Modeling of process intensification of biodiesel production from Aegle Marmelos Correa seed oil using microreactor assisted with ultrasonic mixing

Conventionally, the batch type reactors were used for the production of biodiesel. However, in recent years, the usage of microreactors has started emerging as a significant substitute for biodiesel production due to its higher conversion rate at a short duration. These microreactors have a significantly high surface to volume ratio and high heat-mass transfer rate. The disadvantage of this type of reactors is its low mixing rate of the reagents. This can be overcome with the assistance of ultrasonic mixing. The main objective of this paper is to study the interlaced effect of a continuous flow microreactor and ultrasonic mixing on trans-esterification of Aegle Marmelos Correa seed oil using sodium methoxide catalyst. Results of microreactors with 0.3 mm and 0.8 mm diameter were compared. The effects of process parameters namely, flow rate (2–10 mL/min), reaction temperature (45–65 °C), catalyst amount (0.5–2.5 wt%), oil to methanol molar ratio (1:6–1:18) and ultrasonic mixing time (30–150 s) were studied using response surface methodology (RSM). The biodiesel yield of 98% and 91.8% were obtained for 0.3 mm and 0.8 mm microreactors, respectively. The maximum biodiesel yield observed in 0.3 mm reactor under following optimum conditions: 6.8 mL/min flow rate, 48 °C reaction temperature, 1.3 wt% catalyst, 1:9 oil to methanol molar ratio and 83 s ultrasonic mixing time. The predictive and generalization abilities of RSM and artificial neural network (ANN) models were evaluated and compared. The study showed that ANN and RSM models could predict the yield with an R² value of 0.9955 and 0.9900 respectively. However, the ANN model predicted the yield with the least mean square error value of 0.00001294, which is much lower than RSM.