Studies on the use of two different magnesia-carbon recyclates as secondary raw material for MgO–C refractories

Moritz, Kirsten; Brachhold, Nora; Küster, Felix; Dudczig, Steffen; Schemmel, Thomas; Aneziris, Christos G.

doi:10.1016/j.oceram.2023.100426

Cited by 6 publications

(1 citation statement)

References 12 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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