Identification of Pressure-Swing Separation Processes for Azeotropic Mixtures Using Deep Reinforcement Learning

Our previously developed deep reinforcement learning (RL) framework for the conceptual design of fluid separation processes showed strong performance in generating flowsheets for multiple chemical systems under single-pressure conditions. This work extends that framework by introducing distillation columns modeled at different pressures into the RL environment. The agent autonomously learns to synthesize flowsheets, uncovering pressure-swing strategies for pressure-sensitive azeotropes without prior knowledge or heuristics. It also continues to identify effective single-pressure processes that rely on entrainers or liquid–liquid immiscibility for mixtures with less pressure sensitivity. This work advances RL-based process synthesis toward a more general and versatile framework.