The effect of C content in MgO–C on dissolution behavior in CaO–SiO2–Al2O3 slag

Lee, Seungwook; Chung, Yoonsun

doi:10.1016/j.ceramint.2022.06.010

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…As mentioned earlier, the SHT apparatus has the advantage of allowing observation of the dissolution behavior of Al2O3 particles in real time. Several studies using this apparatus have taken advantage of this by observing the dissolution behavior of inclusions in real time [5,15,22,23]. However, in this study, there was a problem, as the dissolution behavior of Al2O3 particles could not be observed in real time due to the opacity of the slag at high temperature because it contained FeO, a transition metal.…”

Section: Experimental Conditionsmentioning

confidence: 97%

Activation Energy of Alumina Dissolution in FeO-Bearing Slags

Kwack,

Um,

Chung

2023

Metals

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The dissolution of Al2O3 non-metallic inclusions in slag containing FeO was investigated in this study. The slag system used in the experiments was a quaternary system of CaO-SiO2-Al2O3-FeO. The composition of the slag was studied by fixing the basicity (CaO/SiO2 ratio) to 1 and varying the FeO content to 10 and 20 wt%. In addition, the experimental temperature was varied to 1550 °C, 1575 °C, and 1600 °C to study the effect of temperature on the Al2O3 dissolution behavior. The experimental equipment used was a single hot thermocouple apparatus. The dissolution rate of Al2O3 particles increased linearly with increasing temperature and FeO content. In addition, the mass transfer activation energy of Al2O3 dissolution in FeO 10 wt% and FeO 20 wt% was calculated through an Arrhenius-type analysis. The obtained mass transfer activation energies were 159 and 189 kJ/mole, respectively.

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Section: Experimental Conditionsmentioning

confidence: 97%

Activation Energy of Alumina Dissolution in FeO-Bearing Slags

Kwack,

Um,

Chung

2023

Metals

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“…The described research [68] reveals an extremely important issue in terms of sustainable development, as the production of fused magnesia demands 15 times more energy than the production of sintered aggregates [69]. Even if the corrosion of MgO-C refractories is widely described in the literature [27][28][29][30][31][32][36][37][38][39][70][71][72], scarce information can be found regarding the comparison of high-temperature properties of MgO-C materials based on different magnesia raw materials. Using ML techniques, it was possible to group MgO-C compositions with different ratios of sintered to fused magnesia characterized by comparable corrosion resistance.…”

Section: Corrosion Resistance Of Mgo-c Refractoriesmentioning

confidence: 99%

“…Their addition to MgO-C improves decarburization resistance as well as hot properties, like hot strength and thermal shock resistance. The corrosion resistance of MgO-C has also been broadly investigated, with multiple techniques used, like the induction furnace test [26][27][28], finger test [29,30], sessile-drop technique [31][32][33], cup test [34][35][36][37], single hot thermocouple technique [38], and observations of in situ changes using a high-stage microscope [39]. Recently, new calcium-magnesium-aluminate raw materials have been developed, which promote the formation of a protective layer at the hot face of MgO-C bricks during operation [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

Sado,

Jastrzębska,

Zelik

et al. 2023

Materials

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Nowadays, digitalization and automation in both industrial and research activities are driving forces of innovations. In recent years, machine learning (ML) techniques have been widely applied in these areas. A paramount direction in the application of ML models is the prediction of the material service time in heating devices. The results of ML algorithms are easy to interpret and can significantly shorten the time required for research and decision-making, substituting the trial-and-error approach and allowing for more sustainable processes. This work presents the state of the art in the application of machine learning for the investigation of MgO-C refractories, which are materials mainly consumed by the steel industry. Firstly, ML algorithms are presented, with an emphasis on the most commonly used ones in refractories engineering. Then, we reveal the application of ML in laboratory and industrial-scale investigations of MgO-C refractories. The first group reveals the implementation of ML techniques in the prediction of the most critical properties of MgO-C, including oxidation resistance, optimization of the C content, corrosion resistance, and thermomechanical properties. For the second group, ML was shown to be mostly utilized for the prediction of the service time of refractories. The work is summarized by indicating the opportunities and limitations of ML in the refractories engineering field. Above all, reliable models require an appropriate amount of high-quality data, which is the greatest current challenge and a call to the industry for data sharing, which will be reimbursed over the longer lifetimes of devices.

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“…However, the dissolution studies of nonmetallic inclusions of MgO and Al 2 O 3 have recently used single hot thermocouple technique (SHTT). [19][20][21][22][23][24] Their studies appear to provide high resolution images during the dissolution of particles. This technique also easily provides quenched samples and, therefore interfacial reaction information.…”

Section: Introductionmentioning

confidence: 99%

Dissolution Behavior of MgAl₂O₄ Spinel Inclusions in Mold Slag

Kim,

Chung

2024

steel research int.

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This study investigates the dissolution behavior of single crystal MgAl2O4 particle in CaO–SiO2–Al2O3–MgO–CaF2–Na2O slags using single hot thermocouple technique (SHTT) from 1550 to 1650 °C. MgAl2O4 contents of the slag are set at 5, 15, and 25 wt%, and the basicity (CaO/SiO2) of the slags is fixed at 0.9. Dissolution rate is determined by image analysis of the MgAl2O4 spinel size over time in the slag. In the experimental results, the dissolution rate increases as the temperature increases and as the contents of MgAl2O4 in the slag decreases. During the dissolution, the cube or trapezoid cube shaped MgAl2O4 particles change into a truncated cube, a cube with rounded corners and a sphere. The shrinking core model is modified into a volume ratio and found that the MgAl2O4 dissolution kinetic varies with the slag chemical composition. At high temperature, it appears to be chemical controlled. The dissolution firstly occurs at the corners and edges of the cube particle which can be understood by the Gibbs–Thomson equation. The activation energies of MgAl2O4 dissolution are 205 kJ mol−1 at 5 wt% MgAl2O4, 181 kJ mol−1 at 15 wt% MgAl2O4 and 594 kJ mol−1 at 25 wt% MgAl2O4. The activation energy calculated should be started as similar to other inclusions of Al2O3 and MgO.

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The effect of C content in MgO–C on dissolution behavior in CaO–SiO2–Al2O3 slag

Cited by 12 publications

References 21 publications

Activation Energy of Alumina Dissolution in FeO-Bearing Slags

Activation Energy of Alumina Dissolution in FeO-Bearing Slags

Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

Dissolution Behavior of MgAl₂O₄ Spinel Inclusions in Mold Slag

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The effect of C content in MgO–C on dissolution behavior in CaO–SiO2–Al2O3 slag

Cited by 12 publications

References 21 publications

Activation Energy of Alumina Dissolution in FeO-Bearing Slags

Activation Energy of Alumina Dissolution in FeO-Bearing Slags

Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

Dissolution Behavior of MgAl2O4 Spinel Inclusions in Mold Slag

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Product

Resources

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Dissolution Behavior of MgAl₂O₄ Spinel Inclusions in Mold Slag