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- Enhanced Compressive Strength of Fired Iron Ore Pellets: Effects of Blending Fine and Coarse Particle Concentrates
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Ngo Quoc Dung, Tran Xuan Hai, Nguyen Minh Thuyet, Nguyen Quang Tung, Arvind Barsiwal, Nguyen Hoang Viet
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J Powder Mater. 2025;32(4):315-329. Published online August 29, 2025
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DOI: https://doi.org/10.4150/jpm.2025.00129
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 Abstract
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- This study investigated the effects of oxidative firing parameters and raw material characteristics on the pelletization of Australian and Minh Son (Vietnam) iron ore concentrates. The influence of firing temperature (1050°C–1150°C) and holding time (15–120 min) on pellet compressive strength was examined, focusing on microstructural changes during consolidation. Green pellets were prepared using controlled particle size distributions and bentonite as a binder. Scanning electron microscopy and energy-dispersive X-ray spectroscopy analyses revealed that grain boundary diffusion, liquid phase formation, and densification significantly improved mechanical strength. X-ray diffraction confirmed the complete oxidation of magnetite to hematite at elevated temperatures, a critical transformation for metallurgical performance. Optimal firing conditions for both single and blended ore compositions yielded compressive strengths above 250 kgf/pellet, satisfying the requirements for blast furnace applications. These results provide valuable guidance for improving pellet production, promoting the efficient utilization of diverse ore types, and enhancing the overall performance of ironmaking operations.
- [English]
- Thermodynamic and Electronic Descriptor-Driven Machine Learning for Phase Prediction in High-Entropy Alloys: Experimental Validation
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Nguyen Lam Khoa, Nguyen Duy Khanh, Hoang Thi Ngoc Quyen, Nguyen Thi Hoang Oanh, , Le Hong Thang, Nguyen Hoa Khiem, Nguyen Hoang Viet
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J Powder Mater. 2025;32(3):191-201. Published online June 30, 2025
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DOI: https://doi.org/10.4150/jpm.2025.00143
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Abstract
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- High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al₃₀Cu₁₇.₅Fe₁₇.₅Cr₁₇.₅Mn₁₇.₅ alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.
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Citations
Citations to this article as recorded by  - Preparation and Arc Erosion Behavior of Cu-Based Contact Materials Reinforced with High Entropy Particles CuCrNiCoFe
Jiacheng Tong, Jun Wang, Huimin Zhang, Haoran Liu, Youchang Sun, Zhiguo Li, Wenyi Zhang, Zhe Wang, Yanli Chang, Zhao Yuan, Henry Hu
Metallurgical and Materials Transactions B.2025;[Epub] CrossRef - Recent progresses on high entropy alloy development using machine learning: A review
Abhishek Kumar, Nilay Krishna Mukhopadhyay, Thakur Prasad Yadav
Computational Materials Today.2025; : 100038. CrossRef
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