A Supervisory Control Framework for Set Point Tuning in Industrial Processes

The problem of adjusting the set point of industrial processes with unknown characteristics is studied. An agent based supervisory control system framework is proposed. Modeling agents as well as intelligent agents are included. The modeling agents derive local steady - state models around operating points of the process. These models are considered to be constructed during normal process operation using various identification techniques, each of which corresponds to a different instantiation of our system. The intelligent agents are proposed to be implemented using finite automata units. The performance of the proposed scheme is illustrated through simulation results to some well-known benchmark problems of chemical industry