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@ARTICLE{Caspari:877451,
      author       = {Caspari, Adrian and Offermanns, Christoph and Ecker,
                      Anna-Maria and Pottmann, Martin and Zapp, Gerhard and
                      Mhamdi, Adel and Mitsos, Alexander},
      title        = {{A} wave propagation approach for reduced dynamic modeling
                      of distillation columns: {O}ptimization and control},
      journal      = {Journal of process control},
      volume       = {91},
      issn         = {0959-1524},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-02204},
      pages        = {12 - 24},
      year         = {2020},
      abstract     = {Reduced models enable real-time optimization of large-scale
                      processes. We propose a reduced model of distillation
                      columns based on multicomponent nonlinear wave propagation
                      (Kienle 2000). We use a nonlinear wave equation in dynamic
                      mass and energy balances. We thus combine the ideas of
                      compartment modeling and wave propagation. In contrast to
                      existing reduced column models based on nonlinear wave
                      propagation, our model deploys a hydraulic correlation. This
                      enables the column holdup to change as load varies. The
                      model parameters can be estimated solely based on
                      steady-state data. The new transient wave propagation model
                      can be used as a controller model for flexible process
                      operation including load changes. To demonstrate this, we
                      implement full-order and reduced dynamic models of an air
                      separation process and multi-component distillation column
                      in Modelica. We use the open-source framework DyOS for the
                      dynamic optimizations and an Extended Kalman Filter for
                      state estimation. We apply the reduced model in-silico in
                      open-loop forward simulations as well as in several open-
                      and closed-loop optimization and control case studies, and
                      analyze the resulting computational speed-up compared to
                      using full-order stage-by-stage column models. The first
                      case study deals with tracking control of a single air
                      separation distillation column, whereas the second one
                      addresses economic model predictive control of an entire air
                      separation process. The reduced model is able to adequately
                      capture the transient column behavior. Compared to the
                      full-order model, the reduced model achieves highly accurate
                      profiles for the manipulated variables, while the
                      optimizations with the reduced model are significantly
                      faster, achieving more than $95\%$ CPU time reduction in the
                      closed-loop simulation and more than $96\%$ in the open-loop
                      optimizations. This enables the real-time capability of the
                      reduced model in process optimization and control.},
      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:000543364100002},
      doi          = {10.1016/j.jprocont.2020.05.004},
      url          = {https://juser.fz-juelich.de/record/877451},
}