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Frontiers in Energy

Front Energ    2013, Vol. 7 Issue (4) : 495-505
Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm
Sunil DHINGRA1(), Gian BHUSHAN2, Kashyap Kumar DUBEY3
1. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 136119, India; 2. National Institute of Technology, Kurukshetra 136119, India; 3. University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak 124001, India
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This paper described the production of karanja biodiesel using response surface methodology (RSM) and genetic algorithm (GA). The optimum combination of reaction variables were analyzed for maximizing the biodiesel yield. The yield obtained by the RSM was 65% whereas the predicted value was 70%. The mathematical regression model proposed from the RSM was coupled with the GA. By using this technique, 90% of the yield was obtained at a molar ratio of 38, a reaction time of 8 hours, a reaction temperature of 40 oC, a catalyst concentration of 2% oil, and a mixing speed of 707 r/min. The yield produced was closer to the predicted value of 94.2093%. Hence, 25% of the improvement in the biodiesel yield was reported. Moreover the different properties of karanja biodiesel were found closer to the American Society for Testing & Materials (ASTM) standard of biodiesel.

Keywords optimization of karanja biodiesel      genetic algorithm (GA)      response surface methodology (RSM)      percentage improvement in the biodiesel yield      properties of biodiesel     
Corresponding Authors: DHINGRA Sunil,   
Issue Date: 05 December 2013
 Cite this article:   
Sunil DHINGRA,Gian BHUSHAN,Kashyap Kumar DUBEY. Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm[J]. Front Energ, 2013, 7(4): 495-505.
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Kashyap Kumar DUBEY
1.Molar ratio6<X1<36
2.Reaction time/h5.5<X2<11.5
3.Reaction temperature/°C35<X3<255
4.Catalyst (wt)/% concentration1.5<X4<15.5
5.Mixing speed/(r·min-1)75<X5<755
Tab.1  Constraints of process variables
Molar ratio614223038
Reaction time5.578.81011.5
Reaction temperature3590145200255
Catalyst concentration1.558.51215.5
Mixing speed75250425600775
Tab.2  Coded values of variables
Exp. No.TypeMolar ratioReaction time/hTemperature/ oCCatalystconc./ %Mixing speed/(r·min-1)Biodiesel yield/%
Tab.3  Biodiesel yield at different combinations of coded factors
SourceSum of squaresDF a)Mean squareF valueProb.>F b)(P-value)
Tab.4  Sequential model sum of squares
Sum of squaresDFMean squareF valueProb.>F(P-value)
X2 X51.0011.000.0450.8367
Lack of fit203.53633.923.910.0779Not significant
Pure error43.3358.67
Cor total1597.8831
Tab.5  ANOVA for response surface quadratic model
Fig.1  Contour plot of biodiesel yield with molar ratio and reaction time (hour) (Constant values: temperature= 145oC, catalyst concentration= 8.5% by wt. of oil, mixing speed= 425 r/min)
Fig.2  Contour plot of biodiesel yield with molar ratio and reaction temperature (°C) (Constant values: reaction time= 8.5 h, catalyst concentration= 8.5% by wt. of oil, mixing speed= 425 r/min)
Fig.3  Contour plot of biodiesel yield with molar ratio and catalyst concentration (%) ( Constant values: reaction time= 8.5 h, temperature= 145oC , mixing speed= 425 r/min )
Fig.4  Contour plot of biodiesel yield with molar ratio and mixing speed (r/min) (Constant values: reaction time= 8.5 h, temperature= 145oC, catalyst concentration= 8.5% by wt. of oil)
Fig.5  Predicted biodiesel yield vs. actual yield of experimental runs under RSM
Fig.6  Two dimensional representation of residual differences between predicted and actual yield of experimental runs under RSM
Fig.7  Normal probability plot of biodiesel yield
Population size5000
Crossover functionTwo point
Mutation functionUniform
Elite count1
Cross over fraction0.9
Mutation fraction0.01
No. of generations20
Tab.6  Genetic algorithm parameters
Fig.8  Stepwise procedure in genetic algorithm for optimum solution
Fig.9  Biodiesel yield optimization using genetic algorithm
Molar ratioReaction time/hTemperature/ °CCatalyst concentration/%Mixing speed/(r·min-1)Biodiesel (predicted)/%Yield actualPercentage improvement/%
Recommendation using GA38840270794.29025
Response surface methodology design29.458.990.35.06595.907065
Tab.7  Comparison of GA and RSM optimal design
PropertyKaranja oilKaranja ethyl esterASTM standards
Kinematic viscosity/cst a)27.854.371.9-6.0
Calorific value/(J·g-1)410004213339000-43000
Cetane no.564847
Cloud point/ oC414.65-12
Pour point/ oC3.85.13-5
Flash point/ oC205126130
Specific gravity0.9250.900.85-0.94
Iodine value8610585-115
Saponification value194176185-180
Moisture content/% max
Peroxide value1.941.60-
Sulphur content/%0.1NIL15×10-6 max
Tab.8  Comparison of karanja oil, karanja ethyl ester and ASTM standards of biodiesel
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