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@ARTICLE{Fleitmann:972117,
      author       = {Fleitmann, Lorenz and Ackermann, Philipp and Schilling,
                      Johannes and Kleinekorte, Johanna and Rittig, Jan G. and vom
                      Lehn, Florian and Schweidtmann, Artur M. and Pitsch, Heinz
                      and Leonhard, Kai and Mitsos, Alexander and Bardow, André
                      and Dahmen, Manuel},
      title        = {{M}olecular {D}esign of {F}uels for {M}aximum
                      {S}park-{I}gnition {E}ngine {E}fficiency by {C}ombining
                      {P}redictive {T}hermodynamics and {M}achine {L}earning},
      journal      = {Energy $\&$ fuels},
      volume       = {37},
      number       = {3},
      issn         = {0887-0624},
      address      = {Columbus, Ohio},
      publisher    = {American Chemical Society},
      reportid     = {FZJ-2023-01079},
      pages        = {2213–2229},
      year         = {2023},
      abstract     = {Co-design of alternative fuels and future spark-ignition
                      (SI) engines allows very high engine efficiencies to be
                      achieved. To tailor the fuel’s molecular structure to the
                      needs of SI engines with very high compression ratios,
                      computer-aided molecular design (CAMD) of renewable fuels
                      has received considerable attention over the past decade. To
                      date, CAMD for fuels is typically performed by
                      computationally screening the physicochemical properties of
                      single molecules against property targets. However,
                      achievable SI engine efficiency is the result of the
                      combined effect of various fuel properties, and molecules
                      should not be discarded because of individual unfavorable
                      properties that can be compensated for. Therefore, we
                      present an optimization-based fuel design method directly
                      targeting SI engine efficiency as the objective function.
                      Specifically, we employ an empirical model to assess the
                      achievable relative engine efficiency increase compared to
                      conventional RON95 gasoline for each candidate fuel as a
                      function of fuel properties. For this purpose, we integrate
                      the automated prediction of various fuel properties into the
                      fuel design method: Thermodynamic properties are calculated
                      by COSMO-RS; combustion properties, indicators for
                      environment, health and safety, and synthesizability are
                      predicted using machine learning models. The method is
                      applied to design pure-component fuels and binary
                      ethanol-containing fuel blends. The optimal pure-component
                      fuel tert-butyl formate is predicted to yield a relative
                      efficiency increase of approximately $8\%$ and the optimal
                      fuel blend with ethanol and
                      3,4-dimethyl-3-propan-2-yl-1-pentene of $19\%.$},
      cin          = {IEK-10},
      ddc          = {660},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000925002500001},
      doi          = {10.1021/acs.energyfuels.2c03296},
      url          = {https://juser.fz-juelich.de/record/972117},
}