| Zugriffsnummer | 53274 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct |
| Autor(in); Institution | |
| Quelle/Jahr | Journal of Loss Prevention in the Process Industries: 92 (2024), 1 - 7 |
| Artikelnummer | 105474 |
| ISSN | 0950-4230 (print) |
| DOI | |
| URL | |
| Verlag | Amsterdam: Elsevier |
| Freie Schlagworte | industrial explosions ; powder processing ; electrostatics ; measurement ; simulation ; shallow neural network (SNN) ; machine learning (ML) |
| Zusammenfassung | The electrostatic charge acquired by powders during transport through ducts can cause devastating dust explosions. Our recently developed laser-optical measurement technique can resolve the powder charge along a one-dimensional (1D) path. However, the charge across the duct's complete two-dimensional (2D) crosssection, which is the critical parameter for process safety, is generally unavailable due to limited optical access. To estimate the complete powder charge distribution in a conveying duct, we propose a machine learning (ML) approach using a shallow neural network (SNN). The ML algorithm is trained with cross-sectional data extracted from four different three-dimensional direct numerical simulations of a turbulent duct flow with varying particle size. Through this training with simulation data, the ML algorithm can estimate the powder charge distribution in the duct's cross-section based on only 1D measurements. The results reveal an average L1-error of the reconstructed 2D cross-section of 1.63%. |
| Themenbereich der Metrologie | Physikalische Sicherheitstechnik, Explosionsschutz |
| Innovationscluster | Energie ; Digitalisierung |
| Geschäftsfelder | Metrologie für die Wirtschaft |
| Forschungsprojekt | PowFEct |
| Förderinformationen (1) |
Förderername: European Research Council (ERC)
Förderer ID: 0000 0000 8923 0953 Förderer ID Typ: ISNI Titel der Förderung: PowFEct Förderungsnummer: 947606 URI der Förderung: https://cordis.europa.eu/project/id/947606/de |
Zitierung
Wilms, C., Xu, W., Özler, G., Jantac, S., Schmelter, S., & Grosshans, H. (2024). ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct. Journal of Loss Prevention in the Process Industries, 92, 1–7. https://doi.org/10.1016/j.jlp.2024.105474