Relationship between sinter properties and iron ore granulation index

Cores, A.; Muñiz, M.; Ferreira, S.; Robla, J. I.; Mochón, Javier

doi:10.1179/1743281211y.0000000056

Cited by 20 publications

(16 citation statements)

References 11 publications

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“…10 In addition, the effects of particle size distribution of iron ores on granulation performance were widely researched. [11][12][13] However, specific characteristics of particle size have not been proposed to assess the granulation performance. This paper describes setting up a practical structural model of granules, and finding out the method to optimise the granulation through studying the factors influencing granulation.…”

Section: Introductionmentioning

confidence: 99%

Optimising method for improving granulation effectiveness of iron ore sintering mixture

Gan

Fan

et al. 2014

Ironmaking & Steelmaking

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The structure of granules has been investigated and a model of granulation determined. The results show that a granule consists of an adhesive layer and a nucleus. Particles with diameter under 0.5 mm act as adhesive fines, while the remainders act as nuclei. By studying the influencing factors of granulation, two significant particle characteristics assessing granulation performance were determined. One is the relative proportion of adhesive fines and nucleus particles, and the other is the specific surface area of adhesive fines. The method of optimising granulation of sinter mixtures is proposed as follows: the proportion of adhesive fines should be 40-50% and the specific surface area should exceed 1000 cm 2 g 21 , which contributes to achieving a better bed permeability, a faster sintering speed and higher productivity.

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Section: Introductionmentioning

confidence: 99%

Optimising method for improving granulation effectiveness of iron ore sintering mixture

Gan

Fan

et al. 2014

Ironmaking & Steelmaking

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“…Cores et al further set an upper limit of 8 mm for nuclear particles and classified iron ore into four groups based on G-index, with Group A having a G-index less than 15, Group B having a G-index between 15 and 35, Group C having a G-index between 35 and 70, and Group D having a G-index greater than 70. The use of different groups of iron ore together is beneficial for regulating the granulation properties and improving the sinter strength index 13,65) . Although the presence of iron ore ultrafine powder can help the quasi-particle size to grow, it does not significantly affect the quasi-particle strength, which is mainly controlled by the adhesion force of the liquid bridge 66) .…”

Section: 11: Iron Oresmentioning

confidence: 99%

Iron Ore Granulation for Sinter Production: Developments, Progress, and Challenges

et al. 2023

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Iron ore granulation is an indispensable process in the production of sinter that can influence and regulate the yield, efficiency and quality. Although a great deal of research has been done on the granulation process over the past decades, we still need to think about the current and future development of this process, as sinter is still an essential raw material for ironmaking blast furnace today. This paper begins with a review of particle agglomeration theory development for sintering granulation, followed by a summary of existing granulation evaluation methods and indexes. The roles of iron ore, fuel, fluxes, and moisture in sintering granulation are also analyzed, and finally advanced granulation equipment and processes for industrial production, as well as their applications, are summarized. Correspondingly, the challenges in sintering granulation field are proposed to include: (1) Development of iron ore sintering industry; (2) Ore blending optimization based on synergistic coupling of granulation and sintering; (3) Optimization of granulation process and equipment; (4) Methods and tools for granulation scientific research. These issues will be tackled and overcome in the future by both steel enterprises and academic researchers, generating suggestions for future development in the field of sintering granulation.

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“…The main literature that offers the samples used in the present study is shown in Table 1. In addition, the sample set was also built with some other references [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The correlation analysis of the sample set is shown in Figure 1, which revealed that there was a strong correlation between tumbler index (TI) and CaO content and ignition temperature (IT), an increase of CaO content or ignition temperature could increase the sinter drum strength when other experimental conditions were fixed.…”

Section: Samples Setmentioning

confidence: 99%

The strength prediction model of iron ore sinter based on an artificial neural network

Huang

Liu

et al. 2022

Ironmaking & Steelmaking

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The iron ore sinter is still the main raw material for the blast furnace ironmaking process, its properties, such as strength and reducibility, are of vital importance to the productivity and the smooth operation of the blast furnace. In the present study, one model based on an artificial neural network (ANN) was established to predict the sinter strength. The ANN model was trained with the sample set, which was generated from the credible data from the published papers. The comparison between the direct prediction model and the indirect prediction model with the amount of liquid phase and spinal phase calculated with thermodynamic theory as the middle layer was carried out. The results show that the indirect ANN model gave much higher accurate prediction results than that of the direct one without the middle layer.The parametric study with the validated model shows that the sinter strength increased first with increasing the SiO 2 to 5.4% and then decreased with further increasing the SiO 2 .

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Relationship between sinter properties and iron ore granulation index

Cited by 20 publications

References 11 publications

Optimising method for improving granulation effectiveness of iron ore sintering mixture

Optimising method for improving granulation effectiveness of iron ore sintering mixture

Iron Ore Granulation for Sinter Production: Developments, Progress, and Challenges

The strength prediction model of iron ore sinter based on an artificial neural network

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