Nonlinear Model-Based Control: Using First-Principles Models in Process Control
First-principles models (engineering models) are used in industry for process design, troubleshooting, training, online analysis and supervisory optimization. The author's vision is to use them for control.
Why? They effectively handle nonlinearity, nonstationary behavior and interacting variables with just one tuning coefficient per controlled variable (CV). Using optimization, the controller can handle constraints and shape the manipulated variables to achieve desired controlled variable trajectories. Using first-principles models for control can also enhance the operational staff's understanding of the process, support auxiliary process management, and keep the mathematics at the engineers' comfort level. In addition, unifying all models across diverse process management operations ensures continuity and compatibility.
The book explains four control techniques using first-principles models that have been credibly demonstrated for industrial practice: generic model control, process-model-based control, predictive functional control and horizon predictive control. It illustrates their applications and discusses the pros and cons of each. To provide a better understanding of first-principles models, the book includes examples of setting up functions for controllers and discusses inherent properties such as ease of tuning, the handling of nonlinearity and interaction, feedforward constraints and the range of operation.
Table of Contents:
Acknowledgments v
About the Author xvii
Rationale/Preface xix
Section 1 Introductory Material 1
Chapter 1 Introduction 3
Chapter 2 Models 27
Chapter 3 Process Simulation 59
Chapter 4 Model Verification and Validation 75
Chapter 5 Control Issues 83
Chapter 6 Control Metrics Goodness 93
Chapter 7 First- and Second-Order Plus Dead Time Models 109
Chapter 8 III-Behaved Dynamics 125
Section 2 Simple Controllers – MISO and Unconstrained 135
Chapter 9 Simple Internal Model Control – Unconstrained 137
Chapter 10 Generic Model Control with Steady-State Models 149
Chapter 11 Simple Model-Based Control – MISO, Unconstrained 163
Chapter 12 Simple Process-Model-Based Control – Unconstrained MISO 185
Chapter 13 Simple Predictive Functional Control – Unconstrained MISO 197
Chapter 14 Simple Horizon Predictive Control – MISO with Constraints 207
Chapter 15 More on MISO Models 229
Chapter 16 Equivalence to PID 235
Section 3 Supporting Techniques for Multivariable Constrained Control 239
Chapter 17 Data and Action Filtering 241
Chapter 18 Steady- and Transient-State Identification 249
Chapter 19 Data Validity 271
Chapter 20 Optimization 277
Section 4 Constrained MIMO Control 291
Chapter 21 Constraints and Balancing Desirables 293
Chapter 22 Multivariable Processes 303
Chapter 23 MIMO Process-Model-Based Control (PMBC) 309
Chapter 24 MIMO Horizon Predictive Control 317
Section 5 Ending 323
Chapter 25 Insight 325
Section 6 Appendixes 343
Appendix A Car Speed Simulator 345
Appendix B In-Line Hot and Cold Water Mixing Simulator 351
Appendix C pH Neutralization Simulator 365
Appendix D Heat Exchanger Simulator 381
Appendix E Nomenclature 395
Bibliography 399
Index 405
About the Author :
Dr. R. Russell Rhinehart, professor emeritus in the School of Chemical Engineering at Oklahoma State University, has experience in both industry (13 years) and academia (31 years) and was head of the school for 13 years. Russ is a past president of the American Automatic Control Council, was editor-in-chief of ISA Transactions from 1998 to 2012, and Director of the ISA Automatic Control Systems Division (now Control and Robotics). He is a Fellow of both ISA and AIChE and a Process Automation Hall of Fame inductee. He received the 2009 ISA Distinguished Service Award and the 2013 Fray International Sustainability Award.
Inspired by his industrial experience, his mission has been to bridge the gap between industry and academia. Russ was the codirector of two industrial consortia (one at Texas Tech and a second at Oklahoma State) and built pilot-scale laboratories for dual use in undergraduate education and graduate research. He left industry in 1982 with a vision to use engineers’ process models in control and pursued many aspects of doing so in his academic research career. This book is his collection of practicable methods. His goal is for it to be a useful guide to others seeking to use nonlinear models in control.
His 1968 BS in chemical engineering and subsequent MS in nuclear engineering are both from the University of Maryland. His 1985 PhD in chemical engineering is from North Carolina State University.
He maintains a website (www.r3eda.com) to provide open access to software (including simulators to support this text) and technique monographs.
In "retirement," he offers consulting services related to engineering analysis, and serves on several ISA, American Automatic Control Council (AACC), and International Federation of Automatic Control (IFAC) committees.
