Modeling investigation on the viscosity of pure refrigerants and their liquid mixtures by using the Patel–Teja viscosity equation of state

Khosharay, Shahin; Pierantozzi, Mariano; Nicola, Giovanni Di

doi:10.1016/j.ijrefrig.2017.10.004

Cited by 21 publications

(18 citation statements)

References 26 publications

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“…Para la regla de mezcla 1, los valores estimados de Iij son altos porque la regla de mezclado utiliza únicamente este coeficiente de interacción para el parámetro , los otros dos parámetros no tienen coeficiente de interacción por lo tanto Iij debe compensar todos los errores de mezclado. Por otro lado, la regla de mezcla 2 los valores de los coeficientes de interacción son similares aquellos reportados en la literatura (Khosharay et al, 2015;Khosharay et al, 2017). Además, es posible calcular combinaciones de kij y Wij con valores diferentes pero que pueden llevar a desviaciones similares.…”

Section: Resultados Para Mezclasunclassified

“…Bonyadi y Rostami (2017) usaron el modelo basado en la EoS de Soave-Redlich-Kwong para realizar estimaciones de la viscosidad de hidrocarburos, nitrógeno y dióxido de carbono con desviaciones promedio de 7.80%. Recientemente, Khosharay et al (2017) estima viscosidades de refrigerantes y sus mezclas usando la EoS de Patel-Teja con desviaciones promedio de 7.40% para sustancia pura y 3.50% para mezclas.…”

Section: Introductionunclassified

“…En conclusión, no es adecuada la predicción de estos modelos en sustancias polares. Estos resultados están conformes a lo reportado por Bonyadi y Rostami (2017) yKhosharay et al (2017).…”

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Modelamiento de la Viscosidad con Base en una Ecuación Cúbica μTP del Tipo Peng-Robinson

2019

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ResumenSe aplica un modelo para el cálculo de la viscosidad del tipo ecuación cúbica de estado de Peng-Robinson. El modelo ha sido empleado para correlacionar y predecir viscosidades de sustancias puras y mezclas en especial en la región de saturación. Los parámetros del modelo han sido generalizados en términos de la masa molar. Las expresiones generalizadas son estimadas mediante el ajuste de datos experimentales de alcanos y alcoholes. Las desviaciones absolutas promedio para la viscosidad, entre los valores correlacionados y los experimentales, son 6.26% y 7.21% para alcanos y alcoholes, respectivamente. Se realizan cálculos predictivos con desviaciones absolutas promedio de 3.99% para alcanos y 13.76% para alcoholes. Para viscosidades de mezclas se utilizaron tres enfoques de reglas de mezclado. Las desviaciones promedio calculadas son 18.66%, 10.31% y 3.85% para las reglas simples, de uno y dos parámetros respectivamente. Los resultados indican que el modelo propuesto proporciona resultados adecuados y consistentes, teniendo en cuenta la simplicidad de las expresiones generalizadas desarrolladas. Palabras clave: viscosidad; ecuaciones cúbicas de estado; sustancias puras; mezclas Modeling the Viscosity Based on Peng-Robinson Type Cubic TP Equation AbstractA viscosity model based on the Peng-Robinson cubic equation of state is applied. The model has been used to correlate and predict viscosities of pure substances and mixtures especially in the saturation zone. The parameters of the model are generalized in terms of the molar mass. The generalized expressions are estimated by fitting the experimental data for a group of alkanes and alcohols. Average deviations between calculated values and experimental data for alkanes and alcohols are 6.26% and 7.21%. Some predictive calculations are performed for alkanes and alcohols finding average absolute deviation of 3.99% and 13.76%. To extend the model to mixtures, some binary mixtures calculations are performed using three mixing rule approaches. The results obtained with the simple, one and two interaction parameters mixing rules are 18.66%, 10.31% and 3.85%. The results indicate that the new viscosity model provides results that can be considered adequate and consistent taking in account the simplicity of the generalized expressions.

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Section: Resultados Para Mezclasunclassified

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Modelamiento de la Viscosidad con Base en una Ecuación Cúbica μTP del Tipo Peng-Robinson

2019

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“…Thus, new pressure–viscosity–temperature and pressure–thermal conductivity–temperature cubic EoS’s have been recently proposed. 84 − 87 In these equations, the density is replaced for the new property to be calculated.…”

Section: Introductionmentioning

confidence: 99%

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

Mulero

Cachadiña

Cardona

et al. 2022

Ind. Eng. Chem. Res.

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Herein, the geometric similitude concept is applied to propose a cubic equation that relates surface tension, saturation pressure, and temperature for n -alkanes. The input properties for each fluid are the molecular mass, pressure, temperature, and compressibility factor at the critical point. The model is applied to temperatures below 0.93· T c (critical point temperature). A total of 2429 surface tension values have been selected for 32 n -alkanes. The parameters of the model have been obtained with a fit of the surface tension values for 19 pure n -alkanes that are randomly chosen. Then, it is tested for the other 13 pure n -alkanes and used to predict the surface tension for 11 binary and 4 ternary mixtures. These predictions are compared with the reported experimental data. For pure n -alkanes, the overall absolute average deviation is 2.4%, including the correlation and testing sets. No additional adjustable coefficients are used for mixtures, yielding an overall absolute average deviation of 2.98% for the binary systems and 7.97% for the ternary ones. The results show that the model is accurate enough for predictions and that the highest deviations are due to the lack of agreement in the values of surface tension of pure fluids obtained from different sources.

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“…In addition, to simplify and quickly obtain the reservoir fluid viscosity and to analyze the influencing factors of viscosity, a large number of simulation studies on viscosity have appeared in the petroleum industry, and many com-monly used viscosity models have been proposed, including LBS viscosity model [11], CS viscosity model, LLS viscosity model, Pt viscosity model, and PR viscosity model [12,13]. The application of these models can realize the viscosity acquisition of reservoir fluid with specific composition and realize viscosity prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

Shao

et al. 2021

Geofluids

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It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison results show that the random forest (RF) model performs accurately in predicting the viscosity of each phase of the reservoir, with the lowest error percentage and highest R 2 values. And the RF model is tremendously fast in a computing time of 0.53 s. In addition, sensitivity analysis indicates that for a multiphase reservoir system, the viscosity of each phase of the reservoir is determined by different factors. Among them, the viscosity of oil is vital for oil production, which is mainly affected by the molar ratio of gas to oil (MR-GO).

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Modeling investigation on the viscosity of pure refrigerants and their liquid mixtures by using the Patel–Teja viscosity equation of state

Cited by 21 publications

References 26 publications

Modelamiento de la Viscosidad con Base en una Ecuación Cúbica μTP del Tipo Peng-Robinson

Modelamiento de la Viscosidad con Base en una Ecuación Cúbica μTP del Tipo Peng-Robinson

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

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Modeling investigation on the viscosity of pure refrigerants and their liquid mixtures by using the Patel–Teja viscosity equation of state

Cited by 21 publications

References 26 publications

Modelamiento de la Viscosidad con Base en una Ecuación Cúbica &#956;TP del Tipo Peng-Robinson

Modelamiento de la Viscosidad con Base en una Ecuación Cúbica &#956;TP del Tipo Peng-Robinson

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

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Modelamiento de la Viscosidad con Base en una Ecuación Cúbica μTP del Tipo Peng-Robinson

Modelamiento de la Viscosidad con Base en una Ecuación Cúbica μTP del Tipo Peng-Robinson