Process Dynamics and Control
4th Edition
Published on 13. September 2016
Book
Paperback/Softback
512 pages
978-1-119-28591-5 (ISBN)
Description
The new 4th edition of Seborg's Process Dynamics Control provides full topical coverage for process control courses in the chemical engineering curriculum, emphasizing how process control and its related fields of process modeling and optimization are essential to the development of high-value products. A principal objective of this new edition is to describe modern techniques for control processes, with an emphasis on complex systems necessary to the development, design, and operation of modern processing plants. Control process instructors can cover the basic material while also having the flexibility to include advanced topics.
More details
Edition
4th Revised edition
Language
English
Place of publication
New York
United States
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 269 mm
Width: 211 mm
Thickness: 23 mm
Weight
1021 gr
ISBN-13
978-1-119-28591-5 (9781119285915)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
Dale E. Seborg | Duncan A. Mellichamp | Thomas F. Edgar
Process Dynamics and Control 3E
Book
03/2010
Wiley
€162.18
Article exhausted; check for reprint
Persons
Dale E. Seborg is a Professor and Vice Chair of the Department of Chemical Engineering at the University of California, Santa Barbara. He received his B.S. degree from the University of Wisconsin and his Ph.D. degree from Princeton University. Dr. Seborg has published over 200 articles and co-edited three books on process control and related topics. Dr. Seborg has served on the Editorial Advisor Boards for control engineering journals and book series, and has been a co-organizer of several major conferences. He is an active industrial consultant who serves as an expert witness in legal proceedings.
Thomas F. Edgar holds the Abell Chair in chemical engineering at the University of Texas at Austin. He earned a B.S. degree in chemical engineering from the University of Kansas and a Ph.D. from Princeton University. He has published over 300 papers in the field of process control, optimization, and mathematical modeling of processes such as separations, combustion, and microelectronics processing. Dr. Edgar was president of AIChE in 1997 and President of the American Automatic Control Council in 1989-91.
Duncan A. Mellichamp is professor Emeritus and founding member of the faculty of the chemical engineering department at the University of California, Santa Barbara. He is editor of an early book on data acquisition and control computing and has published more than one hundred papers on process modeling, large scale/plantwide systems analysis, and computer control. He earned a B.S. degree from Georgia Tech and a Ph.D. from Purdue University with intermediate studies at the Technische Universitaet Stuttgart (Germany). He presently serves on the governing boards of several nonprofit organizations.
Francis J. Doyle III is the Associate Dean for Research in the College of Engineering at the University of California, Santa Barbara. He holds the Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program. He received his B.S.E. from Princeton, C.P.G.S. from Cambridge, and Ph.D. from Caltech, all in Chemical Engineering. He is a Fellow of IEEE, IFAC, and AIMBE; he is also the recipient of multiple research awards (including the AIChE Computing in Chemical Engineering Award) as well as teaching awards (including the ASEE Ray Fahien Award).
Thomas F. Edgar holds the Abell Chair in chemical engineering at the University of Texas at Austin. He earned a B.S. degree in chemical engineering from the University of Kansas and a Ph.D. from Princeton University. He has published over 300 papers in the field of process control, optimization, and mathematical modeling of processes such as separations, combustion, and microelectronics processing. Dr. Edgar was president of AIChE in 1997 and President of the American Automatic Control Council in 1989-91.
Duncan A. Mellichamp is professor Emeritus and founding member of the faculty of the chemical engineering department at the University of California, Santa Barbara. He is editor of an early book on data acquisition and control computing and has published more than one hundred papers on process modeling, large scale/plantwide systems analysis, and computer control. He earned a B.S. degree from Georgia Tech and a Ph.D. from Purdue University with intermediate studies at the Technische Universitaet Stuttgart (Germany). He presently serves on the governing boards of several nonprofit organizations.
Francis J. Doyle III is the Associate Dean for Research in the College of Engineering at the University of California, Santa Barbara. He holds the Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program. He received his B.S.E. from Princeton, C.P.G.S. from Cambridge, and Ph.D. from Caltech, all in Chemical Engineering. He is a Fellow of IEEE, IFAC, and AIMBE; he is also the recipient of multiple research awards (including the AIChE Computing in Chemical Engineering Award) as well as teaching awards (including the ASEE Ray Fahien Award).
Content
PART ONE INTRODUCTION TO PROCESS CONTROL 1. Introduction to Process Control 1
1.1 Representative Process Control Problems 2
1.2 Illustrative Example-A Blending Process 4
1.3 Classification of Process Control Strategies 5
1.4 A More Complicated Example-A Distillation Column 7
1.5 The Hierarchy of Process Control Activities 8
1.6 An Overview of Control System Design 10
2. Theoretical Models of Chemical Processes 14
2.1 The Rationale for Dynamic Process Models 14
2.2 General Modeling Principles 16
2.3 Degrees of Freedom Analysis 19
2.4 Dynamic Models of Representative Processes 21
2.5 Process Dynamics and Mathematical Models 30
PART TWO DYNAMIC BEHAVIOR OF PROCESSES
3. Laplace Transforms 38
3.1 Laplace Transforms of Representative Functions 39
3.2 Solution of Differential Equations by Laplace Transform Techniques 42
3.3 Partial Fraction Expansion 43
3.4 Other Laplace Transform Properties 45
3.5 A Transient Response Example 47
3.6 Software for Solving Symbolic Mathematical Problems 49
4. Transfer Function Models 54
4.1 Introduction to Transfer Function Models 54
4.2 Properties of Transfer Functions 57
4.3 Linearization of Nonlinear Models 61
5. Dynamic Behavior of First-Order and Second-Order Processes 68
5.1 Standard Process Inputs 69
5.2 Response of First-Order Processes 70
5.3 Response of Integrating Processes 73
5.4 Response of Second-Order Processes 75
6. Dynamic Response Characteristics of More Complicated Processes 86
6.1 Poles and Zeros and Their Effect on Process Response 86
6.2 Processes with Time Delays 89
6.3 Approximation of Higher-Order Transfer Functions 92
6.4 Interacting and Noninteracting Processes 94
6.5 State-Space and Transfer Function Matrix Models 95
6.6 Multiple-Input, Multiple-Output (MIMO) Processes 98
7. Development of Empirical Models from Process Data 105
7.1 Model Development Using Linear or Nonlinear Regression 106
7.2 Fitting First- and Second-Order Models Using Step Tests 109
7.3 Neural Network Models 113
7.4 Development of Discrete-Time Dynamic Models 115
7.5 Identifying Discrete-Time Models from Experimental Data 116
PART THREE FEEDBACK AND FEEDFORWARD CONTROL
8. Feedback Controllers 123
8.1 Introduction 123
8.2 Basic Control Modes 125
8.3 Features of PID Controllers 130
8.4 Digital Versions of PID Controllers 133
8.5 Typical Responses of Feedback Control Systems 135
8.6 On-Off Controllers 136
9. Control System Instrumentation 140
9.1 Sensors, Transmitters, and Transducers 141
9.2 Final Control Elements 148
9.3 Accuracy in Instrumentation 154
10. Process Safety and Process Control 160
10.1 Layers of Protection 161
10.2 Alarm Management 165
10.3 Abnormal Event Detection 169
10.4 Risk Assessment 170
11. Dynamic Behavior and Stability of Closed-Loop Control Systems 175
11.1 Block Diagram Representation 176
11.2 Closed-Loop Transfer Functions 178
11.3 Closed-Loop Responses of Simple Control Systems 181
11.4 Stability of Closed-Loop Control Systems 186
11.5 Root Locus Diagrams 191
12. PID Controller Design, Tuning, and Troubleshooting 199
12.1 Performance Criteria for Closed-Loop Systems 200
12.2 Model-Based Design Methods 201
12.3 Controller Tuning Relations 206
12.4 Controllers with Two Degrees of Freedom 213
12.5 On-Line Controller Tuning 214
12.6 Guidelines for Common Control Loops 220
12.7 Troubleshooting Control Loops 222
13. Control Strategies at the Process Unit Level 229
13.1 Degrees of Freedom Analysis for Process Control 230
13.2 Selection of Controlled, Manipulated, and Measured Variables 232
13.3 Applications 235
14. Frequency Response Analysis and Control System Design 244
14.1 Sinusoidal Forcing of a First-Order Process 244
14.2 Sinusoidal Forcing of an nth-Order Process 246
14.3 Bode Diagrams 247
14.4 Frequency Response Characteristics of Feedback Controllers 251
14.5 Nyquist Diagrams 252
14.6 Bode Stability Criterion 252
14.7 Gain and Phase Margins 256
15. Feedforward and Ratio Control 262
15.1 Introduction to Feedforward Control 263
15.2 Ratio Control 264
15.3 Feedforward Controller Design Based on Steady-State Models 266
15.4 Feedforward Controller Design Based on Dynamic Models 268
15.5 The Relationship Between the Steady-State and Dynamic Design Methods 272
15.6 Configurations for Feedforward-Feedback Control 272
15.7 Tuning Feedforward Controllers 273
PART FOUR ADVANCED PROCESS CONTROL
16. Enhanced Single-Loop Control Strategies 279
16.1 Cascade Control 279
16.2 Time-Delay Compensation 284
16.3 Inferential Control 286
16.4 Selective Control/Override Systems 287
16.5 Nonlinear Control Systems 289
16.6 Adaptive Control Systems 292
17. Digital Sampling, Filtering, and Control 300
17.1 Sampling and Signal Reconstruction 300
17.2 Signal Processing and Data Filtering 303
17.3 z-Transform Analysis for Digital Control 307
17.4 Tuning of Digital PID Controllers 313
17.5 Direct Synthesis for Design of Digital Controllers 315
17.6 Minimum Variance Control 319
18. Multiloop and Multivariable Control 326
18.1 Process Interactions and Control Loop Interactions 327
18.2 Pairing of Controlled and Manipulated Variables 331
18.3 Singular Value Analysis 338
18.4 Tuning of Multiloop PID Control Systems 341
18.5 Decoupling and Multivariable Control Strategies 342
18.6 Strategies for Reducing Control Loop Interactions 343
19. Real-Time Optimization 350
19.1 Basic Requirements in Real-Time Optimization 352
19.2 The Formulation and Solution of RTO Problems 354
19.3 Unconstrained and Constrained Optimization 356
19.4 Linear Programming 359
19.5 Quadratic and Nonlinear Programming 362
20. Model Predictive Control 368
20.1 Overview of Model Predictive Control 369
20.2 Predictions for SISO Models 370
20.3 Predictions for MIMO Models 377
20.4 Model Predictive Control Calculations 379
20.5 Set-Point Calculations 382
20.6 Selection of Design and Tuning Parameters 384
20.7 Implementation of MPC 389
21. Process Monitoring 395
21.1 Traditional Monitoring Techniques 397
21.2 Quality Control Charts 398
21.3 Extensions of Statistical Process Control 404
21.4 Multivariate Statistical Techniques 406
21.5 Control Performance Monitoring 408
22. Batch Process Control 413
22.1 Batch Control Systems 415
22.2 Sequential and Logic Control 416
22.3 Control During the Batch 421
22.4 Run-to-Run Control 426
22.5 Batch Production Management 427
PART FIVE APPLICATIONS TO BIOLOGICAL SYSTEMS
23. Biosystems Control Design 435
23.1 Process Modeling and Control in Pharmaceutical Operations 435
23.2 Process Modeling and Control for Drug Delivery 442
24. Dynamics and Control of Biological Systems 451
24.1 Systems Biology 451
24.2 Gene Regulatory Control 453
24.3 Signal Transduction Networks 457
Appendix A: Digital Process Control Systems: Hardware and Software 464
A.1 Distributed Digital Control Systems 465
A.2 Analog and Digital Signals and Data Transfer 466
A.3 Microprocessors and Digital Hardware in Process Control 467
A.4 Software Organization 470
Appendix B: Review of Thermodynamic Concepts for Conservation Equations 478
B.1 Single-Component Systems 478
B.2 Multicomponent Systems 479
Appendix C: Control Simulation Software 480
C.1 MATLAB Operations and Equation Solving 480
C.2 Computer Simulation with Simulink 482
C.3 Computer Simulation with LabVIEW 485
Appendix D: Instrumentation Symbols 487
Appendix E: Process Control Modules 489
E.1 Introduction 489
E.2 Module Organization 489
E.3 Hardware and Software Requirements 490
E.4 Installation 490
E.5 Running the Software 490
Appendix F: Review of Basic Concepts From Probability and Statistics 491
F.1 Probability Concepts 491
F.2 Means and Variances 492
F.3 Standard Normal Distribution 493
F.4 Error Analysis 493
Appendix G: Introduction to Plantwide Control (Available online at: www.wiley.com/college/seborg)
Appendix H: Plantwide Control System Design (Available online at: www.wiley.com/college/seborg)
Appendix I: Dynamic Models and Parameters Used for Plantwide Control Chapters (Available online at: www.wiley.com/college/seborg)
Appendix J: Additional Closed-Loop Frequency Response Material (Available online at: www.wiley.com/college/seborg)
Appendix K: Contour Mapping and the Principle of the Argument (Available online at: www.wiley.com/college/seborg)
Appendix L: Partial Fraction Expansions for Repeated and Complex Factors (Available online at: www.wiley.com/college/seborg)
Index 495
1.1 Representative Process Control Problems 2
1.2 Illustrative Example-A Blending Process 4
1.3 Classification of Process Control Strategies 5
1.4 A More Complicated Example-A Distillation Column 7
1.5 The Hierarchy of Process Control Activities 8
1.6 An Overview of Control System Design 10
2. Theoretical Models of Chemical Processes 14
2.1 The Rationale for Dynamic Process Models 14
2.2 General Modeling Principles 16
2.3 Degrees of Freedom Analysis 19
2.4 Dynamic Models of Representative Processes 21
2.5 Process Dynamics and Mathematical Models 30
PART TWO DYNAMIC BEHAVIOR OF PROCESSES
3. Laplace Transforms 38
3.1 Laplace Transforms of Representative Functions 39
3.2 Solution of Differential Equations by Laplace Transform Techniques 42
3.3 Partial Fraction Expansion 43
3.4 Other Laplace Transform Properties 45
3.5 A Transient Response Example 47
3.6 Software for Solving Symbolic Mathematical Problems 49
4. Transfer Function Models 54
4.1 Introduction to Transfer Function Models 54
4.2 Properties of Transfer Functions 57
4.3 Linearization of Nonlinear Models 61
5. Dynamic Behavior of First-Order and Second-Order Processes 68
5.1 Standard Process Inputs 69
5.2 Response of First-Order Processes 70
5.3 Response of Integrating Processes 73
5.4 Response of Second-Order Processes 75
6. Dynamic Response Characteristics of More Complicated Processes 86
6.1 Poles and Zeros and Their Effect on Process Response 86
6.2 Processes with Time Delays 89
6.3 Approximation of Higher-Order Transfer Functions 92
6.4 Interacting and Noninteracting Processes 94
6.5 State-Space and Transfer Function Matrix Models 95
6.6 Multiple-Input, Multiple-Output (MIMO) Processes 98
7. Development of Empirical Models from Process Data 105
7.1 Model Development Using Linear or Nonlinear Regression 106
7.2 Fitting First- and Second-Order Models Using Step Tests 109
7.3 Neural Network Models 113
7.4 Development of Discrete-Time Dynamic Models 115
7.5 Identifying Discrete-Time Models from Experimental Data 116
PART THREE FEEDBACK AND FEEDFORWARD CONTROL
8. Feedback Controllers 123
8.1 Introduction 123
8.2 Basic Control Modes 125
8.3 Features of PID Controllers 130
8.4 Digital Versions of PID Controllers 133
8.5 Typical Responses of Feedback Control Systems 135
8.6 On-Off Controllers 136
9. Control System Instrumentation 140
9.1 Sensors, Transmitters, and Transducers 141
9.2 Final Control Elements 148
9.3 Accuracy in Instrumentation 154
10. Process Safety and Process Control 160
10.1 Layers of Protection 161
10.2 Alarm Management 165
10.3 Abnormal Event Detection 169
10.4 Risk Assessment 170
11. Dynamic Behavior and Stability of Closed-Loop Control Systems 175
11.1 Block Diagram Representation 176
11.2 Closed-Loop Transfer Functions 178
11.3 Closed-Loop Responses of Simple Control Systems 181
11.4 Stability of Closed-Loop Control Systems 186
11.5 Root Locus Diagrams 191
12. PID Controller Design, Tuning, and Troubleshooting 199
12.1 Performance Criteria for Closed-Loop Systems 200
12.2 Model-Based Design Methods 201
12.3 Controller Tuning Relations 206
12.4 Controllers with Two Degrees of Freedom 213
12.5 On-Line Controller Tuning 214
12.6 Guidelines for Common Control Loops 220
12.7 Troubleshooting Control Loops 222
13. Control Strategies at the Process Unit Level 229
13.1 Degrees of Freedom Analysis for Process Control 230
13.2 Selection of Controlled, Manipulated, and Measured Variables 232
13.3 Applications 235
14. Frequency Response Analysis and Control System Design 244
14.1 Sinusoidal Forcing of a First-Order Process 244
14.2 Sinusoidal Forcing of an nth-Order Process 246
14.3 Bode Diagrams 247
14.4 Frequency Response Characteristics of Feedback Controllers 251
14.5 Nyquist Diagrams 252
14.6 Bode Stability Criterion 252
14.7 Gain and Phase Margins 256
15. Feedforward and Ratio Control 262
15.1 Introduction to Feedforward Control 263
15.2 Ratio Control 264
15.3 Feedforward Controller Design Based on Steady-State Models 266
15.4 Feedforward Controller Design Based on Dynamic Models 268
15.5 The Relationship Between the Steady-State and Dynamic Design Methods 272
15.6 Configurations for Feedforward-Feedback Control 272
15.7 Tuning Feedforward Controllers 273
PART FOUR ADVANCED PROCESS CONTROL
16. Enhanced Single-Loop Control Strategies 279
16.1 Cascade Control 279
16.2 Time-Delay Compensation 284
16.3 Inferential Control 286
16.4 Selective Control/Override Systems 287
16.5 Nonlinear Control Systems 289
16.6 Adaptive Control Systems 292
17. Digital Sampling, Filtering, and Control 300
17.1 Sampling and Signal Reconstruction 300
17.2 Signal Processing and Data Filtering 303
17.3 z-Transform Analysis for Digital Control 307
17.4 Tuning of Digital PID Controllers 313
17.5 Direct Synthesis for Design of Digital Controllers 315
17.6 Minimum Variance Control 319
18. Multiloop and Multivariable Control 326
18.1 Process Interactions and Control Loop Interactions 327
18.2 Pairing of Controlled and Manipulated Variables 331
18.3 Singular Value Analysis 338
18.4 Tuning of Multiloop PID Control Systems 341
18.5 Decoupling and Multivariable Control Strategies 342
18.6 Strategies for Reducing Control Loop Interactions 343
19. Real-Time Optimization 350
19.1 Basic Requirements in Real-Time Optimization 352
19.2 The Formulation and Solution of RTO Problems 354
19.3 Unconstrained and Constrained Optimization 356
19.4 Linear Programming 359
19.5 Quadratic and Nonlinear Programming 362
20. Model Predictive Control 368
20.1 Overview of Model Predictive Control 369
20.2 Predictions for SISO Models 370
20.3 Predictions for MIMO Models 377
20.4 Model Predictive Control Calculations 379
20.5 Set-Point Calculations 382
20.6 Selection of Design and Tuning Parameters 384
20.7 Implementation of MPC 389
21. Process Monitoring 395
21.1 Traditional Monitoring Techniques 397
21.2 Quality Control Charts 398
21.3 Extensions of Statistical Process Control 404
21.4 Multivariate Statistical Techniques 406
21.5 Control Performance Monitoring 408
22. Batch Process Control 413
22.1 Batch Control Systems 415
22.2 Sequential and Logic Control 416
22.3 Control During the Batch 421
22.4 Run-to-Run Control 426
22.5 Batch Production Management 427
PART FIVE APPLICATIONS TO BIOLOGICAL SYSTEMS
23. Biosystems Control Design 435
23.1 Process Modeling and Control in Pharmaceutical Operations 435
23.2 Process Modeling and Control for Drug Delivery 442
24. Dynamics and Control of Biological Systems 451
24.1 Systems Biology 451
24.2 Gene Regulatory Control 453
24.3 Signal Transduction Networks 457
Appendix A: Digital Process Control Systems: Hardware and Software 464
A.1 Distributed Digital Control Systems 465
A.2 Analog and Digital Signals and Data Transfer 466
A.3 Microprocessors and Digital Hardware in Process Control 467
A.4 Software Organization 470
Appendix B: Review of Thermodynamic Concepts for Conservation Equations 478
B.1 Single-Component Systems 478
B.2 Multicomponent Systems 479
Appendix C: Control Simulation Software 480
C.1 MATLAB Operations and Equation Solving 480
C.2 Computer Simulation with Simulink 482
C.3 Computer Simulation with LabVIEW 485
Appendix D: Instrumentation Symbols 487
Appendix E: Process Control Modules 489
E.1 Introduction 489
E.2 Module Organization 489
E.3 Hardware and Software Requirements 490
E.4 Installation 490
E.5 Running the Software 490
Appendix F: Review of Basic Concepts From Probability and Statistics 491
F.1 Probability Concepts 491
F.2 Means and Variances 492
F.3 Standard Normal Distribution 493
F.4 Error Analysis 493
Appendix G: Introduction to Plantwide Control (Available online at: www.wiley.com/college/seborg)
Appendix H: Plantwide Control System Design (Available online at: www.wiley.com/college/seborg)
Appendix I: Dynamic Models and Parameters Used for Plantwide Control Chapters (Available online at: www.wiley.com/college/seborg)
Appendix J: Additional Closed-Loop Frequency Response Material (Available online at: www.wiley.com/college/seborg)
Appendix K: Contour Mapping and the Principle of the Argument (Available online at: www.wiley.com/college/seborg)
Appendix L: Partial Fraction Expansions for Repeated and Complex Factors (Available online at: www.wiley.com/college/seborg)
Index 495