Prediction of azeotrope formation in binary mixtures with pure component properties and limiting activity coefficients

Li, Aihong; Paluch, Andrew S.; Liu, Zhiyong

doi:10.1016/j.fluid.2022.113664

Cited by 1 publication

(1 citation statement)

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“…For example, Brito Alves et al 10 presented a neural network approach for the prediction of azeotrope formation using a series of macroscopic and microscopic properties of the pure components. Li et al 11 proposed a method to predict homogeneous azeotropes in binary mixtures using pure component properties and activity coefficients at quasi-infinite dilution, which were estimated by the modified separation of cohesive energy density model, although any suitable method could be used. In other works, azeotropy is analyzed from a theoretical basis with the aim of calculating the conditions for such a phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Phase Stability Analysis: A Consistent Guide for Drawing Maps for Safe Traveling through the World of Azeotropy and Miscibility

Marcilla,

Carbonell-Hermida,

Olaya

2024

Ind. Eng. Chem. Res.

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Phase equilibria for systems that present different types of azeotropy have been analyzed by using the dimensionless Gibbs energy of mixing (g M ) for the liquid and vapor phases along with both the common tangent criterion and the phase stability test. First, the g M,L function for the liquid phase and its derivatives have been analyzed to obtain the boundary function between the homogeneous (L) and heterogeneous (LLE) regions as a function of the parameters for different activity coefficient models. Next, the excess Gibbs free energies and their first derivatives for the liquid and vapor phases have been analyzed to determine the mathematical conditions and the limits of different types of azeotropy (VLE and VLLE) and the possible combinations of two azeotropes in the same binary system. Finally, as a result of the combination of all this information, azeotropy and miscibility maps have been obtained as a function of the parameter values for Margules, van Laar, Wilson, and NRTL activity coefficient models.

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Section: Introductionmentioning

confidence: 99%