Machine learning in process systems engineering

Challenges and opportunities

Journal article (2024)

Authors

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
A.M. Schweidtmann ChemE/Product and Process Engineering - AS
Iiro Harjunkoski Aalto University
Mehmet Mercangöz Imperial College London
Ali Mesbah University of California
More Authors... External organisation
Research Group
ChemE/Product and Process Engineering (AS) (TU Delft)
Copyright: © 2024 Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, A.M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangöz, Ali Mesbah, More Authors
DOI: https://doi.org/10.1016/j.compchemeng.2023.108523
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Published Date

2024

Language

English

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Faculty

Applied Sciences

Department

ChemE/Chemical Engineering

Research Group

ChemE/Product and Process Engineering

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.

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