Deep reinforcement learning for process design: Review and perspective
This is a review paper, so it is incremental, summarizing existing research for researchers in chemical engineering and AI.
The paper surveys the use of deep reinforcement learning to address complex decision-making in sustainable chemical process design, highlighting its potential to accelerate the transition to renewable energy and feedstocks.
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.