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@ARTICLE{Schfer:877454,
      author       = {Schäfer, Pascal and Caspari, Adrian and Mhamdi, Adel and
                      Mitsos, Alexander},
      title        = {{E}conomic nonlinear model predictive control using hybrid
                      mechanistic data-driven models for optimal operation in
                      real-time electricity markets: {I}n-silico application to
                      air separation processes},
      journal      = {Journal of process control},
      volume       = {84},
      issn         = {0959-1524},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-02207},
      pages        = {171 - 181},
      year         = {2019},
      abstract     = {Optimization of the energy consumption at fluctuating
                      short-term electricity markets is a promising measure to
                      increase the economic efficiency of energy-intense
                      processes. This can be addressed by integrating the economic
                      perspective directly into the process control, i.e., by
                      using economic nonlinear model predictive control (eNMPC).
                      We present a single-layer eNMPC framework for optimal
                      operation of an industrial-scale nitrogen plant
                      participating in real-time electricity markets. To achieve
                      real-time capability, we utilize suboptimal updates as well
                      as our reduced modeling approach for rectification columns
                      combining compartmentalization and artificial neural
                      networks (Schäfer et al., AIChE J., doi:10.1002/aic.16568).
                      We demonstrate the real-time capability of the approach
                      in-silico. We explicitly account for model-plant mismatch by
                      using a detailed full-order stage-by-stage model that is
                      common in literature as plant replacement. Our results show
                      that close-to-optimal savings in electricity costs are
                      enabled via the eNMPC strategy even under consideration of
                      inherently uncertain market forecasts whilst safely
                      satisfying production targets. Furthermore, the disturbance
                      rejection capability of the control structure is
                      investigated, showing that severe unmeasured disturbances
                      with slow dynamics can be rejected effectively without
                      violating product requirements.},
      cin          = {IEK-10},
      ddc          = {004},
      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:000501410500014},
      doi          = {10.1016/j.jprocont.2019.10.008},
      url          = {https://juser.fz-juelich.de/record/877454},
}