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.

翻译：向化工行业中可再生能源与原料供应转型需要新的概念性过程设计方法。近年来，人工智能领域的突破为加速这一转型提供了机遇。具体而言，深度强化学习作为机器学习的一个子类，已展现出解决复杂决策问题并助力可持续过程设计的潜力。我们通过三个关键要素综述了强化学习在过程设计中的最前沿研究：（i）信息表征，（ii）智能体架构，（iii）环境与奖励函数。此外，我们讨论了面临的潜在挑战及未来有前景的研究方向，旨在充分释放强化学习在化学工程过程设计中的潜力。