The development and evaluation of an intelligent supervisory system for process control.

Abstract

As industrial plants become more complex. there is a growing need for new approaches to control and supervision. This research investigates the issues involved in applying Artificial Intelligence (AI) techniques in the real-world engineering problem of process control supervision. Current AI theory is examined and some techniques modified to design a general-purpose, reactive planner. The planner forms the basis of a supervisory control system. The system is implemented and interfaced with an existing Laboratory plant, so that its performance can he tested and evaluated by comparing with a conventional feedback controller This real life testing necessitates explicit treatment of issues such as data: sampling. situation assessment and CPU scheduling. The case study shows that by combining AI techniques with conventional control, a system can be built which displays superior performance under normal operating conditions and which can deal with abnormal conditions such as equipment failures.