Hybrid XGBoost-RF-MLP model and PSO optimization for performance and emissions of CI engine using waste cooking biodiesel blends
M S Gad, M Sami Soliman, Emad B Helal · 2025
Read Hybrid XGBoost-RF-MLP model and PSO optimization for performance and emissions of CI engine using waste cooking biodiesel blends by M S Gad, M Sami Soliman, Emad B Helal (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12706068.txt.
Description
Gad, Sami Soliman, and Emad Helal blend machine learning with engine tests to squeeze the best from biodiesel made of waste cooking oil. Their study was published in twenty twenty-five. Turning fryer waste into fuel cuts both waste and diesel use.
The team made biodiesel from waste oil and blended it with diesel at four levels. Pure biodiesel cut carbon monoxide, unburned fuel, and smoke at full load, but raised nitrogen oxides and fuel use. Three learning models were stacked so their predictions fed a final learner, which forecast performance and emissions with very low error. A swarm search then found the best engine settings. The hybrid gives engineers a fast way to tune engines for waste-based fuels.
Catalog record
| CREATOR | M S Gad, M Sami Soliman, Emad B Helal |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 81 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |