Bonvin Advanced Control of Chemical Processes 1994

1. Auflage 2014
ISBN: 978-1-4832-9759-0
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 560 Seiten, Web PDF

Reihe: IFAC Postprint Volume

This publication brings together the latest research findings in the key area of chemical process control; including dynamic modelling and simulation - modelling and model validation for application in linear and nonlinear model-based control: nonlinear model-based predictive control and optimization - to facilitate constrained real-time optimization of chemical processes; statistical control techniques - major developments in the statistical interpretation of measured data to guide future research; knowledge-based v model-based control - the integration of theoretical aspects of control and optimization theory with more recent developments in artificial intelligence and computer science.

Autoren/Hrsg.

Bonvin, D.

Inhaltsverzeichnis

1;Front Cover;1
2;Reprinted from Advanced Control of Chemical Processes (ADCHEM'94);2
3;Copyright Page;3
4;Table of Contents;8
5;IFAC SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSES (ADCHEM'94);4
6;Preface;6
7;PART I: TUTORIAL PAPER;14
7.1;Chapter 1. Nonlinear Input/Output Modeling;14
7.1.1;1 INTRODUCTION;14
7.1.2;2 MODEL STRUCTURES;14
7.1.3;3 MODEL BEHAVIOR;17
7.1.4;4 STRUCTURE SELECTION;18
7.1.5;5 HIGHER-ORDER STATISTICS;19
7.1.6;6 IDENTIFIABILITY AND INPUT SEQUENCE DESIGN;20
7.1.7;7 AN EXAMPLE;21
7.1.8;8 SUMMARY;26
7.1.9;9 REFERENCES;27
8;PART II: MODELING AND SIMULATION I;30
8.1;CHAPTER 2. SYSTEMATIC TECHNIQUES FOR DETERMINING MODELING REQUIREMENTS FOR SISO AND MIMO FEEDBACK CONTROL PROBLEMS;30
8.1.1;1. Introduction;30
8.1.2;2. Control Relevant Parameter Estimation;30
8.1.3;3. Solving the MIMO Estimation Problem;32
8.1.4;4. Conclusions;34
8.1.5;References;34
8.2;CHAPTER 3. DYNAMIC SIMULATION FOR INTEGRATED DESIGN AND CONTROL OF PROCESS FLOWSHEETS;36
8.2.1;1. INTRODUCTION;36
8.2.2;2. MODELLING ASPECTS;36
8.2.3;3. COMPUTATIONAL ASPECTS;38
8.2.4;4. APPLICATION EXAMPLE;39
8.2.5;5. CONCLUSION;41
8.2.6;6. REFERENCES;41
8.3;CHAPTER 4. DYNAMIC SIMULATION OF A MULTISTAGE REACTOR;42
8.3.1;1. INTRODUCTION;42
8.3.2;2. OVERVIEW AND MODELLING OF THE MULTISTAGE REACTOR EQUIPMENT;42
8.3.3;3. STUDY OF CONTROLLABILITY THROUGH DYNAMIC SIMULATION;44
8.3.4;4. CONCLUSION;46
8.3.5;5. REFERENCES;46
8.4;CHAPTER 5. POISSON WAVELETS APPLIED TO MODEL IDENTIFICATION;48
8.4.1;1. INTRODUCTION;48
8.4.2;2. POISSON WAVELET TRANSFORM;49
8.4.3;3. PARAMETER ESTIMATION;49
8.4.4;4. EXAMPLE: TANKS IN SERIES;49
8.4.5;5. MODEL VALIDATION;51
8.4.6;6. SUMMARY;51
8.4.7;7. REFERENCES;52
9;PART III: MODELING AND SIMULATION II;54
9.1;Chapter 6. Low Order Empirical Modeling for Nonlinear Systems;54
9.1.1;1 INTRODUCTION;54
9.1.2;2 THE EMPIRICAL MODEL;54
9.1.3;3 IDENTIFICATION FROM INPUT/OUTPUT DATA;55
9.1.4;4 EXAMPLES;56
9.1.5;5 SUMMARY/CONCLUSIONS;59
9.1.6;6 REFERENCES;59
9.2;CHAPTER 7. BILINEAR ID..TIF.C..I.. OF NONLINEAR PROCESSES;60
9.2.1;1 INTRODUCTION;60
9.2.2;2 INPUT/OUTPUT MODEL STRUCTURE;60
9.2.3;3 CALCULATION OF VOLTERRA KERNELS;62
9.2.4;4 PARAMETER CONSTRAINTS;62
9.2.5;5 PARAMETER ESTIMATION;63
9.2.6;6 FCCU EXAMPLE;64
9.2.7;7 CONCLUSIONS;64
9.2.8;REFERENCES;65
9.3;CHAPTER 8. SYSTEM IDENTIFICATION OF AN ADSORPTION PROCESS USING NEURAL NETWORKS;66
9.3.1;1. Introduction;66
9.3.2;2. The adsorption process for wastewater treatment;67
9.3.3;3. Artificial neural networks and their training;67
9.3.4;4. Results;68
9.3.5;5. Recurrent networks for full trajectory prediction;70
9.3.6;6. Discussion and Conclusions;70
9.3.7;References;71
9.4;CHAPTER 9. DYNAMIC MODELLING AND SIMULATION OF A MULTI-PURPOSE BATCH PILOT PLANT;72
9.4.1;1. INTRODUCTION;72
9.4.2;2. BATCH PILOT PLANT;73
9.4.3;3. PLANT MODEL;73
9.4.4;4. OPERATIONS MODEL;74
9.4.5;5. SIMULATIONS;75
9.4.6;6. CONCLUSION;77
9.4.7;7. NOMENCLATURE;77
9.4.8;8. REFERENCES;77
9.5;Chapter 10. Improved Training of Neural Networks with Complex Search Spaces;78
9.5.1;1 Introduction;78
9.5.2;2 Conventional offline training;79
9.5.3;3 The presented improvements;79
9.5.4;References;81
10;PART IV: NONLINEAR CONTROL AND OPTIMIZATION I;84
10.1;CHAPTER 11. ON-LINE SCHEDULE OPTIMIZATION FOR MIXED-BATCH/CONTINUOUS PLANTS;84
10.1.1;1. INTRODUCTION;84
10.1.2;2. SCHEDULING STRATEGY;84
10.1.3;3. REACTIVENESS;85
10.1.4;4. IMPLEMENTATION AND RESULTS;89
10.1.5;5. CONCLUSIONS;89
10.1.6;6. REFERENCES;89
10.2;CHAPTER 12. EFFICIENT COMPUTATION OF BATCH REACTOR CONTROL PROFILES UNDER PARAMETRIC UNCERTAINTY;90
10.2.1;1. INTRODUCTION;90
10.2.2;2. CONCEPT OF OPTIMIZATION UNDER UNCERTAINTY;91
10.2.3;3. SOLUTION STRATEGY;91
10.2.4;4. SIMULATION EXAMPLE;93
10.2.5;5. CONCLUSIONS;95
10.2.6;6. REFERENCES;95
10.3;CHAPTER 13. PARAMETER ESTIMATION AND NONLINEAR PREDICTIVE CONTROL FOR RTP;96
10.3.1;1. INTRODUCTION;96
10.3.2;2. MODEL DESCRIPTION;97
10.3.3;3. NONLINEAR PARAMETER ESTIMATION;98
10.3.4;4. MODEL TEANSFORMATION;99
10.3.5;5. NONLINEAR MODEL PREDICTIVE CONTROL;100
10.3.6;6. CONCLUSION;101
10.3.7;7. ACKNOWLEDGEMENT;101
10.3.8;8. REFERENCES;101
10.4;CHAPTER 14. MODEL-BASED PREDICTIVE CONTROL: THEORY AND IMPLEMENTATION ISSUES;102
10.4.1;1 INTRODUCTION;102
10.4.2;2 BASIC PHILOSOPHY OF LRPC;102
10.4.3;3 DERIVATION OF SISO LRPC;103
10.4.4;4 STABILITY ISSUES IN LRPC;104
10.4.5;5 CONSTRAINED LRPC;104
10.4.6;6 IMPLEMENTATION ISSUES;106
10.4.7;7 CONCLUDING REMARKS;106
10.4.8;REFERENCES;106
10.4.9;APPENDIX A;107
10.5;CHAPTER 15. INTEGRATED ADVANCED CONTROL AND CLOSED-LOOP REAL-TIME OPTIMIZATION OF AN OLEFINS PLANT;108
10.5.1;1. INTRODUCTION;108
10.5.2;2. OPTIMIZATION SYSTEM;109
10.5.3;3. ADVANCED CONTROL SYSTEM;110
10.5.4;4. COMPUTER SYSTEM;113
10.5.5;5. ECONOMIC BENEFITS;113
10.5.6;6. CONCLUSION;113
10.5.7;7. REFERENCES;113
11;PART V: KNOWLEDGE-BASED AND MODEL-BASED CONTROL I;114
11.1;Chapter 16. A Genetic Algorithm for MIMO Feedback Control System Design;114
11.1.1;1. INTRODUCTION;114
11.1.2;2. A CASE STUDY: THE SHELL PROBLEM;114
11.1.3;3. FEEDBACK CONTROL DESIGN USING SSV;115
11.1.4;4. A GENETIC ALGORITHM FOR FEEDBACK CONTROL DESIGN;116
11.1.5;5. SOLUTIONS TO THE SHELL PROBLEM;117
11.1.6;6. CONCLUSIONS;119
11.1.7;ACKNOWLEDGEMENT;119
11.1.8;REFERENCES;119
11.2;CHAPTER 17. DYNAMIC SYSTEM MODELLING USING MIXED NODE NEURAL NETWORKS;120
11.2.1;1. INTRODUCTION;120
11.2.2;2. NEURAL NETWORKS WITH MIXED TYPES OF HIDDEN NEURONS;120
11.2.3;3. SEQUENTIAL ORTHOGONAL TRAINING OF NEURAL NETWORKS;121
11.2.4;4. APPLICATION TO A DISTILLATION COLUMN;124
11.2.5;5. CONCLUSIONS;125
11.2.6;6. REFERENCES;125
11.3;CHAPTER 18. REAL-TIME CONTROL OF A WASTE WATER NEUTRALIZATION PROCESS USING RADIAL BASIS FUNCTIONS;126
11.3.1;1. INTRODUCTION;126
11.3.2;2. SYSTEM REPRESENTATION;127
11.3.3;3. CONTROLLER DESIGN;128
11.3.4;4. WASTE WATER pH NEUTRALIZATION;128
11.3.5;5. CONCLUSIONS;130
11.3.6;6. REFERENCES;131
12;PART VI: POSTER PAPERS I;132
12.1;CHAPTER 19. MODEL VALIDATION TEST;132
12.1.1;1. INTRODUCTION;132
12.1.2;2. PROBLEM FORMULATION;132
12.1.3;3. STRATEGY;133
12.1.4;4. INFINITY NORM AND STABILITY;133
12.1.5;5. DATA TRANSFORM;133
12.1.6;6. SELECTION OF ALL PASS FILTER;134
12.1.7;7. STABILITY DETERMINATION;135
12.1.8;8. NUMERICAL EXAMPLE;136
12.1.9;9. DISCUSSION;137
12.1.10;10. CONCLUSION;137
12.1.11;11. REFERENCES;137
12.2;CHAPTER 20. IDENTIFICATION OF COMBINED PHYSICAL AND EMPIRICAL MODELS USING NONLINEAR A PRIORI KNOWLEDGE;138
12.2.1;1. INTRODUCTION AND LITERATURE REVIEW;138
12.2.2;2. DATA EXTRACTION METHOD;139
12.2.3;3. THE CVA IDENTIFICATION METHOD;139
12.2.4;4. SIMULATION RESULTS;140
12.2.5;5. EXPERIMENTAL RESULTS;141
12.2.6;6. CONCLUSIONS;143
12.2.7;ACKNOWLEDGMENTS;143
12.2.8;REFERENCES;143
12.3;CHAPTER 21. COMPUTER-AIDED MODELLING : SPECIES TOPOLOGY;144
12.3.1;AIMS AND GOALS OF COMPUTER-AIDED MODELLING;144
12.3.2;PHYSICAL TOPOLOGY;145
12.3.3;CONSTRUCTION OF THE SPECIES TOPOLOGY;145
12.3.4;MODIFICATION OF SPECIES TOPOLOGY;146
12.3.5;A BRIEF EXAMPLE;147
12.3.6;CONCLUSION;149
12.3.7;References;149
12.4;CHAPTER 22. OPTIMIZATION OF PROCESS SYSTEMS WITH DISCONTINUITIES;150
12.4.1;1. INTRODUCTION;150
12.4.2;2. AN NLP FORMULATION FOR DAOP;151
12.4.3;3. NEW NLP FORMULATION WITH SMOOTH APPROXIMATION;153
12.4.4;4. CONCLUSIONS;155
12.4.5;5. REFERENCES;155
12.5;CHAPTER 23. STEAM BALANCE OPTIMIZATION IN CHEMICAL PLANT;156
12.5.1;1. INTRODUCTION;156
12.5.2;2. PROCESS DESCRIPTION;156
12.5.3;3. SYSTEM CONFIGURATION;157
12.5.4;4. SYSTEM FUNCTIONALITY;157
12.5.5;5. CURRENT STATUS OF THE PROJECT;158
12.5.6;6. CONCLUSION;159
12.6;CHAPTER 24. ADAPTIVE CONTROL OF MIMO NON-LINEAR SYSTEMS USING LOCAL ARX MODELS AND INTERPOLATION;160
12.6.1;1 INTRODUCTION;160
12.6.2;2 MODEL REPRESENTATION USING LOCAL MODELS AND INTERPOLATION;160
12.6.3;3 PARAMETER ESTIMATION;162
12.6.4;4 ADAPTIVE CONTROL;163
12.6.5;5 DISCUSSION;164
12.6.6;6 SIMULATION EXAMPLE;164
12.6.7;7 CONCLUDING REMARKS;166
12.6.8;ACKNOWLEDGMENTS;166
12.6.9;References;166
12.6.10;APPENDIX;166
12.7;CHAPTER 25. A DISTURBANCE ESTIMATOR FOR MODEL PREDICTIVE CONTROL;168
12.7.1;1. INTRODUCTION;168
12.7.2;2. DISTURBANCE PREDICTOR;169
12.7.3;3· EXAMPLES;170
12.7.4;4. CONCLUSIONS;172
12.7.5;5. REFERENCES;172
12.7.6;APPENDIX;173
12.8;Chapter 26. Controller Synthesis for Two-Time-Scale Nonlinear Processes;174
12.8.1;Introduction;174
12.8.2;Two-Time-Scale Processes: Preliminaries;174
12.8.3;Controller Synthesis for Two-Time-Scale Nonlinear Processes with Stable Fast Dynamics;175
12.8.4;Definitions of the various concepts of relative order;175
12.8.5;Controller Synthesis for Two-Time-Scale Nonlinear Processes with Unstable Fast Dynamics;177
12.8.6;Closed loop stability;178
12.8.7;Acknowledgement;178
12.8.8;References;178
12.9;CHAPTER 27. ANALYSIS AND SYNTHESIS METHODS FOR ROBUST MODEL PREDICTIVE CONTROL;180
12.9.1;1. INTRODUCTION;180
12.9.2;2. MULTIVARIABLE EQDMC;181
12.9.3;3. ROBUST STABILITY OF MIMO EQDMC;181
12.9.4;4. EQDMC PERFORMANCE;182
12.9.5;5. EQDMC TUNING METHODOLOGY;182
12.9.6;6. SIMULATION STUDIES;183
12.9.7;7. CONCLUSIONS;185
12.9.8;8. REFERENCES;185
12.10;CHAPTER 28. REDUCED HESSIAN SUCCESSIVE QUADRATIC PROGRAMMING FOR REALTIME OPTIMIZATION;186
12.10.1;1. REDUCED HESSIAN SQP;186
12.10.2;2. SOLUTION TECHNIQUES;187
12.10.3;3. PROCESS OPTIMIZATION;188
12.10.4;4. CONCLUSION;190
12.10.5;5. ACKNOWLEDGEMENTS;191
12.10.6;6. REFERENCES;191
12.11;Chapter 29. A real-time CAD environments for model predictive controllers;192
12.11.1;1. INTRODUCTION;192
12.11.2;2. CONCEPT OF MIPCON;193
12.11.3;3. APPLICATION STUDY;196
12.11.4;4. CONCLUSION;196
12.11.5;5. REFERENCES;197
13;PART VII: TUTORIAL PAPER;198
13.1;CHAPTER 30. NONLINEAR MODEL PREDICTIVE CONTROL: A TUTORIAL AND SURVEY;198
13.1.1;1. INTRODUCTION;198
13.1.2;2. MPC FOR LINEAR PLANTS;199
13.1.3;3. MPC FOR NONLINEAR PLANTS;203
13.1.4;4. CONCLUSIONS AND FUTURE OUTLOOK;208
13.1.5;5. REFERENCES;209
14;PART VIII: SURVEY PAPER;212
14.1;CHAPTER 31. THE PROCESS INDUSTRY REQUIREMENTS OF ADVANCED CONTROL TECHNIQUES: CHALLENGES AND OPPORTUNITIES;212
14.1.1;1. INTRODUCTION;212
14.1.2;2. THE HISTORICAL PERSPECTIVE;212
14.1.3;3. HISTORICAL PERSPECTIVE IN OTHER MANUFACTURING INDUSTRIES;214
14.1.4;4. REVIEW;215
14.1.5;5. STRENGTHS OF THE CHEMICAL PROCESS INDUSTRIES;215
14.1.6;6. WEAKNESSES;215
14.1.7;7. OPPORTUNITIES IN THE PROCESS INDUSTRIES;217
14.1.8;8. THREATS;218
14.1.9;9. MARKETING PROCESS CONTROL;219
14.1.10;10. INCREASING USER FRIENDLINESS;219
14.1.11;12. BENCHMARKING;220
14.1.12;13. TRAINING/EDUCATION;220
14.1.13;14. NEW PROCESS TECHNOLOGY;220
14.1.14;15. CONCLUSIONS;221
14.1.15;16. REFERENCES;221
14.1.16;17. ACKNOWLEDGEMENTS;221
15;PART IX: NONLINEAR CONTROL AND OPTIMIZATION II;222
15.1;CHAPTER 32. NONLINEAR PREDICTIVE CONTROL USING LOCAL MODELS - APPLIED TO A BATCH PROCESS;222
15.1.1;1 INTRODUCTION;222
15.1.2;2 LOCAL MODELLING;223
15.1.3;3 MODEL PREDICTIVE CONTROL;224
15.1.4;4 SIMULATION EXAMPLE;224
15.1.5;ACKNOWLEDGMENTS;227
15.1.6;5 CONCLUSIONS;227
15.1.7;REFERENCES;227
15.2;Chapter 33. Iterative refinement of model predictive control;228
15.2.1;1 Introduction;228
15.2.2;2 Modeling and predictive control;228
15.2.3;3 Parameter centering using adaptive control;229
15.2.4;4 Summary and Conclusions;232
15.2.5;References;232
15.3;CHAPTER 34. A CASE-STUDY IN ON-LINE OPTIMAL CONTROL;234
15.3.1;INTRODUCTION;234
15.3.2;REFERENCES;236
15.4;CHAPTER 35. OVERRIDE CONFIGURATION OF GENERALIZED PREDICTIVE CONTROL FOR A MULTI-PURPOSE CONTROL PROBLEM;242
15.4.1;1. INTRODUCTION;242
15.4.2;2. CONTROLLER;243
15.4.3;3. SIMULATION;244
15.4.4;4. CONCLUSION;246
15.4.5;5. Reference;246
16;PART X: MODELING AND SIMULATION III;248
16.1;CHAPTER 36. DYNAMICS AND STABILITY OF POLYMERIZATION PROCESS FLOWSHEETS USING POLYRED;248
16.1.1;1. INTRODUCTION;248
16.1.2;2. THE POLYRED PACKAGE;248
16.1.3;3. PROCESS STABILITY ANALYSIS;249
16.1.4;4. SOME EXAMPLES;250
16.1.5;5. CONCLUSIONS;254
16.1.6;6. ACKNOWLEDGMENTS;254
16.1.7;7. REFERENCES;254
16.2;CHAPTER 37. OPERATION SUPPORT SYSTEM USING DYNAMIC SIMULATION FOR A COMBINED BATCH/CONTINUOUS PLANT;256
16.2.1;1. INTRODUCTION;256
16.2.2;2. A COMBINED BATCH/CONTINUOUS PLANT;256
16.2.3;3. MODELING THE COMBINED BATCH/CONTINUOUS PLANT;257
16.2.4;4. OPERATION SUPPORT SYSTEM;258
16.2.5;5. OPERATIONAL GUIDANCE;258
16.2.6;6. REAL TIME IMPLEMENTATION IN THE ACTUAL PLANT;260
16.2.7;7. CONCLUDING REMARKS AND FUTURE DEVELOPMENT;261
16.2.8;8. ACKNOWLEDGMENTS;261
16.2.9;9. REFERENCES;261
16.3;CHAPTER 38. A DYNAMIC SIMULATION STRATEGY FOR CYCLED DISTRIBUTED PARAMETER SYSTEMS;262
16.3.1;1. INTRODUCTION;262
16.3.2;2. PROCESS DESCRIPTION;263
16.3.3;3. MATHEMATICAL MODELLING;263
16.3.4;4. SIMULATION STRATEGY AND NUMERICAL METHODS;264
16.3.5;5. EXAMPLE SIMULATION;265
16.3.6;6. CONCLUSIONS;266
16.3.7;7. NOMENCLATURE;267
16.3.8;8. REFERENCES;267
16.4;CHAPTER 39. LOCAL THERMODYNAMIC MODELS FOR DYNAMIC PROCESS SIMULATION;268
16.4.1;1. INTRODUCTION;268
16.4.2;2. MODEL;268
16.4.3;3. DISCUSSION;273
16.4.4;4. REFERENCES;273
16.5;CHAPTER 40. RIGOROUS DYNAMIC SIMULATION OF DISTILLATION COLUMNS BASED ON UV-FLASH;274
16.5.1;1 INTRODUCTION;274
16.5.2;2 DYNAMIC DISTILLATION MODELS;274
16.5.3;3 FLASH CALCULATIONS;276
16.5.4;4 THERMODYNAMICS;277
16.5.5;5 EXAMPLE COLUMN;278
16.5.6;6 CONCLUSION;279
16.5.7;REFERENCES;279
17;PART XI: NONUNEAR CONTROL AND OPTIMIZATION III;280
17.1;CHAPTER 41. A TRUST REGION STRATEGY FOR NEWTON-TYPE PROCESS CONTROL;280
17.1.1;1. INTRODUCTION;280
17.1.2;2. OVERVIEW OF THE CONTROL FORMULATION;281
17.1.3;3. TRUST REGION STRATEGIES FOR NONLINEAR OPTIMIZATION;281
17.1.4;4. PROCESS EXAMPLES;282
17.1.5;5. CONCLUSIONS;284
17.1.6;ACKNOWLEDGMENTS;285
17.1.7;6. REFERENCES;285
17.2;CHAPTER 42. A MULTIMODEL MIXED H2/H8 PROBLEM FOR PLANTS WITH STRUCTURED UNCERTAINTY;286
17.2.1;1. INTRODUCTION;286
17.2.2;2. PROBLEM FORMULATION;287
17.2.3;3. SOLUTION PROCEDURE;288
17.2.4;4. GRADIENT EXPRESSIONS;289
17.2.5;5. A DISTILLATION COLUMN EXAMPLE;290
17.2.6;6. CONCLUSIONS;290
17.2.7;ACKNOWLEDGMENTS;291
17.2.8;7. REFERENCES;291
17.3;CHAPTER 43. ROBUST MODEL PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS WITH CONSTRAINTS;292
17.3.1;1. INTRODUCTION;292
17.3.2;2. MPC ALGORITHM;293
17.3.3;3. BASIC ASSUMPTIONS;295
17.3.4;4. ANALYSIS OF THE ALGORITHM;296
17.3.5;5. REFERENCES;297
17.4;Chapter 44. A Kalman filter based robust model predictive control with constraints;298
17.4.1;1. INTRODUCTION;298
17.4.2;2. ALGORITHMS;299
17.4.3;3. APPLICATION STUDY;302
17.4.4;4. CONCLUSION;303
17.4.5;5. REFERENCES;303
17.4.6;6. APPENDIX;303
17.5;CHAPTER 45. A PRACTICAL APPROACH TO APPROXIMATE INPUT-OUTPUT LINEARIZATION;304
17.5.1;1. INTRODUCTION;304
17.5.2;2. APPROXIMATE INPUT-OUTPUT MODELS;305
17.5.3;3. APPROXIMATE INPUT-OUTPUT LINEARIZING CONTROLLER SYNTHESIS;306
17.5.4;4. APPLICATION - VAN DE VUSSE REACTOR;307
17.5.5;5. CONCLUSIONS;308
17.5.6;Acknowledgements;309
17.5.7;REFERENCES;309
17.5.8;6. APPENDIX;309
18;PART XII: KNOWLEDGE-BASED AND MODEL-BASED CONTROL II;310
18.1;CHAPTER 46. FUZZY BASED CONTROL OF A DISTILLATION PLANT START-UP;310
18.1.1;1. INTRODUCTION;310
18.1.2;2. START-UP CONTROL STRUCTURING;310
18.1.3;3. EXPERIMENT;312
18.1.4;4. RESULTS AND DISCUSSION;314
18.1.5;5. CONCLUSION;315
18.1.6;REFERENCES;315
18.2;CHAPTER 47. DERIVATION OF FUZZY RULES FOR PARAMETER FREE PID GAIN TUNING;316
18.2.1;1. INTRODUCTION;316
18.2.2;2. STATEMENT OF THE PROBLEM;316
18.2.3;3. DERIVATION OF THE RULE BASE;317
18.2.4;4. APPLICATIONS;319
18.2.5;5. BUILDING ON THE EXPERT;320
18.2.6;6. CONCLUSION;320
18.2.7;7. ACKNOWLEDGEMENTS;321
18.2.8;8. REFERENCES;321
18.3;CHAPTER 48. A COMPARISON OF VARIOUS CONTROL SCHEMES FOR CONTINUOUS BIOREACTOR;322
18.3.1;1 INTRODUCTION;322
18.3.2;2 CONTROLLABILITY MEASURES;323
18.3.3;3 DYNAMIC MODEL;323
18.3.4;4 CONTROLLABILITY STUDY;325
18.3.5;5 SIMULATION RESULTS;326
18.3.6;6 CONCLUSIONS;327
18.3.7;7 REFERENCES;327
18.4;CHAPTER 49. CONTROLLER VERIFICATION UNDER NON-PARAMETRIC UNCERTAINTY;328
18.4.1;INTRODUCTION;328
18.4.2;NON-PARAMETRIC MONTE-CARLO;329
18.4.3;THE NSIM ALGORITHM;329
18.4.4;CSTR PROCESS;330
18.4.5;RESULTS;331
18.4.6;CONCLUSIONS;331
18.4.7;REFERENCES;332
18.5;CHAPTER 50. AUTOMATIC TUNING OF PID CONTROLLERS FOR UNSTABLE PROCESSES;334
18.5.1;1. INTRODUCTION;334
18.5.2;2. STABILIZABILITY OF UNSTABLE SYSTEMS BY PID CONTROLLERS;335
18.5.3;3. EXTENSION OF THE TUNING TECHNIQUE TO UNSTABLE SYSTEMS;336
18.5.4;4. TEST PROCESS;337
18.5.5;5. RESULTS;337
18.5.6;6. CONCLUSIONS;339
18.5.7;7. REFERENCES;339
19;PART XIII: POSTER PAPERS II;340
19.1;CHAPTER 51. A COMPARISON OF DEDUCTIVE AND INDUCTIVE MODELS FOR PRODUCT QUALITY ESTIMATION;340
19.1.1;1. INTRODUCTION;340
19.1.2;2. DEDUCTIVE MODEL;341
19.1.3;3. NEURAL NETWORK APPROACH;342
19.1.4;4. COMPARISON;343
19.1.5;5. HYBRID MODEL;344
19.1.6;6. CONCLUSIONS;345
19.1.7;7. REFERENCES;345
19.2;CHAPTER 52. EXTRACTION OF OPERATING SIGNATURES BY EPISODIC REPRESENTATION;346
19.2.1;1. INTRODUCTION;346
19.2.2;2. EPISODIC REPRESENTATION;346
19.2.3;3. SCALING FOR SPIKES AND TRENDS;348
19.2.4;4. ILLUSTRATIVE EXAMPLES;350
19.2.5;5. CONCLUDING REMARKS AND FUTURE DEVELOPMENT;351
19.2.6;6. REFERENCES;351
19.3;CHAPTER 53. MONITORING CHEMICAL REACTION SYSTEMS USING INCREMENTAL TARGET FACTOR ANALYSIS;352
19.3.1;1 Introduction;352
19.3.2;2 Target Factor Analysis;352
19.3.3;3 Incremental TFA;353
19.3.4;4 Monitoring Chemical Reaction Systems Using IncTFA;356
19.3.5;5 Conclusions;357
19.3.6;6 References;357
19.4;CHAPTER 54. SEQUENTIAL CONTROL ISSUES IN THE PLANT-WIDE CONTROL SYSTEM;358
19.4.1;1. INTRODUCTION;358
19.4.2;2. SEQUENTIAL CONTROL SYSTEM;359
19.4.3;3. GLOBAL STATE TRANSITION GRAPH;360
19.4.4;4. VERIFICATION OF THE SEQUENTIAL CONTROL SYSTEM WITH THE RULE TRANSITION GRAPH;361
19.4.5;5. TRANSLATION OF THE RULE TRANSITION GRAPH INTO THE PSEUDO-STATE TRANSITION GRAPH;362
19.4.6;6. CONCLUDING REMARKS AND FUTURE DEVELOPMENT;363
19.4.7;7. REFERENCES;363
19.5;CHAPTER 55. COMPARISON OF ADVANCED DISTILLATION CONTROL TECHNIQUES;364
19.5.1;1. INTRODUCTION;364
19.5.2;2. DISTILLATION CONTROL DIFFICULTY;365
19.5.3;3. CASE STUDY AND SIMULATOR;365
19.5.4;4. IMPLEMENTATION APPROACH FOR EACH CONTROLLER;366
19.5.5;5. RESULTS;367
19.5.6;6. CONCLUSION;369
19.5.7;7. REFERENCES;369
19.6;CHAPTER 56. A PROTOTYPE PACKAGE FOR SIMULTANEOUS PROCESS AND CONTROL SYSTEM DESIGN;370
19.6.1;1. INTRODUCTION;370
19.6.2;2. HDA PROCESS CONTROL SYSTEM SYNTHESIS;372
19.6.3;3. CONCLUSION;374
19.6.4;4. REFERENCES;374
19.7;Chapter 57. Opportunities and Difficulties with 5 x 5 Distillation Control;376
19.7.1;1 Introduction;376
19.7.2;2 5 x 5 Distillation Model;377
19.7.3;3 Controllability analysis;377
19.7.4;4 H8/µ control;379
19.7.5;5 Model Predictive 5 x 5 control;381
19.7.6;6 Conclusions;382
19.7.7;References;382
19.8;CHAPTER 58. ROBUST MULTIVARIABLE CONTROL SYSTEM DESIGNS THROUGH REAL-TIME SUPERVISORY KNOWLEDGE-BASED SYSTEMS;384
19.8.1;1. INTRODUCTION;384
19.8.2;2. PROCESS, CONTROL AND KBS DESCRIPTION;385
19.8.3;3. AUTOMATIC H8 CONTROLLER TUNING;385
19.8.4;4. CLOSED-LOOP ROBUSTNESS;386
19.8.5;5. PERFORMANCE ASSESSMENT;386
19.8.6;6. SUPERVISORY KBS PERFORMANCE;386
19.8.7;7. KBS VALIDATION TESTS;387
19.8.8;8. SUMMARY AND CONCLUSIONS;389
19.8.9;REFERENCES;389
19.9;CHAPTER 59. A COMPARISON OF NEURAL NETWORK BASED CONTROL STRATEGIES FOR A CSTR;390
19.9.1;1. INTRODUCTION;390
19.9.2;2. CONTROL PROBLEM;390
19.9.3;3. CONTROL STRATEGIES USING THE NEURAL NETWORKS;391
19.9.4;4. SIMULATION RESULTS;392
19.9.5;5. CONCLUSION;395
19.9.6;6. REFERENCES;395
19.10;CHAPTER 60. NONLINEAR MODELING USING NEURAL NETWORKS WITH MULTIRESOLUTION REPRESENTATIONS;396
19.10.1;1. INTRODUCTION;396
19.10.2;2. MULTIRESOLUTION REPRESENTATIONS;396
19.10.3;3. MODELING USING NEURAL NETWORKS;397
19.10.4;4. EXAMPLES;397
19.10.5;5. CONCLUSION;398
19.10.6;6. REFERENCES;399
19.11;CHAPTER 61. USING KNOWLEDGE-BASED NEURAL NETWORK PROCESS MODELS FOR MODEL-BASED CONTROL;402
19.11.1;1. INTRODUCTION;402
19.11.2;2. MANNIDENT NETWORK MODELS;403
19.11.3;3. THE CONTROL EXPERIMENTS;403
19.11.4;4. ANN MODEL-BASED CONTROLLERS;404
19.11.5;5. DISCUSSION AND CONCLUSIONS;407
19.11.6;6. REFERENCES;407
19.12;CHAPTER 62. RULE BASED COMBUSTION DISTURBANCE PREDICTION AND CONTROL SYSTEM;408
19.12.1;1. INTRODUCTION;408
19.12.2;2. OUTLINE OF REFUSE INCINERATOR;408
19.12.3;3. REFUSE INCINERATOR OPERATION EXPERT SYSTEM;409
19.12.4;4. INTELLIGENT COMBUSTION CONTROL FOR REFUSE INCINERATOR;411
19.12.5;5. SUMMARY;413
19.12.6;6. REFERENCES;413
19.13;CHAPTER 63. OPTIMAL OPERATION OF MULTICOMPONENT BATCH DISTILLATION - A COMPARATIVE STUDY USING CONVENTIONAL AND UNCONVENTIONAL COLUMNS;414
19.13.1;1. INTRODUCTION;414
19.13.2;2. COLUMN MODEL;415
19.13.3;3. CRITERION FOR SELECTING THE BEST COLUMN CONFIGURATION;416
19.13.4;4. EXAMPLE;416
19.13.5;5. DISCUSSION AND CONCLUSIONS;418
19.13.6;6. REFERENCES;419
19.14;CHAPTER 64. A NEW DESIGN METHOD OF SLIDING MODE CONTROL SYSTEMS BASED ON THE CONSTRUCTION OF LIAPUNOV FUNCTIONS;420
19.14.1;1. INTRODUCTION;420
19.14.2;2. CONNECTION BETWEEN SLIDING MODE CONTROL THEORY AND VARIABLE GRADIENT METHOD;420
19.14.3;3. NUMERICAL EXAMPLES;421
19.14.4;4. PILOT PLANT EXPERIMENTS;424
19.14.5;5. CONCLUSION;425
19.14.6;6. REFERENCES;425
19.15;CHAPTER 65. OPTIMAL AVERAGING LEVEL CONTROL FOR MULTI-TANK SYSTEMS;426
19.15.1;1. INTRODUCTION;426
19.15.2;2. OPTIMAL AVERAGING LEVEL CONTROL FOR TANKS IN SERIES;426
19.15.3;3. COMPARISON AMONG DIFFERENT OPTIMAL AVERAGING LEVEL CONTROL SCHEMES;428
19.15.4;4. APPLICATION IN AN INDUSTRIAL TEST PROBLEM;428
19.15.5;5. CONCLUSIONS;429
19.15.6;Appendix;430
19.15.7;References;431
19.16;CHAPTER 66. CONTROL OF COMPLEX DISTILLATION CONFIGURATIONS USING A NONLINEAR WAVE THEORY;432
19.16.1;1. Introduction;432
19.16.2;2. Profile Position Control of Distillation Column section;432
19.16.3;3. Control of Sidestream/Sidestripper configuration;434
19.16.4;4. Control of Complex Prefractionater/sidestream Column;436
19.16.5;5. Conclusion;437
19.16.6;6. Nomenclature;437
19.16.7;7. References;438
20;PART XIV: TUTORIAL PAPER;440
20.1;CHAPTER 67. STATISTICAL PROCESS CONTROL OF MULTIVARIATE PROCESSES;440
20.1.1;1. INTRODUCTION;440
20.1.2;2. MULTIVARIATE CHARTS FOR STATISTICAL QUALITY CONTROL;441
20.1.3;3. MULTIVARIATE STATISTICAL PROCESS CONTROL;444
20.1.4;4. SUMMARY;446
20.1.5;5. REFERENCES;447
21;PART XV: STATISTICAL CONTROL TECHNIQUES I;452
21.1;Chapter 68. Predictive Maintenance using PCA;452
21.1.1;1. INTRODUCTION;452
21.1.2;2. PCA MODELLING;452
21.1.3;3. SMART ALARM MONITORING;453
21.1.4;4. TOWARDS FAILURE PREDICTION;454
21.1.5;5. EXAMPLE APPLICATION;456
21.1.6;6. CONCLUSIONS;457
21.1.7;ACKNOWLEDGEMENT;457
21.1.8;REFERENCES;457
21.2;CHAPTER 69. AUTOASSOCIATIVE NEURAL NETWORKS IN BIOPROCESS CONDITION MONITORING;458
21.2.1;1. INTRODUCTION;458
21.2.2;2. BIOPROCESS DESCRIPTIONS;459
21.2.3;3. LINEAR PATTERN RECOGNITION;459
21.2.4;4. APPLICATIONS OF PCA TO BIOPROCESSES;459
21.2.5;5. AUTOASSOCIATIVE NEURAL NETWORKS;460
21.2.6;6. APPLICATIONS OF AUTOASSOCIATIVE NETWORKS TO BIOPROCESSES;460
21.2.7;7. CONCLUDING REMARKS;461
21.2.8;8. ACKNOWLEDGEMENTS;462
21.2.9;9. REFERENCES;462
21.3;CHAPTER 70. STATISTICAL PROCESS MONITORING AND DISTURBANCE ISOLATION IN MULTIVARIATE CONTINUOUS PROCESSES;464
21.3.1;1. INTRODUCTION;464
21.3.2;2. STATISTICAL MONITORING OF MULTIVARIABLE PROCESSES;464
21.3.3;PLANT DESCRIPTION;467
21.3.4;RESULTS;467
21.3.5;CONCLUSIONS;467
21.3.6;REFERENCES;468
21.4;Chapter 71. Detection of Unmodelled Disturbances Effects by Coherence Analysis;470
21.4.1;1 INTRODUCTION;470
21.4.2;2 COHERENCE ANALYSIS;470
21.4.3;3 DISTURBANCE MODELS;471
21.4.4;4 SPECIAL CASES;472
21.4.5;5 SIMULATION EXAMPLES;472
21.4.6;6 PROCESS EXAMPLE;473
21.4.7;7 SUMMARY;474
21.4.8;8 REFERENCES;474
22;PART XVI: MODELING AND SIMULATION IV;476
22.1;CHAPTER 72. ILL-CONDITIONEDNESS AND PROCESS DIRECTIONALITY - THE USE OF CONDITION NUMBERS IN PROCESS CONTROL;476
22.1.1;1. INTRODUCTION;476
22.1.2;2. THE SCALING DEPENDENCY OF THE CONDITION NUMBER;476
22.1.3;3. CONDITION NUMBERS AND CONTROL DIFFICULTIES;479
22.1.4;4. DISCUSSION AND CONCLUSIONS;480
22.1.5;5. ACKNOWLEDGMENTS;480
22.1.6;6. REFERENCES;480
22.2;CHAPTER 73. CONTROLLABILITY ANALYSIS OF SISO SYSTEMS;482
22.2.1;1 INTRODUCTION;482
22.2.2;2 LINEAR CONTROL THEORY;483
22.2.3;3 CONTROLLABILITY ANALYSIS;484
22.2.4;4 NEUTRALIZATION PROCESS;487
22.2.5;5 REFERENCES;487
22.2.6;APPENDIX. Scaling procedure;487
22.3;CHAPTER 74. PROCESS IDENTIFICATION USING DISCRETE WAVELET TRANSFORMS;488
22.3.1;1. INTRODUCTION;488
22.3.2;2. SOME ISSUES IN PROCESS IDENTIFICATION;488
22.3.3;3. WAVELET TRANSFORMS;489
22.3.4;4. PROCESS IDENTIFICATION USING WAVELETS;490
22.3.5;5. SIEVED PARAMETER ESTIMATION;492
22.3.6;6. ILLUSTRATIVE EXAMPLE;492
22.3.7;7. CONCLUSIONS;493
22.3.8;8. REFERENCES;493
22.4;CHAPTER 75. DETERMINING NECESSARY MODEL RESOLUTION IN MODEL BASED CONTROL OF DISTRIBUTED PARAMETER PROCESSES;494
22.4.1;1 INTRODUCTION;494
22.4.2;2 MODEL REDUCTION;494
22.4.3;3 FACTORS THAT INFLUENCES THE CHOICE OF N;495
22.4.4;4 CRITERION FOR CHOOSING N;496
22.4.5;5 SIMULATION STUDY;497
22.4.6;6 CONCLUSION;499
22.4.7;7 ACKNOWLEDGEMENT;499
22.4.8;REFERENCES;499
22.5;CHAPTER 76. LEAST SQUARES FORMULATION OF STATE ESTIMATION;500
22.5.1;ABSTRACT;500
22.5.2;1. INTRODUCTION;500
22.5.3;2. LEAST SQUARES FORMULATION OF STATE ESTIMATION;501
22.5.4;3. LINEAR STATE ESTIMATION;503
22.5.5;4. NONLINEAR ESTIMATION;504
22.5.6;References;505
23;PART XVII: NONLINEAR CONTROL AND OPTIMIZATION IV;506
23.1;CHAPTER 77. ELEMENTARY NONLINEAR DECOUPLING CONTROL OF COMPOSITION IN BINARY DISTILLATION COLUMNS;506
23.1.1;1. INTRODUCTION;506
23.1.2;2. ELEMENTARY NONLINEAR DECOUPLING (END);506
23.1.3;3. A DYNAMIC MODEL OF A BINARY DISTILLATION COLUMN;507
23.1.4;4. ILLUSTRATION OF END CONTROL OF A DISTILLATION COLUMN;507
23.1.5;5. CONCLUSION;508
23.1.6;6. ACKNOWLEDGEMENT;508
23.1.7;7. REFERENCES;508
23.2;CHAPTER 78. APPLICATION OF GEOMETRIC NONLINEAR CONTROL IN THE PROCESS INDUSTRIES - A CASE STUDY;512
23.2.1;1. Introduction;512
23.2.2;2. Model Reduction;514
23.2.3;3. State Observer;514
23.2.4;4. Experiments;515
23.2.5;5. Summary;517
23.2.6;6. Nomencalture;517
23.2.7;7. References;517
23.3;CHAPTER 79. GRADE TRANSITION CONTROL FOR AN IMPACT COPOLYMERIZATION REACTOR;518
23.3.1;1. INTRODUCTION;518
23.3.2;2. PROCESS DESCRIPTION;519
23.3.3;3. QUALITY & PROCESS MODELS;519
23.3.4;4. OPTIMIZATION PROBLEM;521
23.3.5;5. IMPLEMENTATION OF MODEL PREDICTIVE CONTROL;522
23.3.6;6. CONCLUSION;523
23.3.7;7. REFERENCES;523
23.4;CHAPTER 80. NONLINEAR ADAPTIVE CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR;524
23.4.1;1. INTRODUCTION;524
23.4.2;2. MODELLING OF THE REACTOR;524
23.4.3;3. CONTROLLER DESIGN;526
23.4.4;4. SIMULATION RESULTS;528
23.4.5;5. CONCLUSION;529
23.4.6;6. REFERENCES;529
23.5;CHAPTER 81. EXTERNAL MODEL CONTROL OF A PERISTALTIC PUMP;530
23.5.1;1. INTRODUCTION;530
23.5.2;2. PROCESS MODEL;531
23.5.3;3. PROBLEM STATEMENT;531
23.5.4;4. DISTURBANCE REJECTION IN SMITH PREDICTOR;531
23.5.5;5. DEADBEAT DISTURBANCE PREDICTION;532
23.5.6;6. ALGEBRAIC DISTURBANCE REJECTION CONDITION;533
23.5.7;7. SIMULATION;534
23.5.8;8. CONCLUSIONS;534
23.5.9;ACKNOWLEDGEMENT;535
23.5.10;REFERENCES;535
23.5.11;A PROOF OF THEOREM 1;535
24;PART XVIII: STATISTICAL CONTROL TECHNIQUES II;536
24.1;CHAPTER 82. MULTIVARIATE STATISTICAL PROCESS CONTROL OF BATCH PROCESSES USING PCA AND PLS;536
24.1.1;INTRODUCTION;536
24.1.2;NATURE OF BATCH DATA;537
24.1.3;PROJECTION METHODS MULTI - WAY PCA AND PLS;537
24.1.4;EXAMPLE OF MPCA APPLICATION;538
24.1.5;CONCLUDING REMARKS;538
24.1.6;REFERENCES;539
24.2;CHAPTER 83. BIAS DETECTION AND ESTIMATION IN DYNAMIC DATA RECONCILIATION;542
24.2.1;1. INTRODUCTION;542
24.2.2;2. BACKGROUND;542
24.2.3;3. EXAMPLES;545
24.2.4;4. CONCLUSION;547
24.2.5;5. REFERENCES;547
24.3;CHAPTER 84. MONITORING AND FAULT DETECTION FOR AN HVAC CONTROL SYSTEM;548
24.3.1;1. INTRODUCTION;548
24.3.2;2. CONTROL SYSTEM PERFORMANCE;548
24.3.3;3. FAULT DETECTION STRATEGY;550
24.3.4;4. SIMULATION STUDY;550
24.3.5;CONCLUSIONS;551
24.3.6;REFERENCES;551
24.3.7;ACKNOWLEDGEMENTS;552
24.4;Chapter 85. Modelling of a continuous digester for process surveillance and prediction;554
24.4.1;1 INTRODUCTION;554
24.4.2;2 CONCEPTUAL MODEL;555
24.4.3;3 MATHEMATICAL MODEL;556
24.4.4;4 SIMULATION RESULTS;557
24.4.5;5 MODEL REDUCTION;558
24.4.6;6 CONCLUSIONS;559
24.4.7;7 ACKNOWLEDGMENTS;559
24.4.8;REFERENCES;559
25;AUTHOR INDEX;560