The Potential of Hybrid Mechanistic/Data-Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns

Pascal Schäfer, Adrian Caspari, Artur M. Schweidtmann, Yannic Vaupel, Adel Mhamdi, Alexander Mitsos*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1910-1920
Number of pages11
JournalChemie-Ingenieur-Technik
Volume92
Issue number12
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Distillation columns
  • Dynamic model reduction
  • Hybrid modeling
  • Machine learning
  • Surrogate models

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