Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge

Abstract

Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recent years. Existing work on reinforcement learning for flowsheet synthesis shows promising concepts. We further develop those concepts and present a general deep reinforcement learning approach for flowsheet synthesis. We demonstrate the adaptability of an agent to the general task of separating binary azeotropic mixtures. The agent is trained to set up the discrete process topology alongside choosing continuous specifications for the individual flowsheet elements (e.g., distillation columns and recycles). Without prior knowledge, it learns within one training cycle to craft flowsheets for multiple chemical systems, considering different feed compositions and conceptual approaches. The agent discovers autonomously fundamental process engineering paradigms as heteroazeotropic distillation or curved-boundary distillation.