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@ARTICLE{Schulze:1021904,
      author       = {Schulze, Jan C. and Doncevic, Danimir and Erwes, Nils and
                      Mitsos, Alexander},
      title        = {{D}ata-{D}riven {M}odel {R}eduction and {N}onlinear {M}odel
                      {P}redictive {C}ontrol of an {A}ir {S}eparation {U}nit by
                      {A}pplied {K}oopman {T}heory},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-01052},
      year         = {2023},
      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 $\%.$},
      keywords     = {Systems and Control (eess.SY) (Other) / Machine Learning
                      (cs.LG) (Other) / FOS: Electrical engineering, electronic
                      engineering, information engineering (Other) / FOS: Computer
                      and information sciences (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2309.05386},
      url          = {https://juser.fz-juelich.de/record/1021904},
}