Year: 2020
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Midgley, Laurence Illing
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3089296146