(345c) Data-Driven Dynamic Optimization Using Continuous-Time Surrogate Models
AIChE Annual Meeting
Tuesday, November 9, 2021 - 3:30pm to 5:00pm
In this work, we address the control optimization of time-varying chemical systems without the full discretization of the underlying high-fidelity models and derive optimal control trajectories using surrogate modeling and data-driven optimization. We postulate nonlinear continuous-time control action trajectories and derive the parameters of these functional forms using data-driven optimization. We test exponential and polynomial functional forms as well as various data-driven optimization strategies (local vs. global and sample-based vs. model-based) to test the consistency of each approach for controlling dynamic systems. Path constraints are also considered in the formulation and are handled as grey-box constraints. We demonstrate the applicability of our approach on a motivating example and a CSTR control case study.
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