Machine Learning in Chemical Engineering

A Perspective

Review (2021)

Authors

A.M. Schweidtmann ChemE/Product and Process Engineering - AS , Rheinisch-Westfälische Technische Hochschule
Erik Esche Technical University of Berlin
Asja Fischer Ruhr-Universität Bochum
Marius Kloft Technische Universität Kaiserslautern
Jens Uwe Repke Technical University of Berlin
Sebastian Sager Otto-von-Guericke University
Alexander Mitsos Rheinisch-Westfälische Technische Hochschule, Forschungszentrum Jülich, JARA Center for Simulation and Data Science (CSD)
Research Group
ChemE/Product and Process Engineering (AS) (TU Delft)
Copyright: © 2021 A.M. Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft, Jens Uwe Repke, Sebastian Sager, Alexander Mitsos
DOI: https://doi.org/10.1002/cite.202100083
More Info
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Published Date

2021

Language

English

Faculty

Applied Sciences

Department

ChemE/Chemical Engineering

Research Group

ChemE/Product and Process Engineering

Abstract

The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio-)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering (CE) communities will unfold the full potential. We identify six challenges that will open new methods for CE and formulate new types of problems for ML: (1) optimal decision making, (2) introducing and enforcing physics in ML, (3) information and knowledge representation, (4) heterogeneity of data, (5) safety and trust in ML applications, and (6) creativity. Under the umbrella of these challenges, we discuss perspectives for future interdisciplinary research that will enable the transformation of CE.