THERMAL SCIENCE
International Scientific Journal
Thermal Science - Online First
online first only
Exergy analysis and machine learning for enhanced EAF steel recycling
ABSTRACT
This study relies on exergy principles to analyze the sustainability of the steel recycling process in electric arc furnaces. Focusing on a balance between material and energy efficiencies, the research addresses the degradation of elements such as manganese and silicon from steel to slag phase. Machine learning techniques were employed to predict and optimize element distribution coefficients. By leveraging HSC v. 9 software, a detailed exergy analysis was performed, utilizing precise coefficients for element distribution in steel and slag, with energy consumption. The results demonstrate the potential of integrating exergy analysis and machine learning to enhance the sustainability of steel production, aligning with circular economy principles.
KEYWORDS
PAPER SUBMITTED: 2024-06-07
PAPER REVISED: 2024-09-09
PAPER ACCEPTED: 2024-10-24
PUBLISHED ONLINE: 2024-12-07
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