Synthesis of separation processes with reinforcement learning
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Abstract (arXiv)
This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its individual components by utilising distillation. While doing so it tries to maximise the profit produced by the distillation sequence. All actions of the agent were set by the SAC agent in Python and communicated in Aspen Plus via an API. Here the distillation column was simulated by use of the build-in RADFRAC column. With this a connection was established for data transfer between Python and Aspen and the agent succeeded to show learning behaviour, while increasing profit. Although results were generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable for parallelisation. This makes that Aspen is incompatible for solving RL problems. Code and thesis are available at https://github.com/lollcat/Aspen-RL
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 3 Nov 2022.
Builds on
4 of 10 references.
References · 4
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
- 2021Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learningcited by 10
- 2022Flowsheet synthesis through hierarchical reinforcement learning and graph neural networkscited by 10
- 2020Deep Reinforcement Learning for Process Synthesiscited by 6
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Citing papers per year: 2023: 2.
2 papers.