Predictive Model To Assess the Molecular Structure of Biodiesel Fuel

Chuck, Christopher J.; Bannister, Christopher D; Hawley, J G; Davidson, Matthew G.; Bruna, Ivan La; Paine, A

doi:10.1021/ef801085s

Cited by 30 publications

(21 citation statements)

References 23 publications

(29 reference statements)

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“…Among 97 data points on biodiesel viscosity from the literature, 10,18-34 32 data points from two different references 20,21 appear to be either too high or too low (Table 23 and Figure 12). …”

Section: 1b Comparison Of Biodiesel Viscosity Predictionsmentioning

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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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: 1b Comparison Of Biodiesel Viscosity Predictionsmentioning

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 addition, equations to relate properties to each other have been developed. Chuck et al [45] propose using the refractive index, viscosity and density to predict the fatty acid profile. Demirbas [46] fits linear equations to relate higher heating value, flash point and density to each other.…”

Section: The Influence Of Feedstock On Biodiesel Propertiesmentioning

confidence: 99%

Alternative crops for biodiesel feedstock.

Razón¹

2009

CABI Reviews

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Biodiesel, the name popularly given to fatty acid alkyl esters, has become an attractive option for the replacement of petroleum diesel ('petrodiesel'). While its overall impact on the greenhouse effect continues to be debated, other qualities of biodiesel are unquestioned. Biodiesel is biodegradable, generally non-toxic and has superior lubricity to petrodiesel. Overall emissions are greatly reduced. The bulk of the feedstock for its production comes from renewable resources and when biodiesel is used as a blend (up to 20%); no changes are required for existing diesel engines. However, it is still too costly and about 60-80% of the cost for biodiesel comes from the feedstock. Concerns have been raised about the propriety of using food for fuel. Proposed alternative feedstock such as Jatropha curcas address some of these concerns but have their own disadvantages. This paper is a comprehensive review of recent progress on alternative crops for feedstock and addresses the issues that may ultimately lead to their success or failure: engine performance, yield, fatty acid profile, unique minor components, unique fatty acids, toxicity and harvestability. The use of agricultural wastes, used or waste oil and microbial fats is also discussed. The conclusion is reached that a blend of oils and used oils may be the best approach for the immediate future while genetically modified plants and microalgae may provide a longer term solution.

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“…Concerning the refractive index also at 40 • C , the highest value corresponds to grape biodiesel (1.4500), whereas the lowest value corresponds to olive biodiesel (1.4443), showing the very small difference in absolute values of this parameter for all these samples. According to [105], biodiesel refractive index increases as the unsaturation and the number of carbon atoms increase. Again, this would explain the difference in the refractive index experimental results.…”

Section: Properties Of Biodieselmentioning

confidence: 99%

Flash point, kinematic viscosity and refractive index: variations and correlations of biodiesel–diesel blends

Alviso

Saab

Clevenot

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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This paper presents characterization studies of flash point, kinematic viscosity and refractive index for several biodiesel/ diesel blends. Biodiesel is produced from soybean, corn, olive, canola, almond, grape and peanut oils. Regression equations are presented to estimate the kinematic viscosity, refractive index and flash point of the studied biodiesel and their blends with diesel fossil fuel as a function of the blend composition. Moreover, as viscosity and refractive index measurements are simple, fast and require very small volume of samples, a correlation study for the blends is conducted to estimate the flash point from these two properties. It is shown that when kinematic viscosity and refractive index are both used for the estimation of the blends flash point, the R 2 values are higher than those found using simple correlations.

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Predictive Model To Assess the Molecular Structure of Biodiesel Fuel

Cited by 30 publications

References 23 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

Alternative crops for biodiesel feedstock.

Flash point, kinematic viscosity and refractive index: variations and correlations of biodiesel–diesel blends

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