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This review synthesizes state-of-the-art deep reinforcement learning applications for chemical process design, focusing on information representation, agent architecture, and reward mechanisms to accelerate the industry's shift toward renewable energy. By mapping current challenges and future directions, the paper offers a structured roadmap for leveraging AI in complex decision-making scenarios within chemical engineering. The work underscores the practical potential of these ML techniques to solve sustainable process design problems, bridging theoretical advancements with industrial implementation needs.
Deep reinforcement learning for process design: Review and perspective
arXiv:2308.07822v2 Announce Type: replace 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.
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.
Machine Learning (cs.LG); Systems and Control (eess.SY)
arXiv:2308.07822 [cs.LG]
(or arXiv:2308.07822v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2308.07822 Focus to learn more arXiv-issued DOI via DataCite
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