From Plant Data to Process Control: Ideas for

Process engineering spans industrial applications in the manufacturing sector from petrochemical to polymer to mineral production. From Plant Data to Process Control covers the most up-to-date techniques and algorithms in the area of process identification (PID) and process control, two key components of process engineering, essential for optimizing production systems. It examines both the theoretical advances in process design and control theory, and a wide variety of implementations. A wide variety of approaches are presented for building models of dynamical systems based on observed data (process identification) and for making the output of a system behave in a desired fashion by properly selecting the process input (process control).

Series Introduction

xi

Acknowledgements

xiii

Introduction

1

(8)

The Laguerre Model: Process Identification from Step Response Data

1

(2)

Use of Press for Model Structure Selection in Process Identification

3

(1)

Frequency Sampling Filters: An Improved Model Structure for Process Identification

3

(2)

Pid Controller Design: A New Frequency Domain Approach

5

(2)

Relay Feedback Experiments for Process Identification

7

(2)

Modelling from Noisy Step Response Data Using Laguerre Functions

9

(50)

Introduction

9

(1)

Process Representation Using Laguerre Models

10

(7)

Approximation of the process impulse response

10

(2)

Approximation of the process transfer function

12

(2)

Laguerre model in state space form

14

(1)

Generating the Laguerre functions

15

(2)

Choice of the Time Scaling Factor

17

(11)

Modelling errors with respect to choice of p

18

(1)

Optimal choice of p

19

(5)

Optimal time scaling factor for first order plus delay systems

24

(4)

Estimation of Laguerre Coefficients from Step Response Data

28

(7)

Statistical Properties of the Estimated Coefficients

35

(7)

Bias and variance analysis

35

(2)

Some special cases of disturbances

37

(5)

A Strategy for Improving the Laguerre Model

42

(8)

Modelling of a Polymer Reactor

50

(5)

Appendix

55

(4)

Least Squares and the PRESS Statistic using Orthogonal Decomposition

59

(16)

Introduction

59

(1)

Least Squares and Orthogonal Decomposition

60

(3)

Least squares for dynamic models

60

(1)

Orthogonal decomposition algorithm

61

(2)

The Press Statistic

63

(1)

Computation of the Press Statistic

64

(2)

Use of Press for Process Model Selection

66

(3)

Use of Press for Disturbance Model Selection

69

(6)

Frequency Sampling Filters in Process Identification

75

(24)

Introduction

75

(1)

The Frequency Sampling Filter Model

76

(3)

Properties of the FSF Model with Fast Sampling

79

(4)

Reduced Order FSF Model

83

(4)

Parameter Estimation for the FSF Model

87

(2)

Nature of the Correlation Matrix

89

(10)

From FSF Models to Step Response Models

99

(32)

Introduction

99

(1)

Obtaining a Step Response Model from the FSF Model

100

(6)

Smoothing the Step Response Using the FSF Model

106

(5)

Error Analysis

111

(4)

Confidence Bounds for Frequency Response and Step Response Estimates

115

(4)

Generalized Least Squares Algorithm

119

(1)

Industrial Application: Identification of a Refinery Distillation Train

120

(11)

Process description

120

(3)

Dynamic response testing

123

(2)

Results

125

(1)

Use of PRESS for model structure selection

126

(1)

Use of noise models to remove feedback effects

127

(1)

Use of confidence bounds for judging model quality

128

(3)

New Frequency Domain PID Controller Design Method

131

(40)

Introduction

131

(1)

Control Signal Specification

132

(10)

Specification for stable processes

134

(4)

Specification for integrating processes

138

(4)

PID Parameters: Least Squares Approach

142

(6)

Illustrative example

144

(4)

PID Parameters: Use of only Two Frequencies

148

(4)

Choice of Frequency Points

152

(3)

Ensuring a Positive Integral Time Constant

155

(2)

Simulation Studies

157

(14)

Tuning Rules for PID Controllers

171

(30)

Introduction

171

(1)

First Order Plus Delay Case

171

(10)

Evaluation of the New Tuning Rules: Simulation Results

181

(6)

Experiments with a Stirred Tank Heater

187

(5)

Integrating Plus Delay Case

192

(9)

Recursive Estimation from Relay Feedback Experiments

201

(16)

Introduction

201

(1)

Recursive Frequency Response Estimation

201

(6)

Recursive Step Response Estimation

207

(10)

Simulation case study

209

(6)

Automated design of an identification experiment

215

(2)

Bibliography

217

(6)

Index

223

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From Plant Data to Process Control: Ideas for Process Identification and PID Design

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Table of Contents

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