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Ultrasound assisted synthesis of biodiesel from karanja oil by interesterification: Intensification studies and
Shubham S Kashyap1, Parag R Gogate1, Saurabh M Joshi1
1Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai 400019, India.
Abstract:
The present work deals with the optimization of interesterification of karanja oil using response surface methodology (RSM) analysis with intensification studies based on the use of ultrasound. Esterification of karanja oil was performed as a pretreatment under fixed optimum conditions of molar ratio of 1:10, catalyst loading of 3.5% and temperature of 60 °C to reduce the acid value from initial of 10.5 mg of KOH/g to 1.8 mg of KOH/g. The pretreated oil was used for interesterification where the process parameters considered for optimization were time (X1), catalyst loading (X2), reactant ratio (X3) and duty cycle (X4), each varied at three levels. The maximum yield of FAME achieved using optimum parameters as time of 35 min, catalyst loading of 1 wt%, reactant ratio of 1:9 (mol:mol) and duty cycle of 60% was 91.56% (on the basis of theoretical ester formation). The effect of reaction temperature was also studied keeping other parameters constant at optimum conditions and it was observed that yield increases continuously with an increase in the temperature over the entire range of temperature. It was also demonstrated that ultrasound assisted interesterification approach gives less requirement of methyl acetate and catalyst as compared to the conventional approach. It was also observed that higher yield was obtained in the presence of ultrasound (91%) as compared to the conventional approach (60%). Kinetic studies established that second order rate equation fits the obtained data well. A mathematical model in RSM was also successfully developed which can be used to make predictions about the expected conversion. Overall the work demonstrated the intensification benefits of using ultrasound and established the optimum conditions for maximum benefits using RSM analysis.
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