Journal Article

FZJ-2020-05264

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns

Schäfer, P.RWTH* ; Caspari, A.RWTH* ; Schweidtmann, A. M.RWTH* ; Vaupel, Y.RWTH* ; Mhamdi, A.RWTH* ; Mitsos, A. (Corresponding author)FZJ*RWTH*

2020
Wiley-VCH Verl. Weinheim

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Please use a persistent id in citations: http://hdl.handle.net/2128/26533  doi:10.1002/cite.202000048

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.

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Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 899 - ohne Topic (POF3-899) (POF3-899)

Appears in the scientific report 2020

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Wiley ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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