TY - JOUR
T1 - Machine learning in process systems engineering
T2 - Challenges and opportunities
AU - Daoutidis, Prodromos
AU - Lee, Jay H.
AU - Rangarajan, Srinivas
AU - Chiang, Leo
AU - Gopaluni, Bhushan
AU - Schweidtmann, Artur M.
AU - Harjunkoski, Iiro
AU - Mercangöz, Mehmet
AU - Mesbah, Ali
AU - Boukouvala, Fani
AU - Lima, Fernando V.
AU - del Rio Chanona, Antonio
AU - Georgakis, Christos
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/2
Y1 - 2024/2
N2 - 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.
AB - 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.
KW - Control
KW - Machine learning
KW - Modeling
KW - Molecule discovery
KW - Optimization
KW - Process monitoring
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U2 - 10.1016/j.compchemeng.2023.108523
DO - 10.1016/j.compchemeng.2023.108523
M3 - Article
AN - SCOPUS:85177795287
SN - 0098-1354
VL - 181
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 108523
ER -