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Thermodynamic Analysis of Vapor-Liquid and Vapor-Solid Equilibria Data to Obtain Interaction Second Virial Coefficients

  • C.-h. Chiu
  • F. B. Canfield
Part of the Advances in Cryogenic Engineering book series (ACRE, volume 12)

Abstract

In the prediction of thermodynamic properties of liquid and gaseous mixtures, often it is necessary to know the potential energy of interaction between like and unlike species. If sufficient data are available for each pure component comprising the mixture, the potential energy for like interactions will be known. However, the potential energy for the unlike interactions is generally to be predicted from pure component data. For example, in the derivation of Prigogine’s [1] average potential model for liquid mixtures and in some of the more refined theories of corresponding states for mixtures [2], it is assumed that
$$ { \in _{12}} = {({ \in _{11}}{ \in _{22}})^{\frac{1}{2}}} $$
(1)
$$ {\sigma _{12}} = \frac{{{\sigma _{11}} + {\sigma _{22}}}}{2} $$
(2)
where εij- and σ tj are energy and length parameters in a two parameter potential energy function such as the Lennard-Jones (12: 6) potential. That these rules are inadequate has been demonstrated recently by several investigators [3–6]. If substantial progress is to be made in the prediction of mixture behavior from the theory of inter-molecular forces, it is necessary that more accurate mixing rules be developed. The values of ε 12 and σ12 can be calculated from a variety of observed data; for example, binary diffusion coefficients, thermal diffusion coefficients, viscosity of mixtures, and second interaction virial coefficients. It should be noted that each property yields its characteristic force constants and one cannot in general work interchangeably with force constants derived from different properties.

Keywords

Force Constant Condensed Phase Thermodynamic Analysis Virial Coefficient International Advance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1967

Authors and Affiliations

  • C.-h. Chiu
    • 1
  • F. B. Canfield
    • 1
  1. 1.University of OklahomaNormanUSA

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