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@ARTICLE{Schfer:888850,
      author       = {Schäfer, Pascal and Caspari, Adrian and Schweidtmann,
                      Artur M. and Vaupel, Yannic and Mhamdi, Adel and Mitsos,
                      Alexander},
      title        = {{T}he {P}otential of {H}ybrid
                      {M}echanistic/{D}ata‐{D}riven {A}pproaches for {R}educed
                      {D}ynamic {M}odeling: {A}pplication to {D}istillation
                      {C}olumns},
      journal      = {Chemie - Ingenieur - Technik},
      volume       = {92},
      number       = {12},
      issn         = {1522-2640},
      address      = {Weinheim},
      publisher    = {Wiley-VCH Verl.},
      reportid     = {FZJ-2020-05264},
      pages        = {1910 - 1920},
      year         = {2020},
      abstract     = {Extensive literature has considered reduced, but still
                      highly accurate, nonlinear dynamic process models,
                      particularly for distillation columns. Nevertheless, there
                      is a need for continuing research in this field. Herein,
                      opportunities from the integration of machine learning into
                      existing reduction approaches are discussed. First, key
                      concepts for dynamic model reduction and their limitations
                      are briefly reviewed. Afterwards, promising model structures
                      for reduced hybrid mechanistic/data‐driven models are
                      outlined. Finally, crucial future challenges as well as
                      promising research perspectives are presented.},
      cin          = {IEK-10},
      ddc          = {660},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000575310100001},
      doi          = {10.1002/cite.202000048},
      url          = {https://juser.fz-juelich.de/record/888850},
}