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@ARTICLE{Fleitmann:911223,
      author       = {Fleitmann, Lorenz and Pyschik, Jan and Wolff, Ludger and
                      Schilling, Johannes and Bardow, André},
      title        = {{O}ptimal experimental design of physical property
                      measurements for optimal chemical process simulations},
      journal      = {Fluid phase equilibria},
      volume       = {557},
      issn         = {0378-3812},
      address      = {New York, NY [u.a.]},
      publisher    = {Science Direct},
      reportid     = {FZJ-2022-04528},
      pages        = {113420 -},
      year         = {2022},
      abstract     = {Chemical process simulations depend on physical properties,
                      which are usually available through property models with
                      parameters estimated from experiments. The required
                      experimental effort can be reduced using the method of
                      Optimal Experimental Design (OED). OED reduces the number of
                      experiments by minimising the expected uncertainty of the
                      estimated parameters. In chemical engineering, however, the
                      main purpose of an experiment is usually not to determine
                      property parameters with minimum uncertainty but to simulate
                      processes accurately. Therefore, this paper presents the OED
                      of physical property measurements resulting in the most
                      accurate process simulations: c-optimal experimental design
                      (c-OED). c-OED aims to minimise the uncertainty of the
                      process simulation results, which is estimated by linear
                      uncertainty propagation from uncertain property parameters
                      through the process model. We use c-OED to design
                      liquid-liquid equilibrium and diffusion experiments
                      minimising thermodynamic and economic performance metrics of
                      three solvent-based processes. In all three case studies,
                      the c-optimal design substantially reduces the experimental
                      effort for the same simulation accuracy compared to
                      state-of-the-art OED that neglects the process. Our findings
                      are confirmed by a Monte-Carlo simulation of the designed
                      experiments. Furthermore, we assess the limits of c-OED for
                      highly nonlinear process models. Thus, the work shows how
                      c-OED can successfully reduce experimental effort required
                      for accurate process simulations by tailoring experimental
                      designs to the process model.},
      cin          = {IEK-10},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000821374700006},
      doi          = {10.1016/j.fluid.2022.113420},
      url          = {https://juser.fz-juelich.de/record/911223},
}