  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.
