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

Publikationsart: Wissenschaftliche Artikel
Titel: Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge
Medien: Computers and Chemical Engineering
DOI: 10.1016/j.compchemeng.2024.108975
Band: 194
Autor:innen: Quirin Göttl, Jonathan Pirnay, Jakob Burger, Dominik Grimm
Seiten: 108975
Veröffentlichungsdatum: 25.01.2025
Projekttitel: Reinforcement Learning for Automated Flowsheet Synthesis of Steady-State Processes