Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models

In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m²/g) and pore volume (cm³/g) of the MOFs and the output was CO₂ uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO₂ adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO₂ adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications.