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Renewable Energy 34 (2009) 1806–1811

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Renewable Energy

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Theoretical modeling of iodine value and saponification value of biodiesel fuelsfrom their fatty acid composition

A. Gopinath, Sukumar Puhan*, G. NagarajanInternal Combustion Engineering Division, Department of Mechanical Engineering, Anna University, Chennai 600 025, Tamil Nadu, India

a r t i c l e i n f o

Article history:Received 18 October 2007Accepted 25 November 2008Available online 4 January 2009

Keywords:BiodieselFatty acid compositionIodine valueSaponification valueRegression model

* Corresponding author. Tel.: þ91 9444489013.E-mail addresses: gopinathmeice@yahoo.co.in (A

yahoo.co.in (S. Puhan), nagarajan@annauniv.edu (G. N

0960-1481/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.renene.2008.11.023

a b s t r a c t

Biodiesel is an alternative fuel consisting of alkyl esters of fatty acids from vegetable oils or animal fats.The properties of biodiesel depend on the type of vegetable oil used for the transesterification process.The objective of the present work is to theoretically predict the iodine value and the saponification valueof different biodiesels from their fatty acid methyl ester composition. The fatty acid ester compositionsand the above values of different biodiesels were taken from the available published data. A multiplelinear regression model was developed to predict the iodine value and saponification value of differentbiodiesels. The predicted results showed that the prediction errors were less than 3.4% compared to theavailable published data. The predicted values were also verified by substituting in the available pub-lished model which was developed to predict the higher heating values of biodiesel fuels from theiriodine value and the saponification value. The resulting heating values of biodiesels were then comparedwith the published heating values and reported.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

The use of biodiesel derived from vegetable oils or animal fats asa substitute for conventional petroleum fuel in diesel engines hasreceived increased attention. For the combustion analysis of bio-diesels, the chemical properties of the biodiesels are to be studied toa greater extent. The chemical properties of biodiesel fuels includechemicalstructure, iodine value(IV), saponificationvalue (SV), heatingvalue, peroxide value, etc. Therefore predicting biodiesel properties isthe first and foremost stimulating task for the studies of biodiesel indiesel engines. In the present work the IV and the SV of ten biodieselswere predicted using their fatty acid methyl esters composition by theregression model and compared with the reported data.

The objective of this work is to predict the iodine value and thesaponification value of any given biodiesel from their fatty acidester composition, so that there may be no need for testing pro-grammes to determine these properties.

2. Method

2.1. Fatty acid methyl ester composition for different biodiesels

Fatty acid methyl esters present in various biodiesel fuels usedfor predicting the IV and SV obtained from Ayhan Demirbas [1],

. Gopinath), sp_anna2006@agarajan).

All rights reserved.

Graboski and McCormick [2], Senthil Kumar et al. [3] and Ghadgeand Raheman [4] are presented in Table 1.

2.2. Data for iodine value and saponification value for differentbiodiesels

IV and SV of ten biodiesel fuels obtained from Ayhan Demirbas[1], Graboski and McCormick [2], Senthil Kumar et al. [3] andGhadge and Raheman [4] are presented in Table 2.

2.3. Correlation analysis

To evaluate the degree of linear association between the iodinevalue and FAMEs and between saponification value and FAMEs,correlation analysis was conducted and the Pearson coefficient ofcorrelation between the properties (iodine value and saponifica-tion value) and FAMEs were found out as listed in Table 3.Thescatter plot of iodine value vs FAMEs and of saponification vsFAMEs with fitted regression line are shown in Fig. 1 and Fig. 2,respectively.

2.3.1. Pearson product moment correlation coefficient (r)It is the measure of degree of linear relationship between two

variables. The correlation coefficient lies between�1 andþ1. If onevariable tends to increase as the other decreases, the correlationcoefficient is negative. Conversely, if the two variables tend toincrease together the correlation coefficient is positive [5].

Table 1Fatty acid methyl esters in different biodiesels.

S.No. Biodiesel Fatty acid methyl esters, wt%

Palmitic16:0

Stearic18:0

Oleic18:1

Linoleic18:2

Linolenic18:3

Erucic22:1

Others

1 Ailanthus 31.0 0.0 8.1 51.1 7.3 0.0 2.02 Corn 11.8 2.0 24.8 61.3 0.0 0.0 0.03 Poppy seed 12.6 4.0 22.3 60.2 0.5 0.0 0.34 Rapeseed 3.5 0.9 64.1 22.3 8.2 0.0 0.05 Safflower

seed7.3 1.9 13.6 77.2 0.0 0.0 0.0

6 Soybean 13.9 2.1 23.2 56.2 4.3 0.0 0.07 Palm 43.6 4.5 40.5 10.1 0.2 0.1 08 Sunflower 6.0 5.9 16.0 71.4 0.6 0.0 09 Mahua 24.2 25.8 37.2 12.8 0 0 010 Jatropha 14.9 9.5 40.5 34.7 0.3 0 0.1

Table 2Iodine value and saponification value for different biodiesels.

S.No. Biodiesel Iodine value(g iodine/100 g oil)

Saponification value(mg KOH/g oil)

1 Ailanthus 107.18 206.342 Corn 119.41 194.143 Poppy seed 116.83 196.824 Rapeseed 108.05 197.075 Safflower seed 139.83 190.236 Soybean 120.52 194.617 Palm 59 2058 Sunflower 136 1939 Mahua 80 18710 Jatropha 105 198.85

Table 3Pearson product moment correlation coefficient (r) between ‘‘iodine value vsFAMEs’’ and ‘‘saponification value vs FAMEs’’.

S.No. FAMEs Pearson product moment correlationcoefficient (r)

Iodine valuevs FAME

Saponification valuevs FAME

1. Palmitic �0.126 0.2022. Stearic �0.232 0.4013. Oleic �0.827 0.8874. Linoleic 0.884 �0.8935. Linolenic 0.413 �0.363

A. Gopinath et al. / Renewable Energy 34 (2009) 1806–1811 1807

r ¼ SðX � XÞðY � YÞðX � XÞ2ðY � YÞ2

where X and Y are the two variables:

From the correlation analysis (Table 3), it can be observed thatthe palmitic and stearic fatty acids has a low degree of negative

Fatty Acid Methyl Esters (Wt%)

Iodi

ne V

alue

(g

lodi

ne/1

00 g

oil)

302010 21.5

80400

140

120

100

80

Palmitic 16: 0 Stearic

Linoleic 18: 2 Linolen

Scatter Plot of Iodine value vs FA

Fig. 1. Scatter plot of iodine value vs F

correlation with iodine value and the oleic acid has a high degree ofnegative correlation with iodine value. The linoleic acid has a highdegree of positive correlation and the linolenic acid has a moderatedegree of positive correlation with the iodine value. Fig. 1 shows thescatter plot of iodine value and FAMEs with the fitted regressionline. On the other hand, the correlation analysis between thesaponification value and FAMEs shows that stearic fatty acid ismoderately positively correlated with the saponification value. Theoleic acid is highly positively correlated and the linoleic acid ishighly negatively correlated with the saponification value. Fig. 2shows the scatter plot of saponification value and FAMEs with thefitted regression line.

2.4. Regression model

After evaluating the Pearson correlation coefficient, tworegression models as given by Eqs. (1) and (2) were developed topredict the iodine value and the saponification value of biodiesels.

Iodine value ðIVÞ ¼ 35:9� ð0:212� PÞ þ ð0:660� SÞþ ð0:448� OÞ þ ð1:23� LÞ þ ð1:73� LLÞ

(1)

3.5.5 906030

140

120

100

16

18: 0 Oleic 18: 1

ic 18: 3

MEs with fitted Regression Line

AMEs with fitted regression line.

Fatty Acid Methyl Esters (Wt %)

Sapo

nifi

cati

on V

alue

(m

g K

OH

/g o

il)

15105 432 704520

210

205

200

195

190

704520

210

205

200

195

190

1680

Palmitic 16:0 Stearic 18:0 Oleic 18:1

Linoleic 18: 2 Linolenic 18: 3

Scatter Plot of Saponification value Vs FAMEs with fitted Regression Line

Fig. 2. Scatter plot of saponification value vs FAMEs with fitted regression line.

A. Gopinath et al. / Renewable Energy 34 (2009) 1806–18111808

R� Sq ¼ 0:978; R� Sq ðadjÞ ¼ 0:965

Saponification valueðSVÞ ¼ 268�ð0:418�PÞ�ð1:30�SÞ�ð0:695�OÞ�ð0:77�LÞ�ð0:847�LLÞ (2)

R� Sq ¼ 0:858; R� Sq ðadjÞ ¼ 0:788

where, P¼ Palmitic, S¼ Stearic, O¼Oleic, L¼ Linoleic andLL¼ Linolenic.

0

20

40

60

80

100

120

140

IV(g

Iod

ine

/100

g oi

l)

Determined value 59 80.000 103.000 107.18 108Predicted Value 60.54 80.21 100.36 108.44 106

Palm Mahua Jatropha Ailanthus Rape

Fig. 3. Determined and predicte

In the above regression models, the resulting iodine value willbe in ‘‘g iodine/100 g oil’’ and saponification value will be in mgKOH/g oil, and the individual fatty acid methyl esters are to besubstituted in weight%.

3. Results and discussion

The relationship between iodine value and the fatty acidcomposition was investigated. For iodine value Eq. (1) shows anincrease with increasing weight percentage of unsaturated fattyacid esters. In other words, the IV increases with increase in thenumber of double bonds. The relationship between SV and the fattyacid composition was also investigated. For saponification values

Biodiesels

.05 116.83 119.41 120.52 132.32 139.83

.08 120.77 121.23 121.30 134.55 136.66

seed Poppyseed Corn Soybean Sunflower Safflower seed

d iodine value of biodiesels.

Table 4Determined and predicted iodine value of biodiesels from Eq. (1).

S.No. Biodiesel Iodine value(g iodine/100 g oil)determined

Iodine value(g iodine/100 g oil)predicted

Error %

1 Ailanthus 107.18 108.44 1.1742 Corn 119.41 121.23 1.5223 Jatropha 103.000 100.36 �2.5684 Mahua 80.000 80.21 0.2595 Palm 59 60.54 2.6106 Poppy seed 116.83 120.77 3.3737 Rapeseed 108.05 106.08 �1.8208 Safflower seed 139.83 136.66 �2.2709 Soybean 120.52 121.30 0.64510 Sunflower 132.32 134.55 1.685

Table 5Determined and predicted saponification value of biodiesels from Eq. (2).

S.No. Oil Saponification value (mgKOH/g oil) determined

Saponification value (mgKOH/g oil) predicted

Error %

1 Ailanthus 206.34 203.88 �1.1912 Corn 194.14 196.03 0.9743 Poppy seed 196.82 195.26 �0.7944 Rapeseed 197.07 196.70 �0.1875 Safflower seed 190.23 193.58 1.7626 Soybean 194.61 196.42 0.9307 Palm 205 207.83 1.3818 Mahua 187 188.63 0.8749 Sunflower 193 191.22 �0.92410 Jatropha 198.85 194.30 �2.288

A. Gopinath et al. / Renewable Energy 34 (2009) 1806–1811 1809

Eq. (2) shows a decrease with increasing weight percentage ofunsaturated fatty acid esters.

The determined and the predicted iodine values using Eq. (1) arecompared in Fig. 3. Table 4 lists the determined and the predicted IVof different vegetable oil methyl esters. It can be noticed thata maximum error of 3.373% is obtained for the fitted values.

Fig. 4 shows the comparison of the determined and the pre-dicted SV using Eq. (2) while Table 5 lists the same for differentvegetable oil methyl esters showing a maximum error of 2.288% forthe fitted values.

3.1. Evaluation of higher heating value of biodiesels from theiriodine value and saponification value using available publishedmodel

Ayhan Demirbas [1] has developed a model to predict the higherheating value of biodiesels using their iodine value and thesaponification value. The model is given by,

HHV ¼ 49:43� ð0:015� IVÞ � ð0:041� SVÞ (3)

175

180

185

190

195

200

205

210

SV,m

g K

OH

/g o

il

Biodi

Determined value 187 190.23 193 194.14

Predicted Value 188.63 193.58 191.22 196.03

MahuaSafflower

seedSunflower Corn

Fig. 4. Determined and predicted sa

The higher heating values for biodiesels using Eq. (3) were pre-dicted [1] from the available published iodine values and saponi-fication values [1]. The predicted heating values were thencompared with the published heating values by Ayhan Demirbas[1] as listed in Table 6.

3.2. Evaluation of higher heating values of biodiesels from theirpredicted iodine value and saponification value

The higher heating values of biodiesels were once again pre-dicted from Eq. (3) using the predicted IV and SV from Eqs. (1) and(2). The predicted heating values are listed in Table 7.

It can be observed that the maximum error is about 1.072% forthe predicted higher heating values when the predicted iodine andsaponification values were used in Eq. (3). Table 8 shows thecomparison between the ‘‘higher heating values predicted by thedetermined iodine and saponification values [1] and the higherheating values predicted by the predicted iodine and saponificationvalues using Eq. (3)’’ for different biodiesels. The comparison showsa maximum difference of 1.037% between the two different heatingvalues.

esels

194.61 196.82 197.07 198.85 205 206.34

196.42 195.26 196.70 194.30 207.83 203.88

Soybean Poppyseed Rapeseed Jatropha Palm Ailanthus

ponification value of biodiesels.

Table 6Predicted higher heating values of biodiesels from their iodine value and saponifi-cation value [1].

S.No. Biodiesel Iodine value (giodine/100 goil) determined

Saponificationvalue (mg KOH/goil) determined

Higher heating value (MJ/kg)

Determined Predicted Error %

1 Ailanthus 107.18 203.88 39.38 39.46 þ0.2032 Corn 119.41 196.03 39.64 39.60 þ0.1013 Jatropha 103.000 195.26 39.70 39.82 þ0.0314 Mahua 80.000 196.70 40.563 40.15 �1.025 Palm 59 193.58 41.025 40.75 �0.676 Poppy

seed116.83 196.42 39.59 39.61 þ0.05

7 Rapeseed 108.05 207.83 39.73 39.73 0.008 Safflower

seed139.83 188.63 39.52 39.53 þ0.025

9 Soybean 120.52 191.22 39.63 39.64 þ0.02510 Sunflower 132.32 194.30 39.45 39.29 �0.41

Table 7Predicted higher heating values of biodiesels from their predicted iodine value andsaponification value.

S.No. Biodiesel Iodine value (giodine/100 goil) predicted

Saponificationvalue (mg KOH/goil) predicted

Higher heating value (MJ/kg)

Determined Predicted Error %

1 Ailanthus 108.44 203.88 39.38 39.444 þ 0.1632 Corn 121.23 196.03 39.64 39.574 �0.1663 Jatropha 100.36 195.26 39.7 39.919 þ 0.5524 Mahua 80.21 196.7 40.563 40.162 �0.9895 Palm 60.54 193.58 41.025 40.585 �1.0726 Poppy

seed120.77 196.42 39.59 39.565 �0.063

7 Rapeseed 106.08 207.83 39.73 39.318 �1.0388 Safflower

seed136.66 188.63 39.52 39.646 þ 0.320

9 Soybean 121.3 191.22 39.63 39.770 þ 0.35510 Sunflower 134.55 194.3 39.45 39.445 �0.012

Table 8Comparison of higher heating value predicted by the determined iodine andsaponification values [1] and the higher heating value predicted by the predictediodine and saponification values for different biodiesels.

S.No. Biodiesel Higher heating valuepredicted by thedetermined iodine valuesand saponification valuesusing Eq. (3) (MJ/kg)

Higher heating valuepredicted by the predictediodine values andsaponification valuesusing Eq. (3) (MJ/kg)

Difference%

1 Ailanthus 39.46 39.444 �0.0412 Corn 39.6 39.574 �0.0663 Jatropha 39.82 39.919 0.2494 Mahua 40.15 40.162 0.0305 Palm 40.75 40.585 �0.4056 Poppy

seed39.61 39.565 �0.114

7 Rapeseed 39.73 39.318 �1.0378 Safflower

seed39.53 39.646 0.293

9 Soybean 39.64 39.770 0.32810 Sunflower 39.29 39.445 0.395

38.5

39

39.5

40

40.5

41

HH

V, M

J / K

g

Predicted heating value by Determined IV and SV 39.29 39.46 39.53

Predicted heating value by Pedicted IV and SV 39.445 39.444 39.646

Sunflower AilanthusSafflower

seed

Fig. 5. Comparison between ‘‘predicted higher heating values from the determined iodine aiodine and saponification values from Eq. (3)’’.

A. Gopinath et al. / Renewable Energy 34 (2009) 1806–18111810

Fig. 5 shows the comparison between ‘‘predicted higher heatingvalues from the determined iodine and saponification values [1]’’and ‘‘predicted higher heating values from the predicted iodine andsaponification values from Eq. (3)’’. The maximum differencebetween these two values was found as 1.037%.

The predicted IV and the SV were substituted in Eq. (3) to predictthe higher heating value of biodiesels and compared with thepredicted heating values [1] from the published iodine andsaponification value for different biodiesels. The maximum differ-ence between these two values is found to be 1.037%. Thus, thepredicted iodine and saponification values were validated. There-fore for the calculation of iodine value (g iodine/100 g oil) of bio-diesels, Eq. (1) is suggested and for the calculation of saponificationvalue (mg KOH/g oil) of biodiesels, Eq. (2) is suggested.

Biodiesels

39.6 39.61 39.64 39.73 39.82 40.15 40.75

39.574 39.565 39.770 39.318 39.919 40.162 40.585

Corn Poppyseed Soybean Rapeseed Jatropha Mahua Palm

nd saponification values [1]’’ and ‘‘predicted higher heating values from the predicted

A. Gopinath et al. / Renewable Energy 34 (2009) 1806–1811 1811

4. Conclusions

The iodine and the saponification values of the fatty acid methylesters obtained in this work are in agreement with the data given inthe literature. The iodine value and saponification value of a givenbiodiesel can be calculated using fatty acid composition ofa particular biodiesel.

The predicted iodine values of biodiesels vary from 60.54 to136.66 g iodine/100 g oil with a maximum error of 3.373%. Thepredicted saponification values of biodiesels vary from 188.63 to207.83 mg KOH/g oil with a maximum error of 2.288%.

The predicted iodine value and the saponification values weresubstituted in Eq. (3) to predict the higher heating value of bio-diesels and compared with the predicted heating values [1] fromthe published iodine and saponification value for different

biodiesels. The maximum difference between these two values isabout 1.037%.

References

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[2] Graboski Michael S, McCormick Robert L. Combustion of fat and vegetable oilderived fuels in diesel engines. Progress in Energy and Combustion Science1998;24:125–64.

[3] Senthil Kumar M, Ramesh A, Nagalingam B. An experimental comparison ofmethods to use methonal and Jatropha oil in a compression ignition engine.Biomass and Bioenergy 2003;25:309–18.

[4] Ghadge Shashikant Vilas, Raheman Hifjur. Biodiesel production from mahua(Madhuca indica) oil having high free fatty acids. Biomass and Bioenergy2005;28:601–5.

[5] Bali NP. Comprehensive engineering mathematics. Laxmi Publications (P) Ltd.;1997.