Distributed/boundary model predictive control for transport-reaction systems (crystal growth processing)

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The state-of-the-art semiconductor processing, pharmaceutical, petrochemical and chemical process plants operational improvement lies within the realm of optimization and controller synthesis which can meet tighter environmental regulations, provide desired product specifications and ensure profit at the time of increasing energy costs and worldwide competition. In all these processes, the goal is to design a ``best'' possible controller, a model predictive controller (MPC), which should: 1) provide an optimal solution for the process plant regulation; 2) include naturally present constraints (e.g., limitations on available energy used for process regulation-valves opening to heat up or cool down the process, limitations due to the performance requirements imposed by the product specifications, safety or hazard considerations); 3) consider the moving and scanning capability of an actuator/sensor so as to outperform standard fixed actuator/sensor configurations. The theoretical and industrial application related objectives of this research program are to explore three relevant examples which account for 95% of the processes present in the current semiconductor crystal processing industry: 1) Czochralski crystal growth process, 2) Bridgman crystal growth process, 3) melted zone refining process, and within each of the above processes to explore dominant transport-phenomena and to design relevant model based model predictive controllers which should improve upon the current industrially applied controllers. The expected outcome of this program is to provide an ultimate optimal controller that can handle high cost crystal growth processing, and at the same time result in capital cost savings, chemical cost reduction, improved quality of products, and provide for more efficient use of power. Amongst other benefits, the program will provide for the training of highly qualified personnel by active involvement of PhD and MS students capable of mastering the control of semiconductor crystal growth processing, and it will draw attention of current members of the semiconductor industry interested in the process improvements.