Iron ore sintering

Lü, Liming; Ishiyama, Osamu; Higuchi, Takahide; Matsumura, Masazumi; Higuchi, Kenichi

doi:10.1016/b978-0-12-820226-5.00006-9

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Sintering usually occurs below the melting points of the powders in the process. During sintering, harmful impurities such as sulfur in the chemical structure of the ore are removed from the environment [16]. This process, which is formed by the combustion of the coke powder in the mixture, is carried out with fixed or mobile sinter machines.…”

Section: Methodsmentioning

confidence: 99%

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Analysis of Iron Ore Sinter RUL Test Results

Bölükbaşı¹

2022

JCHAR

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The sintering process is to bring the powdered iron ores to the desired particle size, strength and gas permeability for the blast furnace. During sintering, important events such as bonding between particles, size change and coarsening of the microstructure take place. Reduction Under Load (RUL) test is performed to determine the metallurgical properties of the sinter sample. Statistical process control techniques are used for iron and steel plants in which data are measured for certain periods to increase physical, chemical and reducibility properties of sinter production. Thus, when some important advantages are provided in terms of control and applicability. In this study, static and dynamic test low temperature reduction disintegration and reduction under load of the samples taken out regularly from sinter samples were investigated to determine capability of the process. For each parameter, X-R control charts using moving range method were drawn. Thereby, deviations of lower and upper limit values were determined It was concluded that the amount and grade of raw ore in the sinter blend should be fixed by using appropriate amounts.

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Section: Methodsmentioning

confidence: 99%

“…Particles that come into contact with each other while sintering are formed by the motions of atoms in the solid state at high temperature (temperature below the melting point). Bonding is manifested by neck formation between contacting particles [16].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Iron Ore Sinter RUL Test Results

Bölükbaşı¹

2022

JCHAR

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“…The mineral processing of iron ore generates a large quantity of fines, considering particles under 5 mm in size [1,2]. Blast furnace (BF) ironmaking involves a vertical counter-current process designed to be fed with materials ranging from 5 mm to 50 mm [3]. Therefore, depending on the mineral-processing consequences over the particle size distribution, agglomeration unitary processes could be required to input specific fluid dynamics conditions, such as terminal velocity, to allow the raw material application in the BF ironmaking route.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, depending on the mineral-processing consequences over the particle size distribution, agglomeration unitary processes could be required to input specific fluid dynamics conditions, such as terminal velocity, to allow the raw material application in the BF ironmaking route. Under this perspective, sintering is an agglomeration process, based on thermal activated systems, used to transform the independent fine particles into a solid, aggregated material with adequate size and porosity, as well as with the mechanical properties required for efficient BF operation [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Batch Sintering of FeO·OH and Fe2O3 Blends: Chemical and Metallurgical Characterization

Pereira,

Coelho,

Santos

et al. 2024

Metals

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A sample of goethite iron ore sinter feed (G_SF) was employed as a raw material in a sintering bed. This sample partially replaced hematite sinter feed (H_SF), which is currently used as raw material in a sintering plant in the state of Minas Gerais, Brazil. This substitution did not adversely affect the chemical and metallurgical proprieties of the sinter mix product, provided that the utilization of G_SF was kept below 30% in weight. Despite the higher proportion of fines in G_SF, the presence of argillaceous minerals in the sample led to an improvement in the granulation index (GI) of the sinter mix product. The GI value increased from 68.4 to 82.7% for the experiments conducted without the presence of goethite ore and with 40% of goethite ore in the sintering mix, respectively. Consequently, the qualities of both the process and the produced sinter product were not compromised. The raw materials and the various sinters produced were characterized through X-ray fluorescence (XRF) and X-ray diffraction (XRD), as well as thermal gravimetric analysis (TGA). The XRD results were used to perform a quantitative assessment of the mineral phase using the Rietveld method (RM). This technique allowed for the determination of goethite content in the studied sample, which was 35.5%. Finally, the incorporation of G_SF in the sintering bed led to a 20% reduction in the cost of raw materials.

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“…A moisture content that is too high or too low can have a detrimental effect, leading to poor mixing and granulation and affecting the permeability of the mixture. Ultimately, the sintered ore is under-or over-sintered, reducing product quality and yield [1]. To be able to govern and control the production process in real time and with consistency, the relevant parameters and control indicators need to be predicted so that the moisture content of the mixture is kept within a certain range during the sintering process.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

Ren

Huang

Jiang

et al. 2022

Metals

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The design and optimization of a sinter mixture moisture controlling system usually require complex process mechanisms and time-consuming field experimental simulations. Based on BP neural networks, a new KPCA-GA optimization method is proposed to predict the mixture moisture content sequential values with time more accurately so as to derive the optimal water addition to meet industrial requirements. Firstly, the normalized input variables affecting the output were dimensionalized using kernel principal component analysis (KPCA), and the contribution rates of the factors affecting the water content were analyzed. Then, a BP neural network model was established. In order to get rid of the randomness of the initial threshold and weights on the prediction accuracy of the model, a genetic algorithm is proposed to preferentially find the optimal initial threshold and weights for the model. Then, statistical indicators, such as the root mean square error, were used to evaluate the fit and prediction accuracy of the training and test data sets, respectively. The available experimental data show that the KPCA-GA model has high fitting and prediction accuracy, and the method has significant advantages over traditional neural network modeling methods when dealing with data sets with complex nonlinear characteristics, such as those from the sintering process.

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Iron ore sintering

Cited by 8 publications

References 47 publications

Analysis of Iron Ore Sinter RUL Test Results

Analysis of Iron Ore Sinter RUL Test Results

Batch Sintering of FeO·OH and Fe2O3 Blends: Chemical and Metallurgical Characterization

Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

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