Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study
Abstract
Zinc recovery from Electric Arc Furnace (EAF) dust represents a significant challenge in the iron and steel industry. This study aims to classify zinc quality in slag produced through the Waelz process, where zinc is reduced and volatilized at high temperatures (>1000°C) in rotary kilns, using machine learning techniques. The classification of zinc quality in slag is crucial for process optimization and environmental sustainability, as it directly impacts both resource recovery efficiency and waste management strategies. The dataset utilized for developing classification models was obtained from chemical analyses of Waelz process raw materials and slag samples. Four distinct classification algorithms (Support Vector Machine SVM, Decision Tree - DT, Naive Bayes - NB, and Random Forest - RF) were evaluated on the data labeled by experts according to zinc content in slag. The reliability of the models was assessed through 10-fold cross-validation. In experimental studies, the DT algorithm demonstrated superior performance with 100.0% accuracy, precision, sensitivity, and F1 score. The RF algorithm achieved second-place performance with 96.0-98.0% accuracy and 100.0% precision, followed byNB with 91.0-94.0% accuracy, and SVM with 84.0-88.0% accuracy. The results indicate that the DT algorithm can serve as a reliable tool for quality classification in the zinc re-covery process. These findings contribute significantly to the advancement of automated quality control systems in metallurgical processes, potentially enabling real-time monitoring and optimization of zinc recovery operations.
Keywords
References
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Details
Primary Language
English
Subjects
Materials Science and Technologies, Material Characterization
Journal Section
Research Article
Publication Date
December 31, 2024
Submission Date
June 16, 2024
Acceptance Date
November 10, 2024
Published in Issue
Year 2024 Volume: 4 Number: 4
APA
Özcan, D., Karaoğlan, K. M., & Çelik, M. (2024). Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study. Engineering Perspective, 4(4), 171-177. https://doi.org/10.29228/eng.pers.79502
AMA
1.Özcan D, Karaoğlan KM, Çelik M. Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study. engineeringperspective. 2024;4(4):171-177. doi:10.29228/eng.pers.79502
Chicago
Özcan, Didem, Kürşat Mustafa Karaoğlan, and Mehmet Çelik. 2024. “Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study”. Engineering Perspective 4 (4): 171-77. https://doi.org/10.29228/eng.pers.79502.
EndNote
Özcan D, Karaoğlan KM, Çelik M (December 1, 2024) Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study. Engineering Perspective 4 4 171–177.
IEEE
[1]D. Özcan, K. M. Karaoğlan, and M. Çelik, “Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study”, engineeringperspective, vol. 4, no. 4, pp. 171–177, Dec. 2024, doi: 10.29228/eng.pers.79502.
ISNAD
Özcan, Didem - Karaoğlan, Kürşat Mustafa - Çelik, Mehmet. “Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study”. Engineering Perspective 4/4 (December 1, 2024): 171-177. https://doi.org/10.29228/eng.pers.79502.
JAMA
1.Özcan D, Karaoğlan KM, Çelik M. Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study. engineeringperspective. 2024;4:171–177.
MLA
Özcan, Didem, et al. “Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study”. Engineering Perspective, vol. 4, no. 4, Dec. 2024, pp. 171-7, doi:10.29228/eng.pers.79502.
Vancouver
1.Didem Özcan, Kürşat Mustafa Karaoğlan, Mehmet Çelik. Classification of Zinc Recovery Quality from EAF Dust Using Machine Learning: A Waelz Process Study. engineeringperspective. 2024 Dec. 1;4(4):171-7. doi:10.29228/eng.pers.79502
Cited By
Zinc Kiln Slag Recycling Based on Hydrochloric Acid Oxidative Leaching and Subsequent Metal Recovery
Sustainability
https://doi.org/10.3390/su172210171