Estimation of cetane index for esters of fatty acids

Klopfenstein, W. E.

doi:10.1007/bf02636316

Cited by 32 publications

(27 citation statements)

References 2 publications

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“…Method of Klopfenstein. 67 We find that all six biodiesel properties discussed in this study correlate well with the number of carbon atoms and the number of double bonds (or the composition of unsaturated FAMEs) and further improved the methods previously proposed by our group 48 for predictions of viscosity and cetane number. The two-parameter method in this study is easy to use, and it generates reliable predictions for biodiesel properties.…”

Section: Recommended Methods For Predicting Biodiesel Product Propertiessupporting

confidence: 79%

“…We also apply this approach to predict cetane numbers of biodiesels (eq A.57) and obtained results comparable to those of Klopfenstein 67 ( Figure 17). In section 6.2, Klopfenstein's method, 67 based on the number of carbon atoms, the number of double bonds, and a regressed constant, is our recommended prediction method for cetane number.…”

Section: Recommended Methods For Predicting Biodiesel Product Propertiesmentioning

confidence: 99%

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Selection of Prediction Methods for Thermophysical Properties for Process Modeling and Product Design of Biodiesel Manufacturing

Liu

Tovar

et al. 2011

Ind. Eng. Chem. Res.

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To optimize biodiesel manufacturing, many reported studies have built simulation models to quantify the relationship between operating conditions and process performance. For mass and energy balance simulations, it is essential to know the four fundamental thermophysical properties of the feed oil: liquid density (ρ L ), vapor pressure (P vap ), liquid heat capacity (C p L ), and heat of vaporization (ΔH vap ). Additionally, to characterize the fuel qualities, it is critical to develop quantitative correlations to predict three biodiesel properties, namely, viscosity, cetane number, and flash point. Also, to ensure the operability of biodiesel in cold weather, one needs to quantitatively predict three low-temperature flow properties: cloud point (CP), pour point (PP), and cold filter plugging point (CFPP). This article presents the results from a comprehensive evaluation of the methods for predicting these four essential feed oil properties and six key biodiesel fuel properties. We compare the predictions to reported experimental data and recommend the appropriate prediction methods for each property based on accuracy, consistency, and generality. Of particular significance are (1) our presentation of simple and accurate methods for predicting the six key fuel properties based on the number of carbon atoms and the number of double bonds or the composition of total unsaturated fatty acid methyl esters (FAMEs) and (2) our posting of the Excel spreadsheets for implementing all of the evaluated accurate prediction methods on our group website (www.design.che.vt.edu) for the reader to download without charge.ii Acknowledgement

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Section: Recommended Methods For Predicting Biodiesel Product Propertiessupporting

confidence: 79%

Section: Recommended Methods For Predicting Biodiesel Product Propertiesmentioning

confidence: 99%

Selection of Prediction Methods for Thermophysical Properties for Process Modeling and Product Design of Biodiesel Manufacturing

Liu

Tovar

et al. 2011

Ind. Eng. Chem. Res.

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“…In this study, several important biodiesel properties (CN, IV, CFPP, υ, ρ and HHV) were calculated from the FAME composition. Fuel properties were calculated directly from FAME profiles [14,15,25,26]. In addition, CN was estimated using FAME profiles directly and using FAME-derived fuel properties SV and IV) [15], to investigate whether the two different approaches would yield different predictions of cetane values.…”

Section: Calculation Of Fuel Properties From Fatty Acid Profilesmentioning

confidence: 99%

Microalgal Species Selection for Biodiesel Production Based on Fuel Properties Derived from Fatty Acid Profiles

et al. 2013

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Physical and chemical properties of biodiesel are influenced by structural features of the fatty acids, such as chain length, degree of unsaturation and branching of the carbon chain. This study investigated if microalgal fatty acid profiles are suitable for biodiesel characterization and species selection through Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA) analysis. Fatty acid methyl ester (FAME) profiles were used to calculate the likely key chemical and physical properties of the biodiesel [cetane number (CN), iodine value (IV), cold filter plugging point, density, kinematic viscosity, higher heating value] of nine microalgal species (this study) and twelve species from the literature, selected for their suitability for cultivation in subtropical climates. An equal-parameter weighted (PROMETHEE-GAIA) ranked Nannochloropsis oculata, Extubocellulus sp. and Biddulphia sp. highest; the only species meeting the EN14214 and ASTM D6751-02 biodiesel standards, except for the double bond limit in the EN14214. Chlorella vulgaris OPEN ACCESSEnergies 2013, 6 5677 outranked N. oculata when the twelve microalgae were included. Culture growth phase (stationary) and, to a lesser extent, nutrient provision affected CN and IV values of N. oculata due to lower eicosapentaenoic acid (EPA) contents. Application of a polyunsaturated fatty acid (PUFA) weighting to saturation led to a lower ranking of species exceeding the double bond EN14214 thresholds. In summary, CN, IV, C18:3 and double bond limits were the strongest drivers in equal biodiesel parameter-weighted PROMETHEE analysis.

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“…Equation (11) developed by Klopfenstein [20] was compared with the present equation (16). To use equation (11) Table 13 for use in the predictive equations.…”

Section: Analysis Of Regression Coefficientsmentioning

confidence: 99%

Relating the cetane number of biodiesel fuels to their fatty acid composition: A critical study

Gopinath

Puhan

Nagarajan

2009

Proceedings of the Institution of Mechanical Engineers, Part D:

133

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The cetane number is one of the most significant properties to specify the ignition quality of a fuel for use in a diesel engine. The cetane number of biodiesel fuels is considerably influenced by their fatty acid methyl ester composition. The determination of cetane number of biodiesel is an expensive and time-consuming process. The objective of the present work is to predict the cetane numbers of different biodiesels using their fatty acid ester composition. Fifteen different biodiesel (including three blends) fuels were prepared and their cetane numbers were experimentally measured. An extensive literature review was made and the measured cetane numbers were compared with the reported values. To predict the cetane numbers of biodiesel fuels from their fatty acid methyl ester composition, a multiple linear regression model was developed. The cetane numbers and fatty acid compositions of 57 biodiesel fuels and 7 pure fatty acid methyl esters from the available literature were given as inputs. The experimentally measured cetane numbers and fatty acid compositions of four biodiesel fuels were also given as inputs to develop the regression model. The regression model has yielded an R 2 value of 0.953 and a standard deviation of 2.271. The predicted cetane numbers are comparable with the experimentally measured cetane numbers. The maximum prediction error from the present model was found to be 8 per cent. Similarly, the present model was compared with the available litrature models. The maximum prediction error from the literature models was found to be 15 per cent. The present model also shows a good agreement with the measured cetane numbers.

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Estimation of cetane index for esters of fatty acids

Cited by 32 publications

References 2 publications

Selection of Prediction Methods for Thermophysical Properties for Process Modeling and Product Design of Biodiesel Manufacturing

Selection of Prediction Methods for Thermophysical Properties for Process Modeling and Product Design of Biodiesel Manufacturing

Microalgal Species Selection for Biodiesel Production Based on Fuel Properties Derived from Fatty Acid Profiles

Relating the cetane number of biodiesel fuels to their fatty acid composition: A critical study

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