Process Dynamics and Control Modeling for Control and Prediction

by Roffel, Brian; Betlem, Ben

• ISBN13: 9780470016640
• ISBN10: 0470016647
• Edition: 1st
• Format: Paperback
• Copyright: 2007-01-02
• Publisher: WILEY

Summary

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 Biography

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.

Table of Contents

Foreword	p. xi
Preface	p. xiii
Acknowledgement	p. xv
Introduction to Process Modeling	p. 1
Application of Process Models	p. 1
Dynamic Systems Modeling	p. 2
Modeling Steps	p. 5
Use of Diagrams	p. 16
Types of Models	p. 20
Continuous versus Discrete Models	p. 23
References	p. 23
Process Modeling Fundamentals	p. 25
System States	p. 25
Mass Relationship for Liquid and Gas	p. 29
Energy Relationship	p. 38
Composition Relationship	p. 48
Extended Analysis of Modeling for Process Operation	p. 57
Environmental Model	p. 57
Procedure for the Development of an Environmental Model for Process Operation	p. 58
Example: Mixer	p. 68
Example: Evaporator with Variable Heat Exchanging Surface	p. 69
Design for Process Modeling and Behavioral Models	p. 71
Behavioral Model	p. 71
Example: Mixer	p. 77
Transformation Techniques	p. 81
Introduction	p. 81
Laplace Transform	p. 81
Useful Properties of Laplace Transform: limit functions	p. 83
Transfer Functions	p. 84
Discrete Approximations	p. 89
z-Transforms	p. 90
References	p. 95
Linearization of Model Equations	p. 97
Introduction	p. 97
Non-linear Process Models	p. 97
Some General Linearization Rules	p. 100
Linearization of Model of the Level Process	p. 102
Linearization of the Evaporator model	p. 103
Normalization of the Transfer Function	p. 105
Linearization of the Chemical Reactor Model	p. 105
Operating Points	p. 109
Introduction	p. 109
Stationary System and Operating Point	p. 109
Flow Systems	p. 110
Chemical System	p. 111
Stability in the Operating Point	p. 113
Operating Point Transition	p. 116
Process Simulation	p. 119
Using Matlab Simulink	p. 119
Simulation of the Level Process	p. 119
Simulation of the Chemical Reactor	p. 124
References	p. 126
Frequency Response Analysis	p. 127
Introduction	p. 127
Bode Diagrams	p. 129
Bode Diagram of Simulink Models	p. 135
References	p. 137
General Process Behavior	p. 139
Introduction	p. 139
Accumulation Processes	p. 140
Lumped Process with Non-interacting Balances	p. 142
Lumped Process with Interacting Balances	p. 144
Processes with Parallel Balances	p. 148
Distributed Processes	p. 151
Processes with Propagation Without Feedback	p. 154
Processes with Propagation With Feedback	p. 157
Analysis of a Mixing Process	p. 161
The Process	p. 161
Mixer with Self-adjusting Height	p. 164
Dynamics of Chemical Stirred Tank Reactors	p. 169
Introduction	p. 169
Isothermal First-order Reaction	p. 169
Equilibrium Reactions	p. 172
Consecutive Reactions	p. 175
Non-isothermal Reactions	p. 178
Dynamic Analysis of Tubular Reactors	p. 185
Introduction	p. 185
First-order Reaction	p. 186
Equilibrium Reaction	p. 188
Consecutive Reactions	p. 188
Tubular Reactor with Dispersion	p. 188
Dynamics of Adiabatic Tubular Flow Reactors	p. 192
References	p. 194
Dynamic Analysis of Heat Exchangers	p. 195
Introduction	p. 195
Heat Transfer from a Heating Coil	p. 195
Shell and Tube Heat Exchanger with Condensing Steam	p. 198
Dynamics of a Counter-current Heat Exchanger	p. 205
References	p. 206
Dynamics of Evaporators and Separators	p. 207
Introduction	p. 207
Model Description	p. 208
Linearization and Laplace Transformation	p. 209
Derivation of the Normalized Transfer Function	p. 210
Response Analysis	p. 211
General Behavior	p. 212
Example of Some Responses	p. 212
Separation of Multi-phase Systems	p. 213
Separator Model	p. 214
Model Analysis	p. 215
Derivation of the Transfer Function	p. 217
Dynamic Modeling of Distillation Columns	p. 219
Column Environmental Model	p. 219
Assumptions and Simplifications	p. 220
Column Behavioral Model	p. 221
Component Balances and Equilibria	p. 222
Energy Balances	p. 225
Tray Hydraulics	p. 228
Tray Pressure Drop	p. 233
Column Dynamics	p. 236
Notation	p. 240
Greek Symbols	p. 242
References	p. 243
Dynamic Analysis of Fermentation Reactors	p. 245
Introduction	p. 245
Kinetic Equations	p. 245
Reactor Models	p. 247
Dynamics of the Fed-batch Reactor	p. 248
Dynamics of Ideally Mixed Fermentation Reactor	p. 252
Linearization of the Model for the Continuous Reactor	p. 254
References	p. 258
Physiological Modeling: Glucose-Insulin Dynamics and Cardiovascular Modeling	p. 259
Introduction to Physiological Models	p. 259
Modeling of Glucose and Insulin Levels	p. 260
Steady-state Analysis	p. 262
Dynamic Analysis	p. 263
The Bergman Minimal Model	p. 264
Introduction to Cardiovascular Modeling	p. 264
Simple Model Using Aorta Compliance and Peripheral Resistance	p. 265
Modeling Heart Rate Variability using a Baroreflex Model	p. 268
References	p. 271
Introduction to Black Box Modeling	p. 273
Need for Different Model Types	p. 273
Modeling steps	p. 274
Data Preconditioning	p. 275
Selection of Independent Model Variables	p. 275
Model Order Selection	p. 276
Model Linearity	p. 277
Model Extrapolation	p. 277
Model Evaluation	p. 277
Basics of Linear Algebra	p. 279
Introduction	p. 279
Inner and Outer Product	p. 280
Special Matrices and Vectors	p. 281
Gauss-Jordan Elimination, Rank and Singularity	p. 281
Determinant of a matrix	p. 283
The Inverse of a Matrix	p. 284
Inverse of a Singular Matrix	p. 285
Generalized Least Squares	p. 287
Eigen Values and Eigen Vectors	p. 288
References	p. 290
Data Conditioning	p. 291
Examining the Data	p. 291
Detecting and Removing Bad Data	p. 292
Filling in Missing Data	p. 295
Scaling of Variables	p. 295
Identification of Time Lags	p. 296
Smoothing and Filtering a Signal	p. 297
Initial Model Structure	p. 302
References	p. 304
Principal Component Analysis	p. 305
Introduction	p. 305
PCA Decomposition	p. 306
Explained Variance	p. 308
PGA Graphical User Interface	p. 309
Case Study: Demographic data	p. 310
Case Study: Reactor Data	p. 313
Modeling Statistics	p. 314
References	p. 316
Partial Least Squares	p. 317
Problem Definition	p. 317
The PLS Algorithm	p. 318
Dealing with Non-linearities	p. 319
Dynamic Extensions of PLS	p. 320
Modeling Examples	p. 321
References	p. 325
Time-series Identification	p. 327
Mechanistic Non-linear Models	p. 327
Empirical (linear) Dynamic Models	p. 327
The Least Squares Method	p. 328
Cross-correlation and Autocorrelation	p. 329
The Prediction Error Method	p. 331
Identification Examples	p. 332
Design of Plant Experiments	p. 337
References	p. 340
Discrete Linear and Non-linear State Space Modeling	p. 341
Introduction	p. 341
State Space Model Identification	p. 342
Examples of State Space Model Identification	p. 343
References	p. 348
Model Reduction	p. 349
Model Reduction in the Frequency Domain	p. 349
Transfer Functions in the Frequency Domain	p. 350
Example of Basic Frequency-weighted Model Reduction	p. 351
Balancing of Gramians	p. 353
Examples of Model State Reduction Techniques	p. 356
References	p. 360
Neural Networks	p. 361
The Structure of an Artificial Neural Network	p. 361
The Training of Artificial Neural Networks	p. 363
The Standard Back Propagation Algorithm	p. 364
Recurrent Neural Networks	p. 367
Neural Network Applications and Issues	p. 370
Examples of Models	p. 372
References	p. 379
Fuzzy Modeling	p. 381
Mamdani Fuzzy Models	p. 381
Takagi-Sugeno Fuzzy Models	p. 382
Modeling Methodology	p. 384
Example of Fuzzy Modeling	p. 384
Data Clustering	p. 386
Non-linear Process Modeling	p. 391
References	p. 397
Neuro Fuzzy Modeling	p. 399
Introduction	p. 399
Network Architecture	p. 399
Calculation of Model Parameters	p. 401
Identification Examples	p. 403
References	p. 410
Hybrid Models	p. 413
Introduction	p. 413
Methodology	p. 414
Approaches for Different Process Types	p. 424
Bioreactor Case Study	p. 436
Literature	p. 438
Introduction to Process Control and Instrumentation	p. 439
Introduction	p. 439
Process Control Goals	p. 440
The Measuring Device	p. 444
The Control Device	p. 449
The Controller	p. 451
Simulating the Controlled Process	p. 452
References	p. 453
Behaviour of Controlled Processes	p. 455
Purpose of Control	p. 455
Controller Equations	p. 457
Frequency Response Analysis of the Process	p. 458
Frequency Response of Controllers	p. 460
Controller Tuning Guidelines	p. 462
References	p. 464
Design of Control Schemes	p. 465
Procedure	p. 465
Example: Desulphurization Process	p. 472
Optimal Control	p. 475
Extension of the Control Scheme	p. 478
Final Considerations	p. 485
Control of Distillation Columns	p. 487
Control Scheme for a Distillation Column	p. 487
Material and Energy Balance Control	p. 495
Summary	p. 500
References	p. 501
Impact of Vapor Flow Variations on Liquid Holdup	p. 501
Ratio Control for Liquid and Vapor Flow in the Column	p. 502
Control of a Fluid Catalytic Cracker	p. 503
Introduction	p. 503
Initial Input-output Variable Selection	p. 505
Extension of the Basic Control Scheme	p. 509
Selection of the Final Control Scheme	p. 510
References	p. 514
Modeling an Extraction Process	p. 515
Problem Analysis	p. 515
Dynamic Process Model Development	p. 517
Dynamic Process Model Analysis	p. 521
Dynamic Process Simulation	p. 524
Process Control Simulation	p. 530
Hints	p. 534
Index	p. 535
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