Göttl, Q., Pirnay, J., Burger, J., & Grimm, D. G. (2025). Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge. Computers & chemical engineering, 194, 108975. https://doi.org/10.1016/j.compchemeng.2024.108975
Chicago-Zitierstil (17. Ausg.)Göttl, Quirin, Jonathan Pirnay, Jakob Burger, und Dominik G. Grimm. "Deep Reinforcement Learning Enables Conceptual Design of Processes for Separating Azeotropic Mixtures Without Prior Knowledge." Computers & Chemical Engineering 194 (2025): 108975. https://doi.org/10.1016/j.compchemeng.2024.108975.
MLA-Zitierstil (9. Ausg.)Göttl, Quirin, et al. "Deep Reinforcement Learning Enables Conceptual Design of Processes for Separating Azeotropic Mixtures Without Prior Knowledge." Computers & Chemical Engineering, vol. 194, 2025, p. 108975, https://doi.org/10.1016/j.compchemeng.2024.108975.