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@ARTICLE{Caspari:877453,
      author       = {Caspari, Adrian and Tsay, Calvin and Mhamdi, Adel and
                      Baldea, Michael and Mitsos, Alexander},
      title        = {{T}he integration of scheduling and control: {T}op-down vs.
                      bottom-up},
      journal      = {Journal of process control},
      volume       = {91},
      issn         = {0959-1524},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-02206},
      pages        = {50 - 62},
      year         = {2020},
      abstract     = {The flexible operation of continuous processes often
                      requires the integration of scheduling and control. This can
                      be achieved by top-down or bottom-up approaches. We compare
                      the two paradigms in-silico using an air separation unit as
                      a benchmark process. To demonstrate the top-down paradigm,
                      we identify data-driven models of the closed-loop process
                      dynamics based on a mechanistic model and use them in
                      scheduling calculations that are performed offline. The
                      resulting target trajectories are passed to a linear model
                      predictive control (LMPC) system and implemented in the
                      process. To demonstrate the bottom-up paradigm, we define an
                      economic nonlinear model predictive control (eNMPC) scheme,
                      which performs dynamic optimization using the full model in
                      closed-loop to directly obtain the control variable profiles
                      to be implemented in the process. We provide implementations
                      of the process model equations as both a gPROMS and a
                      Modelica model to encourage future comparison of approaches
                      for flexible operation, process control, and/or handling
                      disturbances. The performance, advantages, and disadvantages
                      of the two strategies are analyzed using demand-response
                      scenarios with varying levels of fluctuations in electricity
                      prices, as well as considering the cases of known,
                      instantaneous, and completely unknown load changes. The
                      similarities and differences of the two approaches as
                      relevant to flexible operation of continuous processes are
                      discussed. Integrated scheduling and control leverages
                      existing infrastructure and can be immediately applied to
                      real operation tasks. Both operation strategies achieve
                      successful process operation with remarkable economic
                      improvements (up to $8\%)$ compared to constant operation.
                      eNMPC requires more computational resources, and is – at
                      the moment – not implementable in real-time due to maximum
                      optimization times exceeding the controller sampling time.
                      However, eNMPC achieves up to 2.5 times higher operating
                      cost savings compared to the top-down approach, owing in
                      part to the more accurate modeling of key process dynamics.},
      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:000543364100005},
      doi          = {10.1016/j.jprocont.2020.05.008},
      url          = {https://juser.fz-juelich.de/record/877453},
}