001     1021904
005     20240712112912.0
024 7 _ |a 10.48550/ARXIV.2309.05386
|2 doi
024 7 _ |a 10.34734/FZJ-2024-01052
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037 _ _ |a FZJ-2024-01052
100 1 _ |a Schulze, Jan C.
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245 _ _ |a Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
260 _ _ |c 2023
|b arXiv
336 7 _ |a Preprint
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336 7 _ |a WORKING_PAPER
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336 7 _ |a Electronic Article
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336 7 _ |a preprint
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336 7 _ |a ARTICLE
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520 _ _ |a 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 %.
536 _ _ |a 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Systems and Control (eess.SY)
|2 Other
650 _ 7 |a Machine Learning (cs.LG)
|2 Other
650 _ 7 |a FOS: Electrical engineering, electronic engineering, information engineering
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650 _ 7 |a FOS: Computer and information sciences
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700 1 _ |a Doncevic, Danimir
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700 1 _ |a Erwes, Nils
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700 1 _ |a Mitsos, Alexander
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773 _ _ |a 10.48550/ARXIV.2309.05386
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910 1 _ |a RWTH Aachen
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913 1 _ |a DE-HGF
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