Frontiers of Chemical Science and Engineering
>> 2022,
Volume 16,
Issue 2
doi:
10.1007/s11705-021-2055-9
Automated synthesis of steady-state continuous processes using reinforcement learning
Received: 15 Nov 202
Revised: 15 Mar 202
Accepted: 18 May 202
Available online: 2022-02-15
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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 or 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 build 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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