Comparing Ensemble and Standalone Machine Learning Models for Optimizing Biodiesel Production via Esterification
Melike İmge Şenoymak Tarakçı · 2026
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Description
Melike İmge Şenoymak Tarakçı compares four machine learning models for making biodiesel from oleic acid in a 2026 study. Inputs were reaction time, catalyst amount, and the methanol to oleic acid ratio, and the target was the share of acid converted. A voting ensemble that joined three single models beat each model on its own in tests.
That combined model reached the strongest test scores and pointed to best settings near 18 percent catalyst and a 15 to 1 ratio. Under those settings it predicted a conversion close to 99 percent. The findings suggest ensemble methods can cut lab trial work when engineers tune fuel production.
Catalog record
| CREATOR | Melike İmge Şenoymak Tarakçı |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45329 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |