Estimation of Exhaust Gas Concentrations from a Diesel Engine Powered by Diesel Fuel and Rapeseed Oil Operating Under Dynamic Conditions Using Machine Learning
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Kuszneruk Michał, Longwic Rafał, Górski Krzysztof, Tziourtzioumis Dimitrios |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 7 |
| Wolumen/Tom: | 19 |
| Numer artykułu: | 1750 |
| Strony: | 1 - 15 |
| Impact Factor: | 3,2 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 2 kwietnia 2026 |
| Abstrakty: | angielski |
| This paper presents an analysis of the exhaust gas concentration of a compression ignition engine powered by diesel fuel and rapeseed oil under dynamic conditions. The measure- ment cycle consisted of a 100 s segment of the WLTC cycle. An attempt was then made to estimate the exhaust gas concentration using predictive algorithms based on parame- ters recorded using the OBD-II diagnostic interface. The model was validated based on previously unobserved measurements of the measurement cycle, and the procedure was repeated several times with random parameter changes. Due to the dynamic nature of the combustion process (taking into account its non-linearity and inertia), a delayed feature design was used. A consistent time horizon of input information was selected for the tabular and sequential models used. The results obtained indicated that Gradient-Boosted Regression Trees class algorithms achieved the highest quality of fit and were characterised by the greatest stability. |
