Deep reinforcement learning uncovers processes for separating azeotropic
  mixtures without prior knowledge
Quirin Göttl Jonathan Pirnay Jakob Burger Dominik G. Grimm
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, but focuses on narrow problems in a single chemical system, limiting
its practicality. We present a general deep reinforcement learning approach for
flowsheet synthesis. We demonstrate the adaptability of a single agent to the
general task of separating binary azeotropic mixtures. Without prior knowledge,
it learns to craft near-optimal flowsheets for multiple chemical systems,
considering different feed compositions and conceptual approaches. On average,
the agent can separate more than 99% of the involved materials into pure
components, while autonomously learning fundamental process engineering
paradigms. This highlights the agent's planning flexibility, an encouraging
step toward true generality.