Robust smart schemes for modeling carbon dioxide uptake in metal - organic frameworks

Abstract

The emission of greenhouse gases such as carbon dioxide (CO2) is considered the most acute issue of the 21st century around the globe. Due to this fact, significant efforts have been made to develop rigorous techniques for reducing the amount of CO2 in the atmosphere. Adsorption of CO2 in metal–organic frameworks (MOFs) is one of the efficient technologies for mitigating the high levels of emitted CO2. The main aim of this study is to examine the aptitudes of four advanced intelligent models, including multilayer perceptron (MLP) optimized with Levenberg-Marquardt (MLP-LMA) and Bayesian Regularization (MLP-BR), extreme learning machine (ELM), and genetic programming (GP) in predicting CO2 uptake in MOFs. A sufficiently widespread source of data was used from literature, including more than 500 measurements of CO2 uptake in13 MOFs with various pressures at two temperature values. The results showed that the implemented intelligent paradigms provide accurate estimations of CO2 uptake in MOFs. Besides, error analyses and comparison of the prediction performance revealed that the MLP-LMA model outperformed the other intelligent models and the prior paradigms in the literature. Moreover, the MLP-LMA model yielded an overall coefficient of determination (R2) of 0.9998 and average absolute relative deviation (AARD) of 0.9205%. Finally, the trend analysis confirmed the high integrity of the MLP-LMA model in prognosticating CO2 uptake in MOFs, and its predictions overlapped perfectly the measured values with changes in pressure and temperature