Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Stops, L. (TU Delft ChemE/Product and Process Engineering)
Leenhouts, Roel (Student TU Delft)
Gao, Q. (TU Delft ChemE/Product and Process Engineering)
Schweidtmann, A.M. (TU Delft ChemE/Product and Process Engineering)

2022

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.

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

http://resolver.tudelft.nl/uuid:457e1062-732d-4574-9197-1cf8c4439365

https://doi.org/10.1002/aic.17938

0001-1541

AIChE Journal, 69 (1), 14

Institutional Repository

journal article

© 2022 L. Stops, Roel Leenhouts, Q. Gao, A.M. Schweidtmann