THERMAL SCIENCE

International Scientific Journal

Jelena B. Ivanović

,

Vaso D. Manojlović

,

Nataša M. Gajić

,

Nigel T. Phuthi

,

Jelena P. Zakonović

,

Miroslav D. Sokić

,

Željko J. Kamberović

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 ma-chine learning to enhance the sustainability of steel production, aligning with circular economy principles.
KEYWORDS
electric arc furnace, exergy analysis, carbon footprint, circular economy
PAPER SUBMITTED: 2024-06-07
PAPER REVISED: 2024-09-09
PAPER ACCEPTED: 2024-10-24
PUBLISHED ONLINE: 2024-12-07
DOI REFERENCE: https://doi.org/10.2298/TSCI240607258I
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 3, PAGES [2167 - 2183]
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PDF VERSION [DOWNLOAD]
Exergy analysis and machine learning for enhanced EAF steel recycling

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence