Determination of Coal and Biomass Co-Combustion Process States Using Convolutional Neural Networks
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Kotyra Andrzej, Gromaszek Konrad |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 19 |
| Wolumen/Tom: | 18 |
| Numer artykułu: | 5219 |
| Strony: | 1 - 17 |
| Impact Factor: | 3,2 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| 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: | 1 października 2025 |
| Abstrakty: | angielski |
| The paper presents the application of high-speed flame imaging combined with convolutional neural networks (CNNs) for determining different states of biomass–coal co-combustion in terms of thermal power and excess air coefficient. The experimental setup and methodology used in a laboratory-scale co-combustion system are described, highlighting tests conducted across nine defined operational variants. The performance of several state-of-the-art CNN architectures was examined, focusing particularly on those achieving the highest classification metrics and exploring the dependence of input image resolution and applying a transfer learning paradigm. By benchmarking various CNNs on a large, diverse image dataset without preprocessing, the research advances intelligent, automated control systems for improved stability, efficiency, and emissions control, bridging advanced visual diagnostics with real-time industrial applications. The summary includes recommendations and potential directions for further research related to the use of image data and machine learning techniques in industry. |
