Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge

Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recen...

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Veröffentlicht in:	Computers & chemical engineering Jg. 194; S. 108975
Hauptverfasser:	Göttl, Quirin, Pirnay, Jonathan, Burger, Jakob, Grimm, Dominik G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2025
Schlagworte:	Automated process synthesis Conceptual design Process simulation Reinforcement learning Automated process synthesis Conceptual design Process simulation Reinforcement learning
ISSN:	0098-1354
Online-Zugang:	Volltext
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