ANN-GWO optimization of biolubricants from black soldier fly: A value-added approach to animal waste conversion

• Investigating tribological properties of biolubricants from black soldier fly. • Optimization using artificial neural network techniques. • Higher BSF biolubricant blends (20 %, 30 %, 40 %) showed slightly higher COF values. • Biolubricant exhibits high viscosity index and oxidative stability. • Contributes to sustainable, environmentally acceptable lubricant alternatives. This study aims to explore the potential of biolubricants derived from BSF larvae in addressing the demand for sustainable and environmentally friendly alternatives to petroleum-based lubricants. Specifically, it investigates the tribological properties of BSF-based biolubricants and explores their formulation using a combination of ANN and the GWO. The COF of BSF was optimised based on time (60–3600 s), load (391–392 N), and temperature (74–83 °C). BSF bio-lubricant was blended with commercial 15W-40 lubricant in varying ratios, and tribological tests were conducted to evaluate key performance indicators such as COF, wear scar diameter, and kinematic viscosity. The optimum parameters by ANN-GWO are as follows: time=90 (sec), load=392.12 N, and temperature 82.5 °C with the predicted COF is 0.0145, and the experimental COF is 0.0140, with a difference of 3.57 %. The BSF biolubricant blend (Biol 10) demonstrated a coefficient of friction (COF) of 0.068, comparable to the commercial 15W-40 lubricant. Additionally, Biol 10 exhibited a kinematic viscosity of 83.35 mm²/s and a low wear scar diameter of 376.67 µm at 75 °C. Other BSF blends (Biol 20–40) showed slightly higher COF values. Overall, the results indicate that the ANN-GWO significantly predict the COF-Biol10. The research highlights the feasibility of using BSF larvae for biolubricant production, contributing to the growing interest in bio-based lubricants and offering a sustainable alternative to traditional petroleum-based lubricants with comparable or improved performance. ANN-GWO optimization techniques would estimate their tribological properties, making them a promising candidate for reducing dependence on fossil fuel-based lubricants in industrial applications.