Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Process synthesis experiences a disruptive transformation accelerated by 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. 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. The method is predestined to include large action-state spaces and an interface to process simulators in future research.

Original languageEnglish
Article numbere17938
Pages (from-to)14
JournalAIChE Journal
Issue number1
Publication statusPublished - 2022


  • artificial intelligence
  • graph convolutional neural networks
  • graph generation
  • process synthesis
  • reinforcement learning


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