Process dynamics and control : modeling for control and prediction

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Title:

Personal Author:

Roffel, Brian

Publication Information:

Chichester, England : John Wiley & Sons, 2006

ISBN:

9780470016640

Subject Term:

Chemical process control

Added Author:

Betlem, B. H. (Ben H.)

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010127044	TP155.7 R63 2006	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010087545	TP155.7 R63 2006	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010151861	TP155.7 R63 2006	Open Access Book	Book	Searching... Unknown

Offering a different approach to other textbooks in the area, this book is a comprehensive introduction to the subject divided in three broad parts. The first part deals with building physical models, the second part with developing empirical models and the final part discusses developing process control solutions. Theory is discussed where needed to ensure students have a full understanding of key techniques that are used to solve a modeling problem.

Hallmark Features:

Includes worked out examples of processes where the theory learned early on in the text can be applied. Uses MATLAB simulation examples of all processes and modeling techniques- further information on MATLAB can be obtained from www.mathworks.com Includes supplementary website to include further references, worked examples and figures from the book

This book is structured and aimed at upper level undergraduate students within chemical engineering and other engineering disciplines looking for a comprehensive introduction to the subject. It is also of use to practitioners of process control where the integrated approach of physical and empirical modeling is particularly valuable.

Author Notes

Professor Brian Roffel, University of Twente, The Netherland
sProfessor Roffel has been teaching researching and managing research in the areas of analysis, simulation, control and optimization of process for over twenty years. In addition twelve years spent working in the chemical process industry gives his theoretical knowledge a practical grounding. Professor Roffel is part of a consortium of eight European Universities working on nonlinear multivariable control. He has also been involved in the practical implementation of advanced control in the chemical industry, in particular multivariable control and optimization.

Dr.Ben H. L. Betlem , University of Twente, The Netherlands.

Foreword	p. xi
Preface	p. xiii
Acknowledgement	p. xv
1 Introduction to Process Modeling	p. 1
1.1 Application of Process Models	p. 1
1.2 Dynamic Systems Modeling	p. 2
1.3 Modeling Steps	p. 5
1.4 Use of Diagrams	p. 16
1.5 Types of Models	p. 20
1.6 Continuous versus Discrete Models	p. 23
References	p. 23
2 Process Modeling Fundamentals	p. 25
2.1 System States	p. 25
2.2 Mass Relationship for Liquid and Gas	p. 29
2.3 Energy Relationship	p. 38
2.4 Composition Relationship	p. 48
3 Extended Analysis of Modeling for Process Operation	p. 57
3.1 Environmental Model	p. 57
3.2 Procedure for the Development of an Environmental Model for Process Operation	p. 58
3.3 Example: Mixer	p. 68
3.4 Example: Evaporator with Variable Heat Exchanging Surface	p. 69
4 Design for Process Modeling and Behavioral Models	p. 71
4.1 Behavioral Model	p. 71
4.2 Example: Mixer	p. 77
5 Transformation Techniques	p. 81
5.1 Introduction	p. 81
5.2 Laplace Transform	p. 81
5.3 Useful Properties of Laplace Transform: limit functions	p. 83
5.4 Transfer Functions	p. 84
5.5 Discrete Approximations	p. 89
5.6 z-Transforms	p. 90
References	p. 95
6 Linearization of Model Equations	p. 97
6.1 Introduction	p. 97
6.2 Non-linear Process Models	p. 97
6.3 Some General Linearization Rules	p. 100
6.4 Linearization of Model of the Level Process	p. 102
6.5 Linearization of the Evaporator model	p. 103
6.6 Normalization of the Transfer Function	p. 105
6.7 Linearization of the Chemical Reactor Model	p. 105
7 Operating Points	p. 109
7.1 Introduction	p. 109
7.2 Stationary System and Operating Point	p. 109
7.3 Flow Systems	p. 110
7.4 Chemical System	p. 111
7.5 Stability in the Operating Point	p. 113
7.6 Operating Point Transition	p. 116
8 Process Simulation	p. 119
8.1 Using Matlab Simulink	p. 119
8.2 Simulation of the Level Process	p. 119
8.3 Simulation of the Chemical Reactor	p. 124
References	p. 126
9 Frequency Response Analysis	p. 127
9.1 Introduction	p. 127
9.2 Bode Diagrams	p. 129
9.3 Bode Diagram of Simulink Models	p. 135
References	p. 137
10 General Process Behavior	p. 139
10.1 Introduction	p. 139
10.2 Accumulation Processes	p. 140
10.3 Lumped Process with Non-interacting Balances	p. 142
10.4 Lumped Process with Interacting Balances	p. 144
10.5 Processes with Parallel Balances	p. 148
10.6 Distributed Processes	p. 151
10.7 Processes with Propagation Without Feedback	p. 154
10.8 Processes with Propagation With Feedback	p. 157
11 Analysis of a Mixing Process	p. 161
11.1 The Process	p. 161
11.2 Mixer with Self-adjusting Height	p. 164
12 Dynamics of Chemical Stirred Tank Reactors	p. 169
12.1 Introduction	p. 169
12.2 Isothermal First-order Reaction	p. 169
12.3 Equilibrium Reactions	p. 172
12.4 Consecutive Reactions	p. 175
12.5 Non-isothermal Reactions	p. 178
13 Dynamic Analysis of Tubular Reactors	p. 185
13.1 Introduction	p. 185
13.2 First-order Reaction	p. 186
13.3 Equilibrium Reaction	p. 188
13.4 Consecutive Reactions	p. 188
13.5 Tubular Reactor with Dispersion	p. 188
13.6 Dynamics of Adiabatic Tubular Flow Reactors	p. 192
References	p. 194
14 Dynamic Analysis of Heat Exchangers	p. 195
14.1 Introduction	p. 195
14.2 Heat Transfer from a Heating Coil	p. 195
14.3 Shell and Tube Heat Exchanger with Condensing Steam	p. 198
14.4 Dynamics of a Counter-current Heat Exchanger	p. 205
References	p. 206
15 Dynamics of Evaporators and Separators	p. 207
15.1 Introduction	p. 207
15.2 Model Description	p. 208
15.3 Linearization and Laplace Transformation	p. 209
15.4 Derivation of the Normalized Transfer Function	p. 210
15.5 Response Analysis	p. 211
15.6 General Behavior	p. 212
15.7 Example of Some Responses	p. 212
15.8 Separation of Multi-phase Systems	p. 213
15.9 Separator Model	p. 214
15.10 Model Analysis	p. 215
15.11 Derivation of the Transfer Function	p. 217
16 Dynamic Modeling of Distillation Columns	p. 219
16.1 Column Environmental Model	p. 219
16.2 Assumptions and Simplifications	p. 220
16.3 Column Behavioral Model	p. 221
16.4 Component Balances and Equilibria	p. 222
16.5 Energy Balances	p. 225
16.6 Tray Hydraulics	p. 228
16.7 Tray Pressure Drop	p. 233
16.8 Column Dynamics	p. 236
Notation	p. 240
Greek Symbols	p. 242
References	p. 243
17 Dynamic Analysis of Fermentation Reactors	p. 245
17.1 Introduction	p. 245
17.2 Kinetic Equations	p. 245
17.3 Reactor Models	p. 247
17.4 Dynamics of the Fed-batch Reactor	p. 248
17.5 Dynamics of Ideally Mixed Fermentation Reactor	p. 252
17.6 Linearization of the Model for the Continuous Reactor	p. 254
References	p. 258
18 Physiological Modeling: Glucose-Insulin Dynamics and Cardiovascular Modeling	p. 259
18.1 Introduction to Physiological Models	p. 259
18.2 Modeling of Glucose and Insulin Levels	p. 260
18.3 Steady-state Analysis	p. 262
18.4 Dynamic Analysis	p. 263
18.5 The Bergman Minimal Model	p. 264
18.6 Introduction to Cardiovascular Modeling	p. 264
18.7 Simple Model Using Aorta Compliance and Peripheral Resistance	p. 265
18.8 Modeling Heart Rate Variability using a Baroreflex Model	p. 268
References	p. 271
19 Introduction to Black Box Modeling	p. 273
19.1 Need for Different Model Types	p. 273
19.2 Modeling steps	p. 274
19.3 Data Preconditioning	p. 275
19.4 Selection of Independent Model Variables	p. 275
19.5 Model Order Selection	p. 276
19.6 Model Linearity	p. 277
19.7 Model Extrapolation	p. 277
19.8 Model Evaluation	p. 277
20 Basics of Linear Algebra	p. 279
20.1 Introduction	p. 279
20.2 Inner and Outer Product	p. 280
20.3 Special Matrices and Vectors	p. 281
20.4 Gauss-Jordan Elimination, Rank and Singularity	p. 281
20.5 Determinant of a matrix	p. 283
20.6 The Inverse of a Matrix	p. 284
20.7 Inverse of a Singular Matrix	p. 285
20.8 Generalized Least Squares	p. 287
20.9 Eigen Values and Eigen Vectors	p. 288
References	p. 290
21 Data Conditioning	p. 291
21.1 Examining the Data	p. 291
21.2 Detecting and Removing Bad Data	p. 292
21.3 Filling in Missing Data	p. 295
21.4 Scaling of Variables	p. 295
21.5 Identification of Time Lags	p. 296
21.6 Smoothing and Filtering a Signal	p. 297
21.7 Initial Model Structure	p. 302
References	p. 304
22 Principal Component Analysis	p. 305
22.1 Introduction	p. 305
22.2 PCA Decomposition	p. 306
22.3 Explained Variance	p. 308
22.4 PGA Graphical User Interface	p. 309
22.5 Case Study: Demographic data	p. 310
22.6 Case Study: Reactor Data	p. 313
22.7 Modeling Statistics	p. 314
References	p. 316
23 Partial Least Squares	p. 317
23.1 Problem Definition	p. 317
23.2 The PLS Algorithm	p. 318
23.3 Dealing with Non-linearities	p. 319
23.4 Dynamic Extensions of PLS	p. 320
23.5 Modeling Examples	p. 321
References	p. 325
24 Time-series Identification	p. 327
24.1 Mechanistic Non-linear Models	p. 327
24.2 Empirical (linear) Dynamic Models	p. 327
24.3 The Least Squares Method	p. 328
24.4 Cross-correlation and Autocorrelation	p. 329
24.5 The Prediction Error Method	p. 331
24.6 Identification Examples	p. 332
24.7 Design of Plant Experiments	p. 337
References	p. 340
25 Discrete Linear and Non-linear State Space Modeling	p. 341
25.1 Introduction	p. 341
25.2 State Space Model Identification	p. 342
25.3 Examples of State Space Model Identification	p. 343
References	p. 348
26 Model Reduction	p. 349
26.1 Model Reduction in the Frequency Domain	p. 349
26.2 Transfer Functions in the Frequency Domain	p. 350
26.3 Example of Basic Frequency-weighted Model Reduction	p. 351
26.4 Balancing of Gramians	p. 353
26.5 Examples of Model State Reduction Techniques	p. 356
References	p. 360
27 Neural Networks	p. 361
27.1 The Structure of an Artificial Neural Network	p. 361
27.2 The Training of Artificial Neural Networks	p. 363
27.3 The Standard Back Propagation Algorithm	p. 364
27.4 Recurrent Neural Networks	p. 367
27.5 Neural Network Applications and Issues	p. 370
27.6 Examples of Models	p. 372
References	p. 379
28 Fuzzy Modeling	p. 381
28.1 Mamdani Fuzzy Models	p. 381
28.2 Takagi-Sugeno Fuzzy Models	p. 382
28.3 Modeling Methodology	p. 384
28.4 Example of Fuzzy Modeling	p. 384
28.5 Data Clustering	p. 386
28.6 Non-linear Process Modeling	p. 391
References	p. 397
29 Neuro Fuzzy Modeling	p. 399
29.1 Introduction	p. 399
29.2 Network Architecture	p. 399
29.3 Calculation of Model Parameters	p. 401
29.4 Identification Examples	p. 403
References	p. 410
30 Hybrid Models	p. 413
30.1 Introduction	p. 413
30.2 Methodology	p. 414
30.3 Approaches for Different Process Types	p. 424
30.4 Bioreactor Case Study	p. 436
Literature	p. 438
31 Introduction to Process Control and Instrumentation	p. 439
31.1 Introduction	p. 439
31.2 Process Control Goals	p. 440
31.3 The Measuring Device	p. 444
31.4 The Control Device	p. 449
31.5 The Controller	p. 451
31.6 Simulating the Controlled Process	p. 452
References	p. 453
32 Behaviour of Controlled Processes	p. 455
32.1 Purpose of Control	p. 455
32.2 Controller Equations	p. 457
32.3 Frequency Response Analysis of the Process	p. 458
32.4 Frequency Response of Controllers	p. 460
32.5 Controller Tuning Guidelines	p. 462
References	p. 464
33 Design of Control Schemes	p. 465
33.1 Procedure	p. 465
33.2 Example: Desulphurization Process	p. 472
33.3 Optimal Control	p. 475
33.4 Extension of the Control Scheme	p. 478
33.5 Final Considerations	p. 485
34 Control of Distillation Columns	p. 487
34.1 Control Scheme for a Distillation Column	p. 487
34.2 Material and Energy Balance Control	p. 495
Summary	p. 500
References	p. 501
Appendix 34.I Impact of Vapor Flow Variations on Liquid Holdup	p. 501
Appendix 34.II Ratio Control for Liquid and Vapor Flow in the Column	p. 502
35 Control of a Fluid Catalytic Cracker	p. 503
35.1 Introduction	p. 503
35.2 Initial Input-output Variable Selection	p. 505
35.3 Extension of the Basic Control Scheme	p. 509
35.4 Selection of the Final Control Scheme	p. 510
References	p. 514
Appendix A Modeling an Extraction Process	p. 515
A1 Problem Analysis	p. 515
A2 Dynamic Process Model Development	p. 517
A3 Dynamic Process Model Analysis	p. 521
A4 Dynamic Process Simulation	p. 524
A5 Process Control Simulation	p. 530
Hints	p. 534
Index	p. 535

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