Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning

• URL: http://arxiv.org/abs/2101.04422v2
• Date: Mon, 15 Mar 2021 09:42:10 GMT
• Title: Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning
• Authors: Quirin G\"ottl, Dominik G. Grimm, Jakob Burger
• Abstract summary: Reinforcement learning can be used for automated flowsheet synthesis without prior knowledge of conceptual design. Flowsheet synthesis is modelled as a game of two competing players. The method is applied successfully to a reaction-distillation process in a quaternary system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning 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 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 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.

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