Interpretation on Iron Ore Granulation Process Based on Particle-size Analysis

Huang, Xiaobo; Lv, Xuewei; Xue, Zhengliang

doi:10.2355/isijinternational.isijint-2016-096

Cited by 11 publications

(6 citation statements)

References 24 publications

(29 reference statements)

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“…According to the distribution coefficient, we can get the properties of the granules. There are some parameters for granules of the ith grain size (dÀd l ), such as the ratio of the particles of the adhesive layer to the nuclei particles in the granules, R i , shown in Equation (8); the mass fraction of the granules, ω gi , %, described in Equation ( 9); the thickness of the adhesive layer and the maximum particle size of granules, Δ i and x i , mm, depicted in Equations ( 10) and (11); the average diameter of granules, d, mm, obtained by Equation (12).…”

Section: Litster's Modelmentioning

confidence: 99%

“…[ 7 ] For the adhesive powder boundary, some scholars defined it as −0.25, [ 8 ] −0.5 [ 9 ] or −1 mm. [ 10 ] In addition, there are some models used for predicting the granulation effective of the sinter feeds, including nucleation models, [ 11 ] aggregation models, [ 12 ] kinetic models, [ 13 ] and numerical simulation models. However, nucleation models are limited by parameters that are difficult to measure, such as the size distribution of the droplets.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Prediction Model for the Granulation Effectiveness of Sintering Mixtures

Dai

Fan

Huang

et al. 2023

steel research int.

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The optimization model is proposed on the basis of the Litster's granulation model for the prediction of the granulation effectiveness of the sinter feed. Formulas are proposed to calculate parameters for the adhesive layer of the granules, including its mass‐averaged particle size and the water saturation. Based on small‐scale granulation and sieving experiments, a functional correlation is established that relates the granules properties to the distribution coefficient curve, confirming the validity of the model. The prediction of model is discussed with adhesive layer boundary conditions of −0.25, −0.5, and −1.0 mm, and when the boundary is −0.5 mm, the relative error is minimal. The optimization of the distribution coefficient curve based on the binomial interpolation method can make the model achieve a hit rate of 92.9%. Based on this model, the granulation effectiveness of the mixtures could be evaluated.

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Section: Litster's Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Prediction Model for the Granulation Effectiveness of Sintering Mixtures

Dai

Fan

Huang

et al. 2023

steel research int.

View full text Add to dashboard Cite

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“…The granules bed permeability highly influences productivity and sinter properties by determining the gas flow rate through the bed. This is a very complex parameter, which is sensitive to changes in moisture content, granule size and shape, size dispersion, void fraction and angle of repose [23][24][25][26][27]. To elucidate such effects, the permeability data were analysed by a combination of three size parameters: Sauter mean diameter (SMD), size distribution width (W -8+1 ), and the weight percentage of granules under 1 mm (W -1 ).…”

Section: Permeabilitymentioning

confidence: 99%

Granulation behaviour of a sinter mixture with high additions of pellet feed and ground pellet feed

Lopes

Flores

Oliveira

et al. 2020

Ironmaking & Steelmaking

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The present study investigated the influence of high proportions of pellet feed (two different particle sizes) on the cold agglomeration behaviour of a sinter mixture and the properties of the granules. Using high proportions of adherent particles (pellet feed) lead to a decrease in the mixtures' mean diameter, producing wider size distributions with higher contents of granules below 1 mm. The bed permeability was impaired for higher additions of pellet feed, which correlated with the alterations of granules size distribution. Degradation tests so simulate handling and transported sharply affected granules size distribution and permeability, showing that larger granules are more susceptible to degradation. Moreover, both the ratio and the size distribution of the pellet feed were found to influence the granulation mechanism to different extents. Using finer adherent particles at adherents/nuclei ratios bigger than 50% changed the agglomeration mechanism from layering towards a combination of coalescence and layering.

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“…Recently, the fractal parameter was introduced for interpretation of granulation process. 14) Nevertheless, from a quantitative view, 11) the moisture available for granulation (total moisture less the moisture absorbed by feed materials) and the size distribution of raw materials are the paramount factors, whereas other ore properties such as particle shape and surface characteristics are of secondary importance because these properties actually take effect through their impact on the moisture absorption capability. 15) These experiments in J.D.…”

Section: Introductionmentioning

confidence: 99%

Granulation Effectiveness of Iron Ore Sinter Feeds: Effect of Ore Properties

et al. 2018

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This paper developed a quantitative understanding of the effect of ore properties on granulation effectiveness for different iron ore types based on the mechanistic model proposed by J.D. Litster et al. The granulation effectiveness was measured by the magnitude of x 0.5 , which was postulated to be a function of the layer binding strength. Water was adopted as the only binder for all granulation tests. A wide range of moisture contents, up to the optimum moisture content that yielded an optimum permeability, was tested for each ore type. The typical structure of granules made from different ores was investigated to reveal the effect of mineralogical properties of ore particles on the growth of granules. The results indicate that the granulation effectiveness x 0.5 varies markedly with ore type and moisture content. Feed particle size distribution and moisture for granulation are paramount factors determining the granulation effectiveness x 0.5 , which has a single relationship with the mass mean size of layering particles independent of ore type. The mass mean size of layering particles is dependent on the inherent size distribution of raw materials and the mass fraction of layering particles, while the latter is linear to available moisture content. The model works well for some ore types except for those with unreasonable size distributions: 1) excessive intermediate particles meanwhile lacking of fine particles; or 2) excessive ultrafine particles. An effective criteria closely related to layer binding strength, (1 − ε)S/ε d , is proposed to determine the applicability of the model.

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Interpretation on Iron Ore Granulation Process Based on Particle-size Analysis

Cited by 11 publications

References 24 publications

Optimized Prediction Model for the Granulation Effectiveness of Sintering Mixtures

Optimized Prediction Model for the Granulation Effectiveness of Sintering Mixtures

Granulation behaviour of a sinter mixture with high additions of pellet feed and ground pellet feed

Granulation Effectiveness of Iron Ore Sinter Feeds: Effect of Ore Properties

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