Process Control: Modeling, Design, and SimulationMaster process control hands on, through practical examples and MATLABreg; simulations This is the first complete introduction to process control that fully integrates software toolsenabling professionals and students to master critical techniques hands on, through computer simulations based on the popular MATLAB environment. Process Control: Modeling, Design, and Simulation teaches the field's most important techniques, behaviors, and control problems through practical examples, supplemented by extensive exerciseswith detailed derivations, relevant software files, and additional techniques available on a companion Web site. Coverage includes: bull; Fundamentals of process control and instrumentation, including objectives, variables, and block diagrams Methodologies for developing dynamic models of chemical processes Dynamic behavior of linear systems: state space models, transfer functionbased models, and more Feedback control; proportional, integral, and derivative (PID) controllers; and closedloop 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 enhancements to improve disturbance rejection Splitrange, selective, and override strategies for switching among inputs or outputs Control loop interactions and multivariable controllers An introduction to model predictive control (MPC) Bequette walks step by step through the development of control instrumentation diagrams for an entire chemical process, reviewing common control strategies for individual unit operations, then discussing strategies for integrated systems. The book also includes 16 learning modules demonstrating how to use MATLAB and SIMULINK to solve several key control problems, ranging from robustness analyses to biochemical reactors, biomedical problems to multivariable control. 
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pages in range of 200 or greater or less
Contents
Introduction  1 
Student Exercises  24 
Fundamental Models  31 
Suggested Reading  68 
Solving Algebraic Equations  76 
References  120 
Empirical Models  127 
Files Used to Generate Example 4 4  152 
Introduction to MATLAB  539 
Additional Exercises  557 
FeedbackControl Simulations  563 
Developing Alternative Controller Icons  570 
MATLAB odeOptions  577 
Summary  584 
Forming DiscreteTime Models  593 
Converting Discrete Models to Continuous  599 
References  185 
PID Controller Tuning  195 
References  210 
Internal Model Control  245 
References  279 
The IMCBased PID Procedure  285 
References  306 
Cascade and FeedForward Control  313 
References  333 
Student ExercisesFeedForward Control  340 
References  367 
ControlLoop Interaction  381 
References  409 
Derivation of the Relative Gain for an nInputnOutput System  415 
References  448 
References  482 
References and Relevant Literature  510 
Summary  521 
Student Exercises  533 
Internal Model Control Chapter 8  613 
IMCBased PID Control  628 
Stable SteadyState Operating Point  634 
CSTR  641 
Detailed Model  651 
657  
Feedback Controller Design  663 
FeedForward Controller Design  666 
Reference  676 
Jacket Inlet Temperature Manipulated  685 
Biomedical Systems  691 
Blood Pressure Control in PostOperative Patients  698 
Singular Value Analysis  712 
Fluidized Catalytic Cracking Unit  725 
Flow Control  733 
Digital Control  749 
SIMULINK mdl File for Example M16 2  762 
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Common terms and phrases
assume behavior Bode plot cascade control Chapter chemical reactor closedloop response closedloop transfer function concentration Consider constant control loop control strategy control system control valve controller design controller gain CSTR delay design procedure develop deviation variable discrete distillate dynamic example feedforward controller feedback control filter flow rate gp(s IMCbased PID inlet inputoutput integral jacket temperature Laplace Laplace transform linear manipulated input manipulated variable MATLAB minutes Model Predictive Control module multivariable nonlinear Notice Nyquist plot openloop openloop control operating point Pade approximation performance phase margin PID controller plot poles pressure problem process gain process model process output process transfer function proportional gain reactor temperature recycle relative gain RHP zero Routh stability criterion secondorder shown in Figure simulation SIMULINK SISO space model stability steadystate step change step input step response step setpoint change stream tank temperature controller timedelay tuning parameters unstable vector ZieglerNichols