期刊
FLUID PHASE EQUILIBRIA
卷 382, 期 -, 页码 116-126出版社
ELSEVIER
DOI: 10.1016/j.fluid.2014.09.002
关键词
PC-SAFT; DFT; Adsorption; Pore size distribution
资金
- Swedish research council
- Key Project of the National Natural Science Foundation of China [21136004]
- National Basic Research Program of China [2013CB733500]
The developed hybrid PC-SAFT-DFT model, a coupling of density functional theory (DFT) with perturbed-chain statistical associating fluid theory (PC-SAFT), was used to study the adsorption of pure- and mixed-fluids on nano-porous materials, and carbons and zeolites were chosen as examples of nano-porous materials in this work for model performance evaluation. In the PC-SAFT-DFT model, the modified fundamental measure theory was used for the hard sphere contribution, the dispersion free energy functional was represented with a weighted density approximation, and the chain free energy functional from interfacial SAFT was used to account for the chain connectivity. The fluid was modeled as a chain molecule with molecular parameters taken from those in the bulk PC-SAFT. The external force field was used to describe the interaction between the solid surface of a nano-porous material and fluid. Application of this model was demonstrated on the gas adsorption on porous carbons and zeolites which were assumed to have slit- and cylinder-shaped pores with mean pore sizes, respectively. The parameters of the adsorption model were obtained by fitting to the pure-gas adsorption isotherms measured experimentally. With parameters of the model fitted to the pure-gas adsorption at one temperature, the model was used to predict the pure-gas adsorption at other temperatures as well as the adoption isotherms of mixtures. The model prediction was compared with the available experimental data, which shows that the predictions are reliable for most of the systems studied in this work. The effect of the pore size distribution on the model performance was further investigated, and it was found that the consideration of the pore size distribution (PSD) can improve the accuracy of the model results but the PSD analysis requires much more computing time. (C) 2014 Elsevier B.V. All rights reserved.
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