Vapor-liquid equilibriums of the quinary system hexane, methylcyclopentane, cyclohexane, benzene, and toulene

Weatherford, Robert M.

doi:10.1021/je60046a011

Cited by 14 publications

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“…We thus expect that the NRTL model will at least give reasonable results. Table 4 presents comparisons between NRTL-RK K-value predictions and four sets of multicomponent data (Arai et al, 38 Saito, 39 Rowan and Weber, 40 and Weatherford and Van Winkle 41 ). The last column in Table 4 presents the average sum-squared percentage differences between the NRTL-RK model predictions and the data.…”

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Sensitivity of Process Design to Phase Equilibrium—A New Perturbation Method Based Upon the Margules Equation

Mathias

2013

J. Chem. Eng. Data

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It is widely recognized by experts that the computer-based design of chemical processes depends strongly on the correlated thermodynamic and transport properties, and the effect of property uncertainties should be incorporated into the design. The most significant source of property uncertainties on process design is from the correlations of phase equilibrium. Many approaches have been proposed, but uncertainty analysis is not a routine component of today's industrial practice, mainly because education and awareness is lacking, and the proposed methods are difficult to apply. The purpose of this paper is to report a new approach to uncertainty analysis that is intuitively appealing and easy to apply in process simulation. The proposed approach focuses on activity coefficients and is based upon the simplification that, for the purpose of perturbation, the liquid mixture can be treated as a set of pseudobinaries described by the Margules equation. The resulting perturbation in the activity coefficient of component i goes to zero when its mole fraction approaches unity, and also is relatively small when its estimated activity coefficient is close to unity (i.e., near-ideal systems are likely to be modeled more accurately than highly nonideal systems). In practice, the proposed uncertainty analysis is performed by varying a single parameter for each component in the mixture. The utility of the proposed uncertainty method is demonstrated by application to two problems: (1) a propylene−propane superfractionator for which small changes in correlated relative volatilities have a large effect on the design of the distillation column, and (2) a dehexanizer column that separates a mixture containing many close-boiling components. It is demonstrated that the proposed analysis provides quantitative insight into the effect of property uncertainties and helps to quantify the safety factors that need to be imposed upon the design. While the proposed method is applied to activity-coefficient models, the same idea is applicable to other models such as equations of state.

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Section: ■ Example Applicationsmentioning

confidence: 99%

Sensitivity of Process Design to Phase Equilibrium—A New Perturbation Method Based Upon the Margules Equation

Mathias

2013

J. Chem. Eng. Data

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“…1. Гексан (1) [ 2 ] 0,007 0,013 0,003 0,006 метилциклопентан (2) 0,006 0,028 0,007 0,025 бензол (3) 0,008 0,012 0,52 0,82 0,003 0,007 0,38 1,89 циклогексан (4) 0,003 0,007 0,004 0,008 толуол (5) 0,005 0,016 0,006 0,010 2. Ацетон (1) [ 3 ] 0,005 0,018 В таблице результаты расчета по предлагаемому методу в форме среднеарифметических и максимальных абсолютных расхождений с экспериментальными данными сопоставлены для ряда систем с результатами расчета по методу Вильсона.…”

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“…Метиладетат(1) П 0,004 0,013 0,008 0,018 хлороформ (2) 0,005 0,018 0,25 0,71 0,007 0,025 0,56 1,25 метанол(3) 0,008 0,020 0,009 0,028 бензол (4) 0,004 0,012 0,005 0,017 4. Ацетон (1)[ 4 ] 0,011 0,027 0,008 0,017 2,3-диметилбутан(2) 0,010 0,020 0,63 1,0 0,006 0,023 1,47 3,4 хлороформ (3) 0,005 0,024 0,006 0,013 5. Ацетон (1)[ 5 ] 0,006 0,005 0,013 метанол (2) 0,005 СЛ со * 0,005 0,014 8,4 * изопропанол(3) 0,005 0,004 0,0106.…”

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“…Ацетон (1)[ 5 ] 0,006 0,005 0,013 метанол (2) 0,005 СЛ со * 0,005 0,014 8,4 * изопропанол(3) 0,005 0,004 0,0106. Бутанон (1)[ 7] 0,011 0,022 бензол(2) 0,008 0,027 3,9 * 11,3 * изопропанол(3) 0,007 0,020 ч 7. Муравьиная кислота (1)[ 8 ] 0,008 0,037 уксусная кис-.лота (2) 0,003 0,029 2,0 3,7 пропионовая кислота (3) 0,008 0,0228.…”

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Расчет Равновесия Жидкость–пар В Многокомпонентных Системах

Eisen¹,

Kudryavtseva²,

Täht³

1978

Proceedings of the Academy of Sciences of the Estonian SSR. Che

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Activity coefficients for binary and multicomponent liquid mixtures from unary property model for Wilson parameters

1985

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A model has been developed for prediction of Wilson parameters from single component properties. Activity coefficients and VLE data for miscible binary or multicomponent systems can be calculated from the standard form of the Wilson equation using these properties. The major advantage of this model is that experimental determinations of binary data are not required. Relevant properties for a fairly large number of chemical species have been provided. This method has been compared with UNIFAC, which is generally used for prediction of activity coefficients and has been found to give comparable results with considerably less computational effort. (Tassios, 1971;Ladurelli et al., 1975;Hiranuma and Honma, 1975;Krumins et al., 1980) or to predict the binary parameters by using empirical correlations (Ghosh and Chopra, 1975;Gothard et al., 1976), but none has been of general utility. An approximate theoretical model, based on the assumption that the solution is locally regular, has been developed that successfully expresses Wilson parameters in terms of pure compound properties. The model is quite general in nature and uses the solubility parameter, "effective molar volume," and two deviation parameters for pure substances to predict the Wilson parameters. These properties for a number of polar and nonpolar compounds have been obtained. The results obtained by this method for a large number of binary and multicomponent systems have been compared with those predicted by the UNIFAC method. S. N. ASH CONCLUSIONS AND SIGNIFICANCEThe correlation obtained from the proposed model for prediction of Wilson binary parameters uses only three types of single component properties; the solubility parameter, effective molar volume, and the deviation parameter. This has been tested for a large number of binary and multicomponent systems, including hydrocarbons (aliphatic, cyclic, and aromatic), alcohols (primary and secondary), carbonyl compounds, esters, halogenated hydrocarbons, nitriles, and water. The computed average deviation in bubble point pressure and vapor phase The Wilson equation (Wilson, 1964) has been quite satisfactory for correlation of binary vapor-liquid equilibrium (VLE) data and for the prediction of multicomponent VLE behavior from relevant binary data. In his model Wilson used the energies of interaction At{ and hj or A, , between like and unlike pairs of molecules, but was not successful in attributing precise physical significance to or in obtaining expressions for these quantities. Fortunately, however, the model was so constructed that only the difference terms, (Xi, -hi) and (A, -A j j ) , appeared in the final expressions.Hence, Wilson could use these as adjustable parameters to be obtained from fitting of experimental binary equilibrium data. composition is small, normally within 2%. The results have also been compared with and found to be as good as those predicted by the UNIFAC method.The proposed correlations and expressions are remarkably simple and require only pure compound data. The method will con...

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Vapor-liquid equilibriums of the quinary system hexane, methylcyclopentane, cyclohexane, benzene, and toulene

Cited by 14 publications

References 3 publications

Sensitivity of Process Design to Phase Equilibrium—A New Perturbation Method Based Upon the Margules Equation

Sensitivity of Process Design to Phase Equilibrium—A New Perturbation Method Based Upon the Margules Equation

Расчет Равновесия Жидкость–пар В Многокомпонентных Системах

Activity coefficients for binary and multicomponent liquid mixtures from unary property model for Wilson parameters

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