Dynamic optimization of conditional models with application to chemical process operation and design

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Conditional models may be defined as those in which the system equations depend on where the model solution lies.  In a dynamic setting, the system could operate in different modes, with the transition between modes triggered by discrete events.  Examples of chemical process systems that exhibit behavior of this type are commonplace, and include vessels that could overflow or run dry, appearance or disappearance of a phase in a multiphase system, and actuator saturation.  Discontinuous behavior may also be induced by control systems such as logic-based supervisory controllers.  This research proposal addresses the optimization of conditional dynamic models, focusing primarily on situations in which the model discontinuities are induced by control systems.  Applications to be considered include regulatory and supervisory control system design, optimization of systems in transition, and optimal design of dynamically operable plants.  The broad goals of this research are to develop effective formulation and solution strategies for such problems, characterize the convergence properties for specific problem classes, and develop novel applications of industrial relevance.  Dynamic optimization of conditional models has only recently received attention within the process systems community.  It is hoped that through this research, reliable and efficient techniques will be developed for the solution of such problems in novel design and control applications of industrial significance.