论文标题
深度强化学习过程合成
Deep Reinforcement Learning for Process Synthesis
论文作者
论文摘要
本文展示了增强学习（RL）通过呈现蒸馏健身房的应用来处理综合，这是一组RL环境，其中RL代理的任务是设计蒸馏列车，鉴于用户定义的多组件进料流。蒸馏健身房与工艺模拟器（可可和Chemsep）的界面，以模拟环境。本文讨论了两个蒸馏问题示例的证明（苯，甲苯，P-二甲苯分离问题和碳氢化合物分离问题），其中深层RL药剂能够成功地在蒸馏体育馆内学习以产生合理的设计。最后，本文提出了化学工程体育馆的创建，这是一种用于化学工程过程合成的通用增强软件工具包。
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.
