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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
in Cooperation with on an Cooperation-Score of 37%
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
Importance of textural information in mathematical modelling of iron ore fines sintering performance
Abstract
Predicting the sintering performance of iron ore fines and the possibility of targeted optimisation of specific sinter properties are very important for the iron ore industry and related research organisations. A comprehensive database of pilot-scale sintering experimental results was established and empirical modelling conducted to predict values for sintering performance parameters such as Tumble Index, low temperature Reduction Disintegration Index and productivity. Together with other variables, the models developed include the abundances of several different ore textures which were combined into different textural factors corresponding to different sinter properties. Coefficients for the variables within specific regression equations can provide a better understanding of the effect of the variables on the corresponding sintering performance. The modelling results were also used to predict the sintering performance of tested mixtures that were not part of the database used to establish the models, so all models were thus verified on an independent set of data.