Relationship Between Thermodynamic Modeling and Experimental Process for Optimization Ferro-Nickel Smelting

Saprolite ores in nickel laterite deposits are pyrometallurgically processed to produce Fe-Ni alloy and Ni matte. The key to achieving the highest recovery degrees from nickel ore in electric arc furnaces and producing top-quality ferro-nickel alloys lies in maintaining optimal carbon consumption and carefully controlling the composition of the slag. This research work focused on finding the optimal smelting procedure for extracting ferro-nickel from calcined nickel ore. Comparing experimental data to the results of thermodynamic modeling using Factsage 8.2 software was a key part of the study. The nickel smelting process, which involved a carbon consumption of 4 wt.%, resulted in ferro-nickel with an Fe/Ni ratio of 4.89 and slag with a nickel content of just 0.017%. The structure and properties of nickel slag in the MgO-SiO2-FeO system were investigated by observing the changes in the MgO/SiO2 ratio. This study found a significant nickel recovery degree of 95.6% within the optimal M/S ratio range of 0.65 to 0.7. When the M/S ratio exceeds 0.7, iron-rich magnesium silicates (MgxFeySiO2+n) are generated within the slag. These compounds are released downwards due to their higher specific weight, restricting the movement of small metal particles and contributing to increased metal loss through the slag. Optimized slags could revolutionize smelting, increasing metal recovery while minimizing environmental impact.

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Publication Details

Journal
Minerals
Published
2025-01-22
DOI
https://doi.org/10.3390/min15020101
Citations
4
Primary Topic
Metallurgical Processes and Thermodynamics
Type
article
Field-Weighted Citation Impact
2.85

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article

Relationship Between Thermodynamic Modeling and Experimental Process for Optimization Ferro-Nickel Smelting

Jei‐Pil Wang, Seungmin Heo, Changho Jung, Erdenebold Urtnasan et al.
4 citations
Minerals
Metallurgical Processes and Thermodynamics
2.85
article

Relationship Between Thermodynamic Modeling and Experimental Process for Optimization Ferro-Nickel Smelting

Jei‐Pil Wang, Seungmin Heo, Changho Jung, Erdenebold Urtnasan, Joo-Won Yu
article en
4 citations

Abstract

Saprolite ores in nickel laterite deposits are pyrometallurgically processed to produce Fe-Ni alloy and Ni matte. The key to achieving the highest recovery degrees from nickel ore in electric arc furnaces and producing top-quality ferro-nickel alloys lies in maintaining optimal carbon consumption and carefully controlling the composition of the slag. This research work focused on finding the optimal smelting procedure for extracting ferro-nickel from calcined nickel ore. Comparing experimental data to the results of thermodynamic modeling using Factsage 8.2 software was a key part of the study. The nickel smelting process, which involved a carbon consumption of 4 wt.%, resulted in ferro-nickel with an Fe/Ni ratio of 4.89 and slag with a nickel content of just 0.017%. The structure and properties of nickel slag in the MgO-SiO2-FeO system were investigated by observing the changes in the MgO/SiO2 ratio. This study found a significant nickel recovery degree of 95.6% within the optimal M/S ratio range of 0.65 to 0.7. When the M/S ratio exceeds 0.7, iron-rich magnesium silicates (MgxFeySiO2+n) are generated within the slag. These compounds are released downwards due to their higher specific weight, restricting the movement of small metal particles and contributing to increased metal loss through the slag. Optimized slags could revolutionize smelting, increasing metal recovery while minimizing environmental impact.

MineralsVol. 15(2)
Mongolian University of Science and Technology (MN), Pukyong National University (KR)
Ministry of Trade, Industry and Energy, Korea Institute of Energy Technology Evaluation and Planning
Openalex Percentile: Top 11%
Metallurgical Processes and Thermodynamics
2.85
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Relationship Between Thermodynamic Modeling and Experimental Process for Optimization Ferro-Nickel Smelting — Jei‐Pil Wang, Seungmin Heo, et al. · Minerals (2025) | TGRS Research Map | TGRS