Advanced Control of Chemical Processes 1994 (eBook)

Front Cover 1
Reprinted from Advanced Control of Chemical Processes (ADCHEM'94) 2
Copyright Page 3
Table of Contents 8
IFAC SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSES (ADCHEM'94) 4
Preface 6
PART I: TUTORIAL PAPER 14
Chapter 1. Nonlinear Input/Output Modeling 14
1 INTRODUCTION 14
2 MODEL STRUCTURES 14
3 MODEL BEHAVIOR 17
4 STRUCTURE SELECTION 18
5 HIGHER-ORDER STATISTICS 19
6 IDENTIFIABILITY AND INPUT SEQUENCE DESIGN 20
7 AN EXAMPLE 21
8 SUMMARY 26
9 REFERENCES 27
PART II: MODELING AND SIMULATION I 30
CHAPTER 2. SYSTEMATIC TECHNIQUES FOR DETERMINING MODELING REQUIREMENTS FOR SISO AND MIMO FEEDBACK CONTROL PROBLEMS 30
1. Introduction 30
2. Control Relevant Parameter Estimation 30
3. Solving the MIMO Estimation Problem 32
4. Conclusions 34
References 34
CHAPTER 3. DYNAMIC SIMULATION FOR INTEGRATED DESIGN AND CONTROL OF PROCESS FLOWSHEETS 36
1. INTRODUCTION 36
2. MODELLING ASPECTS 36
3. COMPUTATIONAL ASPECTS 38
4. APPLICATION EXAMPLE 39
5. CONCLUSION 41
6. REFERENCES 41
CHAPTER 4. DYNAMIC SIMULATION OF A MULTISTAGE REACTOR 42
1. INTRODUCTION 42
2. OVERVIEW AND MODELLING OF THE MULTISTAGE REACTOR EQUIPMENT 42
3. STUDY OF CONTROLLABILITY THROUGH DYNAMIC SIMULATION 44
4. CONCLUSION 46
5. REFERENCES 46
CHAPTER 5. POISSON WAVELETS APPLIED TO MODEL IDENTIFICATION 48
1. INTRODUCTION 48
2. POISSON WAVELET TRANSFORM 49
3. PARAMETER ESTIMATION 49
4. EXAMPLE: TANKS IN SERIES 49
5. MODEL VALIDATION 51
6. SUMMARY 51
7. REFERENCES 52
PART III: MODELING AND SIMULATION II 54
Chapter 6. Low Order Empirical Modeling for Nonlinear Systems 54
1 INTRODUCTION 54
2 THE EMPIRICAL MODEL 54
3 IDENTIFICATION FROM INPUT/OUTPUT DATA 55
4 EXAMPLES 56
5 SUMMARY/CONCLUSIONS 59
6 REFERENCES 59
CHAPTER 7. BILINEAR ID..TIF.C..I.. OF NONLINEAR PROCESSES 60
1 INTRODUCTION 60
2 INPUT/OUTPUT MODEL STRUCTURE 60
3 CALCULATION OF VOLTERRA KERNELS 62
4 PARAMETER CONSTRAINTS 62
5 PARAMETER ESTIMATION 63
6 FCCU EXAMPLE 64
7 CONCLUSIONS 64
REFERENCES 65
CHAPTER 8. SYSTEM IDENTIFICATION OF AN ADSORPTION PROCESS USING NEURAL NETWORKS 66
1. Introduction 66
2. The adsorption process for wastewater treatment 67
3. Artificial neural networks and their training 67
4. Results 68
5. Recurrent networks for full trajectory prediction 70
6. Discussion and Conclusions 70
References 71
CHAPTER 9. DYNAMIC MODELLING AND SIMULATION OF A MULTI-PURPOSE BATCH PILOT PLANT 72
1. INTRODUCTION 72
2. BATCH PILOT PLANT 73
3. PLANT MODEL 73
4. OPERATIONS MODEL 74
5. SIMULATIONS 75
6. CONCLUSION 77
7. NOMENCLATURE 77
8. REFERENCES 77
Chapter 10. Improved Training of Neural Networks with Complex Search Spaces 78
1 Introduction 78
2 Conventional offline training 79
3 The presented improvements 79
References 81
PART IV: NONLINEAR CONTROL AND OPTIMIZATION I 84
CHAPTER 11. ON-LINE SCHEDULE OPTIMIZATION FOR MIXED-BATCH/CONTINUOUS PLANTS 84
1. INTRODUCTION 84
2. SCHEDULING STRATEGY 84
3. REACTIVENESS 85
4. IMPLEMENTATION AND RESULTS 89
5. CONCLUSIONS 89
6. REFERENCES 89
CHAPTER 12. EFFICIENT COMPUTATION OF BATCH REACTOR CONTROL PROFILES UNDER PARAMETRIC UNCERTAINTY 90
1. INTRODUCTION 90
2. CONCEPT OF OPTIMIZATION UNDER UNCERTAINTY 91
3. SOLUTION STRATEGY 91
4. SIMULATION EXAMPLE 93
5. CONCLUSIONS 95
6. REFERENCES 95
CHAPTER 13. PARAMETER ESTIMATION AND NONLINEAR PREDICTIVE CONTROL FOR RTP 96
1. INTRODUCTION 96
2. MODEL DESCRIPTION 97
3. NONLINEAR PARAMETER ESTIMATION 98
4. MODEL TEANSFORMATION 99
5. NONLINEAR MODEL PREDICTIVE CONTROL 100
6. CONCLUSION 101
7. ACKNOWLEDGEMENT 101
8. REFERENCES 101
CHAPTER 14. MODEL-BASED PREDICTIVE CONTROL: THEORY AND IMPLEMENTATION ISSUES 102
1 INTRODUCTION 102
2 BASIC PHILOSOPHY OF LRPC 102
3 DERIVATION OF SISO LRPC 103
4 STABILITY ISSUES IN LRPC 104
5 CONSTRAINED LRPC 104
6 IMPLEMENTATION ISSUES 106
7 CONCLUDING REMARKS 106
REFERENCES 106
APPENDIX A 107
CHAPTER 15. INTEGRATED ADVANCED CONTROL AND CLOSED-LOOP REAL-TIME OPTIMIZATION OF AN OLEFINS PLANT 108
1. INTRODUCTION 108
2. OPTIMIZATION SYSTEM 109
3. ADVANCED CONTROL SYSTEM 110
4. COMPUTER SYSTEM 113
5. ECONOMIC BENEFITS 113
6. CONCLUSION 113
7. REFERENCES 113
PART V: KNOWLEDGE-BASED AND MODEL-BASED CONTROL I 114
Chapter 16. A Genetic Algorithm for MIMO Feedback Control System Design 114
1. INTRODUCTION 114
2. A CASE STUDY: THE SHELL PROBLEM 114
3. FEEDBACK CONTROL DESIGN USING SSV 115
4. A GENETIC ALGORITHM FOR FEEDBACK CONTROL DESIGN 116
5. SOLUTIONS TO THE SHELL PROBLEM 117
6. CONCLUSIONS 119
ACKNOWLEDGEMENT 119
REFERENCES 119
CHAPTER 17. DYNAMIC SYSTEM MODELLING USING MIXED NODE NEURAL NETWORKS 120
1. INTRODUCTION 120
2. NEURAL NETWORKS WITH MIXED TYPES OF HIDDEN NEURONS 120
3. SEQUENTIAL ORTHOGONAL TRAINING OF NEURAL NETWORKS 121
4. APPLICATION TO A DISTILLATION COLUMN 124
5. CONCLUSIONS 125
6. REFERENCES 125
CHAPTER 18. REAL-TIME CONTROL OF A WASTE WATER NEUTRALIZATION PROCESS USING RADIAL BASIS FUNCTIONS 126
1. INTRODUCTION 126
2. SYSTEM REPRESENTATION 127
3. CONTROLLER DESIGN 128
4. WASTE WATER pH NEUTRALIZATION 128
5. CONCLUSIONS 130
6. REFERENCES 131
PART VI: POSTER PAPERS I 132
CHAPTER 19. MODEL VALIDATION TEST 132
1. INTRODUCTION 132
2. PROBLEM FORMULATION 132
3. STRATEGY 133
4. INFINITY NORM AND STABILITY 133
5. DATA TRANSFORM 133
6. SELECTION OF ALL PASS FILTER 134
7. STABILITY DETERMINATION 135
8. NUMERICAL EXAMPLE 136
9. DISCUSSION 137
10. CONCLUSION 137
11. REFERENCES 137
CHAPTER 20. IDENTIFICATION OF COMBINED PHYSICAL AND EMPIRICAL MODELS USING NONLINEAR A PRIORI KNOWLEDGE 138
1. INTRODUCTION AND LITERATURE REVIEW 138
2. DATA EXTRACTION METHOD 139
3. THE CVA IDENTIFICATION METHOD 139
4. SIMULATION RESULTS 140
5. EXPERIMENTAL RESULTS 141
6. CONCLUSIONS 143
ACKNOWLEDGMENTS 143
REFERENCES 143
CHAPTER 21. COMPUTER-AIDED MODELLING : SPECIES TOPOLOGY 144
AIMS AND GOALS OF COMPUTER-AIDED MODELLING 144
PHYSICAL TOPOLOGY 145
CONSTRUCTION OF THE SPECIES TOPOLOGY 145
MODIFICATION OF SPECIES TOPOLOGY 146
A BRIEF EXAMPLE 147
CONCLUSION 149
References 149
CHAPTER 22. OPTIMIZATION OF PROCESS SYSTEMS WITH DISCONTINUITIES 150
1. INTRODUCTION 150
2. AN NLP FORMULATION FOR DAOP 151
3. NEW NLP FORMULATION WITH SMOOTH APPROXIMATION 153
4. CONCLUSIONS 155
5. REFERENCES 155
CHAPTER 23. STEAM BALANCE OPTIMIZATION IN CHEMICAL PLANT 156
1. INTRODUCTION 156
2. PROCESS DESCRIPTION 156
3. SYSTEM CONFIGURATION 157
4. SYSTEM FUNCTIONALITY 157
5. CURRENT STATUS OF THE PROJECT 158
6. CONCLUSION 159
CHAPTER 24. ADAPTIVE CONTROL OF MIMO NON-LINEAR SYSTEMS USING LOCAL ARX MODELS AND INTERPOLATION 160
1 INTRODUCTION 160
2 MODEL REPRESENTATION USING LOCAL MODELS AND INTERPOLATION 160
3 PARAMETER ESTIMATION 162
4 ADAPTIVE CONTROL 163
5 DISCUSSION 164
6 SIMULATION EXAMPLE 164
7 CONCLUDING REMARKS 166
ACKNOWLEDGMENTS 166
References 166
APPENDIX 166
CHAPTER 25. A DISTURBANCE ESTIMATOR FOR MODEL PREDICTIVE CONTROL 168
1. INTRODUCTION 168
2. DISTURBANCE PREDICTOR 169
3· EXAMPLES 170
4. CONCLUSIONS 172
5. REFERENCES 172
APPENDIX 173
Chapter 26. Controller Synthesis for Two-Time-Scale Nonlinear Processes 174
Introduction 174
Two-Time-Scale Processes: Preliminaries 174
Controller Synthesis for Two-Time-Scale Nonlinear Processes with Stable Fast Dynamics 175
Definitions of the various concepts of relative order 175
Controller Synthesis for Two-Time-Scale Nonlinear Processes with Unstable Fast Dynamics 177
Closed loop stability 178
Acknowledgement 178
References 178
CHAPTER 27. ANALYSIS AND SYNTHESIS METHODS FOR ROBUST MODEL PREDICTIVE CONTROL 180
1. INTRODUCTION 180
2. MULTIVARIABLE EQDMC 181
3. ROBUST STABILITY OF MIMO EQDMC 181
4. EQDMC PERFORMANCE 182
5. EQDMC TUNING METHODOLOGY 182
6. SIMULATION STUDIES 183
7. CONCLUSIONS 185
8. REFERENCES 185
CHAPTER 28. REDUCED HESSIAN SUCCESSIVE QUADRATIC PROGRAMMING FOR REALTIME OPTIMIZATION 186
1. REDUCED HESSIAN SQP 186
2. SOLUTION TECHNIQUES 187
3. PROCESS OPTIMIZATION 188
4. CONCLUSION 190
5. ACKNOWLEDGEMENTS 191
6. REFERENCES 191
Chapter 29. A real-time CAD environments for model predictive controllers 192
1. INTRODUCTION 192
2. CONCEPT OF MIPCON 193
3. APPLICATION STUDY 196
4. CONCLUSION 196
5. REFERENCES 197
PART VII: TUTORIAL PAPER 198
CHAPTER 30. NONLINEAR MODEL PREDICTIVE CONTROL: A TUTORIAL AND SURVEY 198
1. INTRODUCTION 198
2. MPC FOR LINEAR PLANTS 199
3. MPC FOR NONLINEAR PLANTS 203
4. CONCLUSIONS AND FUTURE OUTLOOK 208
5. REFERENCES 209
PART VIII: SURVEY PAPER 212
CHAPTER 31. THE PROCESS INDUSTRY REQUIREMENTS OF ADVANCED CONTROL TECHNIQUES: CHALLENGES AND OPPORTUNITIES 212
1. INTRODUCTION 212
2. THE HISTORICAL PERSPECTIVE 212
3. HISTORICAL PERSPECTIVE IN OTHER MANUFACTURING INDUSTRIES 214
4. REVIEW 215
5. STRENGTHS OF THE CHEMICAL PROCESS INDUSTRIES 215
6. WEAKNESSES 215
7. OPPORTUNITIES IN THE PROCESS INDUSTRIES 217
8. THREATS 218
9. MARKETING PROCESS CONTROL 219
10. INCREASING USER FRIENDLINESS 219
12. BENCHMARKING 220
13. TRAINING/EDUCATION 220
14. NEW PROCESS TECHNOLOGY 220
15. CONCLUSIONS 221
16. REFERENCES 221
17. ACKNOWLEDGEMENTS 221
PART IX: NONLINEAR CONTROL AND OPTIMIZATION II 222
CHAPTER 32. NONLINEAR PREDICTIVE CONTROL USING LOCAL MODELS - APPLIED TO A BATCH PROCESS 222
1 INTRODUCTION 222
2 LOCAL MODELLING 223
3 MODEL PREDICTIVE CONTROL 224
4 SIMULATION EXAMPLE 224
ACKNOWLEDGMENTS 227
5 CONCLUSIONS 227
REFERENCES 227
Chapter 33. Iterative refinement of model predictive control 228
1 Introduction 228
2 Modeling and predictive control 228
3 Parameter centering using adaptive control 229
4 Summary and Conclusions 232
References 232
CHAPTER 34. A CASE-STUDY IN ON-LINE OPTIMAL CONTROL 234
INTRODUCTION 234
REFERENCES 236
CHAPTER 35. OVERRIDE CONFIGURATION OF GENERALIZED PREDICTIVE CONTROL FOR A MULTI-PURPOSE CONTROL PROBLEM 242
1. INTRODUCTION 242
2. CONTROLLER 243
3. SIMULATION 244
4. CONCLUSION 246
5. Reference 246
PART X: MODELING AND SIMULATION III 248
CHAPTER 36. DYNAMICS AND STABILITY OF POLYMERIZATION PROCESS FLOWSHEETS USING POLYRED 248
1. INTRODUCTION 248
2. THE POLYRED PACKAGE 248
3. PROCESS STABILITY ANALYSIS 249
4. SOME EXAMPLES 250
5. CONCLUSIONS 254
6. ACKNOWLEDGMENTS 254
7. REFERENCES 254
CHAPTER 37. OPERATION SUPPORT SYSTEM USING DYNAMIC SIMULATION FOR A COMBINED BATCH/CONTINUOUS PLANT 256
1. INTRODUCTION 256
2. A COMBINED BATCH/CONTINUOUS PLANT 256
3. MODELING THE COMBINED BATCH/CONTINUOUS PLANT 257
4. OPERATION SUPPORT SYSTEM 258
5. OPERATIONAL GUIDANCE 258
6. REAL TIME IMPLEMENTATION IN THE ACTUAL PLANT 260
7. CONCLUDING REMARKS AND FUTURE DEVELOPMENT 261
8. ACKNOWLEDGMENTS 261
9. REFERENCES 261
CHAPTER 38. A DYNAMIC SIMULATION STRATEGY FOR CYCLED DISTRIBUTED PARAMETER SYSTEMS 262
1. INTRODUCTION 262
2. PROCESS DESCRIPTION 263
3. MATHEMATICAL MODELLING 263
4. SIMULATION STRATEGY AND NUMERICAL METHODS 264
5. EXAMPLE SIMULATION 265
6. CONCLUSIONS 266
7. NOMENCLATURE 267
8. REFERENCES 267
CHAPTER 39. LOCAL THERMODYNAMIC MODELS FOR DYNAMIC PROCESS SIMULATION 268
1. INTRODUCTION 268
2. MODEL 268
3. DISCUSSION 273
4. REFERENCES 273
CHAPTER 40. RIGOROUS DYNAMIC SIMULATION OF DISTILLATION COLUMNS BASED ON UV-FLASH 274
1 INTRODUCTION 274
2 DYNAMIC DISTILLATION MODELS 274
3 FLASH CALCULATIONS 276
4 THERMODYNAMICS 277
5 EXAMPLE COLUMN 278
6 CONCLUSION 279
REFERENCES 279
PART XI: NONUNEAR CONTROL AND OPTIMIZATION III 280
CHAPTER 41. A TRUST REGION STRATEGY FOR NEWTON-TYPE PROCESS CONTROL 280
1. INTRODUCTION 280
2. OVERVIEW OF THE CONTROL FORMULATION 281
3. TRUST REGION STRATEGIES FOR NONLINEAR OPTIMIZATION 281
4. PROCESS EXAMPLES 282
5. CONCLUSIONS 284
ACKNOWLEDGMENTS 285
6. REFERENCES 285
CHAPTER 42. A MULTIMODEL MIXED H2/H8 PROBLEM FOR PLANTS WITH STRUCTURED UNCERTAINTY 286
1. INTRODUCTION 286
2. PROBLEM FORMULATION 287
3. SOLUTION PROCEDURE 288
4. GRADIENT EXPRESSIONS 289
5. A DISTILLATION COLUMN EXAMPLE 290
6. CONCLUSIONS 290
ACKNOWLEDGMENTS 291
7. REFERENCES 291
CHAPTER 43. ROBUST MODEL PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS WITH CONSTRAINTS 292
1. INTRODUCTION 292
2. MPC ALGORITHM 293
3. BASIC ASSUMPTIONS 295
4. ANALYSIS OF THE ALGORITHM 296
5. REFERENCES 297
Chapter 44. A Kalman filter based robust model predictive control with constraints 298
1. INTRODUCTION 298
2. ALGORITHMS 299
3. APPLICATION STUDY 302
4. CONCLUSION 303
5. REFERENCES 303
6. APPENDIX 303
CHAPTER 45. A PRACTICAL APPROACH TO APPROXIMATE INPUT-OUTPUT LINEARIZATION 304
1. INTRODUCTION 304
2. APPROXIMATE INPUT-OUTPUT MODELS 305
3. APPROXIMATE INPUT-OUTPUT LINEARIZING CONTROLLER SYNTHESIS 306
4. APPLICATION - VAN DE VUSSE REACTOR 307
5. CONCLUSIONS 308
Acknowledgements 309
REFERENCES 309
6. APPENDIX 309
PART XII: KNOWLEDGE-BASED AND MODEL-BASED CONTROL II 310
CHAPTER 46. FUZZY BASED CONTROL OF A DISTILLATION PLANT START-UP 310
1. INTRODUCTION 310
2. START-UP CONTROL STRUCTURING 310
3. EXPERIMENT 312
4. RESULTS AND DISCUSSION 314
5. CONCLUSION 315
REFERENCES 315
CHAPTER 47. DERIVATION OF FUZZY RULES FOR PARAMETER FREE PID GAIN TUNING 316
1. INTRODUCTION 316
2. STATEMENT OF THE PROBLEM 316
3. DERIVATION OF THE RULE BASE 317
4. APPLICATIONS 319
5. BUILDING ON THE EXPERT 320
6. CONCLUSION 320
7. ACKNOWLEDGEMENTS 321
8. REFERENCES 321
CHAPTER 48. A COMPARISON OF VARIOUS CONTROL SCHEMES FOR CONTINUOUS BIOREACTOR 322
1 INTRODUCTION 322
2 CONTROLLABILITY MEASURES 323
3 DYNAMIC MODEL 323
4 CONTROLLABILITY STUDY 325
5 SIMULATION RESULTS 326
6 CONCLUSIONS 327
7 REFERENCES 327
CHAPTER 49. CONTROLLER VERIFICATION UNDER NON-PARAMETRIC UNCERTAINTY 328
INTRODUCTION 328
NON-PARAMETRIC MONTE-CARLO 329
THE NSIM ALGORITHM 329
CSTR PROCESS 330
RESULTS 331
CONCLUSIONS 331
REFERENCES 332
CHAPTER 50. AUTOMATIC TUNING OF PID CONTROLLERS FOR UNSTABLE PROCESSES 334
1. INTRODUCTION 334
2. STABILIZABILITY OF UNSTABLE SYSTEMS BY PID CONTROLLERS 335
3. EXTENSION OF THE TUNING TECHNIQUE TO UNSTABLE SYSTEMS 336
4. TEST PROCESS 337
5. RESULTS 337
6. CONCLUSIONS 339
7. REFERENCES 339
PART XIII: POSTER PAPERS II 340
CHAPTER 51. A COMPARISON OF DEDUCTIVE AND INDUCTIVE MODELS FOR PRODUCT QUALITY ESTIMATION 340
1. INTRODUCTION 340
2. DEDUCTIVE MODEL 341
3. NEURAL NETWORK APPROACH 342
4. COMPARISON 343
5. HYBRID MODEL 344
6. CONCLUSIONS 345
7. REFERENCES 345
CHAPTER 52. EXTRACTION OF OPERATING SIGNATURES BY EPISODIC REPRESENTATION 346
1. INTRODUCTION 346
2. EPISODIC REPRESENTATION 346
3. SCALING FOR SPIKES AND TRENDS 348
4. ILLUSTRATIVE EXAMPLES 350
5. CONCLUDING REMARKS AND FUTURE DEVELOPMENT 351
6. REFERENCES 351
CHAPTER 53. MONITORING CHEMICAL REACTION SYSTEMS USING INCREMENTAL TARGET FACTOR ANALYSIS 352
1 Introduction 352
2 Target Factor Analysis 352
3 Incremental TFA 353
4 Monitoring Chemical Reaction Systems Using IncTFA 356
5 Conclusions 357
6 References 357
CHAPTER 54. SEQUENTIAL CONTROL ISSUES IN THE PLANT-WIDE CONTROL SYSTEM 358
1. INTRODUCTION 358
2. SEQUENTIAL CONTROL SYSTEM 359
3. GLOBAL STATE TRANSITION GRAPH 360
4. VERIFICATION OF THE SEQUENTIAL CONTROL SYSTEM WITH THE RULE TRANSITION GRAPH 361
5. TRANSLATION OF THE RULE TRANSITION GRAPH INTO THE PSEUDO-STATE TRANSITION GRAPH 362
6. CONCLUDING REMARKS AND FUTURE DEVELOPMENT 363
7. REFERENCES 363
CHAPTER 55. COMPARISON OF ADVANCED DISTILLATION CONTROL TECHNIQUES 364
1. INTRODUCTION 364
2. DISTILLATION CONTROL DIFFICULTY 365
3. CASE STUDY AND SIMULATOR 365
4. IMPLEMENTATION APPROACH FOR EACH CONTROLLER 366
5. RESULTS 367
6. CONCLUSION 369
7. REFERENCES 369
CHAPTER 56. A PROTOTYPE PACKAGE FOR SIMULTANEOUS PROCESS AND CONTROL SYSTEM DESIGN 370
1. INTRODUCTION 370
2. HDA PROCESS CONTROL SYSTEM SYNTHESIS 372
3. CONCLUSION 374
4. REFERENCES 374
Chapter 57. Opportunities and Difficulties with 5 x 5 Distillation Control 376
1 Introduction 376
2 5 x 5 Distillation Model 377
3 Controllability analysis 377
4 H8/µ control 379
5 Model Predictive 5 x 5 control 381
6 Conclusions 382
References 382
CHAPTER 58. ROBUST MULTIVARIABLE CONTROL SYSTEM DESIGNS THROUGH REAL-TIME SUPERVISORY KNOWLEDGE-BASED SYSTEMS 384
1. INTRODUCTION 384
2. PROCESS, CONTROL AND KBS DESCRIPTION 385
3. AUTOMATIC H8 CONTROLLER TUNING 385
4. CLOSED-LOOP ROBUSTNESS 386
5. PERFORMANCE ASSESSMENT 386
6. SUPERVISORY KBS PERFORMANCE 386
7. KBS VALIDATION TESTS 387
8. SUMMARY AND CONCLUSIONS 389
REFERENCES 389
CHAPTER 59. A COMPARISON OF NEURAL NETWORK BASED CONTROL STRATEGIES FOR A CSTR 390
1. INTRODUCTION 390
2. CONTROL PROBLEM 390
3. CONTROL STRATEGIES USING THE NEURAL NETWORKS 391
4. SIMULATION RESULTS 392
5. CONCLUSION 395
6. REFERENCES 395
CHAPTER 60. NONLINEAR MODELING USING NEURAL NETWORKS WITH MULTIRESOLUTION REPRESENTATIONS 396
1. INTRODUCTION 396
2. MULTIRESOLUTION REPRESENTATIONS 396
3. MODELING USING NEURAL NETWORKS 397
4. EXAMPLES 397
5. CONCLUSION 398
6. REFERENCES 399
CHAPTER 61. USING KNOWLEDGE-BASED NEURAL NETWORK PROCESS MODELS FOR MODEL-BASED CONTROL 402
1. INTRODUCTION 402
2. MANNIDENT NETWORK MODELS 403
3. THE CONTROL EXPERIMENTS 403
4. ANN MODEL-BASED CONTROLLERS 404
5. DISCUSSION AND CONCLUSIONS 407
6. REFERENCES 407
CHAPTER 62. RULE BASED COMBUSTION DISTURBANCE PREDICTION AND CONTROL SYSTEM 408
1. INTRODUCTION 408
2. OUTLINE OF REFUSE INCINERATOR 408
3. REFUSE INCINERATOR OPERATION EXPERT SYSTEM 409
4. INTELLIGENT COMBUSTION CONTROL FOR REFUSE INCINERATOR 411
5. SUMMARY 413
6. REFERENCES 413
CHAPTER 63. OPTIMAL OPERATION OF MULTICOMPONENT BATCH DISTILLATION - A COMPARATIVE STUDY USING CONVENTIONAL AND UNCONVENTIONAL COLUMNS 414
1. INTRODUCTION 414
2. COLUMN MODEL 415
3. CRITERION FOR SELECTING THE BEST COLUMN CONFIGURATION 416
4. EXAMPLE 416
5. DISCUSSION AND CONCLUSIONS 418
6. REFERENCES 419
CHAPTER 64. A NEW DESIGN METHOD OF SLIDING MODE CONTROL SYSTEMS BASED ON THE CONSTRUCTION OF LIAPUNOV FUNCTIONS 420
1. INTRODUCTION 420
2. CONNECTION BETWEEN SLIDING MODE CONTROL THEORY AND VARIABLE GRADIENT METHOD 420
3. NUMERICAL EXAMPLES 421
4. PILOT PLANT EXPERIMENTS 424
5. CONCLUSION 425
6. REFERENCES 425
CHAPTER 65. OPTIMAL AVERAGING LEVEL CONTROL FOR MULTI-TANK SYSTEMS 426
1. INTRODUCTION 426
2. OPTIMAL AVERAGING LEVEL CONTROL FOR TANKS IN SERIES 426
3. COMPARISON AMONG DIFFERENT OPTIMAL AVERAGING LEVEL CONTROL SCHEMES 428
4. APPLICATION IN AN INDUSTRIAL TEST PROBLEM 428
5. CONCLUSIONS 429
Appendix 430
References 431
CHAPTER 66. CONTROL OF COMPLEX DISTILLATION CONFIGURATIONS USING A NONLINEAR WAVE THEORY 432
1. Introduction 432
2. Profile Position Control of Distillation Column section 432
3. Control of Sidestream/Sidestripper configuration 434
4. Control of Complex Prefractionater/sidestream Column 436
5. Conclusion 437
6. Nomenclature 437
7. References 438
PART XIV: TUTORIAL PAPER 440
CHAPTER 67. STATISTICAL PROCESS CONTROL OF MULTIVARIATE PROCESSES 440
1. INTRODUCTION 440
2. MULTIVARIATE CHARTS FOR STATISTICAL QUALITY CONTROL 441
3. MULTIVARIATE STATISTICAL PROCESS CONTROL 444
4. SUMMARY 446
5. REFERENCES 447
PART XV: STATISTICAL CONTROL TECHNIQUES I 452
Chapter 68. Predictive Maintenance using PCA 452
1. INTRODUCTION 452
2. PCA MODELLING 452
3. SMART ALARM MONITORING 453
4. TOWARDS FAILURE PREDICTION 454
5. EXAMPLE APPLICATION 456
6. CONCLUSIONS 457
ACKNOWLEDGEMENT 457
REFERENCES 457
CHAPTER 69. AUTOASSOCIATIVE NEURAL NETWORKS IN BIOPROCESS CONDITION MONITORING 458
1. INTRODUCTION 458
2. BIOPROCESS DESCRIPTIONS 459
3. LINEAR PATTERN RECOGNITION 459
4. APPLICATIONS OF PCA TO BIOPROCESSES 459
5. AUTOASSOCIATIVE NEURAL NETWORKS 460
6. APPLICATIONS OF AUTOASSOCIATIVE NETWORKS TO BIOPROCESSES 460
7. CONCLUDING REMARKS 461
8. ACKNOWLEDGEMENTS 462
9. REFERENCES 462
CHAPTER 70. STATISTICAL PROCESS MONITORING AND DISTURBANCE ISOLATION IN MULTIVARIATE CONTINUOUS PROCESSES 464
1. INTRODUCTION 464
2. STATISTICAL MONITORING OF MULTIVARIABLE PROCESSES 464
PLANT DESCRIPTION 467
RESULTS 467
CONCLUSIONS 467
REFERENCES 468
Chapter 71. Detection of Unmodelled Disturbances Effects by Coherence Analysis 470
1 INTRODUCTION 470
2 COHERENCE ANALYSIS 470
3 DISTURBANCE MODELS 471
4 SPECIAL CASES 472
5 SIMULATION EXAMPLES 472
6 PROCESS EXAMPLE 473
7 SUMMARY 474
8 REFERENCES 474
PART XVI: MODELING AND SIMULATION IV 476
CHAPTER 72. ILL-CONDITIONEDNESS AND PROCESS DIRECTIONALITY - THE USE OF CONDITION NUMBERS IN PROCESS CONTROL 476
1. INTRODUCTION 476
2. THE SCALING DEPENDENCY OF THE CONDITION NUMBER 476
3. CONDITION NUMBERS AND CONTROL DIFFICULTIES 479
4. DISCUSSION AND CONCLUSIONS 480
5. ACKNOWLEDGMENTS 480
6. REFERENCES 480
CHAPTER 73. CONTROLLABILITY ANALYSIS OF SISO SYSTEMS 482
1 INTRODUCTION 482
2 LINEAR CONTROL THEORY 483
3 CONTROLLABILITY ANALYSIS 484
4 NEUTRALIZATION PROCESS 487
5 REFERENCES 487
APPENDIX. Scaling procedure 487
CHAPTER 74. PROCESS IDENTIFICATION USING DISCRETE WAVELET TRANSFORMS 488
1. INTRODUCTION 488
2. SOME ISSUES IN PROCESS IDENTIFICATION 488
3. WAVELET TRANSFORMS 489
4. PROCESS IDENTIFICATION USING WAVELETS 490
5. SIEVED PARAMETER ESTIMATION 492
6. ILLUSTRATIVE EXAMPLE 492
7. CONCLUSIONS 493
8. REFERENCES 493
CHAPTER 75. DETERMINING NECESSARY MODEL RESOLUTION IN MODEL BASED CONTROL OF DISTRIBUTED PARAMETER PROCESSES 494
1 INTRODUCTION 494
2 MODEL REDUCTION 494
3 FACTORS THAT INFLUENCES THE CHOICE OF N 495
4 CRITERION FOR CHOOSING N 496
5 SIMULATION STUDY 497
6 CONCLUSION 499
7 ACKNOWLEDGEMENT 499
REFERENCES 499
CHAPTER 76. LEAST SQUARES FORMULATION OF STATE ESTIMATION 500
ABSTRACT 500
1. INTRODUCTION 500
2. LEAST SQUARES FORMULATION OF STATE ESTIMATION 501
3. LINEAR STATE ESTIMATION 503
4. NONLINEAR ESTIMATION 504
References 505
PART XVII: NONLINEAR CONTROL AND OPTIMIZATION IV 506
CHAPTER 77. ELEMENTARY NONLINEAR DECOUPLING CONTROL OF COMPOSITION IN BINARY DISTILLATION COLUMNS 506
1. INTRODUCTION 506
2. ELEMENTARY NONLINEAR DECOUPLING (END) 506
3. A DYNAMIC MODEL OF A BINARY DISTILLATION COLUMN 507
4. ILLUSTRATION OF END CONTROL OF A DISTILLATION COLUMN 507
5. CONCLUSION 508
6. ACKNOWLEDGEMENT 508
7. REFERENCES 508
CHAPTER 78. APPLICATION OF GEOMETRIC NONLINEAR CONTROL IN THE PROCESS INDUSTRIES - A CASE STUDY 512
1. Introduction 512
2. Model Reduction 514
3. State Observer 514
4. Experiments 515
5. Summary 517
6. Nomencalture 517
7. References 517
CHAPTER 79. GRADE TRANSITION CONTROL FOR AN IMPACT COPOLYMERIZATION REACTOR 518
1. INTRODUCTION 518
2. PROCESS DESCRIPTION 519
3. QUALITY & PROCESS MODELS
4. OPTIMIZATION PROBLEM 521
5. IMPLEMENTATION OF MODEL PREDICTIVE CONTROL 522
6. CONCLUSION 523
7. REFERENCES 523
CHAPTER 80. NONLINEAR ADAPTIVE CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR 524
1. INTRODUCTION 524
2. MODELLING OF THE REACTOR 524
3. CONTROLLER DESIGN 526
4. SIMULATION RESULTS 528
5. CONCLUSION 529
6. REFERENCES 529
CHAPTER 81. EXTERNAL MODEL CONTROL OF A PERISTALTIC PUMP 530
1. INTRODUCTION 530
2. PROCESS MODEL 531
3. PROBLEM STATEMENT 531
4. DISTURBANCE REJECTION IN SMITH PREDICTOR 531
5. DEADBEAT DISTURBANCE PREDICTION 532
6. ALGEBRAIC DISTURBANCE REJECTION CONDITION 533
7. SIMULATION 534
8. CONCLUSIONS 534
ACKNOWLEDGEMENT 535
REFERENCES 535
A PROOF OF THEOREM 1 535
PART XVIII: STATISTICAL CONTROL TECHNIQUES II 536
CHAPTER 82. MULTIVARIATE STATISTICAL PROCESS CONTROL OF BATCH PROCESSES USING PCA AND PLS 536
INTRODUCTION 536
NATURE OF BATCH DATA 537
PROJECTION METHODS MULTI - WAY PCA AND PLS 537
EXAMPLE OF MPCA APPLICATION 538
CONCLUDING REMARKS 538
REFERENCES 539
CHAPTER 83. BIAS DETECTION AND ESTIMATION IN DYNAMIC DATA RECONCILIATION 542
1. INTRODUCTION 542
2. BACKGROUND 542
3. EXAMPLES 545
4. CONCLUSION 547
5. REFERENCES 547
CHAPTER 84. MONITORING AND FAULT DETECTION FOR AN HVAC CONTROL SYSTEM 548
1. INTRODUCTION 548
2. CONTROL SYSTEM PERFORMANCE 548
3. FAULT DETECTION STRATEGY 550
4. SIMULATION STUDY 550
CONCLUSIONS 551
REFERENCES 551
ACKNOWLEDGEMENTS 552
Chapter 85. Modelling of a continuous digester for process surveillance and prediction 554
1 INTRODUCTION 554
2 CONCEPTUAL MODEL 555
3 MATHEMATICAL MODEL 556
4 SIMULATION RESULTS 557
5 MODEL REDUCTION 558
6 CONCLUSIONS 559
7 ACKNOWLEDGMENTS 559
REFERENCES 559
AUTHOR INDEX 560