Online Sample Chapters
Introduction to Process Control and Instrumentation
Process Control: Understanding Dynamic Behavior
Table of Contents
Preface.
1. Introduction.
Introduction. Instrumentation. Process Models and Dynamic Behavior. Control Textbooks and Journals. A Look Ahead. Summary. Student Exercises.
2. Fundamental Models. Background. Balance Equations. Material Balances. Constitutive Relationships. Material and Energy Balances. Form of Dynamic Models. Linear Models and Deviation Variables. Summary. Suggested Reading. Student Exercises. Appendix 2.1: Solving Algebraic Equations. Appendix 2.2: Integrating Ordinary Differential Equations.
3. Dynamic Behavior. Background. Linear State Space Models. Introduction to Laplace Transforms. Transfer Functions. First-Order Behavior. Integrating System. Second-Order Behavior. Lead-Lag Behavior. Poles and Zeros. Processes with Dead Time. Padé Approximation for Dead Time. Converting State Space Models to Transfer Functions. MATLAB and SIMULINK. Summary. References. Student Exercises.
4. Empirical Models. Introduction. First-Order + Dead Time. Integrator + Dead Time. Discrete-Time Autoregressive Models. Parameter Estimation. Discrete Step and Impulse Response Models. Summary. References. Student Exercises. Appendix 4.1: Files Used to Generate Example 4.4. Appendix 4.2.
5. Introduction to Feedback Control. Motivation. Development of Control Block Diagrams. Response to Setpoint Changes. PID Controller Algorithms. Routh Stability Criterion. Effect of Tuning Parameters. Response to Disturbances. Open-Loop Unstable Systems. SIMULINK Block Diagrams. Summary. References. Student Exercises.
6. PID Controller Tuning. Introduction. Closed-Loop Oscillation-Based Tuning. Tuning Rules for First-Order + Dead Time Processes. Direct Synthesis. Summary. References. Student Exercises.
7. Frequency-Response Analysis. Motivation. Bode and Nyquist Plots. Closed-Loop Stability Concepts. Bode and Nyquist Stability. Robustness. MATLAB Control Toolbox: Bode and Nyquist Functions. Summary. Reference. Student Exercises.
8. Internal Model Control. Introduction to Model-Based Control. Practical Open-Loop Controller Design. Generalization of the Open-Loop Control Design Procedure. Model Uncertainty and Disturbances. Development of the IMC Structure. IMC Background. The IMC Structure. The IMC Design Procedure. Effect of Model Uncertainty and Disturbances. Improved Disturbance Rejection Design. Manipulated Variable Saturation. Summary. References. Student Exercises. Appendix 8.1: Derivation of Closed-Loop Relationships for IMC.
9. The IMC-Based PID Procedure. Background. The Equivalent Feedback Form to IMC. IMC-Based Feedback Design for Delay-Free Processes. IMC-Based Feedback Design for Processes with a Time Delay. Summary of IMC-Based PID Controller Design for Stable Processes. IMC-Based PID Controller Design for Unstable Processes. Summary. References. Student Exercises.
10. Cascade and Feed-Forward Control. Background. Introduction to Cascade Control. Cascade-Control Analysis. Cascade-Control Design. Cascade IMC. Feed-Forward Control. Feed-Forward Controller Design. Summary of Feed-Forward Control. Combined Feed-Forward and Cascade. Summary. References. Student Exercises-Cascade Control. Student Exercises-Feed-Forward Control. Student Exercises-Feed-Forward and Cascade.
11. PID Enhancements. Background. Antireset Windup. Autotuning Techniques. Nonlinear PID Control. Controller Parameter (Gain) Scheduling. Measurement/Actuator Selection. Implementing PID Enhancements in SIMULINK. Summary. References. Student Exercises.
12. Ratio, Selective, and Split-Range Control. Motivation. Ratio Control. Selective and Override Control. Split-Range Control. SIMULINK Functions. Summary. References. Student Exercises.
13. Control-Loop Interaction. Introduction. Motivation. The General Pairing Problem. The Relative Gain Array. Properties and Application of the RGA. Return to the Motivating Example. RGA and Sensitivity. Using the RGA to Determine Variable Pairings. MATLAB RGA Function File. Summary. References. Student Exercises. Appendix 13.1: Derivation of the Relative Gain for an n-Input-n-Output System. Appendix 13.2: m-File to Calculate the RGA.
14. Multivariable Control. Background. Zeros and Performance Limitations. Scaling Considerations. Directional Sensitivity and Operability. Block-Diagram Analysis. Decoupling. IMC. MATLAB tzero, svd, and LTI Functions. Summary. References. Student Exercises. Appendix 14.1.
15. Plantwide Control. Background. Steady-State and Dynamic Effects of Recycle. Unit Operations Not Previously Covered. The Control and Optimization Hierarchy. Further Plantwide Control Examples. Simulations. Summary. References. Student Exercises.
16. Model Predictive Control. Motivation. Optimization Problem. Dynamic Matrix Control. Constraints and Multivariable Systems. Other MPC Methods. MATLAB. Summary. References and Relevant Literature. Student Exercises. Appendix 16.1: Derivation of the Step Response Formulation. Appendix 16.2: Derivation of the Least Squares Solution for Control Moves. Appendix 16.3.
17. Summary. Overview of Topics Covered in This Textbook. Process Engineering in Practice. Suggested Further Reading. Notation. Student Exercises.
Module 1. Introduction to MATLAB. Background. Matrix Operations. The MATLAB Workspace. Complex Variables. Plotting. More Matrix Stuff. For Loops. m-Files. Summary of Commonly Used Commands. Frequently Used MATLAB Functions. Additional Exercises.
Module 2. Introduction to SIMULINK. Background. Open-Loop Simulations. Feedback-Control Simulations. Other Commonly Used Icons. Developing Alternative Controller Icons. Summary. Additional Exercises.
Module 3. Ordinary Differential Equations. MATLAB ode-Basic. MATLAB ode-Options. SIMULINK sfun (.mdl Files). SIMULINK sfun (.mdl Files)-Advanced. Summary.
Module 4. MATLAB LTI Models. Forming Continuous-Time Models. Forming Discrete-Time Models. Converting Continuous Models to Discrete. Converting Discrete Models to Continuous. Step and Impulse Responses. Summary. Reference. Additional Exercises.
Module 5. Isothermal Chemical Reactor. Background. Model (Chapter 2). Steady-State and Dynamic Behavior (Chapter 3). Classical Feedback Control (Chapters 5 and 6). Internal Model Control (Chapter 8). Reference. Additional Exercises.
Module 6. First-Order + Time-Delay Processes. Motivation. Closed-Loop Time-Domain Simulation. Bode Analysis. Ziegler-Nichols Tuning. IMC-Based PID Control. Summary. References. Additional Exercises. Appendix M6.1.
Module 7. Biochemical Reactors. Background. Steady-State and Dynamic Behavior. Stable Steady-State Operating Point. Unstable Steady-State Operating Point. SIMULINK Model File. Reference. Additional Exercises.
Module 8. CSTR. Background. Simplified Modeling Equations. Example Chemical Process-Propylene Glycol Production. Effect of Reactor Scale. For Further Study: Detailed Model. Other Considerations. Summary. References. Additional Exercises. Appendix M8.1.
Module 9. Steam Drum Level. Background. Process Model. Feedback Controller Design. Feed-Forward Controller Design. Three-Mode Level Control. Appendix M9.1: SIMULINK Diagram for Feed-Forward/Feedback Control of Steam Drum Level. Appendix M9.2: SIMULINK Diagram for 3-Mode Control of Steam Drum Level.
Module 10. Surge Vessel Level Control. Background. Process Model. Controller Design. Numerical Example. Summary. Reference. Additional Exercises. Appendix M10.1: The SIMULINK Block Diagram.
Module 11. Batch Reactor. Background. Batch Model 1: Jacket Temperature Manipulated. Effect of Scale (Size). Quasi-Steady-State Behavior IMC-Based Design. Batch Model 2: Jacket Inlet Temperature Manipulated. IMC-Based PID Tuning Parameters. Batch Model 3: Cascade Control. Summary. Reference. Additional Exercises.
Module 12. Biomedical Systems. Overview. Pharmacokinetic Models. Intravenous Delivery of Anesthetic Drugs. Blood Glucose Control in Diabetic Patients. Blood Pressure Control in Post-Operative Patients. Critical Care Patients. Summary. References. Additional Exercises.
Module 13. Distillation Control. Description of Distillation Control. Open-Loop Behavior. SISO Control. RGA Analysis. Multiple SISO Controllers. Singular Value Analysis. Nonlinear Effects. Other Issues in Distillation Column Control. Summary. References. Additional Exercises. Appendix M13.1.
Module 14. Case Study Problems. Background. Reactive Ion Etcher. Rotary Lime Kiln Temperature Control. Fluidized Catalytic Cracking Unit. Anaerobic Sludge Digester. Drug Infusion System. Suggested Case Study Schedule. Summary. Additional Exercises.
Module 15. Flow Control. Motivating Example. Flowmeters. Control Valves. Pumping and Piping Systems. Summary. References. Additional Exercises.
Module 16. Digital Control. Background. PID Controllers. Stability Analysis for Digital Control Systems. Performance of Digital Control Systems. Discrete IMC. Summary.
References. Additional Exercises. Appendix M16.1: SIMULINK .mdl File for Example M16.2. Appendix M16.2: SIMULINK .m and .mdl Files for Example M16.3. Index. About the Author.
Preface
Preface
Background
There are a variety of courses in a standard chemical engineering curriculum, ranging from the introductory material and energy balances course, and culminating with the capstone process design course. The focus of virtually all of these courses is on steady-state behavior; the rare exceptions include the analysis of batch reactors and batch distillation in the reaction engineering and equilibrium stage operations courses, respectively. A concern of a practicing process engineer, on the other hand, is how to best operate a process plant where everything seems to be changing. The process dynamics and control course is where students must gain an appreciation for the dynamic nature of chemical processes and develop strategies to operate these processes.
Textbook Goals
The major goal of this textbook is to teach students to analyze dynamic chemical processes and develop automatic control strategies to operate them safely and economically. My experience is that students learn best with immediate simulation-based reinforcement of basic concepts. Rather than simply present theory topics and develop analytical solutions, this textbook uses "interactive learning" through computer-based simulation exercises (modules). The popular MATLAB software package, including the SIMULINK block-diagram simulation environment, is used. Students, instructors, and practicing process engineers learning new model-based techniques can all benefit from the "feedback" provided by simulation studies.
Depending on the goals of the instructor and the background of the students, roughly one chapter (± 0.5) and one module can be covered each week. Still, it is probably too ambitious to cover the entire text during a typical 15-week semester, so I recommend that instructors carefully choose the topics that best meet their personal objectives. At Rensselaer Polytechnic Institute, we teach the one-semester, 4-credit course in a studio-based format, with students attending two 2-hour sessions and one 2-hour recitation (which also provides plenty of "catch-up" time) each week. During these sessions we typically spend 45 minutes discussing a topic, then have the students spend the remaining hour performing analysis and computer simulation exercises, working in pairs. During the discussion periods the students face the instructor station at the front of the room, and during the simulation periods they swivel in their chairs to the workstations on the countertops behind them. This textbook can also be used in a more traditional lecture-based course, with students working on the modules and solving homework problems on their own.
Chapters
An introduction to process control and instrumentation is presented in Chapter 1. The development and use of models is very important in control systems engineering. Fundamental models are developed in Chapter 2, including the steady-state solution and linearization to form state space models. Chapter 3 focuses on the dynamic behavior of linear systems, starting with state space models and then covering transfer function-based models in detail. Chapter 4 we cover the development of empirical models, including continuous and discrete transfer function models.
Chapter 5 provides a more detailed introduction to feedback control, developing the basic idea of a feedback system, proportional, integral, derivative (PID) controllers, and methods of analyzing closed-loop stability. Chapter 6 presents the Ziegler-Nichols closed-loop oscillation method for controller tuning, since the same basic concept is used in the automatic tuning procedures presented in Chapter 11. Frequency response analysis techniques, important for determining control system robustness, are presented in Chapter 7.
In recent years model-based control has lead to improved control loop performance. One of the clearest model-based techniques is internal model control (IMC), which is presented in Chapter 8. The PID controller remains the most widely used controller in industry; in Chapter 9 we show how to convert internal model controllers to classical feedback (PID) controllers.
In Chapter 10 the widely used cascade and feed-forward strategies are developed. Many control loops suffer from poor performance, either because they were not tuned well originally, or because the process is nonlinear and has changed operating conditions. Two methods of dealing with these problems, automatic tuning and gain scheduling, are presented in Chapter 11. The phenomenon of reset windup and the development of antireset windup strategies are also presented in Chapter 11.
Many control strategies must be able to switch between manipulated inputs or select from several measured outputs. Split-range, selective and override strategies are presented in Chapter 12. Process units contain many control loops that generally do not operate independently. The effects of these control-loop interactions are presented in Chapter 13. The design of multivariable controllers is developed in Chapter 14.
The development of the control instrumentation diagram for an entire chemical process is challenging and remains somewhat of an art. In Chapter 15 recycle systems are shown to cause unique and challenging steady-state and dynamic control problems. In addition, an overview of corporate-wide optimization and control problems is presented. Model predictive control (MPC) is the most widely applied advanced control strategy in industry. The basic step response model-based MPC method is developed in Chapter 16. This is followed by a discussion of the constrained version of MPC, and enhancements to improve disturbance rejection.
Learning Modules
The chapters are followed by a series of learning modules that serve several purposes; some focus on the software tools, while others focus on particular control problems. The first two provide introductions to MATLAB and SIMULINK, the simulation environment for the modules that follow. The third module demonstrates the solution of ordinary differential equations using MATLAB and SIMULINK, while the fourth shows how to use the MATLAB Control Toolbox to create and convert models from one form to another. The modules that follow focus on a particular unit operation, to provide a detailed demonstration of various control system design, analysis or implementation techniques. Module 5 develops a simple isothermal CSTR model that is used in a number of the chapters. Module 6 details the robustness analysis of processes characterized by first-order + deadtime (FODT) models.
Module 7 presents a biochemical reactor with two possible desired operating points; one stable and the other unstable. The controller design and system performance is clearly different at each operating point. The classic jacketed CSTR with an exothermic reaction is studied in Module 8. Issues discussed include recirculation heat transfer dynamics, cascade control, and split-range control. Level control loops can be tuned for two different extremes of closed-loop performance, as shown in Module 9 (steam drum, requiring tight level control) and Module 10 (surge drum, allowing loose level control to minimize outflow variation). Challenges associated with jacketed batch reactors are presented in Module 11. Some motivating biomedical problems are presented in Module 12. Challenges of control loop interaction are demonstrated in the distillation application of Module 13. Module 14 provides an overview of several case study problems in multivariable control. Here the students can download SIMULINK .mdl files for the textbook web page and perform complete modeling and control system design. These case studies are meant to tie together many concepts presented in the text. Issues particular to flow control are discussed in Module 15, and digital control techniques are presented in Module 16.
Textbook Web Page
MATLAB and SIMULINK files, as well as additional learning material and errata, can be found on the textbook web pages.
www.rpi.edu/dept/chem-eng/WWW/faculty/bequette/books/Process_Control/
www.rpi.edu/~bequeb/Control