Adaptive, hybrid modeling and optimization for design and control of startup processes

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Almost all production processes in Canada (and worldwide) go through transients: shutdowns and startups resulting from scheduled maintenance or unforeseen events. Current startup practices rely on recipes or a series of pre-decided sequence of actions. Such policies, while feasible, are far from optimal, and significant environmental and financial gains can be made by applying modeling, optimization and feedback control principles to these processes. While the notions of feedback and control have been universally adopted for continuous operation, they have not been widely utilized for the purpose of handling startups due to two primary reasons. The first is the difficulty in developing good models that could be then used for optimization and control, and the other being the unique nature of optimization problems that naturally arise in this context (due to the requirement of minimizing for instance the startup time). The proposed research will focus on both these aspects and present adaptive data driven hybrid modeling, optimization and control approaches with applications to startup of a hydrogen and air separations unit in collaboration with the industrial partner (Praxair Inc). Owing to the ubiquitous presence of these transient operations in all productions processes across Canada, the research results will benefit all manufacturing industries in Canada.