# Deep Reinforcement Learning for Process Synthesis

arXiv: https://arxiv.org/abs/2009.13265 · HTML: https://mlsanity.com/paper/2009.13265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks](https://mlsanity.com/paper/2207.12051/) (2022)
- [Learning from flowsheets: A generative transformer model for autocompletion of flowsheets](https://mlsanity.com/paper/2208.00859/) (2022)
- [Deep reinforcement learning for process design: Review and perspective](https://mlsanity.com/paper/2308.07822/) (2023)
- [Transfer learning for process design with reinforcement learning](https://mlsanity.com/paper/2302.03375/) (2023)
- [Synthesis of separation processes with reinforcement learning](https://mlsanity.com/paper/2211.04327/) (2022)
- [Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge](https://mlsanity.com/paper/2310.06415/) (2023)

## Abstract (arXiv)

This paper demonstrates the application of reinforcement learning (RL) to process synthesis by presenting Distillation Gym, a set of RL environments in which an RL agent is tasked with designing a distillation train, given a user defined multi-component feed stream. Distillation Gym interfaces with a process simulator (COCO and ChemSep) to simulate the environment. A demonstration of two distillation problem examples are discussed in this paper (a Benzene, Toluene, P-xylene separation problem and a hydrocarbon separation problem), in which a deep RL agent is successfully able to learn within Distillation Gym to produce reasonable designs. Finally, this paper proposes the creation of Chemical Engineering Gym, an all-purpose reinforcement learning software toolkit for chemical engineering process synthesis.