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This paper presents an Automated Optimization of Mixed-Matrix Membrane Fabrication (AOMMF) framework that utilizes Bayesian Reinforcement Learning (BRL) to enhance the CO2 capture process. By integrating a digital twin simulation, the framework efficiently navigates the complex parameter space of membrane fabrication, significantly reducing the need for physical experimentation and improving performance metrics. The results demonstrate a marked increase in CO2 permeability and selectivity, indicating the po

Author

freederia

Language

EN