Green Nanocatalyst-driven Ultrasonic Synthesis of Biodiesel from Madhuca longifolia Oil: Integrated Taguchi, ANN and Genetic Algorithm Optimization
J. Environ. Nanotechnol., Volume 15, No 2 (June 2026) pp. 804-815
Abstract
The swift exhaustion of fossil fuel reserves and increasing demand for sustainable energy have increased the need for eco-friendly alternative fuels. This study investigated biodiesel production from Madhuca longifolia (Mahua oil), a cost-effective non-edible feedstock, utilizing an ultrasonic-assisted two-step esterification-transesterification process catalyzed by green-synthesized ZnO nanoparticles. The ZnO nanocatalyst was synthesized utilizing Moringa oleifera leaf extract and extensively evaluated by FTIR, XRD, SEM–EDX, and particle size analysis, validating its structural integrity, shape, and nanoscale characteristics. Process parameters, such as ultrasonication duration (30–40 min), catalyst concentration (0.5–1.5 wt%) and methanol-to-oil molar ratio (6–12) were adjusted utilizing Taguchi design in conjunction with ANOVA. The molar ratio was determined to be the primary factor influencing biodiesel yield. Despite the statistical regression model demonstrating substantial predictive power (R2 = 98.76%), its capacity to depict intricate nonlinear relationships was constrained. An artificial intelligence-based strategy was employed to surmount this limitation. The constructed backpropagation artificial neural network (BP-ANN) model exhibited exceptional predictive efficacy (R2 = 0.999), markedly surpassing Radial Basis Function (RBF) and Random Forest (RF) models regarding accuracy and error reduction. Subsequent optimization employing a hybrid BP-ANN combined with a genetic algorithm (GA) determined the ideal operating conditions to be 40 minutes of ultrasonication, 1.2317 wt% ZnO catalyst loading, and a molar ratio of 12. Under the conditions, maximum anticipated biodiesel yield of 96.042% were attained and experimentally corroborated with a yield of 94.5%, reflecting a minimal deviance of 1.605%. The results indicate that ultrasonication significantly accelerates reaction kinetics by enhancing cavitation and mass transfer, facilitating high conversion efficiency with less catalyst loading. The integration of Taguchi design, ANN modeling, and GA optimization provides a stable and efficient framework for biodiesel synthesis. This study highlights the promise of green nanocatalytic systems combined with modern computational methods for the scalable and sustainable synthesis of biofuels from non-edible oil sources.
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