Accurate predicting the viscosity of biodiesels and blends using soft computing models

Aminian, Ali; ZareNezhad, Bahman

doi:10.1016/j.renene.2017.12.038

Cited by 35 publications

(19 citation statements)

References 35 publications

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“…Table 9 presents the statistics for viscosity predictions of the diesel fuels. The composition of the diesel fuels in this study contain significant concentrations of iso-alkanes (i.e., 26 Table 9. The MAPD (%), bias (%), SD (%), and Max Deviation (%) for pseudo-component viscosity predictions of diesel fuels compared to experimental data [13].…”

Section: Diesel Fuelsmentioning

confidence: 99%

“…Recently, several researchers demonstrated advantages when incorporating advanced equations of state (EoS), such as the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) within CFD [22][23][24]. Given that complex mixtures (e.g., diesel and biodiesel fuels, crude oils, bitumens, heavy oils) are often composed of hundreds of different compounds [13,[25][26][27][28][29], CFD simulation of every fuel compound would be computationally intensive. Often, a smaller number of components are chosen as a surrogate to closely match the thermophysical properties of the mixture [13,[30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

Rokni

Moore

Gupta

et al. 2019

Fuel

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An entropy scaling based technique using the Perturbed-Chain Statistical Associating Fluid Theory is described for predicting the viscosity of hydrocarbon mixtures and diesel fuels up to high temperatures and high pressures. The compounds found in diesel fuels or hydrocarbon mixtures are represented as a single pseudo-component. The model is not fit to viscosity data but is predictive up to high temperatures and pressures with input of only two calculated or measured mixture properties: the number averaged molecular weight and hydrogen to carbon ratio. Viscosity is predicted less accurately when the mixture contains high concentrations of iso-alkanes and cyclohexanes. However, it is shown that predictions for these mixtures are improved by fitting a 2 third parameter to a single viscosity data point at a chosen reference state. For hydrocarbon mixtures, viscosity is predicted with average mean absolute percent deviations (MAPDs) of 12.2% using the two-parameter model and 7.3% using the three-parameter model from 293 to 353 K and up to 1,000 bar. For two different diesel fuels, viscosity is predicted with an average MAPD of 21.4% using the two-parameter model and 9.4% using the three-parameter model from 323 to 423 K and up to 3,500 bar.

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Section: Diesel Fuelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

Rokni

Moore

Gupta

et al. 2019

Fuel

View full text Add to dashboard Cite

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“…Se a viscosidade é baixa, as gotículas formadas podem ser tão pequenas que há deficiência de lubrificação causando desgaste no motor e vazamentos. 22 Assim como a viscosidade, a tensão superficial também é uma propriedade importante para a atomização de um combustível. Altos valores de tensão superficial dificultam a formação de gotas.…”

Section: Figura 1 Esquema Da Reação Geral De Transesterificaçãounclassified

Prediction the Physical Properties of Pure Esters for Modeling Biodiesel Combustion

Arvelos¹,

Romanielo²

2018

Rev. Virtual Quim.

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Adequate knowledge of biodiesel's physical properties is necessary to simulate its dispersion, atomization and combustion when it is being used in engines. The objective of this work was to evaluate predictive models of physical properties of seven main methyl and ethyl esters that compose biodiesel to model its combustion. The properties evaluated in this study were: normal boiling temperature, critical temperature, critical pressure, critical volume, acentric factor, saturation pressure, liquid density, enthalpy of vaporization and combustion, liquid viscosity, and surface tension. Satisfactory adjustments were obtained for each property evaluated by comparing estimated values to experimental data available in the literature.

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“…Then, they used the Grunberg‐Nissan mixing rule equation (Grunberg and Nissan, ) to predict the viscosity of different biodiesels with good accuracy. Aminian and ZareNezhad () used some branches of artificial intelligence to correlate the viscosity of biodiesel and the blends of biodiesel and diesel fuel as a function of temperature, the average numbers of carbon atoms, and double bonds in the mixture.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Correlation for Dynamic Viscosity of Fatty Acid Methyl Esters and Biodiesel

Roosta

Bardool

2019

J Americ Oil Chem Soc

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The viscosity of biodiesel, which is a mixture of fatty acid methyl esters (FAME), is an important physical property in the injection and efficient combustion. In this study, a simple correlation, with only one adjustable parameter, is proposed for predicting the viscosity of FAME and their mixtures (biodiesel) as a function of temperature. First, the adjustable parameter of the correlation is calculated for various FAME. The average absolute relative deviation (AARD) is obtained to be 0.97% for 226 data points. Second, the adjustable parameter of FAME is connected to the number of carbon atoms and the number of double bonds to build a predictive correlation for the calculation of viscosity. The AARD for 226 data points is obtained to be 2.28%. Third, the proposed model is employed to predict the viscosity of biodiesel without introducing any new adjustable parameter. To predict the viscosity of biodiesel, the average of the adjustable parameter is applied to the correlation. The AARD of 2.96% is obtained for 185 data points comprised of 23 different biodiesels. To better understand the ability of the correlation in the estimation of the viscosity of biodiesel and FAME, a comparison is made between the present correlation and a number of correlations available in the literature.

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Accurate predicting the viscosity of biodiesels and blends using soft computing models

Cited by 35 publications

References 35 publications

Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

Prediction the Physical Properties of Pure Esters for Modeling Biodiesel Combustion

A Predictive Correlation for Dynamic Viscosity of Fatty Acid Methyl Esters and Biodiesel

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