1) The document describes research optimizing the production of biodiesel from Jatropha oil through alkali-catalyzed transesterification. 2) A central composite design was used to optimize reaction conditions including methanol-to-oil ratio, sodium hydroxide concentration, and reaction time. 3) The optimal conditions found were a methanol-to-oil ratio of 6.0, 1.0% sodium hydroxide concentration, and a 90 minute reaction time, producing a 99.87% fatty acid methyl ester content.

1. Kasetsart J. (Nat. Sci.) 44 : 290 - 299 (2010)
Optimization of Biodiesel Production from Jatropha Oil
(Jatropha curcas L.) using Response Surface Methodology
Kanthawut Boonmee1*, Sawitri Chuntranuluck1, Vittaya Punsuvon2 and Pinya Silayoi3
ABSTRACT
The main purpose of this research was to develop a biodiesel production technique from Jatropha
oil (Jatropha curcas). Special attention was paid to the optimization of alkali-catalyzed transesterification
for converting fatty acid methyl ester (FAME). Jatropha oil contained 2.59 mg KOH/g of acid and a
molecular weight of 900 g/mol with high oleic acid (41.70%) and linoleic acid (36.98%). A central
composite design (CCD) technique was applied for the experimental design. There were 20 experiments
involving the three investigated variables of methanol-to-oil molar ratio (0.95-11.50), sodium hydroxide
(0.16-1.84% w/w) and reaction time (39.55-140.45 min). The data was statistically analyzed by the
Design-Expert program to find the suitable model of % fatty acid methyl ester (% FAME) as a function
of the three investigated variables. A full quadratic model was suggested by the program using response
surface methodology (RSM) with an R2 and adjusted R2 of 97 and 94%, respectively. The optimum
conditions for transesterification were a methanol-to-oil molar ratio of 6.00, 1.00% w/w sodium hydroxide
and 90 min reaction time. The optimum condition obtained a FAME content of 99.87%. The resulting
Jatropha biodiesel properties satisfied both the ASTMD 6751 and EN 14214 biodiesel standards. The
production technique developed could be further applied in a pilot plant.
Key words: Jatropha curcas L. oil, non-edible oil, transesterification, biodiesel, fatty acid methyl ester
(FAME)
INTRODUCTION vegetable oils and animal fats, biodiesel feedstock
may affect food supplies in the long-term. The
Due to the availability of recoverable recent focus has been to seek a source of non-
agricultural resources, the environmental problems edible oils, as a feedstock for biodiesel production.
caused by fossil fuel consumption, as well as the Jatropha curcas L. (Jatropha) has been chosen as
dramatic impact of oil imports on Thailand’s an optimal supply source.
economy, biodiesel production is being considered Jatropha curcas L. is a non-edible oil-
as an alternative to petrodiesel. Biodiesel is bearing plant widespread in arid, semi-arid and
believed to be able to decrease the dependence on tropical regions of Thailand. Jatropha curcas L.
and improve the adverse environmental impact of is a drought-resistant perennial tree that grows in
using oil. However, as it is produced from marginal lands and can live over 50 years
1 Department of Biotechnology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand.
2 Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.
3 Department of Packing Technology and Materials, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand.
* Corresponding author, e -mail: Kanthawut@hotmail.com
Received date : 06/08/09 Accepted date : 30/10/09

2. Kasetsart J. (Nat. Sci.) 44(2) 291
(Bosswell, 2003). Jatropha curcas L. has several deposits and thickening of the lubricating oil
benefits, such as its stem can be used as a natural (Silvio et al., 2002). Transesterification is a process
toothpaste and toothbrush, latex from the stem can for the reduction of triglyceride molecules (Van
be used as a natural pesticide and to heal wounds, Dyne et al., 1996; Muniyappa, et al., 1996). The
while its leaves are used as fodder for silkworms use of chemically altered or transesterified
(Chhetri et al., 2008). vegetable oil, called biodiesel, does not require
Compared to any other economic plants, any modification in the engine or its injection
Jatropha curcas L. is very durable in hot climates, system or fuel lines and can be used in any diesel
such as Thailand experiences, The oil content in engine. The stoichiometric equation requires one
Jatropha curcas L. seed is reported to be in the mole of triglyceride and three moles of alcohol to
range from 30 to 50% by weight of seed (Kandpal form three moles of methyl ester and one mole of
and Madan, 1995; Pramanik, 2003) and from 45 glycerol in the presence of a strong base or acid
to 60% by weight of the kernel itself (Pramanik, (Muniyappa et al., 1996). Methanolysis is the
2003). Therefore, Jatropha oil has a potential to process where methanol is used in biodiesel
be used as a substitute fuel in biodiesel production. production (Gervasio, 1996; Ma and Hanna, 1999).
In addition, Jatropha oil not only has a high level Response surface methodology (RSM)
of fat and unsaturated fatty acids, but also low is a useful statistical technique, which has been
levels of free fatty acids (Foidl et al., 1996). The applied in the research of complex variable
oil can be used directly in agricultural diesel processes (Myers and Montgomery, 2002).
engines, electric generators, tractors and water Multiple regression and correlation analysis are
pumps without any additives and does not cause used as tools to assess the effects of two or more
any physical damage. For diesel engine use, independent factors on the dependent variables.
Jatropha oil has to undergo a transesterification Furthermore, the central composite design (CCD)
process. In Thailand, Jatropha oil has been placed of RSM has been applied in the optimization of
on the national agenda to encourage its production several biotechnological and chemical processes.
in the rural community for transportation and Its main advantage is the reduction in the number
agriculture, as a substitute for bio-diesel fuel. of experimental runs required to generate sufficient
A few attempts have been made to information for a statistically acceptable result.
produce biodiesel from non-edible sources, such RSM has been applied successfully for
as used frying oil, grease, tallow and lard optimization of biodiesel production in fat and oil
(Alcantara et al., 2000; Canakci and Gerpen, 2001; feedstocks, including mahua oil (Madhuca indica)
Dorado et al., 2002). The production of biodiesel (Ghadge and Raheman, 2006), Jatropha oil (Tiwari
would be inexpensive because it could be extracted et al., 2007), waste rapeseed oil (Yuan et al., 2008)
from the non-edible oil sources and from certain and animal fat (Jeong et al., 2009).
species that are common in many parts of Thailand. The current study concentrated on
Jatropha curcas L. has ecological advantages and developing a technique for biodiesel production
has been found to be an appropriate, renewable, from Jatropha oil. RSM was applied to optimize
alternative source of biodiesel production in the alkali-catalyzed transesterification to produce
Thailand. However, extracted Jatropha oil cannot fatty acid methyl ester (FAME) as a function of
be used directly in diesel engines because of its three factors: the methanol-to-oil molar ratio,
high viscosity. The high viscosity of pure vegetable sodium hydroxide and the reaction time. The fuel
oils reduces fuel atomization and increases fuel properties of Jatropha biodiesel for vehicle use
spray penetration, which results in high engine were determined.

3. 292 Kasetsart J. (Nat. Sci.) 44(2)
MATERIALS AND METHODS 1.20 (% by weight of oil) NaOH (Alacantara et
al., 2000). The acid value was defined as
Alkali catalyzed transesterification milligrams of potassium hydroxide necessary to
Crude Jatropha oil used in the neutralize fatty acids in 1 g of sample. If the acid
experiments was obtained from the Department value of the oil used was greater than 5 mg KOH/
of Chemical Engineering at Kasetsart University. g, more NaOH would be required to neutralize the
Methanol (from the J. T. Baker Chemical Co.) and free fatty acids (Wright et al., 1944). The reaction
sodium hydroxide (from Merek Ltd.) were time was 90 min, after which the reactant was
analytical reagent grade. Oil was partially purified transferred to a separation funnel (Foidl et al.,
by filtration and boiling at 105-110°C for 0.5 h to 1996).
remove the insoluble portion and water, A five-level-three-factor CCD was
respectively. The experiments were conducted at employed in the optimization study, requiring 20
the Department of Chemical Engineering, experiments. The methanol-to-oil molar ratio,
Kasetsart University. catalyst concentration and reaction time were the
In the production of Jatropha biodiesel independent variables selected to optimize the
by the alkali-catalyzed transesterification conditions for FAME production of sodium
technique, methanol was chosen as a catalyst hydroxide-catalyzed transesterification. The 20
because of its low cost. Sodium hydroxide was experiments were carried out and data was
chosen, since it was reasonably priced and reacted statistically analyzed by the Design-Expert
much faster than the acid catalyst (Freedman et program to find the suitable model for the % fatty
al., 1984). The important factors affecting the acid methyl ester (% FAME) as a function of the
transesterification reaction were the excessive above three variables.
amount of methanol and sodium hydroxide, and The coded and uncoded levels of the
the reaction time (Demirbas, 2003). In order to independent variables in this step are given in
optimize the amount of excess methanol required Table 1. Two replications were carried out for all
for the reaction, the experiments were conducted experimental design conditions. The central values
with various methanol-to-oil molar ratios, because (zero level) chosen for the experimental design
the transesterification reaction required 3 moles were a methanol-to-oil molar ratio of 6:1, 1%
of methanol to react with 1 mole of vegetable oil w/w catalyst concentration and 90 min reaction
(Kavitha, 2003). Most researchers used 0.10 to time.
Table 1 Independent variables and levels used in the central composite design for the alkali catalyzed
transesterification process.
Variable Symbol Levela
(uncoded variable) -1.68 -1 0 +1 +1.68
(-α) (+α)
Methanol-to-oil molar ratio M 0.95 3.00 6.00 9.00 11.50
Catalyst concentration C 0.16 0.50 1.00 1.50 1.84
(%w/w)
Reaction time T 39.55 60.00 90.00 120.00 140.45
(minutes)
Note:
a Transformation of variable levels from coded variables of X , X and X in Equation 3 to uncoded variables are: M = 6.00+3.00X ,
1 2 3 1
C = 1.00+0.50X2 and T = 90.00+30.00X3.

4. Kasetsart J. (Nat. Sci.) 44(2) 293
The following experimental procedure where :
was adopted for the production of Jatropha C = the FAME content (% w/w)
biodiesel. Some Jatropha oil was placed in a three- ΣA = the total peak area from the
necked round-bottomed flask. A water-cooled methyl ester
condenser and a thermometer with cork were ASI = the peak area of methyl
connected to both sides of the round-bottom flask. heptadecanoate
The required amount of NaOH and methanol were CSI = the concentration of used methyl
weighed and dissolved completely, using a heptadecanoate solution (mg/ml)
magnetic stirrer. The Jatropha oil was warmed by VSI = the volume of used methyl
placing the round-bottomed flask in a water bath heptadecanoate solution (ml)
maintained at 60°C. The sodium methoxide m = the weight of sample (g)
solution was added into the oil using fixed
vigorous mixing (400 rpm). The mixture was Statistical analysis
poured into the separating funnel overnight settling The experimental data was analyzed by
by gravity into two layers, with the clear, golden the response surface regression procedure using a
liquid-Jatropha biodiesel on the top and the light second-order polynomial (Equation 2):
brown glycerol on the bottom. After 24 h, the k k k k
glycerol was drained off. The raw Jatropha y = β 0 + ∑ β i X i + ∑ β ii X 2 i + ∑ ∑ β ij X i X j
(2)
biodiesel was collected and water-washed to bring i =1 i =1 ii> j j
down the pH of bio-diesel to 7 (the pH of water). where, y is the response variable; xi and xj are the
The percentage of FAME content in the resulting coded independent variables and βo, βi, βii and βij
biodiesel was measured by gas chromatography are the intercept, linear, quadratic and interaction
(GC). constant coefficients respectively, and k is the
number of factors studied and optimized in the
Quantitative analysis of fatty acid methyl ester experiment.
content The Design-Expert program was used in
Chromatographic analysis was the regression analysis and analysis of variance
performed on a Shimadzu GC-2010 gas (ANOVA). The Statistica software program was
chromatograph equipped with a DB-WAX column used to generate surface plots, using the fitted
(30 m × 0.32mm, 0.25µm) and flame ionization quadratic polynomial equation obtained from the
detector (FID). The operating conditions involved regression analysis, holding one of the independent
injector and detector temperatures at 260°C and a variables constant. Experiments were carried out
split ratio at 1:25. Helium was used as the carrier to validate the equation, using combinations of the
gas. Methyl heptadecanoate (Supelco Inc.) was independent variables, which were not part of the
used as the internal standard of fatty acid methyl original experimental design, but within the
ester. The analysis was performed by dissolving experimental region (Ghadge and Raheman,
0.05 g of the biodiesel sample in 1 ml of methyl 2006).
heptadecanoate and injecting 1 µl of this solution
mixture into the gas chromatograph. The Analysis of Jatropha biodiesel properties
percentage of FAME was calculated by the The analysis of Jatropha biodiesel
Equation 1: qualities considered the density at 15°C, acid
value, iodine value, linolenic methyl ester, flash
C = (Σ A-ASI)/ASI × (CSI×VSI)/m×100 (1) point, cloud point, viscosity at 40°C, free

5. 294 Kasetsart J. (Nat. Sci.) 44(2)
glyceride, monoglyceride, diglycerides, Alkali catalyzed transesterification
triglycerides and total glyceride. The analysis was The central composite design conditions
carried out using the methods developed by the and responses, and the statistical analysis of the
Center of Excellence on Palm Oil, Kasetsart ANOVA are given in Tables 2 and 3, respectively.
University and compared with the ASTMD6751 The multiple regression coefficients were obtained
and EN 14214 biodiesel standards. by employing a least square technique to predict a
quadratic polynomial model for the FAME content
RESULTS AND DISCUSSION (Table 4). The model was tested for adequacy by
analysis of variance. The regression model was
Properties of Jatropha oil found to be highly significant with the correlation
The fatty acid composition of Jatropha coefficients of determination of R-Squared (R2),
oil was 41.70% w/w oleic acid and 36.98% w/w adjusted R-Squared and predicted R-Squared
linoleic acid with an acid value of 2.59 mg KOH/ having a value of 0.97, 0.94 and 0.75, respectively.
g, which was an acceptable result for the The predicted model for percentage of FAME
transesterification process (lower than 5.00 mg content (Y) in terms of the coded factors is shown
KOH/g), according to Gerpen (2005). The average in Equation 3:
molecular weight was 900 g/mole.
Table 2 Central composite design arrangement and response for alkali catalyzed transesterification.
Treatment X1 X2 X3 Methanol NaOH Reaction
/oil molar concentration time Fatty acid methyl ester
ratio (%w/w) (minutes) (%)
(M) (C) (T) Experimental Predicted
1 -1 -1 -1 3.00 0.50 60.00 57.08 60.79
2 -1 -1 +1 3.00 0.50 120.00 89.98 90.63
3 -1 +1 -1 3.00 1.50 60.00 57.43 52.90
4 -1 +1 +1 3.00 1.50 120.00 90.14 91.42
5 +1 -1 -1 9.00 0.50 60.00 94.31 92.73
6 +1 -1 +1 9.00 0.50 120.00 78.13 82.36
7 +1 +1 -1 9.00 1.50 60.00 93.71 92.76
8 +1 +1 +1 9.00 1.50 120.00 95.08 91.07
9 0 0 -1.68 6.00 1.00 39.55 71.60 73.45
10 0 0 +1.68 6.00 1.00 140.45 98.55 97.13
11 0 -1.68 0 6.00 0.16 90.00 89.17 84.86
12 0 +1.68 0 6.00 1.84 90.00 80.83 85.56
13 -1.68 0 0 0.95 1.00 90.00 65.61 64.81
14 +1.68 0 0 11.05 1.00 90.00 90.14 91.37
15 0 0 0 6.00 1.00 90.00 100.00 99.87
16 0 0 0 6.00 1.00 90.00 99.42 99.87
17 0 0 0 6.00 1.00 90.00 100.00 99.87
18 0 0 0 6.00 1.00 90.00 99.89 99.87
19 0 0 0 6.00 1.00 90.00 100.00 99.87
20 0 0 0 6.00 1.00 90.00 100.00 99.87

6. Kasetsart J. (Nat. Sci.) 44(2) 295
Y = + 99.87 + 7.90 X1 + 0.21 X2 + 7.04 X3 At the same time, there was a significant mutual
- 7.70 X12 - 5.18 X22 - 5.16 X32 + 1.98 X1X2 interaction between the methanol to oil molar ratio
- 10.05 X1X3 + 2.17 X2X3 (3) and the catalyst concentration (X1X2) and the
The RSM was used to optimize the interaction between catalyst concentration and
conditions of conversion for Jatropha biodiesel and reaction time (X2X3). These results were similar
to understand the interaction of the factors to Jeong et al. (2009), who studied RSM and the
affecting Jatropha biodiesel production. Figures effect of five-level-three-factors in optimizing the
1, 2 and 3 show surface plots between the reaction conditions of biodiesel production from
independent and dependent variables for different animal fat.
fixed parameters. From Figure 1, the % FAME A statistical model (Equation 3) predicted
amount increased with increasing catalyst that the highest conversion yield of Jatropha
concentration at a low methanol-to-oil molar ratio. biodiesel was 99.87% FAME content, when the
From Figure 2, the % FAME amount increased optimized reaction conditions were a catalyst
with the increasing methanol-to-oil molar ratio for concentration of 1.00% w/w, a methanol-to-oil
a low reaction time. From Figure 3, the % FAME molar ratio of 6.00 and a reaction time of 90 min.
amount increased with increasing reaction time at Additional experiments were carried out to
a high catalyst concentration. The methanol-to-oil validate the equation using these optimal values.
molar ratio (X1) was the limiting condition and a It was found that the experimental value of 99.88% of
small variation in its value altered the conversion. FAME content agreed well with the predicted value.
Table 3 Analysis of variance (ANOVA) for the quadratic polynomial model from the transesterification.
Model Sum of squares df Mean square F Sig.
Regression 3779.179 9 419.909 34.253 .000a
Residual 122.589 10 12.259
Total 3901.768 19
a Predictors: (Constant), X1, X2, X3, X1X2, X1X3, X2X3, X12, X22, X32.
Table 4 Regression coefficients of the predicted quadratic polynomial model for alkali-catalyzed
transesterification.
Model Unstandardized Standardized t Sig.
coefficients coefficients
B Std. error Beta
(Constant) 99.871 1.428 69.943 0.000
X1 7.901 0.948 0.467 8.336 0.000
X2 0.209 0.948 0.012 0.220 0.830
X3 7.041 0.948 0.416 7.429 0.000
X1 2 -7.710 0.924 -0.472 -8.346 0.000
X22 -5.186 0.924 -0.317 -5.613 0.000
X3 2 -5.159 0.924 -0.316 -5.585 0.000
X1X2 1.980 1.238 0.090 1.599 0.141
X1X3 -10.052 1.238 -0.455 -8.121 0.000
X2X3 2.170 1.238 0.098 1.753 0.110

7. 296 Kasetsart J. (Nat. Sci.) 44(2)
Analysis of Jatropha biodiesel methyl esters (Yuan et al., 2008) with oleic acid
The chromatogram of Jatropha oil as the predominant fatty acid.
methyl ester is shown in Figure 4. The major The quality of the Jatropha biodiesel was
FAME components were palmitic acid (C16:0), designed to obtain a high percentage FAME. The
oleic acid (C18:1) and linoleic acid (C18:2), which Jatropha biodiesel process consisted of a filtration
are required for the biodiesel standard. The GC process, reaction process (alkali-catalyzed
analysis of the FAME from Jatropha oil (Figure transesterification process), separation process,
4) showed that FAME mainly contained fatty acid washing process, recovery process and
Figure 1 The effect of catalyst concentration (% w/w) and methanol-to-oil molar ratio on predicted
value of % FAME at 90 min.
Figure 2 The effect of reaction time (minutes) and methanol-to-oil molar ratio on predicted value of
% FAME at 1% w/w catalyst concentration.

8. Kasetsart J. (Nat. Sci.) 44(2) 297
dehydration process. In the experiment, the 14214). It was found that its properties met the
temperature and the agitation were maintained at ASTMD6751 and EN 14214 standards. Therefore,
60°C and 400 rpm, respectively. Jatropha biodiesel was an environmentally
Table 5 shows the comparison between friendly, alternative diesel fuel from non-edible oil
the properties of Jatropha biodiesel obtained and feedstock.
the biodiesel standards (ASTMD6751 and EN
Figure 3 The effect of reaction time (minutes) and catalyst concentration (% w/w) on predicted value
of % FAME at methanol-to-oil molar ratio of 6.
Figure 4 GC chromatogram of fatty acid methyl ester from Jatropha oil under optimum conditions for
transesterification.

9. 298 Kasetsart J. (Nat. Sci.) 44(2)
Table 5 Fuel properties of Jatropha biodiesel.
Parameter Unit Method Jatropha ASTM EN 14214
biodiesel D 6751
Density at 15oC Kg/m3 ASTM D 1298 880.53 - 860-900
Acid value mg KOH/g AOCS Ca5a-40 0.27 <0.80 <0.50
Iodine value g iodine /100g AOCS Cdl-25 98.41 - <120
Linolenic methyl ester %wt EN 14103 0.17 - <12
Flash point °C ASTM D-93-02a >206 >130 >120
Cloud point °C ASTM D 2500 4.90 Report -
Viscosity at 40°C mm2/s ASTM 445 4.36 1.90-6.00 3.50-5.00
Free glyceride %wt EN 14105 0.01 ≤0.02 <0.02
Monoglyceride %wt EN 14105 0.47 - <0.80
Diglyceride %wt EN 14105 0.09 - <0.20
Triglyceride %wt EN 14105 <0.01 - <0.20
Total glyceride %wt EN 14105 0.14 ≤0.24 <0.25
CONCLUSION ACKNOWLEDGEMENTS
A CCD technique was applied as the This work was partly supported by the
experimental design. There were 20 experiments KU-biodiesel project, Kasetsart University,
involving the three investigated variables of Bangkok. The authors would like to thank the
methanol-to-oil molar ratio (X 1 ), sodium Department of Chemical Engineering at Kasetsart
hydroxide (X2) and reaction time (X3). The data University for the raw Jatropha oil extractions and
was statistically analyzed by the Design-Expert Assoc. Prof. Dr. Sawitri Chuntranuluck, Assoc.
program. The full quadratic model for the Prof. Dr. Vittaya Punsuvon and Asst. Prof. Dr.
percentage of FAME content (Y) as a function of Pinya Silayoi for assistance in setting up the
the above three variables was: Y = + 99.87 + 7.90 experimental stage of the research.
X1 + 0.21 X2 + 7.04 X3 - 7.70 X12 - 5.18 X22
- 5.16 X32 + 1.98 X1X2 - 10.05 X1X3 + 2.17 X2X3.
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