A METHODOLOGY FOR FORMULATING, FORMALIZING, VALIDATING, AND EVALUATING A REAL-TIME PROCESS-CONTROL ADVISER

This research presents guidelines to design control processes where im proving quality is achieved by improving the manufacturing consistency through the use of intelligent process control. Conventional control processes cannot include the theoretical knowledge, experimental knowl edge, and expert knowledge available concerning the product. A hybrid intelligent process control (IPC) combining a continuous simulation (C S) and an artificial neural network (ANN) can make this knowledge avai lable to the operator for process control. This paper presents a metho dology for combining the CS and ANN to achieve real-time process contr ol. A human-machine interface (HMI) is included in the process to aid operators in communication with the CS/ANN hybrid IPC. The result of t he new process is a real-time process control advisor (RTPCa). A case example for the methodology of formulating, formalizing, validating, a nd evaluating the RTPCa is given. The case studied concerns galvanizin g continuous sheet steel at a steel plant. The CS is written in SIMAN, and the ANN in C. The research validates and evaluates the RTPCa usin g plant data, simulation output, and face validation by plant personne l. The authors conclude that the benefits of the RTPCa over other form s of IPC include better process communication to the operator, robustn ess to moderate changes in system parameters, the flexibility to retra in the ANN if conditions change dramatically, and the computation spee d necessary for real-time process control. This methodology has furthe r applications to other continuous processes where quality is determin ed by manufacturing consistency of the product, such as in the pulp pa per and film processing industries.