Algorithmic Advances for Large-Scale Dynamic Process Optimization

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Research:Dynamic optimization is an essential component of many process design activities. In previous work the PI developed an efficient simultaneous approach for dynamic optimization that handles both unstable and path-constrained differential-algebraic systems. He developed an improved nonlinear programming strategy based on interior point or barrier methods, which leads to significant gains in performance. For example, on his dynamic process optimization case, nonlinear programs with over 1.2 million variables can be solved in approximately two hours of CPU time on a 1.0 GHz computer. In this project he will extend his simultaneous optimization strategies to large systems. To do this, he will consider several improvements in the formulation and algorithm for dynamic optimization problems as well as extending the scope of the dynamic optimization problems to include discrete decisions that involve phase equilibriums and other complementary relationships. Because these features are exploited in a novel way by the interior point algorithm, difficult optimization problems that include a class of discrete decisions can be treated with little additional computational cost.He plans to incorporate these into a modeling framework that will combine well-developed user interfaces and object-oriented software as well as demonstrate the application of the algorithms within a state-of-the-art parallel computing environment on a wide variety of process optimization problems drawn from process control, operations and design.Broader Impact:The Chemical Process Industries (CPI) could greatly benefit from the development of more efficient dynamic optimization methodologies.