Computational modeling of CO₂ adsorption on the activated biochars derived from biomasses: Implications for energy, environment, and climate change

Global efforts are being intensified to capture CO₂ to mitigate its alarming effects on environment and climate change. Among several known solid adsorbents for CO₂ capture, activated biochars developed from biomasses have proven to be efficient adsorbents for CO₂ capture. Herein, we propose the support vector regression (SVR) model to aid the fast prediction of CO₂ adsorption on biochar adsorbents. The SVR model employed surface area, pore volume, micropore diameter, temperature and pressure of experimental data to predict the CO₂ sorption capacity of biochars adsorbents. Predicted CO2 uptakes by various biochar adsorbents closely align with experimental results. The developed strategy demonstrated excellent accuracy, signified by the low root mean square error (RSME) values of 0.163 and 0.624 for the training and testing dataset. Besides, the model showed 99.67 % and 77.38 % correlation coefficients (CC) for the CO₂ adsorption in the training and testing dataset, respectively, thereby ratifying the excellent agreement between the measured and the predicted results. The outcomes displayed the simple but effective model proposed in this study will enhance future studies on CO₂ capture not limited to only activated biochars but all kinds of solid adsorbents. The proposed SVR model serves as a rapid and reliable tool for predicting CO₂ adsorption, facilitating the efficient screening and optimization of biochar adsorbents for large-scale carbon capture applications.