Process Control: Modeling, Design, and Simulation

Master Process Control Hands On, through Updated Practical Examples and MATLAB® Simulations Process Control: Modeling, Design, and Simulation, Second Edition, is a complete introduction to process control and has been fully updated, integrating current software tools to enable professionals and students to master critical techniques hands on through simulations based on modern versions of MATLAB. This revised edition teaches the field's most important techniques, behaviors, and control problems with even more practical examples and exercises. Wide-ranging enhancements include safety considerations, an expanded discussion of digital control, additional process examples, and updates throughout for newer versions of MATLAB and SIMULINK. Fundamentals of process control and instrumentation, including objectives, variables, block diagrams, and process flowsheets Methodologies for developing dynamic models of chemical processes, including compartmental models Dynamic behavior of linear systems: state-space models, transfer function-based models (including conversion to state space), and more Empirical and discrete-time models, including relationships among types of discrete models Feedback control; proportional, integral, and derivative (PID) controllers; and closed-loop stability analysis Frequency response analysis techniques for evaluating the robustness of control systems Improving control loop performance: internal model control (IMC), automatic tuning, gain scheduling, and enhanced disturbance rejection Split-range, selective, and override strategies for switching among inputs or outputs Control loop interactions and multivariable controllers An introduction to model predictive control (MPC), with a new discrete state-space model derivation exercise Bequette walks step by step through developing control instrumentation diagrams for an entire chemical process, reviewing common control strategies for individual unit operations, then discussing strategies for integrated systems. This edition also includes 16 learning modules demonstrating how to use MATLAB and SIMULINK to solve many key control problems, including new modules on process monitoring and safety, as well as a detailed new study of artificial pancreas systems for Type 1 diabetes. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

B. Wayne Bequette is a Professor of Chemical and Biological Engineering and Technology Manager for the Smart Manufacturing Innovation Center (SMIC) at Rensselaer Polytechnic Institute, where his research efforts are focused on the modeling and control of chemical process, biomedical, biopharma, and food manufacturing systems. He serves as the Board Secretary for the American Automatic Control Council (AACC) and as a Trustee of the Computer Aids for Chemical Engineering (CACHE) Corporation. Dr. Bequette is a founding member of the editorial board of the Journal of Diabetes Science and Technology and serves on the editorial board of Industrial & Engineering Chemistry Research. He is a Fellow of IEEE, AIChE, and the American Institute of Medical and Biological Engineers (AIMBE), and was inducted into the Arkansas Academy of Chemical Engineers. He is the author of Process Control: Modeling, Design, and Simulation, Second Edition, and Process Dynamics: Modeling, Analysis, and Simulation (both from Pearson), and has published 17 book chapters and more than 125 refereed journal articles. While completing a BS in chemical engineering at the University of Arkansas, Dr. Bequette worked at Arkansas Eastman (handling utility and waste treatment problems) and Cosden Oil and Chemical. After his undergraduate studies, he was a process engineer at American Petrofina, where he had the chance to serve as a process operator during two work stoppages. This sparked his interest in process automation and control, enticing him to the University of Texas at Austin to earn a PhD with a focus on multivariable control-system analysis and design. He spent a year as a visiting lecturer at the University of California at Davis before becoming a professor at Rensselaer in 1988. While at Rensselaer, he has had the good fortune to serve as the advisor for 23 PhD students, in addition to teaching chemical process dynamics and control to at least 1500 undergraduate students. His outside interests include bicycling and pole-vaulting.

Preface to the Second Edition xxvii About the Author xxxiii Chapter 1: Introduction 1 1.1 Introduction 2 1.2 Instrumentation 14 1.3 Process Models and Dynamic Behavior 15 1.4 Redundancy and Operability 18 1.5 Industrial IoT and Smart Manufacturing 19 1.6 Control Textbooks 21 1.7 A Look Ahead 22 1.8 Summary 22 References 23 Student Exercises 24 Chapter 2: Fundamental Models 31 2.1 Background 32 2.2 Balance Equations 33 2.3 Material Balances 36 2.4 Constitutive Relationships 41 2.5 Material and Energy Balances 44 2.6 Form of Dynamic Models 48 2.7 Linear Models and Deviation Variables 50 2.8 Summary 55 Suggested Reading 57 Student Exercises 57 Chapter 3: Dynamic Behavior 69 3.1 Background 70 3.2 Linear State-Space Models 70 3.3 Laplace Transforms 75 3.4 Transfer Functions 87 3.5 First-Order Behavior 88 3.6 Integrating Behavior 94 3.7 Second-Order Behavior 100 3.8 Summary 107 References 107 Student Exercises 107 Chapter 4: Dynamic Behavior: Complex Systems 115 4.1 Introduction 116 4.2 Poles and Zeros 116 4.3 Lead-Lag Behavior 119 4.4 Processes with Deadtime 120 4.5 Padé Approximation for Deadtime 123 4.6 Converting State-Space Models to Transfer Functions 124 4.7 Converting Transfer Functions to State-Space Models 127 4.8 MATLAB and SIMULINK 128 4.9 Summary 130 Student Exercises 130 Chapter 5: Empirical and Discrete-Time Models 139 5.1 Introduction 140 5.2 First-Order + Deadtime 141 5.3 Integrator + Deadtime 144 5.4 Other Continuous Models 147 5.5 Discrete-Time Autoregressive Models 148 5.6 Parameter Estimation 152 5.7 Discrete Step and Impulse Response Models 156 5.8 Converting Continuous Models to Discrete 158 5.9 Digital Filtering 160 5.10 Summary 163 References 163 Student Exercises 164 Appendix 5.1: Discretization 170 Chapter 6: Introduction to Feedback Control 171 6.1 Motivation 172 6.2 Control Block Diagrams 176 6.3 Closed-Loop Analysis 179 6.4 PID Controller Algorithms 185 6.5 Routh Stability Criterion 192 6.6 Effect of Tuning Parameters 196 6.7 Open-Loop Unstable Systems 197 6.8 SIMULINK Block Diagrams 199 6.9 ODEs to Solve PID Problems 200 6.10 Summary 202 References 205 Student Exercises 205 Chapter 7: Model-Based Control 215 7.1 Introduction 216 7.2 Direct Synthesis 216 7.3 Internal Model Control 218 7.4 IMC-Based PID 223 7.5 IMC-Based PID for Time-Delay Processes 231 7.6 IMC-Based PID for Unstable Processes 237 7.7 Summary 240 References 242 Student Exercises 242 Appendix 7.1: SIMC-Based PID Design 252 Chapter 8: PID Controller Tuning 255 8.1 Introduction 256 8.2 Closed-Loop Oscillation-Based Tuning 257 8.3 Tuning Rules for First-Order + Deadtime Processes 261 8.4 Digital Control 263 8.5 Stability of Digital Control Systems 265 8.6 Performance of Digital Control Systems 267 8.7 Summary 268 References 268 Student Exercises 269 Chapter 9: Frequency-Response Analysis 275 9.1 Motivation 276 9.2 Bode and Nyquist Plots 279 9.3 Effect of Process Parameters on Bode and Nyquist Plots 284 9.4 Closed-Loop Stability 288 9.5 Bode and Nyquist Stability 290 9.6 Robustness 294 9.7 MATLAB Control Toolbox: Bode and Nyquist Functions 295 9.8 Summary 297 Reference 298 Student Exercises 298 Chapter 10: Cascade and Feedforward Control 305 10.1 Background 306 10.2 Introduction to Cascade Control 306 10.3 Cascade-Control Analysis 310 10.4 Cascade-Control Design 312 10.5 Feedforward Control 313 10.6 Feedforward Controller Design 315 10.7 Summary of Feedforward Control 320 10.8 Combined Feedforward and Cascade 321 10.9 Summary 321 References 321 Student Exercises 322 Chapter 11: PID Enhancements 333 11.1 Background 333 11.2 Antireset Windup 334 11.3 Autotuning Techniques 342 11.4 Nonlinear PID Control 347 11.5 Controller Parameter (Gain) Scheduling 348 11.6 Measurement/Actuator Selection 350 11.7 Implementing PID Enhancements in Simulink 351 11.8 Summary 353 References 354 Student Exercises 354 Chapter 12: Ratio, Selective, and Split-Range Control 357 12.1 Motivation 357 12.2 Ratio Control 358 12.3 Selective and Override Control 359 12.4 Split-Range Control 360 12.5 SIMULINK Functions 363 12.6 Summary 364 References 364 Student Exercises 365 Chapter 13: Control-Loop Interaction 371 13.1 Introduction 372 13.2 Motivation 372 13.3 The General Pairing Problem 375 13.4 The Relative Gain Array 382 13.5 Properties and Application of the RGA 385 13.6 Return to the Motivating Example 387 13.7 RGA and Sensitivity 389 13.8 Using the RGA to Determine Variable Pairings 392 13.9 MATLAB RGA Function File 396 13.10 Summary 397 References 398 Student Exercises 398 Appendix 13.1: Derivation of the Relative Gain for an n-Input-n-Output System 404 Appendix 13.2: m-File to Calculate the RGA 406 Chapter 14: Multivariable Control 407 14.1 Background 408 14.2 Zeros and Performance Limitations 408 14.3 Scaling Considerations 412 14.4 Directional Sensitivity and Operability 416 14.5 Block-Diagram Analysis 422 14.6 Decoupling 423 14.7 MATLAB tzero, svd 427 14.8 Summary 430 References 431 Student Exercises 431 Appendix 14.1 433 Chapter 15: Plantwide Control 435 15.1 Background 436 15.2 Steady-State and Dynamic Effects of Recycle 437 15.3 Unit Operations Not Previously Covered 444 15.4 The Control and Optimization Hierarchy 448 15.5 Further Plantwide Control Examples 451 15.6 Simulations 456 15.7 Startup, Safety, and the Human-in-the-Loop 458 15.8 Summary 459 References 460 Student Exercises 461 Appendix 15.1 463 Chapter 16: Model Predictive Control 467 16.1 Motivation 468 16.2 Optimization Problem 468 16.3 Dynamic Matrix Control 471 16.4 Constraints and Multivariable Systems 482 16.5 Other MPC Methods 485 16.6 MATLAB 487 16.7 Summary 487 References and Relevant Literature 488 Student Exercises 489 Appendix 16.1: Derivation of the Step Response Formulation 491 Appendix 16.2: Derivation of the Least-Squares Solution for Control Moves 492 Appendix 16.3: State Space Formulation for MPC 493 Chapter 17: Summary 497 17.1 Overview of Topics Covered in This Textbook 497 17.2 Process Engineering in Practice 502 17.3 Suggested Further Reading 504 Student Exercises 505 Module 1: Introduction to MATLAB 507 M1.1 Background 508 M1.2 Matrix Operations 509 M1.3 The MATLAB Workspace 513 M1.4 Complex Variables 514 M1.5 Plotting 514 M1.6 More Matrix Stuff 517 M1.7 for Loops 519 M1.8 m-Files 520 M1.9 Summary of Commonly Used Commands 523 M1.10 Frequently Used MATLAB Functions 524 Additional Exercises 524 Module 2: Introduction to SIMULINK 527 M2.1 Background 528 M2.2 Open-Loop Simulations 529 M2.3 Feedback-Control Simulations 530 M2.4 Summary 534 Additional Exercises 534 Module 3: Ordinary Differential Equations 537 M3.1 MATLAB ode--Basic 538 M3.2 MATLAB ode--Options 541 M3.3 SIMULINK sfun 541 M3.4 Summary 545 Additional Exercises 545 Module 4: MATLAB LTI Models 547 M4.1 Forming Continuous-Time Models 548 M4.2 Forming Discrete-Time Models 555 M4.3 Converting Continuous Models to Discrete 557 M4.4 Converting Discrete Models to Continuous 558 M4.5 Step and Impulse Responses 558 M4.6 Summary 560 Additional Exercises 561 Module 5: Isothermal Chemical Reactor 563 M5.1 Background 564 M5.2 Model 564 M5.3 Steady-State and Dynamic Behavior 565 M5.4 Closed-Loop Control 569 Reference 571 Additional Exercises 571 Module 6: Biochemical Reactors 573 M6.1 Background 573 M6.2 Steady-State and Dynamic Behavior 575 M6.3 Stable Steady-State Operating Point 577 M6.4 Unstable Steady-State Operating Point 578 M6.5 SIMULINK Model File 580 Reference 581 Additional Exercises 582 Module 7: CSTR 585 M7.1 Background 586 M7.2 Simplified Modeling Equations 586 M7.3 Example Chemical Process--Propylene Glycol Production 590 M7.4 Effect of Reactor Scale 591 M7.5 For Further Study: Detailed Model 594 M7.6 Other Considerations 598 M7.7 Summary 599 References 600 Additional Exercises 601 Appendix M7.1 602 Module 8: Steam Drum Level 605 M8.1 Background 605 M8.2 Process Model 606 M8.3 Feedback Controller Design 607 M8.4 Feedforward Controller Design 609 M8.5 Three-Mode Level Control 609 Appendix M8.1: SIMULINK Diagram for Feedforward/Feedback Control of Steam Drum Level 611 Appendix M8.2: SIMULINK Diagram for Three-Mode Control of Steam Drum Level 612 Module 9: Surge Vessel Level Control 613 M9.1 Background 613 M9.2 Process Model 614 M9.3 Controller Design 614 M9.4 Numerical Example 616 M9.5 Summary 619 Reference 620 Additional Exercises 620 Appendix M9.1: The SIMULINK Block Diagram 621 Module 10: Batch Reactor 623 M10.1 Background 624 M10.2 Batch Model 1: Jacket Temperature Manipulated 625 M10.3 Batch Model 2: Jacket Inlet Temperature Manipulated 629 M10.4 Batch Model 3: Cascade Control 632 M10.5 Summary 633 Reference 634 Additional Exercises 634 Module 11: Biomedical Systems 635 M11.1 Overview 635 M11.2 Pharmacokinetic Models 636 M11.3 Intravenous Delivery of Anesthetic Drugs 637 M11.4 Blood Glucose Control in ICU Patients 638 M11.5 Critical Care Patients 640 M11.6 Summary 641 References 641 Additional Exercises 642 Module 12: Automated Insulin Delivery 643 M12.1 Background: Physiology of Blood Glucose Regulation 644 M12.2 Type 1 Diabetes 644 M12.3 Closed-Loop Components and Diagram 646 M12.4 Simulation Model 648 M12.5 Open-Loop Responses to Meal and Insulin 649 M12.6 Closed-Loop Responses 652 M12.7 Summary 654 References 655 Suggested Further Study 655 Additional Exercises 656 Module 13: Distillation Control 657 M13.1 Description of Distillation Control 658 M13.2 Open-Loop Behavior 659 M13.3 SISO Control 661 M13.4 RGA Analysis 662 M13.5 Multiple SISO Controllers 663 M13.6 Singular Value Analysis 664 M13.7 Nonlinear Effects 667 M13.8 Other Issues in Distillation Column Control 667 M13.9 Summary 668 References 668 Additional Exercises 668 Module 14: Case Study Problems 671 M14.1 Background 671 M14.2 Reactive Ion Etcher 673 M14.3 Rotary Lime Kiln Temperature Control 674 M14.4 Fluidized Catalytic Cracking Unit 674 M14.5 Anaerobic Sludge Digester 675 M14.6 Suggested Case Study Schedule 676 M14.7 Summary 678 Additional Exercises 679 Module 15: Process Monitoring 681 M15.1 Concise Review of Probability 682 M15.2 Statistical Process Control 685 M15.3 Characteristic Process Noise 689 M15.4 Filtering and Smoothing 690 M15.5 Data Reconciliation 690 M15.6 Gross Error Detection 694 M15.7 Summary 696 References 696 Additional Exercises 696 Appendix M15.1 702 Module 16: Safety 705 M16.1 Overview 706 M16.2 Chemical Process Disasters 707 M16.3 Aircraft Disasters 708 M16.4 Fault Detection Algorithms and Safety Science 710 M16.5 Summary 710 References 711 Additional Exercises 713 Index 715