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@INPROCEEDINGS{Kleinekorte:877627,
      author       = {Kleinekorte, Johanna and Kröger, Leif and Leonhard, Kai
                      and Bardow, André},
      title        = {{A} {N}eural {N}etwork-{B}ased {F}ramework to {P}redict
                      {P}rocess-{S}pecific {E}nvironmental {I}mpacts},
      volume       = {46},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2020-02342},
      series       = {Computer Aided Chemical Engineering},
      pages        = {1447 - 1452},
      year         = {2019},
      abstract     = {Growing environmental concern and strict regulations led to
                      an increasing effort of the chemical industry to develop
                      greener production pathways. To ensure that this development
                      indeed improves environmental aspects requires an
                      early-stage estimation of the environmental impact in early
                      process design. An accepted method to evaluate the
                      environmental impact is Life Cycle Assessment (LCA).
                      However, LCA requires detailed data on mass and energy
                      balances, which is usually limited in early process design.
                      Therefore, predictive LCA approaches are required. Current
                      predictive LCA approaches estimate the environmental impacts
                      of chemicals only based on molecular descriptors. Thus, the
                      predicted impacts are independent from the chosen production
                      process. A potentially greener process cannot be
                      distinguished from the conventional route. In this work, we
                      propose a fully predictive, neural network-based framework,
                      which utilizes both molecular and process descriptors to
                      distinguish between production pathways. The framework is
                      fully automatized and includes feature selection, setup of
                      the network architecture, and predicts 17 environmental
                      impact categories. The pathway-specific prediction is
                      illustrated for two examples, comparing the CO2-based
                      production of methanol and formic acid to their respective
                      fossil production pathway. The presented framework is
                      competitive to LCA predictions from literature but can now
                      also distinguish between process alternatives. Thus, our
                      framework can serve as initial screening tool to identify
                      environmentally beneficial process alternatives.},
      month         = {Jun},
      date          = {2019-06-16},
      organization  = {29th European Symposium on Computer
                       Aided Process Engineering, Eindhoven
                       (The Netherlands), 16 Jun 2019 - 19 Jun
                       2019},
      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)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000495452400053},
      doi          = {10.1016/B978-0-12-818634-3.50242-3},
      url          = {https://juser.fz-juelich.de/record/877627},
}