Newcastle University School of Chemical Engineering and Advanced Materials
 

ADVANCED PROCESS CONTROL

by: Mark J. Willis & Ming T. Tham

© Copyright

CONTENTS

  SUMMARY
1. WHAT IS ADVANCED CONTROL?
2. PROCESS MODELS
 
2.1. Mechanistic Models
2.2. Black Box Models
2.3. Qualitative Models
2.4. Statistical Models
3. 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
4. STATISTICAL PROCESS CONTROL
 
4.1. Conventional SPC
4.2. Algorithmic SPC
4.3. Active SPC
5. DEALING WITH DATA PROBLEMS
 
5.1. Inferential Estimation
5.2. Data Conditioning and Validation
5.3. Data Analysis
6. HIGHER LEVEL OPERATIONS
 
6.1. Process Optimisation
6.2. Process Monitoring, Fault Detection, Location and Diagnosis
6.3. Process Supervision via Artificial Intelligence Techniques
7. ADVANCED CONTROL
8. 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
7. ADVANCED CONTROL

The techniques described in the previous sections have been applied to a wide variety of systems. In the process industries, they have been applied to reactors, separation processes, power generation systems including boilers, HVAC and so on. Many of these are reported by academics, academics involved in industrial collaborative projects or by consultants. There are also many unreported cases of successful advanced control applications, primarily because of commercial confidentiality. An illustrative list of reported applications is given in Appendix A. Many of the applications reported in the literature describe the use of single techniques. However, our philosophy of advanced control is depicted in the following diagram.

Advanced Control
Figure 9. Hierarchical Layers in Integrated Modern Control

Local control is implemented, using appropriate controllers, to keep the process operating at desired conditions. Here, the type of local controllers employed depends on the task at hand. Although it is easier to tune and maintain simple controllers, some processes do require control by more sophisticated algorithms. However, unless such sophisticated controllers are installed and maintained by well trained trained personnel, they can be prone to failure. Until the last decade, the higher level tasks of monitoring, optimisation, and supervision were mainly carried out by human beings. Due to the advent of modern technology, and advances in the field of AI, these can now be automated. In particular, the installation, operation and integrity of modern controllers can be supervised by higher level systems.

Advanced control is the implementation of this hierarchical information and control structure. The flow of information is bi-directional, from management layer to process level and vice versa. The task here is to be able to integrate the various components in an efficient and manageable fashion. This can be facilitated by ensuring that each component is designed as a modular, yet integrable element.


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© Copyright 1994-2009
Mark Willis and Ming Tham
School of Chemical Engineering and Advanced Materials
Newcastle University
Newcastle upon Tyne
NE1 7RU, UK.

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