Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

被引:15
|
作者
Gottl, Quirin [1 ]
Tonges, Yannic [1 ]
Grimm, Dominik G. [2 ,3 ,4 ]
Burger, Jakob [1 ]
机构
[1] Tech Univ Munich, Lab Chem Proc Engn, Campus Straubing Biotechnol & Sustainabil, D-94315 Straubing, Germany
[2] Tech Univ Munich, Bioinformat, Campus Straubing Biotechnol & Sustainabil, D-94315 Straubing, Germany
[3] Weihenstephan Triesdorf Univ Appl Sci, Petersgasse 18, D-94315 Straubing, Germany
[4] Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Garching, Germany
关键词
Artificial intelligence; Automated process synthesis; Flowsheet synthesis; Machine learning; Reinforcement learning; ARTIFICIAL-INTELLIGENCE; DESIGN; STATE; OPTIMIZATION; CHALLENGES; GO;
D O I
10.1002/cite.202100086
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Recently we showed that reinforcement learning can be used to automatically generate process flowsheets without heuristics or prior knowledge. For this purpose, SynGameZero, a novel two-player game has been developed. In this work we extend SynGameZero by structuring the agent's actions in several hierarchy levels, which improves the approach in terms of scalability and allows the consideration of more sophisticated flowsheet problems. We successfully demonstrate the usability of our novel framework for the fully automated synthesis of an ethyl tert-butyl ether process.
引用
收藏
页码:2010 / 2018
页数:9
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