# Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge

arXiv: https://arxiv.org/abs/2310.06415 · HTML: https://mlsanity.com/paper/2310.06415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [Learning values across many orders of magnitude](https://mlsanity.com/paper/1602.07714/) (2016)
- [Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks](https://mlsanity.com/paper/2012.06051/) (2020)
- [Observe and Look Further: Achieving Consistent Performance on Atari](https://mlsanity.com/paper/1805.11593/) (2018)
- [Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning](https://mlsanity.com/paper/2101.04422/) (2021)
- [Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks](https://mlsanity.com/paper/2207.12051/) (2022)
- [Deep Reinforcement Learning for Process Synthesis](https://mlsanity.com/paper/2009.13265/) (2020)
- [Transfer learning for process design with reinforcement learning](https://mlsanity.com/paper/2302.03375/) (2023)
- [Policy-Based Self-Competition for Planning Problems](https://mlsanity.com/paper/2306.04403/) (2023)

## Abstract (arXiv)

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.