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 CO(2) into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO(2). In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO(2) adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO(2) adsorption will open new doors for their further application in CO(2) 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 CO(2) adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO(2) 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 R(2) value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO(2) adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO(2) adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools

INTELLIGENT ROUTE TO DESIGN EFCIENT CO2 REDUCTION ELECTROCATALYSTS USING ANFIS OPTIMIZED BYGA AND PSO

Recently, electrochemical reduction of CO2 into value-added fuels has been noticed as a promising process to decrease CO2 emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS–PSO and ANFIS–GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO2 reduction