Najim / Dufour Advanced Control of Chemical Processes (ADCHEM'91)

Selected Papers from the IFAC Symposium, Toulouse, France, 14-16 October 1991
1. Auflage 2014
ISBN: 978-1-4832-9897-9
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 307 Seiten, Web PDF

Reihe: IFAC Symposia Series

This volume contains 40 papers which describe the recent developments in advanced control of chemical processes and related industries. The topics of adaptive control, model-based control and neural networks are covered by 3 survey papers. New adaptive, statistical, model-based control and artificial intelligence techniques and their applications are detailed in several papers. The problem of implementation of control algorithms on a digital computer is also considered.

Autoren/Hrsg.

Najim, K.

Dufour, E.

Inhaltsverzeichnis

1;Front Cover;1
2;Advanced Control of Chemical Processes (ADCHEM'91);4
3;Copyright Page;5
4;Table of Contents;10
5;Preface;8
6;CHAPTER 1. ADAPTIVE CONTROL: AN OVERVIEW;14
6.1;1. INTRODUCTION.;14
6.2;2. PLANT MODEL REPRESENTATION.;15
6.3;3. FIRST GENERATION ADAPTIVE CONTROL.;15
6.4;4. MORE GENERAL CONTROL DESIGNS.;16
6.5;5. ROBUST PARAMETER ESTIMATION.;18
6.6;6. SECOND GENERATION ADAPTIVE CONTROL;20
6.7;7. CONCLUSION.;20
6.8;REFERENCES;21
7;CHAPTER 2. SELFTUNING COMBUSTION CONTROL FOR A FURNACE WITH LOW POWER;24
7.1;INTRODUCTION;24
7.2;MATHEMATICAL MODEL OF THE COMBUSTION PROCESS;25
7.3;SIMULATION AND VERIFICATION;27
7.4;DESIGN OF THE COMBUSTION SYSTEM;27
7.5;EXPERIMENTAL RESULTS;29
7.6;CONCLUSION;29
7.7;REFERENCES;29
8;CHAPTER 3.MULTIVARIABLE ADAPTIVE PREDICTIVE CONTROL OF THERMAL PROCESSES;30
8.1;I. INTRODUCTION;30
8.2;II. STATEMENT OF THE PROBLEM;30
8.3;Ill. DEVELOPMENT OF THE ALGORITHM;31
8.4;IV. PROCESSES DESCRIPTION - THE ENVIRONMENTAL TEST CHAMBERS;31
8.5;V. EXPERIMENTAL RESULTS. ÌÉÌÏ ADAPTIVE CONSTRAINED PREDICTIVE CONTROL;32
8.6;VI. SOME CONCLUSIONS;32
8.7;REFERENCES;32
9;CHAPTER 4. PID ADAPTIVE CONTROL OF A NON ISOTHERMAL CONTINUOUS STIRRED TANK REACTOR;36
9.1;1. INTRODUCTION;36
9.2;2. PLANT DESCRIPTION.;36
9.3;3. THE ADAPTIVE PID CONTROLLER.;37
9.4;4. EXPERIMENTAL RESULTS;39
9.5;5. CONCLUSION;39
9.6;REFERENCES;39
10;CHAPTER 5. GENERALIZED PREDICTIVE CONTROL OF AN INDUSTRIAL CALCINATION REACTOR;42
10.1;THE PLANT AND CONTROL PROBLEM OUTLINE;42
10.2;PROCESS MODELING AND IDENTIFICATION;43
10.3;GENERALIZED PREDICTIVE CONTROL ALGORITHM;43
10.4;RECURSIVE ESTIMATION ALGORITHM;44
10.5;IMPLEMENTATION AND APPLICATION RESULTS;44
10.6;CONCLUSION;45
10.7;REFERENCES;45
11;CHAPTER 6. ON-LINE AUTOMATIC TUNING PID CONTROL OF DISSOLVED OXYGEN;48
11.1;INTRODUCTION;48
11.2;THEORY;49
11.3;RESULTS;49
11.4;CONCLUSION;50
11.5;ACKNOWLEDGMENTS;50
11.6;APPENDIX A;50
11.7;NOMENCLATURE;50
11.8;REFERENCES;51
12;CHAPTER 7. ADVANCED REGULATION OF COLORATION SYSTEM IN PAPER INDUSTRY;54
12.1;I. INTRODUCTION;54
12.2;II. PROCESS DESCRIPTION AND MODEL;54
12.3;Ill. CONTROL ALGORITHMS;54
12.4;IV. SOME CONTROL RESULTS;55
13;CHAPTER 8. ADVANCED CONTROL OF A PAPER MACHINE WET END;60
13.1;1 INTRODUCTION;60
13.2;2 CONTROL STRATEGIES;60
13.3;3 RETENTION CONTROL;61
13.4;4 HEAD BOX CONTROL;62
13.5;5 CONTROL STATION AND SOFTWARE;62
13.6;6 EXPERIMENTS;63
13.7;7 CONCLUSIONS;66
13.8;REFERENCES;64
14;CHAPTER 9. NONLINEAR MODELLING AND LINEAR PREDICTIVE CONTROL OF A DISTILLATION PLANT;65
14.1;INTRODUCTION;65
14.2;MODELLING OF THE PLANT;65
14.3;CONCENTRATION CONTROL;68
14.4;SIMULATION RESULTS;69
14.5;CONCLUSIONS;70
14.6;REFERENCES;71
15;CHAPTER 10. NONLINEAR PREDICTIVE CONTROL OF EXOTHERMIC CHEMICAL REACTORS;72
15.1;INTRODUCTION;72
15.2;NONLINEAR PREDICTIVE CONTROL;72
15.3;CSTR EXAMPLE;74
15.4;SUMMARY;75
15.5;ACKNOWLEDGEMENT;75
15.6;NOMENCLATURE;75
15.7;REFERENCES;76
16;CHAPTER 11. NONLINEAR QUALITY CONTROL OF PSUEDO-BINARY DISTILLATION COLUMNS;78
16.1;1 Introduction;78
16.2;2 Puttingqualitative behaviour into control;79
16.3;3 A nonlinear control law;80
16.4;4 Output feedback synthesis and real-time results;80
16.5;References;81
17;CHAPTER 12. APPLICATION OF A NEW MULTIVARIABLE ADAPTIVE DECOUPLING CONTROLLER TO A BINARY DISTILLATION COLUMN;84
17.1;INTRODUCTION;84
17.2;DECOUPLING CONTROLLER;84
17.3;DECOUPLING ADAPTIVE CONTROL ALGORITHM;86
17.4;GLOBAL CONVERGENCE ANALYSIS;87
17.5;EXPERIMENT STUDY;88
17.6;CONCLUSIONS;89
17.7;REFERENCES;89
18;CHAPTER 13. ADAPTIVE CONTROL WITH.MULTI OUTPUT ARX MODELS;90
18.1;1. INTRODUCTION;90
18.2;2. PROCESS;91
18.3;3. FULLY PARAMETERIZED ARX-MODEL STRUCTURES ;91
18.4;4. RESULTS;91
18.5;5. SIMPLIFICATION OF MODEL COMPLEXITY;92
18.6;6. CONCLUSIONS;93
18.7;REFERENCES;93
19;CHAPTER 14. ADAPTIVE LINEARIZING CONTROL OF A CATALYTIC FLUIDIZED-BED REACTOR;96
19.1;1. INTRODUCTION;96
19.2;2. DYNAMICAL MODEL OF THE FBR;97
19.3;3. MODEL REFERENCE LINEARIZING CONTROL;97
19.4;4. SIMULATION STUDIES;99
19.5;6. CONCLUSIONS;100
19.6;REFERENCES;100
19.7;NOMENCLATURE ;100
20;CHAPTER 15. MODEL REFERENCE ADAPTIVE ESTIMATION AND CONTROL APPLIED TO A CONTINUOUS FLOW FERMENTATION PROCESS;102
20.1;1. INTRODUCTION;102
20.2;2. PLANT MODEL;103
20.3;3. ADAPTIVE ESTIMATION;103
20.4;4. ADAPTIVE CONTROL;104
20.5;5. SIMULATION RESULTS;105
20.6;6. CONCLUSION;106
20.7;REFERENCES;106
21;CHAPTER 16. MODEL APPROXIMATIONS AND FILTERING IN ADAPTIVE CONTROL OF DISTRIBUTED PARAMETER BIOREACTORS;108
21.1;INTRODUCTION;108
21.2;DISTRIBUTED SYSTEM MODELS;109
21.3;ORTHOGONAL COLLOCATION;109
21.4;LUMPED SYSTEM MODEL;110
21.5;STATE ESTIMATION;110
21.6;CONTROLLER DESIGN;111
21.7;SIMULATION STUDY;111
21.8;CONCLUDING REMARKS;111
21.9;REFERENCES;113
22;CHAPTER 17. MULTIVARIATE IDENTIFICATION: A STUDY OF SEVERAL METHODS;114
22.1;INTRODUCTION;114
22.2;NON-PARSIMONIOUS MODEL STRUCTURES;114
22.3;IDENTIFICATION METHODS;115
22.4;TEST EXAMPLE;116
22.5;SIMULATION AND IDENTIFICATION STUDIES;116
22.6;IDENTIFICATION IN THE PRESENCE OF FEEDBACK;117
22.7;SUMMARY;117
22.8;ACKNOWLEDGEMENTS;117
22.9;REFERENCES;118
23;CHAPTER 18. AN ON-LINE OPTIMIZATION STRATEGY FOR FAST BATCH AND SEMI-BATCH REACTION SYSTEMS;122
23.1;INTRODUCTION;122
23.2;ADAPTIVE OPTIMIZATION OF DISCONTINUOUS REACTORS;122
23.3;OPTIMIZATION STRATEGIES;123
23.4;OPTIMIZATION EXAMPLES;125
23.5;CONCLUSIONS;127
23.6;ACKNOWLEDGEMENT;127
23.7;REFERENCES;127
24;CHAPTER 19. USE OF PROGRAMMING METHODS FOR FLEXIBLE RECIPES IN BATCH PROCESS CONTROL;128
24.1;INTRODUCTION;128
24.2;PROBLEM FORMULATION;128
24.3;EXAMPLE;130
24.4;CONCLUSIONS;131
24.5;Acknowledgements;131
24.6;REFERENCES;131
24.7;APPENDIX;132
25;CHAPTER 20. HIGH PERFORMANCE DISTILLATION - CASE STUDIES IN CONSTRAINED PREDICTIVE CONTROL;134
25.1;INTRODUCTION;134
25.2;THE MULTIVARIABLE GPC;134
25.3;APPLICATIONS TO HIGH PURITY DISTILLATION;136
25.4;CONCLUSIONS;137
25.5;ACKNOWLEDGMENTS;137
25.6;REFERENCES;137
26;CHAPTER 21. MODEL-BASED CONTROL: A SURVEY;140
26.1;INTRODUCTION;140
26.2;LINEAR MODEL PREDICTIVE CONTROL;141
26.3;NONLINEAR MODEL-PREDICTIVE CONTROL;143
26.4;NONLINEAR VARIABLE TRANSFORMATION;144
26.5;ACKNOWLEDGEMENTS;145
26.6;LITERATURE CITED;145
27;CHAPTER 22. MODEL-BASED OPTIMIZATION AND CONTROL OF KAMYR DIGESTER OPERATION;150
27.1;INTRODUCTION;150
27.2;DESCRIPTION OF OPTIMIZATION METHOD;151
27.3;MAXIMIZATION OF PULP YIELD AT A SPECIFIC KAPPA NUMBER;151
27.4;MINIMIZATION OF OFF-SPECIFICATIONS PULP PRODUCED DURING A SWING;152
27.5;THE APPLICATION OF THE PURDUE MODEL TO THE ON-LINE CONTROL OF THE KAMYR DIGESTER;154
27.6;CONCLUSIONS;155
27.7;REFRENCES;155
28;CHAPTER 23. MODEL-BASED PREDICTIVE CONTROL AND INTERNAL MODEL PRINCIPLE;156
28.1;INTRODUCTION;156
28.2;FORMULATION OF MODEL-BASED PREDICTIVE CONTROL;156
28.3;SINGLE-INPUT SINGLE-OUTPUT CASE;157
28.4;MULTI-VARIABLE CASE;159
28.5;CONCLUSION;159
28.6;REFERENCES;160
29;CHAPTER 24. A CONSTRAINED MULTIVARIABLE NONLINEAR MODEL PREDICTIVE CONTROLLER BASED ON ITERATIVE QDMC;162
29.1;INTRODUCTION;162
29.2;THE CLASSICAL QDMC;162
29.3;NONLINEAR QDMC;163
29.4;EXAMPLES;164
29.5;ACKNOWLEDGEMENTS;165
29.6;REFERENCES;165
30;CHAPTER 25. MULTIVARIATE STATISTICAL PROCESS CONTROL AND PROPERTY INFERENCE APPLIED TO LOW DENSITY POLYETHYLENE REACTORS;168
30.1;INTRODUCTION;168
30.2;HIGH PRESSURE, LOW DENSITY POLYETHYLENE PROCESS;168
30.3;INFERENTIAL MODEL DEVELOPMENT FOR POLYMER PROPERTIES;169
30.4;PROCESS MONITORING VIA MULTIVARIATE SPC CHARTS;170
30.5;SUMMARY;170
30.6;REFERENCES;170
31;CHAPTER 26. NONLINEAR CONTROL OF BIOTECHNOLOGICAL PROCESSES WITH GROWTH/PRODUCTION DECOUPLING;174
31.1;1 Introduction;174
31.2;2 Problem statement;174
31.3;3 Stability of equilibrium states;175
31.4;4 Feedback linearization with stability : a theoretical review;175
31.5;5 Feedback stabilization with Sin as control input;176
31.6;6 Feedback stabilization with Das control input;177
31.7;7 Some final comments;178
31.8;References;179
32;CHAPTER 27. NONLINEAR CONTROL OF A DOUBLE EFFECT EVAPORATOR;180
32.1;1. Introduction;180
32.2;2. Models fora Double Effect Evaporator;180
32.3;3. SISO Nonlinear Control Tools . Consider the following non linear system;181
32.4;4. Nonlinear Control of a Double Effect Evaporator;182
32.5;5. Disturbance Decoupling Control;183
32.6;6. Simulation Results;184
32.7;REFERENCES
;184
33;CHAPTER 28. COMPUTATION OF THE OPTIMAL CONTROL-FUNCTION FOR A CHEMICAL REACTOR;186
33.1;INTRODUCTION;186
33.2;PROBLEM FORMULATION;187
33.3;VARIATIONAL FORMULATION;188
33.4;Á-PRIORI ESTIMATES;189
33.5;CONTINUOUS CONTROL PROBLEM;189
33.6;DISCRETIZED CONTROL PROBLEM;190
33.7;CONCLUSION;191
33.8;REFERENCES;191
34;CHAPTER 29. STRUCTURE OF REACTION NETWORKS AND CONTROLLABILITY OF OPEN ISOTHERMAL REACTORS;192
34.1;1. Introduction;192
34.2;2.Reaction Networks;192
34.3;3.State-space model;193
34.4;4.Accessibility;193
34.5;5.Controllability;194
34.6;6.Stability of uncontrollable modes;195
34.7;7.Conclusions;196
34.8;8. References;196
35;CHAPTER 30. COUPLING OF NONLINEAR CONTROL WITH A STOCHASTIC FILTER FOR STATE ESTIMATION: APPLICATION ON A CONTINUOUS FREE RADICAL POLYMERIZATION REACTOR;198
35.1;INTRODUCTION;198
35.2;NONLINEAR MODEL OF A FREE RADICAL POLYMERIZATION REACTOR;198
35.3;NONLINEAR GEOMETRIC CONTROL;199
35.4;STOCHASTIC FILTER FOR STATE ESTIMATION;199
35.5;COUPLING OF THE STOCHASTIC FILTER WITH A NONLINEAR CONTROLLER;199
35.6;CONCLUSIONS;199
35.7;NOMENCLATURE ;199
35.8;REFERENCES;200
35.9;APPENDIX 1 FREE RADICAL POLYMERIZATION;200
36;CHAPTER 31. QUALITATIVE MODELLING OF DISTILLATION COLUMNS;204
36.1;INTRODUCTION;204
36.2;QUALITATIVE PHYSICS.;204
36.3;CAUSAL GRAPH AND QUALITATIVE TRANSFER FUNCTIONS
;205
36.4;SIMULATION ALGORITHM;206
36.5;APPLICATION
;207
36.6;CONCLUSION;207
36.7;REFERENCES;207
37;CHAPTER 32.CONTROLLER DESIGN IN PACKED BED DISTILLATION PROCESSES;210
37.1;1 INTRODUCTION;210
37.2;2 PROCESS MODEL;211
37.3;3 DESIGN APPROACH;211
37.4;4 DISCUSSION OF RESULTS;212
37.5;Notation;213
37.6;References;213
38;CHAPTER 33. FEEDFORWARD DESIGN USING THE DISTURBANCE CONDITION NUMBER;216
38.1;1. INTRODUCTION;216
38.2;2. THE DISTURBANCE CONDITION NUMBER and DISTURBANCE COST;216
38.3;3. DISTURBANCE REJECTION BY "ACTIVE" FEEDFORWARD CONTROL;219
38.4;4. CONCLUSIONS;221
38.5;ACKNOWLEDGEMENTS;221
38.6;REFERENCES;221
39;CHAPTER 34. IMPROVING THE CONTROLLABILITY OF VISCOSE FIBRE PROCESS;222
39.1;INTRODUCTION;222
39.2;VISCOSE FIBRE PROCESS;222
39.3;PROCESS CONTROL AND PROBLEMS;223
39.4;PROCESS MODEL;225
39.5;ON-LINE ESTIMATION OF ALPHA CELLULOSE;225
39.6;FILTERING;226
39.7;ESTIMATION OF COAGULABILITY;226
39.8;CONCLUSIONS;227
39.9;REFERENCES;227
40;CHAPTER 35. IMPACT OF MODEL UNCERTAINTY ON CONTROL STRUCTURE SELECTION FOR THE FLUID CATALYTIC CRACKING PROCESS;228
40.1;1 Introduction;228
40.2;2 Models of the FCC process used in this work;228
40.3;3 Measures for e valuating controllability;229
40.4;4 Analysis of FCC models;230
40.5;5 Effect of different model features;230
40.6;6 Effect of o peratingpoint;231
40.7;7 Sensitivity to parametric uncertainty;231
40.8;8 Sensitivity to input uncertainty;231
40.9;9 Disturbances;231
40.10;10 Effect of controlling the riser temperature;231
40.11;11 Conclusion;232
40.12;References;232
41;CHAPTER 36. H8 OPTIMAL CONTROL OF AN ABSORPTION COLUMN;234
41.1;INTRODUCTION;234
41.2;PROCESS DESCRIPTION;234
41.3;PROBLEM STATEMENT IN HARDY SPACE RH8;235
41.4;SIMULATION RESULTS;237
41.5;CONCLUSION;238
41.6;REFERENCES;238
42;CHAPTER 37. ESTIMATORS FOR ILL-CONDITIONED PLANTS: HIGH-PURITY DISTILLATION;240
42.1;1 Introduction;240
42.2;2 Distillation Column Application;241
42.3;3 Estimators;241
42.4;4 Analysis of the Estimators;242
42.5;5 Results;243
42.6;6 Discussion;244
42.7;NOMENCLATURE;245
42.8;REFERENCES;245
43;CHAPTER 38. REAL TIME DISTILLATION RESEARCH SOFTWARE;246
43.1;INTRODUCTION;246
43.2;PROCESS;246
43.3;HARDWARE;247
43.4;SOFTWARE;248
43.5;CONTROL ALGORITHMS;249
43.6;EXPERIMENTS;250
43.7;RECENT AND COMING DEVELOPMENTS;251
43.8;CONCLUSION;251
43.9;REFERENCES;251
44;CHAPTER 39. A SOFTWARE PACKAGE FOR INTEGRAL SLURRY MILLING CONTROL IN CEMENT PRODUCTION PLANTS;252
44.1;1 INTRODUCTION;252
44.2;2 SOFTWARE PACKAGE CHARACTERISTICS;252
44.3;3 FUNCTIONAL REQUIREMENTS;253
44.4;4 REQUIREMENTS INTEGRATION. DATA FLOW;255
44.5;5 CONCLUSIONS;255
44.6;6 REFERENCES;255
45;CHAPTER 40. ON A PROCESS CONTROL FRAMEWORK FOR QUALITY ASSURANCE;258
45.1;INTRODUCTION;258
45.2;STATE OF THE ART IN QUALITY CONTROL;258
45.3;QUALITY AS A STATE FUNCTION;259
45.4;HIERARCHICAL PROCESS CONTROL CONCEPT;259
45.5;CONTROL ON PROCESS LEVEL;260
45.6;CONCLUSIONS;262
45.7;REFERENCES;262
46;CHAPTER 41. NEURAL NETWORKS IN PROCESS CONTROL - A SURVEY;264
46.1;INTRODUCTION;264
46.2;NEURAL NETWORK CHARACTERISTICS;264
46.3;CONTROL APPLICATIONS;266
46.4;A SIMPLE ILLUSTRATIVE EXAMPLE;270
46.5;CONCLUSION;272
46.6;REFERENCES;272
47;CHAPTER 42. ARTIFICIAL NEURAL NETWORK BASED PREDICTIVE CONTROL;274
47.1;INTRODUCTION;274
47.2;ARTIFICIAL NEURAL NETWORK MODELS;275
47.3;NONLINEAR PREDICTIVE CONTROL USING NEURAL NETWORKS;275
47.4;APPLICATION TO A BINARY DISTILLATION COLUMN.;276
47.5;CONCLUDING REMARKS;278
47.6;REFERENCES;278
48;CHAPTER 43.NEURAL NETWORK BASED CONTROL OF MODE-SWITCH PROCESSES;280
48.1;INTRODUCTION;280
48.2;STORAGE AND RETRIEVAL IN A SUPERVISORY SYSTEM;280
48.3;CONTROLLER SELECTION BASED ON CREDIT ASSIGNMENT;281
48.4;DISTURBANCE REJECTION IN A CHEMICAL REACTOR;282
48.5;CONCLUSIONS;283
48.6;APPENDIX: CHEMICAL REACTOR DYNAMICS AND NUMERICAL VALUES;283
48.7;ACKNOWLEDGEMENT;284
48.8;REFERENCES;284
49;CHAPTER 44. LEARNING BY INTERPOLATING MEMORIES FOR MODELLING OF FERMENTATION PROCESSES;286
49.1;1 INTRODUCTION;286
49.2;2 ARCHITECTURE AND WORKING PRINCIPLES OF A M S AND MIAS;287
49.3;3 EXPERIMENTAL SETUP AND MODELLING RESULTS;288
49.4;4 PHYSIOLOGICAL STATE APPROACH;290
49.5;5 CONCLUSIONS;290
49.6;6 ACKNOWLEDGEMENT;291
49.7;REFERENCES;291
50;CHAPTER 45. KINETIC MODELLING AND CONTROL OF BIOACTIVE SUBSTANCE SYNTHESIS;292
50.1;INTRODUCTION;292
50.2;1.DESCRIPTION OF THE PROCESS;293
50.3;2. PROCESS MODEL;293
50.4;3. ADAPTIVE CONTROL SCHEME;295
50.5;4, CONCLUSIONS;297
50.6;REFERENCES;297
51;CHAPTER 46. AN EXPERT SYSTEM FOR A CONTROL COORDINATION PROBLEM;298
51.1;INTRODUCTION;298
51.2;PROCESS DESCRIPTION;298
51.3;ROBLEM FORMULATION MATHEMATICAL SOLUTION AN D;299
51.4;SYSTEM ELEMENTS;299
51.5;MANIPULATION OF VARIABLES;301
51.6;IMPLEMENTATION;302
51.7;RESULTS AND CONCLUSIONS;302
51.8;REFERENCES;303
52;CHAPTER 47.THE CEMENT KILN: AI APPROACH TO MODEL AND CONTROL;304
52.1;1 Introduction;304
52.2;2 The cement kiln;304
52.3;3 RIGAS program description;305
52.4;4 RIGAS Implementation.;306
52.5;Conclusions;306
52.6;References;307
53;AUTHOR INDEX;308
54;KEYWORD INDEX;310