Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm

doi:10.1007/s11708-013-0267-5

Front Energ

2013, Vol. 7

Issue (4) : 495-505 https://doi.org/10.1007/s11708-013-0267-5

RESEARCH ARTICLE

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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Abstract

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,Email:pecdhingra@gmail.com

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.

URL:

http://journal.hep.com.cn/fie/EN/10.1007/s11708-013-0267-5
http://journal.hep.com.cn/fie/EN/Y2013/V7/I4/495

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Tab.1 Constraints of process variables

Tab.2 Coded values of variables

Tab.3 Biodiesel yield at different combinations of coded factors

Tab.4 Sequential model sum of squares

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

Tab.6 Genetic algorithm parameters

Fig.8 Stepwise procedure in genetic algorithm for optimum solution

Fig.9 Biodiesel yield optimization using genetic algorithm

Tab.7 Comparison of GA and RSM optimal design

Tab.8 Comparison of karanja oil, karanja ethyl ester and ASTM standards of biodiesel

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