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.

转向可再生能源和原料供应的化工行业需要新的概念性工艺设计方法。最近，人工智能的突破为加速这一转型提供了机会。特别是深度强化学习作为机器学习的一个子类，已显示出解决复杂决策问题和辅助可持续工艺设计的潜力。我们通过三个主要元素调查了强化学习在工艺设计中的最新研究：（i）信息表示，（ii）智能体体系结构和（iii）环境和奖励。此外，我们讨论了潜在挑战的观点和有前景的未来工作，以充分发挥化工工程中强化学习的潜力。