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Intelligent Systems in Process Control

Technical Resources:

Process Industry Applications Process Industry Applications An Old Cement Plant Control Room Operations Monitoring Nuclear Plant Control Room


Overview of successful application areas for intelligent systems in process control

The following are proven successful application areas for a Knowledge-Based Expert System (KBES):
Fault diagnosis:  detection, root cause analysis, repetitive problem recognition
Supervisory control
Complex control schemes
Recovery from extreme conditions
Emergency shutdown
Heuristic optimization, e.g., debottlenecking
Startup or shutdown monitoring
Batch phase transition detection and subsequent control mode switching
Process and control performance monitoring
Statistical Process Control (SPC)
Real Time Quality Management (combination of the above)
Online "smart" operator and troubleshooting manual
Sequential or batch control
Control system validation
Object-oriented simulation of processes and control systems

Other applications have been successful as well.

A survey paper of knowledge-based systems in process control

The survey paper below was presented by Greg Stanley as the plenary session for an IFAC (International Federation of Automatic Control) conference.  It is a summary of the state of the art in applying real-time knowledge-based systems (Artificial Intelligence & expert systems) in process control.  After a brief overview of the features of a Knowledge-Based Expert System (KBES) useful in industrial control, several case studies are reviewed.  The lessons learned are summarized. 

Experiences Using Knowledge-Based Reasoning in Online Control Systems (pdf)

Expert system requirements to meet the needs of process control

The G2 product by Gensym is the most popular real time expert system used by the process industries and others.  The following technical paper by Moore, Rosenof, and Stanley describes the requirements and issues addressed by a real time expert system:  Process Control Using a Real Time Expert System (pdf)

BDAC - Big Data Approximating Control

In more recent years, there has been substantial progress in machine learning, pattern recognition, and dealing with large volumes and varieties of of data. A new technical paper by Stanley outlines an entirely new method of process estimation and control based on approximate pattern matching.  An overview and references are found in the section on Big Data Approximating Control.



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