Prediction of thermal conductivity for characterized oils and their fractions using an expanded fluid based model

Ramos-Pallares, F.; Schoeggl, F. F.; Taylor, Shawn D.; Yarranton, Harvey W.

doi:10.1016/j.fuel.2018.06.112

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…Teja and Tarlneu introduced an effective carbon number concept to calculate the thermal conductivity of pure liquids and liquid mixtures. Ramos-Pallares et al , applied the expanded fluid theory to the thermal conductivity of defined and undefined hydrocarbon systems. Rokni et al used a residual entropy scaling based thermal conductivity correlation of Hopp and Gross and the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) (EoS) …”

Section: Introductionmentioning

confidence: 99%

Friction Theory Model for Thermal Conductivity

2021

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Thermal conductivity is an important transport property in nonequilibrium processes, for example, heat transfer between bodies. Compared to viscosity, there are relatively few experimental studies, and a model of engineering accuracy is needed to calculate the thermal conductivity in the absence of these experiments. This model also needs to apply to mixtures at conditions relevant to industrial processes. Numerous thermal conductivity correlations are available in the literature. However, most of them are for specific single fluids and cannot be extended to the prediction of mixture thermal conductivity. A new approach of a general nature, based on nonequilibrium properties and a common cubic equation of state, is presented in this investigation. This new approach delivers accurate and straightforward wide-range correlations for pure compounds and good predictions of mixture thermal conductivity (based on the studied test cases).

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Section: Introductionmentioning

confidence: 99%

Friction Theory Model for Thermal Conductivity

2021

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“…The accuracy of these CFD models depends on accurate representation of the thermophysical fuel properties, especially viscosity and thermal conductivity, often up to extreme pressure conditions. Various viscosity [1,11,12] and thermal conductivity [2,13,14] models have been proposed for fuels in the literature. These models range from empirical [15][16][17][18][19][20][21][22] to mixture [23] and pseudo-component models [1,2,13,14,[24][25][26][27][28][29][30][31][32][33][34][35], such as expanded fluid theory (EFT) [13,14,[25][26][27][28], friction theory (FT) [29][30][31][32][33], and free volume theory (FVT) [34,35].…”

Section: Introductionmentioning

confidence: 99%

Entropy-scaling based pseudo-component viscosity and thermal conductivity models for hydrocarbon mixtures and fuels containing iso-alkanes and two-ring saturates

Rokni

Moore

Gavaises

2021

Fuel

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“…Xu et al 32 predicted binary-interaction parameters of a cubic equation state for petroleum fluids represented by pseudo-components. Ramos-Pallares et al 33 predicted the thermal conductivity of oils characterized into pseudo-components. Liu et al 8 extended the pseudo-component representation to characterize molecular information of pseudo-components.…”

Section: ■ Introductionmentioning

confidence: 99%

Prediction of the Octane Number: A Bayesian Pseudo-Component Method

Tipler

Fürst

Haute³

et al. 2020

Energy Fuels

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The energy transition leads to the development of unconventional liquid fuels. Unconventional liquid fuels are produced at a small scale so they are produced with a limited budget and they must be characterized at a cheap price. When liquid fuels are burned in piston engines, they are characterized by the Research Octane Number (RON) and the Motor Octane Number (MON). As the measurement of the RON and the MON is expensive, a cheaper alternative, like the pseudo-component method, is sought. Nevertheless, this method was only developed for the RON, it is not applicable for complex fuels with olen and oxygenates, and its uncertainty has not been characterized. Moreover, it does not dierentiate the isomers. For instance, the iso-parans 1 are considered as a blend of 2-methyl-alkane, 3-methyl-alkane, 2,2-dimethyl-alkane and 2,3-dimethyl-alkane in equal proportions. The authors address the limitations of the pseudo-component method using a Bayesian approach. The validity of the method is demonstrated for three gasoline blendstocks mixed with ve oxygenated molecules: 1propanol, 2-propanol, 1-butanol, 2-butanol and 2-methyl-1-propanol. As a result, the octane numbers are predicted within the theoretical uncertainty bounds and with less than 2% of error.

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Prediction of thermal conductivity for characterized oils and their fractions using an expanded fluid based model

Cited by 6 publications

References 27 publications

Friction Theory Model for Thermal Conductivity

Friction Theory Model for Thermal Conductivity

Entropy-scaling based pseudo-component viscosity and thermal conductivity models for hydrocarbon mixtures and fuels containing iso-alkanes and two-ring saturates

Prediction of the Octane Number: A Bayesian Pseudo-Component Method

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