Biodiesel synthesis from Blighia sapida seed oil: comparative modelling via RSM and ANFIS–PSO hybrid optimization

Abstract

The depletion of fossil fuels and growing environmental concerns necessitate the development of renewable biofuels. Blighia sapida seed oil, an inedible and underutilized resource, presents a viable option for biodiesel production. This study optimized its transesterification process using advanced modelling to improve yield and fuel quality. A two-step esterification–transesterification approach was used, with process variables, methanol-to-oil molar ratio (6:1–12:1), catalyst concentration (0.5–1.5 wt%), reaction temperature (50°C–70 °C), and time (30–90 min), optimized via Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System integrated with Particle Swarm Optimization (ANFIS–PSO). Model performance was evaluated using R 2, RMSE, and MAE. ANFIS–PSO outperformed RSM with R 2 = 0.9997, RMSE = 0.0925, and MAE = 0.0721, compared to RSM’s R 2 = 0.9896, RMSE = 0.6384, and MAE = 0.4162. Optimal ANFIS–PSO conditions, methanol-to-oil ratio of 9.8:1, catalyst concentration of 1.2 wt%, temperature of 62 °C, and reaction time of 78 min, yielded 96.21% biodiesel. The biodiesel met international standards: kinematic viscosity (3.38 mm2/s), flash point (167 °C), density (0.8765 g/cm3), acid value (1.178 mg KOH/g), and cetane number (58.06). These findings validate B. sapida as a promising biodiesel feedstock and position ANFIS–PSO as a superior modelling tool for scalable biofuel production from non-edible sources.