Deep reinforcement learning for process design: Review and perspective

• URL: http://arxiv.org/abs/2308.07822v1
• Date: Tue, 15 Aug 2023 14:56:37 GMT
• Title: Deep reinforcement learning for process design: Review and perspective
• Authors: Qinghe Gao and Artur M. Schweidtmann
• Abstract summary: Deep reinforcement learning has shown potential to solve complex decision-making problems and aid sustainable process design. We discuss perspectives on underlying challenges and promising future works to unfold the full potential of reinforcement learning for process design in chemical engineering.
• Score: 1.1603243575080535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accelerate this transition. Specifically, deep reinforcement learning, a subclass of machine learning, has shown the potential to solve complex decision-making problems and aid sustainable process design. We survey state-of-the-art research in reinforcement learning for process design through three major elements: (i) information representation, (ii) agent architecture, and (iii) environment and reward. Moreover, we discuss perspectives on underlying challenges and promising future works to unfold the full potential of reinforcement learning for process design in chemical engineering.

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