Machine learning in process systems engineering: Challenges and opportunities

• DOI: 10.1016/j.compchemeng.2023.108523
• Publication Date: 2023-11-22T13:35:26Z
• Authors: Prodromos Daoutidis; Jay H. Lee; Srinivas Rangarajan; Leo Chiang; Bhushan Gopaluni; Artur M. Schweidt...; Iiro Harjunkoski; Mehmet Mercangöz; Ali Mesbah; Fani Boukouvala; Fernando V. Lima; Antonio del Rio C...; Christos Georgakis
• Keywords: Exposition (narrative)

This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27–29, 2022. The session included two invited talks and three short contributed presentations followed by extensive discussions. This paper does not intend to provide a comprehensive review on the subject or a detailed exposition of the discussions; instead its aim is to distill the main points of the discussions and talks, and in doing so, highlight open problems and directions for future research. The general conclusion from the session was that machine learning can have a transformational impact on the PSE domain enabling new discoveries and innovations, but research is needed to develop domain-specific techniques for problems in molecular/material design, data analytics, optimization, and control. ; Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; ChemE/Product and Process Engineering