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Prediction of performance and assessment of reusability and recycling of refractory materials using non-destructive online evaluation and machine learning algorithms

Authors: GOPE, Amit Kumar; BOULLE, Alexandre; RICHAUD, Johan; BIRKELBACH, Felix; REBOUILLAT, Lionel; ROMERO BAIVIER, Severine; HUGER, Marc;

Prediction of performance and assessment of reusability and recycling of refractory materials using non-destructive online evaluation and machine learning algorithms

Abstract

Refractory materials are necessary for the Iron and Steel (I&S) sector to endure the demanding conditions of its manufacturing processes. These materials' brief service lives, which can range from a few minutes to several months, create substantial problems for consumption, disposal, and environmental effect. There is a rising need for sustainable solutions that are compliant with the European Green Deal's standards for reduced greenhouse gas emissions, increased energy efficiency, and life cycle assessments. The Concerted European Activity on Sustainable Applications of REFractories (Doctoral Network CESAREF), which aims to enhance research and practices linked to refractory material in the Iron & Steel industry, was founded in 2022 to address these concerns.

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Keywords

Sustainability, Online Sensoring, Refractories, Recycling, Machine learning (ML), Non-destructive evaluation

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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