(712g) Combining First-Principles and Empirical Modeling for Computation Time Reduction of Economic Model Predictive Control
AIChE Annual Meeting
Thursday, November 2, 2017 - 2:24pm to 2:43pm
Motivated by this, the present work investigates the trade-off between computation time and model accuracy for an EMPC design that utilizes a first-principles model for the first several sampling periods of the prediction horizon (including the first sampling period since the control actions computed for the first sampling period are implemented on the process according to the receding horizon implementation strategy of EMPC) and an empirical model for the remaining sampling periods (for computation time reduction). The number of sampling periods for which the first-principles model is used, the sampling period length, and the prediction horizon length are evaluated with respect to their impact on closed-loop economic performance and computation time. The use of the error-triggered on-line empirical model update strategy from  is investigated for updating the empirical model used within the EMPC with the combined first-principles/empirical model as the closed-loop state moves throughout state-space. Conditions that guarantee closed-loop stability and feasibility of the EMPC with the combined first-principles/empirical model are developed. In addition, the impact of changing the empirical model partway through a sampling period (i.e., allowing the EMPC to start using the empirical model after a non-integer multiple of a sampling period has passed) is investigated both from a closed-loop stability and feasibility standpoint and from a practical computation time and economic performance standpoint. A chemical process example compares the computation time and economic performance of a nonlinear process under the proposed combined first-principles/empirical EMPC design with the computation time and performance under both an EMPC utilizing only a first-principles model and an EMPC using only an empirical model.
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