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 biodiese...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 ℃, 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.展开更多
文摘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 ℃, 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.
