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Optimisation and estimation of biodiesel by Karanja oil (KO) was done by response surface methodology (RSM) and artificial neural networks (ANNs) models through domestic microwave heating transesterification. Box–Behnken experimental design was adopted. Four process parameters are methanol/oil mole ratio (30–50%), catalyst concentration (1–2 wt%), volume (100–300 mL) and time (4–8 min). Biodiesel with a yield of 87.34% was obtained using 1.5 wt. % NaOH, 35% methanol to oil molar ratio, 150 mL amount and 5 min of reaction at 700 W power with 100 rpm stirring. The physico-chemical characteristics of KO methyl ester are measured using standard methods. Quality of RSM model is analysed by analysis of variance. ANN tool was adopted for modelling and prediction. Correlation coefficient values were 0.85 and 0.8663 with RSM and ANN, respectively. The concentration of the catalyst, volume, methanol to oil molar ratio and time required producing maximum yield of biodiesel were obtained. The predictive capacities of RSM and ANN are evaluated and compared by statistical parameters, namely, R2 ,  RMSE ,  ADJ-R2  and  MSE.  Hence, results typify the strength and excellence of ANN over RSM specifically in the transesterification of biodiesel.

Optimisation and prediction of Karanja oil transesterification with domestic microwave by RSM and ANN