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 Universität 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).

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

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