Magnetic iron nanoparticles (Fe₃O₄) were synthesized and characterized using Fourier Transformed Infrared ((FT-IR), UV-Visible spectrophotometer, Scanned Electron Microscopy (SEM) equipped with an Energy Dispersive X-ray spectrometer (EDX), and X-ray Diffraction (XRD). The synthesized nano catalyst was used in the transesterification of mahogany seed oil with methanol. The optimized reaction conditions gave a reaction yield of 88% at a catalyst concentration of 1.5% wt., a volume ratio of methanol to oil of 5:1, a reaction temperature of 60 °C, and a reaction time of 120 minutes. The Fe₃O₄ nanoparticles was regenerated from the mixture and reused for various circles by applying the optimum conditions obtained during the present study. The results showed that the biodiesel yield decreased by increasing the number of cycles when the regenerated catalyst was used. However, good conversion (81.9%) was obtained up to the 5th cycles. The elemental analysis of the synthesized magnetic iron nanoparticles Fe₃O₄) revealed the highest proportion of iron with 64.37 and 74.40% for atomic and weight concentration respectively, followed by oxygen with 34.27 and 24.50% for atomic and weight concentrations respectively. It could be concluded that the synthesized nano catalyst would serve as an excellent catalyst for the transesterification of vegetable oils.

In this study, oil was extracted from Moringa seed using mechanical and solvent methods. To transesterify the oil into biodiesel, factorial design of experiment of 2⁴ was used to obtain different combination factors at different level of reaction temperature, catalyst amount, reaction time and alcohol to oil ratio, giving rise to 48 experimental runs. The oil sample was transesterified in 48 experimental runs, in each case the biodiesel yield was recorded in percentage. The biodiesel was then characterized according to ASTM test protocol. Factorial design model was developed using Design Expert 7.0, the model generated R of 0.987 and Mean Square Error (MSE) of 5.0453 and was used to predict and optimize biodiesel yield. Artificial Neural Network (ANN) model from MATLAB R2016a was developed using 4 input variables and 30 runs, the remaining 18 runs were tested with the ANN model to predict and compare the biodiesel yield with the experimental biodiesel yield, the model generated R value of 0.99687 and MSE of 3.50804. It was found that solvent method yielded more oil than mechanical method, the biodiesel has good thermo-physical property, optimum biodiesel yield of 91.45 % was obtained at 5:1 alcohol/ oil molar ratio, 18.89 wt% catalyst amounts, 45 minutes reaction time and at 45 reaction temperature. The experimental validation yielded 88.33 % biodiesel. The ANN model adequately predicted the remaining 18 runs with R² value of 0.99649 and MSE of 4.914243. Both models proved adequate enough to predict biodiesel yield but ANN model proved more adequate.