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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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Lu, Liming
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2021Characterisation of SFCA phases in iron ore sinter by combined optical microscopy and electron probe microanalysis (EPMA)
- 2021Positive Influence of WHIMS Concentrate on the Sintering Performance of Roy Hill Fines
- 2018Importance of textural information in mathematical modelling of iron ore fines sintering performancecitations
- 2015Automated optical image analysis of natural and sintered iron orecitations
- 2015Utilization of biomass as an alternative fuel in ironmakingcitations
- 2014Effect of sintering conditions on the formation of mineral phases during iron ore sintering with New Zealand ironsand
- 2014Current status and future direction of low-emission Integrated Steelmaking Processcitations
- 2013Substitution of charcoal for coke breeze in iron ore sinteringcitations
Places of action
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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.