2003Industrial & Engineering Chemistry ResearchRequires access

Vapor−Liquid Equilibrium of Systems Containing Alcohols Using the Statistical Associating Fluid Theory Equation of State

Xiao‐Sen Li, Peter Englezos

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Abstract

The statistical associating fluid theory (SAFT) equation of state is employed for the correlation and prediction of vapor−liquid equilibrium (VLE) of binary mixtures of alcohols with water, carbon dioxide, butane, hexane, benzene, and other alcohols. In addition, ternary VLE for water/1,2-propanediol (propylene glycol)/1,2-ethanediol (ethylene glycol), carbon dioxide/methanol/ethanol, and water/1,3 propanediol/1,2,3-propanetriol (glycerol) mixtures is predicted. In the SAFT equation, three molecular parameters, the Lennard-Jones (L-J) potential well depth, the soft-sphere diameter of the segments, and the number of segments of the molecule, are needed. These parameters are obtained from the thermodynamic properties of pure substances. For self-associating pure substances, two additional parameters are needed, namely, the bonding volume and the association energy. The binary interaction parameters are fitted to experimental vapor−liquid equilibrium data for binary systems. These binary parameters are used to predict the phase equilibria for ternary mixtures without any additional adjustment. The results were found to be in good agreement with the experimental data.

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What this paper is about

The statistical associating fluid theory (SAFT) equation of state is employed for the correlation and prediction of vapor−liquid equilibrium (VLE) of binary mixtures of alcohols with water, carbon dioxide, butane, hexane, benzene, and other alcohols. In addition, ternary VLE for water/1,2-propanediol (propylene glycol)/1,2-ethanediol (ethylene glycol), carbon dioxide/methanol/ethanol, and water/1,3 propanediol/1,2,3-propanetriol (glycerol) mixtures is predicted. In the SAFT equation, three molecular parameters, the Lennard-Jones (L-J) potential well depth, the soft-sphere diameter of the segments, and the number of segments of the molecule, are needed. These parameters are obtained from the thermodynamic properties of pure substances. For self-associating pure substances, two additional parameters are needed, namely, the bonding volume and the association energy. The binary interaction parameters are fitted to experimental vapor−liquid equilibrium data for binary systems. These binary parameters are used to predict the phase equilibria for ternary mixtures without any additional adjustment. The results were found to be in good agreement with the experimental data.

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Available abstract

The statistical associating fluid theory (SAFT) equation of state is employed for the correlation and prediction of vapor−liquid equilibrium (VLE) of binary mixtures of alcohols with water, carbon dioxide, butane, hexane, benzene, and other alcohols. In addition, ternary VLE for water/1,2-propanediol (propylene glycol)/1,2-ethanediol (ethylene glycol), carbon dioxide/methanol/ethanol, and water/1,3 propanediol/1,2,3-propanetriol (glycerol) mixtures is predicted. In the SAFT equation, three molecular parameters, the Lennard-Jones (L-J) potential well depth, the soft-sphere diameter of the segments, and the number of segments of the molecule, are needed. These parameters are obtained from the thermodynamic properties of pure substances. For self-associating pure substances, two additional parameters are needed, namely, the bonding volume and the association energy. The binary interaction parameters are fitted to experimental vapor−liquid equilibrium data for binary systems. These binary parameters are used to predict the phase equilibria for ternary mixtures without any additional adjustment. The results were found to be in good agreement with the experimental data.

Key concepts: Thermodynamics, Chemistry, Equation of state, Ternary operation, Ethylene glycol, Binary number, Methanol, Butane

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Vapor−Liquid Equilibrium of Systems Containing Alcohols Using the Statistical Associating Fluid Theory Equation of State — Research Paper | ScholarLens