Equations for predicting the cetane number of diesel fuels from their physical properties

Ladommatos, Nicos; Goacher, John

doi:10.1016/0016-2361(95)00040-c

Cited by 50 publications

(38 citation statements)

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“…The CN is an important factor in measuring the ignition situation of a fuel . The CN is measured with a special single‐cylinder engine according to the ASTM D613 method . High CN value causes a short ignition delay time that reduces the knocking, emissions, etc.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Ardabili

Najafi

Shamshirband³

2019

Env Prog and Sustain Energy

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The aim of present study is to develop an accessible accurate estimation of CN based on fatty acid methyl esters and to provide a proper solution for presenting a user‐friendly method. In fact, this study calculates the density, viscosity, and HHV based on FAMEs and predicts the CN by employing fuzzy method within a set. This is an interesting approach that has not been used in similar articles. Gaussian membership functions with 81 roles were employed to develop the fuzzy model. The model was developed based on Carbon number, Double bond, Saponification number, and Iodine value to predict the CN. Performance factors of r, RMSE, MAE, and R2 were calculated as 0.9912, 1.0723, 0.63427, and 0.9828, respectively to predict the CN. The results of FAMEs effect on properties of biodiesel showed, increasing Carbon number of FAMEs increases the CN, viscosity, and HHV, but increasing the number of Double bonds decreases CN, viscosity, and HHV. While the effect of increasing Carbon number of FAMEs on density was vice versa. Based on results, C16:00, C18:00, C18:1, and C18:02 FAMEs are approximately in components of all conventional oils; therefore, they can be effective on physical properties of biodiesels. © 2019 American Institute of Chemical Engineers Environ Prog, 38: 584–599, 2019

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Section: Introductionmentioning

confidence: 99%

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Ardabili

Najafi

Shamshirband³

2019

Env Prog and Sustain Energy

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“…Although the ASTM D613 method is the widely accepted test method for CN, it exhibits several inherent disadvantages, including a considerable amount of fuel sample requirement (*1 L), a time-consuming process, a relatively high reproducibility error, and a relatively high cost [4]. Therefore, there have been many attempts to develop theoretical models to predict the CN quickly and reliably from bulk properties of diesel such as density, viscosity, aniline point, distillation temperatures, chemical composition, saponification number, iodine value, and so on [5][6][7][8]. Ladommatos et al [7] tested the accuracy of 22 equations for predicting the CN of diesel from the above mentioned properties, by comparing the predicted values of 563 fuels with the measured values.…”

Section: Introductionmentioning

confidence: 99%

Cetane Number Prediction of Biodiesel from the Composition of the Fatty Acid Methyl Esters

Tong

Jiang

et al. 2010

J Americ Oil Chem Soc

108

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In the present work, the measured cetane numbers (CN) of pure fatty acid methyl esters (FAME), as well as the FAME compositions and the reported CN of 59 kinds of biodiesels collected from literature were used to develop a simple model involving as more FAME component as possible for predicting CN of biodiesel from its FAME composition. Two different regression equations correlating the CN of pure FAME with the carbon number of fatty acid chain were obtained by regression analysis, which shows that the dependence of the CN on the carbon number varies with the unsaturated degree of fatty acid chain. The 59 biodiesels were divided into two categories and used, respectively to develop and test a multiple linear regression model (MLRM) correlating the CN of biodiesel with its FAME composition. A simple and convenient regression equation with a high accuracy and a good reproducibility (average absolute error of 0.49 CN for testing set and 1.52 CN for all data) were developed, showing excellent correlation (R 2 : 0.9904 for testing set). The model developed in the present work can be used conveniently to give a satisfactory predicted CN of biodiesel from the FAME composition.

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“…The CNs for isostructural aliphatic hydrocarbons increase with increasing carbon number [13], as one would expect from the change in octane number. Many similar correlations can be found in the literature, employing the physical and chemical characteristics of the diesel fuel [13][14][15][16][17]. Crude oil refiners often make use of the cetane index as an alternative to the CN.…”

Section: Minimum Densitymentioning

confidence: 62%

Diesel Fuel

Klerk

2011

Fischer‐Tropsch Refining

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Diesel fuel refers to the fuel that is used in compression-ignition engines (diesel engines). It is generally material boiling in the C 11 -C 22 hydrocarbon range. The requirements for diesel fuel are often diametrically opposed to that of motor-gasoline (Chapter 13). In motor-gasoline, it is important to suppress autoignition of the fuel to allow spark ignition to be correctly timed for good engine performance. In diesel fuel, it is important that the fuel autoignites and that the delay between fuel injection and the start of combustion is short.Compression-ignition engines operate at high compression ratios, typically around 15-17 : 1. This improves the thermodynamic efficiency of the engine, and on average compression-ignition engines are more efficient than spark-ignition engines. The engine itself is heavier, and traditionally it has been applied mainly for heavier vehicles. This situation has changed and, despite the higher cost of compression-ignition engines as compared to spark-ignition engines, there has been a marked shift in preference for diesel-powered passenger vehicles in Europe. This change is partly due to an increase in environmental awareness. Better engine efficiency leads to lower fuel consumption and can be translated into less CO 2 emitted per distance traveled. The move to vehicles with lower rated emissions (in grams of CO 2 per kilometer) is supported by the policies of the European Union.This shift in transportation fuel preference for the passenger vehicle market had a marked impact on refineries. The hydrogen in a typical conventional crude oil refinery is obtained from catalytic naphtha reforming. As the demand for motor-gasoline decreases, the need for reformate also decreases, which in turn reduces hydrogen production. Yet, refining crude oil to diesel fuel requires more hydrogen than refining crude oil to motor-gasoline. In each refinery there is a minimum motor-gasoline to diesel fuel ratio beyond which refining becomes inefficient and costly. The shift toward diesel fuel has created an imbalance in refinery production and in practice this imbalance in Europe is corrected by exporting motor-gasoline and importing diesel fuel. In the long run, it will be interesting to see how this might affect diesel fuel specifications, which over time have made diesel fuel production more difficult.Diesel fuel, like motor-gasoline, is a major consumer product and exerts a tremendous influence on the economy and the environment. Diesel fuel specifications are consequently subject to technical, environmental, and political pressures (Section 13.1).Fischer-Tropsch Refining, First Edition. Arno de Klerk.

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Equations for predicting the cetane number of diesel fuels from their physical properties

Cited by 50 publications

References 18 publications

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Cetane Number Prediction of Biodiesel from the Composition of the Fatty Acid Methyl Esters

Diesel Fuel

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