ANN-GWO optimization of biolubricants from black soldier fly: A value-added approach to animal waste conversion
Bibliographic record
Abstract
• 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".