Machine learning in process systems engineering
Challenges and opportunities
Prodromos Daoutidis (University of Minnesota Twin Cities)
Jay H. Lee (University of Southern California)
Srinivas Rangarajan (Lehigh University)
Leo Chiang (The Dow Chemical Company)
Bhushan Gopaluni (University of British Columbia)
Artur M. Schweidtmann (TU Delft - ChemE/Product and Process Engineering)
Iiro Harjunkoski (Aalto University)
Mehmet Mercangöz (Imperial College London)
Ali Mesbah (University of California)
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Abstract
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.