Hybrid Semi‐parametric Modeling in Separation Processes: A Review. Issue 7 (26th May 2020)

• Record Type: Journal Article
• Authors: McBride, Kevin; Sanchez Medina, Edgar Ivan; Sundmacher, Kai
• Other Names: Zeck Sebastian guestEditor.; Bart Hans‐Jörg guestEditor.
• Is Part Of: Chemie Ingenieur Technik. Volume 92:Issue 7(2020)
• Page Start: 842
• Page End: 855
• Publication Date: 2020-05-26
• Subjects: Chemical separation -- Hybrid modeling -- Machine learning -- Thermodynamics; Chemical engineering -- Patents -- Periodicals; Chemical engineering -- Periodicals; Chemical industry -- Periodicals; Chemistry, Technical -- Periodicals; 660.05
• DOI: 10.1002/cite.202000025
• Languages: English
• ISSNs: 0009-286X

Abstract

Abstract: Separations of mixtures play a critical role in chemical industries. Over the last century, the knowledge in the area of chemical thermodynamics and modeling of separation processes has been substantially expanded. Since the models are still not completely accurate, hybrid models can be used as an alternative that allows to retain existing knowledge and augment it using data. This paper explores some of the weaknesses in the current knowledge in separations design, simulation, optimization, and operation, and presents many examples where data‐driven and hybrid models have been used to facilitate these tasks. Abstract : The combination of mechanistic and data‐driven models is a promising approach for the generation of more accurate and efficient mathematical models for design, control, and optimization of separation processes. This has been proved in several applications. However, some issues are still open for research in this direction.