Machine Learning in Chemical Engineering: A Perspective

Artur M. Schweidtmann*, Erik Esche, Asja Fischer, Marius Kloft, Jens Uwe Repke, Sebastian Sager, Alexander Mitsos

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)
10 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)2029-2039
JournalChemie-Ingenieur-Technik
Volume93
Issue number12
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Deep learning
  • Hybrid modeling
  • Machine learning
  • Optimization
  • Reinforcement learning

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