Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

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.

Publikationsart
Zeitschriftenbeiträge (peer-reviewed)
Titel
Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept
Medien
Chemie Ingenieur Technik
Heft
12
Band
93
Autoren
Quirin Göttl, Yannic Tönges, Prof. Dr. Dominik Grimm , Prof. Dr.-Ing. Jakob Burger
Seiten
2010-2018
Veröffentlichungsdatum
26.08.2021
Zitation
Göttl, Quirin; Tönges, Yannic; Grimm, Dominik; Burger, Jakob (2021): Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept. Chemie Ingenieur Technik 93 (12), S. 2010-2018. DOI: 10.1002/cite.202100086