Recyclate-containing magnesia-carbon refractories – Influence on the non-metallic inclusions in steel

Moritz, Kirsten; Kerber, Florian; Dudczig, Steffen; Schmidt, Gert; Schemmel, Thomas; Schwarz, Matthias; Jansen, Helge; Aneziris, Christos G.

doi:10.1016/j.oceram.2023.100450

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…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]. With the increased demand for widely understood decarbonization and sustainable development, much effort is also being put into the recycling of MgO-C materials [43][44][45][46]. Ludwig et al [43] obtained satisfactory results for 20% and even 30% additions of recycled MgO-C aggregate in a composition of new MgO-C brick.…”

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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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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Eco-friendly lactose/tannin-based binder in MgO–C refractories produced from MgO–C recyclate

Lauermannová,

Jankovský,

Stadtmüller

et al. 2024

Ceramics International

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Research on the development of recycling technology for magnesium refractory materials

Hao,

Yu,

Chen

et al. 2024

Mater. Res. Express

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Magnesium refractory materials are rich in substances such as alumina and magnesium oxide, and the recycling and reuse of their waste is an important way to achieve energy conservation, environmental protection, and sustainable development of mineral resources. This article analyzes and elucidates the current recycling and utilization technologies of refractory materials from the perspectives of refractory waste used in desulfurization mixing heads, recycled magnesia carbon brick refractory materials, magnesia calcium brick refractory materials, and blast furnace lining refractory materials. By analyzing the key issues in the recycling and utilization technology of Magnesium refractory waste, important references are provided for the development direction of deep processing of high-quality refractory recycled products.

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Recyclate-containing magnesia-carbon refractories – Influence on the non-metallic inclusions in steel

Cited by 9 publications

References 34 publications

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

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

Eco-friendly lactose/tannin-based binder in MgO–C refractories produced from MgO–C recyclate

Research on the development of recycling technology for magnesium refractory materials

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