Predicting the viscosity of biodiesel fuels from their fatty acid ester composition

Allen, C.A.W.; Watts, K.C.; Ackman, R. G.; Pegg, Michael J.

doi:10.1016/s0016-2361(99)00059-9

Cited by 300 publications

(205 citation statements)

References 7 publications

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“…Number of data points ARD (%) Chang and Liu 48 Allen et al 34 Ceriani et al 66 From Figure 13, one can see that the three methods exhibit similar accuracies but tend to underestimate the viscosity of biodiesels. Moreover, we observe the underestimated prediction of biodiesel viscosity in Figure 17 of Chang and Liu, 48 as well as a slight underestimation of all of the predicted viscosities in Table 5 of Allen et al 34 This might suggest that a biodiesel sample could have an elevated viscosity when compared with those of its constituents, pure FAMEs, implying that one should not ignore the interaction parameter, G ij , in the Grunberg-Nissan equation (eq 10).…”

Section: Table 23 Ard Of Viscosity Predictions With Data From Differmentioning

confidence: 98%

“…A.5a Method of Allen et al 34 Allen A.5b Method of Ceriani et al 66 In addition to estimating the vapor pressure and heat capacity, Ceriani et al applied the group contribution approach to predict the viscosity of biodiesel fuel .46) where N k is the number of groups k in the molecule i; M is the component molecular weight that multiplies the perturbation term; A 1k , B 1k , C 1k , D 1k , A 2k , B 2k , C 2k , and D 2k are parameters obtained from the regression of the experimental data (see Table A7); k represents the groups of component i; and Q is a correction term expressed as (A.53) where x i is the weight fraction of FAME and CN FAME,i is the cetane number of pure FAME. where n C,i is number of carbons in the fatty acid chain of FAME and n DB,i is the number of double bonds in the fatty acid chain of each FAME component i.…”

Section: A5 Viscosity Of Biodieselmentioning

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: Table 23 Ard Of Viscosity Predictions With Data From Differmentioning

confidence: 98%

Section: A5 Viscosity Of Biodieselmentioning

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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“…The Smittenberg relation was used to estimate the density of saturated methyl esters at 20 °C and 40 °C [16]. An empirical correlation of saturated and unsaturated FAMEs was proposed for estimating viscosity [17]. In this study, fatty acids were extracted from microalgae biomass and directly transesterified to FAMEs to investigate the suitability of microalgae FAMEs as biodiesel.…”

Section: Introductionmentioning

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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“…The surface tension coefficient mentioned in this table was required for the Ishii-Zuber correlation and was taken from Allen (1999).…”

Section: Wall Lubrication Forcementioning

confidence: 99%

Development and validation of a two phase CFD model for tubular biodiesel reactors

Boer

Bahri

2015

Computers & Chemical Engineering

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The use of biodiesel as an alternative to diesel has gained increasing momentum over the past 15 years. To meet this growing demand there is a need to optimise the transesterification reactor at the heart of the biodiesel production system. Assessing the performance of innovative reactors is difficult due to the liquid-liquid reaction mixture that is affected by mass transfer, reaction kinetics and component solubility. This paper presents a Computational Fluid Dynamic model of a tubular reactor developed in ANSYS CFX that can be used to predict the onset of mixing via turbulent flow. In developing the model an analysis of the reaction mixture is provided before the presentation of experimental data, which includes flow visualisation results and temperature dependant viscosity and density data for each phase. The detailed data and model development procedure represents an advancement in the modeling of the two phase transesterification reaction used in biodiesel production..

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Predicting the viscosity of biodiesel fuels from their fatty acid ester composition

Cited by 300 publications

References 7 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

Development and validation of a two phase CFD model for tubular biodiesel reactors

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