| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Manuel, James
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2021Positive Influence of WHIMS Concentrate on the Sintering Performance of Roy Hill Fines
- 2021Positive Influence of WHIMS Concentrate on the Sintering Performance of Roy Hill Fines
- 2021Automated Optical Image Analysis of Iron Ore Sintercitations
- 2019Characterisation of phosphorus and other impurities in goethite-rich iron ores – Possible P incorporation mechanismscitations
- 2019Totipotent Cellularly-Inspired Materialscitations
- 2018Importance of textural information in mathematical modelling of iron ore fines sintering performancecitations
- 2016Mineralogical quantification of iron ore sintercitations
- 2015Mineralogical quantification of iron ore sinter
- 2015Automated optical image analysis of natural and sintered iron orecitations
- 2014Sintering characteristics of titanium containing iron orescitations
- 2013Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysiscitations
- 2013In situ X-ray and neutron diffraction studies of silico-ferrite of calcium and aluminium iron ore sinter phase formation
- 2011In situ diffraction studies of phase formation during iron ore sintering
Places of action
| Organizations | Location | People |
|---|
article
Automated Optical Image Analysis of Iron Ore Sinter
Abstract
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.