CO₂ adsorption on polymeric amine-functionalized adsorbents by computational intelligence method

Background: Emission of CO₂ has been one of the important challenges as far as global warming is concerned. The global research programs on CO₂ capture are therefore justified based on several environmental problems. The fusion of amine-based polymers on various supports like silica, carbon spheres, zeolite, metal-organic frameworks and soon has shown to be an up-and-coming technology for CO₂ capture. Methods: Here, application of support vector regression (SVR) based model was proposed for quick estimation of CO₂ adsorption on amine-functionalized polymer based solid adsorbents. This model applied attributes such as support pore size, surface area, polymer molecular weight, polymer concentration, temperature, and pressure; obtained from numerous experimental investigations to predict the CO₂ adsorption on amine-based solid materials. Significant findings: Statistical results displayed that the applied SVR model considerably predicted the adsorption capacity of CO₂ by examined amine solid adsorbents. The proposed model is characterized by correlation coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE) of 0.9999, 0.068, and 0.0532; respectively. The computational model approach put forward in this study is believed to be the first of its kind for CO₂ capture on polymeric amine-functionalized solid adsorbents and it is hoped that the proposed model would be useful for fast assessment of polymeric amine-based solid adsorbents developed in laboratories.