From Iron Ore to Iron Sinter – Process Control Using X-Ray Diffraction (Xrd)

König, U.; Norberg, Nicholas; Gobbo, Luciano de Andrade

doi:10.5151/2594-357x-26276

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Background subtraction was applied to account for minor background variation with ore composition, with increasing background with Fe content (Figure 1) consistent with the study of Fransen (2004). Similar to the approach described by König and Norberg (2015), an optimal regression model was found automatically using the PLSR tool in the HighScore Plus software. The optimal number of PLSR factors was found to be 6 with the scaling mode 'Center', and the root-mean-square error of prediction (RMSEP) was 13.41 [ Figure 2(a)].…”

Section: Methodsmentioning

confidence: 99%

“…Partial least-squares regression (PLSR) analysis has been shown by König et al . (2014) and König and Norberg (2015) to enable prediction of sinter basicity (CaO:SiO 2 ratio) and Fe 2+ content – the latter in particular being a key marker of sinter quality and used for process control – from powder XRD data without the need for rigorous mineralogical analysis by the operator. As well, the traditional wet chemical method for Fe 2+ determination can take several hours to complete, and more rapid feedback for the purpose of process control is desirable.…”

Section: Introductionmentioning

confidence: 99%

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Predicting iron ore sinter strength through partial least square regression (PLSR) analysis of X-ray diffraction patterns

et al. 2017

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The decrease in quality of Australian iron ore, coupled with the demand for more efficient energy use, means that closer monitoring and optimisation of process conditions for iron ore sinter production is required. Here, the suitability of using partial least-squares regression analysis of powder X-ray diffraction data, collected for iron ore sinter samples, for the prediction of iron ore sinter strength has been further assessed. In addition, a preliminary assessment of the effect of 2θ range on the quality of prediction has been made. For the purposes of process control, the level of correlation between predicted strength and actual sinter strength would inform an operator whether or not the process was operating within the acceptable limits, or whether there was a potential problem requiring further investigation or rapid intervention. Reducing the 2θ range was found to reduce the level of correlation between predicted and actual strength, to a point where the particular analysis may no longer be suitable for process control.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting iron ore sinter strength through partial least square regression (PLSR) analysis of X-ray diffraction patterns

et al. 2017

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“…Statistics Process Control (SPC) techniques are used at the specified periods at the substance preparation foundation from which datum is obtained to increase the production's quality and output [7,8]. So, some important easiness is provided in terms of time control and practicability.…”

Section: Introductionmentioning

confidence: 99%

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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“…The fast generation of reliable data which can be used for decision making is a key component to the digitalization of the mining industry. This research on copper ore materials and their mineral processing can easily be translated to other industrial activities, where the mineralogical knowledge can spark innovation directed toward sustainable goals [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Value of Rapid Mineralogical Monitoring of Copper Ores

Pernechele¹,

Lopez

Davoise

et al. 2021

Minerals

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An essential operation in the mineral processing of copper ores into concentrates is blending, as it guarantees a constant feed for the flotation cells, increases metal recovery rate and reduces tailings. In this study, copper ores from Huelva province (Spain) were investigated by quantitative XRD (X-ray diffraction) methods to optimize blending and detect penalty minerals, which can affect flotation and concentrate quality. The Rietveld method in combination with cluster analysis, PLSR and more traditional chemical analysis provide a more complete and in-depth characterization of the ore and the whole process. The mineralogical monitoring can be fully automated to enable real-time decision making.

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From Iron Ore to Iron Sinter – Process Control Using X-Ray Diffraction (Xrd)

Cited by 3 publications

References 5 publications

Predicting iron ore sinter strength through partial least square regression (PLSR) analysis of X-ray diffraction patterns

Predicting iron ore sinter strength through partial least square regression (PLSR) analysis of X-ray diffraction patterns

Analysis of Iron Ore Sinter RUL Test Results

Value of Rapid Mineralogical Monitoring of Copper Ores

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