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000888850 1001_ $$0P:(DE-HGF)0$$aSchäfer, Pascal$$b0
000888850 245__ $$aThe Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns
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000888850 520__ $$aExtensive literature has considered reduced, but still highly accurate, nonlinear dynamic process models, particularly for distillation columns. Nevertheless, there is a need for continuing research in this field. Herein, opportunities from the integration of machine learning into existing reduction approaches are discussed. First, key concepts for dynamic model reduction and their limitations are briefly reviewed. Afterwards, promising model structures for reduced hybrid mechanistic/data‐driven models are outlined. Finally, crucial future challenges as well as promising research perspectives are presented.
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000888850 7001_ $$0P:(DE-HGF)0$$aCaspari, Adrian$$b1
000888850 7001_ $$0P:(DE-HGF)0$$aSchweidtmann, Artur M.$$b2
000888850 7001_ $$0P:(DE-HGF)0$$aVaupel, Yannic$$b3
000888850 7001_ $$0P:(DE-HGF)0$$aMhamdi, Adel$$b4
000888850 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b5$$eCorresponding author$$ufzj
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