An expert system-based supervisor in real-time control

Artificial intelligence and expert systems techniques, coupled with historical signal analysis and optimization methods, provide a novel approach to monitoring process dynamics and performance and learning knowledge about how to do a production job better based on past experience. An expert system-based supervisor is proposed to provide such a tool for a simple tank level control problem which adjusts the schedule for supply purchase to attempt to minimize the average cost of operation over time. This research provides a prototype for the applications of expert systems in real-time process supervision and control; however, the result is primitive. The underlying modeling and optimization methods only work for simple situations.<
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