Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning

2021/01/12 by Quirin Göttl, Dominik G. Grimm, Göttl, Quirin +3 · 1 citation
Computer Science · Engineering · #Advanced Control Systems Optimization #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Fault Detection and Control Systems #Finance #Machine Learning (cs.LG) #Process Optimization and Integration #and Science (cs.CE) #cs.AI #cs.CE #cs.LG

paper · pdf · doi:10.48550/arxiv.2101.04422

openalex publication_date 2021/01/12 · arxiv created 2021/03/15 · arxiv updated 2021/03/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

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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