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@ARTICLE{Caspari:877455,
      author       = {Caspari, Adrian and Offermanns, Christoph and Schäfer,
                      Pascal and Mhamdi, Adel and Mitsos, Alexander},
      title        = {{A} flexible air separation process: 2. {O}ptimal operation
                      using economic model predictive control},
      journal      = {AIChE journal},
      volume       = {65},
      number       = {11},
      issn         = {1547-5905},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {FZJ-2020-02208},
      pages        = {e16721},
      year         = {2019},
      abstract     = {The penetration of renewable electricity promises an
                      economic advantage for flexible operation of
                      energy‐intense processes. One way to achieve flexible
                      operation is economic model predictive control (eNMPC),
                      where an economic dynamic optimization problem is directly
                      solved at controller level taking into account a process
                      model and operational constraints. We apply eNMPC in silico
                      to an air separation process with an integrated liquefier
                      and liquid‐assist operation. We use a mechanistic dynamic
                      model as both controller model and plant surrogate. We
                      conduct a closed‐loop case study over a time horizon of 2
                      days with historical electricity prices and input
                      disturbances. We solve the dynamic optimization problems in
                      DyOS. Compared to the optimal steady‐state operation, the
                      eNMPC operating strategy gives a significant improvement of
                      $14\%.$ We further show that the eNMPC enables economic
                      improvements similar to an idealized quasistationary
                      scheduling. While the eNMPC provides control profiles
                      qualitatively similar to those obtained from deterministic
                      global optimization of quasistationary scheduling, the eNMPC
                      satisfies the product purity constraints all the time
                      whereas the quasistationary scheduling sometimes fails to do
                      so. The eNMPC applies local optimization methods and
                      achieves profiles similar to the scheduling solved using
                      deterministic global optimization methods over the complete
                      closed‐loop simulation time horizon.},
      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:000478196000001},
      doi          = {10.1002/aic.16721},
      url          = {https://juser.fz-juelich.de/record/877455},
}