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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Donskoi, Eugene
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Topics
Publications (12/12 displayed)
- 2021Characterisation of SFCA phases in iron ore sinter by combined optical microscopy and electron probe microanalysis (EPMA)
- 2021Characterisation of SFCA phases in iron ore sinter by combined optical microscopy and electron probe microanalysis (EPMA)
- 2021Transformation of Automated Optical Image Analysis Software Mineral4/Recognition4 to Mineral5/Recognition5
- 2021Automated Optical Image Analysis of Iron Ore Sintercitations
- 2020Advances in Optical Image Analysis Textural Segmentation in Ironmakingcitations
- 2018Importance of textural information in mathematical modelling of iron ore fines sintering performancecitations
- 2017Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterizationcitations
- 2016Mineralogical quantification of iron ore sintercitations
- 2015Mineralogical quantification of iron ore sinter
- 2015Advances in optical image analysis of iron ore sinter
- 2015Automated optical image analysis of natural and sintered iron orecitations
- 2013Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysiscitations
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article
Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysis
Abstract
In order to develop downstream processing routines for iron ore and to understand the behaviour of the ore during processing, extensive mineralogical characterisation is required. Microscopic analysis of polished sections is effective to determine mineral associations, mineral liberation and grain size distribution. There are two main imaging techniques used for the characterisation of iron ore, i.e. optical image analysis (OIA) and scanning electron microscopy (SEM). In this article, a QEMSCAN system is used as an example of SEM methodology and results obtained from it are compared against results obtained by the CSIRO Recognition3/Mineral3 OIA system. Both OIA and SEM systems have advantages and drawbacks. Even though the latest SEM systems can distinguish between major iron oxides and oxyhydroxides, it is still problematic for SEM systems to distinguish between iron ore minerals very close in oxygen content, e.g. hematite and hydrohematite, or between different types of goethite. Scanning electron microscopy systems also can misidentify minerals with close chemical composition, i.e. hematite as magnetite and vitreous goethite as hematite. In OIA, iron minerals with slight differences in their oxidation or hydration state are more easily and directly recognisable by correlation with their reflectivity. In both methods, the presence of microporosity can result in some misidentification, but in SEM methods misidentifications due to microporosity can be critical. Low resolution during QEMSCAN analysis can significantly affect the textural classification of particle sections. The main conclusion of this study is that, for low iron content ores or tailings, SEM systems can provide much more detailed information on the gangue minerals than OIA. However, for routine characterisation of iron ores with high iron content and containing a variety of iron oxides and oxyhydroxides, OIA is a faster, more cost effective and more reliable method of iron ore characterisation. A combined approach using both techniques will provide the most detailed understanding of iron ore samples being characterised.