The microstructure evolution and performance enhancement of MgO-C refractories by the addition of MA90 spinel micro-powder

Zhong, Hutang; Han, Bingqiang; Wei, Jiawei; Wang, Xin; Liu, Xuexin; Miao, Zheng; Li, Nan

doi:10.1016/j.jeurceramsoc.2023.08.053

Cited by 11 publications

(1 citation statement)

References 63 publications

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“…Although MgO-C refractories have been used since 1950 in steel and refining plants [10], their service times are still being extended owing to progressing research efforts. One of the main research directions in MgO-C improvement is the application of various metallic and non-metallic additions (e.g., Al, Mg, Si, SiC, Al-Mg, Fe) [11][12][13][14][15], as well as the development of new ones (e.g., c-ZrN nanopowder, Ti 3 AlC 2 , Ti 3 SiC 2 , Cr 3 C 2 C, spinel micro-powder, YAG nanopowder, and other oxide composites) [16][17][18][19][20][21][22][23][24][25]. Their addition to MgO-C improves decarburization resistance as well as hot properties, like hot strength and thermal shock resistance.…”

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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