Towards estimation of CO₂ adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO₂ into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO₂. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO₂ adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO₂ adsorption will open new doors for their further application in CO₂ separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO₂ adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO₂ uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R² value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO₂ adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO₂ adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.