TY  - JOUR
AU  - Schäfer, Pascal
AU  - Caspari, Adrian
AU  - Schweidtmann, Artur M.
AU  - Vaupel, Yannic
AU  - Mhamdi, Adel
AU  - Mitsos, Alexander
TI  - The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns
JO  - Chemie - Ingenieur - Technik
VL  - 92
IS  - 12
SN  - 1522-2640
CY  - Weinheim
PB  - Wiley-VCH Verl.
M1  - FZJ-2020-05264
SP  - 1910 - 1920
PY  - 2020
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000575310100001
DO  - DOI:10.1002/cite.202000048
UR  - https://juser.fz-juelich.de/record/888850
ER  -