Advanced Practical Process Control

Advanced Practical Process Control by Brian Roffel, Ben Betlem.

This text and reference offers an application-oriented approach to process control. It systematically explains process identification, control and optimization, the three key steps needed to solve a multivariable control problem.  Theory is discussed as far as it is needed to understand and solve th...

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Bibliographic Details
Main Authors: Roffel, Brian (Author), Betlem, Ben (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
Edition:1st ed. 2004.
Series:Springer eBook Collection.
Subjects:
Electrical engineering.
Control engineering.
Robotics.
Mechatronics.
Chemical engineering.
Computational intelligence.
Vibration.
Dynamical systems.
Dynamics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 1 Introduction to Advanced Process Control Concepts
  • 1.1 Process Time Constant
  • 1.2 Domain Transformations
  • 1.3 Laplace Transformation
  • 1.4 Discrete Approximations
  • 1.5 z-Transforms
  • 1.6 Advanced and Modified z-Transforms
  • 1.7 Common Elements in Control
  • 1.8 The Smith Predictor
  • 1.9 Feed-forward Control
  • 1.10 Feed-forward Control in a Smith Predictor
  • 1.11 Dahlin’s Control Algorithm
  • References
  • 2 Process Simulation
  • 2.1 Simulation using Matlab Simulink
  • 2.2 Simulation of Feed-forward Control
  • 2.3 Control Simulation of a 2x2 System
  • 2.4 Simulation of Dahlin’s Control Algorithm
  • 3 Process Modeling and Identification
  • 3.1 Model Applications
  • 3.2 Types of Models
  • 3.3 Empirical (linear) Dynamic Models
  • 3.4 Model Structure Considerations
  • 3.5 Model Identification
  • References
  • 4 Identification Examples
  • 4.1 SISO Furnace Parametric Model Identification
  • 4.2 MISO Parametric Model Identification
  • 4.3 MISO Non-parametric Identification of a Non-integrating Process
  • 4.4 MIMO Identification of an Integrating and Non-integrating Process
  • 4.5 Design of Plant Experiments
  • 4.5.1 Nature of Input Sequence
  • 4.5.2 PRBS Type Input
  • 4.5.3 Step Type Input
  • 4.5.4 Type of Experiment
  • 4.6 Data File Layout
  • 4.7 Conversion of Model Structures
  • 4.8 Example and Comparison of Open and Closed Loop Identification
  • References
  • 5 Linear Multivariable Control
  • 5.1 Interaction in Multivariable Systems
  • 5.2 Dynamic Matrix Control
  • 5.3 Properties of Commercial MPC Packages
  • References
  • 6 Multivariable Optimal Constraint Control Algorithm
  • 6.1 General Overview
  • 6.2 Model Formulation for Systems with Dead Time
  • 6.3 Model Formulation for Multivariable Processes
  • 6.4 Model Formulation for Multivariable Processes with Time Delays
  • 6.5 Model Formulation in Case of a Limited Control Horizon
  • 6.6 Mocca Control Formulation
  • 6.7 Non-linear Transformations
  • 6.8 Practical Implementation Guidelines
  • 6.9 Case Study
  • 6.10 Control of a Fluidized Catalytic Cracker
  • 6.11 Examples of Case Studies in MATLAB
  • 6.12 Control of Integrating Processes
  • 6.13 Lab Exercises
  • 6.14 Use of MCPC for Constrained Multivariable Control
  • References
  • 7 Internal Model Control
  • 7.1 Introduction
  • 7.2 Factorization of Multiple Delays
  • 7.3 Filter Design
  • 7.4 Feed-forward IMC
  • 7.5 Example of Controller Design
  • 7.6 LQ Optimal Inverse Design
  • References
  • 8 Nonlinear Multivariable Control
  • 8.1 Non-linear Model Predictive Control
  • 8.2 Non-linear Quadratic DMC
  • 8.3 Generic Model Control
  • 8.4 Problem Description
  • 8.5 GMC Application to the CSTR System
  • 8.6 Discussion of the GMC Algorithm
  • 8.7 Simulation of Reactor Control
  • 8.8 One Step Reference Trajectory Control
  • 8.9 Predictive Horizon Reference Trajectory Control
  • References
  • 9 Optimization of Process Operation
  • 9.1 Introduction to Real-time Optimization
  • 9.2 Model Building
  • 9.3 The Objective Function
  • 9.4 Unconstrained Functions: one Dimensional Problems
  • 9.5 Unconstrained Multivariable Optimization
  • 9.6 Linear Programming
  • 9.7 Non-linear Programming
  • References
  • 10 Optimization Examples
  • 10.1 AMPL: a Multi-purpose Optimizer
  • 10.2 Optimization Examples
  • References
  • 11 Integration of Control and Optimization
  • 11.1 Introduction
  • 11.2 Description of the Desalination Plant
  • 11.3 Production Maximization of Desalination Plant
  • 11.4 Linear Model Predictive Control of Desalination Plant
  • 11.5 Reactor problem definition
  • 11.6 Multivariable Non-linear Control of the Reactor
  • References
  • Appendix I. MCPC software guide
  • I.1 Installation
  • I.2 Model identification
  • I.2.1 General process information
  • I.2.2 Identification data
  • I.2.3 Output details
  • I.3 Controller design
  • I.4 Control simulation
  • I.5 Dealing with constraints
  • I.6 Saving a project
  • Appendix II. Comparison of control strategies for a hollow shaft reactor
  • II.1 Introduction
  • II.2 Model Equations
  • II.3 Proportional Integral Control
  • II.4 Linear Multivariable Control
  • II.5 Non-linear Multivariable Control
  • References.

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