Systematic development of predictive molecular models of high surface area activated carbons for adsorption applications

Abstract Adsorption in porous materials is a promising technology for CO2 capture and storage. Particularly important applications are adsorption separation of streams associated with the coal power plant operation, as well as natural gas sweetening. High surface area activated carbons are a promising family of materials for these applications, especially in the high pressure regimes. As the streams under consideration are generally multi-component mixtures, development and optimization of adsorption processes for their separation would substantially benefit from predictive simulation models. Here, we develop a molecular model of a high surface area carbon material based on a random packing of small fragments of a carbon sheet. In the construction of the model, we introduce a number of constraints, such as the value of the accessible surface area, concentration of the surface groups, and pore volume to bring the properties the model structure close to the reference porous material (Maxsorb carbon with the surface area in excess of 3000 m2/g). We use experimental data for CO2 and methane adsorption to tune and validate the model. We demonstrate the accuracy and robustness of the model by predicting single component adsorption of CO2, methane and other relevant components under a range of conditions.