An XGBoost Framework for Predicting CO2 Adsorption Performance and Adsorbent Classification

Carbon dioxide (CO2) capture through adsorption using porous materials has emerged as a promising strategy for mitigating industrial greenhouse gas emissions. However, selecting an optimal adsorbent material under varying operating conditions remains a complex and time-consuming process when relying solely on experimental studies. In this project, a machine-learning-based framework is developed to predict CO2 adsorption capacity and identify the most suitable adsorbent material using process and material parameters. A comprehensive dataset was constructed comprising multiple classes of adsorbent materials including activated carbon, zeolites, metal–organic frameworks (MOFs), porous organic polymers (POPs), alumina/silica, and amine-functionalized sorbents. The dataset includes key parameters such as temperature, pressure, CO2 mole fraction, humidity, BET surface area, micropore characteristics, amine loading, heat of adsorption, particle density, pellet diameter, and bed void fraction. Two machine learning models based on the XGBoost algorithm were implemented. An XGBoost Regressor was used to predict the experimental CO2 adsorption capacity, while an XGBoost Classifier was trained to identify the type of adsorbent used based on the input parameters. The models were trained and validated using a train–test split approach to ensure reliable performance evaluation. The results demonstrate that gradient boosting models can accurately capture complex nonlinear relationships between adsorption conditions, material properties, and adsorption performance. The developed framework provides a fast and efficient predictive tool that can assist researchers and engineers in screening adsorbent materials and optimizing CO2 capture systems for industrial applications. Using this model, one can predict the adsorption capacity of any adsorbent used in the training dataset and predict its type with 95% accuracy.