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Chemical Engineering Science, Vol.66, No.22, 5447-5458, 2011
A multi-scale approach to the physical adsorption in slit pores
Adsorption isotherms are the foundation of gas storage and separation operations. The isotherm models are classified into three scale levels with empiricisms in macroscopic level, requirements of long computing time and idealized conditions in microscopic level, as well as gaps in knowledge between these two levels. A multi-scale modeling methodology is developed in this paper in order to reduce the identified limitations. Microscopic molecular simulations (MS) based on the grand canonical Monte Carlo (GCMC) method are carried out followed by the development of the localized adsorption isotherms defined as the intermediate level models. They are represented by the Boltzmann factor and the local Langmuir equations. The macroscopic models are then formulated through the integration of small scale models. The following three contributions are achieved in the paper. First of all, guidelines for the validity of the Boltzmann factor are established, showing its practical significance, and the local Langmuir isotherm is justified as a good approximation to the results from microscopic simulations. Secondly, it is demonstrated that the pore size distributions can be determined using GCMC simulations coupled with the measured adsorption isotherms as exemplified by a case study on a coal specimen. Finally, using the measurement data reported by Bae and Bhatia (2006) for carbon dioxide adsorption on coal, we show that the overall adsorption isotherms can be determined from the multi-scale approach through the integration of smaller scale models with pore size distributions without the empiricism, indicating the success of the methodology. Further work is needed to improve the prediction accuracy for methane adsorption on coal specimens. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:Adsorption Isotherm;Grand canonical Monte Carlo simulation (GCMC);Pore size distribution;Boltzmann factor;Parameter identification;Model integration