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The transformation towards renewable energy and feedstock supply in thechemical industry requires new conceptual process design approaches. Recently,breakthroughs in artificial intelligence offer opportunities to accelerate thistransition. Specifically, deep reinforcement learning, a subclass of machinelearning, has shown the potential to solve complex decision-making problems andaid sustainable process design. We survey state-of-the-art research inreinforcement learning for process design through three major elements: (i)information representation, (ii) agent architecture, and (iii) environment andreward. Moreover, we discuss perspectives on underlying challenges andpromising future works to unfold the full potential of reinforcement learningfor process design in chemical engineering.

Deep reinforcement learning for process design: Review and perspective