Prediction of vapor-liquid equilibria for multi-component systems at high pressure with binary interaction function

Authors
Citation
Gh. Liu et Ss. Dai, Prediction of vapor-liquid equilibria for multi-component systems at high pressure with binary interaction function, CHEM J CH U, 21(12), 2000, pp. 1875-1879
Citations number
23
Categorie Soggetti
Chemistry
Journal title
CHEMICAL JOURNAL OF CHINESE UNIVERSITIES-CHINESE
ISSN journal
02510790 → ACNP
Volume
21
Issue
12
Year of publication
2000
Pages
1875 - 1879
Database
ISI
SICI code
0251-0790(200012)21:12<1875:POVEFM>2.0.ZU;2-0
Abstract
The mixing rule recently developed by using the binary interaction function L-ij(T, x) depending on the thermodynamic state of a given system, coupled with FRKS equation of state, had been successfully used for calculating th e excess properties of wide variety of complex systems. In this work, we fu rther indicated that this mixing rule satisfies the invariance condition an d tested the capability of this approach for correlating and predicting vap or-liquid equilibria of highly non-ideal systems at high pressure. Fifteen ternary mixtures and their constituent binaries were selected to do the tes t. The systems selected cover the range from almost ideal to highly non-ide al mixtures. The correlation results of the binary VLE show that for highly non-ideal systems, instead of the single optimum L-ij value, L-ij function has to be used for correlating their VLE quantitatively. The ternary VLE w ere predicted by using parameters of constituent binaries only. The results show that, in the case of simple systems, both single optimum L-ij value a nd L-ij function methods predict the VLE with nearly the same accuracy; nev ertheless, the L-ij function method shows a slight edge over the single opt imum L-ij value method. For the complex systems containing hydrogen, polar and associated components, while the single L-ij method fails to represent their VLE, the L-ij function approach can predict the VLE for all of these complex systems accurately. The predicted results are in good agreement wit h the experimental data.