book

Process Control: Modeling, Design, and Simulation, 2nd Edition

by B. Wayne Bequette

August 2023

Intermediate to advanced

768 pages

20h 59m

English

Pearson

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1.1 Introduction1.2 Instrumentation1.3 Process Models and Dynamic Behavior1.4 Redundancy and Operability1.5 Industrial IoT and Smart Manufacturing1.6 Control Textbooks1.7 A Look Ahead1.8 SummaryReferencesStudent Exercises
2.1 Background2.2 Balance Equations2.3 Material Balances2.4 Constitutive Relationships2.5 Material and Energy Balances2.6 Form of Dynamic Models2.7 Linear Models and Deviation Variables2.8 SummarySuggested ReadingStudent Exercises
3.1 Background3.2 Linear State-Space Models3.3 Laplace Transforms3.4 Transfer Functions3.5 First-Order Behavior3.6 Integrating Behavior3.7 Second-Order Behavior3.8 SummaryReferencesStudent Exercises
4.1 Introduction4.2 Poles and Zeros4.3 Lead-Lag Behavior4.4 Processes with Deadtime4.5 Padé Approximation for Deadtime4.6 Converting State-Space Models to Transfer Functions4.7 Converting Transfer Functions to State-Space Models4.8 Matlab and Simulink4.9 SummaryStudent Exercises
5.1 Introduction5.2 First-Order + Deadtime5.3 Integrator + Deadtime5.4 Other Continuous Models5.5 Discrete-Time Autoregressive Models5.6 Parameter Estimation5.7 Discrete Step and Impulse Response Models5.8 Converting Continuous Models to Discrete5.9 Digital Filtering5.10 SummaryReferencesStudent ExercisesAppendix 5.1: Discretization
6.1 Motivation6.2 Control Block Diagrams6.3 Closed-Loop Analysis6.4 PID Controller Algorithms6.5 Routh Stability Criterion6.6 Effect of Tuning Parameters6.7 Open-Loop Unstable Systems6.8 Simulink Block Diagrams6.9 ODEs to Solve PID Problems6.10 SummaryReferencesStudent Exercises
7.1 Introduction7.2 Direct Synthesis7.3 Internal Model Control7.4 IMC-Based PID7.5 IMC-Based PID for Time-Delay Processes7.6 IMC-Based PID for Unstable Processes7.7 SummaryReferencesStudent ExercisesAppendix 7.1: SIMC-Based PID Design
8.1 Introduction8.2 Closed-Loop Oscillation-Based Tuning8.3 Tuning Rules for First-Order + Deadtime Processes8.4 Digital Control8.5 Stability of Digital Control Systems8.6 Performance of Digital Control Systems8.7 SummaryReferencesStudent Exercises
9.1 Motivation9.2 Bode and Nyquist Plots9.3 Effect of Process Parameters on Bode and Nyquist Plots9.4 Closed-Loop Stability9.5 Bode and Nyquist Stability9.6 Robustness9.7 Matlab Control Toolbox: Bode and Nyquist Functions9.8 SummaryReferenceStudent Exercises
10.1 Background10.2 Introduction to Cascade Control10.3 Cascade-Control Analysis10.4 Cascade-Control Design10.5 Feedforward Control10.6 Feedforward Controller Design10.7 Summary of Feedforward Control10.8 Combined Feedforward and Cascade10.9 SummaryReferencesStudent Exercises
11.1 Background11.2 Antireset Windup11.3 Autotuning Techniques11.4 Nonlinear PID Control11.5 Controller Parameter (Gain) Scheduling11.6 Measurement/Actuator Selection11.7 Implementing PID Enhancements in Simulink11.8 SummaryReferencesStudent Exercises
12.1 Motivation12.2 Ratio Control12.3 Selective and Override Control12.4 Split-Range Control12.5 Simulink Functions12.6 SummaryReferencesStudent Exercises
13.1 Introduction13.2 Motivation13.3 The General Pairing Problem13.4 The Relative Gain Array13.5 Properties and Application of the RGA13.6 Return to the Motivating Example13.7 RGA and Sensitivity13.8 Using the RGA to Determine Variable Pairings13.9 Matlab RGA Function File13.10 SummaryReferencesStudent ExercisesAppendix 13.1: Derivation of the Relative Gain for an n-Input–n-Output SystemAppendix 13.2: m-File to Calculate the RGA
14.1 Background14.2 Zeros and Performance Limitations14.3 Scaling Considerations14.4 Directional Sensitivity and Operability14.5 Block-Diagram Analysis14.6 Decoupling14.7 Matlab tzero, svd14.8 SummaryReferencesStudent ExercisesAppendix 14.1
15.1 Background15.2 Steady-State and Dynamic Effects of Recycle15.3 Unit Operations Not Previously Covered15.4 The Control and Optimization Hierarchy15.5 Further Plantwide Control Examples15.6 Simulations15.7 Startup, Safety, and the Human-in-the-Loop15.8 SummaryReferencesStudent ExercisesAppendix 15.1
16.1 Motivation16.2 Optimization Problem16.3 Dynamic Matrix Control16.4 Constraints and Multivariable Systems16.5 Other MPC Methods16.6 Matlab16.7 SummaryReferences and Relevant LiteratureStudent ExercisesAppendix 16.1: Derivation of the Step Response FormulationAppendix 16.2: Derivation of the Least-Squares Solution for Control MovesAppendix 16.3: State Space Formulation for MPC
17.1 Overview of Topics Covered in This Textbook17.2 Process Engineering in Practice17.3 Suggested Further ReadingStudent Exercises
M1.1 BackgroundM1.2 Matrix OperationsM1.3 The Matlab WorkspaceM1.4 Complex VariablesM1.5 PlottingM1.6 More Matrix StuffM1.7 for LoopsM1.8 m-FilesM1.9 Summary of Commonly Used CommandsM1.10 Frequently Used Matlab FunctionsAdditional Exercises
M2.1 BackgroundM2.2 Open-Loop SimulationsM2.3 Feedback-Control SimulationsM2.4 SummaryAdditional Exercises
M3.1 Matlab ode—BasicM3.2 Matlab ode—OptionsM3.3 Simulink sfunM3.4 SummaryAdditional Exercises
M4.1 Forming Continuous-Time ModelsM4.2 Forming Discrete-Time ModelsM4.3 Converting Continuous Models to DiscreteM4.4 Converting Discrete Models to ContinuousM4.5 Step and Impulse ResponsesM4.6 SummaryAdditional Exercises
M5.1 BackgroundM5.2 ModelM5.3 Steady-State and Dynamic BehaviorM5.4 Closed-Loop ControlReferenceAdditional Exercises
M6.1 BackgroundM6.2 Steady-State and Dynamic BehaviorM6.3 Stable Steady-State Operating PointM6.4 Unstable Steady-State Operating PointM6.5 Simulink Model FileReferenceAdditional Exercises
M7.1 BackgroundM7.2 Simplified Modeling EquationsM7.3 Example Chemical Process—Propylene Glycol ProductionM7.4 Effect of Reactor ScaleM7.5 For Further Study: Detailed ModelM7.6 Other ConsiderationsM7.7 SummaryReferencesAdditional ExercisesAppendix M7.1
M8.1 BackgroundM8.2 Process ModelM8.3 Feedback Controller DesignM8.4 Feedforward Controller DesignM8.5 Three-Mode Level ControlAppendix M8.1: Simulink Diagram for Feedforward/Feedback Control of Steam Drum LevelAppendix M8.2: Simulink Diagram for Three-Mode Control of Steam Drum Level
M9.1 BackgroundM9.2 Process ModelM9.3 Controller DesignM9.4 Numerical ExampleM9.5 SummaryReferenceAdditional ExercisesAppendix M9.1: The Simulink Block Diagram
M10.1 BackgroundM10.2 Batch Model 1: Jacket Temperature ManipulatedM10.3 Batch Model 2: Jacket Inlet Temperature ManipulatedM10.4 Batch Model 3: Cascade ControlM10.5 SummaryReferenceAdditional Exercises
M11.1 OverviewM11.2 Pharmacokinetic ModelsM11.3 Intravenous Delivery of Anesthetic DrugsM11.4 Blood Glucose Control in ICU PatientsM11.5 Critical Care PatientsM11.6 SummaryReferencesAdditional Exercises
M12.1 Background: Physiology of Blood Glucose RegulationM12.2 Type 1 DiabetesM12.3 Closed-Loop Components and DiagramM12.4 Simulation ModelM12.5 Open-Loop Responses to Meal and InsulinM12.6 Closed-Loop ResponsesM12.7 SummaryReferencesSuggested Further StudyAdditional Exercises
M13.1 Description of Distillation ControlM13.2 Open-Loop BehaviorM13.3 SISO ControlM13.4 RGA AnalysisM13.5 Multiple SISO ControllersM13.6 Singular Value AnalysisM13.7 Nonlinear EffectsM13.8 Other Issues in Distillation Column ControlM13.9 SummaryReferencesAdditional Exercises
M14.1 BackgroundM14.2 Reactive Ion EtcherM14.3 Rotary Lime Kiln Temperature ControlM14.4 Fluidized Catalytic Cracking UnitM14.5 Anaerobic Sludge DigesterM14.6 Suggested Case Study ScheduleM14.7 SummaryAdditional Exercises
M15.1 Concise Review of ProbabilityM15.2 Statistical Process ControlM15.3 Characteristic Process NoiseM15.4 Filtering and SmoothingM15.5 Data ReconciliationM15.6 Gross Error DetectionM15.7 SummaryReferencesAdditional ExercisesAppendix M15.1
M16.1 OverviewM16.2 Chemical Process DisastersM16.3 Aircraft DisastersM16.4 Fault Detection Algorithms and Safety ScienceM16.5 SummaryReferencesAdditional Exercises

Content preview from Process Control: Modeling, Design, and Simulation, 2nd Edition

Chapter 3 Dynamic Behavior

The goal of this chapter is to understand dynamic behavior. We begin by working with linear state-space models, often obtained by linearizing a nonlinear model, such as those developed in Chapter 2, “Fundamental Models.” We then introduce Laplace transforms. The main advantage to Laplace transforms is that they allow us to analyze behavior exhibited by linear differential equations by using simple algebraic manipulations. Laplace transforms are used to create transfer function models, which are the basis for many control-system design and analysis techniques.

After studying this chapter, the reader should be able to