Flowsheet synthesis through hierarchical reinforcement learning and
graph neural networks
- URL: http://arxiv.org/abs/2207.12051v1
- Date: Mon, 25 Jul 2022 10:42:15 GMT
- Title: Flowsheet synthesis through hierarchical reinforcement learning and
graph neural networks
- Authors: Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann
- Abstract summary: We propose a reinforcement learning algorithm for chemical process design based on actor-critic logic.
Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs.
- Score: 0.4588028371034406
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Process synthesis experiences a disruptive transformation accelerated by
digitization and artificial intelligence. We propose a reinforcement learning
algorithm for chemical process design based on a state-of-the-art actor-critic
logic. Our proposed algorithm represents chemical processes as graphs and uses
graph convolutional neural networks to learn from process graphs. In
particular, the graph neural networks are implemented within the agent
architecture to process the states and make decisions. Moreover, we implement a
hierarchical and hybrid decision-making process to generate flowsheets, where
unit operations are placed iteratively as discrete decisions and corresponding
design variables are selected as continuous decisions. We demonstrate the
potential of our method to design economically viable flowsheets in an
illustrative case study comprising equilibrium reactions, azeotropic
separation, and recycles. The results show quick learning in discrete,
continuous, and hybrid action spaces. Due to the flexible architecture of the
proposed reinforcement learning agent, the method is predestined to include
large action-state spaces and an interface to process simulators in future
research.
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