Theoretical Advancements in Machine Learning Algorithms for Predicting Methane/Hydrogen Separation Using Adsorbent Methods

The separation of methane (CH۴) and hydrogen (H۲) is a critical process in various industrial and energy applications, including natural gas processing, hydrogen purification, and energy storage. Adsorbent-based methods, such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), have emerged as promising materials due to their tunable properties and high surface areas. The application of machine learning (ML) algorithms has significantly advanced the optimization of these materials, enabling rapid and accurate predictions of their separation performance. This paper explores the theoretical advancements in ML algorithms, including supervised, unsupervised, and reinforcement learning (RL), for predicting CH۴/H۲ separation using adsorbent materials. Supervised learning techniques have been used to predict adsorption and separation properties, with high-throughput screening and ML models enabling the identification of high-performance adsorbents. Unsupervised learning methods, including transfer learning and automated machine learning (AutoML), have provided new avenues for material discovery, while RL has optimized predictive models for adsorption properties. Case studies highlight the practical applications of ML models for identifying top-performing adsorbents for methane and hydrogen separation. The paper also discusses challenges and limitations in data quality, material representation, and computational costs, along with future directions such as multi-task learning and digital twin technology. The integration of advanced ML techniques with experimental validation will further accelerate the discovery of optimal adsorbent materials for industrial applications

کلیدواژه ها:

Methane/Hydrogen Separation ، Machine Learning (ML) ، Supervised Learning ، Unsupervised Learning ، Reinforcement Learning (RL)