Computational prediction of MOFs with the potential to improve the efficiency of industrial CO2 capture

Abstract

Capturing and storing carbon dioxide produced during industrial production operations has become a global focus in terms of energy conservation and emission reduction, especially for high-emission sources such as coal-fired power plants. In this study, the high-throughput Grand Canonical Monte Carlo (HT-GCMC) simulation method was used to assess the performance of 4,188 MOFs that were carefully selected from a comprehensive pool of 10,143 CoRE-MOFs using specific criteria. Exploratory data analysis for the population demonstratedthe effect of structural properties and isosteric enthalpy on MOFs’ adsorption performance. 90 MOFs were identified as superior adsorbents that show a strong adsorption performance when screening based on four filter criteria considered important for improving industrial economic efficiency. As the desorption pressure was increased, numerous MOFs maintained their potential and met the four screening conditions. This implies that there is room to increase the desorption pressure, which suggests a potential energy saving in practical applications. HT-GCMC was applied to calculate the adsorption properties of the 90 potential MOFs using various industrial CO2 concentration levels and showed the versatility of these MOFs for use in various industrial CO2 separation schemes. In addition, the adsorption mechanism of potential MOFs was analyzed, revealing specific numerical ranges for Qst and structural characteristics that correlate with their performance. Character analysis of 90 potential MOFs revealed that the elements O, N, Zn, and Cu have the highest content; C-O, Cu-O, C-N, and Cu-N are the bonded atom pairs most commonly seen in these 90 potential MOFs.