All Days 1984
DOI: 10.2118/12643-ms
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A New Multicomponent Surface Tension Correlation Based on Scaling Theory

Abstract: Thi s paper presents a mult i component surface tension correlation based on scaling theory. In addition to particular exponents employed. the correlation contains two new features: (1) A corresponding-states equation is derived for a correlation coefficient. commonly referred to as a parachor.As a result. the hydrocarbon pseudocomponent parachors can be calculated through this equation. once their pseudocritical properties are estimated.(2) An approach is proposed to calcu-1 ate the parachors of mi xtures. In… Show more

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Cited by 40 publications
(11 citation statements)
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“…The diffusion coefficients were calculated using the da Silva and Belery method. IFT values were obtained using the Lee and Chien correlation. Vapour pressures were determined using the Antoine equation.…”
Section: Physical Specifications and General Assumptionsmentioning
confidence: 99%
“…The diffusion coefficients were calculated using the da Silva and Belery method. IFT values were obtained using the Lee and Chien correlation. Vapour pressures were determined using the Antoine equation.…”
Section: Physical Specifications and General Assumptionsmentioning
confidence: 99%
“…11b for comparison; the surface tension is then under-predicted by nearly 20% by the method of Fanchi. Huygens et al [59] have also applied the parachor method of Hough and Stegemeier [61] and the Lee and Chien method [62] and found average deviations between prediction and experiment of 25 and 29%, respectively. Hence, the predictions of the gradient theory are consistently more accurate than those of these methods.…”
Section: N 2 /Hydrocarbons Mixturementioning
confidence: 99%
“…The parachor method is sensitive to the accuracy of the parachor because of the fourth power relationship between the IFT and the parachor. Gasem et al (1989) compared the Weinaug-Katz (1943), Lee-Chien (1984) and Hugill-van Welsenes (1986) models. The results indicated that each of these models could represent the data with average errors of less than 10% if a scaling exponent of 3.6 was incorporated in the models and some parameters were tuned to fit experimental data.…”
mentioning
confidence: 99%