoring conccpt (see Section 11.2) essentially comprises the specilication of operator displays, reports. and logs. The opcrator station concepts must also be worked out. These define the scope of tasks to be pcrformcd at the various control stations and the association of control stations with plants and plant sections [10.28]. The information structure should be hicrarchical, with high-level diagrams containing few
10.4. Specifications Sensoractuator system
Production process
Process control svstem Product properties
Raw material Process stage 1
Process s t a g e n-1
31 1
9
Q
9
Process I iperties
LLL
Process stage n
Product
items of information, and station-level diagrams, extensive information. Reports should be specified and classified as follows: 0 0 0 0
Alarms Malfunctions (in the measurement and control system) Warnings (advance notice of alarms) Errors (in the peripheral hardware of the measurement and control system)
Safety and availability requirements govern how priorities are to be assigned to the messages and how they are to be presented in the control system (see also “Availability Aspects” below).
Figure 10.24. Property profile of an overall process [10.23]
Logs and records are subdivided into, for example, batch records, shift logs, daily logs, and alarm logs. If, for example, the bidding specifications call for recipe-mode operation, the software specifications (Fig. 10.21) should include the appropriate large-scale function charts, specify the selectable parameters of the recipes, and state how the recipes will be maintained and archived. At this point, the task to be performed has been explained enough that a concrete solution proposal can be submitted with the aid of a process control system. This proposal must, however, take account of the measurement and control provisions described in the framework concept of the operation in which the process will be realized. For example, if a process control system
3 12
10. Design and Consrruction of Process Corrrrol Sysrerns
from a certain manufacturer is already in use, then for maintenance reasons it is recommended that the planner stay with this m a n u k t u r c r unless the adaptation to the task carries unacceptable risks with it. Availability Aspects. Busic Clussrficalion of’ Process Measurement und Control Equipment. Availability requirements must bc examined from several viewpoints: Safety Process engineering 0 Logistics Safety is mandated by legislation and regulations. The safety of persons in the plant (worker protection) and of the surroundings (environmental protection) must be guaranteed. The safety of engineering processes is a key criterion. A high level of safety results from continual and intensive work on safety aspects. Once a high degree of safety has been achieved, it must be maintained and improved. The special handling of safety aspects in the bidding specifications and the specifications is meant to stress this requirement. The term “process” is understood to mean the totality of interacting operations in a system by which matter, energy, or information is transformed, transported, or stored. In the case of technical processes, the physical quantities are determined and controlled by technical means [ 10.291. The safety of a proccss is determined by the processes, plants, and process control equipment (Fig. 10.25). An essential contribution to proc e s s safety is due to process control equipment [10.30]. Such equipment can be classified as shown in Figure 10.25. In assessing process safety. the following three types of operational conditions are distinguished [10.30]: 0 Good range 0 Acceptable range 0 Unacceptable range The good range is the intended range for the values of a process parameter. It is defined such that the plant does not sustain any damage beyond ordinary wear and tear, while the quality and quantity of goods produced satisfy the requirements imposed. The acceptable range is adjacent to the good range. In it, the plant still sustains no darnage beyond ordinary wear and tear; the quality and 0
0
consists o f
consists o f
Figure 10.25. Summary of nieasurcmcnt and control equipment performing process safcty functions
quantity of goods produced still satisfy the requirements to within extended tolerances. In the unacceptable range, the value of a process parameter is such that the plant may sustain or cause greater damage, o r the quality and quantity of goods produced no longer satisfy the requirements stated. These parameter ranges can now be correlated with proccss measurement and control equipment. Devices used for measurement, control. reporting, and so forth serve to keep the plants operating as intended (in the good range). They continually intervene in the process. Only i n the acceptable range d o the process control safety devices first come into operation. On entry into this range, the monitoring devices respond, prompting the operator to take suitable action to bring the process back into the good range. If these actions arc not taken as expected, protective features in the proccss measurement and control system, such as safety valves, rupture disks, and the like, prevent entry into the unacceptable range. Processes can further be provided with damage-limitation features such as spray-blocking walls, warning systems. and special traffic control systems that block off the site of the danger. Figure 10.26 summarizes the correlation between the ranges of process paramctcrs and the proccss measurement and control devices, showing the intervention levels and modes of action [10.30]. The safety requirements mandated by Icgislation for the overall process in a plant to be erccted, and in particular for the proccss control equipment, must be precisely described in the
3 13
10.4. Speclfcations Process parameter
-
1
Damage-Limiting (equipment c
al c
.-Ei W a
= I
0
c L
Monitoring equipment
.,v)
c
U
U
Ul
u 0 U
t Opertional equipment I
-- Time
-
Figure 10.26. Process control devices and process safety [10.30]
bidding specifications, since they influence the process design as well as the design of the process measurement and control system. The required availability from the process engineering standpoint affects both product quality and the protection of the facilities. The logistics-based availability bears on the ability to deliver products and has direct effects on manufacturing costs, The availability requirements imposed by logistics and process engineering must be included in the economic picture, in contrast to the safety requirements. The measures taken to comply with these requirements are set forth in the spccifications. Implementation. The safety and availability requirements are key factors affecting the structure and organization of field-level and systemlevel components. These lead to, among others, the need for the process sections and/or plant sections to be largely autonomous relative to one another and relative to malfunctions in higherlevel operating and/or control computers. To insure the protection of workers and the environment as mandated by law, a number of other technical measures set forth in codes and regulations must also be undertaken. A few of these are listed in Figure 10.37.
Allowance must always be made for defects that limit the proper functioning of technical equipment or even take it out of action. In the case of safety equipment, care must therefore be taken that no impairment or failure of the safety function occurs even when there are defects in the technical equipment performing such safety function. This goal can be achieved through two distinct approaches to safety equipment design, called “fail-safe” and “redundant.” In fail-safe design, all malfunctions deemed possible causc the safety devices to fail in a single well-defined sense, namely so as to trigger the safety or protective function. Redundant design means including more than one component for a certain task. If one component fails, a backup component takes over its function. Redundancy in process control systems means that system components, such as field-level, operating and monitoring units, and/or bus systems, are present two or more times. The types of redundancy practiced in technical systems are as follows [10.31]: Static redundancy Instead of a single component. several components are installed in parallel. The system
31 4
10. Design und Consrruciion 0j‘Prot~es.sControl Sysic.ms
as a whole performs in the intended way as long as at least one unit is free ofdefects. Dynamic backup redundancy In case of malfunction, an additional component is switched in to replace the defective component. The additional component is not in operation when no malfunction is present. Local dynamic backup redundnacy Assigned to cvery component is a redundant component, which is continuously reconfigured (hot standby). Global dynamic backup redundancy A redundant component is assigned to several components. I n case of a malfunction, the redundant component is reconfigured once (warm standby). Dynamic functional redundancy When no malfunction is present, the redundant component performs its own tasks; if a malfunction occurs, it takes ovcr the functions of the defective unit by restricting the capacity or shutting down inessential functions. Backup The general term “backup” refers to dynamic backup redundancy for a single process measurement and control function, such as control. There are a variety of methods for acquiring redundant information about the process. Figure 10.27 prescnts some typical examples. The term homogeneous redundancy is used when the same physical quantities and the same measurement tcchniques are empolyed for data acquisition. Physical diversity USCS distinct physical quantities. Methodological diversity means using the same physical quantity for data acquisition, but measuring it by different methods. In process measurement and control systems, functions are implemented through hardware and software. Process control systems should be used to realize safety devices only when they can be validated, that is, when it can be demonstrated that the software involved in the safety function always performs properly and reliably under all circumstances. If this cannot be. demonstrated, safety functions must be implemented by hardwired control units. Redundancy implies a high-availability concept. Despite, or indeed precisely because of, the presence of multiple process components, such high availability is not always realixd. Problems arising from the number, interconnection, and
a Diversity redundancy -Physical
NH, vapor
NH,
llquId
Brine
. . I
-Methodo(ogical Brine Figure 10.27. Redundancy in rncasurcrncni [ 10.30j
synchronization of components in a redundant system may work against it. Safety concepts and availability rcquircments must also affect the security of thc process power supply system. Thc numbcr and type of incoming supply lines, multibus configurations. switching devices, and standby power systems depend on it [10.32]. Section 6.4 surveys the most important options. Thc specifications contain additional provisions dealing with the infrastructure (Fig. 10.21, p. 309), including process control areas, such as control and switching rooms, as well as overall building equipment. The specifications end by giving cost figures and the profitability calculations based on them. These are crucial for the decision to continue with thc investment project. If the economic criteria established by corporate management are satisfied, the project is continued; otherwise, either its scope is modified or its is terminated. Economic Aspects. The purpose of any investment project is to generate prolits. The specifications must therefore demonstrate that the proposed actions to carry out the tasks describcd in the bidding specifications will contribute to profits. The question of economics is vital. especially with regard to the desired level of automation (Section 4.3) [10.2]. A separate economic calculation can be done for each automation level. 0
Automation of field-level functions of the process control level (Fig. 10.17, p. 306)
This level is mainly concerned with the I‘unctions of measurement, control, operation, and
10.5. Execution Phase Field-level f u n c t i o n s
''
O O'
i
Field devices,. 21 0 O/O Power s y s t e m
6 0Yo
Ins t a 11a t o n
30.0 /'o
Conventional PCE
3 15
Pcrforming these functions generally entails additional investment costs, which are, however, justificd only if the annual profit gained through their use exceeds a certain value specificd in advance by corporate management. The profit is calculated as the savings achieved through the investment minus fixed costs. The profit expressed as a pcrcentage of the total investment is called the return on investment (ROI). ROI [%/a] = savings [DM/a] - [fixed costs [DM/a] x 100 total investment [DM] The reciprocal lOO/ROI is the payback time, i.e., after how many years the total investment is paid back in profits: Either figure can be used as a rough measure of the profitability of an action. Tdbk 10.1 shows a cost analysis done in this way. The benefits can bc broken down in a variety of ways, but essentially into five categories, although not all of these can be quantified. If only the quantifiable items are included, satisfactorily high ROI values o r satisfactorily short payback times (all < 2.5 ycars) are obtained. Thcsc results favor the use of process meassurement and control systems for the implementation of higher-level functions of the process control level. ~~
Field-level f u n c t i o n s
lL.OY0
I
Field devices-27.0
O/o
Power s y s t e m
6 0 o/'
l n s t allat ion
27.0 O/o PCE w i t h c o n t r o l s y s t e m s Figure 10.28. Comparison of cost breakdowns for the implementation of field-level functions of the process control
levels with conventional proccss control tcchnology and with process control systems (10.141
0
reporting. These can be implemented by conventional method or by process control systems. An analysis of several projects has shown that there is, in principle, no difference in financial outlay between these two options. Both investment costs and the breakdown of costs (Fig. 10.28) are roughly comparable. On the basis of this knowledge, control tasks of the type discussed here should always be implemented with process control systems, sincc these have potential for expansion that is generally lacking with conventional methods. 0
Automation of field-level and higher-lcvcl functions of the process control level
As Figure 10.17 shows, process control systems make it possible to carry out recipe-mode operation, tasks having to d o with process logistics, and higher-level control functions. The automation of packaging lines or storage facilities can be included under this heading.
Automation of functions of the production control levels (Figs. 10.17 and 10.18, pp. 306-307)
The economic analysis is problematic because the simple investment return calculation methods described above are no longer adequate. The associated benefits of better information (i.e., comprehensive, up-to-the-minute, error-free, and perhaps condensed) being available as the basis for analyzing both engineering and management weak points and also for the rcaltime control of the operation cannot be quantified. Ultimately, corporate objectives will dictate the implementation of a production control level. Failure t o d o so, however, may result in longterm competitive disadvantages.
10.5. Execution Phase The fine structure of the execution phase is indicated in Figure 10.1, p. 294.
3 16
10. Design and Construction of Process Control Sjisterns
Basic Engineering. In the basic cnginccring phase, the individual tasks are formulated in their final form, in collaboration with the customer, and the functional solution is described. The work to be donc here is listed bclow: Planning cind design ,for process tirid pltinr engineering:
Design of plant sections (apparatus) in accordance with the process Decision on materials of contruction Preparation of site plans and provisional layouts Preparation of piping and instrumentation diagrams Planning and design f o r process control engineering :
Design of process control stations Establishment of structure Establishment of devices to bc used Design of process measurement and control system Dimensioning of process interface Design of data processing software Dimensioning of the control system Dimensioning of control and other rooms Spatial disposition in the plant Preparation of piping and instrumentation diagrams Thc process and plant engineering work includes plant design in accordance with the process, sizing of the plant sections (apparatus). and elucidation of materials questions. With regard to docurnentation, see in particular Section 3.5. Preparation of' Piping and Instrumen la I ion D iugrams. A key task is the preparation of P& I (piping and instrumentation) flowsheets [10.22] (see Fig. 10.29). Provisional layouts and site plans are also drawn up. The process control engineering organization is involved in the preparation of P & I diagrams, since its planning and design work will be based on these. Process control tasks are described in the station symbols (Fig. 10.30) of the P & I diagrams. This description should be sufficiently exact enough that it, together with the binding concept, makes possible the design of the PCE stations. The process control station includes all measurement and control elements needed to perform the functions specified, such as sensors and/ or actuators, signal transducers, power supplies. and processing functions, for example indicating (I), recording (R), controlling (C), etc. [10.34].
10.5. Execution Phase Off-gas
31 7
To o f f - g a s treatment
To tank storage O f f -gas
From plant
T o tank storage
Figure 10.29. P & I flowshcct of a plant
---Function -Station
number
Figure 10.30. Representation of a process control station in the P & l flowshcct 110.241
Process Control Station Data Sheet. The station design is set forth in a “data sheet,” which must list all elements making up the station and also provide information on the substances with which the sensors and actuators come in contact and on the installation location (Fig. 10.31). Givcn the measurement and control ranges of the sensors and actuators, suitable equipment is identified for the individual elements o n the basis of instrument standards (see Chaps. 7 and 8 and Section 10.6). Process Control Station Diagram. Station diagrams document how the equipmcnt is wired together (Fig. 10.32). At this point, the planning and design work on stations utilizing conventional technology is finished. If data processing functions are implemented in process mcasurement and control systems, additional dcsign
work is required for the hardware structure of thc process interface as well as the software structure incorporated into the control system proper. Analog and binary signals coming into the process control system must be transformed into information that this system can understand. The conversion takes place in the process interface and is performed by input/output components for the various types of signal. Backup controllers are also implemented with components. These components are shown in the station data sheet as well as the station hardware diagram (Fig. 10.33; see also Sections 7.2 and 10.2). Station Software Diagram. The station hardware diagrams are supplemented by station software diagrams (Fig. 10.34; see also Section 9.6), which show how standard software modules, provided by the process control system manufacturer, are linked so as to build up the PCE station processing functions. Function Chart. The data processing functions of a station must be performed in a coordinated manner; this requires a coordination function at a higher level in the hierarchy. The essence of this function is a task-specific sequence control that must be specially prepared for each field-level component of a measurement and control system. Sequence controls are for-
u Facility
Plant complex
:Building
'Projcrt number
18-Reak Plant
Prepared 20 10 1989
Statton d a t a sheet
I
Process control station
A 123
Plant section
Type of station Interconnected with
1234567
P b I dlagram No
Station ldentifttation
Steam Inlet R01
Station is labeled
Steam
Circuit type Installation standard
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1
Fl2 FIC Tl 1
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10.5. Execution Phuse
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319
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rnulated in a system-neutral way by using function charts (Fig. 10.35) [10.35]; the same holds for station-level interlocks. The function chart is a proccss-oriented depiction of the control function, independent of the way in which it is implemented. It replaces or supplcments the verbal description of the function. The function chart clearly and unambiguously describes the function and thus aids in understanding between manufacturer and uscr.
pI
+
4% L L U
Figure 10.32. Example of a station plan for a convmtional process control station
Furthermore, it facilitates collaboration between specialists [10.36]. In future projects, Petri networks will be employed at an early stage, from which function charts can be automatically generated (see Chap. 2 and Section 4.3). Dimensioning of Process Measurement and Control Components. Generally, a process measurement and control system consists of a number of field-level components. Like the stationlevel functions of field-level components, these
320
10. Design cmd C'onsrrucrion of Process C'onrrol Systems
L12-5* -Binary
7
-Global
L
1
Current multiplier
Binary input component
input module
memory cell
Number o f destination diagram
Vo,H1lO~E'~Oe,tinatron )r
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Figure 10.33. Example of a hardware station diagram for a station with signal processing (LS+)
must also bc coordinated, but at a higher level, for example, in the context of recipc-mode operation. A process control computer is used for this coordination function. The size and type of control computer will depend on the scope of the coordination function. The selection of the operating computer is based on the number of graphical operating displays (Section 11.2). The number and size of the field-level components assigned to a process section is influenced by the number of stations to be implemented, the functional scope, and the requirements on availability and safety (Section 10.4). The station-level functions are built up from software modules. These differ in memory requirement and machine time, so that flowthrough and pressure-control loops assembled from such modules may require 3 - 4 times as much memory and 4-6 times as much processing time as pure measurement loops.
component
Figure 10.34. Example of a station software diagram for a station with signal processing (LS+) in a process measurement and control system
If a large number of time-comsuming functions must be implcmented in a single section of the process, field-level components whose time properties allow this must be used. Today, memory problems are of little importance. The tasks of basic engineering also include dimensioning the rooms for the installation of process measurement and control equipment (see Section 10.7). Defuil Engineering. In the detail engineering phase, dehiled and comprchcnsive documents are prepared for usc in procurement, construction, operation, and maintenance of a plant. Consistent documentation is a vital consideration at this stage (see Section 3.5). The planning and design work can bc broken down between process and plant engineering and process control as follows: Planning and design for process and plant engineering Detailed layout of plant sections, apparatus Construction drawings
lQ -/-
10.5. Execution Phase
1 Initial
Automatic .( Qmin r e ache d l
321
The plant design activity prepares design drawings for machinery and apparatus. Construction design supplies building plans as well as calculations of foundations, floor loads. vibrational loads due to machinery, and so forth. The piping activity supplements its design documents with piping isometrics and parts lists. Care should be taken here to achieve smooth cooperation with local field instrumentation and cable routing activities (e.g., with the aid of suitable 3 D CAE methods). Planning and design of the process measuremcnt and control system completes the documentation generated in the detail engineering phase. The PCE station hardware diagrams contain additional information making it possible to localize the station elements by giving, for example, the switchbox identification, rack-mount module number, card slot, and also to identify the incoming and outgoing signal lines by referring to the terminal numbers. Figure 10.36 shows a detail of the station diagram of Figure 10.32 with this supplementary information. The station software diagrams, function charts, designs for recipe-mode control operations, and other higher functionalities are transformed into programs understandable by the proccss control system. Because screen displays are associated with variables in the software modules, the preparation of screens for process monitoring and control is an expensive process. It can account for a substantial fraction of software costs. Before this step is performed, the screen must be designed. A useful tool for thc process of agreement with the later operator of the plant is working drawings, usually prepared by CAD methods, which are easy to change and supplement. Special ergonomic requirements for state-oriented opertion are discussed in Section 8.2.
h RELEASED
+(V'
N "Tank empty" light ON
CLOSEO).("Start"
button)
Valve
V, OPEN
TVE D = 30s
S t i r r e r ON
EMPTY
N
Valve V, OPEN
+1
Figure 10.35. Example of a function chart for rank filling
Site plans, layouts Piping design Planning and design for process control enginecring Preparation of data sheets Completion of station diagrams and function charts Programming Working drawings of mimic diagrams for operation and monitoring Preparation of circuit and configuration diagrams Preparation of commissioning checklists Preparation of operating handbook Preparation for letting contract to outside films Orders and contracts Scheduling and resource allocation for installation
x2: Rack no Subrack Module
K1 E4 A2.1
Terminal s t r i p Terminal Terminal b o a r d
x2:
R3
X6:
Current
:'t 1
Figure 10.36. Excerpt from a supplemented station diagram
322
10. Design and Construcrion of Procixs t'ontrol S!,srems
An operating handbook must be written during detail cngineering. I t should assist in the early detection of dcsign errors and thc taking of proper corrective actions. Design errors can be remedied in a relatively cost-neutral way at this point. Corrections upon commissioning are cxpensivc. Unfortunately, little usc is made of this capability at present. The following activities are carried out in collaboration with the purchasing departments: Requests for quotations to vendors and/or installation firms Orders When contracting with outside firms, the nature and content of the contracts are agreed on with the legal and purchasing departments. For installation, schedules should be prepared with allowance for delivery times (see also the network chart in Fig. 10.37). It is also dcsirable to generate a commissioning concept, which should use diagrams and lists to document the following points (among others): 1) Personnel requirement
2 ) Schedule
3) Procedures 4) Type of functional tests, etc. Recent years have seen more and more computer-aided tools for use as planning and design aids. These support not so much the conceptual phase as the manual or material phase of dcsign, where the plans necdcd for the ercction and operation of the plant are drawn u p (sec Chap. 9).
Installation. Installation includes the erection of all classes of equipment involved in an investment project and the interconnection of all components into a completcd, operational plant. The stages in installation arc as follows [10.8]: Preparation Site preparation Site organization Execution Stcel construction Installation of machinery, apparatus, tanks, CLC. Installation of piping Installation of process measurement and control equipment Installation in process control rooms Installation of cable trays in the field Field installation
Inspection Machincry, apparatus, tanks Operability Tight ness Process measuremcnt and control Visual inspection Wiring inspection Functional testing The preparations include thc contruction of a site with site offices, storage areas, and installation areas, as well as thc creation of a site organization to handle scheduling. rcsource allocation. costing, and the monitoring of performance rclative to these plans. Good installation planning reveals itself in that the installation of individual catcgories of assets is coordinated so as to insure smooth. reliable progress. Interferences between categories should not occur. What is more. all resources such as installation personnel. plant sections, components, and other materials rcquircd for the installation of individual asset categorics and all working plans and rclated documcnts should bc available on timc and in adequatc quantities and should mcet statcd quality standards. The on-schedule installation of process measurement and control cquipmcnt rcquires special care, since as a rule it cannot be started until significant parts of the other categorics have been installed. Detailcd network charts (see above) can be helpful here. The installation of process measurement and control equipment PdIdlls into thrce parts: 0 0
Installation in control rooms Installation of cable trays in the ficld Installation of process measuremcnt and control devices in the field
Installation "in thc field" means all installation activities outside the process control rooms. Installation work in the control rooms can begin at a very early timc in the overall installation process, immediately after the buildings have been completed. Some 20% of all process control installation work falls under this heading. Cable tray instakition can proceed roughly in parallel with piping installation. But the inslallation of instruments cannot start until ca. 80% of the piping installation has been done.
‘7.5.
Installation ends with an inspection, which is part of the quality assurance (see Section 10.6). The installation activity must be thoroughly cognizant of the “concurrence” of events in the sense of Petri network theory (see Chap. 2).
Execution Phase
323
Installation offers a broad range of rationalization possibilities for later maintenance practices (see Section 11.4). Thc equipment manufacturers still have some way to go here (see Section 11.1).
324
10. Design ond Construction of Process Control Systenis
10.6 Quality Assurance The quality of the items prepared must be continuously assured during the project. These include plans. software specifications, programs, plant sections, installation, and so forth. Consistent documentation must be provided for all these activities (Section 3.5, [10.37], [10.38]). Major elements of quality assurance arc 0 Provisions governing planning, design. and installation 0 Controls 0 Inspection and testing Today, inspection and testing includes datamodel-oriented consistency checks, as known from software engineering, as well as final installation inspection and commissioning. Governing provisions arc, for example, Icgislation, standards, and internal corporate directives. Legislative provisions describe the constraints under which a plant is to be erected and operated. They safeguard the environment, in the broadest sense, against possible threats (see Fig. 10.37). In addition to legislation, a number of national, regional, and international standards must be considered. These standards essentially serve to ensure consistent planning, design, and execution (see also Chap. 12). Internal corporate guidelines interpret and give concrete form to the provisions of laws and standards as well as corporate objectives (see Fig. 2.9). Examples of provisions for process control engineering are given below: General planning/design guidelines Documentation - Nomenclature, identifications, abbreviations Provisions governing project planning and execution - Description of scope of work Costs; ordering - Schedules Substance data Basis for calculations and design Technical provisions - Interconnection of devices - Protective, safety, reporting systems
-
-
0
Ilardwarc and software structurc of control systems Outfitting of process control rooms Equipment for building. labs, shops
Provisions for fabrication and installation Design of standard modules Design of protective enclosures, distribution boxes, instrument housings
-
“Design standard” is understood here t o nican technical provisions for thc function “planning and design.” The design standard for process control engnccring is supplemented by an equipment standard, which narrows the range of devices available on the market to a few choices, in particular those that have proved reliable in many years’ service or through qualification tests (see Fig. 2.9). The scope of work often goes through marked changes in the planning and design of a plant. The reason is inadequate preparation of the investment project. The nature and quantity of the technology to bc implemented are not described exactly. Concepts change latcr on, and designs already adopted must be rcworkcd. Thc resulting “patchwork” can hardly improve quality and, as a rule. actually degrades it. Situations of this kind cannot be avoidcd, but proper preparation of the bidding specifications and specifications can signiticantly improve them. They must therefore be regarded as an important contribution to improving thc quality of planning and design. Design quality control is a major undertaking. bccause of the large volume of docunicnts generated. It becomes more difficult if the design work is done by outside firms. For this reason. quality control is often reduced to just a spotcheck of results. An object-oriented data model for the plant as a whole is thus all the more important (see Section 3.5 and Chap. 9). I t is shown below for the example of the hardware documentation of a process control engineering project that this procedure is quite adequate for practical use. A differentiated assessment of process control engineering documentation requires breaking it down into units for examination. In the case of hardware documentation. the logical unit is the documentation of a single process control station. comprising the station data sheet, the station diagram, and the corresponding information from thc intcr-
325
10.6. Quality Assurunce
connection lists. These documents can be examined as soon as the execution planning has been completed; if the complete job (planning, design. installation supervision, and commissioning) is carried out by one contractor, they can be inspected after the process facility has been commissioned. In the case of a complete contract, the contractor therefore has to examine the documentation only once. For the documentation of a process control station to be deemed correct after commissioning, the docunicnts included must meet all the following quality criteria: 0
0 0 0
The station identification must be correct The documentation must correctly reflect the equipment installed Any cross-references required must be present Each terminal designation must be correct; that is. the information in the proccss control station diagram and in the associated interconnection lists must agrce with the acutal interconncction of the devices
The results of two sampling inspections of process control engineering documentation for a project comprising 324 PCE stations are shown in Figure 10.38. In the first examination, the sample size was increased in steps of 32 PCE stations (roughly 10 YOof the total number of stations). Above a relative sample size of ca. 30%, the relative error is nearly constant, and it appeared unnecessary to continue the inspcction in this case. In a second inspection by a different inspcctor, only those errors overlooked in the first inspection were noted. The result is around 5 % and must be regarded as the errors of the first
A
1
25[
st/-
s 20
: 0 c
.c “I
‘0
0 0
First inspection
inspector (“slip-through”). Similar slip-through values were found in studies of the manufacturing industries [10.39]. The results of this examination show that inspection of the process control engincering documcntation by the contractor is essential [10.40]. In practice, the following procedure has proven suitable: If the customer’s examination reveals an error lying above a predetermined threshold (c.g.. 5 YO), the customer returns the entire documentation to the contractor for complete revision. However, if the relative error is < Soh,the contractor need only remedy the errors found [10.41]. On economic grounds, only spot checks can be carried out on thc process control engineering documentation. Therefore, the risk exists that the error found in the spot check is greater (risk for contractor) or less (risk for customer) than the “true” error that would be found in a complete examination. To resolve this conflict, model calculations have been performed in order to determine the sample size for various project sizes (number of process control stations). The following assumptions were made: 0 0
0
The error limit for acceptance of process control engineering documentation is 5 YO The sample must be large enough that the probability of accepting the documentation (contractor’s risk) when the documentation contains a relative error > 10% (twice the acceptable error limit) is < 20% The “hypergeometric distribution” is the appropriate mathematical description of the sampling insepction described [10.42]
The results of these model calculations are presented in Table 10.2. The most important result of these model calculations is that in projects with > 100 process control stations, given an acccptable limiting error rate of 5 % , sampling inspection of 50 stations is sufficient to decide on the acceptance of the process control cngineering Table 10.2. Sample sizr for quality inspection of process control cnginmring docurnentation
Second , ,inspection , , 10 2 0
,
,
,
,
Number of PCE stations
30 40 50 60 70 80 90 100
20 50 100 500
Sample size, %-
Figure 10.38. Quality inspection of process control mginwring documentation of a project comprising 324 proccss control stations
1
noo
Sample sire Absolute
Relative
14 35
70 ol0 66 Ye
50 50 50
vo
50 10% 5%
326
1 0 . Design and Construction qf Process Control Systetns
documentation or assurance measures. The advantages of sampling inspection in large projects are greatly reduced insepction costs. The monitoring of software design presents major problems. In principle. software design can be checked only during commissioning. A modular architecture is helpful if it permits stepby-step testing. from the single module up to the entire system. This type of examination cannot, however, eliminate software defects that lie outside the scope of the check. Software always hides certain imponderables: hence the recommendation that functions of immediate safety significancc never be performed by software alone, but always in combination with a conventional safety level (Section 10.4 and [10.30]). It can be expected that the quality of planning and design results will improvc markedly if the computer methods still in development (sce Chap. 9) are adopted. Quality assurance also requires installation practices such as the following: 0
0 0 0
Issuance of installation standards (technical provisions for the function “installation”) Incoming inspection Supervision of installation activities Functional tests
An example for the contents of an installation standard for process measurement and control equipment is listed below: Provisions on the mechanical construction of process measurement and control equipment in control rooms and other rooms Panel designs Cabinet and rack-mount designs Subracks Cooling air supply and distribution Cable basements Cable lead-throughs Cable trays and their routing on pipe bridges Distribution boxes Transducer boxes design and installation Terminal boxes Provisions on electrical design Auxiliary power supply Grounding/equipotential bonding Cable routing Power supply and distribution Hookup methods Provisions o n the construction of process measurement and control stations
Installation material in configuration and interconnection I t contains, for example, provisions on 0
0
0
The mechanical construction of process measurement and control equipment in control rooms and other rooms Electrical design The construction of individual stations
These standards should be supplemented by installation layout diagrams (Fig. 10.39). which use drawings and lists to set forth the installation materials needed for certain tasks as well as their configuration and interconnection. The quality of an installation activity is also enhanced if standardized. prefabricated, and pretested hardware components and other equipment are employed. In process control engineering. the use of such standard elements in the form of panels, racks, cabinets, cable trays, subracks for alarm deviccs. power-supply equipment. and so forth has long been common. Incoming inspections during installation relate not only to the work plans but also to the materials and equipment to be used. The plans must be complete and correct; materials and devices must comply with specifications. It is desirable to have specialist personnel for example, project engineers with knowledge of thc process or installation foremen with competence in the work to be done to supervise the installation activities. Such personnel should be designated by the customer of the project. Only in this way can high-quality execution be guaranteed. An essential element of quality assurance is the final installation inspection (see “lnstallation” in Section 10.5, p. 322, and [10.8]). The process control engineering activity carries out three levels of inspections: 1) Visual inspection 2) Wiring checks 3) Functional testing
This procedure insures that the individual process measurement and control devices pcrform correctly and interact properly. Visual inspection establishes correct mounting. Proper interconnection of the elements is determined by wiring checks. The aim of functional testing is to ensure that the active operating sequences of the process
10.6. Quoliiy Assurance
Name VKP
VKE
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-
BI. BI. 81. BI. BI.
1 1 1 1 1
327
Materii
Polyme Polyme plastic plastic
7.7 BI. 1 steel11 7.1 81. 1 steel/[ 6.1 BI. 1
steel steel
Figure 10.39. Example of an installation configuration plan [lO.S]
measurement and control devices accomplish the functions specified for the process tasks. On the basis of the planning and design documents, the individual process control stations are systematically inspected from sensor to actuator. Process control functions are usually tested by first verifying the lower-level functions and then procceding to successively higher levels. A hicrarchicaiiy organized process simulation would be desirable for functional testing, since only such a tool makes it possible to draw unambiguous conclusions as to whether the plant as a whole will also operate in the intended way. If such a simulation cannot be performed, as is still the case for most processes, this information cannot be acquired until commissioning (see Chaps. 3 and 11). Commissioning is the initial operation of the entire plant, including process measurement and control equipment, by the operator. It is the last test showing that the process runs in the intended manner. Commissioning can thus be regarded as further quality assurance practice. Commissioning is performed in three steps: 0 0 0
firms together with the plant operators. The functions to be performed during commissioning are [10.30]: 0
Preparation Technical training of operating personnel (operating instructions) Technical training of maintenance personnel - Safety training Stocking of spare parts and standby equipment - Securing of feeds. auxiliary media, energy supplies - Preparation of the plant (purging, drainage, venting, drying) - Loading of the process measurement and control system
0
Operation with “inert” media (“water run”) Commissioning of auxiliary systems (steam, cooling water, etc.); loops (heating water loops, etc.) Testing of safety features - Simulated operation Testing in operating modes (startup, shutdown, load changing, etc.) - Performance of needed amendments
Preparation Operation with “inert” media (“water run”) Operation with chemicals
The first two steps are carried out by the departments involved in the planning, design, and erection of the plant. If the project has been contracted out, these steps are done by the outside
0
Operation with chemicals Production test run
-
328
10. Design and Construc,tion ($Process Control Si~sterns
Modification of proccss measurement and control equipment - Guarantcc run After preparations are complete, thc plant is startcd up with inert (i.c.. unreactive) products, beginning with the auxiliary and service facilities. Next. circuits such as heating loops arc commissioncd. After the cntirc plant has been placed in operation. thc safety devices are inspected. The opportunity presents itself for a comprehensive test of all modes that will be cncountercd in later opcration (startup, shutdown, load change). On the basis of thc experience gathered, any amendments needed are now carricd out. The documents are revised at the same time. The next step is operation with chemicals, that is, a production test run. Rcsponsibility for the plant ultimately passes ovcr to the operator. Ordinarily, a plant is startcd up at a low throughput and gradually brought up to rated output. The final modification of the process measurement and control equipment to the plant is carried out during this phase. Included in this modification arc the adjustment of controller parameters, the input of limiting values, and so forth. In particular, if thc plant has been designed and built by an outside firm, evidence must be presented that the plant complies with the design as to capacity, yield, availability, and quality. This evidence takes the form of an extended test run. The plant documentation (see above) must be finalized a t this time. When commissioning has established quality, that is, shown that thc process runs in the contemplatcd way, the project is over. A detailed final report is mandatory.
perience. Details of execution can be found in vendors literaturc [10.46]. Architecture. Relarionship of Process Conlrol Rooms to the Plant. It is advantagcous to locate process control rooms relative to thc production facilities of the plant so as to creatc short paths for installation and for acccss to the plant. The control room should be on a level with those parts of the plant most often accessed. Control room location and structure must afford adequate protection [10.47]. The “main wing” layout (Fig. 10.40) offers an optimal way of mecting these rcquircments. Furthcrmore, direct connection betwcen laboratory, supcrintcndcnt’s ofice, and social spaces promotes necessary communication. Protective functions arc also taken care of, for cxample i n relation to hygienc o r safeguards against undesirable cxtcrnal influenccs. In large-area combinations of facilities (Fig. 10.41). it may be useful to crcct a frecstand-
.
Plant
Main wing
Figure 10.40. Rcladonship of plant and main wing
10.7. Process Control Rooms Introduction. This section describes thc requirements that process control rooms must meet, offers suggestions for their appropriate design, and reports operating experience [10.45]. In the context of optimal human -process communication, it is indicated what rcquircments, stemming from technical, ergonomic, and othcr knowledge, govern the architecture. construction, and outfitting of control rooms. The design suggestions are based on scientific publications, standards. guidclincs, and operating ex-
v/
Figure 10.41. Layout of plant and control room buildings
10.7. Process Controll Rooms
329
Information processing Process control Power distribution Plant management
Figure 10.42. Breakdown of thc main wing
ing control building to take over a variety of operational functions. Arrungement of Conrrol Rooms. Process control rooms and other rooms that house, for example, plant management should be laid out in a functional way so as to permit an optimal flow of operations. This can be done whether these spaces are located in a main wing (Fig. 10.42) or in a separate building. The control room is the central area for the performance of essential process control engineering functions. Such tasks are described in the “level” model of information (SIX Section 2.3). The switch room for information processing holds the equipment necessary for processing and interpreting information. The switch room for electrical power distribution is the site ofcentral process electrical equipment. The control room and switch room for information proccssing should be located close together. The electrical switch room should also be placed in the main wing. If distances are long, however, electrical distribution for the plant can be housed in more than one switch room (to minimize cabling costs). The central communications equipment for a plant needs its own room. Rooms for process analytical instruments should be placcd at crucial sampling points in the plant. Special requirements apply to these rooms [10.48]. The following points, in particular, must be considered when positioning control rooms relative to the plant and one another: 0 0
0 0
Safe access and cscape routes Optimal cable and utilities routing Fire safety Favorable location for firefighting if necessary
Space Requirement for Process Control Rooms. Space requirements can be determined with graphs or ratios derived from operating experience, which offer guidelines (Fig. 10.43) for initial control room design. The increase in individual functions expccted through the end of the projcct has to be estimated and reserve space must be provided. Starting with some minimum size (ca. 25 m2), the space required for a control room is worked out from the number of process control stations to be observed and controlled. The figure of 0.1 m2 per process control station is a lower limit for conventional instrumentation. The use of process control systems and process control computers will reduce the area needed. The space required for the information processing switch room is also governed by the number of process control stations. A minimum figure is again 25 m2. With conventional instrumentation, the minimum additional area required is 0.2 m2 per process control station. The use of process control systems will cut the space requirement. The installed power is the essential factor determining the needed area for the power distribution switch room; the minimum size is 15 m2. About one square meter is required for every 10 20 kW of installed capacity. If there are many variable-speed drives, however, the required area per kilowatt may increase.
Ergonomics. Ergonomics is concerned with the interrelations of human beings and the world in which they work [10.43]. Due attention to ergonomic factors in the design and construction of control rooms is a precondition for good human -process communication (see also Section 11.2).
330
1 0 . Design and Constrirction of' Process Control Systems Optimum field o f view: l l O o
@300[ A
I
250 500 PCE stations
m
1
300
750
1000
750
1000
200
N
E
E 100
a
@
1
250
30@
r
500
PCE stations-
Figure 10.44. Optimal manipulation and viewing arc'ab
200
N
E
;100
a
-
1000 2000 Installed power, k W
3000
Figure 10.43. Space requircrnents for process control rooms
MI? ca. 1000 LUX
Antliropomeiry. Anthropometric considcrations lead to guidelines for spacings, opcnings, and clearances in the placement of individual devices and equipment in control rooms. Pushbutton controls, switches, and display elements should be arranged so as to allow optimal access and visibility (Fig. 10.44) 110.491. [10.50]. An important factor in determining the appearance and size of labels is the distance from which they will bc viewed. When locating display and control panels, care should be taken that thcy are in casy view of the operator. Lighting. A varicty of visual tasks must be performed in control rooms (Fig. 10.45). The
Figure 10.45. Control room lighting
type and intcnsity of illumination must be adapted t o each such task [10.52]. Illuminances bctwccn 200 and 500 lux are rccommcnded for error-frcc reading of meters and other readouts, illuminated symbols on pancls, and characters on scrccns and displays. Lighting propcrly adapted to each visual task features separate. continuously variable brightncss adjustmcnts for
10.7. Process Controll Rooms
331
and extracted through ceiling grilles. The control room must have the capability of shutting down the ventilation of all rooms at any time. Acoustics. Acoustic conditions in the control room should be such that pcrsonnel there experience no difficulty in understanding one another or in communicating with the outside world by telephone or intercom, and also such that acoustic signals can easily be detected by the human ear. Care should be taken that the noise level does not exceed the 55 dB(A) ambient noise, the Switchgear room Control room reverberation time in the middle frequency range Temperature 20-26 C' Temperature 10-30 O C (500 to 3000 Hz) is 5 1 s, and the level of acousR.H. 30-80 /o' R.H. 50-60 o/' tical danger signals in an octave range lies a minOverpressure 0.1-0.2 mbar Overpressure 0.1-0.2 mbar imum of 10 dB(A) and a maximum of 20 dB(A) Figure 10.46. Ambient conditions above the interference. the two 7ones. Fill-in lighting serves to enhance the local illuminance on printers, desks, and so forth. An appropriate configuration of lighting fixtures in combination with glare screens should hclp to prevcnt reflection from displays. Since control rooms are generally occupied day and night, the lamps should provide daylight-balanced lights in order to insure consistency in colo r and illumination. Switchgear rooms should have an illuminance of 300 lux. Backup lighting must be provided in control rooms and is recomrnendcd in switchgear rooms. Safety lighting must be provided for purposes of accident prevention. Details of lighting design can be found in company literature [10.44]. Climate Control. Ambient conditions in the control room must be held constant within a fairly narrow range [10.51]. Furthermore, bccause personnel are present at all times, there must be a continuous, draft-free supply of fresh air (5 to 10 air changes per hour; Fig. 10.46). Switchgear rooms arc not continuously occupied, and the tolerance range for the ambient conditions is therefore wider. In contrast to the control room, much heat is evolved here and must be removed by a waste-heat removal system. Computer components belonging to proccss monitoring and control systems may have stringent climatic requirements, which can best be satisfied by separate climate control devices. Undesirable pollutants must be kept out of all rooms used for proccss control functions. A slight overpressure is therefore maintained in such areas: filtered fresh air from pollutant-free areas is delivered through special floor registers
Control Room Design. Doors and Windows. On grounds of safety and health, pathways bctween the production facility and rooms that house process control functions may be equipped with airlocks. Airlock doors must be fireproof, self-closing, and gasketed, and they must open in the escape direction. Larger monitoring and control rooms must have a second cmergency exit. Because communication with the process takes place exclusively via process monitoring and control devices, windows opening into the plant are not needed for communication. Windows may, however, be required by workplace regulations [10.53]. Whcn control rooms are located near explosion hazard areas, appropriately rated exterior windows must be provided. Floors. Control rooms should be constructed with floating floors. Cables are led through floor penetrations or cable ducts placed in the floor (Fig. 10.47). A variety of special plastic floor tiles are available. Ceramic tile is used where contamination is severe. Double flooring in computer and switchgear rooms (Fig. 10.48) permits free cable routing. Ceilings and Walls. False ceilings are particularly suitable for control rooms. They offer a range of options both for concealing the unifinished ceiling and for the placement of functional elements such as fire alarm and fire-suppression systems, air conditioning, cable ducting, and lighting fixtures. Esthetic, acoustical, and climatic considerations require attention to not only the ceiling but also the walls. All other rooms that house process monitoring and control functions have ceilings and walls painted in light colors.
332
10. Design und Construction of Process Control Systems
Figure 10.49. Operator desk
Figure 10.47. Cablc duct installation
Figure 10.50. Operator station
Figure 10.48. Double floor
The control room should have an unfinished height of 4 m (to bottom face of ceiling joists). The height of the false ceiling should be 3 to 3.50111, depending on the size of the room. A
height of 3 m (to bottom face of air ducts) is recommended for switchgear rooms. Ceilings and walls must comply with pcrtinent fire-protection codes (F30 or F90); this applies particularly to cable penetrations. Desks (Fig. 10.49) hold visual displays and controls such as keyboards, light pens, joysticks, mice, and touch-screen input devices. Horizontally and vertically pivoting display bases permit adjustment to suit individual preferenccs [I O S O J . Additional free space is needed. for example. to accommodate a radio and telephones and to permit occasional writing. Operator stutions in control rooms are used for monitoring and controlling the process. They enable the opcrator to intervene in the process as required. An operator station (Fig. 10.50) generally consists of a color graphics monitor or monitors and controls. The controls usually consist of a keyboard (with movable or touch-sensitive keys) for the input of commands and/or a light pen with which the process can be controlled directly on the screen. The light pen has the advantage that input errors are largely eliminated and performance
10.7. Process Control1 Rooms
Figure 10.51. Process communications wall, furniture, and accessories
speed is roughly doubled. Fatigue-frce operation with the light pen is, however, possible only with the elbow supported. so that the monitor should be located only ca. 40 cm away from the operator. Other control arrangements employ tracking devices such as joystick and mouse o r permit operation by touching the front of the screen (touch-screen devices). Hard-copy devices and printers are used to document the process in printed form. They
333
should be installed in noiseproof, dustproof enclosures in a readily accessible location (unless they have to be placed in a separate room). The process conirnunications wall comprises panels holding extra monitors as well as individual displays and controls (Fig. 10.51). The preferred configurations are straight, L-shaped, and U-shaped. Mimic flowsheets can be laid out along the top. The various functional elements are modular devices. To make the spatial organization clear to the observer, they can be arranged in accord with the production flow. Furniture and Accessories. Besides the monitoring and control devices, the control room should be equipped with the following (Fig. 10.51): Bookcases for documentation as well as storage of supplies such as printer paper Dcskiwork table Bulletin board Flip chart Wall clock showing date, day of the week, and time Storage for protective clothing and safety devices (e.g., respiratory protection, fire extinguishers). This should be placed where the operating personnel can access it quickly and without hindrance on the way to the plant.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
11.1. Principles
11. Operation 11.1. Principles This chapter discusses some points rclating to process control engineering that must be borne in mind when operating process plants. Along with rcliability (environmental, occupational safety, and health issues) [11.1]-111.31, the most important quality of process operation is reproducibility. In other words. the required product properties [11.4] of the end products must lie within the respective tolerances (see also Fig. 2.33). The same applies to all relevant intermediate species, of course. Product properties should always be regarded as “primary nominal values” [I1.51 . As shown in detail in Chapter2, product properties are described in terms of individual process elements. These in turn are described in terms of process properties [l 1.41. Process and product properties arc linked by transformations expressed in “operators,” and the product properties can thus be represented as functions of the process properties [11.6], [11.7]. GII.I,ES [11.8] takes this a step further and, in a first formulation, allows thc set of product properties in the production process to be categorized as a state variable. Production process
Sensoractuator system
335
Because the above propertics or attributes are mostly multidimensional aggregations, it is meaningful to refer to “product spaces” and ”proccss spaces.” Thus. to achieve product properties complying with the requirement profile, the respective process properties must be maintained in each process element. These in turn have their own requirement profilcs with corresponding toleranccs. These requirement profiles of the process propertics in each process elemcnts are to be undcrstood as “secondary” nominal values or setpoints. Thcy are constituents of the control loops (gencrally involving thermodynamic and reaction-kinetic quantities) in the process control system. Figure 11.1 shows a nominal/actual comparison for a production process. This representation is the most important piece of information required for controlling the process. Detailed discussions can be found in Sections 2.3 and 11.2. When production processes are to be optimized with respect to certain target quantities, process models must be created. These are mathematical descriptions including the interdependences of process properties as well as the dependences of product properties on process properties. As shown in Chapter 3, models can be
Process c o n t r o l s y s t e m
Figure 11.1. NominalLactual comparison for a production process
336
1 1 . Operalion
subdivided into purely empirical or statistical models and “theoretical” models. Statistical models are derived from the actual (measured) product and process properties in the real proccss running in a pilot or full-scale facility (see Fig. 1 1 . 1 and Section 3.2). Its equations are purely mathematical in nature. It is difficult to assign physical interpretations to its variables by using techniques to reduce the dimensionality [1 1.91, [ 1 1 .1 01. Static and dynamic process models are essential for effective simulations. In general, “simulation” refers to the use of information technology to forecast the behavior of the independent variables in a system when the independent variables or the initial and boundary conditions change [ l l . I 11, [11.12]. Provided the basic model (see Chap. 3) can be formulated in explicit mathematical form, the fundamental problem is to find suitable numerical procedures for the economical solution of the mathematical problem. As interactive communication between humans and the process becomes more important, the graphical presentation of the data model or functional model (see Chap. 2 and [11.13]) is gaining in significance [11.6]. Problem-oriented “object spaces,” with the “attributes” of the objects as their coordinates, should be designed so as to promote human thought and learning (see Section 11.2). Information reduction is a key tool to doing this. This holds both for training a user concerned with a technical, economic, or socio-cultural system (see Chap. 2) and for devising solution aids for finding “optimal” operating points. ZBITZ [11.11] is therefore justified in according the highest importance to such simulation. The framework for system modeling must be extended beyond the customary metric scaling into the realm of topology. In contrast to current wellknown solution approaches, simulation thus takes in predominantly algebraic and differential-equation-oricnted structures as unified topologies of knowledge, as suggested by GILLES [11.14], in his discussion of knowledge-based process control engineering. Numerical and knowledge-based solution tools will have to be harmonized with one another in the future. For example, the NAMUR Status Reports include noteworthy articles offering many suggestions and solution approaches [11.15]-[11.21]. The modern model-aided procedurc will also lead to new and expanded safety concepts. These
include concrete approaches to thc carly detection of hazardous conditions based on modc h i d e d mcasurcment methods [l 1.41. Experience in biotechnology also suggests that applications will appear in prognostic medical diagnostics. One of the principal tasks of process control engineering is to increase thc dcgrcc of computability in each proccss step and to reprcscnt it in suitable process models. Howcvcr. only a purposcful collaboration with process cnginccring can lead to a fruitful symbiosis here. This holistic mode of analysis will be cstablishcd in a new engineering discipline, process tcchnology [11.22]; see also Section 11.3. Rcprcscntations that are closely related to the phase model of production are the Petri nets [11.23]. While the phase model rcprcscnts the topology of product flow. Petri nets describc thc system of conditions governing product flow. In the context of Petri net theory, any technical system can be representcd in terms of two description categories: 0
0
Existence of a state Occurrence of an event
Each state is terminated by at least one event and/or brought into being by at least one event [11.24]. States can also be interpreted as conditions that must be fulfilled for a transformation specification to be carried out. In order to describe the dynamic sequence, a further description clement --the token is introduced. The token moves through the static net as a traffic unit. The total population of the net by tokens characterizes a global state of the net. The central modeling aspect of Petri nets is the concept of concurrency. Two events are said to be concurrent if they are causally independent of one another [ 11.251. In processes, buffers are used in an attempt to decouple the flow of material and prevent disturbances propagating through the entire plant. Subsystems dccoupled via buffers can operate independently of one another, that is, concurrently, within certain limits. In this way, Petri nets can be applied to production processes. The net theory offers thc possibility of modeling such systems and determining their dynamic properties by computation or simulation. Local analysis and modeling has two effccts: It reduces the complexity and it creatcs the possibility of using a process-logistical procedure along with decoupling via buffers [ 1 1.261.
331
11.1. Principles
In automatic mode (for which recipe-mode opcration is a prerequisitc), thc proccss-logistical problem (Table 11.1) [11.27], [11.28] is that multiple intercoupled process elements operating in distinct modcs must be managed in terms of their mass flow. The definition of operating modcs is less well-known than the “recipe” concept discussed above. It describes all modes of operation within a process clemcnt, including cxceptional and disturbance situations, in discrete form. It must be set in phase-specific fashion for the batch case and section-specific fashion for the continuous case. For a continuous process clement and its transitions, the various operating modes are shown in Figure 11.2 [I 1.291. Because of the wide range of possible mode specifications for various process elements, the process states dcsircd in disturbance-free operation and the process states sought when disturbances are present are specified as functions of the instantaneous state, in well-defined steps, or are calculated in real time and set down in control tables. The contents of these mode-specific tables are derived from simulation calculations or experimental data. Figure 11.3 shows schematically how the categories of importance for operation- reproduction, optimization, and automation-are interrelated.
WCCflNEK [11.30] has discussed which requirements apply to personnel when modern process control systcms arc used. KRAUS [l 1.311 sees the acceptance of process control systems opcrating in information-oriented fashion as essentially insured by correct design of the display and control components of the control systems. What applies to communication between humans and the proccss control system is equally important with regard to the sensor systems, which scrve as “eyes” on the process 111.32). After this general discussion of operation, the following sections deal with human-process communications (Section 1 1 .Z),process analysis and proccss optimization (Section 11.3), and maintenance strategies (Section 11.4). Human -process communication comprises the integration of information acquisition (sensor technology), information processing (control system), information and knowledge utilization (human component), and information feedback to the process (actuator systems). The totality of these processes (Fig. 11.4) insures proper operation of the process. Process analysis and process optimization aimed at achieving permanent gains in reproducibility, safety, and economics is a single engineering task from the viewpoint of process control engineering.
Table 11.1. Levels of automation Proccss elements ~~~~
Modc
Nccessary information management
Systcm hardware
Characteristics
Realization
all sensors, all actuators, individual controls and control systems
reproducible production in all operating modes (process models)
275%
as in 1, plus PCS* with BFE* and BF*, PCC*
reproducible production in all operating modes with recipe operation, process models, simulation, optirniation
520%
as in 2
buffer strategy-oriented 5 1% recipe mode for all operating modes
as in 2, plus net orientation (Petri nets)
divcrgence-orientcd 1995 recipe mode for all operating modes
~
Decoupled
Couplcd
1 manual
all flow paths and all process/product properties manually input as setpoints, managed, and documented 2 computer all flow paths and all process/product propcrties input to computer as setpoints, managed, and structured in documented form BF*: switch net; set parameters; BF* provided with sctpoints 3 logistical as in 2, plus buffer management, buffer strategy, source-point management 4 automatic as in 2 and 3, plus control of divergence of process elements through variation of process properties
BF = basic function; BFE
=
basic functional element; PCC = process control computer; PCS = process control system.
338
11. Operution
Shutdown, termination
Figure 11.2. Opcrating modes of a continuous process section and their possible transitions [ I 1.291
Maintenance strategies aim to preserve and improve the availability of thc production facility and its process control components.
11.2. Human- Process Communications Introduction. Humans today are surrounded by a great number of technical processes. In production, new products are made from many elements by combining operations. Production arrives at new products through the reaction of feed materials. Energy generation and distribution processes are of major importance. Other important technical processes permit the transportation of people, goods, and information on land, by water. and in the air. The human rolc in all these processes is to monitor and control. This can be carried out in a “planning and forethought” mode. The human optimizes technical processes as to structure and sequence, detects hazardous situations in the processes at a n early stage, and can then intervene promptly to remedy them. The complete interaction between human and process, which is gathered togcther under the phrase “human - process communication” in what follows, is governed by the operator, working at an operator station, who runs apparatus
and devices. observes measurements. rind alternately abstracts and concretizes in an attempt t o comprehend what is happening in the process on the basis of individual-signal observations. In a production process, information relating to an individual process apparatus is accessible at the individual measurement stations still to be seen everywhere (Fig. 11 S).There arc also very large control rooms (Fig. 11.6) in which information from large plants is gathered together and displayed on consoles and panels. The color graphics display (Fig. 11.7) is a modern alternative offering an easily and quickly comprehensible represcntation, even for complicated processes. Any production process has an a priori dependence on the combined functioning of physical, chemical, and other sensors, which provide the human operator with knowledge about thc properties of products and processes by means of appropriate information processing. Automation of production processes, in contrast. lcaves process events in a state that can ultimately be comprchended by human senses, cxcept as required by systems-engineering and social-psychological exigencies [l 1.331. The preceding chapters have taken up the problem of what information should bc determined for process control purposes (see Chaps.
11.2. Humun-Process Coinmunications
339
Figure 11.3. Reproduction, o p t h i a t i o n , and automation
M a t e r i a l and Energy f l o w
Information flow
I
Product
Process
Q-P-t-i
Ll-J-wi I
I
Sensor system, actuator system
Information processing system
-0 Human
I
Human-process communication system
Figure 11.4. Human-process communication
2, 3, and 7 and Section 4.5). Now to these is added the question of which presentation of the available information is best suited to human communication abilities. Modern resources of information technology hold much promise in this area. As yet, however, there has been no
exact study of their capabilities in the context of information-oriented process control. Shortcomings are often seen in the selection and presentation of information as commonly performed at present. For example, an intermittently severe cognitive stress is imposed on oper-
340
11. Operalion
ators in “exceptional” situations, which include not only changes in operating values in continuous plants (startup, shutdown, load change), but also unforeseeable changes at the dispositivc and operative levels. Ironically, plant automation often leads to a high degree of monotony during
Figure 11.5. Individual mcasuring stand in process engincerinp
Figure 11.6. Large control room for monitoring operations
Figure 11.7. Color display with light pen and keyboard (Siemens Tcleperm M [I 1.861)
normal operation [11.34], which can bc blamed on the supply of information being inadequatc for the performance of additional duties within the sphere of competence [11.35]. Besides the proper distribution of tasks between human and information-processing systems (see “Degree of Automation” in Chap. 7, p. 219), the rcsponsibility for the control of a process is a question of some imporvance. The question of what information is “appropriate” becomes significant. although it must not be forgotten that furnishing such information is a necessary but far from a sufficient condition for any assumption of responsibility [11.36]. This section discusses the gcncral problcin of human .process communications for the cxample of a production process. In discussing thc procedures and systems used. reference is also be made to other technical processes. The special problems of machine-lcvcl monitoring and control. grouped under the heading of MMI (Man-Machine Interface). is not the point of focus here, although at times i t will be ncccssary to point out crucial differences bctwecn MMI and MPC (Man- Process Communication). A helpful introduction to human- machine communications can be found in [11.37]; a useful reference work is [11.38]. First, it will be beneficial to survey briefly the capabilities of humans in their interaction with large bodies of information today. In the commercial and administrative sphere, the highspeed printer serving as output medium in computing centers generates long columns of numbers and symbols on tall stacks of paper (Fig. 113);this device continues t o play a central role. The term “business graphics” refers to a mode of presentation that is more suitable for summarizing complex information (Fig. 11.9.
Figure 11.8. High-speed printcr with tables
11.2. Human-Process Communications
Until recently, enginccring deviccs first took form on the drawing board. Computer-aided design (CAD) marks the bcginning of a new world in which dcsign is supported by all the resources of modern computer technology. Acquisition and Presentation of Information. In earlier centralized proccss control as well as current dcccntralized systems, all relevant information about the technical process (and today also about the distributed proccss control sys-
tcm) must be assembled at a central location so that process control can be optimized under holistic criteria. This central proccss control location must also have a high availability so that operating personncl can carry out monitoring and intervention at any time. High availability is also required because process condition documentation is often mandated by law. Human -process communication began in the early 1900s with direct display and control by field-lcvel devices (Fig. 11.10). The results gained. togcther with the development of auxiliary-cnergy field devices, led to a concentration of such devices in a local control stand. Increased plant siLe and more stringent documentation requirements, aided by unit controllers and modular control systems, Icd to the central control stand with a flowsheet-like representation of the proccss around 1950. This flowsheet technique has several advantages for human-process communications : 0 The process structure (or “process topology”) and thc condition of the process are displayed together. An example is the combination track diagram and signal/control box used by the German Federal Railway (Fig. 11.1 1).
Figure 11.9. Busincss graphics
1920
Influences Display and c o n t r o l a t field unit 0
1935 Individual c o n t r o l s t a n d
0
Environment Danger Control e f f o r t Automatic c o n t r o l o f individual q u a n t i t i e s Devices w i t h auxiliary energy Manual-dependent o p e r a t i o n C o n t r o l o f many q u a n t i t i e s Increased docurnentation r e q u i r e m e n t Unit c o n t r o l l e r s , modular s y s t e m
1950 Central control stand with flow chart 0
1965 w i t h block s t r u c t u r e 0 0
1980 0
color s c r e e n system
341
P l a n t size Rationalization o f p l a n t c o n s t r u c t i o n M o r e changes Legal documentation C e n t r a l Process Computer Environmental and s a f e t y r e g u l a t i o n s Energy and r a w m a t e r i a l savings Open-end design Workplace analysis D i s t r i b u t e d p r o c e s s computer s y s t e m s . color displays, dialog technology
Figure 11.10. Development of process control room design [I 1.341
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1 1 . Operation
Figure 11.1 1. Combination track diagram and signalicontrol box used by the German Federal Railway 0
The assignment of control and display elements to the process is implemented through a suitable configuration in an abstract, graphically represented process structure. As a result, control elements can be directly addressed and actuated while their place in the process structure is clcarly shown.
With increasing process size and number of process states, the space requirements and costs of hardware process flowsheets havc grown. In the case of extensive processes such as thosc in the petrochemical industry, process control rooms employing flowsheet displays arc up to 100 m long and can be managed only by a correspondingly large and mobile staff (Fig. 11.6), hence the names “bicycle” and “rollerskate” contol rooms. High costs of creating such a display arc followed by high costs of modifying it, so it is difficult to alter the flowsheet-type control room to keep up with changes in the technical process. From 1965 on, increasing plant size and the growing number of display and control devices in the central process control room along with the availability of central process control computers -led to the central control stand with block structure (Fig. 11.12), similar to the quickscan display technique used in aircraft dcsign. Control rooms employing the block-structure principle have the following features: 0 0
0
Like instruments are placed in groups Thc relationship betwccn operating and display elemcnts and the technical process is identificd by names (namcplatcs) The control and display elements arc sclccted and actuated indirectly; thcrc is no direct topological relationship to a graphical rcpresentation of the process.
Figure 11.12. Central control stand with block structurc
The operating pcrsonnel must thcrcfore remember the process structure. I t takes scvcrdl steps to select and actuate the control and display elemcnts, and this operation calls for good mcmory and abstraction abilities. The purely technical capabilities of the block-structure control room as to modification arc ccrtainly superior to and more user-friendly than thosc of the flowsheet-typc room. but it is more difficult for thc operating pcrsonnel to keep up with frequent changes in the room. With the availability of powerful and cconomical computer-supported color display. the represenlation methods of the block-structure control room can be retaincd and adapted to the capabilities of thc display screen. Thc most important types of video display now in use arc discussed on thc basis of the control and monitoring dcviccs employed i n thc process control systems TELEPERM M [I 1.391, MASTER [11.40], and Geamatics [11.41]. Figure 11.13 shows the general block-structure principle with color graphics displays. with details presented in Figures 11.14- 11.16. These are standard or predefincd displays and exist as prcfabncated data rccords with thc basic representation of images for plant arcas, groups, and individual control loops. All the designer must d o is adding plant-specific data to this data record (sec Chap. 8). The so-called free display is the cxact opposite of the standard displays. Here the designcr has a totally free hand. Standard symbol sets offer an aid; elementary symbols can be combincd to make new symbols o r a toolbox with elementary graphics objects such as circle and rectangle. The designer can thus rcpresent anything from a valve to a steam boiler. If certain symbols are not included, a new symbol set can
11.2. Iluman-Process Communications
Plant region overview
Region s e l e c t i o n
Region Group o v e r v i e w
Area selection
Process c o n t r o l Area selection
Group
(8 a r e a s , max.1
Process c o n t r o l Parameter input
Area
Teleperm M
Figure 11.13. Block-structure principle with color displays [ 11.271
Figure 11.14. Summary diagram [11.39]
be put up on the screen. Separately stored subimages can be inserted into various overall images, thus saving much time. State displays are also easy to create. In on-line opcration, they appear on the screen with the true values, thus facilitating a nominal/actual comparison. Many such free displays can be stored in the operating computer as a data record and can be scrolled through sequentially (Figs. 11.17 and 11.18). Standardized menus simplify the creation of curves and groups of curves, the assignment of curves to measurement stations, the assembly of curves into full plots, and the specification of storage form, display form, measurement range, and unit of measurement. The system may request all these details in interactive mode.
343
Curve plots can be presented in a standard or free display. Depending on the physical input device, the selection is made by pointing to a digital or a bar-graph display with a light pen (Fig. 11.19), touching a touch screen, or using a mouse or trackball to position a cursor. Curves arc shown in windows with the required deeper level of detail. Complete standard images can also be incorporated in these windows, for example, the standard representation of a plant group. Window support makes process displays far easier to understand, since only the permanently needed information is always visible; details appear in the window only when needed. The window size and position are determined by the designer; in modern systems, they can be modified on-line by the user (Figs. 11.20 and 11.21). What is more, user procedures and reports can be freely designed. In doing this, the user determines everything: size and layout as well as dynamic information u p to the way in which report creation is initiated. Reports can be generated by specifying a start time and a printing cycle (shift report) or certain events (charge report). All reports can be retrieved at any time, for example, in order to reconstruct a certain plant state (Fig. 11.22). Thus the windows technique pioneered by Xerox PAKC, Apple Macintosh, and Microsoft has found use in color display components of process control systems. The supplementing or replacement of the block-structure control room by the introduction of color video displays has also changed the mode of operation. In keeping with the concept of furnishing information about the process and product properties, apparatus, devices, and field-level (local) and process control systems to a central location at all times as needed for process control, additional analysis of the identification and selection of the desired information was required. The search for input techniques that place an a priori limitation on the number of possible errors has led, for example, to the selection of well-defined screen regions by touching with a light pen, thus avoiding erroneous digital input. The touch screen is another approach to the same problem. Efforts to optimize “input technology” are related to the topics of acuracy, speed. and physiological and psychological burden on the operator. These points are discussed in 111.421, [I 1.431.
344
11. Operation
Figure 11.15. Group diagram [11.41]
Figure 11.16. Loop diagram [I 1.401
At this point, a fundamental difference be(man -machine) communication (MMC) and human-process (manprocess) communication (MPC) should be emphasized. While M M C is based on a specified content and context of the information and goes on to formulate conditions such that thc information flow through thc human -machine interface will be suited to human capabilities, MPC is concerned with tasks arising in the course of process control and the information that must be furnished so that these tasks can be performed. Prom, this standpoint, the first questions raised in MPC relate to historically accrcted information structures, which are largely oriented toward apparatus, dcvices, and control loops, and which supports the interpretation of information in prccisely this context. tween human-machine
After a thorough analysis of the contributions made by the operator to process control (see Section 7.1, Fig. 7.3, and Section 11.1. Table 11.l) and the information that must bc made available for this purpose. thc dcsign of human-process communication must take account of the “cognitive faculties” of humans, which also serve as the basis for the dclivery of task-relevant information. Findings from psychology have a role to play here. Intcrface design in software engineering and cognitive engineering employs an approach borrowed from cognitive psychology in which human beings and their communications capabilities are cxamincd in terms of the “information processor” paradigm. An introduction to the principles and limitations of this approach can bc found in [11.44]. According to the conccptual models of cognitive cngineering, humans act on three planes (Fig. 11.23) [l 1.451. First, from sensory afferents and associated arrays of features, signals arc derived through training and directly transformed into actions (intuitive behavior). The second lcvcl of behavior is based on rulcs sclcctcd in accordance with a recognition procedure and the association of state and task. Finally, knowledge and experiencc arc turned into goal-dependent decisions, which lead via a planning procuss to ncw rules of behavior. Clearly, the last two cognitive lcvcls rcquirc a greater information-proccssing effort. Any arrangcmcnt for human process communication must therefore use an ingenious ar-
11.2. Human-Process Communications
345
Figure 11.17. Free display [11.41]
Figure 11.18. Free display [11.39]
ray of features and rcprcsentation of the proccss state to provide especially good support to, for example, the “intuitive” level if the human operator is rcquired to react quickly in the course of a monitoring task (such as to issue an alarm). In tasks having to d o with quality. where links between process and product properties must be discovered and analyzed, possibly over wide spans of proccss scctions, the cognitivc faculties of thc two higher levels should be supported in the modcl. According to ideas of cognitivc engineering [11.46], [11.47], [11.48], this can bc ac-
complished by providing information at various levels of detail or by using a rule-based advisory system (expert system). How individual mental activities involved in operating and monitoring a (partially) automated technical process can be supported by appropriate software has been shown in [11.35]; see Figure 11.24. In a paper titled “Mozart, or Our Dilemma When Dealing with Genius,” EIGEN [11.49] quotes from ECCLES’work “The Human Brain” [11.50]. Here, a similar distinction is made bctween the dominant hcmisphere, which controls conscious behavior in an analytical, logical, comprehending way, and the subdominant hcmisphere, which reacts an a musical, graphical, holistic way: This can bc understood as implicitly relating to various human information-processing cababilities. Thus, pictorial representations, including graphics, make information comprehensible in a chiefly synthetic (holistic) manner, while texts appcar to stimulate analytical, sequential thought. Deepening the understanding of the perceptive, cognitive, and memory proccdurcs of the human brain is the realm of psychologists. A guide to the abundant literaturc of psychology is given in [ l l S l ] .
346
1 1 . Operation
Figure 11.19. Curvc plot [11.39]
Figure 11.20. Windowing: standard displays in free display [11.36]
Psychophysics deals with the quantification and scaling (see Section 2.3) of psychological meaningful phenomena. It studies the relationships between the "objective" features of the thing perceived, the underlying physiological processes, and the experience of perception. Gestalt psychology has also made contributions in this area [11.52]. Findings from these sciences find use today in software ergonomics among other fields [11.53]. Recommended intro-
ductions to physiology are [11.54], [11.55]; an exposition of perceptual psychology is given in 11 1.561. Now that magnetic-resonance and positronemission imaging techniques have made it possible to localize functions of the human brain by functional activation of localizable glucose metabolism [l 1.571 [11.60]. neuropsychology may provide a bridge between physiology and psychology. Results from this discipline that
11.2. Human-Process Communications
347
Figure 11.21. Windowing: standard displays and control panel in free display [11.40]
Figure 11.22. User report [11.35]
may be significant for human process communication are eagerly anticipated in [11.61]. A fundamental dispute between purely scientific-positivist and phenomenological-anthropological modes of analyzing psychological processes, in particular the concerns of perceptual psychology, was shown by STRAUSas early as 1936 [I 1.621 STRAUSwarns urgently against allowing an anthropomorphic interpretation of mechanical activities to be turned into mechanomorphic interpretations of human and animal behavior. Similar statements have been made by other members of the “holistic” school,
such as ECCLES,POPPER,LORENZ,and GIRSON [11.63]. They point to fundamental problems in the reductionist approach when regarded from the standpoints of neurology, philosophy, biology, and perceptual psychology (see, e.g., [l 1.SO, pp. 34 ff.]) and show how spccific human abilities might be understood in terms of the integration of subfunctions and how the preconditions could thus be created for participation in supraindividual “worlds” such as culture and science (see [ 11.64, pp. 148 ff.]). The presentation of messages relating to the technical process must be oriented toward specific target groups of users. This does not simply refer to the expericnce, for example, that the message coding must correspond to the importance of the message itself-which is reflccted in varying reaction times (Fig. 11.25) or to the limited field of view of human vision (Fig. 11.26); such experiences must be taken into consideration when designing process control rooms and in particular when setting up the configuration of monitors [ 1 1.651 (see also Section 10.7). The presentation of information must also take into account that process engineers, for example, need a different view of the process from day-shift or night-shift operating pcrsonnel, whose spectrum of tasks extends from process analysis and optimization to straight operation
348
I 1. Operution
Action because o f Targets
ldentif y
experience
Rules
Training
Recognize
I
Decide
of state
I
Feature arrangement
Sensor inputs
Stored rules
+ sensor and
t
Action
Figure 11.23. Levels of human behavior [I 1.45)
of thc process in accordance with stated rules (Figs. 2.1 5 and 11.27). Modcrn process control systems, with their display and control components, furnish a flexiblc tool with which, using the many individual process signals availablc, process statcs can bc represented in an anthropotechnically and economically favorable fashion. As Figures 11.1311.22 show, operating personnel build their own models of the technical process, based on the individual signals, so that they are then ablc to interpret these signals correctly and take appropriatc action to control the process state. The availability of high-performance computers suggests using the computer itself to comprcss individual Signals into an overall view of the process- -an image or, mathematically speaking, a model of thc process - and conveying to the operating pcrsonnel this compressed information based on the individual signals. This would complete the transition to “infomxationoriented” process control enginecring. In signaloriented process control, the information philosophy was: “Acquire and prcsent information item by item, in parallcl, and always.” In information-oriented process control engineering, the philosophy can be reformulated as: “Acquire information in thc smallest possiblc amount, but as much as necessary, and not until
it is to be used, and in a form that is clearcst for thc operating personnel”. Thus a holistic point of view will makc it possible to enhancc the cornpctence of thc operating personnel with the objcctive of “statc-oricnted” human - process communication. Information Reduction (see also Section 3.3). Complicated tcchnical processes gcncrate a large number of signals, which thc human operator cannot take in and interpret in direct form. For examplc. if two doi.en signals arc plotted in bargraph form, the task of deducing certain proccss states from certain patterns can generally not be performed by a human. Procedurcs of signal processing and pattcrn recognition must thercforc be employed to reduce this complcxity. Scveral mcthods for reducing thc dimcnsionality are known, for example, from specch processing [ 1 1.66). Orthogonal transformations. the bcst-known of which is thc Fourier transformation, play an important rolc in this proccdure. The idca is to represent an original signal by a linear combination of sinc and cosine signals differing in frequcncy and phase. The wcighting factors in the linear combination are thc new dimensions, the number of which can bc reduced by leaving out, say, high or low frequcncies, with no substantial effect on the signal form.
349
11.2. H u m a n -ProcessCommunications
I
1 I
Ooerator Mental activities Thought basis
_____Higher intelligence
Logical l e v e l -
- - - - - -. - - - - - - - - - - - - - - - - Recognition of standard situations
Rules l e v e l
-___--
- -- - - _ - - - -
- - - - - - -.
1
I-
-
I
Readiness l e v e l
-
~
-
Choice o f standard reactions
Readinessbased behavior
t
i
Oialoq and u s e r s o f t w a r e ~
--
- _ -_ - -_-
1
1
~
A u t o m a t e d technical process
Figure 11.24. Support of operator’s mental activities by interactive and custom software in the control and monitoring of an automated technological process I1 1.351
Another orthogonal transformation is the Karhunen-Loke transformation. in which sines and cosines arc replaced by functions derived from the original signal. These arc represented as vectors whose contributions to thc total signal decrease with incrcasing ordinal number. By forming a linear combination of a few such vectors, the original signal can be rcconstructed well; the weighting Factors in the linear combination are thc new transformed signals with smaller dimensionality [11.67]. [I 1.681. The method of factor analysis takes the large amount of possiblc information about the product and the process and generates a system of mutually independent new variables that can be ordered, so that it is possiblc to say how much each of these new parameters contributcs to the total information. If such mcthods are uscd uncritically, the problem arises that the new dimensions may not
lend themselves to ready interpretation by the user. For example, signal mean values and signal ratios are highly suitable and are also subjectively accepted as indicators. This discussion has mentioned and briefly described just a few methods for reducing the dimension. Applying such a method also reduces the redundancy, since as a rule a redundant signal can be represcnted as a combination of other signals, so that recovcring them does not supply any ncw knowledge about thc process statc. This situation typically occurs in an overinstrumented system where there arc, for examaple, more sensors than are requircd for an unambiguous detcrmination of the state. However, overinstrumcntation also makes it possible to carry o u t signal reconstruction for signal sources that may have failed bccausc of some technical defect (redundant instrumentation).
350
1
700
11. operation
r
Alarm Optical
600
Night s h i f t
Process control
Plant operator
v)
E
.+ _
1
500
Production manager
0 c
._
:LOO
4-
01
IZ
Process engineer Ll L, L, L, None Acoustic Optical Combined Additional t a s k
Process analysis
Figure 11.27. Process communication
Figure 11.25. Reaction times to messages encoded in various ways [ 1 1.591
Figure 11.28. Schcmatic diagram of pattern recognition [I 1.591
Figure 11.26. Field of human vision
‘ I
Even though there may be substantial time shifts bctween signals, especially in extended technical processes, there arc deterministic relationships between them (e.g., measurements upstream and downstrcam of a lag element). In many cases, such shifts can be detected by crosscorrelation analysis, but in general the result for a signal source is that not only thc measurement at a time T but also some number N of measurements obtained a t earlier times must be taken into consideration (see Fig. 11.28). Pattern-recognition techniques afford still more support to the operator. For example, hazardous states in a process can bc detected on the basis of a pattern in the signal. Figure 11.28 illustrates the principle of pattern-recognition systems. The original signals are first subjected to feature extraction. This is analogous to the di-
mcnsion-reduction operation described above; the smallest possible number of features. and hence highly informative featurcs, arc derived from the original signals (see also Chap. 2). This means that the subsequent classilication can be done in a space of few dimensions. Classification involvcs forming regions in the N-dimensional feature space, under the assumption that patterns lying close together in feature space belong to the same class. In a two-dimensional feature space, such regions can be delimited by, for example. a chain of line segments. If these lines are drawn automatically. possibly with operator intcraction, the classifier is said to “learn.” Under certain circumstances, dcpending on operator behavior, such a classifier may be able to adapt to changed process situations. By using cluster analysis [I 1 .@I, [I 1.701, class formation can be carried out automatically without external supervision. Each new pattern is assigned to the region (cluster) to which it is closest. Cluster analysis can be used, tor example. to find automatically and subsequently identify particularly frequent and typical process states. In future, this type of procedure will undoubtedly provide significant aid to the operator (see also Fig. 11.63). These order-reduction procedures make it possible to represent process states in thrcc-dimensional form, making it possible to visualize the product or process space (Fig. 11.29).
11.2. Human-Process Communications
xi (tl
E
Property profile, quality ? p o i n t in product space
P” Recipe, process s t a t e ? p o i n t in process space
1 t
[11.67], [11.68]
A similar mode of analysis is arrived at if thc methods of similarity theory are applicd to a signal pool [11.9], [11.10]. Thc direct presentation of the dimensioned X-quantities in X-space with X-relationships is replaced by the dimensionless prcscntation of rr-quantities in n-space with n-relationships. The prcscntation of statc spaces, as a prerequisite for “state-oriented’’ human -process communication [1 1.351, [11.71], [11.72], rcquires proccdurcs by which relationships between the process and product propertics can bc obtained from thc time dependences of these properties (as conventionally supplied by the instruments currently used for process monitoring). Thus the problcm is to derive the function Xi(Xj) from the time dependences X i ( t )and X j ( t ) of the measurements. It is straightforward to plot the last N pairs of values (Xi, Xi) acquired to givc a two-dimensional scattcr diagram in coordinates Xi versus Xi. This representation may actually make it possible to derivc the dcsircd relationship. Under some circumstances, regression methods can also be used; for example, the mcan and dispersion of Xican be obtained for each X j product. Utmost care is advisable, howcvcr, bccausc no a priori prediction can be made as to the form of the CUNC.
Two further difficult timc problcms must be solved : In gcncral, thc function Xi(Xj) is not stationary. Otherwise it could be dctermincd once for all and would havc no predictive power for the operator. The time constant that dcscribes the behavior governs the size of the “window” over which the function Xi(X,) can be dctcrmincd. The window may be fixed (a new measurement vanishes every timc a ncw
AH
xj ( t )
Figure 11.29. Analogy bctwcen product and process spaces
0
351
xJ ( t ) [
..... ;; x; (tl
(t+
....... x; It1
Figure 113. Effect of time delays
0
scan takes place) or dccaying (c.g., exponential); the computing effort required is then less. The opcrator rcccivcs a picturc of the function that shows a continuous slight variation. X i and X , may be very closely rclatcd but with a (possibly long) time shift or bias. If the X i and X j measured at the same time are plotted, the rcsult may havc no predictive power at all (Fig. 11.30).
The time shift can be identified by cross-correlation analysis, after which measured values Xi(r) and X j ( t + Ar) can be paired. It is also possiblc to crcatc a three-dimensional representation with the time offsct as the third dimension. Thc resulting “mountain range” would revcal where the dependence was most distinct. Changes in time offset, in any case, provide an important indicator of the proccss state (see also Section 3.2). Information Storage under Real-Time Conditions. In future human- process communication systems. a major role will be played by the computer’s internal data model [11.73] [11.76]. This model must contain all data acquired and stored during the operation of the process control systcm (see Chaps. 2, 9. and particularly 8). Such a proccdure will require data storage under realtime conditions, and this objective has given rise to the conception and development of real-time
352
I t . Operation
databases. The following typical features distinguish these from classical databases: 0
0 0
They contain instantaneous data generated and required in real-time process operation. In addition. there are data to be archived for infrequent later interpretation. These archival data will replace the process logger charts common today; the large bodies of data acquired will typically be stored on video-disk memory devices. A real-time database, in the narrow sense. is concerned with the management of instantaneous data. These databases are very small in comparison with commercial databases. Reaction-time requirements arc stringent, both for the storage of data (which must always be carried out such that a consistent data model is preserved) and for its retrieval (since such requests may be made by both the process and the operator). Reaction times in the millisecond range and very short time intervals for insuring data consistency are needed here.
In the implementation of real-time databases, the result is extensive utilization of main memory. The use of a real-time database in human process communication systems will lead to major simplifications in programming. With the software tools belonging to a data base, individual queries can easily be handled; support of a query language with graphical output resources will simplify interaction with the operator.
Graphical Technology. EICEN [l 1.91 has shown that pictorial representations (graphics) make it easier to comprehend information than does text. The use of computer graphics in mathematics and the natural sciences is surveyed in [11.77]. Graphical kernel systems such as GKS/GKS-3D o r PHIGSjPHIGS are device-independent basic software systems for graphical programming; implemented on workstations, they increasingly form the basis for CAD tools [11.76], [11.77]. Multidimensional state-space representation, in particular, requires images with almost photographic realism; today these can be generated with efficient algorithms and data structures o r with ray-optics techniques from physics (ray tracing) [11.78]. Finally. computer graphics can handle demanding visualization tasks only with the aid of +
knowledge-based methods. Conversely. as discussed in Chapter 13, increasingly complex artificial-intelligence (AI) systems depend on graphical visualiLation aids to create viewable images of models and states. As a result, the disciplines of graphical data processing and artificial intelligence are becoming more and more interwoven, as is reported in [l 1.791. What follows is a discussion of the graphics techniques available in current proccss monitoring and control systems. All graphical output devices now in use for human process communication are color displays. Graphics systems in first-generation process monitoring and control systems feature medium resolution (roughly equivalent to the broadcast television standard) and permit the semigraphical rcpresentation of process states and process flowsheets. “Semigraphical” n u n s that the screen is subdivided into predefined fields containing, for example, 8 x 8 pixels, in each of which alphanumeric characters or graphical image elements can be placed. A character generator driven in an 8-bit code contains the bit pattern for the characters and the selected graphical elements. The refresh memories in modern systems arc too large to be displayed on the screen. Thus the process image can be moved back and forth so that the screen appears to represent a segment of a large landscape (rolling image). On the input side, the keyboard is complemented by a variety of pointing devices. These Facilitate both graphical input and selection from menus shown on the screen. Typical devices for this purpose include trackballs; joysticks; touch screens, in which various physical methods are used to detect the location of the finger touching the screen; and light pens. Each of these systems gives xi,.coordinates on the screen, which are accepted as inputs to the operator software. The trend in development of graphical devices for display and control components is toward pixel-oriented color displays. These o f k r much better resolution as well as physical parameters (19” and 21” screens with resolutions of 1280 x 1024 dots, displaying 256 colors siniultaneously out of 16.7 million). There is a trend toward the standardization of hardwarc and software. The devices are based on I T S o r workstations with RISC processors and run under operating systems such as DOS, UNIX, or their derivatives.
11.2. Human-Process Communications
The principle cmbodied in these user interfaces is splitting into components that arc relevant for the user interface and those that are specific to the processing tasks in an application (1 1.801. The distinguishing feature from the user’s standpoint is thc structuring of the entire screen area in movable, variable-size, iconizable subregions or windows, which can accept user input and display representations indcpendently of one another. Screen windowing and network-wide window managcment tasks are performed by basic windowing systems, which are includcd in the operating system or added on as utility librarics (e.g., X Windows [11.81]). “Network-wide” means that the user, from any network node, can interact with an application running on any other computcr(s) in the network. One possible principle for such intcraction is the client-server principle. Abovc the window management level are graphical user intcrfaces (OSF-Motif, Open Look in the UNIX environment, MS Windows or Presentation Manager in the DOS environment). The problems relating to control and monitoring components (see Chap. 7) have now impclled a number of manutcturcrs to creatc their own process visualization features for certain information-processing systems that have no display and control components of their own, especially systems based on stored-program controllers. One of the main components of these visualization dcvices, which can simultaneously carry out control functions, is a database system permanently linked to the process and hence running in real time. Because only a fcw of the user interfaces based on the well-known opcrating systems have been standardized (e.g., X-Motiv in the UNIX realm or Windows in the DOS environmcnt), a number of very diverse “artistically” designed tools have appeared in this area. These require an extremely critical examination bcfore they are put to use. Several systems based on this architecture have been offered especially for process visualization (c.g., InTouch [11.82], Factory Link [11.83], APROL [11.84], Dynavis-X [11.85] COROS-1,s-B [I 1.861). Some process monitoring and control systems have integrated display and control components (e.g., SATTLINE [11.87], Viewstar 750 [11.88], Advant-Station [ 11.go]). The discussion that follows introduces special characteristics of these systems without describing individual products.
353
The functionality of these systems is outstanding because elementary, interactive, and scalable graphics objects arc available. System resources, such as fonts, fill patterns, and color tables. can be utilized, and raster or bitmap graphics, either scanned in or generated by other graphics software, can be incorporated. With the aid of thcse design resources, predefined graphical input and output objects specific to process control engineering can be created and placed in librarics for latcr use. Thesc include virtual keys, switch panels, slider controls, analog displays, and so forth. Another aspcct of the graphical functionality is the “logical zoom” function, in which exccrpts of images can be displaycd in varying sizes and levels of detail and information is added as the zoom level increascs [ 11.851. Visualization systems based on graphical user interfaccs arc further characterized by expanded capabilities for dcfining the interaction (dialog) between user and process. These features includc the interpretation of user inputs, the exccution of appropriate commands, the crcation of graphical objects when certain events take place, and so on. Along with well-known evcnt-oriented and data-oriented concepts, complex control structures made up of command sequences, branches, and loops can be initiated, windows can be opened and closed, and graphical objects can be created and deleted. Flexible, state-dependent image structures can be built up in this way and need not be oriented to fixed image hierarchies. Another characteristic is that these systems can be set up to utilize open system services present in any hardwarc and software platform so as to expand the process visualization functionality. Such mechanisms include system services for data exchange, command transmission, and simple intcgration of application-specific software upgradcs. Examples are Dynamic Data Exchange, the Dynamic Link Library concept and multimedia add-ons under Windows and (in part) OS/2, database query languages such as SQL, and pipe mechanisms under UNIX. In this way, open visualization systems offer better integration capabilities than the display and control components in first-generation process control systems. One example is thc connection of thc visualization system to a spreadsheet analysis in which process data can be linked with mathematical functions on-line. Unificd operator control concepts in a user interface come into play hcre bccause different programs then prescnt
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11. Operation
themselves to the user with the same appearance mode of interaction (dialog). The utilization of system services also includes the possibility of creating help systems and defining macros. For the designer of a process visualization utility, ease of use dictates that the capabilities of the graphical user interface be employed in design. “Toolkits” or interface modules are not adequate for this purpose. They merely provide development interfaces at the programming-language level and require much specialized knowledge. Abstraction levels suitable for the design of display and control components offer classes of tools, such as CAD-like editors for graphical interfaces in combination with editors for formulating “interactive” control, so that interface objects can be selected, constructed, furnished with attributes, and linked to user actions and process data (interface builder and user interface management systems [ 11.801). Resources taken over from the CAD world, such as traps in conjunction with underlaid rasters. further facilitate the work. From the process control engineering standpoint, the high flexibility of visualization systems also holds some risks. In the design of process visualization, the assignment of appropriate parameters should be used to restrict the user’s options in interactive mode. For example, it should not be possible to place other windows on top of alarm windows. Similarly. a basic subdivision of the screen into fixed-position, fixed-size summary and detail windows should be permitted. Application-specific software add-ons and process data manipulations in other software packages may prove far more successful if access rights questions are not handled as functions of user or process state. In “closed” process monitoring and control systems, such mechanisms are made available as system services. Of particular importance in process control engineering is the seamless integration of visualization design into the engineering process as a whole. This includes, for example, the linking of process visualization and dynamic process data generated in the design of process-level (local) components. Capabilities should be provided that make available the engineering data which indicates which process data are used in which visualization functions (design database). Anticipated devcloprnents in graphics hardware for human -process communication arc discussed below. Modern visualization systems intended for use as display and control compo-
nents in process control systems are based on 2D graphics. Three-dimensional images can also be displayed on a 2D screen. The representation of the Space Shuttle in Figure 11.31 shows how hidden lines and surfaces can be removed from the three-dimensional picture by using computer graphics. However, mapping a three-dimensional model onto a two-dimensional surfice (welldefined portion of the image, well-defined angle of view) places a considerable load on the computer that constructs the individual points of the image. Accordingly. 3D graphics on a 2D screen is severely limited in speed. Graphical representation capabilities in process control systems with modern 2D graphics systems are illustrated in Figure 1 1 . 1 7. The performance of graphics systems is improving continuously. Graphics systems from the C A D realm and scientific data processing already hold an internal 3D representation, which they map onto the selected viewport. This implies a substantially larger graphics memory (a complex volume model. for example, requires 500 000 vectors. each represented by several bytes. so that the total memory demand is several megabytes). The enormously high performance required of graphics systems in thcse applications stem from close-to-reality graphical representation combined with animation capabilities. An example is the simulation of poor visibility in the training of aircraft pilots [I 1.91J.Graphics systems, usually implemented on a dedicated board or even as multiprocessor systems, can now plot up to 10’ 3D vectors per second. The workstation CPU and the graphics processors split the work up in a rendering pipeline [ I 1.921.
Figure 11.31. Thrcc-dirncnsional rcprcscnrarion of thc Spacc Shuttle on a two-dimensional Ircrwn [ I 1.891
11.2. Human-Process Communications
Data transmission over an internal bus is a bottleneck. as in many othcr situations. Modern graphics workstations have main memory capacities of 16-128 M B and even more. The capabilities of modern high-performance graphics hold the promisc of real-timc visualization of complex data collections in n dimensions, a procedure that has now become the state of the art in scientific data proccssing. Isolincs. textures, and other resources are used along with color to handle the n-th dimcnsion o r variant i n graphical rcprescntations. Photographs of glowing metal slabs o r bars provide an example. From the color of a glowing piece of metal, it is casy to determine the temperatures of the individual zoncs. The color scale runs from white (highest temperature) to black (lowest temperature). In Figure 11.31, color is used as the fourth dimension in visualizing the temperature distribution of the Space Shuttle. Color can also be used to visualizc othcr quantities such as height, material density, etc. The use of color as a supplementary element in graphics is advantageous chiefly in raster graphics. since colors can be gradated extremely finely in this kind of representation (high color resolution). There have also been new developments in pointing devices. The mouse is now commonplace in oflice automation. In conjunction with the use of workstations as display and control components, it will be uscd in the process control room environment as well. Futuristic concepts of interactive devices go yet further: An accclerometer on the fingertip makes it possible to determine the direction in which the finger is pointing, and observation of the pupil tells where the operator is gazing. Electronic gloves equipped with sensors (datagloves) are already in use, chiefly for research and in virtual reality systems [11.93]. The first commercial applications have come about in the CAD field. In the n-dimensional data or feature space concepts discussed above, an operator using such an interactive tool can manipulate data and freely select a point for viewing images, thus wandering through the feature space.
Multimedia Techniques. Control and monitoring functions in process control systems have recently been expanded by techniques broadly referred to as multimedia. STEINMET% has given a qualitative. application-neutral definition of this term [11.94]. The types of media supported and the options for media processing on a computer
355
system arc key features. Multimedia systems must be able to proccss discrete media (e.g., text and graphics) and continuous media (e.g., audio and video) independently of one another. The computer is extending its capacities to every medium by which information can be propagated and presented. The central points in multimedia are intcgration of media, range of media, distribution of multimedia information over a computer network, and user interaction with a multimedia system. From the standpoint of information technology, crucial tasks for media integration are to create digital representations of the media, to develop suitable storage media, and to augment communications networks. The main problem is the high flow rate of time-critical multimedia data. For example, the digitization of a video sequence at normal resolution requires data rates of over 20 MB/s. Key tcchnologics for implementation are digital signal processors for data comprcssion. optical storage media, and highdata-rate networks. Ultimately, multimedia systems make sense only if eficient system management insures the on-time processing of, in particular, continuous media. Requirements in this area apply to realtime processing in multimedia systems, specific service reservation, and the synchronization of linked information units or information streams relating to discrete and continuous media. Finally, the developers and users of a multimedia systcm should be able to operate in the way they have become accustomed to with discrete media. This means that multimedia functionality must be properly abstracted in development systems, user interfaces, and database systems. The object-oriented approach finds use here. Examples of the abstraction of multimedia capabilities can be found in multimedia add-ons to window-oriented graphical user interfaces running on personal computers and workstations. The desktop metaphor has been expanded to include, for example, a telephone, a dictating machine, and a video conference. These points will not be discussed at length here, nor will the associated problems, in particular the rnanagcment of large bodies of data, diverse data formats, links between distinct media objects, and content-oriented searching. Reference should be made to the specialist literature [11.94]-[11.99]. In what follows, special requirements on the use of multimedia are presented, along with possible application areas in process control engineering.
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1 1 . Operation
Real-time requirements in process control mean that the correlation of data in a variety of media- especially continuous media-to objects, events, states, alarms, etc., is a central problem. Furthermore, because events in technical processes occur in stochastic fashion, it is impossible to predict at what time which multimedia input/output devices need to be available. Conflicts, such as when several audio sequenccs must be output at the same time, must not occur. although it is entirely possible for the operator to be simultaneously occupied with scvcral distinct tasks. Thcse points can be traced back to the problem of multimedia resource management. A conccpt for the integration of multimedia in an information and diagnostic system for use in process control is proposed in [11.loo]. A major role is played by the presentation system, which is responsible for synchronized input and output matched to system and user requirements. Already common in the process industries is the use of camcras and microphones to monitor machinery, plant sections, and products. Generally, this involves self-sufficient analong monitoring systems with no connection to the proccss monitoring and control system, or else hybrid systems in which analog recording deviccs are controlled by computers through interfaces. In both cases, the data stream remains unprocessed and isolated from other information-processing functions. In digital multimedia systems of the future. integrated processing of all media will be possible. First, other information can be appended to audio and video signals (timestamp, links to process data, comments, etc.). The result will be new capabilities for messaging, reporting, and archiving systems. Second, digital image processing will offer new possibilities for human process communication. Digital image proccssing is already employed in industrial robots (machine vision), in computer-aided quality inspection of finished parts, and in the monitoring of machincry [11.101], [11.102]. In human-process communication, digital image processing, perhaps in conjunction with C C D cameras, will provide a route to alternative methods by which product and process states can be determined and classified and the results presented in a visually informative way. An example is the assessment of temperature profilcs in metal working by dctcrmining gray-scale distributions [11.103]. Finally, the operations of digital image processing havc thc goal of presenting the operator with
visual information in an appropriate and easily remcmbcred form. A closely related topic is pattern recognition, a set of tcchniques which pcrmit the identification and grouping of logically like image contents and thus thc identification of single objects [l 1 ,1041. In addition to the question of possible applications for multimedia systems, another point of interest for human--process communication is how these systems can be put to USC.First of all. the modality-i.e., the way in which the operator’s sensory organs take part in the interaction of the operator-system intcraction is expaned, for example by speech, but also by futuristic concepts such as the interpretation of bodylanguage[11.105],[11.106].Theobjectiveis to make user interfaces more “natural” by letting them use a broader sensory spectrum. Information acquisition can be facilitated and supported through input and output in the original medium without transformation. as in voice mail. or through multimodal presentation of information in more than one medium. An application is the audiovisual notebook, in which video records with added comments provide information about special events. A second system-related aspect is “activc” user bchavior. The pertinent conccpt here is hypertext or hypermedia [11.107], which is a concept for organizing and actively “cxploring” information. Essentially. a hypertext or hypermedia system has two components: networks consisting of elementary information units (nodes) and flexible, directed rcferences (links), and tools for creating these networks, rcading them, and so forth. Hypermedia differs from hypertext in that the nodes contain multimedia information. Thc user wanders through the network in survey fashion (browsing) or with a definite dcstination (navigation). The contents of nodes and links need to be static. The concept also includes executablc code as well as the on-linc determination of the destination of dynamic links as an aid to navigation. More complex structures can be generated by aggregating networks. For a more in-dcpth treatment and discussion of further aspects. such as programming and tool/system support, see [ 1 1.531, [1 1 .1081, [ 1 1.1091. The first applications of hypertext concepts came in the organization of documents in “information systems” (information kiosks) and in education. Hypermedia systems havc found industrial use as technical information systems for
11.2. Human-Process Communications
commissioning, repair, and service documentation [11.110]. In technical processes, the operator of the future will obtain required knowledge, such as safety aspects, through information systems of this kind. At present, work in this area is being carried on in a EUREKA project [11.111]. Hypertext concepts in human -process communication can also be put to work in on-line operator support or operator training systems, in particular systems that will guide the unpracticed operator to the correct control actions [11.112]. Similar considerations relating to ship steering are presented in [11.113]. Closely related to hypermedia conccpts in process control is the linkage to knowledge-based methods; hypermedia systems are used as user interfaces for diagnostic systems, since operator behavior can be better supported with them [l 1.1 141 (see also Chap.
357
In power-plant control engineering, the status of a boiler or turbine is displayed as an instantaneous operating point o n a plot of characteristic curves. Any change in operating point can be effected by using a crosshair pointer to specify a new nominal operating point. Individual setpoints are then obtained from a process model [11.119]. New approaches to information-oriented process control engineering are also seen in the chemical industry. In general terms, product properties E can be represented as functions g of proccss properties P. This procedure is reddily illustrated in process space (Fig. 11.33) [11.120]. For a certain product property Eo, there exists a function g* of the process proper-
13).
Trend Forecasts. Instances of informationoriented process control engineering can be found today in aeronautics, the power-plant industry, and automaking. Flying involves very complicated changes in attitude resulting from course changes. In modern aircraft, these trimming or dressing actions are performed for the pilot by a computer-supported control system. Thus the pilot can fully concentrate on determining the course, using a joystick to specify course changes to the autopilot, which in turn generates the control instructions needed to alter the attitude. In order to reduce further the cognitive burdcn on the pilot, aircraft such as the Airbus A310 include a display showing the instantaneous position with the planned course and a proposed course deviation. The justification for the deviation is incorporated into the display; for example, if it is an area of bad weather, a weather radar display is superimposed (Fig. 11.32) [11.115]. Similar developments are under way in automotive engineering. Navigation deviccs use graphics to display the position and the proposed course to the desired destination, taking account of current traffic conditions 111.1 161. Required maintenance is determined by using models and reported to the driver; an example is the service-interval display developed by BM W [I 1.1 171. Another example of information-oriented process communication is a display showing ignition angle plots for a digital motor ignition system [ 11 .I 1 81.
Figure 11.32. Airbus A310. navigational display [11.115]
F-Ae-4 Figure 11.33. Process space (two-dimcnsional)
6
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1 1 . Operation
ties P,and fj.as shown by thecurve. Thc thicker portion of the line characterixs the optimal region for the process, charactcri7ed by compliance with additional constraints. If certain tolerances ( E , _+ AE,,) are set when the values of the product properties are cstablished. then in the process-space representation this corresponds to a certain variation range of the process propertics (shaded region). In a specified technical apparatus, of coursc. the process properties can be varied only within certain rangcs (AP,,AP,); the rectangle shown in thc figure is obtained as a result. Safety and environmental restrictions narrow the acceptablc region of the process space still further. leading to the areas enclosed by dashcd lines in the figure. Figure 11.34 shows another way of thinking about a real process. In process space (normally the coordinates are configuration parameters detcrmincd by order reduction), the hatched region shows the normal production condition for a certain product. The white rectangle locatcd on this is the economically optimum operating region. The variation in the process properties allowed by a specified apparatus is indicated by the outline of a parallelepiped. The uppcr and lower gray areas show the restrictions imposed for safety and environmental reasons. Figure 11.34 also shows how the operating point on the screen can be moved into the target region, for example with a joystick. The change so specified is then automatically transformed to setpoint (control) signals [11.121]. Tracking the operating points in a process as just described indicates the distance from critical regions and, eventually, may also make it possiblc to initiate prompt computer-aided action to remedy disturbances. The analytical preconditions for doing this have long been known from fault-tree analysis.
Figure 11.34. Process control with the joystick
Another type of process communication can be implemented with the help of Wolf diasrams [l 1 ,1221. These examples from a variety of technical application areas suffice to illustrate the prescnt and ,forcsecable capabilities of information-orientcd communication. Information-oriented process control engineering will relieve operating personncl of somc burden, help them avoid errors in disturbance situations, and promotc the optimi~ationof processes through improved modeling. This innovation will also make it easier to produce better and more consistent quality in accordancc with spccified nominal profiles. The operating pcrsonncl will be able to put more effort into studying the process as a basis for modeling. Furthermore. it will be possible to employ operating personnel with a broader spectrum of skills. and at the same time the monotony of continuously watching single signals will be broken up. Finally. the early detection of incipient transitions to undcsirable operating states will lead to improvcd operational safety and reliability. These are all good reasons for supporting information-oriented process control cngincering. “Signal-oriented” process control engineering, which recognized only human -machine and instrument-machine modes of communication. will be replaced by “information-oriented” process control engineering. which includes the concept of human-process communication.
11.3. Process Analysis and Process Optimization In 1984, BLASS[11.123] proposed a mcthod ,that requires a methodically structured procedure in the development of complex systems so that a clear. comprehensible framework can bc obtained for the application of specialist knowledge and creativity and the range of possible approaches to finding a strong solution can bc dcveloped and exhausted. The mcthod of choice should always be a systems approach, which furnishes mcthods, principles, and proven resources in a relatively abstract and easily intcrpretcd fashion, in the form of the system concept, the systems procedure. and project management. Nevertheless. there are still processes whose development has not had the benefit of the systems approach. Thus, retrospective methods
11.3. Process Analysis und Process Optirnizution
were sought for determining the validity of existing processes so that they can be optimized. An example is the electrophotographic reproduction of images. for which BIXBYIl1.1241 and BICKMORE[I 1.125) found an approach that involved breaking complex processes down into steps and interpreting these in physical. chemical. and process-engineering terms. Figure 11.35 shows how the transfer process can be analyzed into single, physically comprehensiblc steps and how information can be gained in this way about the overall process. This representation. often called a Jones diagram [l l .126], is an effective way of taking individual transformation operations into account and converting object information into pictorial form 111.1271. Transfer Theory and Transformation Principle (see Chap. 2). Any optical copying process has the task of transforming the optical density distribution of the object T(x) to an optical density distribution of the image D(x). This process is described by transfer theory. Let the object T ( x ) be expressed in terms of spatial frequencies R by the Fourier transform method: 7(x) = ~ h ( R ) e Z i R x d R R
(11.3.1)
The frequency R denotes lattice periods per unit length. The image D ( x ) is then represented by D (x) = K (K)h (K)eZiRx dK R
(11.3.2)
where K ( R ) is the modulation transfer function (MTF), generally a complex-valued function. Its modulus describes the contrast with which the sublattice of frequency K is transferred from the object to the image. The advantages of formulating the imaging process in this way is that the transfer fucntion collates all the properties of the transfer system in a single function; by multiplication of further transfer functions, processes can be represented in simple form and complicated processes can be broken down into steps. At a high level of abstraction, profiles of product properties (Fig. 11.1) can be comparcd to optical density (blackening) distributions. Each process element then indicates (as discussed in Chap.2) how each element of thc
Potential difference
359
Transmission T = log J/Jo
Ius- u,l
L
VI 0
a
Figure 11.35. Analysis of the clectrophotographic copying process into steps [11.124], (11.251
product property profile is transferred to a new element, namely a property element of the end product, by a process step or process segment. The analogy to the concepts advanced by GILLES [11.8]. [11.128] and POLKE[11.129] is then logical. In this relatively simple procedure, in which the modulation transfer function plays an effective modeling role, it was possible to describe and analyze the individual physical operations in the complex copying process. Later, the term “process analysis” was introduced [I 1.1301 for quite different processes, such as the vulcanization of rubber. Existing processes were examined, from starting products through all process steps to end products, including the operational logistics, technical equipment, and process control engineering. The entire process, the plant, and also the operational surroundings are thus examined with the objectives of separating dependcnt from independent variables; identifying possible improvements on the basis of the relations found; and devising and utilizing new-and possibly unconventional- solution approaches. Figure 11.36 shows the material and data flows in a process, and Figure 11.37 shows the same relationships for the example of a vulcanization process. As early as 1972, a “process indicator,” a precursor of the model-aided measurement technique [11.131], was in use. When there are obvious weak points in the operation, improvements are often carried out
360
?T)=o= i =f7 ).;.( --El 11. Operation
1 Measurement 2 Coupling 3 Control
Process d a t a
3
Raw m a t e r i a l
Process
Finished p a r t
I
Raw m a t e r i a l
Technological properties
indicat or
(3
12t
Figure 11.36. Proms analysis (material and data flows)
2
3
1
!
1
1
1 -
1
1
Figure 11.37. Process analysis (rubber vulcanization)
by taking single actions based on day-to-day experience. Such improvements have to do with the optimization of a unit operation, a plant section, or a single apparatus. Along with the advantage of solving the problem relatively quickly, this procedure has the drawback that the real cause of the problem remains undiscovered and sub-
stantial potential for improvement remains unexploited. As a rule, therefore, process analysis must always be performed from a holistic point of view so that improvements throughout the process and the plant can be effected. Finally, process analysis is a tributary activity of process development (see Fig. 11.57, p. 369).
11.3. Process Analysis and Process Optimization
The first step in a process analysis is to break down the overall process into individual process elements in such a way that the product property changes brought about by each element are covered by the phase model described in Chapter 2 (see Fig. 2.32). As stated above, the transformation principle of Chapter 2 is used extensively (see also Fig. 11.58, p. 369). These relationships were also pointed out by RUMPI;,though he used a different vocabulary, speaking of property functions in the course of the process [11.128]. This fundamental insight into the holistic analysis of a complete process is reinforccd when process control systems are employed; very much more than previously, these presuppose a thorough knowledge of the process. This point was made in 1979 in the documentation of a correspondence course published by the Chamber of Engineering of the German Democratic Republic [I 1.1321. The form description of the dependent variables in a production process, of the product properties, as a consequence of the variation of the independent variables, of the process properties, has since come to be referred to as modeling (see also Chap. 3). Here, the problems of process analysis are treated from the viewpoint of the process control engineer, who must acquire knowledge about the controlled process from the process engineer, the designer, the equipment fabricator, and others; that is, from the people who have the main responsibility for designing the apparatus, the facilities, and the processes to be run there, and whose work therefore must precede as well as supplement that of the process control engineer. This applies, in particular, to the analysis and description of processes in a form that is usable by the process control engineer and accessible to his methods and procedures. The result of such a process analysis with a process control engineering orientation, however, not only provides the process control engineer with information about the process but also provides the process specialist with pointers on the active design of the process. This specialist is then better able to develop the process from the outset in such a way that it lends itself to the process control engineer’s approach. This will greatly facilitate the, design of the process control system. The outcome of such a process analysis can also be a considerable aid in the commissioning and maintenance of the plant 111.1321.
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The restriction to a process-control orientation of the process analysis has the result that the informational context becomes a key factor in the analysis. The work of BLAS (11.123] has set the direction for the overall aspect of process analysis, which must always be kept in mind even when dealing with details. Together with presentations at the annual conferences of the VDI Association for Process Engineering and Chemical Engineering (GVC), BLASS’publications provide an ever-fresh source of new methodological and technical information [ 1 1.1331. BLASSalso stresses the need for heuristic procedures, especially in engineering and scientific fields. For this reason, what follows is a presentation of one such method that has been tested in practice, both for process analysis and for process development [1 1 .A, [ 1 1 ,1341. Under certain conditions, product properties and process properties can be plotted as points in appropriately defined mathematical spaces. Some mappings between these spaces can be interpreted as production models that permit the formal mathematical description of product development in terms of process variation. There are tools, many of them tested in the field, for implementing this generally valid concept [I 1.71. A company that wishes to succeed in the market must adapt the quality of its products to the requirements of the market. This adaptation can be interpreted as a measurement and decision problem. The properties of the products produced by the company and required by the market should be quantifiable and their relative separation should be measurable. In principle, this objective can be achieved by reducing all “technological” properties to physical ones [l 1.130]. The physical properties assigned to a certain product are then interpretable as a point in a metric space. The nearness of the product quality to a required level can be objectivized in this way. The process of deciding whether product or process development should be carried out thus becomes transparent. The question with what resources a quality improvement can be effected is first of all a question of operational capabilities: With what processes are the current products made? Will changes of recipe in the context of existing processes result in the desired quality changes, or will process modifications or even new process development have to be carried out.
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An analytical approach here affords some help in ordering the operational reaction possibilities. If a process is represented in terms of the set of assigned and independently variable process properties, then in analogy to the product space these span the process space. in which a metric is defined (provided the properties are medsurdb\e). A recipe is a certain expression of all the properties characterizing the process and can thus be interpreted as a point in this space (process state; see Fig. 11.29, p. 351). Now product development can bc carried out in a controllable manner if it is possible to construct a mapping that assigns points in process space (recipes) to points in product space (product qualities). It is shown how the rclationships between these spaces must bc utilized in order to achieve product development through process variation. It is further shown that process optimization, viewed from the standpoint just developed, gcnerally reduces to a variational problem with constraints. The mapping from process to product spacc describes, in model terms, the interaction between plant and product during the production process. Accordingly, this mapping can also
be used as an aid to solving control problems. The importance of such process modcls for the construction and utilization of tcchnical information and control systems, as well as for the solution of automation problems with the aid of modern computer systems in the chemical industry, should be expressly reemphasiled in this connection. Product and Process Properties (see Section 2.3). The market exerts a variety of influences on product development. For example, consumer habits change over time, often because of changes in the priority of their needs [l 1.1351. New technologies in the convcrting industries impose other (usually more stringent) requirements on products. The market demand for products with novel or improved properties can also be triggered by the appcarancc of competing products (Fig. 11.38). The company, for its part, offers to this market products whose properties arc governed by the raw materials and energies used, the number and skills of thc operating personnel, and by thc tcchnological condition of thc apparatus available. Figure 11.39 illustrates the decision prob-
7
Raw m a t e r i a l s D
Q Consumer
Energy
D
Personnel
D
Apparatus
D
habits
Market
Company
Q Processing technology Q Competing products
Figure 11.38. Factors affecting product quality (left) and market requirements (right)
El Company
of product
0
properties
Product development
Figure 11.39. Dccision problem fodagainst product development andlor process development
11.3. Process Analysis and Process Optimization
lem arising from the situation just described. If thc existing properties of the products made by the company are no longcr adequate to satisfy the needs of the market, the company must decide whether to risk the loss of competitiveness or to react to the new situation by undertaking product and/or process development. To rendcr this dccision proccss objective and intuitive, it is useful to define the ”properties” of the products more exactly. on the one hand, and on the other to quantify thc company’s ability to react (“process”) [l 1.1 341. Product Properties. Beginning with the idealization that product properties can be measured directly and immediately, every product property can be plotted in a histogram, along with its tolerancc range, and the full histogram can be interpreted as thc property profile (Fig. 11.40). In a number of studies [11.136]-[11.143], this method has proved extremely helpful in the analysis of requirement and qualification profiles (see also Fig. 11.41). Applying this idealization to the concept of product properties often reveals the practical difficulty of relating distinct characterizations of one and the same property as determined by subjective assessments. In principle, of course, even the “technological” properties can be reduced to physical ones, measured, and mapped on metrical scales. For this to happen, however, the usually complex relationships must be elucidated, and only in a few cases can this be expected. The
I
w
I
1
Maualification profile aRequirernent profile
Properties Figme 11.40. Plot showing qualification profile and rcquirement profile
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assessment problems arising here are not dealt with further; reference is made to the literature [11.144] and to Figure 3.1. If the treatment is initially limited to the case where all properties are measurable, at least one distance function is found that satisfies the conditions for a metric. Let E denote the set of product properties and d the distance function; thcn the ordered pair {E,d} is a metric space. In accordance with standard mathematical practice, the distance mcasure is immediately dropped from the symbol and E is referred to as the product property space. The concept of “space” can bc retained even when there are nonmetrical types of attributes, provided the distance concept is suitably generalized (e.g., topology and separation axiom -+ Hausdorff space). As a rule, the property profile of a product includcs redundancy. An analysis of the deterministic and/or statistical relationships of the profile leads to a reduction in the number of attributes needed to describe the object “product.” This process is briefly described in Figure 11.42 and illustrated in Figures 11.43 and 11.44.
The result is that any product property profile can, with the aid of the concepts just devcloped, be treated as a point in a suitably selected product space. These concepts also make possible an exact definition of the hard-topin-down term “quality”: Quality is a point in product space. If the components of the profile are also linearly independent and their realizations can be plotted on the real axis, then the property space is a vector space over the real numbers, and any profile can be interpreted as a vector in this space. Tolerances of the individual product properties allow the point to grow into an elementary volume (Fig. 11.45; see also Fig. 11.30).
Process Properties. A ”process” is the operation of creating products having certain properties. Its operationalization leads to the elements of the process profile, the process properties P,. A given realization of all the quantities characterizing the process (e.g., state variables, parameters, control variables, etc.) is called a process state. By construction, so to speak, all relevant process properties involved in process development and plant design will be measurable (monitorable). In canonical fashion, the independently variable process properties span a vector space. By analogy with the product property
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Legal/judicial m a t t e r s
Nominal
Social policy Organization/management Economics Geosciences Ecology Medicine Chemistry O t h e r engineering sciences E l e c t r i c a l engineering Mechanical engineering Process engineering E x p e r i m e n t a l p h y s i c s (special c o u r s e l T h e o r e t i c a l p h y s i c s [special course1 Experimental physics T h e o r e t i c a l physics I n f o r m a t i o n science/EDP Applied mathematics P u r e mathematics
50
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Figure 11.41. Qualification profiles of electrical cnginwrs in process control engineering Il1.1411
Reduction in number of independent components of p r o f i l e s b y i d e n t i f y i n g a) d e t e r m i n i s t i c r e l a t i o n s h i p s b) s t a t i s t i c a l r e l a t i o n s h i p s
it Examples
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Physical t h e o r y Semi-phenomenological model Empirical model R e g r e s s i o n model
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I = cr E
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Figure 11.42. Analysis of product property profile
(SIX
1
Low
also Scction 3.4)
profile, a recipe can now be intcrpreted as a point in the process space (Fig. 11.30). Since the rcgion of definition (rangc of variation) of each process property in real systcms is an interval of finite length, in analyzing a particular plant the treatment can be restrictcd to a finite region in process space:. All that is necessary is to replace Ej by P j in Figure 11.45.
Product and Process Development. The cntire production proccss is handled as a successivc transformation of product property profiles (see Section 2.3). A process. specificd by thc process properties and represented by a point in process spacc. maps an initial property profile to a final property profile and thus links two points in property space. Now the procedure of “product
11.3. I’rocess Anulysb and Process Optimization
365
0 -
€1
€2
€3
[I.
€5
€6
r
f2
Product p r o p e r t i e s Linear dependence, e.g., F, = f , (€,,EL) Figure 11.43. Correlation between components of the product property profile
Figure 11.45. The product property profile as a multidimensional vector in product space
After these general preliminary comments, the particular question of concern in this discussion-the form of the product property profile comes to thc fore. The initial profile is taken as given and fixed. Equation (11.3.3) then depends on thc variables P and E. If the market now demands from a product a certain nominal property profile Es, this has its formal expression in the condition:
.q(P,)= 0
Figure 11.44. Functional dependences between components of the product property profile
development by process variation” can be formally represented as a function
F[P,,P,, . . .. P j ; E , , E , , . . . , Ei;En] = 0 (11.3.3)
where Pi arc the process properties in process space, Ei are the product properties in product space, and E,, denotes the initial product properties upstream of the proccss (see also Figs. 11.1 and 113).
(11.3.4)
where y is specified by the initial property profile E,, and the nominal property profile E, and by the interaction of plant, apparatus, and product taking place in the process step under examination. Specifying one fixed property profile (a point in property spacc) reduces the number of degrees of freedom in proccss space by one. The set of permitted product states then lies on a hyperplane in process space (Figs. 11.46 and 11.47). After this analysis of the relationships between product and process spaces, an attempt is now made to answer the question of how a product property profilc imposed by the market can be realized with the company’s resources, taking both safety and economics into account. Interpolation. In the first step. this objective is to bc achievcd and thc possiblc experiential horizon of an existing plant is to be exhausted. A “recipe change of the first kind,” which does not lead out of the original hyperplanc in
366
11. Operution
process space (Fig. 11.47), does not change the product properties (Fig. 11.46); such a change is therefore ruled out if the required protile Es and the initial profile EA are different. If, however, one moves off the hyperplane (“recipe change of the second kind”), products are now obtained with new property profiles (Figs. 11.48 and 11.49).
If one of thcse points lics sufficiently close to Es. product dcvelopmcnt can be regarded a s successful and no plant change is requircd. If thc requirements imposed cannot be batisficd in the context of the existing capabilities, the initial experiential horizon in process space niust be enlarged.
E
Figure 11.46. Initial product property profile EA and nominal product property profile Es as points in product space
p3
I
Figure 11.48. Property-changing process-state variations with no modilication of plant
€5
I
Product space Figure 11.47. Hypersurfam of property-equivalent process states in process space
Figure 11.49. Possible property changes after the action described in Fig. 11.48
I 1.3. Process Anul.vsis and Process Optimization
Extrapolation. In a second step. this is to happen while the depcndcnccs i n process space arc preserved. The same set of process properties are selccted as above. If. in the development of products with new properties. thc ability to produce the original assortment is to be prcserved. then the range of variation of the individual process properties must be expanded by constructivc actions. If the stratcgy requires the substitution of individual products in case of (partial) abandonment of the original assortment. subscqucnt proccss devclopnient comes down to gcnerating a new range of variation of the process propcrtics (upgrading, “reboring.” dcbottleneckinp). Figures 11 .SO and 11.51 illustrate this extrapolation step. If such an extrapolation does not lcad to the target, the experiential horizon must be expanded in a third step by dcvelopment of a new process or a new plant. Under some circumstances. this leads to new relationships in process space (Figs. 11.52 and 11 S 3 ) . It remains valid that a hypersurface (generally of a different nature) in process space corresponds to the (desired) property profile. Optimization. Subject to the condition of generating equivalent property profiles, all process states on the respective process-space surface are cquivalent. This fact can now be used in selecting a process statc optimal with respect to the extremalization of a target quantity (cost,
energy consumption, yield, etc.). From the mathematical standpoint. this commonly rcduccs to a variational problem with constraints (Fig. 11.54). Noted that the concept developed abovc makes the actions to be taken in product development by process variation, as well as the way
I
/
\ xxL
..
/
Product space E2 Figure 11.51. Possible property change after the action described in Figure l l S O
F
P
New process with other process properties
Process space Process space pt Figure 11.50. Property-changing process-state variations with modificalion of plant
361
PI
Figure 11.52. Hypersurface of property-cquivdlent process states in the new process space generatcd by process development
368
11. Operalion
I Degree of expression
E
€5
= 'fS,'fS2. . . .fs,'
If
Actions
P r o d u c t space
f2
Figure 11.53. Possible property change after the new process development described in Fig. 11.52
Es2
ESl
Figure 11.55. 'l'hc product property profile as a function ol time
@Requirement D r o f i l e f,
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. -?&
.....: -....'......
.:
. ......' :
P r o p e r t y p r o f i l e fA a t s t a r t o f dpvolnnrnont
P r o c e s s space Figure 11.54. Optimization of the process with respcct to a goal quantity
of evaluating their success, more intuitive, and also makes it possible to check them step by step (see also Figs. 11.55 and 11 3 6 ) . However, a certain effort must be taken in order to prepare the required tools. The question whether product development is necessary is thus answered by comparing the qualification profile EAand the requirement profile f$ of the marketed product, that is, by deciding whether the existing properties of the
Figure 11.56. Dynamics of process development can be traced in product space: the portions of surfaces represent subgoals of development
product correspond to the properties required by the market. If the qualification profile and the requirement profile are in agreement to a great extent (that is, if the point in product space does not change), then a merely desirable improvement in the result does not demand any product change;
11.3. Process Annlysis and Process Oprimiiariori
if the proccss structure remains thc same, however, it will be uscful to optimixc the possible changes in process properties (top row in Fig. 11.57) without moving off thc cquiproduct surface (see also Fig. 11S4). A product modification required by the market can be effccted in either of two different ways: 0
0
If the requirement profile is close to the qualification profile, then it is generally possible to continue working with the same process structure. For a product modification, thc process properties are changed within the technically possible range of variation, but this necessitates moving off the equiproduct surface (middle row of Fig. 11.57). If the requirement profilc and the qualification profile of the dcsired product are far apart, then as a rulc it is also necessary to change the process structure, that is, carry out a new development (bottom row in Fig. 11S7).
Measures for Realization. Production Model. The material flow of a typical multistage production proccss in the chemical industry (phase model) is shown in Fig. 1 1.1, p. 335). By abstraction with the aid of the concepts dcveloped above, this material flow is mappcd onto an in-
Point in product space
Process optimization
does n o t change
Product modification
changes slightly
New product development
changes greatly
P o i n t in process space changes, s t a y s on equiproduct surface moves o f f equiproduct
moves o f f equiproduct surface
Process structure
369
formation flow. Thc elements of thc product spacc are described by thc product propcrties and thc points in the process space correspond to the process properties. Furthcr details can be found in [11.131] and Figurc 11.58 (see also Sections 2.3 and 3.5). Figurc 11.59 shows how thc information acquired can bc utilized to discover the explicit relationships between plant, machine, apparatus. and product (i.e., bctween the proccss propertics and the process) for each process step. In particular, mcthods of “system identification” make it possible to derive statistical process models from production data. Such models are needed for tasks in process control engineering (e.g., process indicators [11.130], observcr model). Multivariate methods (such as factor analysis, cluster analysis, and analysis of variancc) makc it possible to establish functional relationships between process data and quality data (see Section 3.2). In summary, it can be stated that the central objective of any process analysis and process optimization is to achieve reproducible nominal product properties. Naturally, the first step is to describe with care the product properties, from the beginning up to the end of the cntire producIdeal model o f p r o d u c t i o n NOMINAL Starting product with well-defined requirement profile
Process with specified nominal values
End p r o d u c t with well-defined requirement profile
Charge o f a starting product with well-defined past
Deviations f r o m nominal values
B a t c h o f end product with measurable property p r o f i l e and well-defined past
remains unchanged
remains unchanged
changes
ACTUAL Real model of p r o d u c t i o n
Figure 11.58. Ideal and real models of production (phase model)
370
11. Operation -
I
Incomplete k n o w l e d g e
p:ctuai
I
Upsets, deviations during production process
o f production process
I Departure
1
pnnmtnal .
f z 7
f r o m nominal Nominal condition
100%
A c t u a l condition a t f ,
W L YI L W
+
0 3 I
m L
Determination o f p r o d u c t p r o p e r t i e s f,
E,
Determination o f process properties P
Machine X
PI
W
0%
Machine X
t,
Time
-
Breakdown
Figure 11.60. Wear-out reserve versus timc
I
Evaluation svstem
t
1 1
pp. ji -----------+----------E, Machine X lr----------
f = f (.....I!
I ! !
Figure 11.59. Evaluation system for quantifying the relationship between proLrss and product properties (sce Scction 3.2)
tion proccss, and of coursc also for each intcrmediate step. The creation of the phase model as shown in Figure 11.57 is thcrefore the ccntral task of the process technologist. A holistic vicwpoint is of fundamental importance (see also Chap. 10). This information-technical step is the most important task. Execution planning, execution, and quality assurance are described in depth in Chaptcr 10. They arc to be applied, with appropriate modifications, t o the problem and task discussed herc. Process analysis and thus process knowledge are also necessary conditions (as shown in Section 11.2) for state-orientcd human- process communication so that the production process can be managed and controlled at all times and under all conditions.
11.4. Maintenance Strategies Introduction. Before maintenance strategics arc discussed, the key objectives of maintenance should be dcfincd [11.145].
Production facilities arc uscd for making consumcr goods. During this production process. the facilities wear out in various ways. In order to remain functional despite wear, production facilities have a reserve of possiblc function fulfillments, called “wear-out reserve,” which is gradually diminished during scrvice (Fig. 11.60) and ultimately leads to breakdown. In the nominal condition, the production facility has a 100% degree of function fulfillment. After a certain time. it loses its wear-out rcservc. The production facility remains opcrable. however, over a broad range. The objcctive of maintcnance is to keep thc wear-out rescrve always above the “damage limit” in order to prevcnt breakdowns. Maintcnance includes three activities [11.145]: 0 0 0
Routine maintenance to preserve the nominal condition Inspection to mcasure and assess thc actual condition Corrective maintenance to restorc the nominal condition
It is recommended that corrective maintenance be subdividcd into scheduled maintcnance and forced repairs (unschcdulcd maintenance) (Fig. 11.61). This distinction relates solely to the nianagement of maintenance work, not to thc quality of thc work. In the case of unscheduled maintcnance, including forced repairs. the schedule is dictated by the evcnt. and the maintenance action is generally predetermined as well. The maintenance workcr can thus d o nothing but react. In thc case of scheduled maintenance, the sequence of actions is dctermined in advance by the maintenance workcr himself, who also influences the actions taken. In othcr words, the worker can take thc initiative. Naturally. it is an
11.4. Mainrenance Strutegies
‘ I
I 1
Planned (scheduled) maintenance
Inspection
I
371
Scheduled corrective maintenance
.
Figure 11.61. Scheduled and unscheduled maintenancc
important goal to put as much maintenance as possible into the “scheduled” category. Maintenance will become even more important. The reasons are the increasing complexity of plants, the greater depth and breadth of automation, the growing size of production facilities, and the increasingly stringent requirements imposed on product quality, safety, and environmental protection. Modern maintenance practices for process control and automation systems are discussed in [11.3]. As the tasks of maintenancc change, its objectives also change. The goals of a maintenance organization have been stated as follows [ 11.1461: To guarantee the retention of value and functionality of production facilities while insuring compliance with requirements relating to 0 0 0
Increasing safety and quality levels Reliability of on-time performance Economically optimal operation
Only when objectives have been clearly formulated can the “correct” strategies be identified and embodied in company policies so that economy measures in the maintenance area d o not cause certain maintenance tasks to be neglected. It is the increasingly high required safety and quality levels that mandate a change of priorities in cost-reduction strategies. Two common costreduction practices are 0
0
Economical performance of maintenance activities Optimization of material and supply use in maintenance
But the potential of these has been largely exhausted. Now a new requirement has taken on
top priority: reduction or even prevention of maintenance. This practice, which involves the elimination of weak points, still holds a substantial potential for improvement. Production facilities needing fewer repairs have higher availability and can be operated both more economically and more safely. Finally, “maintenance” in a holistic sense has become a topic in the design process (Chap. 10) and must be considered at that stage. Strategies for Performance of Maintenance Activities. Maintenance strategies Serve to make maintenance a more systematic operation. They are oriented toward maintenance activities (Fig. 11.61). One speaks of a certain rnaintenance strategy, say an inspection-oriented strategy, if this activity (inspection) is purposely emphasizcd in the context of changing objectives and is not sacrificed when economy measures are introduced. Naturally, this does not mean that other maintenance activities are wholly de-emphasized. Breakdown Strategy. The dominant role in the past was played by the breakdown strategy, which is based on forced repairs and, by definition, unscheduled maintenance. Despite the high cost of devices used in process control engineering, this strategy is still fairly important because many of the sensors and actuators come into contact with products and hence experience contamination, deposits, plugging, and corrosion. It can be shown statistically that such breakdowns are not greatly reduced by more intensive scheduled maintenance. Some improvement is gained with measuring techniques, such as radiometry, that d o not involve contact with the product.
372
I!.
Operalion
Indced. the breakdown strategy docs have advantagcs: It entails low costs, the work can be donc on site by forcmanjcraft teams, and downtime is gencrally short. In recent years, however, some significant drawbacks have also cmcrgcd: 0 0
0
Costs increase with increasing degrec of plant utilization and investmcnt level Becausc of the “workshop” oricntation. it may takc much elrort and expense to maintain operation at wcak points such as those resulting from interaction with products; this strategy thus achieves the cxact opposite of maintenance prcvcntion More stringent rcquiremcnts on safety, occupational health, and environrncntal protection, coupled with heightened public scnsitivity, mean that a breakdown maintenance strategy cannot be practiccd cxclusivcly
Schcduled maintenance stratcgies have thus become more important. Preventive Maintenance Stralegy. Much was cxpcctcd of preventive maintenance when scheduled maintenance stratcgics were first introduced. It is not just a matter of cost that has made this approach obsolete. The reliability of many devices (scrvicc lives of valves and motors) today is so great that prcventive maintcnance is justificd only where an element wears out after a known time and can then be replaced as a unit, for example in the case of fluorescent tubes. Preventive maintenance is still necessary when there arc special factors, such as plant safety or quality assurance considerations. Inspection-Orienled Strategji. As safety and environmental requirements grow more stringent, the inspection-oriented strategy has come to play an important role. This applies to many devices significant for damage prevention or limitation, such as rupture disks, leakage retaining vessels, and protective and damagc-limiting devices in process control systems. Legislation, such as the Storfdlverordnung in Germany [Accident Regulation], mandatcs thc continuous surveillance (inspection) of such devices in the form of periodic function checks. The performance and results of thcse actions must be documented. These requirements have had dramatic cffects. As a one-time engineering cost, they havc given rise to cxtcnsivc safety analyses with plant upgrades and danger aversion plans (e.g., functional inspection charts). More importantly,
they have led to continuing additional labor costs for plant monitoring and safeguarding and for the documentation of actions carried out (sce Section 3.5). The spccific cost of monitoring and documentation niay dcclinc over a span of years as new and improved inspection methods appear on the market. These might include instrunicnts with automatic self-tcsting and auxiliary diagnostic devices in electronic systems (SCC Chap. 5). as wcll as modcl-aided early fault detection methods (e.g.. [11.147]). Another type of inspcction-oriented strategy, visual inspection, is the basis for dcterrnining and assessing the condition of production facilities and process control equipment. The “prcservative register” is a suitable tcchniquc for documenting thc rcsults [11.1481. Depending on the economic and political situation, another strategy may gain in importance: Maximization of plant availability, and thus safety, by application of thc rcdundancy principle, as is alrcady required in nuclear power plants [11.149]. The redundancy principle rcquires installation of spare devices, which automatically takc up the function of broken-down units and report the malfunctions. This is not, however, a typical maintenance strategy but rather a practice that must be adopted at thc design stage (see Chap. 10). High availability, such as is achievcd under the redundancy principle, improves the operating economics. The application of this principle Icads, however, to highcr costs, at least initially. Additional investmcnt is required, which in turn gives rise to additional maintenance costs, since thc redundant units must also be kept opcrabk even when on standby status. Scheduled Correclive Muintenunce Strutegy. The analysis of strategies would not be complete without the strategy of schcduled correctivc maintenance. Scheduling correctivc maintcnance (repairs) does not have the aim of restoring the nominal condition ; instead, alternative solutions must be sought, with an eyc to economic, ecological, and process-enginecring considerations. Thc following question must be examined: maintenance, investmcnt, or both? This applies to plants, plant scctions, apparatus, and process control equipment. The procedure, also refcrrcd to as “intelligent” maintenance, regards maintenance in the context of the plant cconomy, that is, holistically. The “plant economy” includes
11.4. hfuintrnuncr Strutegies 0 0 0 0
Design Fabrication, installation. setup Use (operation, maintenance. upgrading) Replacement
The centerpiece of the method is a decision model that provides a sort of value analysis to support dccisionmaking about maintenance practiccs. On the basis of a situation analysis covering certain independent variables and constraints. the technique compares alternativc actions and rates them through cost benelit analysis. Decisionmaking is not, however, automatic; an educated interpretation of events is nceded.
Maintenance Control Loop. Maintenance is concerned with the actual and nominal conditions of the facilities and equipment being main-
313
tained. Maintcnance can thus be regarded as a control loop, as illustrated in Figure 1 1.62, which shows a breakdown of maintenancc activities. Fhdings. damage. and malfunctions must be acquired and evaluated; weak points must be idcntified by a process of closing in. Finally, thc loop must generate actions, which function as control actions. They react on the facilities being maintained (except for unschedulable breakdown maintenance). In the maintenancc control loop, planning (design) work is an essential component of the plant cconorny. It is at this stage that maintenancc costs are set by the choice of process and plant design. Planning yields the plant and device documentation. which serve as controlling data, but the working plans for scheduled maintenance can also be generated in this stage. Maintenance-oriented installation is crucially
I t--------- 1
Maintenance
I
Design (planning)
Inspection
I1
Plant and device documentation; maintenance schedules
I
I
Scheduled corrective maintenance
I
I I I I +
e
Figure 11.62. The maintenance control loop [11.146]
'
I I Breakdown maintenance
Acquisition and interpretation o f findings, damage, malfunctions;
I I I I
Additional tasks
374
1 1 . Operation
important. along with the maintainable design of apparatus and equipment (see Section 3.5 and Chap. 10). A holistic maintenance program requires the support of a powerful information system. Maintenance-relevant information stems from all maintenance activities. The documentation, however, must focus more on movement data generated on site by the operating engineers in the course of their work. This information goes into object-related history files, which can then provide the informational basis for documenting maintenance activities or analyzing weak points. Figure 11.63 illustrates the identification of weak points in process control systems. The ordinate is the total time spent in remedying problems, and the abscissa is the number of problems arising with individual objects in the system. Each entry characterizes one object. The weak points are located at top right. They stand out because of the large maintenance time rcquirement and the large number of problems. On the basis of the history files, the numbered groups can be formed by an computational technique called cluster analysis [I1 . I 501. Outlook. Breakdown maintenance continua to play by far the major role in the chemical industry. What is lacking is the logical application of scheduled maintenance strategies. This has to d o with a shortage of methods and tools with which a maintenance program oriented chiefly toward prescrving and restoring productive capacity can be conceptually and informationally integrated into the plant economy. There arc, however, difficulties in achieving this kind of integration; the staggered and often uncoordinated adoption of information technology (hardware and software systems) has meant that bodies of engineering, administration, economic, and logistical data are mutually inconsistent. This underlines the importance of using the methods described above for structuring the
Number of problems p e r year
-
Figure 11.63. Use of cluster analysis 10 identify weak points in a process control system
plant economy, and only then implementing it technically (see Chap. 2 ) . In an integrated data processing system. maintenance should be a component of thc plant economy with at least the following dements [11.147]: A documentation system for thc acquisition, structuring, editing, and storagc of maintenance-relevant plant data Planning and control systems for schcdulcd maintenance A control system for generating action- and object-based cost and performance reports and calculating maintenance performance figures A maintenance information system comprising these blocks should have compatible interfaces to other in-company data processing systems, especially to the production planning and production control. bookkeeping and cost accounting, materials records, and engineering subsystems (see also Fig. 2.19).
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
12.1. Principles
12. Standards, Committees, and Associations 12.1. Principles The committees and associations that are either directly or indirectly concerned with proccss measurement and control are so many and diverse that it is impossible to present a complete list. This chapter merely indicates the points of concentration. functions, and objectives of the most important such standardization bodies. What is Standardization? A clear answer to this question appears in DIN 820 Part 1 ; 01 3 6 ; Pardgraph 2: “Standardization is the unification of material and immaterial objects for the benefit of the general public. carried out jointly and in a planned manner by the interested circles. It must not lead to a special economic advantage for individual parties. It promotes rationalization and quality assurance in business, technology, science, and administration. It contributes to the safety of persons and property as well as the improvement of quality in all areas of life. What is more, it contributes to good order and the dissemination of information in the specific area standardized. Standardization is practiced at the national, regional, and international levels.” Who May Create Standards? In Germany. only D I N and D K E are permitted to issue mandatory standards. Under the standardization agreement between DIN and the Federal Republic of Germany, May 5, 1975, D I N is to represent the standardization interests of the Federal Republic in regional and international standardbation bodies. CEN and CENELEC European standards also become effective only after they have been published by DIN or DKE. IEC and I S 0 international standards can be adopted as national or European standards, but there is no obligation to d o so. German-speakers distinguish between the terms “norm” and “standard.” Norms are always somewhat official, while standards are private in nature and represent agreement among certain groups. Both terms relate to technical specifications. In the case of a norm, such specifications are adopted by a governmental or internationally recognized standardization organization. Common usage, unfortunately, does not
375
always maintain this precise distinction, and one often speaks of I S 0 standards. In the German sense, these are norms, since they are issued by ISO. VDE and VDI standards (called “VDE-Bestimmungen“ and “VDI-Richtlinien”) d o not have the status of norms in law. They are designated as the state of the art. The same applies to the recommendations and work sheets published from time to time by other bodies such as NAMUR, ZVEI, or VIK. The purpose of these publications is to make results in science and technology known to a broad public as promptly as possible, since experience has shown that the issuance of norms is a very slow process. These papers generally become proposed norms for consideration by standardization bodies, are discussed among a broad public, and after some years appear in modified form as norms. The essential function of these associations is thus to create draft norms and evaluate draft norms. Both norms and standards are described by the single word “standard” in American English. A “norm” in Germany (and many other countries) might be equated, quite roughly, to a mandatory standard or a standard embodied in a governmental regulation, while German “standard” might correspond to a “voluntary” standard. Standards issued by ANSI, ASTM, etc., are considered voluntary in the United States, but elsewhere they may be regarded as closer in legal status to “norms.” The remainder of this chapter employs “standard” for both classes but provides clarification when necessary. So-called industry standards are in essence neither standards nor norms, but successful products or specifications that have become established in the market. Situation in the United States. The ISA occupies a dual position in the United States: As a technical-scientific organization (comparable with VDE in Germany), it not only takes part in thc preparation of standards but also has the power to issue its own standards, having been granted this right, for the field of process control engineering, by the American National Standards Institute (ANSI; see Section 12.3). Also deserving of mention is the Institute of Elc-:trical and Electronics Engineers (IEEE), which develops and generates international standards. Its publications, however, are not mandatory standards but recommendations.
316
12. Srunuhrrlv. Commiirees. and A.ssnciritinn.\
Changes in the Standardization of Process Control Engineering. Thc 1980s saw a markcd change in technology. from signal-oriented mcasurcment and control to information-oriented process control cngincering. Not only companies but also national and international standardization bodies have had to deal with this change. While standardization bodies in the 1970s were concerned with embodying thc statc of the art in standards, in thc early 1980s the emphasis changed to standardization in parallel with research. To lower rcscarch costs. a context for dcvclopmcnt was cstablishcd in national and international standardization bodies. The changcs in tcchnology and in standards emphasis also necessitated a new structure. This was acknowledgcd at the end of the 1980s when three ncw bodies werc set up: IEC TC65 (Technical Committee 65- -Industrial Process Measuremcnt and Control), DKE 1B9 (“Spccial Area 9 - Measurement and Control”), and NAMUR (“Standards Working Group for Measurement and Control Engineering in the Chemical Industry”).
12.2. Standardization Bodies and Other Organizations Involved in Standardization The most important standards bodies for process control engineering. with thcir functions and objectives, are listed below: CCITT. Thc Comite Consultatif Internat i o n a l ~Tclegraphiquc et Tilephonique (International Consultative Committec on Telcgraphy and Telephony) unites national postal and telecommunications organizations and has jurisdiction over telccommunications standards.
C E N . The Comitc Europecn de Normalisation (European Committee for Standardiyation) has the European national standardization bodies as its members. Together with CENELEC, it creates European standards and provisional standards. CENELEC. The Cornit6 Europeen de Normalisation Electrique (European Committee for Electrical Standardization) includes European national standardization bodies concerned with electrical tcchnology .
CEPT The Comitk Europeen dcs Administrations des Postes et dcs Tc1cconimunicatic)ns (Europcan Committce of Postal and Telccommunications Administrations) is the confercncc of European postal and tclecommunications agencies. D I N . The Dcutschcs Instiiut fur Normung (German Standardization Institute) is thc national institution that dcvclops and adopts standards (mandatory standards).
DKE. The I h t s c h e clektrotechnische Konimission im DIN und VIIE (Gcrman Elcctrotcchnical Commission of DIN and VDE. VDE = Gcrman Electrical Engincers‘ Socicty) is thc national organization that develops standards and safety provisions in the clectrotechnical ficld.
E TSI. The European Telccomni un ica t ion s Standards Institute was established by European tclecommunications agencics together with thc EC Commission after substantial dcficicncics had becn revealed in thc assimilation of standards in thc tclecommunications field. ETSI‘s task is Europcan hamionization of standards for public-sector telecommunications services. IEC. The International Elcctrotcchnical Commission develops international standards in the electrotechnical ficld. ISO. The International Organization lor Standardization functions as an umbrella organization for ncarly 90 national standardization bodies. In the intcrest of flexibility, I S 0 standards are formulated with many degrees of freedom and must be made more specific by lowcrlevel institutions. The Internatinnu1 Elecrrorcchnicril Commission (IEC) was founded in 1906. Its membcrship includes more than 40 national committces, and the official languages are English, Frcnch, and Russian. The purpose of thc IEC is to support intcrnational collaboration in all matters of standardi7ation and certification in thc elcctrical and clectronics fields. It aims to achieve this by mcans of publications, recommendations. and intcrnational standards serving as modcls for incorporation into national legal codcs. IEC standards scrvc manufacturers. consumcrs. rescarch institutes, and govcrnmcntal agencics concerned with intcrnational trade and elcctrical safety is-
12.2. S!undardiiution Bodies and Other Organizaiions Involved in Standardization sucs. Any country. without rcstriction, can
bc-
come an IEC mcniber. Figure 12.1 summari7es the structurc of the IEC. Thc IEC Suborgani7ation “Conformity Tcsting to Standards for Safety of Electrical Equipment” (IECEE) has the purpose of ccrtifying products for conformity to IEC standards. The ccrtification system applies to electrical devices uscd in the homc. the office, and the workplace. For further information. see 112.11. The IEC Technical Cornmittcc TC65, “Industrial Process Measurement and Control” (Fig. 12.2), has as its scope the crcation of international standards for systems and clcments of industrial proccss control, including batch and continuous processcs, as well as the coordination of standards for measurement and control devices and systems. Subcommittee SC65A, “System Aspects,” is concerned with standardi7ing generic aspects of systems, such as operating conditions. assessment of systems, functional reliability, and so forth. SC65B, “Devices,” deals with standardization of devices and systems in the field, the switch room, and the control room. In SC65C, “Digital Communication,” standards are developcd for communications betwccn subsystems. The European Committee for Stundardizalion (CEN) and the European Commiitee for Electrical Standurdization (CEN E L EC) came into being in the early 1960s as regional standards bodies (Fig. 12.3). The membership of CENjCENELEC includes the national stan-
dards institutes of all EC and EFTA countries. These institutes are also members of I S 0 (Intcrnational Organintion for Standardization) IEC (International Elcctrotechnical Commission). CENELEC has the objective of harmonizing technical standards in Europe. In order to achicve these goals, the harmonization efforts of the Community are (under Article 100 of the EEC Treaty) to concentrate on solutions that establish binding criteria and make standards thc basis for dctcrmining the technical details of products. Morcover, with regard to problems specific to new technologies, it is important that standardi7ation at the trans-European levcl become effective at an early stage. Publications created by the specialist activity of CENjCENELEC and issued by CEN/ CENELEC are called European Standards (EN), Harmonization Documents (HD), Provisional European Standards (ENV), or CEN/ CENELEC Reports. European Standards and Harmonization Documcnts are referred to as “rcgional standards” in the sense of the ISO/IEC definition. The various publications differ in the following ways: 0
0
The basic rule is to create European Standards, since it is important that the national standards of the member countries be identical wherever possible. Harmonization Documents are issued when thc change to identical national standards is unnecessary or impractical, and especially
committee of t h e IEC quality assessment system for
Technical
L-----
Sub-
1comm:ttee S C J
(group
Sub[committee S C ]
1
lcommijtee E C ]
WGI
Figure 12.1. Structure of the I C C (simplified)
317
3 78
12. Siandurds. Commiiiec~s.und Associutions
I
:nCdu6s:ria[-process
Advisory group] I
Measurement and control
IWG1: I Terms and definitions] I
[WGL: Interface characteristics) I
I WG2:
Service conditions]
I
I
(WGL: Electromagnetic i n t e r f e r e n c e s ]
WG9: Safe s o f t w a r e lWG10: Functional s a f e t v o f PESl I (WG11: Batch c o n t r o l systems]
I
I W G S Temperature se n so r s\ 1
WG6: Methods o f te sti n g and evaluation o f performance
WG9: Final c o ntr o l elements Figure 12.2. Structurc of IEC ’IC65
0 0
when a unification can be achieved only by granting national variances. Provisional European Standards are planned standards for use on a preliminary basis. CENjCENELEC Reports are intended to provide information.
The Vilamoura procedure has thc goal of promoting collaboration between national committees on a regional level by decentralizing the earliest stages of work. All new intentions to standardize on the national level should be disclosed in detailed form, so that all members are acquainted with the content of the proposed project and can indicate their interest in active participation if they wish to d o so. “New intentions to standardize on the national level” means all intentions to Standardize at the national level except for: 0 0
Revision of existing national standards (see explanation below) Adoption of the results of CENELEC work
0
Adoption of the results of IEC work
The results of national notifications lead to an EN, an HD. or an ENV. Notifications must be sent to all parties concerned (national committees, the CENELEC Secretariat, and the Technical Committee) for response. If no positive responses are received, the notifying national committee is free to develop a national standard. The notification process for the revision of national standards is based on the assumption that the intention to revise is justified by the necessity of updating the standards. The revision of a national standard for the purpose of taking over an IEC standard, IEC provisional stmdard, or IEC publication, or for the purpose of bringing the national standard into conformity with an H D or an EN need not to be disclosed under this procedure. The Technical Committees of CENELEC are numbered to correspond with those of the IEC.
12.2. Stundurdizution Bodies and Other Organizations Involved in Standardization
The member countries of CENELEC arc listed below: Austria: hterreichisches Elektrotechnisches Komitee (OEK) beim Osterreichischen Verband fur Elcktrotechnik (OVE) (Austrian Electrotechnical Committee of the Austrian Electrotechnical Society) Belgium: Cornit6 Electrotechnique Belge (CEB), Bclgisch Elcctrotechnisch Comitk (BEC) (Belgian Electrotechnical Committee) Switzerland: Comite Electrotechnique Suisse (CES) (Swiss Electrotechnical Committee) Germany: Deutsche Elektrotechnische Kommission im DIN und VDE (DKE) (German Electrotechnical Commission of D I N and VDE) Denmark: Dansk Elektroteknisk Komite (DEK) (Danish Electrotechnical Committee) Spain: Asociacion Espaiiola de Normalizacion y Certificacion (AENOR) (Spanish Standardization and Certification Association)
Finland: Finnish Electrotcchnical Standard Association (SESKO) France: Union Techniquc de I’Electricitc (UTE) (Technical Union of Electricity) United Kingdom: British Electrotcchnical Committee (REC), British Standards Institution (BSI) Grccce: Hellenic Organization for Standardization (ELOT) Ireland: Elcctro-Technical Council of Ireland (ETCI) Iceland : The Icelandic Council for Standardization (STRI). Technological Institute of Iceland Italy: Comitato Elettrotecnico Italian0 (CEI) (Italian Electrotechnical Committee) Luxembourg: Service de I’Energie de I’Etat (SEE) (State Energy Service) Netherlands: Nedcrlands Elektrotechnisch Comite (NEC) (Netherlands Electrotcchnical Commi ttcc)
IEF,TA Secretariat I
peen des constructuers de m a t h 1 aerospatial), associated with CEN
[Europaisches Komitee f u r Eisen- und Stahlnormung)
F Joint presidia1 committee
ITSTC [information technology steering committee)
leuropean workshop Figure 12.3. Organirational chart of CENELEC
379
CEPT leuropean committee o f p o s t a l and telecommunication administrations) ETSl leuropean telecommunications standards i n s t i t u t e )
380
I 2 . Srun&rd.s, Conrnzirtees, ond Associarions
Norway: Norsk Elektrotcknisk Komite (NEK) (Norwegian Electrotechnical Committee) Portugal: Instituto PortuguEs da Qualidade (IPQ) (Portuguese Quality Institute) Sweden: Svenska Elektriska Kommissionen (SEK) (Swcdish Electrical Commission) The EC Commission has therefore devoted spccial attention to the development of standardized test methods for vcrifying compliance with thc standards. The ultimate objective is a European certification framework that would award a publicly rccognized certificate to products that conform to the standards. The ECITI (European Committee for Information Technology Testing and Certification) is a body created by thc EC Commission for the purpose of promoting the testing of‘ standard-conforming products in Europe. CENELEC always follows the principle of using thc results of IEC work whenever possible and also of carrying out new standardization projects within the IEC when thcrc is a prospcct of accomplishing this within a given time span. In this way, multiple treatments of the same topic are avoidcd. The extensive collection of IEC publications and the continuation of standardization in the IEC are important components of this effort. With the information agreement of November 1989 and the cooperation agreement that came into force on January 1, 1991, the essential took for furthcr collaboration between IEC and CENELEC and their members were created. The cooperation agreement aims, above all, to accelerate the standardization process by causing a draft prepared on one organization level to be submitted for agreement simultaneously and in parallel on the international and European levels.
DIN. The German standards system deals with all technical disciplines. DIN standards reflect actuality and document the level of technical experience. Each standard is investigated with regard to its economic impact, and only those that are absolutely necessary are issued. DIN standards contribute to rationalization. quality assurancc, safety, and understanding in business, engineering, science, and administration, and also among the public. DIN and its agencies serve as thc national representation in international and European standards institutions (ISO/IEC, CEN/CENELEC).
DKE. DKE is the Gcrnian organization that develops standards and safcty rules in the field of electrical tcchnology. D K E is dcdicated to the safcty of persons using electricity on the job. at home, and during leisure time. DKE is furthcr involved in solving problems of cnvironmcntal protection, answcring consunier questions. and creating nuclear safcty regulations. DKE has jurisdiction ovcr the harmonization of clectrotechnical standards within the European CommunitY. In Germany, mandatory standards can be created only by DIN or DKE. Each European Standard must bc incorporatcd by all CEN/ CENELEC members into their own standards systcm. EFTA countries that have decided against the European Standards are free of this obligation. Adoption requircments such as those that apply to EN and H D documents d o not exist for I S 0 and IEC international standards. On the European level, ISO/IEC standards are subjected to a questionnaire proccdurc; if thcy meet with agrecrnent, they are uniformly adopted in all EC and EFTA countries. Results of the work of I X E are issued primarily under the titles VDE-Bestimmung (VDE voluntary standard) DIN-Norm (DIN mandatory standard) DIN-EN-Norm (DIN European Standard). with D K E number if applicable DIN-IEC-Norm (DIN - IEC world standard), with D K E number if applicable DKE’s “SDecial Area” 9 (FB91 . , bore the name “Mcasu;ement and Control” up through 1990 and was then rcnamed “Process Control Engineering.” FB9 (Fig. 12.4) creates basic standards for industrial process control as well as specialized standards for thc following application areas: process enginecring. powcr plants. networks, buildings, pipelines, ironmaking and iron founding, mining, shipbuilding, instruction and training, and laboratory practice. It is not the j o b of standardization bodies to carry out preliminary technical and scicntific work. This must be done by the societies that cover the various areas (e.g., NAMUR, GMA. ZVEI). This function includes the dcvelopment of technical concepts, technical and scientific groundworks for standardization projccts, and engineering studies and assessments. The procedure of standardization in parallel with development can only become an cffcctivc aid to both
1-12, Stundurdizarion Bodies and Other Organixztions Involved in Stondardizution
38 1
tional and educational problems. to accident research. and the drafting of standards. VDI. The transfer of technical and scientific knowledge is the primary objective of technical and scientific work performed by the Association of German Engineers (VDI). This is accomplished chicfly in the VDI specialist subdivisions. An important emphasis in these subdivisions is the creation of standards documents known as Richrlinien (guidelines). which reflect the state of the art but d o not have the legal status of mandatory standards.
-
9.5 Information logistics]
4 9 . 6 Devices and s v s t e m s l Figure 12.4. /\reds of interest of DKE FB9
parties when close cooperation is achieved between these technical societies and the standards bodies. VDE. The transfer of technical and scientific knowledge, as a service to all professional electrical and electronics engineers, is the first task of VDE. The spectrum ranges from historical aspects of elcctrical engineering, through occupaAssembl
VCI. Among the diverse committees and working tcams of thc Verband der Chcmischen Industrie (VCI, Association of the Chemical Industry), thc Engineering and Law Committee merits special mention. Through the “Measurement and Control” working team, VCI makes use of NAMUR specialist knowledge. This working team is identical with the govcrning body of N A M U R (see below).
N A M U R . The Normenarbeitsgemeinschaft fur Mess- und Regelungstechnik in der Chemischen Industric (NAMUR, Standards Working Group for Measurement and Control Engineering in the Chemical Industry; Fig. 12.5) takes in users of process control engineering (electrical,
o f members
Business office
Steering committee
Steering committee
Steering committee
Steering committee
Information
1 1 , I I IWorking teams and sections f o r special and current themes1 Figure 12.5. Organizational chart of NAMUR
382
12. Standurcis, Committees. and Associotions
measurement, and control engineering) in the chcmical and allied industries. It was founded in 1949 by BASF, Chemische Wcrke Hiils, DAG Troisdorf, Glanzstoff-Courtaulds, Ilenkel, Ruhrchemic, UK Wcsseling, and Farbenfabriken Baycr. At present, NAMUR has 54 mcmber companics, a third of them located outside Gcrmany (in Switzcrland, Austria, Belgium, and the Nethcrlands). Activities of N A M U R relatc chiefly to: Participation in thc standards committees of DIN and D K E (in particular, DKE FB9, "Process Control Engineering") Participation and input in the creation of standards and specifications at the voluntary standards lcvel (e.g.. VDliVDE-Richtlinien documents) Exchange of experience with process control dcvices, especially with newly devclopcd instrumentation and other hardware Publication of NAMUR recommendations in all fields of process control engineering when no other standards cxist Scicntific support of technical journals Publication of NAMUR Status reports to document thc state of thc art in thc field of process control engineering and in Further details can be found in 12.901 .~ [12.2].
VIK. The Vcreinigung Industrielle Kraftwirtschaft (VIK, Industrial Energy Management Alliance) was founded in 1947. The principal concerns of the Elcctrotechnical Committce arc as follows: Exchangc of experience Influence on the preparation of deviccs suitable for consumer use Influence on the modification of VDE standards to conform with consumer requirements Influence on decreasing the number of designs Efforts to establish optimal conditions for accident prevention Attainment of economic benefit in the use of elcctrotcchnical devices This applies t o elcctrical installations and ~. systems in industrial power supply and production service.
Z VEI. The Zentralvcrband Elektrotechnikund Elektronikindustrie e.V. (ZVEI, Central
Fcdcration of the Elcctrical and Electronics Industry) reprcsents the intcrcsts of thc German electrical and electronics industry in niatters of economic and techology policy. ZVEI has the following tasks: To safeguard the common professional and busincss interests of its members before all agencies. in particular by cooperating with rcgulatory, economic, and other agcncies and institutions and secking to provide input to legislativc measures To promote thc gcncral economic and tcchnical development of the clcctrical and clcctronics industries
ISA. Thc Instrument Society of America (ISA) was established in 1945 in Pittsburgh. Pennsylvania. It is a nonprofit organization active in thc tcchnology, sciencc, and educational sector. Its concerns arc with the theory, design, manufacture, and application of process control devices. systems. and computcrs. It issues the monthly International Journal of Instrumentation and Control (INTECH). In the United States, the ISA made its namc with an cxtensive training program and companion publications such as books. journals. software packages, videos, and CBT (computcrbased training) packages. Moreover, ANSI (American National Standards Institute) has delegated to ISA the right to issue mandatory standards in the field of process control engineering. Valuable comments can be found in numcrous ANSI/ISA standards scctions.
12.3. Technical and Scientific Bodies It is difficult and often impossible to distinguish bctween societies that arc active in the standardization field and bodies whose work is primarily tcchnical and scientific. The cmphasis for tcchnical and scientific committees is on the sharing of experience in rcscarch and development, production and technology, training and continuing education, and safety. Such bodies can frequently bc identified by thc Fact that they regularly organize high-level congresses. In this way they contribute to forming public opinion, and this in turn influences standardization. In Germany, for example, the DVT unites a numbcr of technical and scientific associations. such as DPG, DIN, GI. VDE. and VDI. Also
12.3. Technicul and Scienrific Bodies
important for process control engineering are GMA, Gesellschaft fur Informatik (GI, Society for Information Processing), and Gesellschaft fur Mathematik und Datenverarbeitung (GMD, Society for Mathematics and Data Processing). a public-sector research institution. At the international level, there are a variety of national societies with special objectives. One such society oriented toward automation technology is IFAC; toward metrology, IMEKO; and toward the type testing of measurement and control equipment, the International Instrument Users Association. DVT The Deutsche Verband technisch-wissenschaftlicher Vereine (DVT, German lederation of Technical and Scientific Societies) currently includes 100 technical and scientific organizations. It has the objective of analyzing high-level problems in science and technology and representing the interests of engineers and natural scientists in scientific, economic, social, political, governmental, and administrative circles. Its tasks include the promotion of applied sciences. the unification of common technical principles, the improvement of technical educdtion, and cooperation in shaping legislation in the field of technology and technical management. The DVT regards itself as a link between members and also between the membership and business societies whenever technical and scientific problems are addressed.
A M A . The Arbeitsgemeinschaft Messwertaufnehmer e.V. (AMA, Data Sensors Working Group) is an association of manufacturing firms in the areas of sensor technology, sensor-based measurement and automation, and microsysterns technology. DPG. The Deutsche Physikalische Gesellschaft e.V. (DPG, German Physical Society) has the following objectives: to advance pure and applied physics, to bring its members and all physicists living in Germany closer to one another, to represent physicists as a body to other members of society, and to promote the exchange of experience among physicists in Germany and between them and their colleagues in other countries. Notable special activities in the field of process control engineering are the conferences held by the k r a t e n d e Ausschuss der Industriephysiker ( M I , Advisory Committee of Physicists in
383
Industry): the sensor technology conference in Munster and the automation conference in Bonn. G M A . The Gesellschaft Mess- und Automatisierungstechnik im VDI und VDE (GMA, Metrology and Automation Society of the VDI and VDE) is active in measurement and control technology, automation, and process control engineering. The spectrum of related applications extends from the basic chemicals industry to the process industries, industrial and process engineering. energy generation and distribution, the automotive, transportation, and aerospace industries, construction engineering, medicdl technology, public utilities and waste disposal operations, and special applications in machinery, apparatus, and instruments. Measurement and control, as a basic processlevel technology, forms the basis for the functionally oriented, hierarchically structured field of automation and process control engineering. The principal points of emphasis in the action chain between process, automation system, and human being, as well as the higher-order, associative disciplines and engineering technologies, are communication, design, and simulation, together with information processing and, on this basis, the techniques of process operation and monitoring, automation equipment and systems, the technology of actuators and sensors, and also the dynamics of machinery and processes.
Areas of activity of the G M A : Exchange and evaluation of experience and information Holding conferences and lectures Cooperation with national and international bodies working in this field Stimulation and support of research Publication and support of scientific journals Development of guidelines and other recommendations Sponsoring young engineers Involvement in education National collaborations: DGK DGQ DIN DKE
Deutsche Gesellschaft fur Kybcrnetik Deutsche Gesellschaft fur Qualitiit e. V. Deutsches lnstitut fiir Normung Deutsche Elektrotechnische Kommission im DIN und VDE
384
12. Sfundnrds. Committees. und Associations
GI Gescllschaft fur Informatik e. V. NAMUR Normenarbeitsgemeinschaftrur Me& und Kegelungstechnik in der Chemischen lndustrie I n tcrnational collaborations :
IMEKO
Internationale MeUtcchnische Konfodcration IFAC International Federation of Automatic Control The organization of the G M A is shown in Figure 12.7. GI. The Gcscllschaft fur Informatik (GI, Society for Information Processing) has as its objective the interchange of tcchnical and scientific information in thc field of "informatics" o r information processing. G M D . The Gcsellschaft fur Mathematik und Datcnverarbeitung mblf (GMD, Society for Mathematics and Data Processing) is a publicsector research institution that uses basic research in the field of informatics to further the developrncnt of information technology and its applications. O n the basis of wide-ranging basic research, conceptual work is pcrformed and methods are devised that will lead to tools. GVC. The VDI-Gesellschaft Verfahrenstechnik und Chemieingcnieurwesen (GVC, the VDE Society for Process Engineering and Chemical Engineering) is the specialist organization of engineers active in process and chemical engineering as well as allied occupational groups. One of its central activities is the annual congress of process engincers. Its objectives are as follows: 0 Sharing of technical and scicntific experiencc 0 Employment of specialist competence of the GVC technical Committees 0 Training and continuing education 0 Collaboration with othcr institutions 0 Social policy IFAC. The International Fcdcration of Automatic Control was founded in 1957 as a multinational organization. Its members arc technical and scientific societies from 45 countries. Its objective is to support control science and technology in a holistic sense, that is, technically, physically, biologically, socially, and economically, both in thcory and in practice. The task of IFAC is to support information exchange between national and international organizations active in the fields of automation and process control en-
gineering. WAC holds a world congress every three years; betwecn congresses, it organixs symposia and workshops on spccial themes. Figure 12.6 shows the organi/ational structurc of IFAC. IFAC is a member of the "Fivc Intcrnational Associations Coordinating Committee" ( F I ACC), which also includes the following bodies : IFIP International Federation [or Information Processing IFORS International Fcdcration of Operational Research Societies IMACS International Association for Mathcmatics and Computers in SimulaLion IMEKO International Measurement Confederation Member societies of IFAC by country are as follows: Argentina: Asociacion Argentina de Control Automatico (AADECA) Australia: The Institution of Engineers Austria: Arbeitsgemeinschali fur Automatisierung Belgium: Federation lBRA/BI RA Brazil: Sociedade Brasilcira de Automatica (SBA) Bulgaria: National Council of Automation Canada: Canadian National Comrnittce for I FAC Chile: Asociacion Chilcna de Control Automatico (ACCA)
(1Serretariatjj
I
Administrative and finance committee
Figure 12.6. Organizalional chart o f IF-AC
1
-
7
12.3. leclinical and Scientific Bodies
385
Members con ress
Advisor
IPublicity
counciVexecutive committee
p
.
4
Offices
-]Collaborations1
FB 1 Fundamentals, theory
FB 3 Measurement and control devices, basic functions
FB 5 Operation of automation systems
Information
FB I Measurement technology for automated Production Applied control
Figure 12.7. Organizational chart of GMA
China: Chinese Association of Automation, Institute of Automation Czechoslovakia: CSFR National Committee of Automatic Control Denmark : El. Power Engineering Department, Denmark Technical University, Lyngby Egypt: Egyptian High Committee of Automatic Control Finland: Finnish Society of Automatic Control France: Association Franqaise pour la Cybernetique, Economiqucet Technique AFCET Germany: VDIjVDE Gesellschaft Mess- und Automatisierungstechnik (GMA) Greece: Technical Chamber of Greece Hong Kong: The Hong Kong Institution of Enginccrs Hungary: IFAC National Member Organization Computer and Automation Institute India: India Institution of Engineers Israel: Israel Association of Automatic Control Italy: C N R Commissione IFAC Japan: Science Council of Japan Kuwait: Kuwait Society of Engineering Mexico: Asociacion dc Mexico dc Control Automatica -AMCA
Morocco: Association Marocaine pour le Developpement de l’Electronique, de I’Informatiquc et de L’Automatique-A.M.A.D.E.I.A. Netherlands: Royal Institution of Engineers New Zcaland: Institution of Professional Engineers North Korea: Korean General Federation for Science and Technology, Korean Association of Electronics & Automation Norway: Norsk Forening for Automatisiering (NFA) Pakistan: The Institution of Engineers Poland: Polski Komitet Pomiarow i Automatyki i Robotyki Portugal: Associacao Portuguesa para o Desenvolvimento da lnvestigacao Operacional APDIO, CESUR-IST Romania: Comisia de Automati7ari, Inst. de Ccrcetari si Proiectari Automatizari IPA Singapore : Instrumentation and Control Society South Africa: South African Council for Automation South Korea: Korean Association of Automatic Control Spain: Comite Espaiiol de la IFAC Sweden: Komrnitcn Svenska IFAC
386
12. 5’tandard.Y. Cornmifrees.und Associations
Switzerland: SGA, Schwcizerische Gesellschaft fur Automatik Turkey: Turkish National Member Organization- Otomatik Kontrol Turk Milli Comitcsi (TOK) United Kingdom: United Kingdom Automatic Control Council- UKACC United States: American Automatic Control Council CIS: National Committee of Automatic Control Yugoslavia: Electrotechnic Faculty, Automatics and Systems Engineering Division
IMEKO. The Internationale Messtechnischc Konfodcration (IMEKO, International Mcasurement Confederation) was founded in 1958. It includes technical and scientific organizations in 31 countries. Its objectives are as follows:
To support international sharing of expcrience in science and technology for the dcvelopment of metrology, manufacturing and design of devices, and application of devices in research and industry To promote cooperation of scicntists and engineers To hold a world congress every three years To establish Technical Committces To organize symposia, workshops, etc., on special themes and to publish the proceedings To cooperate with other international organizations having similar objectives (see IFAC) The current list of Technical Committees is as follows: TC 1 TC 2 .l’C3 TC4 TC 5 TC7 TC 8 TC 9 TC 10 TC 1 1 TC 12 TC 13 TC 14 TC 15 TC 16 TC 17
Higher Education Photonic Measurement Measurement of Force and Mass Measurement of Electrical Quantities Hardness Measurement Measurement Theory Metrology Flow Measurement Technical Diagnostics Metrological Requirement for Developing Countries Temperature and Thermal Measurement Measurement in Biology and Medicine Measurement of Geometrical Quantities Experimental Mechanics Pressure Measurement Measurement in Robotics
Member societies of I M E K O by country are as follows: Australia: Institute of Instrumentation and Control
Austria: 8 V E - o I A V Fachgruppe Mcsstcchnik
Belgium : Belgian Measurement Confedcration Brazil : Brazilian Association of Mechanical Sciences Bulgaria: Committee for Mcasurement of the Association of Science China: Chinese Society for Measurement Czechoslovakia: Cxchoslovak Scientific and Technical Socicty - CSVTS Denmark : The Danish Society for Enb’ mecring Metrology Egypt: Egyptian Organization for Standardbation Finland: Finnish Society of Automatic Control France: Association Franqaisc pour la Cybernetique Economique ct Technique- AFCET Germany: VDIjVDE Gesellschaft Mess- und A ut oma tisicrungstechni k (GM A) Hungary: Scientific Society of Mcasurcmcnt and Automation India: Institution of Instrumcntation Scientists and Technologists Italy: National Rescarch Council Japan: The Society of Instrumcnt and Control Engineers Korea : Korea Standards Rescarch Institutc Morocco: Society for Automation and Control Netherlands: Royal Institution of Engineers Division for Automatic Control New Zcaland: The Institute of Measurement and Control (Inc.) Norway: Norwegian Society of Automatic Control Philippines: Philippine Instrumentation and Control Society Poland: Committee for Measurement, Robotics and Automation- NOT Romania: Romanian Measurement Society Spain: Committee for Metrology, Spanish Association for Quality Control Sweden: Instrument Society of Sweden Switzerland: Schweixrische Gescllschaft fur Automati k United Kingdom: The Institute of Measurcment and Control United States: lnstrumcnt Society of America -1SA CIS: All-Union Scientific-Technical Society of the Instrument Industry Yugoslavia: JUREMA
12.3. Technical und Scienrific Bodies
Inrernarionul Instrumen1 Users Associution. The International Instrument Users Association unites thrcc societies, "SIREP-WIB-EXERA" ( E X E R A = Association des Exploitants d'Equipment dc Mcsure. de Rcgulation et d'Automatisme, Association of Users of Equipment for Measurement, Control, and Automation), with the objective of performing suitability tests on measurement and control devices and systcms. The concerns in this type testing are operability. safety, availability, accuracy, frccdom from interfercncc. ease of maintcnance, etc. The results arc communicated only to the member firms and the manufacturing firm. Mcmbership is limited to user firms.
Addresses of Organizations Mentioned in Sections 12.2 and 12.3: Arbcitsgemeinschaft Mcsswertaufnehmer c.V. (AMA) Linprunstrasse 49 80335 Miinchen Germany CF.Ir;ELt..C Central Secretariat Rue d c Strassarl 35 B-1050 Bruxelles Belgium Telephone: t 32-2/519-6871 Fax: + 32-21519-6919 Telex : 26257 Tclctex: 206/2210097 = CENCEL DIN Ileutxhes lnstitut f i r Normung c.V. Burggrafenstrasse 6 10787 Berlin Germany
D K E Deutschc Elektrotechnische Kommission im DIN und VDE Strexmannallcc 15 60596 FrankfurtiMain Germany 1)cutsche Physikalixhe Gesellschaft c.V. (DPG) llauptstrasse 5 53604 Bad Honnef Germany Deutscher Verband tcchnisch-wissenschaftlichcr Vercine (I)V?') Graf-Rcckc-Strasse 84 Postfach 10 11 39 40239 Diisseldorf Germany Association des Exploitants d'Equipment dc Mesure, de Regulation et d'Automatisme (EXEKA) BP Cerchar N o 2 60550 Vemeuil en Halatte Prance Gcsellschaft fur Inforrnatik e.V. (GI) Godesberger Allee 91 53175 Bonn Germany
387
VI)I,'VI)E-<;escllschaft Mess- und Automatisierungstechnik (GMA) Graf-Kecke-Strasse 84 Postfach 10 11 39 40239 Diisscldorf Germany
GMD-Birlinghoven Postfach 1 2 4 0 Schloss Rirlinghoven 53757 Sankt Augustin Germany VDI Gesellschaft Verfahrcnstechnik und Chemieingenieurwesen (GVC) Graf-Rcckc-Straw 84 PoStfach 10 1 1 39 40239 1)usseldorf Germany Internationale Elcktrotechnische Kommission (IEC) 3, rue de Varembi. 1211 Geneva 20 Switrerland IFAC Secretariat Schlossplatz 12 A-2361 Laxenburg Austria IMEKO Secretariat H-1371 Budapcst P.O. Box 457 I lungary Instrument Society of America (ISA) 67 Alexander Drive P.O. Box 12277 Research Triangle Park NC 27709 USA. Contact addrcss in Europe: ISA International Avenue Marcel Thiry 204 1200 Brusscls Belgium NAMUR Ir;ormenarbeitsgemeinschaft fur Mess- und Regelungstcchnik in der chernischen lndustrie 51 368 Leverkusen, Bayerwerk IN-PLT, Gcb. I3 I Germany SlREP International Instrument Users' Association South Hill Chislehurst, Kent BR7 5 E H England Verband der Chemischen lndustrie e.V. (VCI) Karlstrasse 21 60329 Frankfurt Germany Verband Deutschcr Elektrotechniker (VDE) e.V. VDE-Haus. Stresernannallee 15 60596 FrankfuniMain Germany Telephone: 069/6308-0 Verein Deutscher lngenicure (VDI) Graf-Recke-Strasse 84 Postfach 10 11 39 40239 Diisseldorf Germany
388
12. StonJarh. Cornmitires. ond Associations
Vcreinigung Industrielle Kraftwirtschafi ( V I K ) Ausschuss "Elektrotechnik" Kichard-Wagner-Strasse 41 45128 Esscn Germany International Instrument Uscrs' Association WIB Barroniclaan 15 4818 PA Brcda Netherlands Zentralverhand Elektrotechnik- und Elektronikindustrie e.V. (ZVEI) Stresemannallcc 19 64596 FrankfurtiMain Germany
12.4. Shows and Fairs Shows and fairs in the field of process control engineering can be categorized into three major groups : Shows dcvoted to onc specialty (e.g., proccss engineering, manufacturing engineering) and including process control engineering as a sidelinc. Significant here are ACHEMA for process engineering and the Hannovcr Messe Industrie for manufacturing engineering. Shows oriented toward proccss control engineering as a whole. In the forcfront of this group are INTERKAMA in Diisseldorf, BIAS in Milan, MESUCORA in Paris, HET INSTRUMENT in Utrccht, ILMAC/INELTEC in Basel, INSTRURAMA in Brussels, C & I in Birmingham, and the ISA Show in the United States. Shows that concentrate on a single area of process control engineering, such as Sensor in Nurembcrg and Systcc, Systems, Laser, and Analytica, all in Munich. The alphabctical listing that follows is certainly not complete but does g v e more information on the shows mentioned above, citing the emphasis and a contact address for each. AClIEMA International Meeting for Chemical Technology and Biotcchnology; includes a congress in the field of chemistry, with special emphasis on process control engineering Areas of concentration: research and innovation; measurement and control; process control engincering; materials science and materials testing: laboratory and analytical technology; mechanical and thermal processcs; packaging and storage technology ; safety engineering; occupational safety and health; pumps, compressors. and fittings; biotechnology; environmental protection
Contact address: D1:CIIEMA Ikutsche Gcscllxhaft fur Chemischcs Apparatewesen, Chemische Technik und Biotcchnologie Postfach 15 01 04 60061 Frankfurt!Main Germany Anu/~rica-International Technical Show tor Biochemical and Instrumental Analytical l'mhnique. with International Mwting 'Technical show focusing on analytical lahoratorv techniques Areas of concentration : analytical instruments and methods, laboratory technique and apparatus. laboratory data acquisition and processing. industrial chemistry. industrial analytic technique. materials testing, quality control, environmental analysis. biotechnology. medical diagnostics
Contact address: Miinchcner Mcsu- und Ausstellungsgescllxhaft rnbl I Postfach 12 1009 80034 Miinchen Germany B I A S -International Show and congress in the Ficld
o f Automation, Instrumentation. and Microelmtronics Areas of concentration: process computers and process
control systems. measuring instruments. controllers. valves. sensors, transducers, transmitters. components and accessories, control devices for processing and fabrication operations, logistics. communications. bus systems. remote control, robotic systems Contact address: E.I.O.M. BIAS Secretariat Viale Prcmuda, 2 1-20129 Milan Italy CeRf7.-World Center for Ofice, Information. Telecommunications International technicdl show for computcr techniques Arcas of concentration: screen communications, ofliu. communications. text processing, computer tmhnology, data processing, information technology, software. telecommunications, communications technology. communications engineering Contact address: Ikutsche Messc AG MessegclInde 30521 I Iannover Germany Conrrol& Instrwnenrarion National technical show and congress for process control enginwring, with emphasis on the chemical industry; held at Birmingham. England Arras of concentration : process control engincering. computer systems. control technology, actuators. wnsors. warning systems, analytical tcchnology. weighing. fiber optics, testing and inspcction, CAE:CAI>,C'AM software, displays and operator interfaces
12.4. Shows and Fairs Contact address: 41GB Exhibitions I.td. C & I Secretariat Marlowe House 109 Station Road Sidcup. Kent DA 15 7 E T kngland Hutinover Mcwe Indusrrie -The world's largest industrial show, f a u s i n g chiefly on control and process drive engineering but with process control engineering as a secondary theme Areas of concentration: electrical and electronics engineering; microelectronics; energy, air pollution, and environmental engineering: industrial optics and lasers; materials of construction; surface technology; process drive enginwring; control engineering; installation engineering; industrial robots; material-flow engineering
Contact address: Deutsche Mcsse AG Messegellnde 30521 Hannover Germany
Her Insrrwnenr-Technical Show for Industrial Instrumentation Dutch national show in the field of process control engineering; technical show for health care, science, and industry; held in Utrecht Areas of concentration: devices and systems (sensors, measurement. testing and inspection, weighing, and control engineering); medical technology; laboratory technology (analysis); electronics; research and innovation Contact address: Sekretariat Ilet Instrument Postfach 152 Birkenstraat 108 3760 A D Soest Ncthcrlands II.MAC International chemical show for laboratory technology, chemical engineering, and measurement and automation Areas of concentration: analytical laboratory techniques; isotope and radiation chemistry; process engineering; measurement. control, and automation engineering; biotechnology ; laboratory animal management ; environmental protection; hcalth care Contact address: Schweizer Mustermesse ILMAC Secretariat Messeplatz CH-4021 Hasel Switrerland INELTEC-International electrical and electronics engineering show Arras of concentration: power supply and distribution; energy supply systems; communications; control systems; components; measurement, testing, and inspection; power electronics Contact address: Schweizer Mustermesse INELTEC Secretariat Messeplab C1-1-4021 BdSd Switzerland
389
INS7'RURAMA-Technical Show for Metrology, Instrumentation, Control, and Automation National show for the field of process control cngineering and laboratory technology Areas of concentration: apparatus and laboratory equipment; devices, systems, and components for process control and management of industrial automation and informatics in the fields of energy. production, refining, communications, environmental protection Contact address: UDlAS INSTKIJRAMA Secretariat Avenue Slegers 203 B-1200 Brussels Belgium INTERKAMA-Innovation Market for Measurement and Automation Largest international show in the field of process control engineering, taking in all aspects of the field but focusing on rcsearch institutions. professional qualifications, exhibitor seminars, and congress Areas of concentration: process control and monitoring systems; measurement and control engineering; Sensor technology; analysis and weighing; actuators; communications; data processing; displays and output devices; components and accessories; tools; testing and inspection; services Contact address: Diisseldorfer Messegesellschaft bmll -NOWEA-INTERK A M A Referat Postfach 32 02 03 40417 Diisseldorf Germany ISA Show- American technical show for process control engineering, with emphasis on a congress; held at various sites Areas of concentration: measurement and automation technology (see, e.g., INTECH/ISA 91) Contact address: Instrument Society of America (ISA) 67 Alexander Ihive P.O. Box 12277 Research Triangle Park NC 27709 United States LASER-Innovative and Applied Optoelectronics International Technical Show and International Congress Specialty show with stress on laser technolgy Areas ofconcentration: laser and laser systems technology for manuhcturing. communications, environment, medicine, science. optronics. components. optical measurement and inspection, holography, pattern recognition, image processing, fiber optics, sensor technology. microwave engineering Contact address: Mtinchener Messe- und Ausstellungsgesellxhaft mbH Postfach 12 1009 80034 Miinchen Germany M~.~UCORn-Intcrnational technical show and congress for mcasurement and control engineering, automation. and industrial data processing, as well as engineering
390
12. Standardy. Conimitrees. and Associations
Arcas of concentration : dimensional and mechanical measurements. elcctrical measurements. process control and automation devices, inspcction and test equipment, industrial and scientific data proccssing, teleconimunicalions systems, engineering, laboratory apparatus. analytical instruments. biological and biomedical metrology. optics and clcctrooptics, nuclear mcasuremmts. vacuum and cryotechnology, instructional and learning aids Contact address: Association MESIJCORA 11. rue Hamelin F-75783 Paris Cedcx 16 France SENSOR. Niirnbcrg- Specialty show focusing o n sensor technology Areas of concentration: sensors, measurement and testing, analytical and weighing technology Contact address: Niirnberg Mcsse GmbH Messezentrum 90471 Niirnberg Germany
SYS7'EC- International fechnicnl Show and International Congress for Computer Intcgration in Industry Tcchnical show focusing on CIM Areas of concentration: research and experimentation, development, and design of CAD, CAE, and CAM software; consulting; scrvices; system components; networks: workstations Contact address: Miinchener Messe- undAu~stellungsgcsellsch~ftmbH POStfach 12 1009 80043 M b c h e n Germany
S Y S T E M S - Computers and Communications -International Technical Show and lntcrnational Congress Spccialty show for computer technology (from PC to the networked data proccssing scene) Areas of concentration : hardware. software, communications technology, systems components, user-oriented IIP problem solving Contact address: Mihchcner Messe- und Ausstcllungsgesellschaft mbll Postfach 121009 80043 Miinchen Germany
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
13.1. Principles
13. Integration of KnowledgeBased Systems in Process Control Engineering
391
depending on what reprcsentation or typc ol‘ knowledge appears most suitable. The aim of knowledge-based expert systems is to explicitly make available, in a knowledge base, thc knowledge of experts as embodied in 13.1. Principles standards, regulations, and guidelines, but also the knowlcdge they have gaincd through experiInformation tcchnology now permeates the ence. Only later was it recognized that the simple, field of process control engineering, not just in application-neutral processing logic of “inferthc form of computers, storage media, and comence machines” (or inference engines) is insuffimunications devices. but also via conccpts, methods. and techniqucs for structuring a wide cient for handling certain types of problem. In addition to the representation of knowlvariety of information. These concepts, mcthods, and techniques now mark every facct of the edge. an ontological analysis of the problem type field, from thc structuring of knowledge about is needed. Thus another problem-solving strateprocesses to be automated, to the use of incrcasgy comes into play as used for example for seckingly powerful microprocessors in control faciliing errors in a technical system [13.5] (diagnostic ties (sensor/actuator systems, process monitorstrategies) or for designing or planning of a teching and control systems), the structuring of the nical system. The basis for the latter activity is process control engineer‘s work during the dc“planning strategies,” which are generally topdown, beginning with functional requirements sign, construction, and opcration of control facilities, and the availability of computer-aidcd and proceeding through design alternatives to dcsign tools. Automation is no longer conceivengineering specifications. Diagnostic stratcgies are relatively well unable without them. They are discussed in depth derstood today, and special expert-systems tools in preceding chapters. cmploying them are available. Planning strateThe more advanced technologies of knowlgies are not so well developed. A point of imporedge-based and adaptive systems are already betance in the problem of planning is “noncoming important. monotonic inference” : During planning or Expert systems, or knowlegde-based systems, design, the planner may find that an approach arose in the field of A1 (artificial intelligence) already adopted is not leading to succcss, so that research, chiefly at American universities and redcsign decisions must be withdrawn. Expcrt syssearch institutions. The first expert systems aptems must bc able to keep u p with this nonpeared in the carly 1970s, but their high demand for computer resources preventcd their becommonotonic decision-making process. The crucial question in expert systems is still ing established at that time. how to acquire experience. In contrast to encodKnowledge-based systems in the sense of a ed technical knowledge, which is formalized, software technology are distinguished by the arstandardized, documented, and bascd on a conchitectural principle that application-oriented sensus in thc technical world, experience is not knowledge and processing logic are separate. formalized, not standardized, not documented, This can bc referred to as a software technology and often not a mattcr of consensus. Nor can because the field incorporates novel programknowlcdge acquisition be approached by methming methods and techniques. It can also be said ods of classical systems analysis, since it is conthat a ”paradigm shift’’ has taken place in inforcerned with thc understanding of mental promatics, since procedural programming (supported by programming languages such as FORTRAN, cesses. For a certain class of problems, adaptive sysPASCAL,and PL/l) has now been joincd, on an tems or neural networks offer a solution. equal footing, by othcr paradigms: logic proNeural networks arc now bcing used successgramming (PROLOG = programming in logic), fully for pattern recognition and classification. rule programming (OPS 5 ) , functional proPattern recognition refers not just to the typical gramming (LISP = list processing), and objectoriented programming ( FLAVOR,SMALLTALK, recognition of images, handwriting, and sounds, but also to the recognition of relationships bec +, ctc.). tween physical. chemical, and apparatus-specific lntegratcd in a hybrid programming system, all these paradigms can bc utiliyed in parallel. quantitics.
+
392
13. Integration of Knonledge-Based Systems in Process Control Engineering
A neural network is a large. highly connected system of simple processors. Whereas the human brain consists of ca. 10” neurons, artificial neural networks today have few neurons. generally no morc than 100. In such a network, a neuron receives inputs from several others. Only when the sum of the weighted input signals excecds a threshold value does the neuron transmit a signal to the neurons connected to it. The “programming” of such a network, also called learning, consists of modifying the weights of the connections inside the network such that a pattcrn presented at the input causes the desired response at the output. One of many programming methods is to modify the internal weights on the basis of the difference betwecn the actual output and the nominal output. When the Icarning phase is complete, the neural network can interpret new patterns correctly, albeit with a residual error. New network designer’s art consists of finding a design, a learning rule, and suitable representative examples so that an optimal neural network will result at the end of the learning phase. Another approach to dealing with uncertainty and indistinctness is fuzzy logic. This technique, which has bcen brought to industrial maturity in Japan, is based on the mathematical theory of fuzzy sets. In classical logic, a proposition can take only the values “true” or “false,” 0 or 1. In fuzzy logic, a proposition or “linguistic variable” is said to have such-and-such percent of value a and such-and-such percent of value b. For example, consider pressure as the variable. In an engineering system, a process control engineer might say that the pressure can take on qualitative values such as very low, low, moderte, high, and very high. Now a given pressure -whereby “pressure” is now understood as a linguistic variable in fuzzy logic- can be characterized by the degree to which it belongs to the set of “high” pressures. This is expressed by a numerical value between 0 and 1. Zero means that the pressure does not belong to the set of high pressures at all, while 1 means that it belongs to that set entirely. A variable might be located, for example, in an intermediate range from high to very high. It no longer takes on a unique value but can be described by, for example, the expression “pressure = (very low = 0.0, low = 0.0, moderate = 0.0, h g h = 0.3, very high = 0.8).”
Astonishingly, fuzzy logic has found broad applications in control cngincering. This should not, however. be surprising, because many quantities cannot bc measured exactly and noise of various kinds is often present. A study performed by the Fraucnhofcr Gesellschaft gives a comprehensive view of the potential applications of fuzzy control. in which an extensive literature collection can be found [13.18]. Fu7zy-logic control systems in practice often show morc stable bchavior than conventional methods. Interesting developments have arisen through the combination of classical detcrministic control algorithms with fuz7y logic. Combinations with ncural networks make is possible to gain the advantages of both approaches. Another technique for finding and retrieving information involves “hypertext/hypcrmedia” systems. The basis for hypcrtcxt is that electronically stored knowledge bases are accessed not with formal query languages or commands but by associative searching in a network consisting of information nodes and arbitrary links bctween these nodes. Employing the principle of direct manipulation, the user follows a train of associations by clicking the mouse on intercsting objects or interesting passages that appear in a document. Nonlinearity is the guiding principlc of hypertext. The freedom to navigatc within such a network can also lead to disorientation. Tools must therefore aid the user in moving around information space. The technique of hypertext systems, often combined with object-oriented data bases, is used particularly for tcchnical documentation, standards, and regulations. Hypermedia systems result when the information nodes are not exclusively text nodes but can also offer graphics, audio. animation, and video sequences. To summarize, information-oriented process control engineering is already fully operational with no end to its development in sight. This quick review of novel software technologies suggests that the immediate future will see remarkable new advances in information processing. This trend has led to many seminars and tutorials, with special mention being made here of the tutorials at the G M A Congress 1993, in which fuzzy control, neural nets modelling dynamic systems, ER/SADT, and inforniationoriented modelling concepts were rcportcd in detail.
t3.2. Knowledze-Bused Approach
13.2. Knowledge-Based Approach Introduction. The ideal structure of a process control system is prcsentcd in Chapter 7. In Figurc 7.16, the “rcmotc” componcnts include an A1 (artificial intelligence) computer. which forms the hardware basis for knowledge-based systcms. In process control cngineering. the latter term is undcrstood as referring to a combination of conventional proccss automation approaches and A1 methods. Thc need for knowledgc-bascd systems using A1 computers in process control enginecring arises from the development of the object being automated, that is, the processing system. I:or example, the continuing trend toward more intensive process control -a consequence of technological progress in process, manufacturing, and automation engineering -has led to a number of qualitativcly new and distinct features in modern production facilities. These include large product throughputs, extremc process parameters. opcration up to the stability limits, high parametric sensitivity, direct coupling of some process units without intermediate storage, and a high degree of material, energetic. and informational interconnection between production areas and operational divisions. The objectives of process control have come to includc process securing (enhancement of process reliability), plant availability, process efficiency, product quality, production flexibility, and environmental safety. For these reasons, complex control tasks at higher levels arc steadily gaining in importance for process automation. Most of these tasks involve thc solution of the following problems [ 13.191: 0
0 0
The complexity and high dimensionality of control tasks (more than five control variables that can be freely selected) The incomplete observability and the nonlinearity of the subsystems Compliance with complicated side conditions (including a number of logical conditions)
The classical methods of modeling and control often fail on grounds of expensc or because of parameter identification problems or realtime requirements. Generally, higher-lcvel control tasks can bc accomplished only when their complexity and dimensionality arc reduced by situation recognition techniques and when expcriential knowledge (to delimit the solution do-
393
main) is combined with situational models (to define the solution point). In the context of knowledge-based systcms for process control cnginecring, the principal A1 dements are expert systcms, artificial neural nctworks, and fuzzy systems. Expcrt systems and f u n y systems (both employing methods of mathematical logic) are similar in their information structure. They are used as rule-based systems in process automation, in both open loops (consultative systcms) and in closed control loops. The sole difference is that expert systems employ “distinct” knowledge (precisely measured process signals, unambiguous and either/ or rules) whilc fuzzy systems cmploy “indistinct” knowledge (membership functions, fuzzy rules). From the applications standpoint, artificial neural networks are vcry similar to statistical methods of process modeling. This section deals mainly with the usc of expert systems and neural networks. A1 methods are just beginning to find wide use in process control engineering [13.19].
RulcBased Systems. The main types of rulebased systems used in process control engineering are expert systems and fuzzy systems. Thcse seek to imitate the process of human thought on a phenomenological level. If these systems are to bc used as control dements in process control engineering, it is necessary to analyze the process employed by the operator of a production plant in solving problems. Figure 13.1 shows the subtasks and the solution process for the example of a reaction to disturbances in the process plant. An analysis of this problem-solving process shows the following: 0
The solution process involves the steps of diagnosis, optimization, and planning. Each step is separated from the next by a simulation phase. In this way, solutions arc validated on simulation models, which may be both heuristic models and, often, mathematical models “in the operator’s head” (thought models). These simulation techniques, whether practiced consciously or not, form part of a continuous iterative mental process, which ultimately leads to optimal conduct of the process. In other words, the suitability or optimality of the target situation is investigated by simulation and then corrected, leading to a corrected target situation, which in
394
0
13. Inregrution of Knodedge-Uu.ved Sysrerns in Prociw Control Engineering
turn is simulated. and so on until the best result has been attained according to the operator's judgment. A nominal/actual comparison, the consiruction of control algorithms, and the determination of setpoint values, all as compo-
nents of the diagnosis, simulation. and planning steps, can be performed with conventional control resources. The result is a higher degree of automation and hence the accomplishment of process stabilimtion tasks.
Production monitoring and c o n t r o l s y s t e m
___
__-___ -_ _ _ ______--Process monitoring and c o n t r o l s y s t e m
Conflict r e s o l u t i o n Process optimization and securing Compromise making t h r o u g h use o f many technical-economic criteria.
Optimization
- - - - -_ -- - - _ - - - -Simulation
t
Target setting Generation o f c o n t r o l algorithms, simulation of dynamic consequences o f disturbances, determination of loss-reducing and quality-assurance process operating modes
_Simulation ______________
Diagnostics
t
I
I
Simulation
situation
\sit a: t i o n j
t
Situation recognition NominaVactual comparison, diagnosis o f p r i m a r y causes o f disturbances and o f c u r t e n t s y s t e m r e s e r v e s . . s
,
Detection Signal acquisition, problem recognition t h r o u g h c o n t r o l l e d process observation and process monitoring
I ! i
I
-
I I I
I
I I I
Planning
Control decision making Determination o f c o n t r o l variables (setpointsl, planning of new coordinative c o n t r o l
1
I
Interaction Implementation o f c o n t r o l by process monitoring and control system
I
I
I I
Technical process Figure 13.1. Tasks in knowledge-supported process control
t
13.2. Knowledge-Based Approuck 0
0
Conflict-resolution strategies in the context of optimization tasks can generally be implemented only by using artificial intelligence methods, namely the accomplishment of tasks relating to process optimization and securing. As a rule, knowledge-based systems here operate in consultative mode, with no increase in the degree of automation. If these tasks in all of the solution space are algebraically describable, however, it is possible to dispense with A1 componcnts. Knowledge-based systems cannot handle background knowlcdge (e.g., historical knowledge about the design process, knowledge of maintenance strategies or of the market situation). This part of the problem-solving process thus cannot be supported by knowledge-based process control engineering.
There are no fixed procedures for modeling the problem-solving process as a mental process. This is the fundamental distinction to the mathematical modeling of engineering processes, in which the application of balance equations (mass, enthalpy, and momentum balance) leads to an unambiguous and exact mathematical model. Analyzing the problem-solving process from the standpoint of process control theory allows two classes of tasks to be identified: disturbance analysis and control synthesis. Disturbance analysis encompasses solving the following tasks: 0
0
Determination of primary casues of disturbances: Disturbances arise through slow parameter drift due to wear, contamination, and corrosion, or through sudden structural changes due to the failure of subsystems. Determination of secondary consequences of disturbances: Frequently occurring secondary effects are bottlenecks in feedstocks, energy-transfer media, intermediate products, or recirculating streams in adjacent process sections or in downstream process steps, which (depending on the process dynamics) most often manifest themselves immediately but, ultimately, further increase the resulting production losses. On the basis of the primary causes of disturbances, a simulation of the dynamic consequences of disturbances thus provides important supplemental information about the objectives of measures taken against disturbanccs.
395
The following tasks must be carried out in control synthesis: Identification of primary repair measures means deriving the repair measures by which the primary causes of disturbances can be directly remedied. Determination of current system reserves: Along with thc medium-term removal of primary causes of disturbances, in the short term it is necessary to make a control decision that will bridge over the failure of the malfunctioning subsystems for the duration of the repair measures and thus minimize the resulting production losses. Generation of a provisional operating regime: On the basis of the current system reserves, a new process regime is generated that will bridge over the system failure for the time required to remedy it and thus minimize the resulting production losses. Restoration of operation: Once the primary causes of disturbances have been remedied, the system must be restored to its normal state. This also requires for coordination of all subsystems. Process optimization: Current system reserves, as well as feedstock and energy resources, are deployed such that a technicaleconomic criterion takes on an optimum value while compliance with technical and economic constraints is maintained. In what follows, some theoretical principles of rule-based systems are presented. These are essentially based on mathematical logic.
Neural Networks. In contrast to expert systems, artificial neural networks imitate the process of human thought at a biological level [13.19], [13.20]. The aim is a functional simulation of the human brain or of the human nervous system, which comprises some 25 billion nerve cells (neurons). Figure 13.2 shows a single neuron and the way in which neurons are interconnected. A neuron receives its input information via the dendrites, which are connected by synapses to the information outputs of other neurons. Each neuron has only a single information output, the axon, which may be connected to as many as 1 O4 “downstream” neurons through dendrites. From the sum of the inputs, an output dependent on the state of the neuron is generated and passed on to the downstream neurons by
396
13. Integration of Knowledge-Bused S p t e m s in Process Conrrol Engineering
\ \
Dendrites
In this discussion, xi, dcnotcs the instantaneous output states of the j-th neuron of laycr s, w ; ~the wcightings of thc connections of the i-th neuron in layers - 1 to thej-th ncuron i n layer s, and I; the weighted sum of the inputs of thej-th neuron in layer s.
Figure 13.2. A biological ncuron
Figure 13.3. Functional diagram of an artificial neuron
way of the axon. Biological information is thus transported throughout the network (nervous system), whereby it may also be modified. A significant feature of the process is the massive parallelism of information transport and processing. The above properties of neurons allow an abstract model of an artificial neuron (processing element) to be devised. which is described by the following rules (see Fig. 13.3): A neuron can adopt either of two states, the resting state and the action state A neuronj in layers has multiple inputs x;~- I , . . ., x;” (synaptic connections) and one output (axon) x; Thc output of the neuron is connected to the inputs of other neurons, and in this way a neural network is formed Some inputs and outputs of the neurons are connected to the outside world A neuron enters the action (excited) state when a sufficient number of its inputs have been excited
The last rule, in particular, implics that the state of an artificial neural network depends only on the state of its elements. The individual neurons thus operate independently of one another. For process control engineering the fcatures of interest are the basic functions of ncural networks (pattern association. pattern reconstruction). Here “patterns” are vectors (e.g., scts of mcasurements used to characterize a plant state or to assess product quality). Three types of processing elements (units) are distinguished in an artificial neural network : Input units, which acquire their input information from the surroundings of the network (e.g., sensors) and are “visiblc” from outside the system Hidden units, which process incoming information and are “invisible” from outsidc the system Output units, which deliver output information to the surroundings of the network (c.g.. actuators) The individual units are groupcd by function into layers, which are interconnected in a manner that depends on thc problem. Figure 13.4 shows a typical multilayer network. Such networks arc used principally for classification tasks. Note that the behavior of the artificial neural network is strongly influcnccd by the network structure along with other factors. A distinction is often made between thc normal working phase (recall phase) and the learning or training phase of a neural network. Bcforc the network becomes able to carry out given tasks, it must be trained for those tasks. Learning may be supervised or unsupervised. The first mode involves learning on the basis of specified examples. The back-propagation method is well known. In unsupervised learning. the network independently trains itself on the basis of rules in the input data (e.g., patterns). In back-propagation, the output of the j-th element of layers s is determined by transforma-
13.2. Knowledge-Based Approuclt
t A
t
397
7’ A
u
....
A Output layer
(13.2.3)
A/
fu i w
”
The aim of the learning process is to minimize the global error E of the network by selecting weights ”$.Given the current set of weights wJi, the problem is to determine whether and by how much these weights must be changed. The formula
Layer
”
w
Input layer
Figure 13.4. Multilayer network with one hidden layer
is used for this purpose, where a is the learning coeflicient. Each weight is thus corrected in accordance with the magnitude and direction of the negative gradient on the surface of E. The partial derivative in Equation (1 3.2.3) is calculated as
(13.2.4) Setting es=--
Threshold function
Linear function
Sigmoid function
6E
(1 3.2.5)
61;
and applying the chain rule twice gives the following relation between e‘ and e‘+ needed for the itcrative computation:
’,
In
e; = f ( I i ” ) . 1
k= 1
Figure 13.5. Possible transfer functions of a processing unit in a neural network
tion with transfer functionf(1;) (see Fig. 13.5 for an example).
$ ,
=f[.k (w; I-0
’
g-71 =f(l;)
(13.2.1)
In back-propagation networks, the quality of the neural network is usually evaluated by a global error function E, which is needed in order to define local errors in the output layer and thus initiate the back-propagation of errors. If it is assumed that vectors i and d a r e given to the network as the input and output patterns during the learning phase, while o represents the output vector calculated with the instantaneous weights, a global error function for the artificial neural network referred to the expected output d can be defined as
l m (1 3.2.2) C (di - Oi)’ 2i=l where m is the number of components of vectors d a n d 0. E
. w;f’)
( 1 3.2.6)
where m is the number of processing elements in layer s + 1. For the output layer e ; = - - 6 E- -_ _ _SE SOL SI,o 6 0 , 61; = (4 .f(Ik? (1 3.2.7) Combining equations (13.2.3) and (13.2.4) and using Figure 13.3 gives An,s = x . e s . q - ’
(13.2.8)
Equation (1 3.2.8) represents the delta learning algorithm of the back-propagation method. Now the mechanism of back-propagation in the artificial neural network can be made explicit: 0
0
=-
0
Forward propagation of the inputs up to the output layer with the aid of Equation (13.2.1) and determination of the error at the output layer Successive correction of the weightings H J (with Eq. 13.2.8) during the process of backpropagation toward the input layer Recalculation of the output layer error with Equation (13.2.1) and recorrection of the wj, with Equation ( 1 3 . 2 . Q and so forth
~ ~
398
13. Iniegruiion of Knowledge-Rased Sjstcmr in 1'rocc.s.T C'onfrol EtiRinzc8rinK
Algorithmic Integration of Knowledge-Based Systems. Expert systems and neural networks havc both advantagcs and disadvantages for process control (see Table 13.1). A uscful way of compcnsating for thc drawbacks is to employ both kinds of systems togethcr. Figure 13.6 illustrates this point; the formulas prcsented thcre are explained in the text. This also enhances the softwarc reliability. The artificial intelligence components determinc thc solution domain, while the conventional algebraic methods of process control give thc solution point. From thc standpoint of automation theory, the result is thus a localization of the proccss control task. l i b l c 13.1. Comparison of expert systems and neural
networks
System
Advantages
Disadvantages
Expert systems. f u y systems
usc of surface knowledge
ditlicultir~in knowledge a q u i s i tion (c.g.. ruln. membership functions)
Neurdl networks
ability lo learn
dificultics in intcrprelation of solution (c.g.. no possibility of explanation)
Thc mathematical models are significantly simplilicd in this way. because a mathematical description is nccdcd only in the vicinity of the solution point. Linear models may well bc adcquate. Thus the control algorithms relating 10 proccss optimi7ation. stabilization. and securing must be run under real-time-capablc software. eithcr on the process monitoring and control computer or on a special A1 computer. The definition of the solution domain by cxpert systems and ncural nctworks (cssentially in the context of classification tasks) is treated above. N o w let us examine in more detail the application of algebraic mcthods to the detcrmination of the solution point. It is assumed that a proccssing system can bc broken down into subsystems (SS,), intcrconnccted by matcrial and cnergy strcarns (see Figs. 13.7 and 13.8). This m a n s simply that thc phasc modcl of production is employcd in devcloping the software structure (sce Chap. 2).
, ..
.
(GGG)
I
A
A-
B
yi Input quantities u; Control quantities xj Output quantities 2;
4
Disturbances Subsystems
Figure 13.7. I ~ ~ o m p o s i t i oofn a technological systcm
Figure 13.6. Algorithmic integration of knowledge-bad systems
L----J
Figurc 13.8. Example of a coupled total system
13.2. Knowledge-Bawd Approach
The following aspects are crucial in analyzing the system into subsystems: 0
0
0
Topological (breakdown into technological elements. apparatus groups. ctc.) Dynamic (breakdown into subsystems by dynamic properties, e.g., duration of dynamic transient processes, time lag) Combination of topological and dynamic aspects The following notation is used :
X" = ( U " ,Z",Y") represents the vector of the process parameters of the n-th subsystem;
x,,
1 = (X,, . . ., X,) the vector of the process parameters of the entire system; the simplified mathematical model of the n-th subsystem ; C(X)= 0
the coupling conditions of the subsystems; and
Q (x) the goal function in process optimization, the Lyapunov function in process stabilization, and the negative first derivative of the Lyapunov function with respect to time in process reliability enhancement and quality assurance. Thus the algorithmic structure of all processcontrol tasks is unified. As a consequence, marked simplifications have been achicved in the applications software (fewer software errors. better software maintenance). The use of the optimization condition Q ( X ) 4 min naturally requires some explanation. X
With regard to the algorithmic integration of knowledge-based systems, conventional mathematical models are used as simulation systems for generating examples (in the case of neural networks) and of unmeasurable process information (in the case of expert systems). This point will be treated in more depth below.
Technical Integration of Knowledge-Based Systems. General Principles o j Integration. Figure 13.9 shows the technical integration of expert systems and neural networks into the process monitoring and control plane. Knowledge-based process control can be represented in three Ievels :
0
0
0
399
Elementary situation recognition with conventional methods of limit monitoring, deterministic or statistical class ordering. and trend analysis. Expert systems and neural networks combined with simulation systems based on mathematical models of the technological process. The objective is to identify the domain of the desired solution to the control problem. Conventional process control with the use of local or simplified mathematical models.
fntegration of' Expert S.ysterns. The recommended procedure for technical integration of expert systems is to implement a real-time-capable expert system shell (expert system with empty knowledge base, i.e., without rule knowledge) on the control or A1 computer, then fill it with concrete process-control knowledge in the course of knowledge acquisition (see Section 13.3). Such expert system shells must imitate the problemsolving process described here. A successful example is the PROCON expert system shell, which has been implemented in several cases [13.21] -[13.24]. This shell will be described in more detail in what follows. 0 System components: PROCON is subdivided into a development system and a runtime system with the following components (Fig. 13.10): -
-
-
-
Knowledge representation: object-oriented representation concept for explicit structuring of problem spaces Knowledge manipulation: cooperative problem-solving processes for diagnosis and application of expert knowledge toward efficient delimitation of a solution Knowledge acquisition: problem-specific development environment for input, inspection, and transformation of knowledge into a runtime-efficient representation Knowledge consultation : user environment for verification and consultation of implemented knowledge bases
PROCON incorporates process, program, and dialog interfaces with the following features: Preparation of all needed process variables that can be acquired on-line, through coupling with the process control system - Preparation of all needed process variables that can be acquired off-line, through interactive dialog with operating personnel
13. Inregrution .f Knowledge-Rused Systems in Process Control Engineering Technical process
1
I
I
1
Elementary s i t u a t i o n recognition
=
(+---+Hm
i
t
Knowledge-based Drocess c o n t r o l
Control operation
I
Conventional process
Icontrol I
t
Process monitoring and c o n t r o l l e v e l
I Operator
Production monitoring and c o n t r o l l e v e l
Figure 13.9. Technical integration of knowlcdge-based systems -
-
-
-
Automatic interrogation of redundant information sources by the expert system whenever failures of measurement stations are reported Manual configuration of charge-dependent process parameters with parameter sets or in interactive dialog with operating personnel Rule-governed activation of external simulation and optimization programs via the program interface Manual, timed cyclical, or external consultation start
Requirements on the Knowledge Base. Applying expert systems to process control tasks means keeping pace with the process dynamics so that the integration of the knowledge base can be insured and critical reaction times met. Heavy modulariiation of expert knowledge into small knowledge units is dictated by both the need for adequate runtime efficiency of knowledge processing and the need for easy maintainability of the knowledge base as technological conditions change and operational experience is gathered.
One useful modularization principle results lrom a division into disturbance and control classes. The set of all disturbances can be imaged on a set of disturbance classes that are qualitatively distinguished by primary causes and secondary consequences of disturbances. Similarly, all control actions can be placed in control classes on the basis of the disturbance class to be compensated and the available system reserves. If the two classifications are successively refined, a treelike structure of disturbance and control classes is created. In the terminology of expert systems technology, this is referred to as a class hierarchy (taxonomy) of prototypical problem classes (objects). Descending through the problem hierarchy thus corresponds to localizing the current system disturbance and the resulting therapeutic control action. On a path from the root to a leaf of the taxonomy, each object encountered makes a contribution to the total solution of a problem. Each object contains a partial knowledge base made up of declarative (facts), functional (functions), and procedural (rules) knowledge specifications (Fig. 13.1 1).
13.2. Knowledge-Based Approach
- S o r t editor -Fact editor -Rule editor -Function editor -Image e d i t o r
Representation shell -Representation accesses o f special problem-solving
-Access functions
+
/
-
Process
J
'ogram
h=;.?:f
I
IL interface I L
/
-Dialog component -Trace Component
-Heuristic graph search procedures in problem
shell -Process coupling -Necessary and sufficient diagnosis -Application o f e x p e r t
Figure 13.10. PROCON system componcnts
-
S t r u c t u r e d problem space (taxonomy)
&L Heuristic qraph
-Irrevocable, locally ordered -Revocable. locally ordered
t I Object-represented problem class ( o b j e c t )
programs Functional knowledge: -Functions Procedural knowledge: -Expertise rules, necessary and s u f f i c i e n t diagnostic conditions
-
Local problemsolving processes
-Process coupling - S u f f i c i e n t diagnosis -Application o f exper knowledge -Necessary diagnosis
401
Each object has access to the knowledgc of all its prcdcccssors in the problcm hierarchy (inherit ance) : Declarative knowledge includes proccss information that can be automatically updated (valucs that can be acquired by on-linc measurement, values that can be acquired by ofrline laboratory work, etc.); user-configurable (tcchnologicdl) constants; interferencc variablcs dcrivablc in thc course of problem-solving; and rcpresentations pararnetrizable and external programs invokable by the rules of expcrt knowlcdge application (simulation and optimization models).
0
what routc to follow through the problem hierarchy. that is. what problcm classes actually nced to be examined in a concrete disturbance situation. Problem Solving. The object-orientcd siructuring of the problem space permits clficient problcm reduction through two problem-solving levels: 0
Procedural knowledge. The following rule bases can be dcfincd in each object:
0
Rules for application of expert knowlcdge ("expcrtise" rules) are forward-chaincd production rules for thc derivation of interferenccs from a given problem class (Fig. 13.11). They can dcrive interference variables, form solution texts to be displayed. parametrize rcprcsentalions for output, and invokc external programs for simulation and optimization. - Diagnostic rulcs are classification rulcs for the derivation of so-called sympathy ratings for the objects in a problem hierarchy (Fig. 13.12). The expert system uses these ratings to dccide
0
-
7ule 01 f
the the the the
paste pump is not in operation & level in the paste mixer is <5% b level in VE1 is under 5% & level in VE2 is under 5%
lhen METER-DIST-1 90 METER-D!ST-2 50 7ule DZ
f
the the the the
paste vatve level in the level in VE1 level in VE2
is s e t t o recycle & paste mixer is a t Max-Alarm EL is over 5% 6 is over 5%
(hen METER-DIST-1 LO METER-DIST-2 80
-
Figure 13.12. Examples of diagnostic rulcs (METER DlSTJ and METERDIST 2 arc IWO lower-lcvcl objcccs of the current problem class)
Higher problem-solving level: Starting from the root ol'the problcm hierarchy, a hcuristic graph searching proccdure dctcrmincs a routc through the structure of the problem space. In so doing, it cstablishes which problem classes actually necd to be cxamined in a concrctc disturbance situation. and in what order. The examination of a problem class is initiated by an activation message to its local problem-solving process. Lowcr problem-solving level: The local problem-solving of an activated ohjcct begins with a process coupling, thcn continucs with an expertise and diagnostic proccss. Through the process coupling. all process featurcs drfined in the given object (with respcctivc cvupling parameters) are first updatcd. The expertise process (application of cxpcrt knowledgc) that follows next evaluates thc expertise rule base of the objcct in ordcr to dcrive interfercnces from thc problem class. Finally. the diagnostic proccss uses thc diagnostic rulc base of the objcct to rate its successors in the problem hierarchy. The remainder of the problem-solving proccss follows thc rulcs of mathcmatical logic as described abovc.
Developmrtir Envvirnnmcw~.The work of developing practically relevant knowledpc bases is commonly divided among specialist expcns (proccss engincers. automation cnginecrs, cxpcrt operators) and. in the long tcrm. makes possiblc the maintenance of the knowledge base as technological conditions change and operational experience is gathcrcd. For this rcason, special value attaches to interactive tools for the support of incremcn tal, problem-specific knowledge acquisition. In PROCON. the acquisition component integrates the following aids in a self-containcd development environment: 0
Knowledge input: Graphically supported structure editor for taxonomy input. Mask editors for fact input, screen editors for rule and function definition.
13.3. Knowledge Engineering
Knowlcdge inspection: Automatic search for inheritable knowledge specifications anywhere in the knowledge base; compressed explanation of defined knowlcdgc contents of the active inheritance hierarchy during knowledgc input. Knowlcdge transformation : Transformation of a problem-specific external knowledge representation into a runtime-efficient internal representation; formal-language or natural-language rule notation can be selected. Knowledge verification : Testing for syntactic closure and semantic consistency in the active inheritance hierarchy during rule translation; testing for adequacy and completeness in a comprchensivc trace environment with case data management. This development environment is the basis for knowledge acquisition as described in Section 13.3.
Integration of Neural Networks [13.25]. In comparison with the conventional methods of process control engineering neural information processing requires high-performance storage media as well as fast processing units (and possibly the use of special processors). Furthermore, the practical deployment of neural networks calls for ingenuity in network configuration as well as a training technique optimally suited to the application. Hardware and Software Implementations. The recommended hardware support for neural network algorithms differs from the classical Von Neumann structure. The following solution approaches are of interest:
The use of special neural processors to speed up serial operation. These units feature a separate floating-point processor, a supplemental cache memory, and complete primary storage capacity and employ the pipelining principle. Examples are the PC boards made by ShlC Corp. Multiple-instruction, multiple-data structures, which can be implemented with available “transputer” components; a number of software packages are already on the market for transputer boards. The use of vector processors. If thcse devices have a sufficient number of processor components. they make it possible to determine
403
network activity layer by layer (i.e.. to determine thc activation vectors section by section with a high degree of concurrency). At prcsent, software simulators are used to convert the various network types to the computing and monitoring/control hardware currently used in process automation. Their implementation on general-purpose proccssors (e.g., Intel 8086) meets the requirements of flexibility, portability, and high integrability. Only in exceptional C ~ S C Scan a dedicated implementation be recommended, espccially since there are a number of powerful neural shells on the market. These systems generally have communications interfaces to the user and file levels and offer a wide range of distinct network types. Particularly important for integration into process control systems are runtime routines that enable customized, memory-efficient source code runtime modules to be integrated into the applications software. One example of such a software simulator is a development system called Neural Works Professional 11, published by Ncural Ware Inc. and intended for PC and workstation environments. Note, however, that straight software implementations have the drawback of limited capacity, both in the training and in the implementation phase. A combination of dedicated hardware and dedicated software should therefore be preferred. The integration ofparallel processing structures (c.g., in the form of transputers) into monitoring and control systems is anticipated.
13.3. Knowledge Engineering Procedures and Methods. The creation of the knowledge base is the key step in the integration of knowledge-based systems into process control engineering. This procedure employs methods of knowledge engineering. The term knowledge engineering denotes the totality of the processes involved in the design, implementation, and maintenance of expert systems (when expert system shells are used, this essentially means filling a knowledge base). Knowledge engineering thus continues throughout the life cycle of an expert system. The analogy to software engineering is clear. Knowledge engineering is the bottleneck in expcrt systems development (the Same applies to automation
404
13. Integration of Knowledge- Based Systems in I’roc(x7 Control Engineering
engineering), Thus there is a parallcl to the “software crisis” in the realm of conventional software. Knowledge engineering, which can also be termed systems analysis, involves close collaboration among process control engineers, process cngincers, and information specialists (Fig. 13.13). In cmpirical social research, many years’ cxpcrience and a broad methodological potential are available for the acquisition of knowledge and the traditional, nonformalized usc of expert knowledge. Recause these are also available to knowledge engineering, it is not surprising that the techniqucs currently in use for the acquisition of expert knowledge are largely derived from the methodological inventory of empirical social research. The tcchniqucs must, however, be modified to suit the needs of this special application area of process control engineering. Desirable, above all, is an appropriate combination of these techniques with the methodological potential built up over decades in theoretical and experimental process analysis. The knowledge to bc incorporated in expert systems is generally found in the most diverse knowledge sources: technical manuals, operating instructions, case studies, and reports, but also data files and databases that may contain operational data for concrete process situations. In addition, pcrsonal cxperience and the knowledge of technical specialists (experts) is also sought. It is the last of these knowledge sources that is crucial in process automation. Moreover, a theoretical process analysis may also involve the creation of mathematical models that are then available as secondary knowledge sources. Accordingly, a complex utilization of all knowledge sources- -theoretical mathematical models, process data, and operator expcricnceis to be recommended as a way of reducing effort (see Fig. 13.14).
Process c o n t r o l engineer
Process c o n t r o l engineer
Process engineer
Information specialist
Figure 13.13. Interdisciplinary cooperation in knowledge engineering
Formulated in gcncral terms, the knowlcdge acquisition component of an expert system must support the transformation of this very heterogcneous knowledge to a homogeneous form, so that it corresponds to the representation schcmc dcfined by the structure of the knowledge basc and by the interference component. Thus the knowledge acquisition component aids in the dcscription of information or knowledge from the area of the user’s tasks. building up a knowledgc base from this information. The wide variety of knowledge sources makes this an exceedingly complicated problem. which entails a considcrable cfl’ort in the case of an expert system for process automation. In present-day expert systems, various “manual” techniques arc often employed for knowledgc extraction; editors support only the final input to the knowledge basc. When knowledge is prepared manually, howcvcr, relevant information is frequcntly lost or is not acquired at all. It is thus all the more important to develop knowledge acquisition components that will interactively support and assist the knowledge engineer (who is, as a rule. the process control engineer) in performing this task. On the basis of these general aspects. a fourphasc concept of knowledge engineering is recommended. This concept is oriented primarily toward process automation [13.26], [13.27]. Thc fundamental idea is to subdivide the iterative process of developing an expert system into thc following phases (see Fig. 13.15): 0
Definition
a Acquisition a Operationalization a Maintenance Theoretical knowledge
Process information
Mathematical
Simulation
[50%
Heuristic knowledge
reduction of e f f o r t ]
Figure 13.14. Reduction of effort by complex utilization of all knowledge sources
13.3. Knowledge Engineering
The definition phase provides a first global survey of the application domain. A specification of requirements must be created; that is. the problems for solution must bc stated, their transformation into objectives and tasks of the expert system must be defined, and the feasibility of the expert system project must be invcstigated. In other words, the control task is established in this phase. The definition of the control task may be supported, for example, by comparing the disturbance amplitude D, and frequency ./: for each control object. An analysis of this kind is done for the dominant disturbance in each control problem. Figure 13.16 shows that closed regions can be defined in the D,-L plane, in which process securing, optimization. and stabilization are to be performed. These process control components are defined as follows (see also Section 4.2): Problem
1 I
IDefinition p h a s e b
New requirements
I
Requirements
Reformulation
Refining/ redesign (new formalisms)
Formalisms
Prototype
I
Correction
Figure 13.15. Four-phase concept
1
Open l o z s e d loop Securing Optimization
Stabilization 1
fz
-
Figure 13.16. Relationship between automation tasks and properties of the disturbances
405
Process stabilization: Compensation of correctable disturbances with the aim of holding the process parameters constant at specified values Process optimiration: Determination and setting of control variables so as to optimize a specified criterion while complying with specified restrictions Process securing: Compensation of uncorrectable disturbanccs with the aim of preventing unacceptable process states and product qualitics or of minimizing their consequences Figure 13.16 also shows. as a function off,, the trend in the use of expert systems and mathematical algorithms (conventional process control) as well as the use of consultative systems (open loop) and automatic systems (closed loop). In this way, it is possible to identify a certain number of standard situations to which a particular control task applies. Expert systems are especially well-suited to the solution of process securing tasks, since as a rule there are no mathematical models for large disturbance amplitudes (emergencies), although a certain amount of empirical knowledge exists. The requirements worked out in the definition phase must be set forth in an appropriate form (specifications). The acquisition phase includes analysis of disturbanccs, causal relationships, and possible control responses, as well as the aggregation of results into situation and control classes. The process analysis yields a formal model of the problem spacc in which the general questions to be answered in the definition phase should be detailed. Thc real difficulties in this phase lie in the design of knowledge bases. The knowledge should be structured to match the technology. It must directly mirror the operator’s cxperiencc or mode of action and be determined chiefly by automation theory. In the operation phase, the knowledge is structured to match the system, as specified by the representation mechanisms that the expert system shell provides. This transformation of knowledge requircs a knowledge engineer with expericnce and knowledge in many scientific disciplines. Now let us take a closer look at “technologylike” structuring. The structure of the knowledge in this case is dcrived from the structure of the control object; a different form of structuring is
406
13. Integralion
OJ’
Knowledge-Bused Systems in Process Control Engineering
generally meaningful in each disturbance class. Experience has shown that the technological system can be broken down into subsystems, each of which is controlled in autonomous f-dshion while all the subsystems are simultaneously coordinated. The decomposition of the entire tcchnological system is based on the phase model of production (see Chap. 2). This principle can be illustratcd for thc example of process securing. The system is decomposed into a certain number of subsystems or levels, depending on the disturbance class; these subsystems show relevant differences in the dynamic properties relative to the dominant disturbances (Fig. 13.7). The upper (i-th) plane reacts with virtually no time lag to signals from the lower (i + I-th) plane. Each level contains a numbcr of subsystems, which are connected to one another by energy, material, and information streams and exhibit similar dynamic properties. On the basis of the structure of the control object, it is recommended that a hierarchical structure with multiple coordination levels be used for the process securing system. The subsystems should have the greatest possible autonomy (see Fig. 13.17). The subtasks of knowledgebased process control that arc solved are the ones described above under “knowledge-based systems”: determination of causes and consequences of disturbances, identification of control actions for the subsystems and for the system as a whole. One of the fundamental ideas in the technology-oriented structuring of knowledge is this: When an uncorrectable disturbance zi takes place. the application of algorithm AIi detects and compensates this disturbance (determination of primary causes of disturbance, derivation of primary repair measures). The “compensation” of a disturbance zi means controlling a subsystem such that the given system is
Si= Subsystem Aij= Process-securing algorithm
Figure 13.17. Structuring of a proccss securing system
in a failure state for a time AI shorter than some acceptable value At,,, (Az < Atacc).If this fails. coordination of two adjacent subsystems Si and S i _I is effected by using Az+ ,). First, the effect of the disturbed or failed subsystem Si on Si . is identified (determination of secondary consequences of disturbance): second. the instantaneous dynamic reserves of S i _ , for compensating the failure of Si are determined; third, a provisional regime (e.g., part-load or turndown regime) is gencrated. If even thesc steps d o not compcnsate the disturbance, threc subsystems arc coordinated (for example, S,, S i tI , and Si+ by using A3i). then four subsystems, and so forth. This process is continued until the disturbance zi has been compensated. Whenever a new subsystem is included, the above steps are repcatcd : identification of secondary consequences of disturbance, determination of instantaneous system reserves, and generation of a provisional regime. If successful compensation of the dangerous disturbance has not been achieved despite coordination of all subsystems, an emergency shutdown of the technological system is performed. In the context of knowledge-based process control, thesc coordinated shutdown modes can also be treated as provisional regimes. The normal regime is restored when the disturbance zi is no longer present. The operationalization phase includes the “system-like” structuring of the knowledge as well as steps to make the expert system functional. Newly acquired knowledge is added during the maintenance phase (e.g., in the form of new “rules”). In addition, portions of the knowledge basis may have to be replaced altogether if changes take place in the technology. Expert systems are considerably more maintainable than conventional software systems, which is largely due to the fact that software errors d o not necessitate complete reprogramming (software technology aspect). This leads to enhanced reliability of the process control system and lower maintcnance costs.
Application Example. A chemical plant for the production of poly(ethy1ene terephthalatc) fibers (trade name Grisuten) is equipped with a TDC3000 process control system. The main tasks of the plant operator fall into the following categories: 0 Process monitoring, the principal tasks being the detection and diagnosis of disturbances
13.3. Knowledge Engineering 0
Proccss securing, i.e.. the prevention of situations that would endanger the plant
In case of a disturbance, the operator must identify and implement a suitable reaction on the basis of available process information. Frequent operator interventions are needed in the following situations: 0 0 0
0
Startup and shutdown Modifications of thc operating regimc Failure of plant sections Occurrence of critical process states that impair more than one plant section
Localized disturbances can generally be compensated by the existing automation system. In the above situations, the quality of operator dccisions varies widely. Along with qualitatively good decisions by some operators, wrong decisions some of them serious are made by other, less experienced operators, especially when they are under psychological stress. Furthermore, the required specialist knowledge is commonly split up among several operators. The basis for an operator’s decisions is essentially his own experience; theoretical background is less important. The main feedstocks are ethylene glycol and terephthalic acid (TPA). The process is continuous and can be broken down into the following phases (see also Fig. 13.18). Meteringlhomogenizing. The feed materials are metered in fixed proportions into the paste mixer, where they are homogenized. Metering
407
errors or inadequate homogeni7ation of the feeds can lead to serious disturbances in the downstream system and to a major degradation of fiber quality. Esferijicarion. The reaction mixture is converted to diglycol terephthalate in a two-stage esterification reaction. The reaction is strongly endothermic, so heat has to be supplied. This process is carried out under vacuum, and the cxtractcd vapors are distillcd to recovcr ethylcne glycol, which is recycled to the metering/homogcnization stage. The crucial point here is to insure a minimum residence time of the reaction mixture in the reactors, to attain an appropriate degree of esterification. Condensation. The diglycol terephthalate from the esterification stage is polycondensed to poly(ethy1ene terephthalate) in four condensation stages. The strongly endothermic reaction is carried out under vacuum with heating. The important quality criterion is that an appropriate degree of polycondensation must be attained after final condensation. SpinninglJinishinglextrusion. The polymer melt is spun and/or extruded in a melt-spinning stage. A number of treatments are performed on the spun filaments, after which they are ready for shipment. Every process step in the condensation and spinning/finishing/extrusion stages results in a significant increase in the viscosity of the reaction mixture. The task of the expert system is to serve as a decision aid to the operator in carrying out proVacuum
Figure 13.18. Process flowsheet V E = estcrification; VK = precondensdtion; RSK acid; HTF = heat-transfer fluid
=
ring-and-disk reactor; EG = ethylene glycol: TPA
=
terephthalic
408
13. Integration of Knonledge-Bused Systetns in Process Control Engineering
cess monitoring and securing tasks at thc highcr level. In particular, thesc hsks includc: Startup: Cold startup of the chemical section of the plant (mean product tcmpcraturc < 7AiJ Warm startup of the chcmical section of the plant (mean product tempcrature > Tmin) Failure of plant scctions: Failure of heating system for heat-transfcr fluid (HTF) Occurrence of critical proccss states: Vacuum disturbance Viscosity disturbance Disturbance of mole ratio/COOH groups Level and flow rate disturbances The aim in reacting to disturbances is to insure high process reliability and product quality with the minimum reduction in plant capacity. Whenever possible, process data should be acquired from the process on-line, without placing extra demands on the operator. Interviews with experts have shown that all disturbance classes are interrelated in a complex manner (see Fig. 13.19). For this reason, in particular, they have not yct been completely brought under control. Now let us examine the practical creation of a knowlcdge base more closely. The four-phasc concept described above is applied to the disturbance class “ H T F heater f d u r e . ” The heattransfer-fluid heating unit supplics hcat to the strongly endothermic reactions in the VE and VK/RSR reactor groups (Fig. 13.18). A hilurc o r loss of capacity in the H T F heater always results in severe disturbances (Fig. 13.19): Failure of HTF heater
Filling level Flow rate
Levcl and flow rate disturbances: Because of the rapidly cooling product lines, continuous flow of product bctwecn thc individual reactors is not insured aftcr just a short time. The consequences are unstable levels and rcsidcnce-time disturbances. Vacuum disturbances: Unstable filling levels rcsult in foaming in the reactors, which can result in product being carried into the vacuum system. Mole ratio disturbances: Residence timc disturbances lead to an excessively high or low conversion of the reaction medium. The consequence is a severe decrease in fibcr quality and possible disturbances to the viscosity or COOH groups of the product. Viscosity disturbances: If the viscosity of the reaction process departs from the acceptable tolerancc range. Both the processability and the quality of the product fall off drastically. COOH group disturbances: Because of cxccssive residence times, much of the feed ethylene glycol flashes off, leaving an cxccss of TPA and thus of COOH groups. As a rcsult, major dccreases in fibcr quality occur.
For each of these disturbance classes. the proper responses to offset the disturbancc [process securing) werc ascertained by intervicws with experts. Thc typical procedure for process securing (see above) was uscd as the basis for these interviews. Starting from the dynamic properties relevant in the case of an H T F hcater Pailurc, the technological system was broken down into subsystems S , (HTF heater) and S, (homogenidng, esterification, condensation, RSR reactor). The process securing system. in analogy to Figure 13.17, has the structure shown in Figure 13.20. This structure also dcscribes the procedure used by the plant operator to compcnsate for the disturbance. The operator movcs down a tree structure, level by level, until a solution to the process securing problem has been found.
ICOOH groups] Figure 13.19. Ilisturhance classcs and their interrelationships
Figure 13.20. Structure of the process securing system upon a failure of the I ITF heater
13.3. Knowledge Engineering
Figure 13.21 shows this procedure schematically. The respective decisions and actions are listed in accordance with the algorithms Aij in Figure 13.9. Figure 13.21 also dernonstratcs the structure of the technology-oriented knowledge base; re-
409
actions to possible downstream disturbances (level, vacuum. etc.) are merely indicated. Direct conversion to thc expert system would have the consequence that many rules or knowledge items would have to be input more than once (e.g., 12 times for the cold and warm startup knowledge
Failure o f HTF heater Knowledge base contents
Purpose
A,,
Determination of disturbance class le.g., HTF heater failure1
Identification of primary causes of disturbance
A,,
Specific actions t o r e s t o r e function of HTF heater
A,,
Determination o f duration o f heating system failure and mean product temperature T
-
A,, A,,
A Level
L
Warm s t a r t u p
Staggered energy c o n t r o l regime (coordinative control)
Warm s t a r t u p
. Determination o f primary repair measures
Level 1
Identification o f secondary consequences o f disturbance
Determination o f a r e s u l t i n g provisional regime
Cold s t a r t u p
Knowledge base contents
Purpose
T w Tmin? Determination o f diphyl temperature Coordination o f product f l o w rates
Knowledge base contents
Purpose
ldent if ication o f primary causes o f disturbance
T -= T,,?
Identificatior o f primary causes o f disturbance
Restart
Coordination o f levels and product flow r a t e s as functions o f diphyl and product temperatures
Level 2
Restart
Cold s t a r t u p
Level 5
Figure 13.21. Technology-oriented knowledge base
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13. Integration of Knowledge-Bused Systems in Process Control Engineering
turbances must be drawn at the very first level. Thus warm startup. cold startup. and other situations are shown not as consequent situations of an HTF heater Failure but as general and equivalent disturbance classes. Technology-oriented structuring makes i t possible to include the complete knowledge. Situation-oriented structuring, in contrast, improves the eficicncy of knowledge proccssing for process securing problems in which the focus is on coordination of more than one processing unit.
sub-base). This expresses the fact that several disturbance classes can be correlated with a control class. Large memory problems would result. Furthermore, searching problem space with such a tree would narrow down the problem very slowly, so that the solution process would not be efficient enough (real-time problem). For this reason, the knowledge base was structured in a situation-oriented fashion, as shown in Figure 13.22. The drawback to this structuring is that conclusions about possible consequent dis-
Same as plant section 1
I
I
I
startup
I
I startup
I
Mole r a t i o /
COOH
Id
.oups
1
I Viscosity I
t
ti f ica t ion o f 'disturbance
__ - - - -
- --- _
I I t
I
I
I
_.
(VK111 (VK121
Scope o f disturbance
(VK311 (VK321
1
co1 c02 Level 5
Classification and therapy
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - _ ---------Figure 13.22. Structure of the knowlcdye basc in the expert system
13.3. Knonledge Engineering
In general, the final structure of the knowledge base (see Fig. 13.23) is governed by the proccdure learned from the plant operator. by automation theory, and also by the structuring capabilities of the expert system. The reasons for this structure arc as follows: 0
0 0 0 0
The decision tree is simplified (fewer levels) Knowledge items are represented only once Memory requirements are reduced Reliability is improved The system gains in maintainability
Figure 13.23. Structure of the process securing system upon a vacuum disturbance S , = csterification; S, = condensation; S, = ring-anddisk reactor Translate
2nd Editor
41 1
As has been mentioned, each primary cause of disturbances leads to a different decomposition of the technological plant, which logically yields a diffcrent structure for the process securing system. For example, consider a vacuum disturbance. which can also be a secondary consequence of a disturbance. The coordinative control action to be generated (see Fig. 13.23) will depend on how widespread the disturbance is within the chemical section of the plant, which, dynamically and topologically, can be broken down into three subsystems: csterification (Sl). condensation (S2). and ring-and-disk reactor (S3). The method of knowledge acquisition is the same as dcscribed abovc for the case of an H T F heater failure. The knowledge base for process securing (improvement of process reliability) in the chemical fiber plant includes, for one plant section, some 100 objccts (problem classes) in six levels with about 220 process attributes, 40 interference variables as well as process constants, and 500 rules, of which 160 are diagnostic and 340 govern the application of expert knowledge. The expert system developed is coupled to the cxisting T D C 3000 process control system and is in permanent service. Typical response times of the
Display
Rule El0 If t h e level in V K l c 2 5 % and t h e melt pump downstream o f VE2 i s n o t running Then ACTION -Set precondensation s t a r t u p parameters (activate explanation if desired) and
Representations Units
ACTION-E - S t a r t u p parameters f o r precondensation Pressure Level VK1 -A kPa -A OO/ VK2 -A kPa -A%
Number Percent String Temperature Time
P-VK1, L-VK, PEVKZ, L-VK
F12020 HI32081 H62131 LRC2213 LRC2302
...t h e primary diphyl f l o w r a t e ...pa s t e pump 2081 i s in order
...t h e melt pump downstream o f VE2 is running ...t h e level in VK1 ...t h e level in VK2
Object: PROD-TEMP-L-TAl
L a s t changed: Nov. 31. 1989
Figure 13.24. PROCON cxpert system shell, acquisition component. definition of a rule base for application of expert knowledgc (expertise rule base)
412
13. Integration of Knowledge- Rased Spvetns in Process Control Engineering
system are in the range of 4 10 s; roughly 7080% of the time spent is for on-line acquisition of process data needed for expertise. The development extended over a period of about one Arguments
Rule t e x t
Argument values Argument t e x t s
State
Results
year, during which time, 11 experts were intcrviewed. Figures 13.24- 13.26 illustrate the consultation of the expert system by the plant operator.
Secure
nuif
Evaluation o f e x p e r t i s e r u l e s in PROD-TEMP-LTA1 1
Condition 1: LRC2213 Condition 2 : HE2131
= 20% FALSE
RULE El0 Because t h e l e v e l in VK1<25% and t h e melt pump downstream o f VE2 is n o t running RECOMMENDED ACTION=Set precondensation s t a r t u p parameters ( a c t i v a t e explanation i f desired] RECOMMENDED ACTION-E=Startup p a r a m e t e r s f o r precondensation: VK1 VK2
Pressure 100 k P a 120 kPa
Level 50.0% 50.0%
Knowledge b a s e : GRISUTEN
T e s t consultation
Figure 13.25. PROCON expert system shell, consultation component, expcrtise rule lrace w i t h inspection
Solutions
Explanations
Representations
Securing
Solution 1 with r a t i n g 100
nuit 23:59:03
The following r e s u l t s were derived: RECOMMENDED ACTION: - A s precaution, check grades being spun (line assignment) -Set TRC 2461, TRC 2501 and TRC 2811 t o MAN -Wait f o r furnace s t a r t u p t e s t t o end - T u r n o f f position 113, lines 1. 2, and 3 - T u r n o f f viscometer (electrical) -The p a s t e v a l v e m u s t be s e t t o recirculate -Positions 107.1 and 107.2 must be t u r n e d o f f -Set precondensation s t a r t u p p a r a m e t e r s ( a c t i v a t e explanation if desired]
Knowledge base: GRISUTEN
T e s t consultation
Figure 13.26. PROCON expert system shell, consultation component, presentation of results
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
14.1. Glossary
Associated with any abstraction hierarchy is a taxonomy of concepts or terms denoting the objccts in the individual classcs.
14. Appendix 14.1. Glossary Prefatory Note. During the period of transition to a holistic mode of analysis in proccss control engincering, thc terminologies of individual specialties are not adapted to one another, and contradictions may even occur. The development of an intcrnally consistent, generally accepted terminological structure for process control cngineering is a critical and ambitious task. It is the concern of thc standardization bodies with jurisdiction over this field. Thc following list of terms cannot and is not intended to anticipate such undertakings. The purposc here is to create a unified platform of concepts to serve as a basis for thc treatment herc. The list defines only a sampling of terms, the ones most important for understanding, and makes no claim of completeness. The following abbreviations are used: D F Definition EP Explanation LI Literature (references) EX Example SN Synonym CL Classification
Abstraction
DF
Abstraction denotes reducing complexity by neglecting all inessential properties or attributes. Which properties and attributes are essential and which can be regarded as inessential will depend primarily on the vicwpoint adopted and on the question or problem stated. Both viewpoint and formulation of problem will determinc the result of any abstraction. Thcre are several forms of abstraction:
DF 1 Class-forming abstraction Class-forming abstraction takes at set of unrelated objects and orders thc set in accordance with common properties and attributes. All objects that possess a common set of attributes can be assigncd to one class. All objects that belong to this class are referred to as “instances” of the class. EP I
Abstraction can take place in several stages.
413
D F 2 Complex-forming abstraction Related ohjecrs can be combined into a with its own chardctcristic propcrties. When unlike objects are joined into a system, the diversity of properties of the individual objects is mapped onto a few external properties of thc system. This proccdurc is called complex-forming abstraction. sjstem
SN 2 Special case: Aggregation D F 3 Functional abstraction When aggregation is perform, typically not just the complcxity of the object but also the level (plane) of examination changes. With every aggregation step, the examination becomes more “abstract” (14.11. The levels may be characterized, for example, as physical, operative, tactical, strategic, or intensional. Such a hierarchical structure is also referred to as functional abstraction. Aggregation
DF
Combination of related objects into a system having characteristic properties. Aggregation is a case of complex-forming abstraction. The objects examined are clusses or instances.
EX
By means of assembly, a collection of initially noncohcrent objects such as wheels, body, engine, transmission, etc. becomes a system, in this case an automobile. The system exhibits its own characteristic properties, which can only be derived indirectly from the interaction of the element properties. For example, an engine is characterized by the following properties: number of cylinders, way in which valves are operated, numbcr of valves, weight, displacement, etc. The transmission is characterized, among others, by the gear ratios. If the total system “automobile” is examined, the interaction of the properties of the individual elements gencratcs a huge varicty of overall system properties. From a givcn standpoint,
414
14. Appendix
however, only a certain subset of these will bc of interest. For example, if an automobile is regarded as an element of a transportation system, its behavior can be characterized by a few properties, such as maximum number of passengers, maximum speed. fuel economy, and range. The properties of the individual objects from which the automobile was assembled are no longer of interest. EP
Aggregation forms complex objects by stepwisc combination of simple objects.
SN
Complex-forming abstraction
for the acquisition. processing, and output of analog and binary quantities. Ilcre, basic functions includc, for example. acquisition via analog input, 1 3 0 encoding, and the formation of a reference value. D F 5 Synonymous with group control. D F 6 Synonymous with monitoring and control function. EP
Here, “basic function” is exclusively uscd in the definitions given in DF 1 and D F 2 .
Basic Functional Element Attribute
DF
A characteristic or feature of an object (see Proper t v ).
SN
Property
Automatic
DF
Performance of a task by a self-contained technical system without outside aid.
LI
DIN 19233
Basic Function
D F 1 Function of process control. Establishment of an independently set process property in a plant section. DF 2 Functional unit with the capabilities of establishing certain process properties in a plant section. The functionality that a plant section possesses for intervention in the process is disjointly distributed over the basic functions. At a higher level of abstraction, the functionality of a plant section for process control is obtained by superposition of the functionalities of its basic functions. D F 3 Softwareihardware module with the capability of realizing the functionality of a basic function (DF2) (see also Functional Unit). The term “basic function” is also used with another meaning in process control engineering: D F 4 In the sense of DIN 19 243, Part 1, basic functions are the smallest building blocks
D F 1 Function, element of a basic function ( D F 1). DF 2 Functional unit or jiunctiotial element (DF2) from which a basicjknction (DF2) is constructed. In this sense, a basic functional element can be a process intervention, a discrete control device, a group control, or any ,functional element orfunctional unit that is used to construct a basic function. D F 3 Software/hardware module with the capability of implementing a single basic functional element (DF2) (see note under Functional Unit). Breakdown (Analysis)
DF
Elucidation of certain propcrties of an object through a system of simpler objects interacting in an ordered Fdshion. The breakdown process is subject 10 the nesting principle.
EP
The system is to be viewed as a model such that, for the question or problem statcd, it can explain the external behavior on the basis of the interaction of system elements. Quite different breakdowns may be obtained, depending on the question formulated and the model selected.
EX
An automobile might be broken down - from the assembly standpoint, into
wheels, engine, body, etc.
- from the disposal standpoint, into plas-
tic parts, metal parts, textiles, etc.
14.1. Glossary
these functions can be incorporated in the semantics of the module (component) language.
Call
DF
EX
One-time activation of an algorithm (better: the message that initiates the activation). An analog controller opcrates continuously and need not be called; a discrete controller, on the other hand, operates under a cyclically (periodically) called algori thm.
Class
DF
Group of objects characterized by a set of common attributes.
41 5
LI
[14.2]
Control
The term “control” is used with five distinct meanings: D F 1 A sequence of action: “Control” or “feedforward control” denotes an opcn sequence of action over a single transmission element. This meaning is contrasted with “feedback control,” which denotes a closed sequence of action.
Command Mode
LI1
DF
D F 2 A binary operation: “Binary control” de-
The command mode is a characteristic property of technical units of a command hierarchy. It indicates whether the unit in question receives its control values and commands from the plant operator (“manual” command mode) or from a higher-level technical unit (“automatic” command mode).
Connector
DF
EP
Connectors are subobjects of functional modules. Through them, the functional module mediates its message traffic with the surroundings. Each connector thus incorporates certain client and/or server properties of the module in a task-oriented fashion. A connector has an interface to the communications system, its own method for handling the message traffic, and an internal interface with access to the information held and used in the module. Module inputs and outputs, as defined, for example, in the languages of I EC 1131, are spccial connector types. They serve primarily to handle “get” connections, in which the recipient, through an input, reads information from the output of another module. The IEC languages provide no possibility of handling more complex communications functions (e.g.. semaphore-protected write) at the module surface. Separate “communications modules” must be defined for this purpose. With the introduction of connectors,
DIN 19226 notes binary processing of input signals yielding binary output signals.
L12
DIN 19237
EP
Here analog processing is contrasted with binary control. There are two forms of binary control: logic control and sequence control.
D F 3 A functional unit with the capability of
performing all functions occurring on a control level (plane, layer). In the control system, it receives tasks from a functional unit located higher in the hierarchy, executes these tasks o n the basis of a specified method or recipe, and concludes them by specifying appropriate instructions to the next lower level.
CL
Group control, plant section control, plant control, production control, etc.
D F 4 Hardware/software modules that realize the functional unit of DF 3 (see note under Functional Unit).
D F 5 In a hardware context, a programmable logic controller (PLC).
Control Value
DF
A control value is a time-varying setpoint of a controlled process property. The expression “controlled process property” signifies that there is a concrete process control function that (in normal opcration) guides the actual value of the process
416
14. Appendix
trolling and monitoring the device itself and functional elements for the processing of useful information, for example. to output control commands or to accept and prepare measured values. In the actuator branch, the discrete device acts as a group conlrol. Here, the task is to bring about certain process properties in accordance with the specified control values.
property so as to track the nominal value in a specified fashion.
EP
The control task can be carried out in various ways, for example by closed-loop control - with event-controlled feedback by open-loop control
Decentralimd Control System
DFI Totality of all technical apparatus that makes possible the function “monitoring and control”.
DF2 In a hardware sense, the monitoring and
Element
DF
Object that is in a well-defined relationship to other objects in the context of a system and that cannot or is not to be further decomposed in the context of the present analysis.
EP
In contrast to an object in general, associated with an element per se are the relations (relationships, dependences) to the other elements in the system. Certain functions are transferred to an clement in the context of the system.
EX
A screw in a 100-pack is an object. In a certain system, the screw that holds two parts A and R together is an element of the system. In the system, the function “connect A and R” is transferred to this element.
SN
System element
control system is part of the process monitoring and control hardware.
Degree of Automation
DF
The degree of automation is the fraction of the process control tasks that is performed automatically by the monitoring and control system.
LI
[14.3]
Device Control Function
DF
EP
Function for the control and monitoring of devices included in the monitoring and control system but not acting directly on the process. In the case of a sensor, for example, the following functions are carried out: select measuring range, initiate calibration, select place of measurement, etc. In the case of an actuator, the device control functions might include the selection of certain actuator characteristics (e.g.,‘converter), the initiation of a calibration (e.g., metering scales), or the selection of a redundant resource (e.g., dual pump).
Entity
D F l A certain state of being of an existing thing; also the thing itself.
DF2 In connection with object orientation. an-
other name for an object that exists in reality or conceptually and is of essential importance to the problem under consideration. Entities thus form an excerpt of the real world that is necessary for the handling of the problem under consideration. Abstract concepts such as “function” can also be entities.
The functional unit discrete control device possesses the functionality to take over the device control function.
Discrete (Control) Device; Discrete Device Technology
LI
DF
Entity - Relationship Model
Functionul unit with the capability of controlling and monitoring an actuator or sensor. This functional unit generally contains both functional elements for con-
DF
[14.4]
The entity-relationship (E/K) model describes the entities of an analysis space to-
14.1. Glossurj-
gethcr with the relations existing between these entities.
EP
E/R models arc suitable for describing static relations between entities (consists o f . . . , is a . . . . is derived from.. . ).
EP
E/R models in general offer a graphical syntax for the intuitive representation of entities and their rclations.
LI
[14.5]
Execution Unit (see also Functional Unit) DF
EP
Execution units are functional units that are capable of realizing a certain role. They arc based on resources that possess the required functionality; spatially and logically, they are placed in a suitable layer; and they are configured so that they exhibit the special functionality needed for the role. See Role.
SN 1 Device Here the term “device” should, however, be understood as including units rcalized logically and in software. SN 2 Here, execution units are referred to as functional units. Framework Concept DF
Document describing the structure within which long-term development is to be accomplished.
Function D F 1 Clearly defined task within a larger totality. Functions are a separate category of objects.
EP1 Note: A function is the task proper, and must be strictly distinguished from the capability (functionality) of a unit to perform a task. In the context of the structure of a system, functions are mapped onto units yunctional units) that possess the functionalities required for the functions. This transfer makes the units into elements of the
417
technical system, which now implement the functions. (See also Functionnlity. Functionul Unit, Element, Substance). EP2 The term “function” is also used informally in relation to concepts that d o not have to d o with functions (see, e.g., Functionul Unit, Functional Element, Basic Function; also the commonly used term “functional module” frequently refers to a program module). Note: Although these expressions are inappropriate with regard to clear categorization of objects, they are not changed here but arc used in accordance with common practice. D F 2 In the process control engineering context, the only other meaning of “function” that is of any interest is the mathematical function. /Z function is a variable quantity whose value depends in an unambiguous way on the value of certain other quantities. (It is a mapping between algebraically structured sets.) Functional Element An ambivalent term denoting both the elements of functions and the elements of functional units. D F 1 Element of aJunction. EX 1 Display analog value, transform signal, link values, form limiting value, verify status signal, control valve, block motor, control,. . . D F 2 Functional unit with elementary functionality. EX 2 Functional elements, defined as the smallest units of functionality under consideration, include display elements, signal-transforming elements, linking elements, and limit pickups; or, specified from a technological standpoint, process monitoring and control station element, basic functional element, . .., motor control, valve control, PID controller,. . . EP
Functional elements can be regarded as the bridge between the top-down analysis of functions and the bottom-up synthesis of functionality. A function must be broken down in such a way that it ultimately
418
14. Appendix
consists of functional elements (Funclions, DE‘l), each of which can be fully implemented by functional elements (Functional Units. DF2). Functionality
DF
The capability of an object to perform certain tasks (function.v).
EP
Functionality denotes a fundamental capability of an object and in this sense is a resource. This resource can be utilized by employing the object as an element of a system. In the system context, certain tasks are assigned to the object (as an element of the system). In this way, a part of the functionality of the object is used.
EX
A drive, as an object (functional unit), has
CL
the following capabilities: “can be at a stop,” “can turn to the left,” and “can turn to the right.” The drive M 2 associated with roller R 4 of a roller conveyor has the tasks of turning to the right or of being at a stop. The capability of the object “drive M 2” expressed as “can turn to the left” is not utilized in this system environment. Functionality denotes not an object but a set of properties of an object.
Functional Unit (see also Execuiion Unit)
DF
An object with a certain required functionality; that is, an object having certain properties that are necessary for the implementation of the tasks to be performed.
CL
Functional units are not functions but play the role of substances. Functional units have the character of resources and can be called on once and only once at any given time.
EX
In the planning of a roller conveyor system (see the example under Functionality), the system is built up, say, from functional units such as drives, rollers, and so on. These are neither functions nor devices but postulated objects with well-defined functionality. In the planning of the roller conveyor system, these objects, viewed as available resources, are used as system ele-
ments; they arc combined and linked in such a way that the roller conveyor system as a whole can fulfill the task allotted to it. EP
The definition of functional units is arbitrary. It would be possible to define any functional units with any functionality. It is, however, expedient to choose functional units in such a way that the set of properties (functionality) coincides with, or is a subset of, the property set of available substances. In the software realm. for example. this mcans that, in constructing a system, functional units may be used only if there are program modules corresponding to them. Because of the correspondcnce between functional unit and object, synonymous expressions are used for the two even though this is not wholly correct (see Function). Examples include the expressions “plant control,” “discrete control device.” “group control,” and so forth. An even more unfortunate situation arises when a synonymous term is used for the function, the functional unit, and the object. This happens with, for example, basic functions and basic functional elements. Metering is a function; there is a functional unit “metering device” for it; ultimately, there is a software module that implements the functionality of the metering device. In common usage, however. the expression “basic function” is applied to all three objects (see Section 4.5).
EPl Examples of the naming of functional
units: Classes of functional units includeproduclion control, plant control, plant seclion control. group control, discrete device control, basic function. Types of functional units include metering device, motor, valve, level control, tcmperature measurement, reactor control. tower control, and so on. Instances of functional units include (standard plant and apparatus coding) motor V13R4K12 and level control
V13R4L6.
EP2 Here, the expression “functional unit“ is also used for a subordinate concept, dcfined under Execution Unit.
14.1. Gtossurj.
Croup Control
D F 1 Functional unit with the capability of establishing certain process properties in accordance with specified control values. The group control uses internal methods to calculate setpoint values, which it passes on to group controls or discrete control devices at the next lower level. D F 2 Software/hardware module with the capability of implementing the firnctionul unit “group control” (DF1) (see note under Functional Unit).
D F 3 Functional unit with permanently defined, gencrally applicable control functionality. LI
[14.6]
Heuristics
DF
Doctrine of the methodical acquisition of new knowledge that leads to acceptable results even though not guaranteeing a definite solution of a problem (since there is no proof of convergence). Heuristic procedurcs are frequently used in the engineering sciences and in operations research.
Inspection
DF
Determination and evaluation of the actual state of a plant or apparatus.
LI
D I N 31 051 (1985)
D F 2 Objects representing the lowermost level of a hierarchy of classes. An instance in this meaning cannot be further instantiated. EX 2 The motor with serial number XXX is an instance in this sense. Every feature, at every time, has a certain expression (serial number, power, construction, voltage, explosion protection. instantaneous rotation angle and rotation speed,. . .). EP2 The instance is itself a n abstraction of the concrete object and takes account only of the properties needed for classification and identification. Other properties of the real object d o not become properties of thc instance. EP2 Note: The instance of a functional unit, even though it has many of the same properties Vuncfionality),is not identical with the instance of the corresponding physical object. They arc correlated only temporarily, for the time of installation. If the physical object is removed (for example, taken into the workshop), the two objects may under some circumstances have different relationships. Instruction Acceptor
DF
The instruction acceptor is the connector through which a functional module receives a control instruction.
EP
Each functional module can have more than one instruction acceptor. The control instruction is a command specification with a write character. The instruction acceptor must accept the control instruction immediately after its arrival, independently of cyclic module processing. It checks the content of the control instruction from a formal and a semantic standpoint, sends a reply to the operating system; on acceptance of the command, it writes the new values to the module as effective values.
Instance
D F 1 A11 objects that belong to a class are rcferred to as instances of the class. EP 1 This definition is general. In a hierarchy of classes, each class can be regarded as an instance of the next higher class. The definition makes n o restriction as to the form of class formation. EX 1 The electric motor is an instance of the class of motors. The asynchronous motor is an instance of the class of electric motors. A certrain model of motor (manufxturer X, model Z) is an instance of the class of asynchronous motors. The motor with serial number XXX is an instance of a model of motor.
419
SN
Operator control. control instruction.
Logic Control
DF
LI
Control using Boolean logical operations to assign certain output signal states to the input signal states. DIN 19237
420
14. Appendix
Maintenance D F 1 Measures taken to keep a plant in functioning condition. D F 2 Mcasurcs to insure the nominal state of a plant.
LI
DIN 31 051 (1985)
Model DF
Representation of the important relations bctween the properties of an object.
SN
Model system.
EX 3 In the case of physical objects, as in examples EX 1 and EX 2, the nesting principle is trivial. The important point is that it also applies to instances of functional units considered as “resources of functionality.” If a functional unit A is an clement of another functional unit R, functional unit A is no longer available for other functional units (in B. a certain task has been allotted to it that makes claims on its resources).
EP
Module DF
In general, a module is an object that forms a closed unit and can be manipulated as such.
EP
The concept of “module” should not be further dclimited. Subtle distinctions to other concepts, such as block, building block, unit, and so forth, based on thc degree of complexity or other attributes, are not meaningful.
SN
Apparatus module: device unit Software module: functional module Functional module: functional unit
Nesting Principle DF
Specification relating to the aggregation of functional units. Instances of functional units are resourccs that are available once. In the framework of an overall structure, they can be used only once and thus also aggregated only once.
EX 1 If gcar Z 1 is installed in gearbox G 1, the gcar is no longcr available; the gearbox as a whole, however, is still available and can be installed in vehicle F 1. Once this has been done. again gearbox G 1 is no longer available; that is, it cannot simultaneously be installed in vehicle F2.
EX 2 If a grcen box is packed inside a yellow box, the green box is no longer available and cannot also be packed insidc a blue box. It is, howcver. possible to pack the yellow box (with the hidden green box inside it) in the bluc box.
The nesting principle applies only to instances of functional units. It does not apply to the aggregarion of classes or to the aggregation of instntices of Jitncrions. A functional element can be simultaneously an element of more than one function.
Object
DF
EP
Subject of knowledge, perception, thinking, and action. In thc context of object orientation: identifiable patterns that are describable in terms of properties. The internal structure of objects is not directly perceivable but may be deduced from the properties describing the object. Both functions and substances can bc treated as objects in this scnse i14.71.Object is not an antonym of function.
Operating Mode This term is used with several meanings. DF 1 Degree of automation of a plant. Here, the operating mode is thc planc (level) of operator control currently selected.
EX1 A coarse classification includes the fol-
lowing modes: manual. computer, logistical, and automatic [14.3]. Manual mode: the monitoring and control system takes over only certain process control tasks for the automatic execution of individual loops. Computer mode: the monitoring and control system takes over all process control tasks for the automatic pcrformance of a process section in a plant section. Logistical mode: the monitoring and control system takes over all process
-
control tasks lor the automatic fulfillment of scheduled orders within a plant. Automatic mode: the monitoring and control system takes over all process control tasks including fine production control for the automatic scheduling and fulfillment of orders within a plant.
Partial Recipe
DI:
DF 2 One of the fundamentally different behavior patterns of functional units. Alternatively, certain technological function units can carry out distinct (selectable) tasks.
EX2 The functional unit “inert-gas blanketing” has the modes “pressurizing” and “purging.” In the first, the task is to establish the pressure in accordance with the specified control value; in the second, to establish the flow of nitrogen through the system. D F 3 Way in which a plant is run in dependence on the control of the operating value or throughput-to-capacity ratio (startup, normal running, load changing, emergency shutdown, ctc.). L13
Phase
DF
Phases subdivide process elements into a strict temporal order.
EP
Each process element, at each point in time, is in precisely one phase. There is no parallelism of phases in a plant section.
EP
Remark: In many cases, the phases are named after the unit operations that predominate in them. This often leads to misunderstanding. In a phase referred to as “meter starting substances,” the unit operations “meter A,” “meter B,” “temper,” and “mix” (agitate) can take place in parallel. The sequcnce of phases, however, is strictly serial.
EP
The subdivisions of the phase model at the lowermost level of refinement correspond to the phases.
[14.6]
Operating Value (Throughput-to-Capacity Ratio)
DF
The ratio of product throughput through a plant to design throughput.
LI
[14.6]
Parameter
Phase Model
DF
The phase model is a special network model with which a procedure can be systematically described. The phase model consists of two elements: - Process elements in which n inlet products are transformed to m end products by mcans of operations. - Product nodes with well-defined product properties, which connect the proccss elements to one another.
EP
The phase model offers two substantial advantagcs: - Logical network structure The network structure permits systematic refinement (top-down method) from a coarse structure to a detailed description.
D F 1 Parameter (of an equation). In functions and equations, an auxiliary quantity appearing alongside the variables proper and either left undefined or held constant. D F 2 Parameter (of a path or trajectory). Special case of a parameter of an equation. D F 3 Parameter (of a process). Variable that influences a technical, economic, or other process. EP
In the proccss control context, this term is often used in an imprecise and poorly understood way, and it should be largely avoided.
Complete description for making a ccrtain end product or intermediate product in a particular type of plant section. A partial recipe consists of: - A specification as to the properties of the inlet products - A specification as to the state of the plant section at the beginning of the process - A manufacturing specification for the control of the proccss.
422
14. Appendix
The specification of nodes in terms of products makes it possible to describe and refine the individual process elements independently of one another. The phase model is not concerned with the form of the temporal and engineering realization of the coupling. -- The possibility of establishing conditions that must be observed, by specifying profiles of process and product properties in parallel with the design process. The spccification of profiles for the process properties to bc brought about in the process elements and the product properties required in the nodes can be done in advance and refined with the network structure. The phase model is the basis for the construction of quality assurance systems.
EP
Among the points charactcrihg a plant section is that the product treated in it is to be regarded as substantially located in a balance space. This balance space may well be distributed (tower, fixed-bed reactor, etc.) but still forms one unit from the standpoint of process engineering. Two stirred-tank reactors should bc viewed as two separate plant sections because, in general, each is characterized by its own balance space.
LI
DIN 28004
Plant Section Control D F 1 Functional unit with the capability ofcontrolling the group controls or basic functions of a plant section and of automatically controlling a process section in accordance with a specified recipe.
[14.3]
D F 2 Hardware/software module for the realbation of the “functional unit plant scction control” (see note under Function).
DF
Combination of plant sections into a delimited tcchnological unit.
Process (General)
EP
The context in which the unit is delimited is not fixed. The important point is that each plant section can bc assigned to only one plant.
EP
An appropriate delimitation can be found if, for example, the plant is viewed as an analytical space for a fine production control system.
LI
DIN 28004
LI
Plant
Plant Complex DF
Organizational combination:of plants and other units into an overall heterogeneous unit.
Plant Control DF
Functional unit with the capability of initiating and coordinating the processing elements in the plant sections of a plant on the basis of a spccified operating plan.
DF
EP
EP
LI
Process Control DF
Process control takes in all measures that effect the desired course of a process in the sense of established objectives. These measures either can be executed automatically by the process monitoring and control hardware itself, or may have to be performed by human operators.
LI
DIN 19222
Plant Section DF
Self-contained unit for the realization of a technical task (see Fig. 2.25).
A totality of interacting trains ofevents in a system by which matter, energy, or information is transformed, transported, or stored. The smallest unit for analysis is the process element. Note: The term is used to refer to the actual train of events taking place in the real world, not to the system characterized by this process or the specification underlying the process. In a processing system, “process” denotes the dynamic variation of product properties as a result of the action of unit operations or the variation of process properties due to the action of basic functions. DIN 66201
14.1. Glossary
Process Control Function DF
Process monitoring and control funclion
with the task of influencing the course of a process in such a way that certain process properties will track specified control values. A process control function is realized by hierarchically structured process control units in the feedforward control branch and a network of process information processing units in the feedback branches (see also Control Value).
DF 3 A combination of functions. In this scnse, the process control station is given by the sum of the defined processing functions assigned to the process connection points (used so, at least in part, in the station detail of the piping and instrumentation diagram). LI DIN 19226 Process Control Station Element DF
Process Control Station (Process Monitoring and Control Station; formerly Measurement and Control Station) The concept of the process control station is a key one in process control engineering. Even so, it is difficult to define and delimit. In common usage, the term refers to a variety of concepts: DF 1 A point of connection of the process. In this sense, a process control station is the intefiace between the process monitoring and control functional unit and an engineering functional unit (used so, at least in part, in the station data sheet or in the station detail of the piping and instrumentation diagram). DF 2 A functional unit of process control. This functional unit is characterized by the fact that it possesses at least one process connection point. The task of the functional unit “process control station” is primarily to execute the assigned processing functions (0,I, C, S,. ..). Two remarks should be made, however: - Only in a few cases is a processing function realized, as a whole, by a functional unit. In many cases (e.g., the S function, central A, I, 0 evaluating functional units), multiple functional units (and multiple process control stations, in the case of the S function) are involved in realizing a processing function. The differentiation between the functional unit “process control station” and more complex interconnections is indistinct (at what complexity does a functional unit with process connection points cease to be a process control station ?).
423
Process control station elements are the smallest functional units into which a process control station (DF2) is broken down. Process control station elements have functionality established such that they can be realized by available substances (deviccs, software modules, etc.). Each process control station element is needed for the performance of one or more processing functions.
Process Element DF EP
Smallest unit used in analysis of a process. A process element can be realized by a process step or a process section, depending on whether the analysis is time-based or spacc-based.
Processing Function
DF
EP
Term denoting process monitoring and control functions allotted to a process control station. The processing functions are represented in the station detail. Note that a processing function is gcnerally realized through more than one functional unit and, in certain cases, through more than one process control station, despite the unambiguous allocation (see note under Process Control station).
Process Intervention DF
Monitoring and control functional unit that combines all functional elements (process control station elements) of control hardware between discrete control device and process.
Process Monitoring and Control Function DF
Function realized with the aid of the mon-
itoring and control hardware. In general,
424
CL
14. Appendix
monitoring and control functions are closed lines of action between process elements, between operator and process, between operator input and operator output, or in general the response of the outputs of the process control system to certain input signals. Monitoring and control functions in process control enginecring can be specified by control action in the following ways (among othcrs): Process control functions After operation DOS-3 ON, meter in x liters in minutes - After operation T5. ON, turn on plant section 5 Process securing (stabilization) functions - If temperature T15 > 80”C, turn off Pump 3 Process information functions - Display temperature T 15 in on-screen flowsheet Device control functions - - Calibrate pH probe Q 14 System control functions - Select screen page plant section ST3 Monitoring and control functions also occur in other settings such as manufacturing engineering or building management engineering.
Production Control
DF 1 Functional unit with the task of scheduling production requirements, preparing a production schedule, allocating the needed resources, and monitoring the performance of the schedule. DF 2 Software/hardware module with the capabilities of realizing precisely the function “production control” (DF 1) (see note under Function; see also Fig. 2.30). Product Properties
DF
Product propcrties are the categories of information about a product. They include physical quantities, chemical quantities, technological properties, and product indicators.
LI
[14.8]
Project
DF
Expressed generally in the terminology used here, a project is the complete temporal framework of conditions governing all substances for the realization of tasks represented by functions. The substances consist of procedures, systems, and elements (see Figs. 2.9 and 3.26) and arc also referred to as resources.
DF
A characteristic or feature of an object. A property consists of the type of feature (category, size) and the expression of the feature (intensity, value). The type of expression (metrical, topological, etc.) depends on the type of feature.
EX
A house might have the properties Expression: Type of feature: Address Hauptstrak 3 D-1 1 111 Kleinhausen Owner Mrs. Mcier Height 8.25 m Building style Jugendstil (Art Nouveau)
EP
Classes are marked by common feature types. The expression is an instance quantity.
SN
Attribute
Process Monitoring and Control Hardware DF
All devices employed for the task of process monitoring and control.
LI
DIN 19222
SN
Process control system
Process Property DF
Process properties are the categories of information about a process. They include state variables, process parameters, control variables, setpoint parameters, and process indicators.
LI
[14.8]
Process State
DF
Expression of the process property vcctor at a given time (see Section 11.3).
EP
See also Quality.
14.1. Glossary
Quality DI?
Expression of thc product property vector at a given time (scc Section 11.3).
EP
See also Process Stute.
0
0
Recipe DF
Homogeneous redundancy: the actual value of the desircd process property is determined by using thc same measuring principlc and by multiple measurements of the same auxiliary process propcrties. Diversity: the term “diversity” (diverse rcdundancy) is used when: -
A recipe consists of the sum of the partial
recipes needed to makc an end product and the explicitly stated relationships between the partial recipes. EP
The term “rccipe” has been introduced but is still subject to various interprctations. “Production instructions” or “production specification” would be a more prccise term and should bc given prcfcrence in the future.
Redundancy (General) D F 1 Concept in information theory, denoting that a message contains elements that can be omitted as not providing any additional information but merely supporting the desired basic information. Beneficial redundancy: constituents that can be omitted would back up the information if other constituents wcre omitted. -- Empty redundancy: constituents that can be omitted would not back up the information content if other constituents were omitted. D F 2 Severalfold presence of a functionality in order to enhance the availability of a function.
EP
The measurement of a certain process property is said to be redundant when the actual value of this process property can be acquired from at least two disjoint groups of available measurements. If a measurement becomes unavailable, the disjointness means that only onc group is affected, and the actual value continues to be available to the system. This is an example of beneficial redundancy.
The actual valuc of the desircd process property is determined from mcasuremcnts of different auxiliary proccss properties (example: determination of flow rate via pressurc drop and with magnetic flowmeter). The actual (instantaneous) value of the desired process property is determined on the basis of the same auxiliary process propcrties, but the auxiliary process properties are measured by different methods (example: level measurement with float and ultrasonic instruments).
Repair@) DF
The restoration of the nominal condition of a plant.
LI
DIN 31051 (1985)
Requirements (see also Specifications)
DF
Document describing the tasks Vuncfions) that arc to be implernentcd in the framework of a project. The initial description is solution-invariant.
SN
Bidding specifications
Role
DF
Roles are unambiguously described tasks within a larger context. Thc realization and performance of a role requires a functional unit (execution unit), which realizes the role in a certain layer at a certain time.
EX
The relationships between roles, rcsources, functional (execution) units, and layer can be illustrated with three examples: - Theatrical performance Consider the role of “Gessler” in the play “William Tell” (virtual system). The realization of this role demands an actor. Any actor who knows the text and is capable of playing Gesslcr is a
Redundant Measurement
DF
425
426
14. Appendix
potential candidate and thus belongs to the pool of resources. Now suppose the play is to be performed on October 16, 1995, at venue X. The role system thus requires that an actor suitable for the representation of Gessler be available at X on October 16, 1995. For the theater manager. this means that an actor Y must be sought and signed for the performance; that is, a resource must be selected and allocated. This can take place ahead of time when the theater season is being planned. For the play to be actually performed, it is important that Mr. Y be available and prepared for the role at X on October 16, 1995. He is configured for his role through makeup and appropriate costuming. Mr. Y is then an available function (execution) unit for the role of “Gessler” at X on October 16, 1995. - Hardware component Consider the following role: discrete control component for reactor X. It belongs to the process monitoring and control system being planned (virtual system). Each component of type Y made by manufacturer A possesses the required functionality and is thus a potential resource. As a functional (execution) unit, it is present (available) if, as a hardware part, it has been delivered, configured (e.g., jumpers placed), and installed in the proper slot. (This job is performed by installation.) In this state, the component can take over the role envisioned for it in the process monitoring and control system. - Motor control module Consider the role of the control of motor V 12 TA45 K 2. The process control being designed is the virtual system. For the realization of the role, a software module must be placed in the process monitoring and control system and made available as a functional (execution) unit. Any instance of the motor control type MOT becomes a candidate. Thus, an instance MOT. V I2 TA45 K 2 is defined, configured (here this may mean parameter assignment: number of feedback messages, startup time, etc.). and installed in the component provided (which here corre-
sponds to the layer). Now this software module is capable of reahdng the role of the motor control V 12TA45K2. SN
Function
Securing (Stabilization) Functions
DF
Functions for prevcnting unacceptable dcviations of the process, of the process control, or of the process nzoriitoriiig rrud control hardware from the nominal state. Securing functions d o not intervene in the sequence of events under normal operating conditions and permit the process control functions to perform their task. Before a deviation enters the unacceptable range, the securing functions are activated, interrupting the access of the process control functions.
EX
Typical securing functions are intcrlocking of functional units and STOP conditions in sequence controls.
EP
Other expressions of this concept refer to improving or enhancing the reliability or safety of the process.
Sequence Control
DF
Control in which the program is constrained to progress step by step and the advance from one step to the next step in the program depends on advancing conditions.
LI
DIN 19237 Depending on the type of conditions for advance, the sequence control is said to be time-dependent or process-dependent. One also speaks simply of timed control or event control.
Specifications (see also Requirements)
DF 1 Document that contains all information required for the realization of a certain system.
EP 1 The specifications describe how the func-
tionality of a system as set forth in the requirements can be realized with the help of given resources. “Given resources“ mean functional units to be used. objects (hardware/software) to be used, rules and procedures t o be applied, and constraints
14.1. Glossary
to be observed (c.g., limitations on time. money, material. space, and pcrsonnel resources). The specifications arc linked to the solution adopted. D F 2 Separate document describing the constraints, rules. and data that must be followed in the procedure.
down in accordance with the nesting principle.
LI
DF
In philosophy, the concept “substance” is EP employed with a variety of meanings. Here it is used in the sense of CAS~IRRR [14.7]:
Substances are objects in the real world that can be described and identified in terms of attributes. Substances either are real objccts themselves or correspond virtually to real objects. Functions always require appropriate substances for their realization.
EP
Substances include systems and their elements (devices), as well as skills and methods (procedures). With the aid of procedures, functions are realized from systems and their elements through the use of a part of their properties. Of the great number of possible substances (see Fig. 2.8), “standards” (e.g., design, installation, and hardware standards) are derived by means of appropriate definitions. Substances, as real objects, can be either hardware objects or software objects.
EX
Subsystem
DF
EP
Part of a system, having the following properties: - Elements of a subsystem are always elements of the surrounding system as well. - The relationship between elements in a subsystem always corresponds to the same relationship between the same elements in the surrounding system. - Distinct subsystems of a system can overlap. A special case is the complete subdivision of a system into nonoverlapping subsystems. This case corresponds to a break
(14.91
System
Substances
DI:
427
EP
LI
A delimited, ordered totality of interacting subsystems or system elements. This configuration is separated, or regarded as being separated, from its surroundings by an envelope. One characteristic of a system is that it simultaneously admits of an external and an internal view. In the external view, the system is examined from outside its envelope, which is thought of as opaque. The system behaves as an aggregated object (black box). In the internal view, the system is examined from inside its envelope, which is thought of as opaque. From this standpoint, the structure and mode of operation of the system can be seen. The propcrties of a system cannot generally be elucidated by superposing or aggregating the properties of its elements. A gearbox is a system of gears, locking mechanisms, bearings, and shafts. From the outside, in the technological view, all that is interesting about a gearbox is the transmission ratio between the driven shaft and the driving shaft. The internal geometry and the number of gears are not important. From the inside, on the other hand, it is prccisely the geometrical structure of shafts and gears that is of interest. The only way to determine the transmission ratio effective at the envelope is through a complicated computation based on the individual gears of the active branch. It is important that a system admits of both views at the same time. It can be said to represent an object at the moment of aggregation or disaggregation.
DIN 66201, DIN 19226, [14.3]
Taxonomy
DF
A methodologywed in science. consisting
in the assignment of concepts or individual phenomena to a general ordering scheme.
428
14. Appcwdix
Type
CENELEC
DF
Class whosc objects exhibit a high dcgrce of intcrrclatcdncss.
Unit Operations DI.'
The smallest physical or chemical operation pcrforrned, which acts on a material system in a plant section.
EP
CL
Ll
By means of physical or chemical operations, thc niatcrial systcm in a plant section is transformed from a state A to a state B. Thcse opcrations can be broken down into unit operations. As a rulc, unit opcrations are carried out onc after another, but therc arc unit opcrations that take place in parallel. Examplcs: conveying and drying in the pneumatic conveying dryer, reaction and mixing in the stirred-tank rcactor, etc.
CPU CSMAICA CSMAICD DBMS DCS DEC DES DI DI F DGK
[14.6]
DKE
AMA ANSI A0 AR ASN. 1 ASP C+ C Methods CAD
CEN
co
CI1'
DGQ
A1
CClT
CIM
Unit operations can be classified under conveying, mechanical processes, forccfield processcs, processes with molecular driving force, and chemical proccsses.
14.2. Abbreviations
CAE CASE
CEPT
Analog Input, Artificial Intclligcncc Arbei tsgemeinschaft MeUwertaufnchmer American National Standards Institutc Analog Output Archival Functions Abstract SynVax Notalion One Abstract Scrvicr Primitive Programming languagc (objcct oriented) Computer-supported methods Computcr Aidcd Drafting, Drawing, Design Computcr Aided Engincering Computer Aided Software Engineering Comitc Consultatif Internationale Telegraphiquc et Tklephonique Comite E u r o p k n de Normalisation
DLL DO DOS DPG DS DVT EAPROM EClTC EEPROM EIA ELCS ElexV EPROM EPS EIR
Comite Europken de Normalisation Electriquc Comitc E u r o p k n des Administrations des Postes et dcs Te~Pcommunications Computcr Integrated Manufacturing Control Functions Computer Integratcd Processing; Cleaning in Process; Cleaning in Place Ccntral Processing Unit Carrier Scnsc, Multiple Acccss. Collision Avoidance Carrier Sense, Multiplc Acccss. Collision Dctection Data Basc Managcmcnt System Distributed Control System Digital Equipment Corporation Discrete Event System Digital Input Data Interchange Format Deutsche Gescllschaft fur Kyberneti k Deutsche Gescllschaft fur Qualitat Deutsche Elektrotechnische Kommission Data Link I.ayer Digital Output Disk Operating System Deutsche Physikalische Cesellschaft Dircctory Service Deutscher Verbund Technischwissenschaftlicher Vereinc Electrically Alterable Programmable Read Only Memory European Comrnittce for Information Technology Testing and Ccrtification Electrically Erasable Programmable Read Only Memory Electronics Industries Association Extcnded List of Control Software Verordnung iibcr clektrischc Anlagen in explosionsgefihrdetcn RHumcn Erasablc Programmablc Read Only Memory European Physical Socicty Entity- Relationship
14.2. Abbreviations
ESPRIT ETSI EUREKA EXERA FIP FLAVOR GA GI GMA GMD GMR GVC HART HD HDLC HHT HPC IEC IECEE lEEE I FAC
IFC IFIP IFORS IMACS IMEKO IMC
European Strategic Program for Research in Information Tcchnology European Telecommunications Standards Institute European Rescarch Coordination Agcncy Association dcs Exploitants d’Equipemcnt dc Mesure, de Rigulation et d’Automatismc Factory Instrumentation Protocol Programming language Gateway Functions Gesellschaft f i r Informatik VDI/VDE-Gesellschaft fur MeU- und Automatisierungstechnik Gesellschaft fur Mathematik und Datenverarbeitung VDI/VDE-Gesellschaft f i r MeB- und Regelungstcchnik, (now: GMA) VDI-Gesellschaft Verfahrenstechnik und Chemieingenieurwesen Highway Adrcssable Remotc Transducer Harmonization Documents High Level Data Link Control Hand Held Terminal Human- Proccss Cornmunication Intcrnational Electrotechnical Commission IEC Sub-committee: Conformity Testing to Standard for Safety of Electrical Equipment Institute of Electrical and Electronics Engineers International Federation of hutomatic Control Intcrnational Ficldbus Consortium International Federation for Information Processing International Federation of Operational Research Societies Intcrnational Association for Mathematics and Computers in Simulation Internationalc MeBtechnischc Konfoderation Internal Modcl Control
429
Information Management System Input Product I I’ Instrument Society of America ISA Integrated Services Digital NetISDN work International Organization for IS0 Standardization International Systems Projcct ISP Local Area Network LAN List Processing Language LISP Logical Link Control L1.C Loop Transfer Recovery LTR Medium Access Control MAC Manufacturing Automation MAP Protocol Management Information MIS System Man -Machine Communication MMC Manufacturing Messagc SpecifiMMS cation Modulator and Demodulator MODEM Man - Process Communication M PC Multi-Virtual System MVS Normenarbeitsgesellschaft fiir NAMUR MeU- und Regeltechnik in der chemischen Industrie Near Infrared NI R Object Oriented Analysis 0014 Object Oricnted Design OOD Output Product OP Operating System or Open 0s System Open Software Foundation OSF Open Systems Interconnection OSI Proportional (controller) P Private Branch Exchange PBX Pcrsonal Computer PC Plant Design System PDS Protocol Data Unit PDU Proportional Integral (controller) PI Proportional lntcgral DifferenPID tial (controller) Programming Language NumPLI1 ber 1 Programmable Logical ConPLC troller Reversed Frame Normalizing RFN Process PROFIBUS Process Field Bus Programming in Logic (proPROLOG gramming language) Programmable Read Only PROM Memory
IMS
430 PTB RAM ROM ROI SA SADT SAP SAS
SIREP SNA SPARC ST STEP 5 SQL TC TIRC
14. Appendix
Physikalisch Technischc Bundcsanstalt Random Access Memory Read Only Mcmory Return on Investment Structured Analysis Structured Analysis and Dcsign Technique Service Access Point Statistical Analysis System. Statistikprogramm der Firma SAS Institute International Instrument User's Associa tion Systems Network Architccturc (IBM) Standards Planning and Rcquirements Committee (RISC hrchitccturc) Structured Text Programming language Standard Query Language Technical Committee (IEC) Temperature Indicating Recording Controlling (DIN 19227)
UNIX UTE
uvv VbF VCI VDI VDE VIK VLSI VMD VMS VOB WAN XP ZVEI
Multitasking and Multiuscr operating system Union Technique de I'ElcctricitC Unfallverhutungsvorschrift Vcrordnung uber brennbarc Flussigkeiten Vcrband der Chemischen Industrie Vercin Deutscher Ingenieure Verband Deutschcr Elektrotcchniker Vereinigung Industrielle Kraftwirtschaft Very Large Scale Integration Virtual Manufactoring Device Operating system from DEC Verdingungsordnung fur Bauleistungen Wide Area Network Expert System Zcntralverband Elektrotechnikund Elektronikindustrie
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
15. References
15. References General Referrocn
Automation and Process Confro1 Engineering D. Balzer: Wissmsbasiertc, Systenie in der Auromurisirritngsrechnik, Carl Hanser Verlag, Miinchen 1992. K. Render (ed.):Profibas -Uer Feldbusfur die Auromurion, Carl Hanser Verlag, Miinchen 1990. G. Brack: Auromarisierungsrechnili /iir Anwender, VEB Ileutscher Verlag fur Grundstoffindustrie, LeipzigStuttgart 1993. G. Rrack. A. lielms: Auromurbie~igsrechnik, VER Dcut%her Verlag fur Grundstollindustrie, Leiprig 1985. G. Farber: ProzeJrechenrechnik : Allgemeines, Hard- iind Sqfrwure, Plunungshinweise,2nd ed., Springer Verlag, Berlin 1992. E. L. Grant, W. G. Ireson. R. S. Leavenworth: Principles of Engineering Economy. John Wiley and Sons, Xew York 1982. J. Hengstenberg, B. Sturm, 0. Winkler. K . H. Schmitt (eds.): Messen. Sleuern und Regeln in der Chemischen Technik, vols. 1- 5, Springer Verlag, Berlin 1980. K. Hiroi : Basis and Application ofDigirul Conrrol Sysrems, Japan. ISA: Human Engineering for Control Centers, ISA Recommended Practice RP 60.3, 1SA 1985. R. Lauber: Proie~uuromurisierung, 2nd ed., vol. 1, Springer Verlag. Berlin 1989. K. Magin. W Wiichner: Digitule ProieJleiltechnik, Verlag Vogel, Wiirzburg 1987. P. Metzing, D. Balzer, M. Reinhardt : ProzeJsreuerung, VEB Ilcutscher Verlag fur Grundstoffindustrie, Leipzig 1988. 1. Morishita: Digiral Control Sysremc, Society of Instrumentation and Process Control, Japan. W. Motsch: ProzeJrechner - Srrukruren. Verlag Vieweg, Braunschwcig 1993. NAMUR (Hng.): NAMUK-Statusberichte 1985, 1987, 1990. 1993, R. Oldenbourg Verlag, Miinchen. G. Platt : P ~ O C KControl. SS a Primer for rhe Nonspeciulisr and the Newcomer, ISA 1988. M. Polke: ProzeDleittechnik, 2nd ed.,Oldenbourg, Miinchen 1984. R. Riick, A. Stockert, F. 0. Vogel: CIM und LOGISTIK im Unternehmen, Carl Hanser Verlag, Miinchen 1992. E. Schriifer (ed.): VDI-Lexikon Me#- und Aurumurisierungsrechnik, VDI-Vcrlag, Diisseldorf 1991. G. G. Seip (ed.):Elekrrische Insrallarionsreehnik, vols. 13, Siemens AG, Berlin 1971. A. Shoten: PID Conrrol, Systems Control and Information Society, Japan. G. Strohrmann: Auromarbierungsrechnik. vols. 1 and 11, R. Oldenbourg Verlag. Miinchen 1990. M. Syrbe, M. Thomd (eds.): Fuchberichre Me.sst-n-SreuernRegeln. vols. 1 - 10, Springer Verlag, Berlin. H. Topfer. P. Besch: Grundlugen der Auromutisierungsrechnik, VEB Deutscher Verlag Technik, Berlin 1990. H. TBpfer, W. Kriesel: Funkrionseinheiren der Airtomurisierungsrechnik, VEB Deutscher Verlag Technik, Berlin 1988. Kompaktscminar ProzeDlcittechnik:Themen in Einzeldarstellungcn, R. Oldenbourg Verlag, Miinchcn 1986 ff. Sysrems Engineering
M. Bruns: S~~sremrechnik, Springer Verlag, Heidelberg 1991.
431
W. E Daenzer (ed.): Sysrems Engineering. 6nd cd., Verlag lndustricllc Organisation, Zurich 1988. J. Raasch: Sysremenrwicklungm i l srrukrurierren Merhoden, Carl Hanser Verlag, Munchen 1992. R. 1Jnbehauen: Sysremrheorie: Grundlugen fur Ingeniewe, R. Oldenbourg Verlag. Miinchen 1993.
Compurer Science I>. Abcl: Pefri-Neriefur Ingenieure, Springer Verlag, Berlin 1990. L. Christov: Objekrorienrierre Progrummierspruche C + +- + : Vollstundige Einfuhnmg unhand von Beispie/en, VDI-Verlag, Diisseldorf 1992. J. Fiedler, K. F. Rix. H. ZBller: Objekr-orienrierte Progrunmierung in der Auromurisierung. VDI-Verlag, Diisseldorf 1991. G.GIBe (ed.): So~,vare-Zuverliirsigkeit, VDI-Verlag, Iliisseldorf 1992. M. Maser: Grundlugen der ullgemeinen Kommunikutionsrheorie, Verlag Bcrlincr Union, Stuttgart and Kohlhammer, Stuttgart 1971.
Experiensysreme: Einfuhrung in Echnik und Anwendung, Siemens AG, Berlin 1987. U. Kernbold: Einfuhrung in die Informurik, Carl Hanser Verlag, Munchen 1991. ProieJ?informurion: Einfihnmg mit PeE. Schnieder (4.): h e t i e n , 2nd ed.,Vcrlag Vicweg, Braunschweig 1993. S. Wcndt: NichrphysikulischeGrundlugen der Informutionsrechnik, Springer Verlag, Berlin 1989. Various editors: Kcihe Infomatik Fachberichtc, Springer Verlag, Berlin. Process Engineering E. BlaO: Enlwicklung verfahrenctechnischer Prozesse, Salle und Saucrlander Verlag, Frankfurt 1989. W. Biichner, R. Schliebs. G. Winter, K. H. Biichel: Indusrrielle Anorganische Chemie, 2nd ed., Verlag Chemie, Weinheim 1986. M. Engshuber, R. Miiller: Grundlugen der Verfuhrenstechnik f i r Auromurisierungsingenieure, VEB Ilcutscher Verlag fur Grundstomndustrie. Leipig -Stuttgart 1993. E. lgnatowitir (ed.): Chemierechnik, Europa-Lxhrmittel Vcrlag, Wuppertal 1980. H. Robel et al.: Lehrbuch der chemischen Verfahrencrechnik, VEB Deutscher Verlag fur Grundsfofindustne, Leipzig 1983. Ullmunn, 4th ed.. 5, 891 . K. Wcisscrmel, H.-J. Arpe: Industrielle Organische Chemie, 3rd ed., VCH Verlagsgesellschaft. Weinheim 1990. Control Engineering und Simulurion A. Aucr: Speicherprogrummierte Regelung, Hiithig, Hcidelberg 1990. A. Bottiger: Regelung.?rechnik: Eine Einfhrung f i r Ingenieure und Nuturwissenschufrler, 2nd ed., R. Oldenbourg Verlag, Miinchen 1991. F. DBrrscheidt. W. Latzel: Gnmdlugen der Regelwigsrechnik. 2nd ed., B. G. Teubner Verlag, Stuttgart 1992. ‘r. Ebel: Regelungsrechnik, 6th ed., B. G. Teubner Verlag (Studienskripten), Stuttgart 1991. 0. Follinger: Nichrlineure Regelungen, vols. I and 11, R. Oldenbourg Vcrlag, Miinchen, vol. I(5th ed.) 1989, vol. I1 (6th ed.) 1991.
432
IS.
Rcferenca
0. Fiillingcr: Regelirngsrechnik: Eit@hrung irr die Merhodeli rind ihre Aiimenduiig, 7th ed., Hiithig, Heidelberg 1992. J. <:. Gille, M. Pclcgrin, P. I)ecaulne: Ixhrgurig der Regelungsiechnik, R. Oldcnbourg Vcrlag, Miinchcn 1963. E.-D. G i k s : S~vsrememir verreilren Purumetenr. R. Oldcnbourg Vcrlag, Miinchcn 1973. H.-M. Hanisch: Pelri-Netze in der Verfulirensrechnik: Modellierung und Slewrung ~,erf~irensrechnischerSysreme, R. Oldenbourg Vcrlag, Miinchcn 1992. R. Iscrmann: Idenrrfikurion dynumischer S.v.WmeI : Grundlegende Merhoden, 2nd cd., Springer Vcrlag, Berlin 1992. R. lsermann : Idenrifikurion dvnumischer Sysreme 2: Besondere Methoden. Anmendungeti, 2nd ed.,Springer Vcrlag, Berlin 1992. D. P. F.Mbller: Modellhildung. Simrrlaiion imd Idenrrfikulion dynamischer Sysreme. Springer Verlag, Berlin 1992. W. Oppclt : Kleines Handbuch rechnischer Regelvorgange. Verlag Chcmie, Weinhcim 1972. M. Papageorgiou: Oprimierung: Srurische, &numitehe. stochasrische Verfuhren f i r die Anwendung, R. Oldenbourg Vcrlag, Miinchcn 1991. E. Samal: Grundrg der prakrischen Regelungsrechnik, 17th ed.,R. Oldenbourg Vcrlag, Miinchcn 1991. G. Schmidt: Grunrllagen der Regelungsrechnik, Springer Vcrlag, Berlin 1989. G. Schmidt: Simularionsrechnik, R. Oldenbourg Vcrlag, Miinchen 1980. J. R. Schwarr, B. Fricdland (eds.) Lbieur Systems, McGrdw-Hill, New York 1965. W. W. Solodownikow : Grundlugen der selhstSindigen Regelung, vols. 1 2. R. Oldenbourg Vcrlag, Miinchen 1959. M. Thoma: Theorie lineurer Regelsysienie. Verlag Vicwcg, Braunschweig 1973. J. G. Truxal: Enrwurf uuromarischer Regelsysreme, K. 01dcnbourg Vcrlag, Miinchcn 1964. G. Wunsch, H. Schreibcr: Stochaviischr Sysreme, 3rd cd., Springer Verlag, Berlin 1992.
Conrrol Technology A. Aucr: S P S - Progammierung: Beispiele und Aufgaheti, 3rd ed., Hiithig, lleidclbcrg 1991. A. Aucr: S P S - AuJbau und I'rogrummierung, 4th ed., lliithig, Heidelberg 1991. P. Conrads: Vom Umgang mir S P S , Hiithig, Heidelberg 1991. K. Haug : Pneumarische Sieuenmgsrechnik, 2nd ed., B. G. Teubncr (Studicnskripten), Stuttgart 1991. J. Pctry : S P S - Projekrierung und Programmierung. 2nd ed., Hiithig, lleidclbcrg 1990. G. Wellenreuthcr, I>. Zastrow : Sreuerungsrechnik mii SPS, Vcrlag Vieweg, Braunxhwcig 1991. G. Wellenreuthcr, D. Zastrow : U.wngsbuch Sreuenmgstechnik mil SPS, Verlag Vicweg, Braunschweig 1992.
Merrology R. Best: Digirale MeJwerrverarbeirung, R. Oldenbourg Verlag, Miinchen 1991. Comprehensive Dictionary of MeaW. II. Cubberly (d.): surement und Control. 2nd d.,ISA 1992. 0. Ficdlcr: Srr6mungs- und DurchfluBmeJ3rechnik. R. 01dcnbourg Verlag, Miinchen 1992. H. Gatzmanga c t al.: ProzeJmeJtechnik, VEB Deutschcr Vcrlag f i r Grundstoflindustrie, Lcipzig 1989. W. GBpel, J. Hessc, J. N. Zcmcl (cds.): Sensors A Comprehensive Survey, vols. 1 8, VCH Verlagsgcscllschaft. Weinheim 1992.
K.-H. Conschorek. I I . Singer (eds.): Elekironiognrii.c.chc Veriraglichkeii: Grrmdlugrn. Anuljsen. Mapnahnren. H. C . Teubncr Verlag. Stuttgart 1992. E. Hcring, K. Brcssler. J. Gutekunst: Elekrronik fur Iirgeniertre, VDI-Verlag, Diisseldorf 1992. J. W. Klein. P. I>ullenkopf, A. Glasmachers: Elekironischr Meprechnik ; MeJsysreme loid Schalrungen (reubner Studienbiichcr). B. G. Tcubner Vcrlag. Stuttgart 1992. 11. Kronmiillcr: Prinzipien der ProzeJineflrechnik, vols. 1 and 2, Schnikcr Verlag, Karlsruhe 1986. S. Miyazaki ct al. : Insiumenrcition und Conrrol for Process, Nikkan Kohgyo, Shuppan-sha. Japan. P. Profos. T. Pfeifer (cds.): Grundlugen der MeJrechnik, 3rd ed.. R. Oldcnbourg Vcrlag, Miinchcn 1992. P. Profos. T. Pfcifcr (eds.): Hundhuch der indusrricllen Meflrechnik. 5th ed., R. Oldcnbourg Verlag, Miinchcn 1992. W. Richter: Grundugen der eleklrixhen Meplechrrik, Vcrlag 'l'echnik, Bcrlin 1989. G . W. Schanz: Sensoren Fiihler der MrJfechnik. 3rd ed., lliithig, Heidelberg 1993. 11. Schaumburg: Sensoren, B. G. Teubner Vcrlag, Stuttgart 1992. <;. Schnell (ed.): Sensoren in der Auroinarisierungsrech,liX., 2nd cd., Verlag Vieweg, Braunschweig 1993. G. Strohrmann: MeJrechnik im Chmiehereich. K. Oldenbourg Vcrlag, Miinchcn 1991. H.-R. Triinkler: Tawhenbuch der MeJtechnik mit Schmerpunkr Sensorrechnik, K. Oldcnbourg Verlag, Miinchen 1992. Ullmunn. 4th ed.. 5, 797 -890. Insrrumenrution Ilandbook. Instrumcntation and Process Control Engineer's Association, Japan.
Conference Proceedings (see also Chap. 12) Bcratender AusschuD dcr Industriephysiker der Dcutschen Physikalischcn Grsellschaft: Physikcrtagung, c.g., P r d i n g s o f Dcr Physikcr in der Automatisierungstechnik (1989); Scnsorphysik und Sensortcchnik ( 1984). IFAC: IFAC-World Congress: Automatic-Control, Selected Papers of the IFAC World Congress, Pergamon Press, Oxford. INTEKKAMA: INTERKAMA-proceedings as series: "Fachbenchtc Messen, Stcucrn, Regch", P. Thoma (ed.),Springer Verlag, Berlin, sincc 1989: G . Schmidt. H. Steusloff (eds.), R. Oldenbourg Vcrlag. Miinchen. GVC: Jahrestreffcn der Vcrfahrensingenicure, Kurzfassung dcr Vortrigc und Poster. VDI : Automatisierungstcchnik Tagungsband, VDI-Verlag. Diisseldorf. Journrrls automatica Automatisicrungstcchnik (at) Automatisierungstechnischc Praxis (atp) Chemic Ingenieur Tcchnik (CIT) Computers & Chemical Engineering Control Engineering Practice Informatik Spcktrum INTECH (ISA) Journal of Process Control messen & priifcn Process Control and Quality Tcchnisches Mcssen (tm) VDI-z ZWFCIM
References for Chapter 1
1 1 . 1 1 N. I>. Kondratieff: “Die langen Wellen dcr Konjunktur.” in: Archiv .fir So;ialn i.s.remchufr irnd Sozialpolitik, vol. 56, Mohr Verlag, Tubingen 1926. 11.21 L.eo A. Nefiodow: “Der finftc Kondratieff.” in: Srraregieii rum Stntkrurwuiidel in Wirrschafr rind Ge.sell.schafr,Gabler-Vcrlag. WiCSbadKn 1990. 11.31 J. Bardeen. W. 11. Bratlain: “Transistor. A Semiconductor Triode,“ Phys. Rev. 74 (1 948) 230 - 23 1 . 11.41 D. P. <:hen: “The Entity Relationship Model.“ ACM Trans. Database Sys~.1 (1976) 9 36. 11.51 C. A. Petri: “Kommunikation mit Automaten.” Disscrtation. ’I‘H Darmstadt 1961. 11.61 H. Nottebohm: “Entwicklungen der Informationstechnik,“ Autoinarisierungster.hiii.sc/ie Praxi.s f a r p ) (1985) nos. 5, 6, 224-230, 267 273. 11.71 M. Polkc: “Information als kritische Ressourcc.” in H. A. Henzlcr (ed.): Handbuch der srrutegbchen Fuhrung, <;ablcr-Vcrlag, Wiesbaden 1988, pp. 353 378. [ 1 .8] M. E. Porter: Werrben~erhsvorreile.Campus Vcrlag, New York-Frankfurt 1986. 11.91 J. Schumpeter: Theorie der wirrschafilichen Enrwicklung. 6th ed., Iluncker und llumbolt Verlag. Berlin 1964. [l.lO] M . Polke: Technology and Srrucrural Change, prcsented at Scottish Council, International Forum, 1986. [1.11] B. Sturm, B. Will, M. Polke: “Die NAMUK gestern, heute und morgen,” Auromarisieningsrechnische Praxis ( a l p ) 30 (1988) 1 1 29. 11.121 A. Lippert et al.: Baycnnterne Untersuchungen und Organisationsanwcisungen, Leverkuscn 1979 80. 11.131 R. Lauber: “ProLeOautomatisierung, Pro7eBleittcchnik oder ProreDdatenverarbritung,” Auromarbierungsrechnische Praxis ( a t p ) 28 (1 9x6) 2 4 4 245. Il.141 R. Lauber: ProzeJauromarbierung uls Hochrchulrexr, 2nd ed., Springer Verlag, Berlin I-leidelbergNew York 1989. 11.151 VDI/VIlE Gesellschaft MKO- und Automatisierungstechnik (GMA): Aufgaben - Srrukrur Akrivitaten, Diisseldorf 1991. 11.161 M. Polke: Der Physiker in der Auromarisierwigsrechnik, IlPG, KongreR, Ausstcllungs- und Venvaltungs-GmbH, Bad Honncf 1989. [I ,171 R. Magin, W. Wiichner: Digirale ProzeJIeirrechnik, Vogel-Buch Verlag, Wiirrburg 1987. [I ,181 G . Strohrmann: “ProLeUlcittcchnik.” rfp-Seminar, Regulungsrechnische Praxis ( r i p ) 26 (1989) no. 10, 543 -550. 11.1 91 G . Gruhn et al.: Verfuhrensrechnik ProieJmeJtechnik. ProzeJsreuerung, Auromatisierungsrechnik, VEB Dcutscher Verlag fur Grundstofindustrie, Leipig 1985. [1.20] H. TBpfer, P. Besch: Grundlagen der Auromarisierungsrechnik, 2nd ed.,Verlag Technik, Berlin 1989. References for Chapter 2 12.11 M. Polke: “Erfordcrliche lnformationsstrukturen fur die Qualitatssicherung.” G VC-Fachrugung “Qualirar”, Heidelberg, June 1992. [2.2] A,-W. Qheer: Wirrschafrsinformurik, Springer Verlag. Berlin 1988. 12.31 M. Vetter: Aufbau hetriehlicher Informalionrsysrenle mirrels konzeprioneller Darenmodellierung. Teubner, Stuttgart 1989.
12.41 G. Schlageter, W. Stucky : Darenbanksssremet Konieprc und Modelle. Teubncr, Stuttgart 1983. 12.51 11. Wdeking. E. Ortner: Sysrernarisches Koiistrrrieren von Darenhankamvendungen : Zur Mcthodologie der ungewandten Informarik, Hanser Verlag, Miinchen 1980. 12.61 1’. Kruck: “Methodische Software-Entwicklung,” Dissertation, UnivcrsitHt-<;esamthochrhule Wuppertal. 1987. 12.71 E. E Codd: “A Relational Model of Data for Large Shared Data Banks.“ Commun. ACM 13 (1970) 377 3x7. 12.81 P. P.-S. Chcn : The ~,ntity-Krlationship-Model ’10ward a IJnificd View of Data. ACM Transactions on Duruhase Sysrems, vol. 1, Cambridge, Mass., March 1976, pp. 9 36. 12.91 A.-W. Schecr: EDV-orienrierre ~erriebs,virtschafislehre, Springer Verlag, Berlin 1990. 12.101 11. Vossberg et al.: “Die Informations- und Steuerungssystenie der Sparten,” Bayer, Leverkusen. pp. 1970ff. 12.1 11 R. Durchholz: “Konzcptionelles Schema,” Informurik Spektrum l ( l 9 8 4 ) 245-247. 12.121 E. Ortner: “Semantischc Modellierung - Datenbankentwurf auf der Ebene der BenutLer.” Informarik-Spekrrum 8 (1985) 20 28. 12.131 W. Wendt : Nichrphvsikalbehe Grundlage der Informuriomsrechnik. Springer Verlag, Berlin 1989. 12.141 E. Ortner, B. Sijllner: “Semantische Datenmodellierung nach der Objckttypcnmethode,” InformarikSpekrrwn 12 (1989) 31 -42. 12.151 E Gcbhart: “Scmantisches Wissen in Datenbanken - cin Literaturbericht,” Informarik-Spektrum 10 (1987) 79 88. 12.161 H. Pcschke: “Partizipative Entwicklung und Einfiihrung von Informationssystemcn,” in H. Balzerl (4.Einfuhrung ): in die Sofr,c.are-Ergonomie, Ile Gruytcr, Berlin 1988, pp. 299- 322. 12.171 D. B. PreOmar: “Strategien fur den EinsatL von Methoden und Werkzeugen in der Softwaretcchnologie,” in T. GutLwiller, El. C)sterlc (eds.): Anleitung i u einer praxi.sorienrierten Software-Enrwicklitngsumgehung, vol. 2, Angewandte Informationstcchnik, Verlag, Hallbcrgmoos 1988, pp. 9 24. 12.181 S. Lofiler, A. Warner: “Integration von SoftwareF.ntwicklungswerkLeugen,”in H. C)stcrle (ed.): Anleitung zu einer praxisorientierren Sofware-Enrwicklungsumgebung, vol. 1, Angewandte Informationstechnik, Verlag, Hallbergmoos 1988, pp. 29-37. 12.191 U. Rembold (ed.): Einfuhrung in die Informatik, llanser Vcrlag, Miinchen 1987. [2.201 K. Heuer: “Die Rolle von Dictionary-Systemen in Software-Produktionsumgebungcn,” in H. osterlc (ed.): Anleirung zu einer praxisorientierren Softwure-Enrwicklungswngebung,vol. 1, Angewandte Informatik, Verlag, Hallbergmoos 1988. pp. 117 133. 12.211 S. Holloway: “Data Dictionary as an Administrative Tool - How to Succecd in the Nineties,” in: Pcrgamon lnfotcch (ed.): Inforniarion Updore 1 (1987) no. 1 , 17-25. 12.221 F.. Ortner, J. Kossner, B. Sollncr: “Entwicklung und Vcrwaltung standardisierter Ilatenelcmcnte,” Informatik-Spekrrwn 13 (1990) 17--30. 12.231 H. Bahert: Die Enrivicklung von Software-Systemen; Priniipien, Merhoden, Sprachen, Werkzeuge, Bibliographisches Institut, Mannheim 1990.
434
IS. References
(2.24) H. Balzert (ed.): CASE-Systeme irnd Werkzeuge, Wisscnschaffsvcrlag, Mannheim 1990. (2.251 Aristoteles: Kategorien, Akadcmic Vcrlag, Berlin 1984. 12.261 C. v. Linnc: Sysremu ncirurae, British Museum of Natural History Publishers. London 1958. 12.27) C. Darwin: Die Enrstehung der Arlen (In dcr Uhersctrung von C. W Neumann), Rcclam, Stuttgart 1963. (2.281 F.. Cassirer: Suhslanzhegrif/ und FunkrionshegrgJ 6th ed., Wissenschaftlichc Buchgcsellschaft, Darmstadt 1990. 12.291 W Ahrcns: “Einsatz von Expcrtcnsystemen in der ProrcLllcittechnik,” Auronurtisierungsrechni~che Praxi.s utp 10 (1987) 475 -485. [2.30] W Gotthard: “Ddtenbanksysteme fur SoftwareProduktionsumgcbungen.” Iiflormatik. Fuchherichte fY93, Springer Verlag, Berlin 1988. 12.311 i f . Stoyan, i f . Wedekind: Objekrorienfierte Sofrware- und Ilard,carearchirekruren, Teubner, Stuttgat? 1983. (2.321 F.Vester: Unsere Welt - ein vernerzres System. Deutschcr Taschenbuch Vcrldg, Miinchen 1983. 12.331 F. Vestcr: Leirmotiv vernenfes Denken, Heine, Vcrlag, Miinchen 1988. [2.34] M. Polkc: “Inrormationshaushdlt technischer Prozcsse, in ProzeJrechner 1984, Springcr Verlag, Berlin 1984. (2.351 H Zwenna, P. tlaubncr: “Gestaltung von Informationcn auf Bildschirmcn,” in K. P. Fihnrich (cd.): Software-Ergonomic, R. Oldcnbourg Vcrlag, Miinchen 1987. (2.361 H. Zwerina: “Masken und Formulare,” in H. Balzert (4.): Einfdrung in die Software-Ergonomie. De Gruyter. Berlin 1988. pp. 163 - 174. (2.371 W. Hacker: “Software-Ergonomie: Gestalten rechncrgestiitzter geistiger Arbeit, in W. Schanpflug (ed.): Sofrwure-Ergonomie 87. Tcubncr, Stuttgart 1987, pp. 31 -54. (2.38) W. Ahrens, M . Polke: “Nctrmodelle als systemtcchnischc Informationsbasis fur die ProzcDleittechnik,” Aufomutisienrngsrechnisc}re Pruxb alp 37 (1 989) no. 3, 94 103; 37 (1989) no. 4, 138 -144. 12.391 B. Shneiderman: “Direct Manipulation: A Step beyond Programming Languages,” IEEE Compurer 16 (1983) Nov. 8. 57-69. (2.40) R. Ilg. J. Ziegler: “Direkte Manipulation. in H. Balmrt (cd.):Einfdrung in die Soflulare-Ergonomic, De Gruytcr, Berlin 1988, pp. 135- 194. 12.411 K.-P. Fahnrich, J. Ziegler: “Direktc Manipulation als lnteraktionsform an Arbeitsplatzrrchnern,” in H. J. Bullingcr (ed.): Software-Ergonomie 85. Teubner, Stuttgart 1985. [2.42] K. Klose: “Software-Entwicklung fiir ProzeOlcitsystcmc,” NAMUR-Sroruberichr 87. ProzeJleifrechnik f i r die Chemische Industrie, R. Oldcnbourg Vcrlag, Miinchen 1987, pp. 224-229. (2.433 CAE2000: Bayerinterner Code fur die Modcllicrung der Ingenicurfunktion, Leverkuscn 19891992. 12.44) M. Chaineux: “Ausrichtung und Konception von DV-Anwendungssystemen auf der Basis von Unternehmungsmodellen, CAE 2000,” GMA KongreJ, Drcsden, Sept. 1993. (2.451 A. L. Luft : Informarik als Technik- Wissenschaf. Eine Orientierungshilfe .fir dos Informarik-Sfudium. EI, Wisscnschaftsverlag, Mannheim 1988.
(2.461 K. Hagemiiller, L. Max, J. Wolf: “Das Entwicklungssystem DOMINO von Siemens,“ in T. Gutzwillcr, t i . Ostcrlc (cds.): Anleirung zir einerpraxisorienrierfeii Softnore- Giricicliliriijisirnigehung, vol. 2, Angewandte lnformationstechnik Vcrlag, Hallbcrgmoos 1988, pp. 41 -61. (2.471 W. Ahrcns: Ohjekr-orientierre Wirsensrepr&senrarioii in der Pro;eJ3leirfechnik. Pro~c~recheirss.~trnre ‘88. Springer Verlag, Berlin 1989. pp. 498-513. 12.481 E. F. Cox: Object Orienred Programming. Addison Wesley, Kcading. Mass.. 1986. 12.49) H. Stoyan: Progranrmiermethoden der Kfiiisrlichen Inrelligenz, vol. 1 (1988). vol. 2 (1991). Springer Vcrlag, Berlin. (2.501 F.. Ignatowitz: Chemiereclinik. Vcrlag Europa-Lchrmittel, Wuppertal 1982. (2.511 G. Frcgc in G. Pauing (ed.):Funklion. Begrfl; Eedeurung. Fiinf logische Studien, G. Pauing Verlag. G6ttingen 1966. (2.521 DIN 32 705, Klassifikationssysfcme, Beuth-Verlag, Berlin 1987. 12.531 I. Rasmusscn: The Role of [Iierdrchical Knowlcdge Rcprescntation in Decision Making on System Management. IEEE Trdn.saCtiOnS on Systcms. Muti and Cybernetics, SMC-15 (1985) no. 2, 234 -243. (2.54) E-J. Kaufcls: Rechnertierzwerk-Sysremarchirekfuren und Darenkommunikation, Wissenschaftsverlag. Mannheim 1989. 12.551 R. Pernc, M. Polke: “Produktentwicklung durch Vcrfahrensvariation,” Regelungsfechnik 30 (1982) no. 5, 147- 156. (2.561 DIN 19222, Lcittechnik, Begriffe, Beuth-Verlag, Berlin 1985. I2.571 B. Polke, G. Rauprich: Operaforenzur Reschreihung zeirahh&giger Zustandsiinderungen, in press. (2.581 T. Suckut : ”Informationsmodell fur MehrprojcktManagement,” Disscrlation RWTi1. Aachen 1993. 12.591 E. Schnieder: ProzeJinjormurik, Einfrtriig mi! Petri-Nerien, Vieweg & Sons, Brdunschweig 1986. [2.60] C. A. Petri: “Kommunikdtion mit Automaten,” Dissertation, TH Darmstadt 1961. (2.611 W Reisig: Pefri-Nerze,Springer Verlag, Berlin 1982. (2.621 E. Schnieder. H. GBckel: “Petri-Nctrc in der Automatisierungstechnik,” Aufomarisierung.srecliik ar 37 (1989) no. 5, 173 -181; no. 6, 234-241. 12.631 D. Abel: “Modellbildung und Analysc diskref gestcucrtcr Systcme mil Petri-Neucn,” Auloniutisierungstechnik a f 36 (1988) no. 12, 455-462. (2.641 W. Ahrens. M. Polke: “Informationsstrukturcn in dcr Automatisicrungstechnik,” in G. Schmidt, H. Stcuslofi (eds.): “Mil vernetztcn, intelligenten Komponenten zu leistungsfiihigen McU- und Automatisierungssystemcn”, INTERKAMA-KoiigreJ 89. R. Oldenbourg Verlag, Miinchen 1989. pp. 47 67. 12.651 E. C. Yamalidou, J. C. Kantor: “Modcling and Analysis of Discrete-Event Processes Using Petri Net Theory” ATCHE Annual Meetwig, San Francisco 1989. (2.663 P. Winkler: “Anforderungsbeschreibung mit Nctzmodellcn.” Automarisierunb.srechnbchePro..ris t u t p ) 28 (1986) no. 1, 32-39. (2.67) J. C. Kantor, E. P. Parsidou: “On the Sceduling and Control of Multipurpose Batch Plants,“ A K H E Annual Meering 1989, Paper no. 1436, San Frdncisco 1989. 12.681 A. Hartramph: personal communication 1989.
15. References [2.69] Anlagen- und Apparatekennzeichen der Bayer-A<;, Wcrknorm 9060, Leverkusen 1989. (2.701 KKS-Krafwerk-Kennzeichensystem. 4th ed., VGB Kraftwerkstechnik GmbH, Essen 1991. 12.71) DIN 28004, FlieDbilder Verfahrcnstcchnischer Anlagen. Beuth Verlag, Berlin 1977. 12.721 TGL 25 000. M a n 1974 Verfahrenstechnik, Grundoperationen, Klassifikation. (2.733 Ullmunn. 4th ed., 4. 1. 12.741 R. J. Uhlig: “Erstcllen von Ablaufsteuerungen fur Chargcnproresse mit wechsclnden Kezepturen, Automatisierungsrechnische Praxis ( a l p ) 29, (1987) no. 1, 17-23. [2.75] J. Lauber, M. Polke: “Das Phasenmodell dcr Produktion: Ein semantisches CAE-Werkzeug zur ProzeObeschreibung.” Regelungsrechnicchen Kolloquiwn, Boppard, March 1993. [2.76] 0. Grdf: “Standardisierte Grundfunktionen verringern Softwareaufwand bei Teleperm M,” Eng. & Aurom. 3 (1991) 2-5. [2.77] U. Epple: “Funktionsorientierte Strukturierung prozeBleittechnischcr Aufgaben.” Automurbierungstechnische Praxis atp 31 (1989) no. 10, 468-474. (2.781 U. Epple, H. Kopec, R. Schmidt; “Strukturierung von Pro~eDfiihrungsaufgaben und Leitsystemsoftware,” AutomurisierungstechnbcIie Praxis (arp) 31 (1989) no. 10, 468-474. 12.79) T. G. Fisher: “Batch Control Systems - Design, Application and Implementation, ISA, Paris 1990. [2.80] F. J. Kersting et al.: “Anforderungen an Systeme 7ur Rezeptfahrweise.” NAMUR Empfehlung NE 25, Version vom 19.5.1992. 12.811 M. Polke, F. Portele: ”Stand und Entwicklung der ProzeBleittechnik f i r die Texlilindustrie,” Melliand Textifher.65 (1984) 291 - 297. (2.821 M. Polke: “Produktionsplanung in der Chemischen Industrie, in D. Hahn. G. La5mann (eds.): Produktionswirrschaf, vol. 2, Physica-Verlag, Heidelberg 1989, pp. 369 413. [2.83] M. Polke: “Pro~eOleittechnik:Trend zur Automatisierung in der Chemischen Industrie halt ungebrochen an Achema 91 ,” Auromatisierungsrechnische Praxis ( a t p ) 33 (1991) no. 5, 219- 222. (2.841 Bayer AG: CAE 2000 Bayerinternc Ilokumentation, Lcverkusen 1992. (2.851 M. Polke: “Information als kritische Ressource,” in A. 1Ienzler (ed.): Handhuch Straregische Fiihrung, Beuicbswirtschaftlicher Verlag, Wiesbaden 1988, pp. 353-378. [2.86] G. Schmidt: “Was sind und wie entstehen komplexe Systeme und wclche spezifischen Aufgaben stellen sic fur die Regelungstechnik?”, Regelungsrechnik 30 (1982) no. 10, 331 339. (2.871 W. Hofmann: “Aufgabe der Produktionslcitebene in der Chemischen Industrie,” NA MUR-Srarwbericht 85, ProzeJleirtechnik f i r die Chemische Indusrrie, R. Oldenbourg Verlag, Miinchen 1985, pp. 44-50. [2.88] K. W. Peters: “Informationshaushalte in Labor und Produktion,” NAMUR-Storusherichr 85. ProzeJleirrechnik f i r die Chemische Indusrrie. R. Oldenbourg Vcrlag, Miinchen 1985, 26 35. [2.89] S. Riemcr: “Funktionen der Produktionslogistik in der Verfahrenstechnik.” in 0. Schmidt, H. Steusloff (eds.): “Mit vernctzten, intelligenten Komponenten zu leistungsfahigen Me5- und Automatisierungssystemen, INTERKAMA-Kongre] 8Y, R. Oldenbourg Verlag, Miinchen 1989, pp. 381 -390.
435
12.901 B. Sturm. B. Will, M. Polke: ”Die NAMUR gcstern, heute und morgen.“ Autoniurisie~ti~strchnische Praxis (arp) 30 (1988) no. 1. 11-29. [2.91] K. F. Friih: “Optimalstruktur dercntrdler P r o d leitsystemc.” RTP Regelungsrech. P r a s b 21 (1979) 273ff. [2.92] W. KBrner, P. Moll. M. Polke: “Zur funktionalen Gliederung von Leitsystemen, Auromarisierwigsrechnische Praxi.s ( a t p ) 30 (1987) no. 9, 393-400. 12.931 W. Kaufmann (ed.): Diagnosrische Entscheidungsprozessr in drr inneren Medizin, Schattaur Verlag, Stuttgart 1986. [2.94] M. Wienand: “Miindliche und unveroffentlichte Hinweise auf das I>V-Phasenmodell bei der Konzeption von Unternehmens-Informationsmodcllen,” Baycr AG, Levcrkusen 1974K. (2.951 W. Busing: Ergehnis vieler Diskussionen zur Klcitung des von Polkel Wienond intuitiv vorgeschlagenen P h senmodells, personal communication, 1983. (2.961 E. I). Gilles, M.Wurst: “ProzeOfiihrung zur Sicherung reprodwierbdrer Qualitit in der verfahrenstcchnischen Produktion,” lecture held on GVCJahresrreflen, Tagungsband, Stuttgart 1990, pp. 138, 139. 12.971 E. D. Gilles: “ProzeOfihrung zur Sicherung rcproduzierbdrer Qualitit in der verfahrenstechnischen Produktion,” Chem. Ing. Tech. 63 (1991) no. 9, 910-918. [2.98] E. D. Gilles, E. Nicklaus, M. Polke: “Sensonechnik in der Chemie Status und Trend,” Chem.-1ng.Tech. 58 (1986) 557-564. [2.99] K. H. Padberg, P.-Th. Wilrich: “Die Auswertung von I>aten und ihrc Abhingigkeit von der Merkmalsart, part 1. Skalentypen,” QZ 26 (1981) no. 6, 179 ff. [2.100] H. Rumpf “uber die Eigenschaften von Nutzstiuben,” Stuuh Reinhalr. Luff 27 (1967) 3 -13. [2.101] R. Hcker, H. Krdmer, H. K. Miiller, M. Polke: “Neue Aspekte bei der Priifung von Kautschuk und Gummi,” Kautsch. Gummi Kunstsr. 25 (1972) no. 1, 5 10. (2.1021 E. D. Gilles, E. Nicklaus, M. Polke: “Sensortechnik in der Chemie - Status und Trend, Automarisierungstechnische Praxis atp 28 (1986) no. 9, 423 437; no. 10. 479-484. 12.103) M. Polke: “CAE mischen Betreihern und Anlagenbauern.” Parrnerschufr im Anlagenbou, Haus der Technik, Essen, Nov. 1992. [2.104] M. Polke: “ProzeOleittechnik; Sicherheit und Qualitat in der ProzeDindustrie,” Vorrrag agfdem V D F h n g r e ] 1993, Rerlin, Januar 1993. [2.105] J. KBnig: “Sicherhcitstechnische Aspckte der ProzcOleittechnik.” Chem. Ing. Tech. 59 (1987) no. 3, 196- 204. 12.1061 B. Bieker, H. Riickert: Enrwicklung eines Expertensystems zur ProzeBfiihrung von Bioreuktoren einer Haranluge der Bayer AG, Leverkusen. Aug. 1989. 12.1071 DIN 40719, Part 6, Schaltungsunterlagen, Regeln und graphische Symbole fiir Funktionspline, Beuth-Verlag, Berlin, Mai 1992. References for Chapter 3 [3.11 H. D. Gills, E. Nicklaus, M. Polke: “Sensortcchnik
in der Chemie - Status und Trend,” arp 28 (1986) no. 6,423-431.
436
15. References
[3.2] K. H. Padberg. P.-Th. Wilrich: "Die Auswertung von h t e n und ihrc Ahhiingigkeit von dcr Merkmalsart," Z.Quulitur Sicherheir 26 ( I 981) no. 179 183. [3.31 M. Polke: "ProzcDlcittcchnik f i r die Chemie StaIUS und Trend," Auronintisierung.srrchtiische Praxis orp 28 (1985) no. 5. 214-223. (3.41 E. D. Gillcs: "Auf dem Wegc zu cincr modcllgc-
stiitztcn ProzeDleittechnik," Aurntnurisieriuigstec/inische Praxis urp 31 (1888) 265 270. 441 449. [3.5] R. Pcrne. M. Polkc: "Produktcntwicklung durch Ver~dhrcnsvariation," Regelungsrechnik 30 (1 982) no. 5, 147 156. [3.6] I. L. Casti: Dynumicul Sysretm und [heir Appliculions. Academic Prcss. New York 1977. (3.7) C. 'I. Chen: Itirroductinri 10 Lineur Sysretn Theory, Ilolt Rinehart and Winston, NKWYork 1970. [3.81 L. A. Zadeh, C. A. Dcsocr: Linear S-vstem Thenr.v, Mc Graw-llill, New York 1963. [3.9] K.Unhchaucn: Systemtheorie, R. Oldenhourg Vcrlag, Miinchen 1969. [3.10] R. E. Kalman. P. L. Falh, M. Arbih: Tipics in Mathemurical Systems Theory, Mc Graw-Hill, New York 1969. (3.111 L. Padulo, M. Arhib: System Theory, Hemispherc, Washington 1974. [3.12] F. Pichlcr: Marhemurische Sysremrheoriet Dynamische Konsrruktintien, Dc Gruyter, Berlin 1975. [3.13] H. W. Knobloch, 11. Kwakemaak: Lineare Konrrolltheorie, Springer Vcrlag, Berlin 1985. [3.141 S. Wend1 : Nichrphysikulische Grundlagen der If$&tnurionsrechnik, Springer Verlag, Bcrlin-HcidclbergNew York 1989. (3.151 W. Hahn: Theorie und Anwendung der direkren Merho& yon Ijupunow, Springer Vcrlag, Berlin 1959. [3.16] W. Hahn. Srubi1ir.v nf' Morinn. Springer Vcrlag, Bcrlin 1967. 13.171 J. La Szalle. S. Lefschctz: Die Stobilirursrheorie von Ljapunow. Bibliographisches Institut, Mannheim
1967. [3.18] H. Haken: Synergetik, Springer Verlag, Berlin 1983. 13.191 T. Kailath: Lineur Sysrems, Prcnticc Hall, Engelwood, N.J., 1980. (3.201 E. Gilbert: "Controllability and Ohservability in Mukivariahle Control Systems," J. Soc. Ind. Appl. Math. Ser. A l(1963) 128 151. [3.21] R. E. Kalman: "On the Gencral Theory of Control Systems," I'roc. Isr. Cong. Aurom. Conrrol, 1960, 481 - 493. 13.221 R. E. Kalman: "Mathematica~Dncription of Linear Systems," J. Soc. Ind. Appl. Murh. Ser. A 1 (1963) 152 192. [3.231 R. E. Kalman: Lrcrures on Conrrollahiliry and Obsenwbiliry. C.I.M.E., Bologna 1968. [3.24] I>. G. Luenhcrgcr: "Observers for Mukivariate Systcms," IEEE TTans. Aurom. Conrrol A C - l l (1966) 190 197. [3.25] D. G. Luenberger: "An Introduction to Observers." IEEE Truns. Aurom. Conrrol AC-16 (1971) 596603. [3.26] R. Mctz: Auswerresysreme. Der Physiker in der Aic-
romrrtisierungsrechnik. Dcutsche Physikalischc Gcscllschaft c.V., Had Honnef, 1989, pp. 125-141. [3.27) W. Masing: Handbuch der Quufiturssicherung. Hanscr Verlag. Miinchen Wien 1988. (3.281 H. Stachowkak: Allgemeine Modellrheorie, Springer Verlag, Berlin 1973.
[3.29] K. R. Poppcr: I,ngik der Forscliuiig. J. (1. R. Mohr (Paul Siebeck). Tiihingen 1982. [3.30] R. Courant: "Variational Methods for the Solution
of Problems of Equilibrium and Vibration." Rull. Am. Murh. Snc. 49 (1943) 1 --23. [3.31] F. A. Ascltinc et al.: "A Survey of Adaptive Control Systems," IRE Trum. Autnm. Control6 (1958) 102-
108. [3.32] R. E. Kalman: "Design of a Self-Optimizing Control System." Trans. ASME 80 (1958) 468 47X. (3.331 A. Roscnhluth, N. Wiener: "The Rolc of Model in Science," Philns. Science 10 (1945), 316 -321. 13.341 P. Eykhoff: Sy.creni Idenrtficatinti. J. Wilcy. I.ondon 1974. (3.351 K . E. Brennan, S . L. Campbell, L. K.Petzold: Nitmericul Solurion of' Inirial- Value Problems in Dif/cr-
enrial-Algebruic f,~quurinris,North-I lolland. Amstcrdam 1989. (3.361 C. W. Gcar: "Simultaneous Numcrical Solution of I)ifferential-Algchraic Icquations," IEEE Tim\. Circuit Theory 18 (1971) 89-95. 13.371 R. Bachmann, L. Hriill, U. Pallaskc: "A Contrihution of the Numerical Treatment of Ilifferential Algebraic Equations Arising in Chemical Engineering," DEC//f:MA-Monogr.116 (1989) 343 - 349. [3.381 I.. R. Pelzold: "Diffcrcntial-Algebraic Equationb are not ODE'S," SIAM J. Sci. Slur. Compur. 3 (1982)
367-384. [3.39] C. W. Gcar. L. R. Pelzold: "ODE Methods for the Solution of Diffcrcntial-Algebraic Systems," S I A M J. Nitmer. Atiol. 21 (1984) 716 728. (3.401 R. E. Kalman, R. S. Bucy: "New Rcsults in Linear Filtering and Prcdiction lheory," J. Busic. Eng. 83 (1961) 95 108. [3.41] G. 0. Kallianpu: Srochaqtir Filrering Theory, Springer Verlag, Bcrlin 1980. [3.42] G. Doctsch: Einihrung in Thenrie itnd Anwendung der Lnpluce-Trutisf'oronnurioti,Birkhiuser-Vcrlag, Hcrlin 1976. (3.431 0. Follingcr: Laplure- wid Fnitrier-Tiutisforninrion, Elitera-Verlag, Bcrlin 1977. [3.44] M. Polke: "Zur Physik dcr Elcktrophotographie,"
in: Fesrkiirperprobleme VIII, Vieweg/Pcrgamon Press, Oxford 1968, pp. 131 -174. [3.45] W. G. Huntcr. A. M. Reiner: Technonierrics 7 (1965)
307 323. (3.461 A. C. Atkinson, V. V. Fcdorov: Rinmerriku 62 (1975) 57 - 70. (3.471 F. J. I>umer, <;. F. Fromcnt: Ind. Eng. Chem. Process Ues. DO. 15 (1976) 291 -301. [3.48] G. E. P. Box, W. J. Hill: "1)iscrimination Among Mechanistic Models," Technornerrics 9 (1 967) 57 71. [3.49] J. Pawlowski : Die Ahnlichkeirstheorie in der pliysi-
kdisch-rechnischen Forschung, Springer Verlag, Berlin 1971. 13.501 H. Giirtlcr: Dimen~ionsunalyse, Springer Vcrlag, Berlin 1975. [3.51] A. Bctz: "Ahnlichkeitstheorie und Modelltcchnik," in: Physikhtirtre, vol. I: "Mcchanik," Ernst u. Sohn, Berlin-Munchen-Dusseldorf 1971. (3.521 E. Eitelhcrg: "Modellreduktion durch Minimieren dcs Glcichungsfehlers," Regelungsrechnik 26 (1978)
320 322. [3.53] F. D. Galiana: "On the Approximation of Multiple Input - Multiple Output Constant Linear Systcms." h i ! . J. Conrrol 17 (1973) no. 6, 1313 1324.
15. Kejerences 13.541 K . Glover: “All Optimal Hankcl-Norm Approximations of Linear Multivariable Systems and Their La,-Error Bounds.” Inr. J. Control 39 (1985) no. 6, 1115 1193. (3.551 D. C. Hyland, D. S. Uernstein: “The Optimal Projection Equations for Model Reduction and the Rclationship among the Methods of Wilson. Skclton and Moore,“ IEEE 7rans. Autom. Control AC-30 (1985) no. 12, 1201 1211. 13.56) L. Litz: Redukrion der Ordnung lineurer Zusrundsrawnmodelle niirtels modaler Verfnhren, HochschulVerlag, Stuttgart 1979. [3.57] B. C. Moore: “Principal Component Analysis in Linear Systems: Controllabilily, Observabilily, and Model Reduction,” IkEE Truns. Autom. Control AC-26 (1981) 17 -31. [3.58] U . Pallaskc: “Ein Verfahren zur Ordnungsreduktion mathematischcr Prozehodelle,” Chem. big. Tech. 59 (1987) no. 7, 604-605. [3.59] R. E. Skelton: “Cost Decomposition of Linear Systems With Application to Model Reduction,” Int. J. Control 32 (1980) 1031 - 1055. [3.60] D. A. Wilson: “Optimum Solution of Model-Reduction Problem,’’ Proc. IEEE 58 (1970) 1161 1165. 13.611 E. Welfonder: “Leittechnikdokumentation aus Betreibersicht,” ETtiIVtiB-Fuchragung Berriebsgerechre Dokumenturion in Kraftwerken. Ein/luJ der modernen Leittechnik und Planungsmirre/, BadenBaden 1986. [3.62] E. IgnatowitL: Chemierechnik, Verlag Europa-Lehrmittcl, Wuppcrtal 1980.
References for Chapter 4 (4.11 I. M. Horowih: Synthesis of Feedback S.vstems, Academic Press, New York 1963. (4.21 W. Oppclt : Kleines Handbuch rechnischer Regelvorgunge, Vcrlag Chernie. Weinhcim 1972. (4.31 0. Fdinger: Regelungrtechnik. Hiithig, lleidelberg 1985. [4.4] 0. Fiillinger: Oprimierung dynamischer Systeme. Eine Einfihrung fur Ingeniewe, Oldenbourg Verlag, Miinchen 1985. [4.5] J. C. Doyle, G. Stein: “Multivariable Feedback Design: Concepts for a Classical/Modern Synthesis,” IEEk Trans. Autom. Control 26 (1981) 4-16. (4.61 M. Mordri, J. C. Doyle: “A Unifying Framework lor Control System 1)csign under Uncertainty and its Implications for Chemical Process Control,” Pror. Chem. Proc. Control 111 (1986) 5 51. 14.71 J. S. Freudenberg, D. P. Looze: Frequencj Domain Properties of Scalar and Mriltivariuhle Feedback S j s tenLv. Springcr Verlag, Berlin 1988. 14.8) H. H. Rosenbrock: Compurer-Aided Control System Design, Academic Press, London 1974. 14.91 D. H. Owens: Feedback and Multivariable Systems, Peter Percgrinus. Stevcnage 1978. [4.10] A. G. J. MacFarlane, D. F. A. Scott-Jones: “ V a l o r Gain,” Inr. J. Control 29 (1979) 65-91. [4.111 Y. Takahashi, M. J. Kabins, D. M. Auslander: Control and Dynamic Systems, Addison-Wesley, Reading, Mass. 1970. (4.121 R. V. Paiel, N. Munro: Multivariuhle Systenis Theory and Design, Pergamon Press, Oxford 1982. (4.131 U. Korn. I I . Wilfert: Mehrgr6&nregelungcn, VEB Verlag Technik, Berlin 1982.
437
14.141 11. ‘lolle: Mehr~r~~en-Regelkrei.ssjnthese, Oldenbourg Verlag, Miinchen 1983. 14.151 J. M. Macicjowski: Multivariable Feedback Design. Addison-Wesley, Reading, Mass., 1989. (4.16) A. Gelb (ed.): Applied Optimal E.stimarion. M.1.I.. Prcss, Cambridge, Mass., 1974. [4.17] K . Brammer, G. Sillling: Stochuvtische tirwidlagen des Kalirian-Bucy-l;ilters: Wuhrscheinlichkeirsrechniing und Zufullproie.we. Oldenbourg Verlag, M iinchen 1975. [4.18] K. Hrammcr, G. Siming: Kalman-Bucy-Filfers Deterministische Beobuchtung iind srochusrische Filtrrung, Oldenbourg Verlag, Miinchen 1975. 14.19) 11. Kwakernaak, R. Siwan: Linear Oprimal Control SjArems. J. Wiley, New York 1972. 14.20) B. D. 0.Anderson, J. B. Moore: Lineur Optimal Control, Prentice-Hall, Englewood Cliffs, NJ, 1972. 14.211 G. Stein. M. Athans: “The LQG/LTR Procedure for Multivariable Feedback Control Design,” IEEE Trims. Aurom. Confro132 (1987) 105-114. [4.22] A. G. J. MacFarlane, B. Kouvaritahs: “A Design Technique for Linear Multivariable Feedback Systems,” Int. J. Control 25 (1977) 837 874. (4.231 Y. S. Ilung, A. G. J. MacFarlane: Mulrivariable Fedbuck :A Quasi-Classical Approach, Springer Verlag, Berlin 1982. (4.241 C. E. Garcia, M. Morari: “lnernal Model Control: 1. A Unifying Review and Some New Results,” Ind. Eng. Chem. Process Des. Dev. 21 (1982) 308323. 14.251 B. A. Francis: A Course in H , Control Theory, Springer Verlag, Berlin 1987. (4.261 J. Iloyle, K. Glovcr, P. Khargonekar, B. Francis: “State-Space Solutions to Standard Ii, and H, Control Problems,” IEEE Trans. Auiom. Control 34 (1989) 831-847. [4.27] V. Manousiouthakis, R. Savage, Y Arkun: ” S p thesis of Decentraliied Control Structures Using the Concept of Block Relativc Gain,” Am. Inst. Chem. Eng. 32 (1986) 991 1003. [4.28) I). D. Siljak: Large-Scale Dynaniic Systems, NorthHolland, New York 1978. [4.29] M. G. Singh, T. Titli: Sysrenis: Decomposirion. Oprimisation and Control, Pergamon Press, Oxford 1978. [4.30] M. G. Singh: Dynumicul Hierarchicul Control, North-Holland, Amsterdam 1980. [4.31] M. G. Singh: Decentralised Control. North-Holland, Amsterdam 1981. 14.321 L. Litz: Dezentrde Regeliing, Oldenbourg Verlag, Miinchen 1983. [4.331 C. J. Harris, S. A. Billings (eds.): Selj-tuning and Aduprise Control: Theory und Application$, Peter Peregrinus, Stevenage 1981. 14.341 M. M. Gupta, C. T. Chen (eds.): Adaptive Merhodr for Control System Design, IEEE Press, New York 1985. (4.353 A. lsidori: Nonlinear Conrrol Sysrems: An Introduction. Springer Verlag, Berlin 1985. 14.361 C. E. Garcia. D. M. Prett, M. Morari: “Model Predictive Control: Theory and Practice - a Survey,” Autornaticu 25 (1989) 335-347. [4.37] D. K. Frederick, C. J. Hcrget. R. Kool, M. Kimvall: “ELCS - The Extended List of Control Software,” unpublished, WGS, Eindhoven University of Technology, Eindhoven 1987.
438
15. References
14.381 M. Jamshidi, C. Hergct : Conipurer-Aidrd Conrrol Sysremc Engineering. North-Holland, Amsterdam 1985. 14.391 P. Holl, W. Marqudrdt, E. I). Gilla: “Diva - a Powerful Tool for Dynamic Process Simulation.” Compur. Chem. Eng. 12 (1988) 421 -425. 14.401 E Allgower, J. Raisch: “Multivariahlc Controller Design for an Industrial Distillation Column,” Proceedings of fhIMA Itirernotional Conference on Confro1 Theory, 1988. [4.41] A. Duschek : Vorlesungen uher hoherr Muthemarik, vol. 3, Springer Verlag. Wicn 1960. (4.421 P.Funk: Variarionsrci~hriunginid ihre Anwendung in Physik und 7kchnik. Springer Verlag. Berlin 1962. 14.43) E. Klinghcil: V~Iriarionsrechnimng.Hihliographischcs Institut, Mannhcim 1977. (4.441 H. Schwarr: Oprimale Regelung linearer Sycreme, Bibliographisches Institut, Mannheim 1976. (4.451 W. Boltjmski: Morheniarische Merhoden der optimulen Sreurrung, Carl Hanser Vcrlag, Munchen 1972. [4.46] J. Hsu. A. Meyer: Modern Conrrol Principles and Applications, part 111, McCraw-Hill, New York 1968. [4.47] L. Pontrjagin ct al.: Marhematische Theorie oprinioler Prozesse, Oldenbourg Verlag. Miinchen 1967. [4.48] R. Bellmann: Dynamic Programming, Princeton University Press. Princeton 1965. (4.491 R. Bcllmann. S . Dreyfus: Applied Dynamic Programming. Princeton Univcrsity Press, Princcton 1964. (4.501 G . Schncider, €1. Mikolcic: Einfuhrung in die Merhode der dynumisclien Progrcunmierung. Oldenbourg Verlag, Miinchen 1972. [4.51] M. Athdns, P.Falb: Oprimul Control, MCGIY~WHill, New York 1966. (4.521 G. BieD ct al.: Oprimale Prozesse und Sysreme. Verlag Harry Deutsch, Thun Frankfurt 1980. 14.531 A. Bryson, Y. H o : Applied Oprimal Control, J. Wiley, New York 1975. [4.54] F.. Hofer, R. Lunderstadt: Numerische Merhoden der Optinlierung, Oldenbourg Vcrlag, Munchen 1975. [4.55] U.Hoffmann, H. Hofmann: Einfuhnuig in die Oprimierung, Verlag Chcmie, Wcinheim 1971. (4.561 H. Tulle: Oprimierungsi,erjahren, Springer Vcrlag, Bcrlin 1971. [4.57] K. Stahl: Indusrrielle Sreuerungsrechnik in schalralgehraisclier Behandlung. Oldenbourg Verlag, Miinchen 1965. 14.581 K. H. Fasol: Binure Sreuerungsrechnik, Springer VKrlag, Bcrlin 1988. 14.591 R. Laubcr: Prozejauromarisierung, vol. 1, Springer Verlag, Berlin 1989. 14.601 W. KBrner, P. Moll. M. Polke: “Zur funktionalcn Gliedcrung von Leitsystemen.” Aufomarisierungsrechnische Praxis (arp) 29 (1987) no. 9, 393-400. (4.61) A. 11. Levis: “Chakngcs to Control: a Collective View.” IEEE Trans. Aurom. Control AC-32 (1987) no. 4, 275 285. Eine [4.62] B. Rosenstcngel, U. Wienand: Perri-Nerze anwendungsorienrierre Ekfuhrung, Vieweg Verlag. Wiesbaden 1982. 14.631 D. Bochrnann, C. Posthoff: Biniire dynamische Sysreme. Oldcnhourg Verlag, Miinchen 1981. 14.641 R. Herschcl: Einfuhrung in die Theorie der Auromalen. Sprochen und Algorirhmen, Oldcnbourg Vcrlag, Miinchen 1974.
14.651 E. Leonhardt: Grundlagen der Digitolrerlinik. Einr systeniarische Einfihrung, Carl Ilanser Verlag. Munchcn 1982. 14.661 S. Wendt : Enrnvrjkomplexer Scholtwerke, Springer Verlag, Bcrlin 1974. [4.67] M. Bromhachcr, M. Polkc: “Perspcktivcn dcr ProreDleittcchnik,” Automuri.~irrungsrech,rische Pruxis !‘Up] 29(1987) 501 -509,558 -567,1987, pp. 22- 35. [4.68] M. Nicse, EP0104411. 1986. 14.691 M. Niesc: “Strukturellcr Aufbau und dercntrale Fiihrung in Produktionsdnlagcn,” INTERKAMA Kongrep, Munchen 1989, pp. 339 -349. [4.70] M. Bruns, M. Eibl. F. J. Kersting, R. J. Uhlig: “Anforderungen an Systemc rur Rercptfahrweisc,” Airroniurisierungsrechnische Pruxis (ntp) 35 ( 1 993) no. 2, 104 108. no. I . 40 -44; (4.711 S. Kiemcr: “Funktionen der Produktionslogistik in der Verfahrenstechnik,” INTERKAMA KongrrJ, Munchen 1989, pp. 381 -390. [4.72] K.-E Gcibig: “Funktionen der Bctriebsleitebcne,” Auromorisierutig.gsrechni.~chePraxis {urp) 34 (1992) no. 2, 68-72. (4.731 M. Polke: Prozejleirrechnik, Univcrsitit Stuttgart. 1984ff. [4.74] L. Prins. J. Stiheli: “Zur Automatisierung von diskontinuierlichen Viclrwcckanlagen in Chemiehetrieben,” Regelutigs~ecli.Prux. 20 (1978) no. 9. 256 261. (4.751 U. Epple, H. Kopec, R. Schmidt: “Strukturicrung von ProzeDfuhrungsaufgaben und Lcitsystcmsoftware.” Auromorisierungsrechni.cche Praxis ( a l p ) 34 (1992) no. 2, 59 67. [4.76] U. Epple: “Die Bausteintechnik - Grundlage einer objektorientierten Netzstruktur fur die Prozeblcittechnik, GMA-Fachbcricht.” VDE-KongreJ, Bcrlin 20.-23.1.1993.
References for Chapter 5 (5.11 E. Pavlik: “Sensorcn als Q U ~ fur C den Informationshaushalt tcchnischer ProIessc,” 46. N A M U R Ihdplsirzung, Ddrmstadt, 7 9 I k c . 1983. 48. Physikertagung and Friihjahrstagung. 15 March 1984, Miinster, pp. 1322ff. Physik-Verlag GmbH, Weinheim 1984. (5.2) R. Weisbeck: “Sensoren fur die Chemische lndustric . einc kritische Bewertung des Standcs der ’fechnik und der7eitigcr Scnsorentwicklung,” 46. N A M U R Hauprsiriung. Darmstadt. 7-9 I k . 1983. (5.31 H. Warncke: “Scnsortcchnik in der Chemischen Industrie: Aufgaben und Moglichkeiten der Stoffgr6DenmeBtcchnik,“ Tech. Mess. 52 (1985) no. 4. (5.41 H. Raab: “Digitalc Sensoren und Sensorsystcme Welche Informationen sollen sie licfern?,” Auromurisierungsreclinisclie Praxis (alp] 30 (1988) no. 11, 534-544. (5.51 VDljVDE 2600, Blatt 3: Metrologie. Gcritetcchnische Begriffc, Bcuth Vcrlag, Berlin 1973. 15.61 11. Warncke: Vorteilc, Prohleme und Moiglichkciten der in-situ-Messung von Stoffgrokn, Baycr-intcrner Bericht, Lcverkusen, Nov. 1982. [5.7] E. 1). Gilles, E. Nicklaus, M. Polke: “Scnsortcchnik in dcr Chcmie - Status und .[rends,” Automarisierungsreclmische Pruxis (utp) 28 (19R6) no. 9/10, 423 432, 479 484. .pp. 797 890. [5.R] Ullmann, 4th 4.5,
IS. References 15.91 H.-J. Schncider: “Sensorsysteme fur die HetriehsmeDtcchnik und Kommunikdtion im Feld,” Automurisierun~sreckiiisclie Pruxis (urpl 33 (1991) no. 10, 51 1-528. [5.10] K. klartmann: “On-line Analytik aus funktionaler Sicht, ~ufomuritierrmn~sfechnische Praxis ! u f p ) 31 (1989) no. 11, 510 -515. [5.11] 14. Schuler, Ch.-U. Schmidt: “Modellgestiitztes Messen in chemischen Reaktoren,” Chem. Ing. Tech. 63 (1991) no. 3. 189 -203. [5.12] R. Perne, M. Polke: “Produktentwicklung durch Verfdhrensvariation.” Xegelungsrech. (rr) 30 (1Y82) no. 5, 147-156. [5.13] K. Garbrecht. A. Prommer: Mikroelekrroirik. Kine 7echnik muthr Epoche. Siemens, Berlin 1981. 15.14) M. Polkc: “lnformationshdushalt technischer Prozesse,” Auromcrfisierungsrechnisrhe Praxis (urp) 27
(1985) no. 4, 214- 217. (5.151 J. Hesse: Sensoren auf dem Weg zur Sysreminregrurion, special edition, Oldcnbourg Verlag, Miinchen 1992. 15.161 J. Lauber: “Das Phasenmodell der Produktion: Ein semantischcs CAE-Werkzeug zur ProxBbeschreibung,” Regelungsfrchnischen Kolloquiunl, Boppdrd, Germany, Feb. 1993. [5.17 International Union for Pure and Applied Physics (IIJPAP): Symbols, Units and Nomenclature in Physics, Document U.I.P.20 (1978). Physik Verlag GmbH. Weinheim 1980. IS.lX] DIN 1313, Physikalische GroDen und Gleichungcn. Begriffe und Schreibweisen, Beuth Verlag, Berlin 1978. [5.19] M. Polke: “Exforderliche Informationsstrukturen fur die Qualidtssicherung.” Chem. Ing. Tech. 65 (1993) no. 7, 791 796. [5.20] W. Konig: Sicherheitstechnische Aspekte zur ProzeBleittechnik, 12. Arbeitsschur;-Sy~posium dpr Buyer AG, Leverkuscn, 22 Nov. 1985. [5.21] DIN V 19250, Mnsen - Steuern - Regeln. Grundlegende Sicherhcitsbetrachtungen fir MSKSchutzeinrichtungen, Beuth Verlag, Berlin 1989. [5.22] NAMUR-Empfehlung 31 : Anlagensicherung mit Mitteln der ProzeBleittechnik, NAMUR, Darmstadt 1992. [5.23] Verordnung zur Durchfuhrung des Bundesimmissionsschutzgesetzes (St6rfalLVerordnung) of 19 May 1988 and 5 July 1991. [5.24] DIN VDE 31 000: Allgcmeine Leitsiitu: fur das sicherheitsgerechte Gcstalten technixher Erreugnisse. Begriffe der Sicherheitstechnik. Grundbegriffe, part 2, Beuth Verlag, Berlin 1987. (5.251 2nd IUPAC-Workshop on Safety in Chemical Production, Pacifico Yokohama, Japan, May 31 -June 4, 1993. (5.261 M. Polke: “Process Automation in Chemical Industry,” 2nd IUPAC- Workshop on Sufery in Chemical Production. Yokohama, Japan, June 1993. (5.271 R. Ecker, H. Kramer, H.K. Mdler, M. Polke: “Neue Aspekte bei der Priifung von Kdutschuk und Gummi,” Kautsch. Gummi Kunstst. 25 (1972) no. 1. 5-10. (5.28) M. Mooney, H . Rolla. H. Taylor, J. H. Fielding: “Development and Standardization of Test for Evaluating Processibility of Rubber.” Symposium on Rubber Testing, American Society for Testing Special Technical Materials, 1974.
439
(5.291 DIN 53 523, Bcstimmung der Plastizitit nach Mooney im Warrnschervcrsuch, Beuth Verlag, Berlin 1965. [5.30] ASTM 1>927/49 T, Method of Test for Viscosity of Rubber and Rubberlike Material by the Sharing Disc-viscorneter, 1962. [5.31] W. Kuhn: “kziehungen zwischen MolekiilgroBe, statistischer Molekiilgestalt und eldstischen Eigenrhaften hochpolymerer Stoffe,” Kolloid-Z. 76 (1936) 3. (5.321 W. Pechold, S.Blasenbrey, S. Woerner, Kolloid-Z. Po/.vm. 189 (1965) 14. 15.331 L. Morbitzer, H. J. KrcD, C. Lindner. K. 13. Ott: “Struktur und Eigcnschaften von Mehrphascnkunststofh,” Angew. Makromol. Chem. 132 (1985) 19 42. (5.341 L. Morbitar, D. KraIv., G. Humme, K. H. Ott: “Structure and Properties of ABS Polymers. X. Innuencc of Particle Size and Graft Structure on Loss Modulus Temperature Dependence and Deformation Behavior,” J. Appl. Polym. Sci. 20 (1986) 2691 2704. 15.351 L. Morbitzer. U. Grigo: “Die wichtigsten polymerphysikalischen Aspekte des Polycarbonats,” Angew. Mukromol. Chem. 162 (1988) 87-107. [5.36] K. Ninomiya, J. Furuta: “Interrelation Between Data from Different Physical Tests,” Annual Meeting qf IIS P R, Mexico City, May 1970. [5.37] J. D. Ferry: Vbcoelu.de Properties of Polymers, John Wiley & Sons, New York 1961. [5.38] DIN I S 0 9001, Qualitiitssicherungsysteme- Modell zur Darlegung der Qualidtssicherung in Design/ Entwicklung, Produktion, Montage und Kundendienst, Beuth Verlag, Berlin 1990. Wilrich: “Die Auswertung I5.391 K. H. Padberg, P.-Th. von Daten und ihre Abhzngigkeit von der Merkmakdrt,” QZ 26 (1981) no. 6, 179 -184, 210214. I5.40) DIN IS0 9000, Qualititsmanagement- und Qualititssicherunysnormcn, Leitfaden 7ur Auswahl und Anwendung. Beuth Verlag, Berlin 1990. 15.411 R. E. Kalmdn: “A New Approach to Linear Filtering and Prediction Problems,” J. Basic. Eng. 82D (1960) 35-45. 15.421 I). G. Luenberger: “Observers for Multivariable Systems,” IEEE Trans. Aurom. Conrrol 11 (1966) 190 - 197. [5.43] A. Gelb (ed.):AppliedOprimal Estimation, The MIT Press, Cambridge, Mass., 1974. [5.44] W. Brammer, C. Siming: Kalmun-Bury-Filter. Determinisrisehe Beobachrung wrd srochasrische Filrerung, R. Oldenbourg Verlag. Miinchen 1975. (5.451 E. D. Gilles: “Modellgestiitzte Verfahren in der Automatisierungstechnik,” Spring Meeting of DPG. Verhandlungen der Deutschen Physikaliwhen Gesellschafr, series Vl, vol. 24 (1989). (5.46) N. R. Iudica: “Expertensysteme,” R 7 P Regdungstech. Prux. 26 (1984) no. 9, 402 -407. [5.47] D. BaLer: Wissensbasierrr Sy.steme in der Automarisierungsrechnik. Hanser Verlag, Miinchcn 1991. [5.48] 13. Fittler: ”Anwendung von Expertcnsystemen in der ProaDautomdtion,” AufomariFirrungsfechnische Praxis (utp) 33 (1991) no. 4, 199 205. [5.49] FI -P. PreuB: “Fuzzy Control - hcuristische Regelung mittels unscharfcr Logik,” Auromurisierungsrechnische Pruxis (urp) 34 (1992) no. 4, 176 184, 239-246.
440
15. References
[5.50] M. Koch, T. Kuhn, J. Wernstedt: “Ein ncues Entwurfskonzept f u r Fur.zy-Rcgclungen,” Arriomaiisierrrrrgsicch. lull 41 (1993) no. 5. 152 158. K. I). Mcycr-Graniann. B. Cuno. [5.51] H. Kiendl, M. Fritsch, Chr. Frcnck: “Fuzzy Control,” Bcitrige 7ur “l‘heorie fiir Anwender,” Arrrofnrriisierungsiechnik ( a i ) 41 (1993) no. 1.AS. 15.521 W. Brauer, D. Hernindes: “Verteilte kiinstliche Intelligenz und kooperativcs Arkiten,” 4 . Iniernarionoler GI-Kongrc$ Wirsensbusirrie Sysieme. Miinchen, Oct. 1991. Informuiik Fachherichie, Springer Verlag, Berlin 1991. [5.53] E. I). Gilles: “Modcllbildung, ein Hilfsmittel der MeUtechnik,“ Tech. Mess. 46 (1979) 225 232, 271 274. 15.541 E. I>. Gilles: “Application of Model Based Measuring Techniques in Chemical Engineering,” Proceedings of Inicrnaiional Conference on Indusiriul Process Modelling and Conrrol. Hangzhou. China, 6 - 9 June. 1985. [S.S5] Ullmunn, 4th ed., 5. [5.56] T. Pfcifer, P. Profos (eds.): Hundhurh der Indusiriel/en Me/liechnik, R. Oldenbourg Verlag. Miinchen 1992. [5.57] K. H. F’cltgen: pcnonal communication, 1972. [ S . S X ] K. H. Schmitt: “Die Automatisicrung von Chemicanlagen mit Mitteln der Systcmtechnik - Analysen und Trends,” Chem. Ing. Tech. 53 (1981) no. 8,620625. [5.59] Knick Elektronische MeOgcrlte GmbIl & Co.: “MeO-System 71 pH,” company brochure no. 71795915000, Berlin 1993. [5.60] K. Schiigerl (cd.):Arralyiisclrr Methoden in der Bioiechnologie, Vieweg Verlag, Braunschweig 1991. 15.611 J. E. Bailey, I). P. Ollis: Biochemical Engineering Fundamenials, McGraw-Hill Book Company. S e w York 1986. 15.62) U.Martens: “Sensor-Systcme liir DurchfluU und Fullstand,” 55. NAMUR Lahnstein Nov. 1992. 15.631 E. Nicklaus: “Auswirkungen modcrner ProzeOleittechnik auf Sensorsysteme,” Chem. Ing. Tech. 59 (1987) no. 8, 615-621. 15.641 I). Ilessc, H. Kiittner: “Entwicklungstendenren der Sensortechnik,” Ind.-Elekironik + Elekironik 28 (1983) no. 5, 36-44. 15.651 M. Polkc: “On-line BctriebsrneUtechnik,” Einfihrungsvortrag in den Themcnkreis der gkichndmigen Postersession aufdcm Jahrestreffcn dcr GVC, Wien, Oct. 1, 1992. [5.66] DIN 31 051, Instandhalten: Begriffe und MaUnahmen, Beuth Vcrlag. Berlin 1985. [5.67] G. .Pinkowski: “lnstrumentierungsteehnik im Wandel,“ Auionraiisierungsreehnische Praxis (alpJ 34 (1992) no. 5 , 247 -254, 325 327. C. Pinkowski: “Portfuhrung der Ex-Fcldbus-Untersuchung,” Auiomaiisierrrngsieclrnische Pruris (nip) 34 (1992) no. 11, 617-622. G. Pinkowski: ”Einhcitliche, hentellerunabhingige AnLeige-Bedicnoberflichc f i r kommunikationsfahige McUumformer,” Auronraiivierwrgsieclrnivch~ Praxis (alp) 34 (1992) no. 2. 530-537. 15.68) 11. Drahten: “Der Feldbus aus Sicht des Anwcndcrs,” Auiomuiisierun~.sreclinische Praxis carp) 33 (1991) no. 2, 55-64. [5.69] H. Kaab: “IXe Kolle der Sensoren in der Verfahrcnstechnik,” Chenr. Ing. Tech. 61 (1989) 199 205.
15.701 W. Stielcr: “Sensorprinsipien zur Mcssung physikalischer Produkteigenschaften,” company report, Bayer AG. Leverkuscn, Nov. 1982. 15.711 11. Raab: “Zur Struktur der An7cige-Bedicnobcrfliche digirdler Sensorsysteme.” Aufomurisierungstechnischc, Praxis (alpj 33 (1991) no. 2, 64-71. [5.72] ISO: MMS 9506, part 1, Service Definitions; MMS. part 2, Protocol Spccification, 1988. 15.731 C. Sskleby: The M M S IniplemcniaiirJrr on ihc PN E T Fieldhus S p i e m . 2nd hrierrruiional Conference on f - N e t Fieldhrrs Sysierns, Dcggendorf 1992. 15.741 A . Valenzano, C. Ilemartini. L. Climiniera: MAP and TOP Communicutions. SiandardF and Applicalions. Addison-Wesley Publi5hing Company. Workingham 1992. [5.75] W. Stadter: “1)evicr-Dcscription-Language. Ein wichtiger Beitrag 7ur herstcllcrunabhingigen AUStauschbarkcit von Feld- und Bcdicngeraten.” Arciomaiisierungsiec/rni~r/rePraxis (orpj 34 (1992) no. 1. 21 IT. 15.761 VDI/VI)E Kichtlinie: Einheitliche Anzeigc- und Bedicnoberfliche auf Pcrsonalconiputcrn fur digitalc Fcldgcrate, Griindruck 1992. [5.77] Krohnc America lnc.: “Flowmeter Selection Guide,” company brochure, Peabody, Mass. 1992. 15.781 P. Profos, T. Pfeifcr: Ilandhuch der Indicsiriellrrr MeJtechnik, K. Oldcnbourg Verlag, Miinchen 1992. [5.79] G. Strohrmann: Einfiihrung in dic .Wc/licchnik inr Chemiebeirieh, R. Oldenbourg Vcrlag, Miinchen 1990. [5.80] 1. Hengstenberg. B. Sturm, 0. Winklcr: Messen. Sieuern rcnd Regelti in der chemischerr 72chnik, Springer Verlag, k r l i n 1980. [5.81] F. Kohlrausch: frukfische fhysik, Teubner Vcrlagsgeselkchaft, Leipzig 1950. 15.823 A. Mangold: ”StcllgerHte f i r verfahrcnstechnischc Anlagen,” Arrtomatbierungsiechnische Praris ( a l p ) 33 (1991) no. 11, 585ff. (5.833 M. Huk: “Stellgerltc f i r verfahrenstechnische Anlagen.” Automniisierimgsi~chnischr Praxis (alp I 3 (1993) no 3. 131 ff. [5.84] H.-J. Schneider: “Sensorsysteme und die Kommunikation im Feld.“ Auiornuii~ieruiig.sie~~/rni.~~~/r~~ Prasis (alp) 35 (1993) no. 2, 71 ff. [5.85] DIN 19227, Graphischc Symbole und Kcnnbuchstaben f i r die ProzeUleittechnik. TI : Darstellung von Aufgaben. Beuth Verlag, Berlin 1990 (draft). [5.86] DIN 40050. IP-Schutzarten. Bcriihrungs-, FremdkBrper- und Wasserschutz Tur elcktrische Betriebsmittel, Beuth Verlag, Berlin 1980. 15.87) DIN VDE 0838, Riickwirkungcn in Stromvcrsorgungsnetzen, die durch Haushaltsgerlte und ahnlichc clektrischc Einrichtungcn versursacht wcrden, T1 T3, Bcuth Verlag, Berlin 1987. (5.881 NAMUR-Empfehlung 21, StBrfestigkcitsanforderungen. Part 1: EMV von Bctriebsmitteln der ProZCO- und Laborleittechnik. NAMUR, Darmstadt 1988. [5.89] DIN VDE 0843, Elektromagnetische Vcrtraglichkeit von Me&, Stcuer- und Regelcinrichtungcn in dcr industricllen ProzcUteehnik, TI -T4. Beuth Verlag, Berlin 1984-1988. 15.901 DIN VDE 0848. T1 -T3: GeGhrdung durch elektromagnetische Felder and 1‘4 T4 At : Sichcrhcit bei elcktromagnetischen Feldern, Beuth Vcrlag, Berlin 1982-1990.
/ 5 . References 15.911 NAMUK-Empfehlung 12,
Analysengerlterlumen,
Explosionsschutz in NAMUR, I>anstadt
1983. (5.921 G. Strohrmann: Anllrgensicherung mir Mirreln drr MSR-7echnik, Oldcnbourg Verlag, Miinchen 1983. l5.93] P. Nctter: “Anlagensicherung mit Mitteln dcr Pro-
zeDleittechnik,” Aictomorisierungrtechnische Pruxis (arpj 35 (1993) no. 1, 45 51. (5.Y41 NAMUR Empfehlung 24. Anfordcrungen an McDeinsdtze fur Temperatursensoren (Widcrstandsthermometer, Thermoclemente) zum Einsatz in eigensicheren Stromkreiscn, NAMUR, I>armstadt 1991. 15.951 F. Pigler: “HliuschutL fiir Leittcchnische Anlagen.” Autonrarisierungstrchnische Praxis ( a t p j 28 (1987) no. 5, 200-205. 15.96) R.-W. Peters. D. Plieninger: “Baycr-interne Arbeiten zu CAE-gestiitzten Arheitsmethoden.” Levcrkusen 1993. 15.971 G. Hwg: “Objektorientierte Systcme,“ company brochure, Georg Idecg Verlag, Dortmund 1993. 15.981 M. Arnold: “Analyse dcr Kommunikation m i schcn Simulationsmodellen technischcr Prozessc und den ProzeOnahcn Komponcnten von ProzeDIeitsystemen,” Diplomarbeit, RWTH, Aachen 1993. (5.991 E. Nicklaus: “Sensortechnik in der Chemischen Produktion,” Mess. PriiJ Auto (1987) 518-522. 15.lOOl M. Polke: “ProzeOtcchnik,” Symposium der KWTH Aachen and Payer Leverkusen 1988. (5.1011 M. Schweizer, 13. J. Warnccke: “Sensoren f i r die Fertigungstcchnik,” 48. P h y s i k e r r a p g Munster fY84, Physik-Verlag, Weinheim 1984, pp. 1331ff. (5.1021 T. Pfeifer: ”Fertigungsmcsslechnik und Qualititssicherung,” Tagungsband zum FachkongreD Microfecnic ’92. VDI Ber. 1006, Oct. 1992. [5.103] H. Kcichenberger: “Sensoren fiir Diagnose und Ubenvachung in der Medizintechnik,” 48. Physikerragung Munsrer lY(14, Physik-Verlag GmbH. Weinheim 1984. pp. 1357ff. 15.1041 E. Krestel (cd): “Bildgebendc Systeme fiir die medizinische Diagnostik,” Siemens AG, Berlin 1980. [5.105] W. Konig (cd.): Klinische-physiologi.~che Untersuchungsmeflroden, Thieme Verlag, Stuttgart 1972. 15.1061 I t . Kresse (cd.): Konrpendium Elektromedizin -
Grundlagrn. Anwendungen. Gerafe, Siemens AG, Berlin 1982. 15.107) S. Kutscha: “Statistische Bewcrtungskriterien fur die Entropieanalyse dynamischer Systeme,” Fortschrirrsher. YDI Z . Reihe 8, 178 (1989). [5.108] S. Kutscha, I t . Haubrich, K. Henning: “Lokalisierung von Epilepsieherdcn durch Einsatz der dynamischen Entropieanalyse.” Aufonurrisierwgstech. ( a t ) 40 (1992) no. 2. 70- 76. (5.1091 H.-W Delank: Neurologie, Enke Verlag, Stuttgart
1991. 15.1101 K. Poeck: Neurologie, Springer Verlag, Berlin 1987. 15.1111 E D . Stott e l al. (eds.): “ISAM-Gent-1981,” Proceedings of the 4rh Internarional Synrposium on Amhularory Monitoring and the 2nd Gem Workshop on
Blood Pressure Variability. Academic Press, London 1982. (5.1 121 Bundesministerium fur Forschung und lochnolopie: Mikrosystemtcchnik 1994- 1999 - Programm im Rahmcn des Zukunftkonzeptes Informationstcchnik, Rcferat 414, Bonn 1994.
441
(5.113) AMA Fachverband liir Scnsorik e.V.: Sensor ’93
KongreD, Technologie-Forum Mikrosystemtechnik, Nurnberg, Oct. 1993. 15.1 141 P. tigelhaaf, t. I-Ieintz, E. Zabler: “Sensoren ini K raft fahrzeug,” 48. Physikertagung Miorcter 1984, Physik-Verlag GmbH, Wcinheim 1984, pp. 1367 ff. [5.115] U. Kiencke: “Sensorik im Kraftfahrzeug vom Sensor zum System,” VDI Ber. 939 (1992). (5.1161 R. Polke: “Qualititssicherung in der Peststoffvcrfahrenstcchnik,” Chem. h g . Tech. 62 (1990) no. 10,
813 821. 15.1171 R. Polke: “The Relevance of Particle Mcasurcnicnt Technology to Process Engineering,’’ Parricle und Parriclc Sysrrnrr Churacreriiarion 10 (1993) 86 -91. 15.1 181 R. Polke, N. Scholz, Parr. Characr. 2 (1985) 39-
40.
15.1 lY] Ullmann’s Encyclopedia of Industrial Chemistry, Vol. 82, VCH, Weinheim-New York 1988, pp. 1-1 - 1-7. [5.120] 11. Kumpf: Slaub Xeinhalr. Luff 27 (1967) 3-13. 15.121I K. Polkc: 6. Fuchtagung Granulometrie, Dresden 19XY. vol. 1, 39 48. 15.122) K. Leschonski, W. Alex, B. Koglin: Chem. Ing. Tech. 46 (1974) 23 26; 47 (1975) 97 100. (5.1231 G. KGnig, R. Polke, N. ScholL: Verfahrenstechnik (Maini) 3 (1986) 10 17. (5.1241 K.Polkc, M. Schlfer, N. Scholz: 4 . Europ. Symp. PartikelmeJrrchnik PAR7EC, Niirnberg 1989, VOI. I, pp. 47- 61. (5.1251 K. Sommer: Probmahme von Pdvern und k6rnigen
Massengutern: Gntndlagen, Verfahren, Gerare, Springer Verlag, Berlin 1979. 15.1261 Laser Sensor Techn. Inc.: “Lasentcc.” company brochure, Bellcvue, WA 98009. 15.1271 D. Horn, Angew. Mokromol. Chem. 166/167 (1989)
139 - 153. 15.1281 K. Polkc. M. Rldle, N. Scholz: “PartikelmeDtechnik Status, ’Trend - Bedarf,” Chem. Ing. Tech. 65 (1993) no. 10, 1191 1199. (5.1291 J. Krekcl, R. Polke: “Qualititssicherung bei der Verfahrensentwicklung,” Chem. Ing. Tech. 64 (1992) no. 6. 528 535. [5.130] H. H. Meyer, W. Noerpel, P. J. Sehrder, W. Stie15.1311
(5.1 321 15.1331 [5.134]
(5.3351
(5.1 361
ler: “MeOtechnik im [Jmweltschutz,” company report, Bayer AG, Leverkusen, July 1988. M. Arnold, N. Ingendahl. M. Polke: “ProzeDautomatisierung in der Chemischcn Industrie,” Innovationsdynainik prozeJ&znaIyrischer,auromarisierungstechnischer und verfuhrensrechnischer Merhoden in drr chemischen Inrfusfrie, VDI-Hallexher Beirksverein. Halle 14 15 Oct. 1993. G. lIeiland: “Sensoren auf Metalloxidbasis,” 48. I’hysikertugung Munster 1984. Physik-Verlag GmbH, Weinheim 1984, pp. 1393ff. H. Warncke, M . Kahl, H. H. Meyer, P. Schurmeyer, DE 2932436, 1984. D. Kit-zelmann: “Elektrochemische Sensoren zur Erfassung von Schadstoffen in lndustrie und Urnwelt,” 48. I’hysikertagung Minster f984, PhysikVerlag GmbH, Weinheim 1984, 1463ff. R. Hotop: “Neuc Methcdcn dcr ProzeDanalysetechnik Wr die verfahrcnstechnische Produktion, Chem. I g . 7ech. 65 (1993) no. 8, 921 928. W.Nocrpel: “Abwasseriibenvachung mittels online Analytik,” company report, Bayer AG. Levcrkusen 1990.
442
IS. References
15.1371 H. Raab, A. Wachter-Buchner: “Kontinuierliche
TO<:-MeBtechnik in dcr Abwasseriibcrwachung,” Auromurisirrungslec/itiisc.hu Praxis ‘arpj 34 (1 992) no. 3. 128 136. [5.138] DIN 38409. Summdrische Wdrlungs- und Stoffkenngrdkn (Gruppe): Hestimmung dcs gesamtcn organisch gebundenen KohlenstofTs (IOC), Beuth Vcrlag, Bcrlin 1983. (5.1391 Endress + Hauser, International Holding AG (eds.): Abwasserhundbuch, Reinach 1992. (5.1401 G. Fikcntscher: Cettulosechemie 13 (1932) 58. [5.141] M. Hoffmann. H. Kromer, K. Kuhn: PolymerunuIytik I, Thicmc Verlag. Stuttgart 1977. (5.142) H.-G. Elias: Mukromolekule, Verlag Hiithig & Wepf, lieidelberg 1975. (5.1431 R. Hotop, J. Konig: “On-line Analytik in Polymerpromsen: Derzitige Situation und Zukunftsaufgaben.” AuromurisierunRsrechnische Praxis lurp) 33 (1991) no. 12, 610 -616. [5.144] W Giinther: “Die HPLC im praktischen Vergleich.“ Messen, Regeln. Auromarisieren 1993, Verlag Hoppenstedt & Co., Berlin 1993, pp. 17- 24. Synthesefosern, ): Vcrlag Che[5.145] B. van Falkai (4. mie. Weinheim 1981. [5.146] B. Adler: “Erkennen van ProrcOtrends durch Methoden der Fuzzy Clusterung und Fuzzy Logic,” lccture at VIII Tagung Innovationsdynmik p r o z e M Iylischer, aurumarisierungstechnischer und verfahrenslechnischer Merhoden in der chemischen Indusrrie, Preprints GVC-VDI, IXsseldorf, Oct. 1993. (5.147) M. Winterstein, C. Briickner. B. Adler: “Einsatz cines multivariantcn Sensorsystems zur Abwasseriibenvachung,” lecture held at the GVC-VDI Tagung Innovurionsdynumik prozeJ3unulyrischer. aufomarisierunRsrechnbcher und verfuhrensrechnischer Merhoden in der chemischen Indusrrie, Preprints GVC-VIII, I>iisseldorf, Oct. 1993. (5.1481 M. Polkc: “Trends der ProzeOlcittechnik,” Auromarisierungstechnisclie Praxis (alp) 31 (1 989) no. 9, 408-415. (5.1491 P. Hauptmann, R. Lucklum: “Sensoren fur die Prozdtcchnik,” lccture held at VDI Tagung Innovurionsdynumik prozeJunalyrischer. uuromaririrrungsrechniseher und verJahrensrechnisclter Merhoden in der chemischen Iuluttrie, Preprints GVCVDI. Diisseldorf, Oct. 1993. [5.150] K.-W. Peters: “Informationsstrukturen fur das Gebiet der ProreBleittechnik und ihre Prlsentation in cinem Hypermediasystem”. Dissertation, RWTH, Aachen 1994. [S.lSl] W. GBpcl,J. Hesse, J. N. Zemel: Sensors.vols. 1-8, VCH Verlagsgesellschaft. Weinheim 1989. 15.1521 G. Tschulena: “Von Mikrosensoren zu Mikrosystemcn - Stand und Perspektiven.” T M Tech. Mess. 60 (1993) no. 9, 313-318. [5.153] AMA: Sensor 91 - KongreBband Marketing, Fachverband fur Sensorik c.V., Niirnberg 1991. [5.154] ISA Directory 1993: Comprehensive Guide for Measurement and Control Market, Instrument Society of America, North Carolina, 1993. (5.1551 Messen. Regeln. Aulomurisieren: Einkaqfsfuhrer der Me&, Regelungs- und Aufomarisierungsrechnik, Verlag Hoppenstedt & Co., Darmstadt 1993. (5.1561 6. Inlernurionate Fachmesse mir Kongre] fur Sensorik & Sysremtechnik, Nurnbcrg. Oct. 1993. [5.157j MessComp ’93-7, KongrcOmesse fur industrielle MeDtechnik, Wicsbadcn. Sept. 1993.
(5.158) Prognos A
Marketplace, Opportunities and lrends in the 1990’s.” final report, ref. no. MC-10. Menlo Park, CA 1991. [5.160] K. Giinrel: ”Trends in der ProzeOmeOtechnik,“ Tech. Mess. 59 (19Y2) no. 9, 333- 339. (5.1611 A. Brucker, H. Umbdch: “Tcndenzen bei dcr Messung von I>urchfluO,” AitromurisirrunRstechn~~c/te Pruxis i a f p j 34 (1992) no. 3, 123 -127. (5.1621 Batelle-lnstitut: Sensors. Minifurisation und Inregralion. Hdtelle-lnstitut e.V., Frdnkfurt 1Y88. (5.1631 G. Mark, R. flcidel, K. P. Frehse: “Wirtschaftlichkeit dcs Feldbuseinsatzes: Analyse des Installationsaufwandcs unterschicdlicher Fcldkommunikationssysteme,” Automati.~ierungsrechniche Pruxis (arpp) 34 (1992) no. 9, 507-512. 15.1641 Rayer AC: Kuhcl- und Rohrwge, company report. Bayer AG, Lcvcrkusen. Oct. 1987, References for Chapter 6 (6.11 H. Janocha (cd.):Akroren. Springer Verlag, Berlin 1992. [6.2] H. Drahten: “ller Feldbus aus Sicht des Anwenders,” AutomarisiPrungsrechnisclie Praxis larp) 33 (1991) no. 2, 55 -64. (6.31 G. Pinkowski: “Instrumentierungstechnik im Wan-
del. Van der Smart-Tcchnik zum herstellcrunabhingigcn Bcdienkonzept,” AuromurisiertrnRsrechnische Praxis (urp) 6 (1992) no. 5, 247- 254. [6.4] H.Junginger, H. Wehkdn: “Der Fcldmultiplexcr aus Anwendersicht,” Auromurisieruttgsrechnische Proxis
(alp) 31 (1989) 12. [6.5] A. Mangold: ACHEMA ‘91: “Stellgcrlte f i r verfahrenstechnischc Anlagen,” Auromuririerungslechnische Praris (urp) 33 (19Y1) no. 1 1 , 585 -593. [6.6] DIN 31 051 : Instandhaltung, Begriffe und MaOnahmen. Beuth Verlag GmbH, Berlin 1985. [6.7] M. BjBrkmann: Elektrische Antriebstcchnik in der Chemischen Industrie, NAMUK I Iauptversammlung 1991, Lahnstcin. [6.8] B. Koenig, 0. Ohligschliiger: “Ein intelligentcr clektropneumatischer Stellungsregler,” Auromurisierungstechnische Praxis (alp) 31 (1989) 8. [6.9] M. Pandit, J. KGnig, H. Iioffman: ”Ein intelligenter ckktropneumdtischer Stellungsregler,” Auromutisierungsrechnische Praxis (urpj 31 ( 1 989) 367. 373. 16.101 DIN 19222: Lcittechnik, Begriffe, March 1985. 16.1 11 M. Polkc: “On-line BetriebsmcOtcchnik.” lccture at the GVC Jahresrreflen, Wien 1992. 16.121 P. Kruck: “Methodische Software-Entwicklung
(ddrgestellt am Beispiel cines CAD-Systems zur Symbolischen Konstruktion). Dissertation, Rergische Universitit GH Wupprtal 1987. (6.131 11.-J. Dorn: “Drehzahlvcrlnderliche Pumpenantriebc: Eine Alternative zu Stellventilen?” Auromarisierungsrechnische Praxis (arp) 29 (1987) no. 6. [6.14] H. Linnenbrink: “Drchzahlveranderbdre Antriebe, ein neuer Arbeitskreis im Arbcitsfeld,“ 3. N A M U R Huupfherichr. Huuptsitzung 53. Lahnstein 1991. [6.151 Antriebstechnik van AEG, Fachbcreich Antriebssystem und Lcistungselcktronik A 95, Gleichstrom-, Drehstrom- und Servoantriebe, Kurzdokumcntation, Bestell-Nr. 029.129951, 1991.
15. References [6.16] Bayer-interner Arbeitskreis PLT-Festlcgungen: Einfiihrung von PLT-Fcstlegungen nach Strukturierung auf Basis von E/R-Iliagrammen. Lcvcrkuscn 1990. [6.17] Canon, various company brochures, EOS Cameras, 1989. [6.18] Veranstahungsprogramm N Internationale Konfercn7 zu neuen Akrorcn - Actuator 1994, Rremen. [6.19] V. Wagner ct al.. Baycr Internal Report, Leverkusen 1990. [6.20] K. I lartmann, W. Schepers: Ilrehzahlgercgelte Pumpenanlagen, Bayer internal Iccture, Leverkusen. Oct. 12, 1988. [6.21] M. Polke: “Trends in dcr ProzcDleittL~hnik,”Autoin~JtisierungstechnischePraxis ( o l p ) 31 (1989) no. 9. 408 -41 5 . [6.22] K.llaby : “IlrehLahlverlnderbarc Pumpen als Stellglieder im Regelkreisen,” Regelungstechnische Praxis lorp) 22 (1986) 6. [6.23] H. Linnenbrink: ”Drehzahlveranderbdre Antriebe. Ein neuer Arbcitskreis im Arbeitsfeld,” 3. NAMURHauprberichr. IIauprsirzung 53, 1990. [6.24] M. Polke: “Trend iur Automatisicrung in der Chemischen lndustrie halt ungebrochen an: Gcdanken zur ACI IEMA 1991,” Automarisieritngstechnische Prunis ( a l p ) 33 (1991) no. 5, 219. [6.25] M. Harmoinen: “SPPdetyt AC kaytot tulevaisuudessa,“ (Variable speed AC drives in the future), ABB Strdmberg Oy, Power Electronics, Vaasa, Finland 1991. [6.26] Sanyo Electric, Semiconductor Division, Sanyo MST Hybrid System, Japan, Oct. 1989. [6.27] W. Mutz: “ProzDnahe Stromvenorgung,” Auromarisierungsrechnischc Praxis farp) Sonderheft NAMUR Stdtusbcricht H7, 1Y87. [6.28] W. Mutr: “Niederspannungstechnik f i r die Betriebe,” EN-PLI-Kolloquium “Prozeklektrotechnik,” Bayer AG, Leverkusen 1987. [6.29] M. Preytag: “Sichere Stromversorgung f i r PLTEinrichtungen,” PLT-Kolloquium “Erfahrungen mit dem Einsatz moderner ProreDleittechnik,” Rayer AG. Leverkusen 1984. [6.30] K. Kling: Stand und Entwicklung der Errichtungsbcstimmungen VDE 0100.etz b.H.6. pp. 128-131, 1974. [6.31] Bayer AG: “Beleuchtungsanlagen,” PLT-Fcstlegungen 06.53.01, Leverkusen 1984. [6.32] Bayer AG : “Die Bedcutung des Sicherheitsschalters bei Arbciten in Gefahrenbereichen technixher Arbeitsmittel,” Arbeitsschutzfestlcgung LE2, Leverkusen 1984. Refercocea for Chapter 7 [7.l] G. U. Spohr: “ProzeDleitsysteme,” in: ProzeJ/eirrechnik. 1st ed.. Oldenbourr! - Verlan.- Miinchen 1992, Chap. 5.2. 17.21 . . M. Polke: “ProzeDleittechnik,” Skriptum zur Vorlesung W S ~ ~ / R Universitit S Stuttgari, Ausgdbc 1984 und folgende. [7.3] U. Epple: “ProzeDfihrung,” in: ProzeJ/eittcchnik, 1st cd.,Oldenbourg Verkdg. Miinchen 1992. Chap. 4.3. [7.4] M. Brombdchcr. M. Polkc: “Perspektiven dcr Pro7cDleittechnik,” Chem. I g . Tech. 60 (1988) no. 7, 539 546. [7.5] K. F. Friih: “Optimalstruktur dezentraler ProzDIcitsysteme,” RTP Regelungsrech. Prax. 21 (1979) 273 ff.
443
[7.6] W. Korner, P. Moll, M. Polke: “Zur funktionalen Gliederung von hitsystemen,” Automatisieristgsrechnische Praxis (arp) 29 (1987) no. 9. 393 400. [7.7] U. Epple: “Funktionsorientierte Strukturierung prozeDleittechnischer Aufgaben,” Automatisierungstechnische Praxis lurp) 31 (1989) no. 10, 468474. [7.8] L. Lit7: “Anfordcrungen an die ProzeDleitsysteme der nlchsten Generation,” 53. NA M U R Ilauptsitiung, Lahnstein 1990. [7.9] ISO: MMS. Part 1 : Service Definitions, MMS Part 2: Protocol Specification, 1988. [7.10] ISO: ISOjDIS 74498 Open Systems Interconnection Basic Reference Model, May 1983. [7.11] M. Arnold: “Analyse der Kommunikation nvischen Simulationsmodellen technischer Prozsse und den prozeDnahen Komponenten von ProzeDleitsystemen,” Iliplomarbeit. Lehrstuhl fur ProzeDleittechnik, RWTH Aachen, 1993. 17.121 M. Polke: “Information als kritische Ressourcc,” in H. A. Henzler (ed.): Handbuch srrategische Fihrung, Gabler, Wiesbaden 1988. 17.131 NAMUR Empfehlung 21: Elektromagnetische Vertriglichkcit von Betriebsmitteln der ProzeD- und Laborleittechnik. NAMUR Gcschiftsstelle. h v e r kusen Dec. 1990. [7.14] DIN VDE 848 T1 T3: Gefahrdung durch elektromagnetische Felder, T4: Sicherheit bei elektromagnetischen Feldern, Beuth Vcrlag, Berlin 1982. [7.15] DIN VDE 84311 -T4: ElektromagnetischeVertrBglichkeit von Me&, Steuer- und Regeleinrichtungen in dcr industriellen ProzeDtechnik, Beuth Verlag, Berlin 1987. References for Chapter 8 [8.1] J. Konig: “Datenkommunikation in der Leittechnik,” AutornatisiertolgsrechnBche Praxis ( a l p ) 28 (1986) no. 5, 213 214. [8.2] M.Freytag: “Datenkommunikation in der Leittechnik - Status und Trend,” Auromatisierungsrec/inische Praxis carp) 28 (1986) no. 5, 215-222. [H.3] M. Polke: “lnformationshaushalt technischer Systeme.” in ProzeDrechner 1984, ProzeDdatenvcrarbeitung im Wandel 4, Pro:eJrechnertagung 1984, Kurlsruhe, Springer Verlag, Berlin-Heidelberg 1984, pp. 62 70. [8.4] M. Syrbc: “lnformationstechnik, Chance und Verantwortung.” Vorrrag Produkrionstechnisches Kolloguium, Berlin 1986, Fraunhofer-Gesellschaft, Miinchen. [8.5] H. SteusloB: “Kommunikation in technischcn Systemcn,” GIjNTG T a p g Kommunikation in verreilten Sysremen, Karlsruhe. 11.- 15.3.1985, Infomrik-Fachberichte 111, Springer Verlag, Berlin 1985, pp. 33 55. [8.6] K.W. Peters: “Funktionale Glicderung der Informationssysteme f i r Produktion, Labor und Infrastruktur; Funktionen der Produktionsleitebenc,” 48. N A M U R Hauptsirzung, Ldhnstein 1985; Berichtsband pp. 210-232. [8.7] D. Powell el al.: “The Delta-4 Approach to Dependability in Open Distributed Computing Systems,” FTCS 18, Tokio 1988. [8.8] ISO: Open Systems Interconnection Basic Reference Model, ISO/IlIS 7498, May 1Y83.
444
15. Rejerences
(8.91 0. Gihr, E.-H. Goldner. P. J. Kiihn, K. Sauer: “Lokale Nctrc und IS1)N-Ncbcnstellenanlagcn. Stand und Entwicklungstendcnzcn,” PIK (Pruxis der Informutionsverarbritung und Koinmunikation), 3, Hanscr Verlag, Miinchen 1986. pp. 26 34. [8.101 General Motors (GM): “Manufacturing Automation Protocol (MAP) I>ocumeni Version 2,” GM EMCC-APMES AiMD-39, General Motors Technical Center, Warren 1984, MI 4H090-9040. [8.11] ESPRIT Project 5104 (CNMA): CNMA Implementation Guide. Revision 5.1, Volume 1 3. University of Porto (ed.), Ref. 5104.92.35/FI.PD, 1992. (8.121 H. Stcusloff: “Twhnische Kommunikation in der industricllcn Automation. INFINA ’89. Informatik fur die industrielle Automation,“ VDI-Eerirhte 723, VDI-Verlag, 1989. pp. 319- 338. (8.131 M. Polke: “Trends der ProzeBlcittcchnik.” Aulomarisierungsrechnische Pruxis l a t p ) 31 (1989) 408 -41 5 . [8.14] Factory Instrumentation Protocol C46-602 Application Layer (MPS). C 46-603 Data Link Layer, C46604, Twisted Pair Physical Layer. Franzosische Normungsorganisation AFNOR, Paris 1988. 18.151 DIN 19245: Mcsscn. Steuern. Reaeln. PROFIBUS. Process Field Bus. Beuth Verlag,bcrlin. (8.161 D. Janetzky, K. Watson: Performance Evaluation of the MAP Token Bus in Real Time Applications. Advances in Local Area Networks (eds.: K. Kiimmcrle, F. Tobagi, J. Limb). IEEE, New York 1987, pp. 411 -425. (8.17 Fieldbus; ESPRIT-CIM-Report: CNMA, Phasc 4, Addendum 2, Chapter 4, in Vorbercitung; Kommission der Europlischen Gemeinschaften, DG XIII, 200 Rue de la Loi, Briissel, Bclgicn. [8.18] J. A. H. Pflcger: “Kommunikationssystcm Feldbus,” Automatisierungstechnitehe Praxis (utp) 28 (1986) 223 227. [8.1Y] I I. Drathen: “Neuorienticrung der leittcchnischcn Normung auf nationaler und intemationalcr Ebcnc,” Auromatisierungstechnisclie Praxis ( a l p ) 33 (1991) no. 11, 5RO -585. (R.201 W. Busing: “MAP f i r die ProzeBlcittcchnik: Die Herausforderung bleibt,“ N A M U R Statusbericht -87, ProzeJleitreclinik in der Chemi.7cht.n Industrie, K.Oldenbourg Verlag, Miinchen 1987, pp. 101 106.
(R.211 G . Schcidcnberger: “Fieldbus Message Specitication (FMS)”. Automatisierungstechnische Praxis (alp) 31 (1989) 152. (8.22) D. Hegcr: “Konformititspriifung offener Kommunikationsprotokolle fur die Automatisierungstcchnik. Fachbcricht Mnsen, Steuern, Regeln. Fortrhritte in der MeB- und Automatisicrungstechnik durch Informationstechnik.” vol. 14. INTERKAMA-KongreJ, Oldenbourg Verlag. Miinchen 1986. (8.231 ISO: “Specification of Abstract Syntax Notation One (ASN.I),” IS 8824. May 1987; “Specification of Basic Encoding Rules for Abstract Syntax Notation One (ASN.1);’ IS 8825, May 1987. 18.241 W. Gora. R. Speyercr: ASN.1. Abstract Synta.r Norurion One, DATACOM Buchvcrlag, Pulhcim 1987. I8.251 M. Syrbe: “Basic Principles of Advanced Process Control System Structures and a Realization with Distributed Microcomputers,” IFAC World Congress, June 1978, Helsinki, Pergamon Press, New York 1979, pp. 393 407.
18.261 M. Siefert: ”Entwurf und lmplcmcntierung eines ASN. 1 -Compliers,” I>iplomarbcit, Fakultit fur Informatik. Ilniversitit Karlsruhe!FraurnhoferInstitut fur Informations- und Datenvcrarbcitung, Karlsruhc 1988.
References for Chapter 9 (9.11 D. T. Ross, K. E. Schomann, Jr.: “Structured Anal-
ysis for Requirements Definition,” 1EEE Transaction~011 Software Engineering SE-3 (1 977) no. 1, 6 15.
(9.21 T. I k Marw: Structured Analysis und System Specificurion, Yourdon Press, New York 1978. lY.31 M. Polke: “ProreBlcittcchnik: Trend fur Auromatisicrung in dcr Chcmischcn lndustric halt ungcbrochen an,” Autornutisier~gsteclinische Praxis (alp) 33 (1991) no. 5. 219- 222. 19.41 mbp: Produktbcschreibung des CAE-Systems IIWPLAN. Dortmund 1990. 19.51 G.-U. Spohr: “CAE fur die ProzeBleitiechnik,” 5 / . NAMUR-Huuprsitzung, Lahnstein, I k c . 1988. [9.6] G.-U. Spohr. R. Schriebcr: “Anforderungen an CAE-Systeme,” INTERKAMA-KongreJ. 1)iisscldorf, Oct. 1989, in: G . Schmidt, 11. Steusloff (eds.): Mi! vernetzten intelligenten Kunlponenten zu leistungsfahigeren Me& und AutomurisirrunRss)slemen. Oldenbourg Verlag, Miinchen 1989, pp. 575584. [9.7] G.-U. Spohr: “CAE in der ProzeDleittechnik,” Vorlesungsmanuskript, RWTH Aachen, Lehrstuhl fur ProzcBleittechnik, 1991.
References for Chapter 10 [ 10.1j HOAl : Kcchtsverordnung zum Gesetz zur Regelung von Ingenicur- und Architektenlcistungen vom 04.1 1.1 971. 110.21 M. Polkc: “Einfuhrung in die ProreBleittcchnik,” Manuskript rur Vorlewng, Univcrsitit Stuttgart 1984. (10.31 DIN 69901, Projektwirtxhaft; Projektmanagement; Bcgriffe, Reuth Verlag. Berlin, August 19R7. (10.41 E. Cassirer: Subsrunzbegri/jc und Funktionsbegr# 6th cd., Wisscnschaftliche Buchgesellschaft, Darmstadt 1990. (10.51 W. E Ilaenzer: Systems Engineering, Verlag Industrielle Organisation, Zurich 1988. (10.61 D. Rerhke, A. Schcllc, K. Schnopp: Handbuch “Projekt~nunugement.” Vcrlag ’ I O V Rheinland, Koln 1989. (10.71 11. H. Schulzc, A. Schembra: “Schdtzung der Investitionskosten und Produktionskostcn bei der Verfahrensentwicklung und Anlagenplanung,” lecture held at the GVC-Tagung iiber Entwicklung und Auslegung verfahrenstechnischcr Prozesse. BadenBaden, 12/13 June 1989. (10.81 E. Willems, H. Henneckc: “Planung, Montage, lnbctriebnahme und Ingenieurbctrcuung von Einrichtungcn zum Messen, Steuern, Regeln,” in J. Ilengstenbcrg, K. H. Schmitt, B. Sturm, D. Winkler (eds.): Messen, Steuern, ReReIn irr der cheniischen fichnik. 3rd ed.. vol. 5. Springer Verlag. Miinchen 1985. [ 10.91 H. Groh, R. <;ut.sch: Netzpluntechnik, VDI-Vcrlag, Diisseldorf 1982.
15. References [lO.lO] S. Dworatschek ct al.: “Project Managcmcnt Software.” 7 h e Inkrnrrtionirl GI’MjINTk‘RNE7-Symposiwn. Garmisch. 15 -18 June 1986. [10.1I ] A. Tampicr: “De7entralc Einzelprojektplanung und zentrale Multiprojcktplanung im automa!ischen Vcrbund VAX-PC-VAX mit IST-KostenUbernahme,” in D. Reschke, A. Schellc (eds.): Beitruge zum Projektmcmu~emmt-Furunr 1989. Tagung Garmisch, 4 6 Oct. 1989. [10.12] MS-Project. software product von Micro-Soft. [10.13] M. Brombacher: “Das Lastenheft als GrundPage dcr Automatisicrung chemischcr Verfahren und seine 1)arstcllung als Expertensystem,” Dissrrtation, Twhnische Ilniversitit, Miinchen 1985. [10.14] M. Polke: “lnformationshaushalt technischer Prozesse,“ Auromatki~rungstechnischePraxis (urp) 27 (1985) 161-171. [lO.lS] M. Polke: “Information als kritische Rcssource,” in 11. A. Flenzler (cd.): ffandhuch “Strutegi.rrhe Fuhrung.” Gablcr-Verlag, Wiesbadcn 1988. (10.16] ‘I‘ H. Miiller-Hcinzerling: “Flexible Standardsoftware zur Automatisierung von Chargenprozcssen mit ProrcOlcitsystem TELEPERM M.” Auromuririer~gsrechnischePruxis, 30 (1988) no. 6. (10.171 R. Schmidt, Diverse Haycr-interne Mitteilungen, Levcrkusen 19RY - 1990. [lO.lS] M. Niesc. kP0104411, 1986. (10.191 N.Reckcr: Diverse Bayer-intcrnc Mittcilungcn. Leverkuscn 1989- 1990. (10.20] Ullmunn, 4th ed., 4, 70. [10.21] G. Bcrneckcr: Plunwrg und Buu ver/uhrensrechnischer Anlugen, VD1-Vcrlag Diisseldorf 1984. (10.221 1>1N28004. FlicObildcr vcrfahrcnstcchnischer Anlagcn; Begriffe, FlieObildartcn, Informationsinhalt, part 1, Bcuth Verlag, Berlin, May 1988. (10.23) M. Polke: “ProzeOleittechnik in dcr Produktion; Voraussetzung fur Diversifikation von ChemiePasern,” Lenzinger Ber. 67 (1989) 12- 23. [10.24] DIN 19227, Bildzcichen und Kennbuchstabcn fur Messcn, Steuern, Regeh in der Vcrfahrenstcchnik; Zeichen fur die funktionelle 1)arstellung. part 1. Beuth Vcrlag, Bcrlin, Scpt. 1973. [10.25] W. Ahrcns, M. Polke: “lnformationsstrukturcn in der Automatisierungstechnik,” lecture held at Intcrkama-KongeO 1989, in G . Schmidt, H. Steusloff (eds.): Mi! vernefzfen,inrelligenren Komponenten zu leistwgs/ahigen Me& w d Automutkierungssysremen, R. Oldcnbourg Verlag, Miinchen 1989, pp. 47-68. [10.26] E. Welfonder: “Leittcchnik - Dokumentation aus Betreibersicht,” ETG,JVGB-Fuchtagung Betriebsgerechfe Dokumenrurion in Krafrrwerken, Einflubl der modernen Leirrechnik und Planringsmiltel, Badcn-Badcn 1986. (10.271 DIN 32 705, Klassi~kationssysteme; Erstellung und Weiterentwicklung von Klassifikationssystemen. Beuth Vcrlag, Berlin 1987. (10.28] K. Kesslcr: “ProzeOorientierte Strukturierung von verfahrenstechnischenAnlagen,”Automut~rierung.rtechnbche Praxis ( u p ) 31 (1989) no. 10, 4 6 467.
[10.29] DIN 66201, Prodhchensystemc; Begriffe, part 1. Beuth Vcrlag, Berlin. May 1981. (10.301 J. K6nig: “Sicherheitstcchnische Aspekte der ProzeOleittechnik,” Chem.-Ing. Tech. 49 (1987) no. 3, 196 204.
445
[10.311 R. Magin, W. Wiichner: Digitale Proxpleiltechnik.
Vogel Buchverlag, Wiirrburg 1987. (10.321 M. Frcytag: “USV und Notstromversorgung,” vI)I-7agung, Fellbach/Stuttgart, 25. -26.10.1989. [10.33] K. Peters: Diverse Bayer-interne Mitteilungcn, Leverkuscn 1985. (10.341 E. Ignatowitz: Chemietechnik. Verlag Europa-I-chrmittel, Wuppertal 1982, p. 112. [10.35] DIN/IF.C 3H (Sec)40, Ausfiihrung von Funktionsplanen fur Stcuerungen, Identisch mit IEC 3B (Scc).lo, Oct. 1985. [10.36] DIN 40719, Kcnnreichnungsrcgcln zur funktionsund ortsbczogcnen Kenrueichnung von Systemen und Anlagen, Einrichtungen und Betriehsmitteln, Anschlussen. Signalcn und technischer Dokumentation, Ted 6, Entwurf, Beuth Verlag, March 1989. [10.37] D. Plieninger: “Qualititssicherung in der leittcchnischen Planung,” NAMUR-tiauptsitzung, Ldhnstein 1991. [10.38] K. 11. Schmitt: Qualititssicherung in der Leittcchnik, NAMUR-Hauptsitzung, Lahnstein 1991. Beuth [10.39] W. Masing: Quulirutslelve. DCQ, 5th 4.. Verlag, Berlin 1977. 110.40) A. Miiller: Qualititspriifung von PLT-Dokumentationen als Vertragsbestandteil l x i der Vergabc von lngenieurleistungen an Fremdfirmcn. Bayerinterne Fcstlcgung, Leverkusen 1992. [10.41] D. Plicninger, A. Miiller: “Ansitze zur Qualititssichcrung in dcr leittechnischen Planung,” Autornutisierungstcchiiirche Praxis (alp] 34 ( I 992) no. 10, 564ff. (10.421 H. Rinne, H.-J. Mittag: Srutistische Merhoden der Quuliratssicherung. 2nd cd.,Hanscr Verlag, Miinchen 1991. 110.431 G. Firbcr, M. Polke, El. Steusloff: “Mensch-Proze8-Komrnunikation.” Chem. Ing. Tech. 57 (1985) no. 4, 307- 317. [10.44] Haycr AG, Beleuchtungsanlagcn, PLT-Festlegungcn, 06.53.01, Leverkucn 1984. (10,451 Bayer AG: ProzeOleitwarten, Leitstande, Ncbenriume. Richtlinien fur die konstruktive Gestaltung, PLT-l;cstlcgungen. Leverkusen, 1989. (10.461 R. Beuchelt, U. Fierus, H. Hcnncckc, E. Willems: PLT-Raumdcsign, interne Richtlinic der Bayer AG Leverkusen 1985. [10.47] VDljVDE 3546: Konstruktive Gestaltung von ProzeDleitwarten. Blatt 1 (19x7): Allgemeiner ‘reil; Blatt 2 (1981): Bautcchnische MaOnahmen; Blatt 3 (1988): Ausfiihrung dcs Leitstandes; Blatt 4 (1986): Nebenraumc; Blatt 5 (1991): Anordnung von Monitoren. Beuth Verlag, Berlin. 110.481 NAMUK Empfehlung NE 12: “Explosionsschuw von Analysegertterlumcn”. NAMUR Geschiftsstelle Leverkuscn, Baycrwerk, Lcverkuscn 1983. (10.491 DIN 33402: K 6 r p m a O c des Mcnschen. Beuth Verlag, Berlin 1986. [lO.SO] DIN 33414: Ergonomische Gestaltung von Warten. Beuth Verlag, Berlin 1988. [10.51] DIN 33403: Klima am hrbeitsplatz und in der Arbeitsumgcbung, Heuth Verlag, Berlin 1988. [10.52] DIN 5035: Beleuchtung mit kijnstlichcm Licht. Teil 2: Richtwcrte fiir Arbeitsstatten in Innenriumen und in Frcien. Beuth Vcrlag. Berlin, 1990. [10.53] Verordnung iibcr Arbeitsstittcn. Bundcsministcrium fiir Arbcit und Sozialordnung, Rcferat t)ffentlichkei tsarbeit, Bonn 1975.
446
15. Reference3
References for Chapter 11 [I 1.1) J. Konig: “Sichcrheitstcchnischc Aspckte dcr Prozd3lcittechnik.” Chem. lng. Tech. 59 (1987) no. 3 216 218. [11.2] R. Bachmann. J. Konig: “Towards lncrcased Safety Some Future Aspects of Modern Process Control for Chemical Plants,“ in Aulomalic Conrrol World Congress 1987. Pergamon Press, Oxford. [l 1.31 R. Isermann (ed.):IFAC-Smposium: Safeprocess, Badcn-Baden 1991. (11.41 8. 11. Gilles: “Von dcr MSK-Tcchnik zur wissensbasiertcn ProLeDlcittcchnik,” Chetn. Anlagen + Verfahren 4 (1985) 62 64. [l 1.51 M. Polke: “Erforderliche lnformationsstrukturcn fur die Qudlitatssichcrung.” Chem. Ing. Tech. 65 (1993) no. 7, 791-796. (11.61 G . Firber. M. Polke, H. Steusloff: “Mensch-ProrcD-Kommunikation.” Chem. Ing. Tech. 57 (1985) no. 4, 307 317. ( I 1.71 R. Pcrne, M. Polkc: “Produktentwicklung durch Vcrfahrcnsvariation,” Regelungstechnik 30 (1 982) no. 5. 147- 156, (11.81 E. I>. Gilles. M.Wurst: “ProzrBf-hrung zur Sicherung reproduzicrbarcr Qualitat in der vcrfahrenstechnischen Produktion,” lecture at GVC-Juhresrreffen, Stuttgart 1990. pp. 138-139. 111.91 J. Pawlowski: Die Ahnlichkeirsfheorie in der physikoli.sch-rechnischeir Forschung, Springer Vcrlag, Bcrlin 1971. [I 1.10] J. Pawlowski: “Ahnlichkeitstheoric - ein wirksdmes Instrument der Vcrfahrcnstechnik,” GVC-Tagung Ahnlichkeirsrheorerische Merhoden und ihr versrurkrer Eimaiz in der Pruxis, Mainr, June 15, 1984. [11.1 I ] M. Zeitz: “Simulationstechnik,” Juhresrreffen der Verfihrensingenieure, StraDburg 1986, Oldenbourg Vcrlag, Munchcn 1987, pp. 164-169. (11.121 H. Schuler: Methoden ZUI ProLeBfiihrung mit Simulationsmodellcn, AufomatisienrnRstechnische Praxis ( a l p ) 31 (1989) no. 10, 475-481; no. 11, 519- 523. (11.13) W. Ahrens, M. Polkc: “Nctzmodclle als systcmtechnische lnformationsbasis fur die ProzeBleittechnik,” Auromatisierungstechnik ( o f ) 37 (1989) no. 3, Y4- 103. [l 1.14) E. D. Gilles: “Auf dem Wegc zu ciner modellgcstiihten ProzeDlcittechnik,” 50. Jahresfagung N A M U R , Lahnstein 1987, Oldenbourg Verlag, Miinchen 1987, pp. 35-45. [11.15] E. D. Gilles, W. Marquardt: “Dynamische Anlagensirnulation in dcr chcmischcn Vcrfahrcnstechnik,” Auromurisierungsrrchnische Pruxis (arp)34 (1986) no. 2, 49- 58. [11.16] W Ahrcns: “Einsatr von Expertensystcmen in der Aufomafisierungsrechnische ProzeBleittechnik.” Praxis (urp) 29 (1987) no. 10. 475-485. (11.171 M. Brombdcher: “Expertcnsystcme zur UntentiitLung der Planung (Ldstenhcft),” Auromurisienurgsrechnische Proxis (nrp) 30 (1988) no. 6, 280291. [11.18] W. Marquardt: “Rechnergestiitzte Entellung vcrfahrcnstechnischer ProLeDmodelle,” Chem. Ing. lech. 64 (1992) no. 1 , 25-40. [11.19] H. Fittler: “Anwcndung von Expertcnsystcme in dcr ProLeDautomation.” Auromurisierungsrechnische Praxis (arp) 33 (1991) no. 4. 199- 205.
(ll.ZOl E. D. Gilles ct al.: “Ein lrainingssimulator zur
Ausbildung von Hetricbspcrsonal in der chcrniwhen Industrie,” Aulomurisierungsrechnis~,hePraxis ( a l p ) 32 (1990) no. 7, 343 -350. [11.21] J. Laube, M. Polke: “Das Phasenmodcll der Produktion: Ein scmdntischcs CAE-Werkicug m r Prozcssbeschrcibung.” Lecture at Regelungsrec/rnische Kolloquun, Boppard, March 1993. [11.22] A. Lippert et al.: “Gedankcn m r Ncugestaltung dcr Vcrfahrcnstcchnik.” company rcport, Baycr. Levcrkuscn, Nov. 1988. [I 1.23) C. A. Petri: “Kommunikation mit Automatcn,” Dissertation, Technische Hochschulc Darmstadt 1961. [ 11.241 W. Keisig: Sysrenrenrwurfmir Nerzen, Springer Vcrlag, Berlin 1985. ( I 1.251 G. Scheschong: ”Eine auf Pctrincuen basicrendc Konstruktionsanalysc und (Tcile-)verifikationsmcthodc zur Modellicrungsuntcrstutzung bci Entwicklung von lnformationssysterncn,” Disscrtation, TU Berlin 1984. (11.261 M. Brombacher, M. Polke: “Perspektiven dcr Pro7eBlcittechnik,” Auromarisierungsrechnische Praxis carp) 29 (1987) no. 11, 503- 509; no. 12, 558 561. [I 1.271 U. Epplc: “Funktionsorientiertc Strukturicrung ProzeDleittechnischcr Aufgaben,” Aurotnufisierungsrechirische Pruxis fofp) 31 (1989) no. 10,468474. [I 1.281 0. Grdf: “Standartisicrte Grundfunktioncn verringern Softwarcaufwand bei Telepcrm M.,” c’ng & U U I O 3 (1991) 2- 5. [11.29] &dyer AG, EPO 104 411, 1986 (M. Niese). [11.30) W. Wiichner: “Anfordcrungcn an das Personal beim Einsau modcrner ProzcOleitsystemc, Arrromutisierung.srechnische Pruxis ( a l p ) 28 (1986) no. 1 13-18. [11.31) G. Krauss: “Akmptanz der ProzcUleitsystcme durch die Bctriebc,” Arrromuri.sierung.sreclrnbcbe Praxis ( a l p ) 10 (1985) no. 10 470 476. (11.321 11. Radb: “Dic Rolle dcr Sensoren in dcr Vcrfahrenstcchnik, und: Digitalc Sensorcn und Sensorsystcme: Welche Informationcn sollen sie licfcrn.” Chetn. Ing. Tech. 61 (1989) no. 3. 41 -47. [I 1.331 Verein Deutschcr Ingcnieure: “Der Ingenicur in Beruf und Gcscllschaft,” ~landlungsempfehlung: Sorialvertragliche GCStdkUng von Automatisicrungsvorhabcn, Dusseldorf, Dec. 198Y. (1 1.341 L. Bainbridge: “lronics of Automation,” Aufofnuficu 19 (1983) no. 6, 775 559. [ t 1.351 H. Grauel, H. Baucr, P. Meting: “Erhohung dcr Kompetenz des Hetrciberpcrsonals - Ziel dcr zustdndsorienticrten Mensch-Prod-Komrnunikation in Chcmiednlagcn,” M S R (Berlin) 30 (1Y87) no. 10, 448-455. [ I 1.361 H. Lcnk: “KBnnen lnformationssystemc moralisch vcrantwortlich sein?” Injormurik Spekrruni 12 (1989) 248-255. (11.371 ti. J. Charwart: Lrxikon der Mensch Mruchine Kommunikurion. Oldenbourg Verlag, Miinchcn 1992. [11.38] B. Geiser: “Was Window-Manager in der Leittcchnik leistcn sollen,” Elekrronik 16 (1991) 52 59. [11.39] Siemcns AG: “Telcperm M. company brochure. no. A 19100 E816-B677 100045 PA 10895, 1991. [11.40] AEG: Schirmbilder f i r Vicwstar 750, Frankfurt. [ I 1.411 ABB ProzcBautomation Gmb1l : Schirrnbildcr fiir Advant Station.
15. References
K.R. Hoff, J. b,. Lincoln: Engineering Datu Compendium Humun Perception und Performunce, I larry G. Armstrong Aerospace Medical Research Lab.. Ohio 1988. 2nd ed.. [11.431 H. Schmidtke: Ilundbirch der Ereonomie.,~ Carl Hanser Verlag. Miinchen 1989. 441 N. Heineken: “Aufnahme und Verarbeitung von Information,“ to be published in H. Schmidtke: Hundbuch dcr Ergonomie, 2nd cd., Carl Hanser Verlag. Miinchen 1989. 451 H. Steusloff: “Mensch-ProieB-Kommunikation.“ DPG-Arbeitstugung, Bad I lonnef, Dec. 1983, pp. 1-23. 461 R. W. Bailey: Hwnmi Performunee Engineering. A Guidefor Systems Designers. Bell Telephone Laboratories, Inc., Prcntice-Hall, Englcwood Cliffs, N.J. 1982. [11.47] 1. Rasmussen: “The Role of Hierarchical Knowledge Representation in Decision Making in System Management,” IEEE Transations on Systems, Man and Cybernetics, vol., SMC -15(1985) 2, pp.234 243. [11.48] G. Johannsen: “Towards a New Quality of Automation in Complex Man-Machine Systems,” Automuricu ZS (1992) no. 2, 355 373. [11.49] M. Rigen: “Mozart - oder unscre Verlegenheit im Umgang mit dem Genie,” in Perspekriven der Wissenschuft, DVA, Stuttgan 1988, pp. 76-104. [l1.50] J. C.Eccles: Das Gehirn des Menschen, 6th ed.. R. Piper, Munchen 1975. (11.511 H. and K. Benesch: dtv-Arlus zur Psychologic, dtv, Miinchcn 1987, pp. 90 123. [11.52] W. Metzger (ed.):“1. Wahrnehmung und BcwuDtsein,” Hundbuch der Psychologie. vol. 1, Hogrete, Gottingen 1966. [11.53] N. A. Streitz, P. A. Gloor (eds.): “Hypertext und Ilypermedia: Von theoretischen Konzepten zur praktischcn Anwendung,” Informatik-Fuchherichre 249, Springer Verlag, Heidelberg 1990. [11.54] A. Despopoulos, S. Silbernagel: Tosrhenotlas der Physiologie, Georg lhieme Verlag, Stuttgarl 198X. [11.55] R. F.Schmidt, G. Thews: Physiologie des Menschen, Springer Verlag, Berlin 1983. (I1.561 A. Hajos: Eittfuhrung in die Wuhmehmungspsychologie, Wissenschaftliche Buchgesellschaft, Darmstadt 1980. (I1.57) W. D. Heiss: “Kernspintomographie und Positronen-Emissions-lomographiezur Lokalisation nienschlicher Hirnfunktion,” Ver. Ges. Dtsch. Nuturfrsch. &re 114 (1986) 241 -269. 111.581 G. Deutsch, S.P. Springer: Linkes und rechtes Gehirn: Funktionule Asymmerrien, 2nd ed.. Spektrum der Wissenschaft . Verlagsgesellschaft. Heidelberg 1988. [ I 1.591 H. Ostertag: “Positronen-Emissions-Tomographie (PET),” Phys. 61. 48 (1992) no. 2, 77. [11.60] K. Poeck (ed.):Klinische Ncuropsychologie, Georg Thieme Verlag, Stuttgart 1989. [11.61] R. J. Trotter: “Das Jahrzehnt des Gehirns.” Psychologie Heute, Jan. 1992. 57- 59. [11.62] E. Straw: Vom Sinn der S h e , Springcr Verlag, Berlin 1978. [11.63] J. J. Gibson: Wuhrnehmwig und Umwelr, Urban & Schwdrzenberg, Miinchen 1982. [11.64] K. Lorcnz: Die Rtickseire des Spiegels. Versuch einer Nuturgeschichre menschlichen Erkcnncns. d t v, Miinchen 1977. [ 11.421
447
[ I 1.651 VDIjVDE Gexllschaft MeD- und Automatisierungstcchnik (GMA), VDIjVDE Richtlinie 3536. Diisseldorf, Jan. 1990. [l 1.661 I). O’Shaughnessy: Speech Communicution, Addison-Wesley, Reading. Mass. 1987. 111.671 .)I Fukunada, W. L. G. Koontz, Inf. Control 16 (1970) 85-101. [ 11.OX] F.. Paulus, I>. LIngle: “Die Anwendung dcr Karhunen-Locve-Entwicklung f i r die digitale Sprachanalyse und -synthese,” NTG-Fuchrugung Signuh~erurheirung,Erlangen 1973, pp. 362- 369. [ I 1.691 R. M. Anderberg: Cluster Anuljsis for Applicutiotrs, Academic Press, New York 1973. [11.70] 11. Splth: Clicster-Anulyse-Algorirhmen, 2nd 4.. Oldcnbourg Verlag. Munchen 1977. [I 1.71) K. Stephan, P.Metzing: “hutomatisierung komplexcr Chargenprozesse.” M S R (Berlin) 29 (1986) 511 -515. (1 1.72) H. Bohme: “Anwendung der Situationssteuerung in komplexen verfahrenstechnixhen Systemen,” in Wissenxhaftliche Berichte. vol. 15, TH Leipig, 1985, pp. 48-81. [11.73] R. Miiller, F. Miiller, A. Parusel: “Planning ManMachine-Communication in Process Control Systems,” Proceedings of the BIAS 1990, Milan, pp. 407-417. [11.74] G. S. Axelby, A. P. Sage (eds.): Zusammenfassung der ‘lagungsbeitrage der IFAC/IFIP/IFORS/IEA Conferenct: on Analysis, Design and Evaluation on Man-Machine-Systems, Auromuricu 19 (19x2) no. 6,593-779. [11.75] J. Richter: “Experimentellc Untersuchung zur aufgabenbwogenen Gestaltung von Informationssystemcn in Lentralen ubcrwachungseinheiten,” Dissertation, Humbold-Univcrsitat. Berlin 1982. [11.76] A. De Meyer: “Software verfahrensgerceht strukturieren,” company report, Bayer Antwerpcn NV. Antwerpen 1991. [11.77 H.Jiiraens. D. Sauoc ( 4 s . ) : Visuulisierunz in Murhe&tik und Nurur.wiss;nschufren, Springer Verlag. Berlin 1989. ,781 H. Miiller: Reulistische Computergruphik. Springer Verlag, Berlin 1988. ,791 K. Kansy, P. Wisskirchen ( 4 s . ) : Gruphik und KI, Springer Verlag, Berlin 1990. ,801 K.P. FLhnrich, C. Janssen, G. Groh: “Rntwicklungswerkzeuge fiir graphische Benutzerschnittstellcn,” Cornpurer Mugarin 2 (1992) 6 13. ,811 I). A. Young: X Window Sysrems, Prentice Iiall, Englewood Cliffs, N.J. 1989. ,821 Eckclmann: Industrieautomation, InTouch. company brochure, Wicsbaden-Erbenheim 1991. ,831 PROLINK AG, Factory Link, company brochure, Seengcn 1992. ,841 ProzeB-Leitteehnik GmbH, APROL. company brochure, Hilden 1991. ,851 G. Geiscr: Mensch--Maschine- Kommunikution, Oldcnbourg Verlag. Miinchen 1990. ,861 Siemens AG: leleperm M: Das ProzeBleitsystem, company brochureno. A 19100-E815-A2-V1.1983. ,871 SattControl GmbH, SATTLINE, company brochure, Wuppertal 1990. 88) AEG, GeAmatics, Company Brochure Frankfurt 1992. ,891 Fxkardt AG: LS 130 Leistation des PLS 80E, Systemiibersicht PLS 180 t. Drucksehrift B 13310692, Frankfurt 1992.
448
15. References
[11.90]ABB ProzcDautomation GmbH: ABB Master, company brochure, Mannheim 1992. [11.91]J. Encarnacao: “Die perfektr Tauschung als
hochgcstccktes Zicl.” forschung . Mitteilungcn dcr IJFG 2 (1992) 4 7. [11.92]M. Utermijhlc, H. G . Pagcndarni: “Sichtwcisen Visualisierung,” Grundlugen iind Lntscheidivrgskrirerien 1X (1992)92 -9X. (1 1 ,931 tl. W. Franke: “Wcltcn aus Bits and Bytes.” VDI Nachr. 9 (1 992) 13 - 17. [Il.94]R. Steinmetr, R. G . tlcrrtwich: “lntcgrierte vcrwile Multimedia-Systeme,” Iifforntulik-Spekrrum 14 (1991) 249 -260. 11 1.951 R. G. Hcrrtwich: “ktriebsmittcivergdbe unter Echtzeitgcsichtspunkten.” Informarik-Spekrrtun 14 (1991).
[I 1 ,961 1). P. Anderson: “Resource Management for Digital Audio and Video,” IEEE Workshop on Rea~-Time-Operdting Systems and Software, Charlottesville. May 1990. (11.97)P. Horpncr: “Synchroniing the Prescntation of Multimedia Objects - ODA Extensions,” Mulrimediu - Sysrems. Interaction and Applicarions, Isr Eurographics Workshop. Stockholm. April 1991. pp. 87- 100.
[I 1.981 N. S. Borenstcin: Mulrimedia Applicarion Develupmenr with rhe Andrew Toolkir, Prentice Hall, Englewood Cliffs 1990. 11 1.991 K. Meyer-Wegener: Mulriinedia-Darenbunken, B. G. ’Ieubner. Stuttgart 1991. [ll.lOO]J. L. Alty, C. D. C. Mccdrtney: ”Managing Multimedia Resources in Proccss Control,” Mulrimediu - Systems. Inreracrion md Applicarions. lsr Eurographics Workshop, Stockholm, April 1991, pp. 293- 306. [ll.lOll E.Golla: “Multimedia-Anwcndung fur die Industrie,’* C I M - P r a i s 1 (1993)46 47. [ll.102]W. Michaeli, K. Borgschulte, M. Philip: “Neuc Konmpte bei dcr Automation in der kunststoKverarbeitenden Industrie durch Anwendung von Bildverarbcitungssystemcn,” Proceedings of rhe 9th hrrernarionul Conference on Atitontared Inspeclion and Prodtic1 Control. Stuttgdrt, May 19x9, pp. 115 -123. [11.103]F.J. Kleincr, M. Licwld: “Einsatz von CCD-Array-Kameras f i r optoelektronische Messungen in der Umformtechnik,” in: Umformtechnik Ideen. KDnzepre und Enrwicklungen, Teubner Verlag. Stuttgdrt 1992, pp. 243 - 260. (1 1.104) P. Haberacker: Digitale Rildverarheitung, Carl Hanser Verlag, Miinchen 1989. [11.105]I>. Weimar: “A Synthetical Sicual h v i r o n ment with Hand Gesturing and Voice Input,” Proceedings of CHI’ 89. May 1989, pp. 235 -
240.
I1 1.1061 D.M.Frohlich : “The Design Space of Interface,”
in Mulrimedia - Systems, Inreraerion and Applications, 1st Eurographics Workshop, Stockholm, April 1991, pp. 53 -69. [11.107]H. Marmolin: “Multimedia from thc Perspectives of Psychology,” in: Mulrimedia - Sysrems, Inreracrion and Applicalions, 1st Eurographics Workshop, Stockholm, April 1991, pp. 39-
52.
[ 11.108) R.Bogaschewsky, “Hypertext-/Hypcrmcdia-Systeme - Ein uberblick,” Informatik-Spekrrum 15
(1992)127-143.
[ I 1.1091 M. Miihlhauscr: “HypcrmedPa-Konreptc zur Verarbcitung muhimedialer Information,” Informarik-Spekrriitn 14 (1991)281 290. 11 I . 1 101 I). Roller: ”’lechnical Information System for Assembly, Test and Service Ilocumentation,” Proc. of (he 3rd Inr. Conf. on Hiitnuit Aspects o j Adr~airced Manufncruring and H,vbrid Auroniation, Gelscnkirchen, Aug. 1992. pp. 273 -278. [I1.1 111 A. Licbcram: “Expertensysteme in der Chcmischen Technik,” Cliem. Ing. Tech. 65 (1993)no. 4, 362 -363. I11 .I 121 11. Kumano: ”Learning with Interactive Multimedia by Plant Operators,“ IFAC Workshop on Producrion in Process Industry, DGsscldorf. March 19Y3,pp. 127 -134. [ 1 1.11 31 M. 2. Conklin: “Operator Performance Support System (OPPS),” Proceedings of 7echnologv 2002, Fcb. 1992. 11 1.1141 J. A. Mullens, L. C. Williams. W. L. Zabriskie: “Integrated 1)Pagnostic System for a Process Controller,” Inrernarional Marhinery Moniroring ruid DiuRnosrir Coi~erence,L u Vegas. Scptembcr 1989. [11.115]J. P.Hack, P. 11. Heldt: “Das Cockpit des Airbus A 310,”Spektrum der Wissennscliaft. March 1984.
38- 52. (11.116)Blaupunkt Gmbll, ALI. company brochure, Frankfurt 1984. [11.117]BMW AG. company brochure no. VS-202 IXi 83 311 500410, Miinchen 19x3. [11.11R]BMWAG,company brochureno.411070111-11 X4 VM, Miinchen 1984. [I 1.1191 Siemens AG. Trleperm M - Das ProzcDleitsys~cm fur
Kraftwerkc,
company
brochure
19100 E661-A2-V1,1984. [ll.l20]M. Polke, M.Wienand: “Anforderungs-
no. A
und Quali~lidtionsprofile,” Festschrift rum 60. Geburtstag von Prof. Dr. Stockmann. Karlsruhc. Oct. 197% [11.1211G . E’arber, M. Polke, H. Steusloff: “MenschProzeD Kommunikation.” Chem. h g . Tech. 57 (1985)no. 4 307-309. 111.1221 P.1. Burton, Cliem. Eng. (Riighy. Engl.1, March 1981. 103. [ I 1.1231 E. Rlass: “Methodische Entwicklung verfahrcnstahnischer Prozcsse,” Chem. lng. Tech. 57 (1985) no. 3, 201. 210. [11.124]W. E. Rixby, P. G. Andrus. L. E. Walkup, Photog‘. Eng. 5 (1954) 195- 201. (11.1251 1. T. Bickmore, R. E. Havford. H. E. Clark. I’hhocogr. Sci. Eng. 3 (1959) i20- 214. ,1261 L.A. Jones, J. Franklin-Insr. 187 (1920)29. ,1271 M. Polke: “Zur Physik der Elektrophotographie,” in: Festkorperprobleme, vol. VIII, Viewegj Pergamon Press, Oxford 1968, pp. 131 174. ,128)11. Rumpf: “ h e r die Eigcnschaften von Nutrslluben,” Sluub Reinh. Lirfr 27 (1967)3- 13. 1291 R. Polke. N. Scholr: ”Quality Control in Solids Process Engineering,” Purr. Characr. 2 (1985)39 -
48. [11.130]K. Ecker, H. Kramer, t1. K . Miillcr, M. Polkc:
“Neue Aspekte h i dcr PrGfung von Kautschuk und Gummi.” Kautsch. Gummi Kunsrsr. 1 (1972) 5- to. [11.131]E. D.Gilles, E. Nicklaus, M. Polke: “Sensortechnik in der Chemie, Status und Trend,” Autoniarisieritngsreckische Pruxis ( a l p ) 28 (1986)no. 9, 423 -431;no. 10,479-484.
[11.132] H. Tbpfer. H. BischofY: “Grundlagen der Prozeljanalyse,” KTD. Wisscnschaftlich-Technische Gesellschaft fur MeB- und Automatisierungstechnik, Lehrbricf 1 his 4, D r c d e n 1979. [11.133] J. Lauber, M . Polkc: ”Das Phascrmodell der Produktion als methodisches Hilfsmittel f i r die ProzeDcntwicklung,” GVC-Sir:itng Prozep- wid Anlugenteclinik. Oct. 1992, Wiesbaden. [11.134] M. Breitenfelder. M . Polke. M . Wienand: “Der Weg cines Produktes vom K o n e p t 7ur Kontrollc,” lecture held by M . Polke at the Arbcitstagung Fororsehutigs~nu~iagetiie~if in der Physik 9. 12. June 1976, Universitit Bielefeld. [11.135] M. Polke, P. Siara: “<:hemiefasern - Chanccn und Probleme in der Zukunft,” Vortrag anliljlich des Zukunftssymposiums Textil ”Mirkte morgcn“, 1975, Verband der Textilindustrie Westfalen, Schriftenrcihe Heft 22. [11.136] M. Polke, M. Wienand: ”Der Physikcr in der Industric . Ausbildung und Beruf,“ Phys. BI. 6 (1975) and l(1976). [11.137] R. W. Peters. M. Polke: “Verfahren zur Objektivierung der Studicnreform,” VDI-llauptgruppe Ingenieurausbildung. Diisseldorf 1972. (11.138j K. W. Peters, M. Polke: “Heruf und Ausbildung der lngenieure - kine empirische Untersuchung,” VIII-llauptgruppe Der Ingcnieur in Beruf und Gesellschaft, IIiisseldorf 1972. [11.139] H. Hillmer. R. W Peters, M. Polke: Studium, Beruf und Quulijikafion der Ingenieure, VDI-Verlag. Diisseldorf 1976. [I 1.1401 M. Polke: “Die Bcdeutung dcs Qualifikationsprofils des lngenieun f i r die Gestaltung der Studienginge in dcr Ingenieur-Aus- und Weitcrbildung.” FEANI-OECP-Seminar. Miinchen 1973. [11.141] H. Drahten, M. Polkc: “Anforderungsprofil und Qualifikationsprofil als Basis zur Ermittlung des Weiterbildungsbcdarfs - Ergcbnis einer Unifrage,” in G. Schmidt. H. SteusloB ( 4 s . ) : “Mit vcrnetzten, intelligenten Komponenten xu leistungsfahigcn MeD- und Automatisierungssystemcn,” Tagungshand hterkama-Kongrep 198Y. Oldenbourg Verlag, Miinchen, pp. 643-651. (11.1421 M. Polke: “Aus- und Weiterbildung in und an moderner ProzeBlcittechnik.” in W. Briickers, N. Meycr (4s.): Arbeit. Teclinik und herufliche Rildung im Merallhereich, vol. 3, Verlagsgesellschaft, Koln 1988, pp. 27 -53. [11.143] M. Polke: “ProxDleittcchnik in der Produktion Voraussctzung m r Diversifikation von Chemicfasern,” Leniinger Ber. 67 (1989) no. 8, 12 24. [11.144] K. H. Padberg, P.-Th. Wilrich: Die Auswertung von Daten und ihre Ahhangigkeit von der Merkmalsart. part 1, Qualitar und ZuverlLwiqkeit 26 (1981) 179 183. 111.145] IIIN 31 051, Regriffe und M a h a h m e n . Bcuth Verlag, Berlin 1985. [11.146] E Zutt. H.-P. liubich: “Sinnvolles Agiercn war friiher kaum mbglich; Praxisbeispiel. Instandhallung aus der Sicht der chemischen Industrie. Instandhaltungsmanagement der 90er Jahre,” in W. Schulte. <;. KiifYner (eds.): Rlick durch die Wirtschafr, Frankfurter Allgemeine Zeitung Gmbll. Frankfurt 1988. pp. 287- 298.
[11.147] R. Isermann: Model Rased Fault Diagnosis of Machines via Parameter Estimation Methods and IFAC-Symposium, Knowledge Processing,“ Badcn-Badcn 1991. [I 1.1481 H. Strickling ct al.: “Erstellung von F.rhaltungskatastern,“ company report, Payer, Leverkuwn FS 1975, PIX 1987. [ 1 1.149) W. Aleite. D. Wach: “Supervision and fault diagnosis,” IFAC-Symposiiim, Badcn-Radcn 1991. [ I I . 1 501 K.Kuxhncrus: “Die EDV-unterstiitzte Planung der Instandhaltung: lnstandhaltung als Teil dcr Anlagcnwirtschaft,” company report, Bayer A<;. Leverkusen 1988. [11.151] W. Minnel: “ M o d e r n s Instandhaltungsmanagcment: Anforderungsgcrwhte IH-Konzcptc mchrcn den Unternehmcnserfolg.” III-Markr, 1989, pp. 4 14. References for Chapter 12 112.11 IEC: Status and Rulcs of Procedure of the IEC. 01 (Central Ofice), 809. 809 A, July 1990. 112.21 M. Polke. B. Will: “Tradition und Fortschritt 40 Jahre X A MU R,” A uromarisierunqsrechnische Praais (arpj 32 (1990) no. 3, 105 109. References for Chapter 13 General References E.xperr Systems 113.1) W. Ahrens: “Einsatz von Expertensystemen in der PraProreDIei ttwhnik,” Aitromarisierun~.sre~hnische xis (utpj 29 (1987) 475-485. [13.21 W Ahrens: “Expertensystcme fur die Prozeljfuhrung, Erfdhrungen aus den1 Verbundprojekt TEXI,” Chem. Ing. 7ech. 62 (1990) no. 8, 635- 644. 113.31 K . Bernhardt. C. Koppermann, U. Miller-Nebler. K.-H. Wietschorke: “F:x.xpertensystem f i r betriebsnahe Problemstellungen.” Automarisierungsreclurische Pruris (arpj 31 (1989) 580 587. 113.41 H. Fittler: “Anwcndung von Expertensystemen in der ProzeUautomation,” Auromu/isierirngsfechnische Praxis (arpj 31 (1989) 74 -80. (13.51 W. Kaufmann (ed.): Diagnosfische Ennrscheidimngsprvzesse in der inneren Medizin, Schattauer, Stuttgart 1986. [13.6] R. Soltysiak: “Praktixbc Anwendung von Expertensystemen in der ProrcBleittcchnik.“ Airromatisiemigsiechnische Pra.xis ( a l p ) 32 (1990) 247 257. (13.71 R . Soltysiak: “HEPROX, ein Expcrtensystcmshell fur ProLeDfuhrungsaufgabcn.” Au/omari.sierirngsterhnische Praxis f a f p j 33 (1991) 74- 80. Fuzzy Logic 113.81 T. Mc Cuskcr: “Neural Setworks and Furiy 1.0gic, Tools of Promise for Controls,” Conrrol Dig. 37 (1990) May, 84 85. [13.9] B. Kosko: Neural Networks and F i c q Svsreins. Prentict: Hall. Englewood Cliffs 1990. [13.10] L. A. Zadeh: Fuizy Sers. Itiformu/ion and Control, 8 (1965) 338-353. [13.11] 11.-I. Zimmermann: I;u:z,v Ser Theory und Ifs Applications, 2nd ed.. Khmer-Nijhoff Publ.. Boston 1990.
450
15. References
Hypertext: Hypermedia
(13.121 I . Conklin: Ilypertcxt: A Introduction and Survey, IEEE Comp. Mag. 20 (1987) no. 9, 17-41. (t3.131 P. A. Gloor, N. A. Streitz (eds.): ”Hypertext und Hypermedia,” Informarik Fadiherichre 249, Springer Verlag, Berlin 1990. (13.141 P. A. Glow: Hj’per~dia-Anwendicngsenrnicklung, Teubncr, Stuttgart 1990. [13.15] ‘I; Nelsen: “A File Structure of thc Complex, thc Changing and the Indeterminate,” Proc. 20th Nat. ACM-ConJ 1%5, 84 - 100. Neural Networks
(13.16) K.Eckmiller, C. Hartmann, G. Hanske (eds.): Parallel Processing in Neural .$?stems and Compurers, Elsevier, Amsterdam 1990. (13.171 14. Ritter: Neuronale Netze, Addison Wesley, Reading, Mass., 1989. (13.181 C. Bretthaucr. G. Straube: “Einsatzmoglichkeitcn f i r Fuzzy-Rcgler,” Frauenhofer-Institut fiir Informations- und Datenverarbeitung, Dresden 1993. Specific References
(13.191 11. Balrer, V. Kirbach, V. May: “Knowledge based Process Control,” in: H. Steusloff, M. Polke (eds.): Integration of Design. Implemenration and Application in Measuremenr. Automation and Control, INTERKAMA Tdgungsband, Oldenbourg Verlag, Miinchen 1992. (13.201 D. Balzcr, V. May, U. Starke: “Wisscnsgestiilztc ProzeDfiihrung,” part 1 : Aictomarisierungstechnische Praxis ( a l p ) 34 (1992) no. 1, 36- 43; pan 2: Automatisienutgstechnische Pruxis (alp) 34 (1992) no. 2, 85-92. (13.211 D. Baker, V. Kirchbach, V. May: ”A Real-Time Expert System for Process Control,” Proceedings of the 2nd IFAC Workshop an Arrijicial Intelligenze in Real-Time Control. AIRTC ‘89, Shcnyang 1989. [13.22] B. Bohme, U.Starke: “Expert Systems in Process Control,” Proceedings IFAC Symposium on ,!.urge Scale Systems, Berlin 1989. [13.23] V. May, K.Wieland, B. Bbhme, D. Balzer: “PROCON-I ein Expertensystemshell zur Lbsung hiihc-
rer Automatisicrungsaufgaben,” Proceedings of the 33rd IWK. Ilmenau 1988. (13.241 V. May: ”Grundlagen und Entwicklung von Rahmen-Expertcnsystemen fiir Aufgaben der ProLeUfiihrung -das Expertensystemshell PROCON,” Dissertation, TlI Leipig 1989. [13.25] D. Halzcr, V. Kirbach: “Neuronale Netze in dcr Pro7.eDautomatisierung.” a f p 34 (1992) no. 7, 395 401. (13.261 N. Kunde: “Methoden und Werbxugc Lur Wissensakquisition in der ProreDautomatisierung.” Dissertation, TH Lcipzig, 1991. (13.271 U.Starke: “Eine Methodologie fiirdas Knowledge Enginecring in dcr ProreUsteuerung.” Dissertation, T I 1 Leipig 1990.
References for Chapter 14 (14.11 1. Rasmussen: “The Role of Hierarchical Knowledge Representation in Decision Making on Systcm Managemcnt,” IEEE Transaction on Systems Man and Cybernetics. vol. SMC-15 (1985) no. 2, 234243. [14.2] U. Epplc: “Modular Technology, Basis of an Object-Oriented Network Structure for Process Control Engineering,” GMA-Fachberichr 4, VDE-Verlag, Berlin 1993. (14.3) M. Nicse, EP 0104411, 1986. (14.4) A. Strickling: “CAE in dcr ProreBlcittechnik.” NAMUR-Statusberichr 1987, pp. 21 1 217. 114.51 P. Chen: “The Entity-Relationship Model Towards a Unified View of Data,”ACM Transucrions on Database Systems (1976) 1. [14.6] H. Robel et al.: Lehrbuch der chemischen Wfuhrenstechnik. VEB Deutschcr Verlag fur GrundstoITindustrie, Lcipzig 1983. [14.7] E. Cassirer: Suhstanrbegrifj und FunktionsbegrgJ Springer Vcrlag. Berlin 1910. [14.8] E. I>. Cilles, E. Nicklaus, M. Polke: “Sensortechnik in der Chemie - Status und Trend,” Aulomorisierungsrechniche Praxi.s l a t p ) 9 (1 986) 423 -43 1 : ia (1986) 479 484. [14.9] M. Polkc: Pro7eDleittechnik, Manuskript der Vorlesung. Univcrsitlt Stuttgart.
Process Control Engineering Edited by M. Polke copyright 0VCH Verlagsgerellrchafr mbH.1994
Index
A
ABB mastcr - functional structurc 232 Absorption measurerncnts optically integrating 158 Abstraction - class-forming, dcfinition of 41 3 - complex-forming, definition of 41 3 - functional, definition of 41 3 Abstraction principle - aggrcgation 19 - class-forming 9, 16, 24 - complex-forming 9, 19 - concept-forming 9. 18 functional 9 - functional abstraction 19 - model-aided measurement tcchniques 16 - models 15 - plant modcl 16 - simulation 16 - steps in process engineering 16 Acceptance testing - international standardization 120 - test conditions 120 Access authorixation 230 Accuracy of positioning 190 ACHEMA 388 Action 95, 106 automatic pcrformancc of 109 - modular form 109 - operating states 107 - planning and scheduling system 107 Action conccpt 97 Actor systems 66 Actual motor control 206 Actuating command 185 Actuating machinc - structure of 198 Actuating signal 185 Actuator 35 Actuator computer - diagnostic test 193 - local parametri/ation 193 - self-test 193
Actuator system 185, 190, 192, 200, 308 analogy with thc phase model of production 185 - complcxity of 185 - continuous 187 - coupling of 200 - discontinuous 187 drivc systcm of 196 - for clcctrical encrgy streams 187 - for material streams 187 - generic propertics 187 - in information-oricnted PCE 185 - intelligent 186, 187, 189 - powcr section of 196 - simple 186 structurc of 191 - taxonomy of 187 with auxiliary cnergy 186 Actuator system, continuous - integrating types 187 - proportional 187 Actuator system, discontinuous 197 Actuator system. taxonomy classification 193 - for energy streams 188 194 - for mass and energy strcams - for mass streams according to functional critcria 194 - for matcrial and cnergy streams 193 - for matcrial streams 188 Actuator systems, taxonomy - for elcctrical energy streams 189 Adaptive control 51 Adaptive process models 54 Adaptive systcms 391 Addressing - access viewpoint 246 - enginccnng viewpoint 246 - hardware viewpoint 246 - open communications viewpoint 246 - programming viewpoint 246 Aggregation 20 - definition of 413 A M A 383 Analysis of weak points 305 Analytica 388 ANSI 375
452
Index
Anthropometry 330 Antibodyiantigen systcms 165 Application layer 270 Application functions 268 Architectural principle - abstraction 9 - decomposition Y - PCE propositions for 15 - transformation 9 Archiving 255 Archiving functions (AR) 218, 232 Aristotle's doctrine of categories 9 Array principlc 143 Artificial intelligcnce (At) 42, 391 Artificial neural network 393, 395 - hiddcn units 396 - input units 3Y6 - lcarning 396 - nctwork structure 396 - output units 396 - pattern association 396 - pattcrn reconstruction 396 - working phasc (recall phase) 396 ASIC (application specific IC) 205 ASTM 315 Attribute - definition of 414 - nominal 117 Automatic - calibration 136 - definition of 414 Automatic cxccution - function, maintenance 127 Automatic maintenance techniques 189 Automation 168 - degree of 2, 110, 185, 218 -- field-levcl 315 - functions 218 - - of functions of the production control lcvels 315 - islands 5 - tasks 405 Automation, degrcc - dcfinition of 416 Automation cquipment 89 - classical mcasurcment and control dcviccs 90 Automation systcms - application arcas of YO Auxiliary encrgy 125 Availability 116, 328 - aspects 312 - state 105 Availability requircments - for protection of thc facilitics 313 - from product quality 313 - from the process engineering standpoint 313 - logistics-bascd 3 13 Axon 395
B Backup 314 Backup controllcrs 317 Backup operation 237 Backup systcms 21Y UAI 383 Balance equation 52 Basic engincering 3 16 function chart 31 7 - information flow and process control in 323 PCE stations 316 piping and instrumentation diagrams 316 - planning and design for PCE 316 -- planning and design for process and plant enginccring 316 - proccss control station data shcct 31 7 - proccss control station diagram 31 7 - station software diagram 31 7 - station symbols 316 Basic function definition of 414 Batch process 51, 108 Batch production process Y7 Battcry-backcd convcrters 21 1 Bellman's principlc of optimality 89 BET method 155 BIAS 388 Bidding spccification - brcakdown of 305 Binary control - binary signals 89 - combinatorial circuit 9 1 - conjunction 91 - disjunction 91 - in operations 89 - mathematical mcthods YO - memory clements 91 negation 91 timers 91 Binary logic functions 220 Binary state - rcprescntations of 249 Binding concept 294, 305 Bioclectrodcs 165 Uioscnsors 165 Biotcchnology - ccll physiology 164 - genetic cnginecring 164 metabolite design 164 process cnginccring 164 protein design 164 Block structure 342 Bodc gain -phase relation 78 Bodc plot 76, 77 - gain margin 80 - lag compensator of 80 lead compensator of 80 - phasc margin 80
Boka pcrformancc indcx 89 Boolean algebra 89 Hoolcan differcntial calculus 91 Bottom-up principle 225 Box-Hill method 58 Brcakdown - definition of 414 - of a process in the phase modcl 96 99 - of plant and process cquipment Ib-oadcasting 266 Buffer 336 Huilding-block modcl external interfaces 223 - implcmcntation 223 - nesting principle 223 types. rolcs, and functional units 224 Bus field 34 systcm 34 Bus cablc 180 Bus systcm - redundant 237 - linking of SPCs and 221 Bus topology 275 Bus-type structure 228 Busbar distributor 21 1
C
C + + 391 Cabinet tcrminal allocations 286 Cable 175 - high-voltage 183 - placement of 176 Cablc, master 175 Cable network in industrial plants 21 1 meshed network 21 1 radial design 21 1 Cablc penctration - to the switch room 184 Cablc pit - chimney effect 183 Cable route 172 in danger zoncs 182 - in zones of hcightcncd firc hward 182 - insidc proccss control rooms 183 - thermal load on 183 - threc-dimcnsional 184 Cabling - convcntional, parallel 178 Cache memory 403 CAE methods, 3 D 321 CAE system 207 acceptancc and efficiency 286 cablc plans 285 - compatiblc interfaces 284 - dcvice data in 285 - hardware and software design 286 - ideal structurc 285
Installation 286 position data in 285 procurcment 286 setup plans 285 - station data in 285 - technical specifications in 285 - terminal allocations 285 CAE system, combincd - alphanumeric tasks 287 graphical tasks 287 - master cablc connections 287 - process control stations 287 - station list 287 - structurc 287 terminal allocations of cabincts and racks 287 text placcholdcrs 287 CAE systcm, intcgratcd coupling of graphics and databasc 288 multitasking operating systcm 288 - structure of 288 Calculus of variations 89 Call - definition of 415 Capacity 328 Cassirer schemc IS Causal model 36 CCITT 376 CeBIT 388 CEN 375, 376, 377 CENELEC 375, 376, 377 - mcmber countries of 379 - organizational chart of 379 Central service 199 CEPT 376 Chain-length distribution 164 Channel Failure 252 Channel level 105 Charts - bar 301, 303 - Gantt 303 - nctwork 303 Chlor-alkali electrolysis 35 Chromatographic methods 164 CIM (Computer-integrated manufiicturing) 1 CIM (Computer-integrated manufacturing) 264 CIP 1 Circuits - cable ducts 176 - conductor bundles 176 - - conduits 176 - maximum power available on 176 - safety barriers 176 - with intrinsic safety 176 Class - definition of 415 Client portions 233 Client-server principle 353 Cluster analysis 350 C N M A (Communication Network for Manufacturing Applications) 274
454
Index
Cognitive psychology 344 Cognitive stress 339 Collective propcrty 133 Collisions 266 Command mode - definition of 415 Commercial process control system - component structure 232 Commissioning 258 opcration with “inert” mcdia (“watcr run”) 327
preparation 327 - structural model of 257 Commissioning chccklist 321 Committecs - technical and scicntific 382 Communication - as a system scrvice 245 connector-to-connector 246 - internal 246 Communication, bidircctional 172 Communication cost - determination of 138, 139 Communication equipment central 329 Communication, human machine 340 Communication, human-proccss 9, 166, 328, -
329, 337
state-oriented 351 Communication, man-process 340 Communication model 24 Communication network - connectors 244 - “get” relation 244 Communication. opcn 246, 270, 276 Communication system 220, 264 - consistency conditions 265 - fault tolerant 265 - Geld-level 138 - flexibility 265 - redundancy 265 Competcnce 340 Complex modulus 119 Componcnt structure 226 Computer-aided control systems design 86 Computer-aidcd management tools 304 Computer-aided methods for design, erection, operation, maintenance of processing facilitics 283 Computer network 264 Computer tomography 144 Computerized revision service 65 Concept, methodological - object-oriented formulation 9 Conceptual proccdurc 306 Conceptual work 293 Conformance test 277 Conformity tests 268 Connection layer 270 Connector 109 -
- dcfinition of 415 Consistency check data-model-oricnted 324 Continuous process opcrating modes 108 Continuous process. discretized 109 Contract 321, 322 Contract administration 301 Control - adaptive 85 - binary 73 - dcfinition of 415 - external 73 - fccdback 73 - optimal 73 Control building, frecstanding 329 Control circuits - clcan scparation of 206 Control command 1YO Control components - dimensioning 31Y Control concept - hierarchical lcvels 74 Control device 191, 198 .. design of 286 Control drive 192 Control element 192 - for mass streams 195 Control element, continuous mass or energy flow 190 Control element, switching 190 Control enginecring 2 Control, feedback and fcedforward 74 Control functions (CO) 232 - definition of 423 Control hardware 217 Control hardware monitoring functions 255 Control & Instrurncntation 388 Control loop frequency response 76 lincar controller 76 - multivariablc systems 79 - noisc suppression 76 - open-loop shaping 78 - operating point 76 - robustncss 78 - sensitivity function 77 - stability 76 Control machine 196, 198 Control model - hierarchical 101 Control module specific to motors 228 Control operations, recipe-modc 321 Control panel 222 Control pathway 190 Control room 329 - acoustics 331 - ambient conditions 331 - arrangement of 329
cable duct installation 332 ceilings and walls 331 climate control 331 - desks 332 - doors and windows 331 - double floor 332 - hard-copy devices and printers 333 - lighting 330 - operator station 332 - process communications wall, furniture, and accessories 333 Control room dcsign 328 Control signal 190 Control signal processing 185 Control system 21 7 - application-oriented 93 - decentralizcd stored-program controller system 220 - electrical and mechanical design in 240 - external functional vicwpoint 218 - first-generation 228 - function-oriented 93 - hierarchy-oriented 93 - ideal structure of 230 - information model 228 - integratcd decentralizcd process monitoring and control systcms 220 - intelligent central systcms with dumb peripherals 220 - internal structural viewpoint 218 -- mathematical process model 75 -- schcmatic diagram of 308 - sccond-generation 228 - sctpoints 75 - signal-oriented 93 - steady-state difference 75 - structural models 222 - structure-oriented 93 - structuring of 223 - technology-oriented 93 Control system, decentralized - definition of 416 Control system, distributed 213 Control system elements 99 Control trajectory 74 Control unit - action as 108 - hardwired 314 Control unit, group 103 Control value - definition of 415 Control valve 191, 195, 200 - for design and installation 193 - structure of 191 Controller - docurncntation 93 - fixed-programmed 92 - rcprogrdmmabk 92 - simulation 92 - specification 92 -
-
Controller design methods adaptive and nonlinear control 85 - classical control enginecring 79 - classification 81 - decentralized and hierarchical control 85 - multivariablc control 81 - optimal strategy 82 Controller. pogrammable logic (PLC) 90 - function block diagram 92 - instruction list 92 - ladder diagram 92 - linear step scqucncc 92 - sequential function chart 92 structured text 92 Controller, sclf-tuning 85 Controller, stored-program 92 Controlling element - structure of 198 Controlling systems 188 Converter 198 - frequency 202 Converter, switchgear 205 Coordination functions (CD) 233 Copper wire 180 Coriolis methods 171 Corporate management 264 Correlation 365 Cost analysis 315 - for the implementation of high-level functions 316 Cost/benefit analysis 305 Cost brcakdown - for thc implementation of field-level functions 315 Cost estimation - by the multiplication factor method 300 Cost managcmcnt 299 Cost of ownership 140 Cost structure - of a chcmical plant investment 302 costs - engineering 299, 300 - erection and installation 299 - procurcment 299 - start-up 299 Critical paths 302 Cross-correlation analysis 351 Cross-linking - degree of 164 CSMAjCD 266 Current source inverter (CSI) 204 -
D Damage-limitation features 312 Danger 116 Darlingtons 204 Darwin 9, 18 Data acquisition system 49
456
bides
Data-basc design conceptual levcl 6 - logical lcvel 6 - physical level 6 Data basc. object-oricntcd 392 Data basc. relational 17. 285 Data-basc systcm - hierarchical network 7 rclational type 7 Data dcsign 6 Data dictionary 7, 284 Data flow 284 Data independence 7 Data integration 7 Data Interchange Format 49 Data managcmcnt 284 - modcl of 232 Data matrix - as interfacc 49 - for observations 46 Data model 7, 284 - consistent data modcl for hardware objects 64 - entity-relationship (ER) modcl 5 , 26, 65 - flowshects 65 - information blocks relating to process plants 65 normal forms 5 - object-oriented managcmcnt of data 64,65 - process models for process control 65 - rccipe procedurcs 65 Data model, company 286 Data modcl, corporatc 24 - ISO-OSI rcference model 21 - levels 27 - process modcl 27 Data model, object-oriented - for thc plant 324 Data-model-oriented planning 295 Data model, semantic 13, 18 Data modcling techniquc 6 Data module 242 Data sheet 24. 321 - for an enamelcd stirred tank 25 Data storage, decentralixd 291 Data structure analysis 6 Data structuring - abstraction relationship 13 - attributes 13 - data-oriented 13 - mapping 13 - object-oriented 13 - relationships 13 dBase 225 Decentralized storcd-program controller (SPC) system - handling intcger and real variables 220 - scrial bus systems for data exchange 220 DECHEMA 388 Decision phasc 304 Decision tables 8
Decomposition principlc design principles 10 - implcmcntation principlcs 10 - type of structure 10 user intcrfacc dcsign 11 Default contcxt 279 Dcyecs of frecdom 44 Dclivcry times 322 Dcndritcs 395 Description, logic-oricntcd algebraic specifications 22 combinational circuits 22 - decision tablcs 22 fourth-gencration languages 22 - logic diagrams 22 - logic-oriented languages 22 Description, scqucncc-oricntcd flowcharts 22 FORTRAN and PASCAL 22 - Michael Jackson diagrams 22 - Nassi -Shncidcrman diagrams 22 - pseudocode 22 - SADT diagrams 22 - structurcd text 22 Description, state-oriented deadlock 22 - Petri nct 22 simulation 22 statc graphs 22 -- transitions 22 Descriptive data 47 Design of a control structurc 224 list of requircmcnts 256 - project specification 256 - rolc systcm 256 - structural model of 257 Dcsign database 354 Design crrors 322 Design specifications 200 Design standard - for intrinsic safcty 181 Design study 305 Design system 168 Detail engineering 320 phases 323 Detcrministic model cornponcnt 82 Development environmcnt 402 Device 14, 24, 64 - input 343 Devicc, compact 171 Dcvicc configuration 200 Devicc control function - definition of 416 Dcvice Description Languagcs (DDL) 132 Dcvicc, discretc (control) - definition of 416 Devicc field-lcvcl - clectromagnetic compatibility (EM) 136 -
Index Device object tree for proccss control sysfcms at the field levcl 20 1 Diagnostic fcaturc 132 Diagnostic routines 132 Diagnostic rulc - cxamples of 402 Diagram - circuit and configuration 321 - mimic 321 Diagram, functional 61 - station (loop) diagram 66 Diagram gcneration - synthetic 290 Diagram, schematic 61 clectrical 62 Iliagram, station (loop) 62, 68 Dicscl engincs clcctronic control of 150 - optimization of 150 Digital data - semantics 266 Ilimensional analysis 59 DIN 375, 376, 380 DIN measuring bus 139 Dircct manipulation 11 Dircctory Scrvice (DS) 268 Discrete-dcvice technology 222 Display 342 - frce 342 standard 342 Distributcd “intelligcnce” 113 Distributcd process control systems 222 DIVA simulation system 86 DKE 375, 376, 380 DKE FB9 376 - areas of interest of 381 DOC (dissolved organic carbon) 160 Documcntation 325 Documentation forms - clcmcnt structural plans 285 - function charts 285 - logic diagrams 285 Documcntation, retrospective 8 DOS 225 Dosing of materials 203 DPG 383 Drive 196 as system clcmcnts 206 - diaphragm 190 - information structurc of 206 - piston 190 - rotary 190 - scwo 190, 191 - size of 205 - standardizing 208 - type of 204 - variable spccd 203 vector-controlled 205 Drive, electrical -
457
areas of use 203 dcvelopment of 202 - variable spccd 202 - world market for 202 Drive system - delivery tests 207 - engineering specification 207 - planning of 207 requircment specification 207 DVT 383 Dynamic data cxchange 353 Dynamic entropy analysis 143 Dynamic link library 353 -
-
E EEG 145 Eigcnvalues 83 Eigenvcctors 83 EKG 145 Elastic (Hookcan) solid 118 Elcctric powcr supply - adcquate capacity undcr normal conditions 209 availability of 21 1 - availability requircments 209 “consumer” levcl 209 distributed gencration 21 1 - for a wastewatcr treatment plant 213 implcmcntation of safety 212 - implcmcntations 210 - levcl model 209, 210 - principle of cogeneration 210 - protection against elcctric shock 213 protection against failure 213 safety of electrical equipment 210 - splitting of the power demand 210 “transport” lcvcl 209 Electric power supply, standby 212 Electrical dcsign - bursts 241 - clcctromagnetic compatibility 240 clectrornagnetic fields 241 environmental conditions 241 -- hybrid voltagc transicnts 241 isolation, earthing concept 240 powcr supply 240 protective enclosures and safcty rcquirements 240 - static discharges 241 Elcctro-viscous clutch 208 Electromagnetic interfcrcncc 231 Electronic frcquency converters - for motor speed 189 Elemcnt - definition of 416 Elcmentary molecular motions 163 EMC (Elcctromagnetic compatibility) 205 Emission calculation of impacts from 162
Emission measurement 157, 162 Emission rcgister 162 EN 378 I.hcyclopcdic treatment 133 Energy - availability of 185 - auxiliary 190 Energy channel - separation of 133 Energy controlling systcms - positioner 189 -- switch 189 Energy flow 114 Energy storagc 194 Energy supply - auxiliary 186 Engincering data - consistency of 68 Engineering functions (EN) 232, 233 Engineering port 128, 129, 130. 131 Entity - definition of 416 Entity- rclationship (E/K) model 6, 8, 134, 2!84, 310 - definition of 416 Entry-lcvel version 227 ENV 378 Environmental compatibility 295 Environmental safety 1 15, 170 Equipotential bonding 176 Ergonomics 328, 329 Error bound 78 ETSI 376 Euclidcan vector 79 European standards 377 Ex ‘5” field bus 181 Exact linearization 86 Execution phase 315 Execution planning 325 Execution unit definition of 417 Expenses 300 Experiential knowledge 393 Expert plant operator 123 Expert system 6, 391, 393 - comparison of neural network with 398 - integration of 399 -- structure of the knowledge base 410 Explosion groups 175 Explosion hazard conditions 192 Explosion protection 137 Extendcd list of control software 86 Extrapolation 367
F Factor analysis 349 Fail safe 187 Failure strategics 127 Fairs 388
Fault compcnsation 136 Fault dctcction 136 Fault reporting 136 Fault tolcrance 266 FDDI 232 Feasibility study 301. 304 - topics covcrcd by 304 Fcc - regulation governing ‘architccts’ and ‘enginccrs’ 301 Feedback control 74. 75 Feedback loop, linear 77 Feedback paths 91 Fcedfonvard control 74 - open loop 75 Feldbaum’s theorcm 89 FEM methods 207 FETh (Ficld effect controlled thyristor) 204 FlACC 384 Fibcr-optic endoscopy 145 Fiber technology, optical - advantages 180 FICIM 276 Field bus 128, 173. 178. 206. 228, 266 in explosion hazard areas 181 Field bus, bidirectional 199 Field bus standard, ISA SP 50 international 132 Field bus system 138, 228, 269 architcctural features 139 - classification of 229 - communications structure 139 - mastcr/slave structure 139 - monomaster systems 139 - multimastcr or peer-to-peer systems t 39 - taxonomy of 138, 139. 140 Field bus technology 172 Field device 270 Field distributor 175 Ficld effect transistor (FET) 204 Field installation 171, 172 - cost analysis for installations 178 - dcfinitions and terms 177 - devclopmcnt of 174 175 - in explosion hazard areas - in the United States 177 - of safety circuits 180 - typCS Of 173 - with a field bus 179 - with field multiplexcr 178 Field lcvel - shift of intelligence into 186 Field-level communications 132 Field-level component 310 Field multiplexer 173 - hardware 178 - twisted pair 178 Field systems, intclligent 170 Field unit 207
Filtcr control 228 1;inal control clement 185, 191 Final installation inspection 326 Final report 328 Finitc elements 51 Finitc-statc automata 8 FIP (1;actory instrumcntation protocol) Fixed spccd motors 202 FLAVOR 391 Flipflops - edge-triggercd 91 - levcl-operated 91 Floating-point value rcprcscntations of 249 Flow cytometry 165 Flow diagrams 90 Flow injection analysis 165 Flowchart 6, 15 Flowmeter 200 Flowshcet - piping and instrumentation 316 Flowshcet, basic 34, 69 Flowshcet tcchniquc 341 Formal decoupling 96 Formal qucry languages 392 FORTRAN 391 Fouricr series expansion 59 Fourier transformation 348 Fracture strain 119 Framework concept 305, 31 1 - - dcfinition of 417 Framework, objcct-orientcd 67 Frequency convcrter 204 Frequency domain methods 81 Frequency response 57 modulus (magnitude) 76 Nyquist locus 76 phasc arg 76 - polar decomposition 76 - polar plot 76 Frequency response method 57 Function - definition of 417 Function. basic 38, 68 Function chart 38. 67, 290, 319, 321 - cxamplc of 321 - largc-scale 311 - via Petri ncts 39 Function diagram 6 Function clemcnts, basic 38 Function maintcnancc 186 Function model 284 Function monitoring 130, 186 Function, technical 68 - instrumcnt types 71 Functional abstraction - internal strength 21 - levels 20 - locality 21 - sLurecy principlc 21
275
Functional chart 23 Functional design 6 Functional element - definition of 417 Functional elcmcnt, basic - dcfinition of 414 Functional modulc 242 Functional modulc type - dcfinition of 258 - feasibility 257 Functional ncts 8 Functional programming 391 Functional role system - location 258 - logical card slot 258 Functional structure 226 - ABB MOD 300 233 - AEG Ceamatics P 233 - Bailey INFI 90 234 - Eckardt PLS 80 E 234 - Fischer & Porter DCI systcm SIX 235 Fisher Controls PROVOXplus 235 Foxboro IAS 236 - H & B Contronic P 236 - lloneywcll T I X 3000 237 - Roscmount Systcm 3 237 Sicmens Telcperm M 238 - Wcstinghouse Controlmatic WDPF I1 238 - Yokogawa Centum XL 239 Functional structuring 12. 14 - dcsign laws 13 Functional system - application level 260 - base system levcl 260 - hardware lcvel 260 - hicrarchical structurc of 260 - process control level 260 260 - with scveral unavailable units Functional unit - availability of 259 - control clemcnts 101 - control units 100 - definition of 418 - grouping of 100 - individual 101, 102, 104 - plant (equipment) units 100 plant scction 102, 104 - type Of 224 Functional unit, engineering information exchange with 131 - maintcnancc function 131 Functional unit, group 106 - basic functions 102 - control hierarchy 104 104 - operational plant control system - plant cnginccring resources 104 Functional unit, local - information exchange with 129 Functionality
definition of 418 Functions archiving 277 auxiliary 127 - central operator 227 - classical 127 - diagnostic and monitoring 149. 227 - cngineering 227 - entity relationship 15 cvaluation and intcrpretation 227 logging 227 - mobility 149 - production control 227 - rccipc management 227 safety 149 Functions, intelligent - cxtraordinary proccss states 127 Fundamental dilemma 77 F u u y clustcring mcthods 166 Fuzzy logic 42 - linguistic variable 392 - potcntial applications of fuzzy control - thcory of 392 F u u y rule 393 F u u y systcrn 393
GTO (gatc turn-off thyristor) 204 384
cv<: H
392
C Galcrkin mcthod 52 Gasoline engines - cngine clcctronics for 150 Gatc-turnoff (GTO) dcvice 189 Gateway functions (GA) 232 Gaussian mcthod, lincar quadratic 81 General message cxchange system 246 General principlcs concerning qualitics, units and symbols 115 Generic categories 17 Ceomctrical variables 142 Gershgorin bands 83, 84 Gestalt psychology 346 Get rclationship 244 GI 383 G K S 352 <;MA 256, 380, 383 - areas of activity of 383 - organizational chart of 385 GMD 383,384 Graphical devicc 352 Graphical documentation - automatic 290 - retrospective 290 Graphical operation 286 Graphical tcchnology 352 Graphical tool 286 Graphical workstation 291 Graphics systcrn 354 Graphics technique 352 Group control - definition of 419
Handheld tcrminal 186 I Iangcr design 183 Ilannovcr Mcssc Industrie 389 llardware interchangcability of 199 Hardware diagnostics 255 - uscd by manufacturing firm 256 - uscd by on-site tcchnician 256 Ilardware redundancy 219 Harmonization documents 377 HART protocol 127 HD 378 Heater control 198 Hct instrumcnt 389 Heuristics - dcfinition of 419 IIierarchical structural model - basc systcrn level 225 - communications protocols 226 functional level 225 operational levels 225 process control 226 process control lcvcl 225 - virtual machincs 225 High-frcquency disturbance 206 High-spccd motor concepts 207 HIP0 8 HPLC mcthods scheme of 164 Human role 338 Human scnses 142 Hurwitz criterion 56 Hybrid system 90 Hypermedia 133. 356 Hypcrmedia concepts 138 IIypermedia system 392 Hypertext 356 I Iypcrtext/hypermedia - arbitrary links 392 - associative searching 392 - basis for 392 - information nodes 392 I I ypcrtext systcm - tcchnique of 392 I Icon 11 Identification codcs - systcm of 309 IEC 256, 375, 376 - structure of 377 IEC 721 241 IEC TC65 376, 377 structurc of 378
375 IFAC 383. 384 - organizational chart of 384 IGBT (insulated gate base transistor) 204 ILMAC 389 Image function 56 Imaging techniqucs 143 I M C (internal modcl control) 84 -- structure 84 IMliKO 383. 385 member socictics of 385 Implantablc dcvicc 147 In-plant product logistics 98 Incoming inspcction 326 Independent standard programs 286 Indcx reduction mcthods 53 Individual signals 348 Industry standards 375 INliLTEC 389 Inference machines 391 Informatics 266 Information - acquisition 115. 124 - economy 115 - feedback 284 - handling 264 - hiding 15 - hierarchy 263 - integration 5 - pathway 264 -- processing systcm 171 - reduction 348 storage systcm 264, 351 structure 5 , 310 - systcm management 5 - task-relevant 344 - technology 1. 391 -- transformation 124 Information channel - scparation of 133 Information flow 36. 284 - associativc linking 114 - for production of a chcmical material 163 - parallel 114 - serial 114 Information flow top to bottom 290 Information logistics 280 - distribution 263 - modeling 263 - retricval 263 storagc 263 - tools 263 Information model 72 - conceptual 5 -- corporate 5 - quality of 117 - safcty 117 - semantic 5 Information-oricntcd process control cnginecring 187 1Elil;
Information proccssing movement of. near the process lcvcl 130 lnformix 225 Input modulc 228 lnspcction - dcfinition of 419 - incoming 326 - of process control enginecring documentation 325 sampling 325. 326 resting 324 - visual 326 Installation - execution 322 in control rooms 322 inspection 322 of cable trays in the field 322 of process measurement and control devices in thc field 322 - preparation 322 - standards 326 - supervision 301 technology 171 Installation activities supcrvision of 326 Installation configuration plan example of 327 Installation layout diagrams 326 Instance - dcfinition of 419 Instant analysis 72 Instruction acceptor - dcfinition of 419 Instruction input connector 245 Instrumentation cost - determination of 133, 134 INSTRURAMA 389 Integral analysis - proccss facilities of 72 Integrated Scrviccs Digital Network (ISDN) 267 Integration - of electrical and nonelectrical subfunction 168 - spatial 168 Intelligent ccntral systems - with unintclligcnt peripherals 222 “Intelligcnt” systems 172 In terfacc - consistcnt 283 - dcfinition 263 - human dcvicc 199 - menu-bascd operator 199 - opcrator and monitoring 218 - structuring of 131 Interface, human -computer 7 Intcrface, human-device - structure of 199 Interface, man - machine 340 Interface, operator 222, 230 Intcrfacc, standard order 105 Interface, standard status 105
Interference, low-frequency 206 INTERGRAPII 283 lntergraph plant design system 295 INTERKAMA 389 Internal (computer) model 286 Interoperability test 277 Interpolation 365 Intrinsic safety 181 Inverter 208 Inverter. matrix 208 IP (International protection) 136 ISA 26, 375, 382 ISA show 389 ISAC 8 I S 0 375, 376 I S 0 standards 375 J Jackson structured programming (JSP) 8 Jackson system development (JSD) 8 Japanese industrial standard 66 Job plan, partial 63 Jones diagram 359
K K value 163 Kalman-Hucy filter 53, 82 Kalman filter 121, 122 Kant’s concept of system 9 Karhunen-Loevc transformation 349 Knowledge acquisition 391 Knowledge base - declarative 400, 402 - functional 400 - knowledge specifications 400 - modularization principle 400 - procedural 400, 402 - structure of, in expert system 410 - taxonomy 400 Knowledge base, technology-oriented 409 - structure of 409 Knowlcdgc-based system 393 - algorithmic intcgration of 398 - linear modcls 398 - phasc model of production 398 - technical integration of 399 Knowlcdgc engincering 403 - four-phase concept of 404 - intcrdisciplinary cooperation 404 - knowledge acquisition 404 - knowledge extraction 404 - theoretical and experimental process analysis 404 Knowledge engineering, four-phase concept - acquisition phase 405 - definition phase 405 - maintenancc phase 406 operationaliiation phasc 406
Knowledge input - structure editor for taxonomy input Knowledge inspection 403 Knowledge transformation 403 Knowledge verification 403 Kondratieff cycle 1 waves 1 Kuhn coil model 119
402
Laboratory analytical techniques 143 1.aboratory mcasuremcnt technology 113 Ladder diagrams 90 Lagrange index 89 Lagrange performance 89 LAN communications modules 245 LAN (Local area network) 266 LASER 389 Lascr scanner 155 Level model 21 of production 306 Lighting control 198 Lightning protection practiccs 137 “Limp home” function 149 Linguistic lcvel 105 Link converter 198 Link laycr 272 Linnaean systcm 9 LISP (list processing) 391 Local componcnts 227 - central 227 - ccntral unit of 228 - periphcral 227 Local data safeguards 243 Locus curve mcthod, characteristic 82 Locus curvcs, characteristic 83 Logic control dclinition of 419 Logic programming 391 Logical operations - elementary 91 Logical m o m 353 Logistical model 24 Logistics 312 Loop transfer recovery 82 Lower tcstcr (LT) 277 LQG/LTR design techniquc 82 Luenbcrgcr observer 45, 53, 82. 121, 122 Lyapunov function 399 Lyapunov stability 43
iM Machine time 320 Magnetic induction flowmeter (MID) Main wing - brcakdown of 329 Main wing layout 328
125
Index Maintenancc breakdown 374 brcakdown strategy 371 correctivc 370 - dcfinition of 420 - forccd repairs 370 - inspection 370 - inspection-oriented strategy 372 - prcventivc strategy 372 - routine 370 - scheduled 236, 370, 371 - scheduled corrective strategy 372 - strategies for performance 371 Maintcnance control loop 373 Maintcnance model 24 Maintenance plan 72 Management level 263 Management level, corporate 27 data bascs 29 - levcl model 29 - logical requirements of intcgration 29 - product orientation 29 - supra-departmcntal view 28 - task-oriented application systems 29 Management Icvel, production 29 - assembly process 30 - CIM 30, 33 - CIP 33 - delivery schcdule 30 - demand disposition 31 - information and report management 32 - inventory disposition 32 - inventory keeping 32 - level-specific relations 30 manufacturing industry 30 mass production 30 - process control 31 - process industries 30 - production control 31 - production planning 31 - warehouse control 32 - warehouse management 32 Management, multi-projcct 304 Management, object-oriented 66 Manufacturing cngineering 264 MAP (Manufacturing Automation Protocols) 267, 268 Market 361 Market requirement 362 Marks 23 MASCOT 8 Mass-transfer coeflicients 165 Master-slave structures 11 Matcrial flow 36, 114 - for production of chemical material 163 Material-identifying spectroscopic methods 164
Material properties phenomenological theory 118 - technological behavior I 18
Mathematical model 50 apparatus-specific part 51 of distillation columns 52 - physicochemical part 51 Maxwell bodies 118 Maycr pcrformance index 89 MCT (MOS controlled thyristor) 204 Measurement - “contactless” methods 170 - flow ratc 170 - in fermentation 165 - temperature 170 Measurcment, ex-line 163 Measurement, in-line 155, 163 Measurcmcnt, invasive - of chemical quantities 147 Measurement methods model-supported 168 Measurerncnt, on-line 133, 155 - in polymer processes 165 Measurement, physiological 143, 145 Measurement, sin&-point 168 Mechanical design 240 Membership function 393 Memory elcments 91 Memory requirement 320 Mental activities 345 Menu trees - hierarchically structured 286 Message - coding 347 distribution 254 function monitoring 132 handling 252 - maintenance required 132 - transmission 254 Message, content 253 Message, failure 132 Mcssage generation, centralized 253 Message gcneration, decentralizcd 252 Message receiving archive unit 254 Message -~system - with centralized mcssage generation 253 - with decentralized message generation 253 MESUCORA 389 Metering clemcnt 200 Method of finite elements 51 Method of multiplication factors 299 Metrology - laboratory 123 - proccss 123 Michael Jackson diagram 22 Microsystems technology 168 MIT X-Windows system 291 MMI function 247 MMI server functions 247 MMI services 246 MMS communications objects 133 M MS (Manufacturing Message Specification) 132, 273 -
463
464
Index
,Mode control - automatic 93 - manual 93 - semiautomatic 93 Model - dcfinition of 420 - heuristic 393 mathcrnatical 393 simulation 393 - situational 393 - thought 393 Modcl-aided mcasuremcnt proccdurc 121 structure of 122, 123 Model-aided mcasuremcnt proccdurcs 120 Model. object-oriented 132 iModel predictive control, nonlinear 86 Model support, information-oriented 165 Modeling - coordinate transformation conccpt 58 - model accuracy 58 - model reduction 59 - order reduction 59 - principal-componcnt analysis method 59 state-space 59 - structure of the modcl 58 - - type of modcl 58 Modeling tcchniquc 134 Modular approach - object-oricntcd 241 - proccdure-oriented 241 Modular architecture 326 Modular conccpt, object-oriented 243 Modular intelligence 241 Modular model - - closurc of modulcs 109 - data cxchangc through conncctors and links 109 modulc-specific parameters 109 - modulc state information 109 - one-lcvel abstraction hierarchy 109 - support of thc type conccpt 109 -. target system 109 Modular switchgear 21 1 - usc of 211 Module -. definition of 420 Module processing - connector-specific methods 247 - cyclic method calling 247 Module typc - instances of 243 Molecular-mass distribution 164 Molecular propcrtics 164 Monitoring 248 - cmission 158 - maintenance of state image 249 methodology of wastcwatcr 161 - of gaseous pollutants 158 - of softwarc design 326 - state displays 249
Monitoring concept 310 Monitoring sensor continuous analytical monitoring 159 - flue-gas 156 - Monitoring of combincd scwcr water 159 - process wastewater monitoring 159 - wastewater 159 Monitoring scrvice - schcmatic diagram 248 Motor. a.c. - flux control 208 Motor control 198 - adaptive control 208 Motor. high specd 208 Motor, integral - applications 208 Motor interlock - malfunction of 252 MS Project 304 MTBE' (mean timc bctwccn failurc) 205 Multi-sensor technology 115 Multilayer nctwork - with onc hiddcn layer 397 Multimedia rcsourcc management 356 Multimedia technique 355 Multiphase chemical substances 119 Multiplexcr 266 Multisensor systcm - fuzzy logic 167 - pattern recognition 167 Multiscnsors 166 Multivariable systcms - complementary 79 - multiple manipulated variables 79 Multivariate method 369 Murphrce effcicncy 52
N NAMUR 2, 26. 228, 276, 375. 376, 380, 381 - activities of 382 - organizational chart of 381 NAMUR recommendation 115 Nassi -Schnciderman diagram 22 Navigation 133. 356 NERES 121 Nesting pnnciplc - dcfinition of 420 Network - local 266 - open 266 Network charting 302 Network data distribution model 232 Network manigemcnt 270, 273 Network Management (NM) 268 Network structure types of 12 Networking 265 Ncural nctwork back-propagation 397
1ndc.x
classification 39 1 comparison of expert systems and 398 forward propagation 397 - integration of 403 - learningphase 392 - successive corrcction of the wcightings 397 - transfcr functions 397 Ncural network algorithms 403 Neural works professional I1 403 Neuron 395 Neuron, artificial - functional diagram 396 Neuron, biological 396 Neuronal structure - of organisms 166 Ncwtonian fluid 118 Nile programming 391 Noisc emission 192 Noisc pollution control 162 Non-monotonic infcrencc 391 Novel software technologies 392 Nuclear magnctic rcsonance tomography 144 Nuclear medicine 144 Nyquist array method, direct 83 Nyquist array method, inversc 84 Nyquist criterion. multivariable 79, 83 Nyquist locus 76 Nyquist stability critcrion 77
0 Object - abstract 13 - concretc 13 - dcfinition of 420 - instancc 243 - properties 17 - type 243 Object idcntification 17, 309 Object instances 18 Object-orientcd programming 241, 391 Object, types of 17 Object world logistics 24 - maintcnance 24 - plant cngineering 24 - process communications 24 - process control enginccring 24 - process enginccring 24 Objcctivc - overall 283 - partial 283 Observer models 113 Occupational safcty 170 On-site assessment 132 Ontological analysis 391 Open network 267 Operating handbook 321, 322 Opcrating modc definition of 420
Operating point 85 Operating state 105 Operating system 241. 245 - asynchronous operation 247 execution propertics 247 Opcrating valuc - dcfinition of 421 Opcration conccpt 310 Opcrational conditions - types of 312 Opcrational control - logistical product management 93 - maintenance 93 - objcct-oricnted 93 - quality assurancc standards 93 - recipe-based process control 93 resource allocation 93 Operational model 24 Opcrational plant control 96 Operator control 250 Operator control system - control instruction 251 - data security 251 - design intervention 250 input 251 - opcrator intervention 250 - processing of instruction acccptor methods 251 - reaction on the part of the process 252 - selcction 250 - structure of 251 - topology of 250 Operator functions (OP) 233 Operator window 251 OPS 5 391 Optical fiber 179, 180, 231, 266 Optimal control - of lincar systems 89 - open- and closed-loop 88 - optimal trajectory 88 - optimization criterion 87 - parametric optimization 88 - pcrformancc criterion 87 - PI or PID controllers 88 - structural optimization 88 - Zieglcr -Nichols rules 88 Optimization 367 Optimization potential 305 Optimization problcm - initial state xo 88 - pcrformancc index J 88 - system cquation 88 Optomagnctic disc, rcwritablc 291 Oracle 225 Organic contaminants 159 Organizational forms 295 Organiiational requircmcnt 295 Organimtions - addresses of 387 Original function 56
465
Orthogonal transformation OSF 291 OSF MOTIF 291 OSI model 266 Ostrowski bands 84 Output modulc 228 Overcngincering 140
348
P P & I flowshcct 60, 66, 316 o f a plant 317 represcntation of a process control station in 317 Paradigm shift 391 Parameter - definition of 421 Partial recipe - definition of 421 Particle measurement 155 Particle-size analysis 155 Particle size distribution 155 Particle-system characterization 155 PASCAL 391 Pattern recognition 350, 391 Payback timc 315 Pcchold kink thcory 119 Pcrformancc indcx 88 - energy-optimal 89 - integral squared error type of thc 89 timc-optimal 89 Performance specification 95, 305 - opcrating manual 106 - rccipc 105 - semantics of 105 Performance systcm - scheduling cntity 106 - scheduling systcm 106 Peripheral modulc 227 - intclligcnt 228 Personnel requirement 322 Petri nets 8, 23, 91, 92, 336 Petri network 303, 319 pH scnsor - automated 169 - calibration of 170 Phase - decision 293 - definition of 421 - execution 293, 294 Phase mode - production of 115 Phase model 24, 293, 310 - definition of 421 - detailing 37 - cxtract from 310 - for chcmical plant construction 294 - from the basic flowshcct 34 - operations 97
process indicators 35 process properties 35 product indicators 35 product properties 35 - production of 34 - production process of a 42 - quality assurancc 66 - quality of 36 - transformation of 39 Phase modcl, production - decomposition 405 PHIGS 352 Photomcter. rotating lascr backscatter 158 Photon corrclation spectroscopy 155 Physical layer 270 Physiology 346 Pickup 113 Pictogram 1 1 Piping isometrics 321 PLjl 391 Plant 308 - definition of 422 - lifetime of 309 Plant activity phases of 69 Plant and apparatus coding (PAC) 24 Plant availability 170 Plant characteristics - analysis of 53 Plant complex - definition of 422 Plant construction - project managcrnent for 293 - project planning and execution for 293 - quality assurancc for 293 Plant control - action model 94 - definition of 422 - functional units 94 - resources 94 Plant documentation 328 Plant engineering - planning and design for 316 Plant equipment 99 - breakdown of 99 - clements 99 Plant management 222 Plant modcl 24. 26, 28 Plant objects - easy-to-use identification of 309 Plant safcty analysis - form used for 70 Plant section 308 - basic connection patterns 100 - definition of 422 real network of conncctions 100 - technical equipmcnt 308 - topological structure 99 Plant scction control - definition of 422 -
1nde.y
Plant security 137 Plant strucure 308 Plant support - maintenance 98 “Plug-and-play“ unit 199 Pneumatic buffcr 194 Point cluster 48 Pointing device 352, 355 Polc assignment 82. 85 Pollution control 147 Pontryagin maximum principlc 89 Position controllcrs 197 Positioncr 191, 197 for a heating system 192 - in a heating systcm 192 Positron cmission tomography 144 Power amplificr, hydraulic 190 Power controllcr 190 Power plant coding systcm 24 Power scmiconductor 203 electrical data 203 junction tcmperaturc 205 Power, standby 21 1 from a second. indcpendent network 211 from a standby gencrating system 21 1 Power supply separate 175 Preprocessing 143 Press control 228 Price - fixed 300 - unit 300 Price basis - unit 300 Pricc systcm fixcd 300 Primary elcment 113 Principle of minimum surprise 11 Private Branch Exchangc (PBX) 267 Problem-solving levcl - higher 402 - lower 402 Procedural programming 391 Procedure 295 f’rocedurc, process-logistical 336 Process - definition of 422 Process A1 computers 33 Process analysis 123, 359 Process analytical tcchniquc 1 13 Process analyler 228 f’rocess automation 89 - continuous YO - material streams 90 - sequcntial operations 90 Process automation, intcgratcd YO Process configuration spaces 168 Proccss control breakdown of 100 - definition of 422
467
information-oriented 123 signal-oriented 123 Process control, central 341 Proccss control computcr 33, 219, 320 Process control device 67 Process control flowsheet 67 Process control function - continuously controlled 74 - control variables 73 - definition of 423 - cvcnt-controlled feedback 74 - hierarchical breakdown of 21 8 - manipulated variables 73 model of 218 - timed 74 ffoccss control, information-oriented 348, 357 Process control. knowledge-based 406 Process control, knowlcdge-supportcd - tasks in 394 Process control level - ccntral functions for 33 - control recipe 33 - coordination functions 33 - idcal structurc 33 - individual functions 33 - system communications functions 34 Process control opcrating systcm - functionality of 225 Process control point functions - lettcr symbols for 135 Process control room 328, 347 - for process analytical instruments 329 space rcquircment for 329 space requircments for 330 Process control station - definition of 423 Process control station elcment - definition of 423 Process control systcm, integrated decentralized 221 - functional-module approach 221 Process control system (PCS) 90, 107 - distributed or dccentralizcd 219 - totally centralized structure 219 - totally parallel structure 219 - with continuous opcrating system platform 231 Process control system (f’CS), second generation 90 Proccss control task 344 Process data intcrprctation 45 - preparation 45 f’rocess devclopmcnt 364 Process clemcnt 34, 307 - attributes 115 - category 1 15 - definition of 423 - WIUC 115 Process cngincering 33, 312 -
automated batch production 89 continuous processcs 89 shutdown 8 Y - startup 89 Process cnvironmcnt 166 Process cquipment - breakdown of 99 Process flowshcet 310 Process idcntification 114 Process indicators 115, 164 Process information modcl 1 15 Process input,ioutput functions (10) 233 Process instrumentation - function 217 -
-
-
structure
217
Process intervention - dcfinition of 423 Process knowlcdgc 73 Process-levcl components (PLC) 33 Process logistics 93, 31 5 Process measurcmcnt 113 - dimensioning 319 Process metrology 123 Proccss model 24, 28, 113. 122, 168, 335 - abbreviation 50 - adaptive 113 - mapping 50, 114 -- optimi7;ltion 114 - pragmatism 50 - transformation 114 Process model, lincar 75 - block diagram 54 - mathcmatical trcatmcnt 55 Process model, mathematical 41 - controllability 44 - observability 44 Process monitoring 33, 34, 217 - definition of 423 - clcctrical and mechanical design in 240 - cxtcrnal functional vicwpoint 218 - internal structural viewpoint 218 - prcparation of screens for 321 - structural models 222 Proccss monitoring control hardware - definition of 424 Proccss monitoring flowsheet 70 Proccss opcrating computers 33 Proccss optimization 405 Proccss plant - information blocks in 64 Proccss port 128, 129, 130 Proccss properties 34, 114, 185. 362,
363 - attributcs 133 - catcgories 133 - controlled variables 117 - definition of 424 - preparation of 45 - proccss parameters 117 - profilcs for 36
sctpoints 117, 118 state variables 1 17 Process property space 171 Proccss reliability 34, 38 Proccss safety 115 - backup component 313 - fail-safe 313 - fail-safc dcsign 313 - redundant 313 - redundant design 3 13 Process sccuring 405 - structuring of 406 Proccss sensor system - economic aspects 139 - general requircmcnts 139 - technology 113, 123 Process simulation 114 - hierarchically organized 327 Process space 357, 366 Proccss stabilization 405 Proccss stagc 307 Proccss state - definition of 424 Proccss structure 308 Process topology 341 Proccss variation 114 Processing function - controlling (C) 316 - dcfinition of 423 - indicating (I) 316 - rccording (R) 316 PROCON expert system - components 401 - knowledge reprcscntation in 401 PROCON expcrt system shell - knowlcdgc acquisition 399 - knowlcdgc manipulation 399 - knowlcdgc rcprcscntation 399 Product data - acquisition 45 - archiving 45 Product development 114, 364 Product clcmcnt - attributes 115 - category 115 - value 115 Prodact indicators 1 17 Product modification 36Y Product net 36. 115 Product properties 35, 114, 185, 335, 363 - chemical 117 - definition of 424 - determination of 163 - - elements of thc requircmcnt profilc 163 - example of a list of 310 - - physical 117. 163 - proccss indicators 163 - profiles for 36 - property function 163 - qualification profile 163 -
-
Index technological 117, 163 Product property space 171 - quality 363 Product quality 1 1 5. 170. 362 Product variation 114 Production, computcr-integrated 279 Production condition 358 Production control 93 - dcfinition of 424 reproducibility 120 Production control computer 310 Production facilities 265 Production instructions general 26 - recipe 26 - recipe-mode operation 26 - taxonomy of 28 - types of rccipc 28 Production modcl 369 functions broken down by level of 306 Production process - operational breakdown 96 - quantitative breakdown 97 Production specifications - process properties 97 - product properties 97 Production units 264 Products - inlet 97 - intermediate 97 - outlet 97 - subprocesses 97 PROFIBUS (International ficld bus) 139, 1181, 275 Profitability calculation 308 Prognos figures 169 Program flowcharts 61 Programrnablc controllcr 220 Programming languagc, procedural 12 Programming languagc, standardizcd - for process control systcms 290 Project 295 - assigning priorities to 304 - definition of 424 Project engineer 297 Project managemcnt 295 - committee 297 - organization as a function of projcct sizc 298 - organizational structure 297 - organizational structure of 297 tasks of 298 Projcct managcr 297 Project organization - influence 296, 297 matrix 295. 296 straight 296 Project responsibility 296 Project structuring 298 PROLOG (programming in logic) 39 1 Property -
469
definition of 424 Property function 35, 361 Property profilc 35 311 - of an overall process Protection environmental 312 worker 312 Protcxtivc features 3 12 Protcctivc mcchanism 245 Protocol -- standardizcd 266 Protocol data units (PDU) 278 Protocol functions (PR) 233 Protocol spxification 278 Prototyping, rapid 6 ProzcDleittechnik 2 Psychophysics 346 PTB tests 181 Pull-down menu systcm 251 Pulse-amplitude-modulated frequency converter 198 Pulse-width-modulated frequency convcrtcr 198 Pump - variable speed 203 - with variable performancc 200 Pump drive - variable-speed I91 Purity guarantee 164 -
Q Qualification profilcs 364 Quality 328 - definition of 425 Quality assurance 34, 67. 164, 295, 324 Quality control, design 324 Quality control, statistical 120 Quality information system 308 Quality inspection of process control cngineering documentation 325 Quality monitoring wastewater 162 Quantity structurc 300 Quasi-procedure-oriented system 245 Quasi-stationary property 44 Quickscan 342 Quinc McCluskey method 91
R Radio transmission 179 Rasmussen’s schemc 20 Ratio factors 299 Real-time-capablc cxpcrt systcm shell Real-time condition 351 Real-time databasc 351 Real-time visualization 355 Recipc
399
470
Index
basic 26 change 365 - concept 337 - definition of 425 plant-dependent control 26 taxonomy of 26 Rccipe control 222 cxample of 309 Recipe-mode operation 31 1, 315. 320 dcscription of 307 Rccipc sequence arrival at a mark in 252 Rcdundancy dcfinition of 425 - dynamic backup 314 - dynamic functional 314 - necessity of 235 - static 313 Redundancy concept, modular 237. 239 Rcdundant measurcmcnt definition of 425 Redundant unit changeover 239 dctection of thc malfunction 239 quality of changcover to 238 restart 240 synchronization 239 Regression mcthod 351 Regression modcl 54 Relaxation spectroscopy 119 Relaxation time - of product properties 163 - spcctrum 119 Rclay-based control systcms 90 Reliability 371 Remote componcnts 227, 228 Repair - definition of 425 Rcport - charge 343 - user procedurcs 343 Representation contcxt 279 Rcproducl bili ty 335 Rcquircments dcfinition of 425 - design of list of 256 list of, for instrumcntation and control systcm 218 - profile of 308 Requirements, safcty 320 Residence-time distribution 56 Residual effects 75 Resonance circuits 206 Rcsource 295 allocation logic 105 - control interface 95 - functional units 95 - personncl 295 - processing of orders 95 sclf-managing 105
self-protccting 105 self-sufficiency 95 - self-sufficient 105 Resource-constrained allocation 303 Rcsource leveling 303 Resource managcmcnt 303. 304 Resource modcl - modular form 109 Rcsource personncl 303 Resource planning 303 Resource structure 96. 99 determination of 103 Return on invcstmcnt (ROI) 315 R F N (reverscd-frame normalizing) 83 Riccati controller 89 Risk 116 Risk asscssmcnt 116 Risk limiting 116 Kit7 Galerkin method 59 Robot, industrial 141 - distance measuring systems for 142 Role - definition of 425 discrete 224 - type of 224 Role system 224 - design 256 functional 224 Root-locus mcthod 81 RSjl 49 RS flipflop 91 Rulc-bascd system - conflict-resolution strategies 395 control synthesis 395 - disturbancc analysis 395 - maintenance strategies 395 - problem-solving process 393 -
S
SADT mcthod 8. 12 Safe position 194 well-defincd 187 Safeguards 1 16 Safety 116, 295, 312, 371 - acceptablc range 312 good rangc 312 - unacceptable range 312 Safety analysis 67 Safety and reliability functions 218 Safety conccpt 3 10 Safety considcrdtions 309 Safety deviccs 312 Safety functions - measurcmcnt and control cquipmcnl pcrforrning 312 Safcty, intrinsic 137, 271 Safety- or reliability-cnhancing function 21 8 Safety position 190, 192 Safety requircmcnts 138. 312
Safety significance, immediate - functions of 326 Sample preparation 125. 165 Sample transport 125 Sampling 125, 165 of powders 155 Sampling techniques 120 SAS 46, 49 Scalc interval 41 metric 41 - metrical 117 - nominal 41 - ordinal 41 - proportional 41 - topological 1 17 - topological information from 41 Scalc control 228 Schcdulc management 301 Schumpeter 1 Screen region 343 Sealing - type of 192 Securing functions - definition of 426 Semantics 267. 278 Scnd relationship 245 SENROB 121 Scnsing element 171 Sensitivity analysis 305 Sensitivity matrix 84, 87 Sensitivity matrix. complementary 84, 87 SENSOR 390 Sensor- actuator systems 133 Sensor arrays 166 Sensor element 1 13 Sensor information - assessment of 130 Scnsor interface - functional 129 Sensor-levcl display and control component 132 Scnsor system 66, 113 - accuracy 123 - adequatc accuracy and predictive power 166 - application-oricntcd description I37 - bidircctional and open communications structures 128 - collectivc 135 - conceptual, object-oriented classification 137 - dependability 123 - development of 124 - - device-oriented I37 - easy adaptation to production variation 166 - cx-line 135 - for environmental protcction 156 - for noisc pollution control 162 - functionality 123
functionality in cxtraordinary proccss states 166 heterogeneous phases 135 homogeneous 135 - imagc-processing, pattcrn recognizing 142 - in automobiles 147 164 - in biotcchnical process engincering - in-linc 135 - in manufacturing 141 - in medical tcchnology 143 - in polymer production 163 - in solid-state proccss cnginccring 151 - information flow 124. 128. 129 - input quantity 114 - maintenance cost 123 - maintenance procedures 123 - method-oriented 137 multi-sensor approach 114 - multivariatc 166 - off-linc 135 - on-line 135 - reliability 123 - reliability at reasonable maintenance cost 166 - robustness 123 - self-diagnosis 166 - self-maintenance 166 - signal quantity 114 - specific properties 135 - taxonomies of 19, 133 Sensor system, complex - general structure 126 Sensor system. ex-line 117 Sensor system tcchnology 166 Sensor technology 1 13 Sensors 35, 113, 308 - accuracy 149 - applications 172 array Of 114, 143 - availability of 136 - cathcter 147 - coupling of 200 electrochemical I47 element 125 - environmental influcnccs on 149 - flow rate and mass flow rate 174 - for automobilc monitoring and diagnostics 148 - for automobilc safety and security purposes 148 148 - for information in automobiles - functional 148 - in intcnsivc care 146 - integrated 125 - intelligcnt 125 market for 169 - market forecasting 171 - miniaturization of 147 142 - multidimensional force and moment - new or modified materials for 167 - reliability and service life 136, 149
servicc conditions 149 space requircment and weight 149 - taxonomy of 133 - use in plant dcsign and construction 172 - usc in process cngincering 172 - Wcstern curopean market 173 - Westcrn curopean sales of flow sensors 174 - world market 173 Sensors. acceleration 151 Sensors, automotive - measuring principlcs of 150 - tcchnologies of 151 Sensors. biological 114, 143, 167 Sensors. chcmical 167 Sensors. digital - coupling of resonant structures 167 - micromcchanical resonant sensors 167 - pie;.oclectric matcrials 167 Sensors, noncontact 141 Sensors, optical 167 Sensors, semiconductor 167 Sensors, surface-active mctal oxidc 167 Sensors, tactile 141, 142 Sensors, torque 151 Sequence control 31 7 - definition of 426 Sequential controller 91 Scrial point-to-point connection 228 Server 132 Server portions 233 Service elcments 278 Service primitivcs 278 Servo drive 185 Setup - proccdurc-oricnted and objcct-oriented 242 Shear modulus, complex 163 Shift - day 347 - night 347 Shift factor 119 Shows 388 Siemens STEP-5 92 Signal conditioning 125 Signal proccssing 125 Similarity theory SY, 351 Simulation 336 Simulation specification 107 Simulation techniqucs 53 Singlc-photon emission computer tomography (SPECT) 144 SIREP-WIB-EXERA 387 Situation recognition tcchniqucs 393 Slack time 302 Sludge incincrator 157 - analytical instrumcntation of 158 SMALLTALK 391 SMART power 209 Software, componcnt-orientcd -
prcparation of 2YO Software dcsign abstract modulcs 289 code generation 290 - construction of scrcen diagrams 290 - cross-refcrcncing 290 - documentation of existing facilities 290 - function charts and structural diagrams 288 - hierarchical organimtion 288 - internal structurc 289 - library of typicals 290 - multi-component structures 290 - progressivc rcfincmcnt 289 - scquence of programming instructions 289 - system-neutral programming 290 Softwarc ergonomics 346 Software implementation 244 Software module 243 Software-typc concept 243 - autonomous objcct 244 - instance of a typc 244 Solid-state process - agglomcration 152 - disperse propertics 152 - physical propertics 152 - process clcments 152 - product statcs 152 - propcrty function 152 - s i x reduction 152 - technological propertics I52 SPARTEN 6 Spatial representativeness 168 Specification - binding concept 293 - definition of 426 - document 200 - process-en$ncering aspects 307 - solution-variant realization 307 - structure and content of 309 Specification, pcrformance 293 Spring - cncrgy storage in 190 SPS 266 Stability analysis 53 Stagc models 51 Standard - classification 375 - dcsign 324 - equipment 67, 324 - installation 67. 200 - instrument 67 - manufacturing 200 - PCE, structure of 71 - systcm 67 Standard. companion 269, 273 Standard elements 326 Standardization bodies 376 Startupirestart behavior 225 -
State display - sclcctivc 250 Slate graph 23 State image change-driven transmission 250 - formation preparation 249 - modular organization 249 - selective transmission 250 - transfer cost minimization 249 Statc image, updating - prcparation of a new image 249 State-spacc models 82 Stale-space representation 54 Station data sheet 317 - example of 318 Station hardward diagram 317 example of 320 Station idcntification 325 Station plan - example of 319 Station softwarc diagram - example of 320 Stationary point 43 Stationary process 43 Statistical analysis - acquisition 45 - archiving 45, 49 - data acquisition 49 - preparation 49 - software, preparation 46 Statistical methods - acquisition 45 elementary statistics 47 - expcrimental design 48 - extrapolation 45 - interpolation 46 - prcparation (formatting) 46 - principal-component analysis 48 - time-series analysis 49 Statistical model 336 Statistical software packages 46 Steady continuous process 51 Steady-state process 43 Stirred tank reactor 189 Stochastic model component 82 Strategies - - diagnostic 391 - - planning 391 problem-solving 391 Stress - strain curve 119 Structural chart - technical scope of performance 298 Structural chart, function-oriented 298 Structural chart, object-oriented 298 - example 299 Structured analysis and design technique (SADT) - diagram 283 283 - structured brcakdown of a problem in Structurcd analysis (SA) 8 Structured design (SD) 8
Structured query language (SOL) 18 Subprocess 97 Substances 295 - definition of 427 - procurement of 300 Subsystem - definition of 427 Subsystem coupling modules 228 Subsystem. intelligent 228 Super-sign formation 143 Switch for control or safety purposes 192 Switch room 171 for electrical powcr distribution 329 - for information processing 329 Switchbox identification 321 Switchgear, low-voltage - classes of 212 - implementation 212 Switching algebra 89. 91 Switching devices 197 Synapses 395 Syntax local 278 - transfcr 278 SYSTEC 390 System 295 - concept 42 - definition of 427 - elements of 9 - quantities 41 - stability of 43 - theory of 42 System analysis 283 System analysis, classical 391 System bus 231 System, consultative 393 System, integratcd 286 System, knowledge-based 391 solution domain 398 - solution point 398 System reaction times 218 System service, general - changeovcr between redundant units 248 communications and organization 248 manufacturer-neutral design procedure 248 manufacturer-specific systcm services 248 monitoring and process control hardware diagnostic 248 - network synchronization 248 - time management 248 Systematic instrumentation 133 SYSTEMS 390
'r TA Luft 157 Task analysis 283 Taxonomy 18 - definition of 427
474
Index
of process sensor systems 134, 135. 137 T C (total carbon) 160 Tcchnical cxpert systcms 121 Tcchnical function 309. 310 Tcchnical knowledge. cncoded 39 1 Tcchnical objects - coding of 310 Tcchnical spccifications - norms 375 - standards 375 Technological modulc type 258 - definition of 256 - manageable functional scope 256 Tcchnological propertics 35 Technological role system - group function 258 - individual function 258 - plant coordination 258 Tcchnological variablcs 142 Temperature classes 175 Test methods - physical 118 tcchnological 117 Testing, functional 326 Thc Fix 49 Thcory of automata 91 Therapeutic systcms 147 Thcrmography 144 Throttling organs 191, 196 Thyristor 203 T I C (total inorganic carbon) 160 Time-discrete systcms 53 Time-domain mcthods 81 Time synchronkation 255 TOC (total organic carbon) 159 - determination 161 - measurcment 160, 161 Token passing 266 Tokens 23, 266 Tolerances - defcctivc, acceptable 117 - defcctivc. unacccptablc 117 Tomographic mcthods 168 Toolbox 342 T O P 268 T O P (Tcchnical and Officc Protocols) 267 Torque shock 208 Training system 357 Transducer 174 Transfer-function models 54 Transfer matrix 57 Transfer model 53, 54 Transfer process 359 Transfer theory 359 Transformation. Fourier 57 Transformation, integral 56 Transformation, Laplace 56, 57 Transformation principle 21, 359 - event-orientcd 9 - logic-oriented descriptions 22
scqucnce-oriented descriptions 22 state-oriented 9. 22 Transformalion proccss 307 Transforming systems 188 Transistor, bipolar 204 Transistor. Darlington-connected 204 Transmission media 266 Transmitter - numbers and values of 174 Transputcr 403 Trce structurc types of 12 TTL circuits 205 Type of allocation 105 Typicals 290 -
-
c'
[Jltrasonic imaging 145 Unit-cost cstimation 300 - examplc of cost structurc for 301 Unit operation 38, 308 - basic flowshect 66 - classification schemc 26 - definition of 427 - taxonomy of 26, 27 - with inlet and outlet products 307 - with process properties 307 - with product properties 307 UNIX 225, 288. 291 Upper tester (UT) 276, 277 User interfacc 290 - design principlcs 11 ergonomics 18 V Va 1ve - slide 190 - solenoid-actuated 190 VCI 381 VDE 381 VDI 381 Vector proccssors 403 VIK 375, 382 Vilamoura procedure 378 Virtual machine - in a proccss control system 226 Viscoelastic behavior 119 Visualifition systcm - flexibility or 354 VMS 225 Voigt bodics 118 Voltage sourcc invertcr 204 Voltage sourcc (PWM) 204 Von Neumann structure 403 W WAN (Wide area network) 266 Watcr run 327 Waveguide techniques, optical 180
Weighting matrices 85 Weighting matrices, distinct frcqucncy-dependent 85 Wiring chcck 326 Work-station networking 291 Worksitc distribution board 21 1 Write ~ C C C S S 245
Y Yicld
328
Z Ziegler- Nichols rules 81 ZVIII 375, 380, 382
