Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning (RL) can be used for automated flowsheet synthesis without any heuristics of prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially built up flowsheets that solve a given process problem. A novel RL method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The RL agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

翻译：自动流程表合成是计算机辅助流程工程中的一个重要领域。当前工作展示了如何在无需事先概念设计知识的超常性知识的情况下,将强化学习(RL)用于自动流程表合成。环境由稳定状态流程表模拟器构成,包含所有物理知识。一个代理器受过培训,可以采取分立行动和连续构建流程表以解决特定流程问题。正在开发名为SynGameZero的新型流程表方法,以确保在复杂问题中制定良好的探索计划。在其中,流程表合成模式是两个竞争玩家的游戏。流程表合成工具在培训过程中与自己对立,由人工神经网络和树前方规划搜索组成。该方法被成功应用到一个顶层系统中的反应蒸馏过程。