Automated Optical Image Analysis of Iron Ore Sinter
| Autor: | A. Poliakov, James Manuel, Michael John Peterson, Sarath Hapugoda, Tom Honeyands, Birgit Kain Bückner, Heinrich Mali, Mark I. Pownceby, E. Donskoi |
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| Rok vydání: | 2021 |
| Předmět: |
Blast furnace
SFCA Materials science 0211 other engineering and technologies iron ore Sintering 02 engineering and technology engineering.material 010502 geochemistry & geophysics 01 natural sciences Texture (geology) hematite chemistry.chemical_compound Larnite image analysis goethite structure sinter 021102 mining & metallurgy 0105 earth and related environmental sciences Magnetite algorithm Mineral Metallurgy Geology Hematite Mineralogy Geotechnical Engineering and Engineering Geology Iron ore chemistry visual_art visual_art.visual_art_medium engineering texture QE351-399.2 |
| Zdroj: | Minerals Volume 11 Issue 6 Minerals, Vol 11, Iss 562, p 562 (2021) |
| ISSN: | 2075-163X |
| Popis: | Sinter quality is a key element for stable blast furnace operation. Sinter strength and reducibility depend considerably on the mineral composition and associated textural features. During sinter optical image analysis (OIA), it is important to distinguish different morphologies of the same mineral such as primary/secondary hematite, and types of silico-ferrite of calcium and aluminum (SFCA). Standard red, green and blue (RGB) thresholding cannot effectively segment such morphologies one from another. The Commonwealth Scientific Industrial Research Organization’s (CSIRO) OIA software Mineral4/Recognition4 incorporates a unique textural identification module allowing various textures/morphologies of the same mineral to be discriminated. Together with other capabilities of the software, this feature was used for the examination of iron ore sinters where the ability to segment different types of hematite (primary versus secondary), different morphological sub-types of SFCA (platy and prismatic), and other common sinter phases such as magnetite, larnite, glass and remnant aluminosilicates is crucial for quantifying sinter petrology. Three different sinter samples were examined. Visual comparison showed very high correlation between manual and automated phase identification. The OIA results also gave high correlations with manual point counting, X-ray Diffraction (XRD) and X-ray Fluorescence (XRF) analysis results. Sinter textural classification performed by Recognition4 showed a high potential for deep understanding of sinter properties and the changes of such properties under different sintering conditions. |
| Databáze: | OpenAIRE |
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