Predictive Modeling of CO2 Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches

Carbon dioxide (CO 2 ) emissions from industrial and energy sources lead to global warming and drive climate change. To address the critical challenge of rising atmospheric CO 2 levels, strategies such as CO 2 capture are essential for limiting emissions and mitigating environmental impact. Among various materials, metal–organic frameworks (MOFs) have emerged as highly promising and effective adsorbents for CO 2 capture, offering high selectivity and capacity. This study evaluates the performance of various machine learning (ML) models, including artificial neural network-particle swarm optimization (ANN-PSO), coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and adaptive neuro-fuzzy inference system (ANFIS), in predicting the CO 2 capture capacity of MOFs. Additionally, gene expression programming (GEP) is utilized to find a mathematical correlation between CO 2 capture capacity and key operating variables such as pressure and surface area. The model development considered the input variables: temperature, pressure, surface area, pore volume, and enthalpy. Performance metrics such as mean square error (MSE) and coefficient of determination ( R 2 ) are calculated for the training and testing phases to assess the model accuracy and reliability. Among the evaluated models, CSA-LSSVM exhibits the best performance, achieving R 2 values of 0.971 and 0.915 and MSE values of 0.0025 and 0.0034 for the training and testing phases, respectively. Sensitivity analysis conducted on the CSA-LSSVM model reveals that pressure significantly influences CO 2 capture capacity, followed by surface area. The results demonstrate the potential of ML models, particularly CSA-LSSVM, as powerful tools for predicting and optimizing the performance of MOFs in CO 2 capture, considering cost, energy efficiency, and environmental sustainability.