APA (7th ed.) Citation

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 Style (17th ed.) Citation

Göttl, Quirin, Jonathan Pirnay, Jakob Burger, and 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 (9th ed.) Citation

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.