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The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns
Author(s) -
Schäfer Pascal,
Caspari Adrian,
Schweidtmann Artur M.,
Vaupel Yannic,
Mhamdi Adel,
Mitsos Alexander
Publication year - 2020
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.202000048
Subject(s) - computer science , distillation , key (lock) , process (computing) , reduction (mathematics) , field (mathematics) , biochemical engineering , nonlinear system , engineering , chemistry , mathematics , physics , geometry , computer security , organic chemistry , quantum mechanics , pure mathematics , operating system
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.