Machine Learning in Chemical Engineering: A Perspective. Issue 12 (22nd October 2021) - Schweidtmann, Artur M.; Esche, Erik; Fischer, Asja; Kloft, Marius; Repke, Jens‐Uwe; Sager, Sebastian; Mitsos, Alexander
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. Abstract : Recent breakthroughs in machine learning provide unique opportunities for chemical engineering, but only joint interdisciplinary research will unfold the full potential of machine learning in chemical engineering. We identify six challenges of interdisciplinary research that will open up new methods for chemical engineering and formulate new types of problems for ML.