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@ARTICLE{Schfer:888850,
author = {Schäfer, Pascal and Caspari, Adrian and Schweidtmann,
Artur M. and Vaupel, Yannic and Mhamdi, Adel and Mitsos,
Alexander},
title = {{T}he {P}otential of {H}ybrid
{M}echanistic/{D}ata‐{D}riven {A}pproaches for {R}educed
{D}ynamic {M}odeling: {A}pplication to {D}istillation
{C}olumns},
journal = {Chemie - Ingenieur - Technik},
volume = {92},
number = {12},
issn = {1522-2640},
address = {Weinheim},
publisher = {Wiley-VCH Verl.},
reportid = {FZJ-2020-05264},
pages = {1910 - 1920},
year = {2020},
abstract = {Extensive 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.},
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)16},
UT = {WOS:000575310100001},
doi = {10.1002/cite.202000048},
url = {https://juser.fz-juelich.de/record/888850},
}