Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory

Schulze, Jan C.; Doncevic, Danimir; Mitsos, Alexander; Erwes, Nils

doi:10.48550/ARXIV.2309.05386

Preprint

FZJ-2024-01052

Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory

Schulze, J. C.RWTH* ; Doncevic, D.FZJ*RWTH* ; Erwes, N.RWTH* ; Mitsos, A. (Corresponding author)FZJ*RWTH*

2023
arXiv

arXiv (2023) [10.48550/ARXIV.2309.05386]

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Please use a persistent id in citations: doi:10.48550/ARXIV.2309.05386 doi:10.34734/FZJ-2024-01052

Abstract: Achieving real-time capability is an essential prerequisite for the industrial implementation of nonlinear model predictive control (NMPC). Data-driven model reduction offers a way to obtain low-order control models from complex digital twins. In particular, data-driven approaches require little expert knowledge of the particular process and its model, and provide reduced models of a well-defined generic structure. Herein, we apply our recently proposed data-driven reduction strategy based on Koopman theory [Schulze et al. (2022), Comput. Chem. Eng.] to generate a low-order control model of an air separation unit (ASU). The reduced Koopman model combines autoencoders and linear latent dynamics and is constructed using machine learning. Further, we present an NMPC implementation that uses derivative computation tailored to the fixed block structure of reduced Koopman models. Our reduction approach with tailored NMPC implementation enables real-time NMPC of an ASU at an average CPU time decrease by 98 %.

Keyword(s): Systems and Control (eess.SY) ; Machine Learning (cs.LG) ; FOS: Electrical engineering, electronic engineering, information engineering ; FOS: Computer and information sciences

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