Sammanfattning
The conditions in the combustion zones, i.e., the raceways, are crucial for the operation
of the blast furnace. In recent years, advancements in tuyere cameras and image processing and interpretation techniques have provided a better means by which to obtain information from this region of the furnace. In this study, a comprehensive approach is proposed to visually monitor the status of the pulverized coal cloud at the tuyeres based on a carefully designed processing strategy. Firstly, tuyere images are preprocessed to remove noise and enhance image quality, applying the adaptive Otsu algorithm to detect the edges of the coal cloud, enabling precise delineation of the pulverized coal region. Next, a Swin–Unet model, which combines the strengths of Swin Transformer and U-Net architecture, is employed for accurate segmentation of the coal cloud area. The extracted pulverized coal cloud features are analyzed using RGB super-pixel weighting, which takes into account the variations in color within the cloud region. It is demonstrated that the pulverized coal injection rate shows a correlation with the state of the cloud detected based on the images. The effectiveness of this visual monitoring method is validated using real-world data obtained from a blast furnace of SSAB Europe. The experimental results align with earlier research findings and
practical operational experience.
of the blast furnace. In recent years, advancements in tuyere cameras and image processing and interpretation techniques have provided a better means by which to obtain information from this region of the furnace. In this study, a comprehensive approach is proposed to visually monitor the status of the pulverized coal cloud at the tuyeres based on a carefully designed processing strategy. Firstly, tuyere images are preprocessed to remove noise and enhance image quality, applying the adaptive Otsu algorithm to detect the edges of the coal cloud, enabling precise delineation of the pulverized coal region. Next, a Swin–Unet model, which combines the strengths of Swin Transformer and U-Net architecture, is employed for accurate segmentation of the coal cloud area. The extracted pulverized coal cloud features are analyzed using RGB super-pixel weighting, which takes into account the variations in color within the cloud region. It is demonstrated that the pulverized coal injection rate shows a correlation with the state of the cloud detected based on the images. The effectiveness of this visual monitoring method is validated using real-world data obtained from a blast furnace of SSAB Europe. The experimental results align with earlier research findings and
practical operational experience.
| Originalspråk | Engelska |
|---|---|
| Artikelnummer | 529 |
| Antal sidor | 14 |
| Tidskrift | Processes |
| Volym | 12 |
| Status | Publicerad - 6 mars 2024 |
| MoE-publikationstyp | A1 Tidskriftsartikel-refererad |
FN:s SDG:er
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