RESUMO
This study investigates Marula (Sclerocarya birrea) seed oil (SBSO) as a novel feedstock for biodiesel production through the transesterification process catalysed by heterogeneous bio-alkali derived from banana (Musa acuminata) peels. Response surface methodology (RSM) and artificial neural network (ANN) tools were used for the modelling and optimization of the process variables. The reaction process parameters considered were methanol/SBSO molar ratio, catalyst loading levels, reaction time and temperature. Central composite design (CCD) was espoused to generate 30 experimental conditions which were deployed in investigating the individual and synergetic effect of the process input variables on Sclerocarya birrea oil methyl ester (SBOME) yield. Appropriate statistical indices were adopted to investigate the predictive aptitude of the two models. Analysis shows that ANN model obtained for the transesterification process has a higher coefficient of determination (R2) of 0.9846 and lower absolute average deviation (AAD) of 0.07% compared to RSM model with R2 of 0.9482 and AAD of 0.12%. The process modelling outcome also confirmed ANN performance to be more precise than RSM. At methanol/SBSO ratio of 6:1, catalyst loading level of 2 wt%, process reaction time of 50 min and temperature of 55°C, the experimental maximum SBOME yield was observed to be 96.45 wt % following the ANN predicted yield of 96.45 wt % and RSM predicted yield of 96.65 wt % respectively. The analysed fuel properties of SBOME was found satisfactory within the biodiesel stipulated standard limit(s). The study establishes that SBSO is a good source for biodiesel production and its biodiesel methyl ester is a potential substitute for petroleum diesel and a bioenergy fuel.
