Machine learning-informed one-pot biodiesel synthesis from an optimally formulated mixed non-edible oil feedstock over magnetic sulfonated biobased catalyst

Abstract

in facilitating MNEO transesterification reaction. Comparative modeling demonstrated satisfactory applications of ANN, SVR and XGB, while error indices established XGB as the most superior model in capturing the complex nonlinear dynamics of the system. Feature ranking established methanol-oil molar ratio as the most influential parameter predicting biodiesel yield, underscoring the important role of methanol in biodiesel synthesis. Furthermore, optimum reaction temperature, catalyst dosage, methanol-oil-ratio and reaction time of 50 °C, 3.01 wt%, 30.0, and 2.4 h, obtained from XGB-MRFO resulted in a yield of 99.68% which was experimentally validated to be 98.16%. It is concluded that MNEO and poultry droppings can be successfully employed for sustainable biodiesel synthesis.