Back to Search
Start Over
The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns.
- Source :
- Chemie Ingenieur Technik (CIT); Dec2020, Vol. 92 Issue 12, p1910-1920, 11p
- Publication 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DYNAMIC models
DISTILLATION
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 0009286X
- Volume :
- 92
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Chemie Ingenieur Technik (CIT)
- Publication Type :
- Academic Journal
- Accession number :
- 147176394
- Full Text :
- https://doi.org/10.1002/cite.202000048