Advanced Process Control

School of Chemical Engineering and Advanced Materials

ADVANCED PROCESS CONTROL

by: Mark J. Willis & Ming T. Tham

© Copyright

CONTENTS

SUMMARY

WHAT IS ADVANCED CONTROL?

PROCESS MODELS

2.1.	Mechanistic Models
2.2.	Black Box Models
2.3.	Qualitative Models
2.4.	Statistical Models

MODEL BASED (MODERN) AUTOMATIC CONTROL

3.1.	PID Control
3.2.	Predictive Constrained Control
3.3.	Multivariable Control
3.4.	Robust Control and the Internal Model Principle
3.5.	Globally Linearising Control

STATISTICAL PROCESS CONTROL

4.1.	Conventional SPC
4.2.	Algorithmic SPC
4.3.	Active SPC

DEALING WITH DATA PROBLEMS

5.1.	Inferential Estimation
5.2.	Data Conditioning and Validation
5.3.	Data Analysis

HIGHER LEVEL OPERATIONS

6.1.	Process Optimisation
6.2.	Process Monitoring, Fault Detection, Location and Diagnosis
6.3.	Process Supervision via Artificial Intelligence Techniques

ADVANCED CONTROL

CURRENT RESEARCH AND FUTURE TRENDS

BIBLIOGRAPHY

APPENDIX A:
Examples of reported applications

	Control
AC1	Reactors
AC2	Separation processes
AC3	Power systems
AC4	HVAC systems
	Optimisation
AO1	Reactors
AO2	Separation Processes

Document Title: ADVANCED PROCESS CONTROL
Base Document URL: http://lorien.ncl.ac.uk/ming/advcontrl/apc.htm
Authors: Mark J. Willis and Ming T. Tham
Date: April 1994

You are not allowed to sell this information, or host copies of any parts of this set of pages on your own server. The copyright of this document belongs to the authors, M.T. Tham and Mark Willis, of the School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK. If you wish to include parts of the document in your non-commercial publications, then an appropriate citation using the above information, would be most appreciated.

Back to the Swot Shop

Please email any link problems or comments to ming.tham@ncl.ac.uk