Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network

Abstract

Abstract Investigation on the use of KOH and NaOH catalysts for waste groundnut oil (WGO) biodiesel production, as well as the comparative adoption of response surface methodology (RSM) and artificial neural network (ANN) for the modelling of yield and process parameters was carried out in this research work. Box–Benkhen experimental design was adopted and the four process parameters considered were methanol-oil mole ratio (6–12), catalyst concentration (0.7–1.7 wt%), reaction temperature (48–62 °C) and reaction time (50–90 min). The results of this research work reveal that KOH catalyst produced higher yield of biodiesel, compared to the yield obtained from NaOH catalysed process. ANN model had 0.9241 regression coefficients (R) and 0.8539 correlation coefficients (R2) while the R and R2 calculated from RSM were 0.9290 and 0.8516 for KOH catalysed transesterification process. Also, the overall regression coefficients R and correlation coefficient R2 in the ANN model were 0.9629 and 0.9272, while the R and the correlation coefficient R2 calculated from RSM were 0.9210 and 0.8791, for NaOH catalysed WGO biodiesel production. Hence, the results typify the robustness and superiority of ANN over RSM in predicting and solving complex problems specifically in the transesterification of biodiesel, due to the larger values of R and R2 as recorded.