Phosphate mine tailings are often treated as waste, even though they contain significant residual phosphorus (P2O5) and rare earth elements (REEs). Reprocessing these tailings presents a promising opportunity for recovering critical raw materials (CRMs) to support the green energy transition and sustainable fertilizer production. Phosphate tailings storage facilities (TSFs) occupy large land areas, pose long-term geotechnical risks and lock up critical raw materials. This study presents an integrated approach to evaluate the residual value of phosphorus and rare earth elements lost within the phosphate tailings from Youssoufia site (Morocco) by combining strategic drill cores sampling, comprehensive physical, chemical and mineralogical characterization and 3D modelling. The chemical analysis results were compiled into a database, which was then geo-referenced and integrated to develop a 3D block model using DATAMINE StudioRM software. On average, the tailings contain between 10 and 16 wt% P2O5 along with approximately 250 ppm of REEs. Around 50% of francolite particles are free, indicating high recovery potential via flotation and demonstrated a positive correlation between P2O5 and REEs. 3D modelling identified spatially enriched zones for targeted re-mining. Results demonstrate that combining drilling, advanced mineralogy and 3D modelling transforms waste into a secondary resource a key step toward circular phosphate supply strategies.
{"title":"Assessing the potential of phosphate mine tailings through advanced characterization and spatial modelling","authors":"Kaoutar Erraihani , Yassine Taha , Noaman Bouhlali , Mustapha El Ghorfi , Manar Derhy , Mostafa Benzaazoua , Yassine Ait-Khouia","doi":"10.1016/j.mineng.2025.110046","DOIUrl":"10.1016/j.mineng.2025.110046","url":null,"abstract":"<div><div>Phosphate mine tailings are often treated as waste, even though they contain significant residual phosphorus (P<sub>2</sub>O<sub>5</sub>) and rare earth elements (REEs). Reprocessing these tailings presents a promising opportunity for recovering critical raw materials (CRMs) to support the green energy transition and sustainable fertilizer production. Phosphate tailings storage facilities (TSFs) occupy large land areas, pose long-term geotechnical risks and lock up critical raw materials. This study presents an integrated approach to evaluate the residual value of phosphorus and rare earth elements lost within the phosphate tailings from Youssoufia site (Morocco) by combining strategic drill cores sampling, comprehensive physical, chemical and mineralogical characterization and 3D modelling. The chemical analysis results were compiled into a database, which was then geo-referenced and integrated to develop a 3D block model using DATAMINE StudioRM software. On average, the tailings contain between 10 and 16 wt% P<sub>2</sub>O<sub>5</sub> along with approximately 250 ppm of REEs. Around 50% of francolite particles are free, indicating high recovery potential via flotation and demonstrated a positive correlation between P<sub>2</sub>O<sub>5</sub> and REEs. 3D modelling identified spatially enriched zones for targeted re-mining. Results demonstrate that combining drilling, advanced mineralogy and 3D modelling transforms waste into a secondary resource a key step toward circular phosphate supply strategies.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"238 ","pages":"Article 110046"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-12DOI: 10.1016/j.mineng.2026.110071
Sayed Janishar Anzoom, Ghislain Bournival, Seher Ata
In a fluidized-bed flotation system, hydrophobic particles are often buoyed in the pulp phase as bubble clusters. However, the impact of these clusters on the flotation process is not thoroughly understood due to a lack of characterization techniques to study their properties. A technique for studying the properties of clusters using micro-computed tomography (micro-CT) was established with single mineral particles (Anzoom et al., 2024). This study aims to extend this approach to real ores, such as copper ore, where the particles consist of different minerals, to gain a more comprehensive understanding of the properties of bubble clusters. The approach integrates micro-CT, X-ray, diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) to perform three-dimensional quantitative characterization of bubble clusters and visualize the internal structure and distribution of different mineral phases present in the particles. Two methods were used to generate clusters: one involving a hand-shaking experiment and another using a fluidised-bed flotation system. Results revealed that cluster characteristics vary depending on their formation method and the particle size distribution. Particle-particle aggregation was more pronounced in the cluster formed using the hand-shaking experiment. The hydrophobic chalcopyrite mineral phase was observed attaching to bubbles, while gangue minerals were engulfed within particles transported by the bubble clusters. While this study focuses on copper ore, which contains a high concentration of chalcopyrite, the method is applicable to studying bubble clusters with any mineral particles.
在流化床浮选系统中，疏水颗粒通常以气泡团簇的形式浮在矿浆相中。然而，由于缺乏表征技术来研究其性质，这些团簇对浮选过程的影响尚未完全了解。一种利用微计算机断层扫描（micro-CT）研究单个矿物颗粒簇性质的技术被建立起来（Anzoom et al., 2024）。本研究旨在将这种方法扩展到真实的矿石，如铜矿石，其中颗粒由不同的矿物组成，以获得对气泡团簇性质的更全面的了解。该方法将微ct、x射线、衍射（XRD）、扫描电子显微镜与能量色散x射线能谱（SEM-EDS）相结合，对气泡团簇进行三维定量表征，并可视化颗粒中不同矿物相的内部结构和分布。研究人员使用了两种方法来生成簇：一种是握手实验，另一种是流化床浮选系统。结果表明，聚类特征随其形成方式和粒径分布的不同而不同。在握手实验形成的团簇中，粒子-粒子聚集更为明显。观察到疏水黄铜矿矿物相附着在气泡上，而脉石矿物被气泡团运输的颗粒吞没。该方法适用于任何矿物颗粒的气泡团簇研究，但研究对象为铜矿石，其中黄铜矿含量较高。
{"title":"Micro-CT imaging of bubble clusters: Extending single mineral observations to a real ore system","authors":"Sayed Janishar Anzoom, Ghislain Bournival, Seher Ata","doi":"10.1016/j.mineng.2026.110071","DOIUrl":"10.1016/j.mineng.2026.110071","url":null,"abstract":"<div><div>In a fluidized-bed flotation system, hydrophobic particles are often buoyed in the pulp phase as bubble clusters. However, the impact of these clusters on the flotation process is not thoroughly understood due to a lack of characterization techniques to study their properties. A technique for studying the properties of clusters using micro-computed tomography (micro-CT) was established with single mineral particles (<span><span>Anzoom et al., 2024</span></span>). This study aims to extend this approach to real ores, such as copper ore, where the particles consist of different minerals, to gain a more comprehensive understanding of the properties of bubble clusters. The approach integrates micro-CT, X-ray, diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) to perform three-dimensional quantitative characterization of bubble clusters and visualize the internal structure and distribution of different mineral phases present in the particles. Two methods were used to generate clusters: one involving a hand-shaking experiment and another using a fluidised-bed flotation system. Results revealed that cluster characteristics vary depending on their formation method and the particle size distribution. Particle-particle aggregation was more pronounced in the cluster formed using the hand-shaking experiment. The hydrophobic chalcopyrite mineral phase was observed attaching to bubbles, while gangue minerals were engulfed within particles transported by the bubble clusters. While this study focuses on copper ore, which contains a high concentration of chalcopyrite, the method is applicable to studying bubble clusters with any mineral particles.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"238 ","pages":"Article 110071"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1016/j.mineng.2026.110224
Ali Taheri Najafabadi, Hossein Aboody, Fatemeh Amiri, Farnaz Tavoli
{"title":"Valorization of spent methanol synthesis catalysts as a secondary source of Cu and Zn: Optimization of an integrated three-stage hydrometallurgical recovery process","authors":"Ali Taheri Najafabadi, Hossein Aboody, Fatemeh Amiri, Farnaz Tavoli","doi":"10.1016/j.mineng.2026.110224","DOIUrl":"https://doi.org/10.1016/j.mineng.2026.110224","url":null,"abstract":"","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"59 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147501682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-20DOI: 10.1016/j.mineng.2026.110230
Sepehr Ghaderi, Wencai Zhang, Braden J. Limb, Jason C. Quinn, Ah-Hyung Alissa Park, Aaron J. Moment, Jingyao Meng, Dwain Michael Veach
{"title":"Integrated green mechano-chemical assisted recovery of nickel with carbon mineralization of olivine: Mechanisms and life cycle assessment","authors":"Sepehr Ghaderi, Wencai Zhang, Braden J. Limb, Jason C. Quinn, Ah-Hyung Alissa Park, Aaron J. Moment, Jingyao Meng, Dwain Michael Veach","doi":"10.1016/j.mineng.2026.110230","DOIUrl":"https://doi.org/10.1016/j.mineng.2026.110230","url":null,"abstract":"","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"16 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effects of impurities on the rheological behavior of rare earth concentrate slurry and the regulatory role of retarders","authors":"Xiaodong Wang, Xiaowei Zhang, Feng Guo, Yanhong Hu, Jinxiu Wu, Wenjun Fan, Likai Zang, Qifu Yuli, Zhao Yu, Ruifeng Ma","doi":"10.1016/j.mineng.2026.110225","DOIUrl":"https://doi.org/10.1016/j.mineng.2026.110225","url":null,"abstract":"","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"16 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-16DOI: 10.1016/j.mineng.2026.110229
Lingyu Gao, Rongjie Zhu, Zhaozhi Zhou, Wenqing Ma, Min Zhao, Hanbing He
Rare earth-bearing phosphate ores (REP), characterized by high P content and abundant Ca and Mg impurities, poses significant challenges for REEs recovery. In this work, a novel two-step HCl leaching strategy was developed to achieve selective dolomite removal and efficient REEs extraction without flotation pretreatment or phosphogypsum generation. In the first low-acid stage, guided by thermodynamic calculations and E–pH diagram analysis, the leaching conditions were optimized via response surface method (RSM) and multi-objective optimization, enabling a high Mg removal rate (93.3%) with minimal P loss (4.9%). XRD, SEM-EDS, XPS, FTIR and ICP analyses revealed phase evolution and selective separation behavior during the leaching process. In the second high-acid stage, kinetic studies fitted by shrinking-core models demonstrated that the leaching of REEs was governed by intra-particle diffusion, with apparent activation energies were all lower than 20 kJ·mol⁻1. Economic analysis suggests that the proposed two-step process exhibits reasonable economic feasibility and promising potential for industrial application. Overall, this strategy provides an economically and environmentally sustainable route for the comprehensive utilization of low-grade REP resources.
{"title":"A two-step acid leaching strategy for impurity reduction and efficient REEs recovery from phosphate ores: mechanism, optimization, and economic evaluation","authors":"Lingyu Gao, Rongjie Zhu, Zhaozhi Zhou, Wenqing Ma, Min Zhao, Hanbing He","doi":"10.1016/j.mineng.2026.110229","DOIUrl":"https://doi.org/10.1016/j.mineng.2026.110229","url":null,"abstract":"Rare earth-bearing phosphate ores (REP), characterized by high P content and abundant Ca and Mg impurities, poses significant challenges for REEs recovery. In this work, a novel two-step HCl leaching strategy was developed to achieve selective dolomite removal and efficient REEs extraction without flotation pretreatment or phosphogypsum generation. In the first low-acid stage, guided by thermodynamic calculations and E–pH diagram analysis, the leaching conditions were optimized via response surface method (RSM) and multi-objective optimization, enabling a high Mg removal rate (93.3%) with minimal P loss (4.9%). XRD, SEM-EDS, XPS, FTIR and ICP analyses revealed phase evolution and selective separation behavior during the leaching process. In the second high-acid stage, kinetic studies fitted by shrinking-core models demonstrated that the leaching of REEs was governed by intra-particle diffusion, with apparent activation energies were all lower than 20 kJ·mol⁻1. Economic analysis suggests that the proposed two-step process exhibits reasonable economic feasibility and promising potential for industrial application. Overall, this strategy provides an economically and environmentally sustainable route for the comprehensive utilization of low-grade REP resources.","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"40 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-14DOI: 10.1016/j.mineng.2026.110227
Ning Han, Yifei Li, Zhiyuan Zhang, Jiabao Gong, Bolin Zhang, Yanfeng Li
Coal plays a pivotal role in the global energy sector, and accurately assessing ash content is a core component of quality management in the coal industry. However, existing predictive models suffer from low accuracy and other issues, failing to meet the demand for rapid and precise coal ash assessment in industrial production. This study proposed a new method utilizing Bayesian algorithms to optimize stacked ensemble learning. Subsequently, this method is employed to predict the ash content of flotation cleaned coal based on XRF oxide data. Preliminary predictions were made using five distinct machine learning techniques. Bayesian optimization is employed to determine optimal hyperparameters of single models. By screening through multiple configuration schemes, the optimal stacking model configuration was identified. Prediction metrics peaked when the base learner module employed Random Forest and Gradient Boosted Decision Trees, while the meta-learner utilized linear regression. Final metrics achieved were MAE = 0.1257, MAPE = 1.5480, MSE = 0.0261, RMSE = 0.1687, and R2 = 0.9875. SHAP analysis indicated that SiO2, Al2O3, Fe2O3, CaO and TiO2 are the five factors exerting substantial influence on ash content. Partial dependency diagrams indicate that SiO2 and Al2O3 constitute the core ash minerals; CaO and MgO regulate ash yield through the synthesis of stabilizing minerals. Industrial trials demonstrate an R2 value of 0.9834, with MAPE falling below the 5% industrial threshold. The findings of this study can help for better understanding of the relationship between the composition of coal oxides and ash content, providing crucial support for advancing the coal industry toward intelligent and efficient development.
{"title":"A Bayesian optimization stacked ensemble learning method: Predicting ash content in flotation cleaned coal using X-ray fluorescence oxides data","authors":"Ning Han, Yifei Li, Zhiyuan Zhang, Jiabao Gong, Bolin Zhang, Yanfeng Li","doi":"10.1016/j.mineng.2026.110227","DOIUrl":"https://doi.org/10.1016/j.mineng.2026.110227","url":null,"abstract":"Coal plays a pivotal role in the global energy sector, and accurately assessing ash content is a core component of quality management in the coal industry. However, existing predictive models suffer from low accuracy and other issues, failing to meet the demand for rapid and precise coal ash assessment in industrial production. This study proposed a new method utilizing Bayesian algorithms to optimize stacked ensemble learning. Subsequently, this method is employed to predict the ash content of flotation cleaned coal based on XRF oxide data. Preliminary predictions were made using five distinct machine learning techniques. Bayesian optimization is employed to determine optimal hyperparameters of single models. By screening through multiple configuration schemes, the optimal stacking model configuration was identified. Prediction metrics peaked when the base learner module employed Random Forest and Gradient Boosted Decision Trees, while the <ce:italic>meta</ce:italic>-learner utilized linear regression. Final metrics achieved were MAE = 0.1257, MAPE = 1.5480, MSE = 0.0261, RMSE = 0.1687, and R<ce:sup loc=\"post\">2</ce:sup> = 0.9875. SHAP analysis indicated that SiO<ce:inf loc=\"post\">2</ce:inf>, Al<ce:inf loc=\"post\">2</ce:inf>O<ce:inf loc=\"post\">3</ce:inf>, Fe<ce:inf loc=\"post\">2</ce:inf>O<ce:inf loc=\"post\">3</ce:inf>, CaO and TiO<ce:inf loc=\"post\">2</ce:inf> are the five factors exerting substantial influence on ash content. Partial dependency diagrams indicate that SiO<ce:inf loc=\"post\">2</ce:inf> and Al<ce:inf loc=\"post\">2</ce:inf>O<ce:inf loc=\"post\">3</ce:inf> constitute the core ash minerals; CaO and MgO regulate ash yield through the synthesis of stabilizing minerals. Industrial trials demonstrate an R<ce:sup loc=\"post\">2</ce:sup> value of 0.9834, with MAPE falling below the 5% industrial threshold. The findings of this study can help for better understanding of the relationship between the composition of coal oxides and ash content, providing crucial support for advancing the coal industry toward intelligent and efficient development.","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":"20 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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