ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT
AZOJETE, September 2019. Vol. 15(3):519-534

Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria.
Print ISSN: 1596-2490, Electronic ISSN: 2545-5818

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519

ORIGINAL RESEARCH ARTICLE

PREDICTION OF DENSITY AND VISCOSITY OF BIODIESEL FUEL FROM FATTY ACID
METHYL ESTER (FAME) COMPOSITION

S.O. Jekayinfa1, A.O. Adebayo1*, M. A. Sulaiman2 and V.O. Ayoola1

(1Department of Agricultural Engineering, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
2Department of Agricultural and Mechanical Engineering, Olabisi Onabanjo University, Ago Iwoye, Ogun State, Nigeria)

* Corresponding author’s email address: aoadebayo57@lautech.edu.ng

ARTICLE INFORMATION

Submitted 29 March, 2018
Revised 3 February, 2019
Accepted 16 February, 2019

Keywords:
Density
Viscosity
Biodiesel
FAME composition
regression analysis
prediction

ABSTRACT
Viscosity and density are important properties that qualify biodiesel
fuel to serve as an alternative fuel because all other properties directly
or indirectly depend on them. The processes involved in determining
the viscosity and density of biodiesel are relatively simple and costly
and there are discrepancies in the results obtained due to the
differences in the oil composition of the feed-stocks used as against
the reported cause of experimental errors. This work aimed at
theoretically determining and developing relationship between the
density and viscosity of different feed-stocks and the fatty acid methyl
ester composition at different temperatures. In the study, density and
viscosity data of biodiesel fuels and the composition of FAME were
collected from literatures. A linear regression analysis was carried out
for density and viscosity on the average values of viscosity and density
data obtained at different temperatures range (10˚C-40˚C) of FAME
composition. Equations relating density and viscosity with the
percentage composition by weight of FAME of biodiesel fuel were
developed. Predicted mean values for kinematic viscosity and density
were respectively between 4.31 - 5.64 mm2/s and 861.67 – 885.66
kg/m3. The developed equations were able to predict with up to 97.1%
accuracy for viscosity and 98.5% accuracy for density. The developed
equations could effectively predict the quality of biodiesel (viscosity
and density) from various feedstocks based on FAME compositions.

© 2019 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved.

1.0 Introduction
Developing countries are often faced with added impasses about environmental protection
owing to their heavy reliance on biomass and fossil fuel. Biomass is gaining more ground
and becoming more important because of rampant fuel scarcity, emissions of dangerous
pollutants and increase in the prices of fossil fuels (Sensoz et al., 2000). Global warming, the
destruction of the ozone layer and acid rain are as a result of economic growth and heavy
consumption of natural resources (Thakur, 2006). As a result of the soaring prices of oil
coupled with its environmental impact worldwide, there is an increasing use of “green” fuels
globally. Most countries these days are taking advantage of crops and even their residues for

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Jekayinfa, et al: Prediction of density and viscosity of biodiesel fuel from fatty acid methyl ester (fame)
composition. AZOJETE, 15(3):519-534. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: aoadebayo57@lautech.edu.ng 520

biofuels production. In the US and Brazil, for example, maize and sugarcane serve as the
main feedstocks for the production of ethanol, while kernel seed and rapeseed are
respectively used for biodiesel production in Malaysia and Europe (Srinivasa et al., 2010).
Biodiesel has also been produced from Nigerian palm kernel, milk bush and sandbox kernel
oil (Alamu, et al., 2007a, 2007b, 2007c, 2008, 2009, Jekayinfa, 2018, Oniya et al., 2016 and
Ogunkunle et al., 2017).

Rudolf Diesel researched into the use of vegetable oils as fuel for diesel Engines (Lang et al.,
2001). It was also established that biodiesel can be obtained from feedstocks like animal fats,
and oils from other organisms such as cyanobacteria, micro algae and is produced from
triglyceride transesterification which serves as the major constituent of vegetable oils or
animal fats in the presence of an alcohol (Jazie etal., 2013). Apart from the fact that biodiesel
has lower carbon monoxide emissions and low viscosity, it is also non-toxic, simple to use
and essentially free of Sulphur (Alcantara et al., 2000). Biodiesel, though very advantageous,
has got some limitations as well especially when using it (Nolte, 2007).
Biodiesel produces relatively high NO2 levels at combustion level due to the high oxygen
content in it. This can however be minimized below that of the fossil fuel levels simply by
adjusting the timing of the engine. A catalytic converter can as well be used (Manohar et al.,
2010). Researchers have attempted to determine the best biodiesel composition that would
result into enhancement of combustion process as well as the quantity of various fatty acid
that can be employed in predicting both the physical and performance properties of
biodiesel (Bamgboye and Hassen, 2008). Viscosity as one of the fuel properties is used as an
indicator to ascertain the following; the ease of starting the engine, effectiveness and
completeness of the ester conversion process, the quality of spray, the penetration of the
injected jet and the quality of the fuel-air mixture combustion is greatly affected by viscosity
but reduces considerably with increase in unsaturation (Alptekin and Canakci, 2008).

Density is another property which is greatly influenced by temperature. It also has the
capacity to affect fuel performance. Fuel properties such as heating value, cetane number,
and viscosity are connected to density (Barabas and Todorut, 2011). The quality of
atomization and combustion is affected by density as well (Barabas and Todorut, 2011).
Molecular properties had influence on the density and viscosity (kinematic) of biodiesel. As
chain length increases, density decreases slightly but larger alkyl esters are all solids at room
temperature. It can be affirmed that the level of un-saturation plays a significant role in the
determination of density of a given Fatty Acid Methyl Esters sample (Alptekin, and Canakci,
2008).

Also, temperature is one of the key factors that have influence on the density of the liquid as
large change in the density at higher temperatures affect the atomization and combustion
processes of the engine(Knotheet al., (2005),Tate et al., (2006)and Knothe and Steidley, 2005)
carried out investigations on the varying effects of temperature on density of methyl soyate,
rapeseed methyl ester and ethyl ester of fish oil and found out that with an increase in
temperature, the density decreases linearly and at 300oC the density was as low as 0.5 gcm-3.
Geller and Goodrum (2004) opined that the longer the fatty acid chains, the more saturated
the molecules and this would automatically result into higher viscosity. However, viscosity is
lowered drastically when the saturation is less. There is a vast variation in the values of

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Arid Zone Journal of Engineering, Technology and Environment, September, 2019; Vol. 15(3):519-534. ISSN 1596-
2490; e-ISSN 2545-5818; www.azojete.com.ng

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521

viscosity and density (Tesfa et al., 2010). Tables 1 and 2 present the value of viscosity and
density of the selected feedstocks respectively as obtained from past studies.
Density and viscosity of biodiesel obtained from soybean oil ranged from 882.80 to 900
kgm-3 and 3.5-9.31 cSt respectively (Mariceli et al., 2012, Omotola, 2011). For palm-oil
derived biodiesel the density and viscosity ranged from 857.80 to 900 kgm-3 and 3.70-4.61
cSt respectively (Leung et al.,2010). Also, density and viscosity values obtained for waste
cooking oil methyl ester ranged from 879.55 to 889.90 kgm-3 and 5.21 to 6.00 cSt
respectively (Tesfa et al., 2010, Leung et al., 2010). The density and viscosity values vary from
one feed input to another. This is dependent on the chemical composition of the fuel. It also
has effect on the NOx emissions. It has also been observed that there is always a less
significant variation in density and viscosity at different temperatures for feedstock of the
same type of oil (McCormick et al., 2001).

Density and viscosity fall among properties that can be easily measured though a bit costly.
Ballantine et al., (1997) and Josse et al., (2001), noted that these properties can be measured
using a Thickness Shear Mode (TSM) resonator or a Guided Shear Horizontal Surface
Acoustic Wave (SH-SAW) device. The shortcoming that the TSM systems have are their
sensitivity and selectivity to only Newtonian liquids, SH-SAW devices are much more
sensitive and selective than their counterparts such as Thickness Shear Mode (TSM), shear
Horizontal Acoustic Plate Mode (SH-APM) and Flexural Plate Wave (FPW). Another method
of measuring the viscosity is to use a miniaturized thermal conductivity sensor made from
molybdenum or another metal and literature reveals that it measures liquids displaying non-
Newtonian properties. (Kuntner et al., 2005). In order to theoretically determine the density
and the kinematics viscosity of FAME and to establish the findings of researchers that have
worked on this subject whether their results conform to the limits set out by the
standardization organization, there is a need to develop models that can be used to predict
the density and kinematic viscosity of some known parameters. Since the composition of
FAME has been established to have great influence on its viscosity and density at varying
temperature, models can be developed relating viscosity and density to FAME composition.

Yuan model (Yuan et al., 2009) with parameters specific for FAMEs was used in the work of
Mesquita et al., (2011). Also, Yuan model as revised by Pratas (Pratas et al., 2010) and do
Carmo et al., (2012) were used to predict biodiesel viscosity. In correlating pure FAME and
biodiesel densities, the Tait Equation of State (EoS) was used (Dymond and Malhotra,1988,
Pratas et al., 2011, Schedemann et al., 2013 and Aparicio et al., 2007). Pratas et al., (2011)
applied the CPA EoS to correlate pure FAME density, and the calculated pure component
parameters were applied to predict the density of methyl biodiesels with deviations ranging
from 0.79% to 2.5%. Schedemann et al., (2013) in his own turn made use of the VTPR
method to predict density data of methyl linoleate and biodiesel as submitted in the findings
of Prieto et al., (2015). In developing the present model, the composition of fatty acid in
percentage of ester in pure form together with the mixtures of esters (biodiesel) and their
experimental density and viscosity were considered (Bamgboye and Hassen 2008). This study
aimed at developing regression equations which can be used to predict the density and
viscosity of biodiesel based on the FAME chain compositions and authenticating the
applicability of the established equations for predicting the density and viscosity based on
the FAME composition of the feedstock oils used for biodiesel production.

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Jekayinfa, et al: Prediction of density and viscosity of biodiesel fuel from fatty acid methyl ester (fame) composition. AZOJETE, 15(3):519-534. ISSN 1596-2490; e-ISSN 2545-5818,
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Table 1: Reported values of Viscosity of different biodiesel feedstock (mm2/s) at varying temperature (10-40°C)

*SUNME: Sunflower methyl Esters; COME: Corn Oil Methyl Ester; SME: Soybean Methyl Ester; CME: Canola Seed Methyl Ester; POME: Palm Oil
Methyl Esters; RME: Rapeseed Methyl Ester; CSOME: Cotton Seed Oil Methyl Ester; WCME: Waste Cooking Oil Methyl Ester; OSME: Olive Seed
Oil Methyl Ester
**The number in the parenthesis represent the different authors from which the viscosities were sourced from literatures for this study: 1.
Barabás & Todoruţ, (2010); 2. Chuepeng & Komintarachat, (2010); 3. Zahan and Kano (2018).; 4. Singh and Singh (2010), 5. Leung et al., (2010), 6.
Auwal et al., (2011), 7. Tesfa et al., (2010), 8. Maricelida et al., (2012). 9. Omotola (2011).

TEMP.°C SUNME* COME SME CME POME RME CSOME WCME OSME
10.00 9.92 [8]** 9.54 [8] 9.31[8] - - - - - -
15.00 - - - - - - - - -
20.00 7.44[8] - - 7.69[8] - - - - -
28.00 - - - - - - - - -
30.00 - 5.58[8] - - - - - -
40.00 4.03 [1]

4.63[11]
4.57[8]
4.9[5]
4.51[9]
4.38-4.90[3]

4.18-4.52[1]
4.89 [7]
3.39 [5]
4.32[8]
3.39-4.36[3]

4.44[8]
4.10[6]
-
4.08 [5,9]
4.08-4.42[3]

4.34-
4.84[1]
4.68[8]

3.70 [1]
4.42 [5]
4.42-4.76[3]
5.7[4]

4.2[4]
4.83-5.65[1]
4.48[2]
5.48[7]
4.3-5.83[5]
-
4.59-5.83[3]

-
4.07 [5]
-
4.0-4.96[3]

-
5.78-6.00[1]
5.81[5,9]
5.21[7]

4.70 [1]
-

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Table 2: Reported values of Density of different biodiesel feedstock (kg/m3) at varying temperature (10-40ºC)
TEMP.°C SUNME COME SME CME POME RME CSOME WCME OSME
10.00 887.60[8] 885.86[8] 886.33[8] 886.43[4] - - - - -
15.00 882.70[9]

880[10]
882.90[9,10]

880-890[10]
882.20[8]
884[1]

885[9,10]
882.80[8]

888[5] 860-
900[10],
867[5]
864.4-
870[1]

883.7 [2]
880.2[1]
880-888[10]

875[10]
884.1[3]

874.0[9]
897[6]

881.50[1]
-

15.60 - - - - - - - - -
20.00 883.41[8] - - - - - - -

25.00 - - - - - - - - -
26.00 - - - - - - - - -
30.00 - - - - - - - - -
32.00 - - - - - - - - -
34.00 - - - - - - - - -
36.00 - - - - - - - - -
38.00 - - - - - - - - -
40.00 874.96[8] 879.65[7] - 874.51[8] - 879.35[7] 864.4[3] 879.55[7]

920[1]
-

*SUNME: Sunflower methyl Esters; COME: Corn Oil Methyl Ester; SME: Soybean Methyl Ester; CME: Canola Seed Methyl Ester; POME: Palm Oil
Methyl Esters; RME: Rapeseed Methyl Ester; CSOME: Cotton Seed Oil Methyl Ester; WCME: Waste Cooking Oil Methyl Ester; OSME: Olive Seed Oil
Methyl Ester
**The number in the parenthesis represent the different authors from which the densities were sourced from literatures for this study:

1. Barabás & Todoruţ, (2010); 2. Chuepeng&Komintarachat, (2010); 3. Prieto et al, 2015; 4. Fan et al., (2009), 5. Nolte (2007) 6. Zahan and
Kano (2018)., 7. Tesfaet al., (2010), 8.Maricelida et al., (2012). 9. Omotola (2011), 10. Leung et al., (2010)

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Jekayinfa, et al: Prediction of density and viscosity of biodiesel fuel from fatty acid methyl ester (fame)
composition. AZOJETE, 15(3):519-534. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: aoadebayo57@lautech.edu.ng 524

2. Materials and Methods
2.1 Fuel Data Selection
The viscosity and density of the biodiesel (FAME Esters) were gotten from different reports
submitted to fuel standards by various testing laboratories (European standard EN 14214,
ASTM D 6751) as shown in Tables 3 – 6. Average values of the data collected for the
percentage composition of the fatty acids in the diverse feedstocks of pure methyl esters
were utilized in this study.

2.2 Regression Analysis
A seven by eight matrix was formed with density and Viscosity as the dependent
variable respectively and pure FAME composition as the independent variables as shown in
Tables 3 and 4.
The linear regression equation conceptualized can be presented as follows;

= ……… 1
and,

………..2
where,

, and B, is the viscosity in
, a, b, c, d, e, f, g, h are constants to be determined in the regression analysis;
…… are % compositions of FAME.

Table 3: Reported average values of Density for pure methyl esters of different fatty
acids (kg/m3) at temperature 40°C

Fatty Acid
Methyl Esters

Density ρ kg/m3 References

Laurate 853.90a, (Barabás and Todoruţ, 2011)a,
Myristate 852.20a (Barabás and Todoruţ, 2011)a
Palmitate 850.80a, 852b (Barabás and Todoruţ, 2011)a, (Lapuertaet al., 2010)b
Palmitoleate 853.80a, 875b (Barabás and Todoruţ, 2011)a, (Lapuertaet al., 2010)b
Stearate 849.80a, 850b (Barabás and Todoruţ, 2011)a, (Lapuertaet al., 2010)b
Oleate 859.50a, 874b (Barabás and Todoruţ, 2011)a, (Lapuertaet al., 2010)b
Linoleate 871.50a, 889b (Barabás and Todoruţ, 2011)a, (Lapuertaet al., 2010)b
Linolenate 887.00a, 895b (Barabás and Todoruţ, 2011)a, (Lapuertaet al., 2010)b

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Table 4: Reported average values of Density for pure methyl esters of different fatty
acids (kg/m3) at temperature (15˚C)a (15.5ºC)b

Fatty Acid Methyl Esters Density ρ kg/m3 References
Laurate 873.70a (Barabás and Todoruţ, 2011)a
Myristate Not determineda, (Barabás and Todoruţ, 2011)a
Palmitate Not determineda, 867b (Barabás and Todoruţ, 2011)a

(Janarthananet al.,1996)b
Palmitoleate 872.80a (Barabás and Todoruţ, 2011)a
Stearate Not determineda, (Barabás and Todoruţ, 2011)a,
Oleate 877.70a, (Barabás and Todoruţ, 2011)a,
Linoleate 889.90a, (Barabás and Todoruţ, 2011)a,
Linolenate 905.70a (Barabás and Todoruţ, 2011)a

Table 5: Reported average values of Kinematic Viscosity for pure methyl esters of different
fatty acids (mm2/s) at temperature 40˚C
Fatty Acid Methyl
Esters

Viscosity (mm2/s) References

Laurate 2.4331a, 2.43c (Barabás and Todoruţ, 2011)a, (Knothe,2005)c
Myristate 3.3381a, 3.23c, (Barabás and Todoruţ, 2011)a, (Knothe,2005)c
Palmitate 4.4136a, 4.38c, (Barabás and Todoruţ, 2011)a, (Knothe,2005)c,
Palmitoleate 3.0642a, 3.67d (Barabás and Todoruţ, 2011)a, (Moser, 2009)d
Stearate 5.8675a , 5.51c (Barabás and Todoruţ, 2011)a,(Knothe,2005)c
Oleate 4.5728a, 4.51c (Barabás and Todoruţ, 2011)a, (Knothe,2005)c
Linoleate 3.7028a, 3.65c (Barabás and Todoruţ,2011)a, (Knothe,2005)c
Linolenate 3.2980a, 3.14c, (Barabás and Todoruţ, 2011)a, (Knothe,2005)c

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Jekayinfa, et al: Prediction of density and viscosity of biodiesel fuel from fatty acid methyl ester (fame) composition. AZOJETE, 15(3):519-534. ISSN 1596-2490; e-ISSN 2545-5818,
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Corresponding author’s e-mail address: aoadebayo57@lautech.edu.ng 526

Table 6: Reported Average % composition of fatty acids in different feedstocks at 40°C
Feedstock Lauric Myristic Palmitic Stearic Palmitoleic Oleic Linoleic Linolenic
SME 0a, 0.08e 0.06a,

0b,d,
0.12e

10.64a 13.9d,11.4e,
12b

3.88a, 2.1d,
3b, 4.14e

0.14a, 0.3d,
0b, 0.16e

32.38a, 23.2d,
23b, 23.47e

46.36a, 56.2d,
55b,53.46e

5.53a, 4.3d,
6b, 6.64e

SUNME 0a, 0.01a,
0b,d
0.04e

6.48a, 6.2d,12b,
6.26e

4.25a, 3b,
2.95d, 3.93e

0a, 0.1d,
0b, 0.06e

18.97a,17.35d,
17b, 20.77e

69.07a,73.45d,
74b, 67.75e

0.26a, 0d,b,
0.15e

CSME 0.02a 0.32a, 0d,
0.72e

22.05a, 28.35d,
25.93e

2.17a,0.95d,
1.74e

0.13a, 0.36e 16.13a, 13d,
15.98e

55.72a, 57.7d,
55.12e

0.25a,0d, 0.16e

POME 0a, 0.37e 0a, 1c,
1.13e

11.1a, 42.6d,41c
42.39e

4.22a, 4.4d,
3b,4.20e

0a,0.3d, 0c,
0.17e

47.23a, 40.5d,
45c,40.91e

32.14a,10.1d,9c,
9.97e

0.68a,0.2d,1c,
0.29e

RME 0a 0.02a,0d,
0.04e

4.06a,3.25d,4.07e 1.2a,0.95d,
1.55e

0.04a, 0.23e 63.12a,64.05d,
62.24e

21.28a,22.15d,
20.61e

8.63a,8.1d,
8.72e

WCME 1.98a,0.20e 0a,0.67e 15.65a, 15.69e 3.10a,6.14e 0.31a, 0.73e 29.57a, 42.84e 41.53a, 29.36e 1.04a,2.03e
OSME 0a, 0a 11.60a, 3.10a, 0.9a, 74.98a, 7.80a, 0.60a

Bamboye and Hassen (2008), b. Bagbyet al.,(1987), c. Ziaziet al.,(1996), d. Singh and Singh, (2010), e. Giakoumis, (2013)

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527

3. Results and Discussion
Table 5 presents the reported average values of Kinematic Viscosity for pure methyl esters of
different fatty acids (mm2/s) at temperature 40˚C while Table6 presents the reported
Average % composition of fatty acids in different feedstocks at 40˚C. Substituting the values
of M, a – h in Eq. (1) and (2) respectively for the coefficients obtained during regression
analysis for each of the feedstocks whose reported data were used,the new equations are
presented in Tables 7 and 8 for viscosity and density respectively.

Equations (1) to (7) show the relationship between Fatty acid composition and viscosity of
feedstocks. From equations (1) to (7), it was observed that coefficient of determination (R2)
of viscosity for SME was 0.775, which indicated that the viscosity of SME can be predicted
with 77.5% accuracy. The coefficients of determination (R2) of viscosity of other feedstocks
include SUNME (R2=0.734), POME (R2=0.609), CME (R2=0.971), RME (R2=0.097), WCME
(R2=0.804) and OSME (R2=0.959). The above results indicated that fatty acid compositions of
methyl esters of different feedstocks are a major predictor of viscosity of biodiesel produced
from them and the quality of biodiesel produced is positively influenced by saturated and
long fatty acids. Also, shorter and more unsaturated chains always increase the viscosity and
flow characteristics at low temperatures (desirable characteristics of fuels). Similarly, Table
8shows that the coefficient of determination (R2) of density for SME was 0.737, which
indicated that the viscosity of SME can be predicted with 73.7% accuracy. The coefficients of
determination (R2) of density of other feedstocks include SUNME (R2=0.524), POME
(R2=0.837), CME (R2=0.985), RME (R2=0.0115), WCME (R2=0.982) and OSME (R2=0.835). This
implies that the density of a methyl ester depends on its molecular weight (saturated and
unsaturated), free fatty acid content, water content, and temperature and indicates that fatty
acid compositions of methyl esters of different feedstocks are a major predictor of density of
biodiesel produced from them. These results agree with the findings of Knothe (2005), who
claimed that an appropriate ratio between saturated and unsaturated fatty acids should be
maintained in order to obtain a biodiesel with appropriate quality characteristics.

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757.010.00.052.00.00.092.00..0897.0)(
2

87654321





R

xxxxxxxxRMEB

(5)

)804.0(
68.1873.4773.290.072.188.110.00..060.3024)(

2
87654321





R
xxxxxxxxWCMEB

(6)

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Jekayinfa, et al: Prediction of density and viscosity of biodiesel fuel from fatty acid methyl ester (fame)
composition. AZOJETE, 15(3):519-534. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: aoadebayo57@lautech.edu.ng 528

)959.0(

05.6965.2495.057.1110.189.045.972.4975.17)(
2

87654321





R

xxxxxxxxOSMEB

(7)
The regression model equations indicating the relationship between Fatty acid composition
and density of feedstocks are given in equations (8) to (14)

)737.0(

02.931.1163.1044.7205.3914.1172.22911.531760.2060)(
2

87654321





R

xxxxxxxxSMEP

(8)

)524.0(

585.2698.5593.60.052.0199.50.00.061.386)(
2

87654321





R

xxxxxxxxSUNMEP

(9)

)837.0(

93.7166.725.213.9781.3384.669.2194.2115.1464)(
2

87654321





R

xxxxxxxxPOMEP

10)

)985.0(
25.194.112.002.353.1736.058.231.949.988)(

2
87654321





R
xxxxxxxxCMEP

(11)

)115.0(
31.811.168.10.00.00..087.60.025.945)(

2
87654321





R
xxxxxxxxRMEP

(12)

)982.0(

14.002.8968.810.010.8514.1350.00.022.2247)(
2

87654321





R

xxxxxxxxWCMEP

(13)

)835.0(

26.138964.1320.2060.20997.1526.297.18279.3423.302)(
2

87654321





R

xxxxxxxxOSMEP

(14)
Where

x1…….x8 are percentage (%) composition of FAME
B= is the viscosity in
P= is the density in kg/m3

The equations developed (equations 1 to 14) were used to predict the viscosity and density
of different biodiesels consisting of different FAME compositions. The predicted values were
plotted against the measured values for both the viscosity and density (Figures 1 and 2). The
viscosity and density of biodiesel predicted were comparable to the standard values (ASTM
D6751). Hence, it could be concluded that the viscosity and density of biodiesel can be
predicted based on FAME compositions. The equation developed was applied to other
studies previously carried out by different researchers. The results gave an indication of
variation of the density and viscosity based on the % composition of FAME. However, the
average range of reported values for density and viscosity of different types of biodiesel as
shown in Tables 1 and 2 differs considerably. Table 6 projects average FAME compositions of
different feedstock for biodiesel fuel as extracted from related past works.
The mean viscosity of SME, SUNME, POME, CME, RME, WCME, and OSME at varying
temperatures (10°C to 40°C) were determined and plotted (Figure1). The means of SME,
SUNME, POME, CME, RME, WCME and OSME were found to be 5.026, 5.514, 4.319, 5.488,
5.266, and 5.64 and 4.894 respectively. The kinematic viscosity at up to 40°C for the selected
biodiesel samples were between 4.319091-5.64cSt which thus satisfies the recommended

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Arid Zone Journal of Engineering, Technology and Environment, September, 2019; Vol. 15(3):519-534. ISSN 1596-
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Corresponding author’s e-mail address: aoadebayo57@lautech.edu.ng

529

limits by ASTMD6751 ranges are 1.9—6.0 cSt for a fatty acid methyl ester. In the same vein,
the observed mean density ranged from 871.2009 to 885.6582kg/m3 while the predicted
mean density also ranged from 861.673 to 885.6582 kg/m3 for the selected feedstocks. CME
reported highest density (885.6582kg/m3) while OSME reported lowest mean density
(871.2009 kg/m3) and is within the ranges (860.0—900.0) recommended for fatty acid methyl
esters (FAME) EN: 14214, 2008.

Key: Blue bars =Observed value while orange bars=Predicted values
Figure 1: Observed and predicted mean values for the selected feedstocks viscosity

Key: Blue bars =Observed value while orange bars=Predicted values
Figure 2: Observed and predicted mean values for the selected feedstocks density

4.0 Conclusions
The following conclusions were drawn:
The study has established the regression equations relating viscosity and FAME compositions
for the selected feedstocks (SME, SUNME, POME, CME, RME, WCME and OSME). Similar
equations were also developed for viscosity and FAME compositions. The equations
developed to predict based on the FAME composition and were able to predict with up to

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Jekayinfa, et al: Prediction of density and viscosity of biodiesel fuel from fatty acid methyl ester (fame)
composition. AZOJETE, 15(3):519-534. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: aoadebayo57@lautech.edu.ng 530

97.1% accuracy for viscosity and 98.5% accuracy for density. The developed equation can
effectively predict the quality of biodiesel (viscosity and density) from various feedstocks
based on FAME composition. The predicted mean values for kinematic viscosity and density
were respectively 4.31 - 5.64 mm2/s and 861.673– 885.6582Kg/m3.

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