On the improvement of the Charpy impact response of high-Mn steel (HMS) alloys via Ti addition for comminution circuits

Lignin-based flotation reagents for sustainable mineral processing

A novel sepiolite-supported Ce-doped black TiO2 composite for the synergistic degradation of xanthates.

Ultrafast degradation of mineral processing collector via magnetic Fe3O4/graphene oxide activating peroxydisulfate: Performance and mechanism

210Po activity in aerosols at Thai Nguyen, Vietnam: An application to assess the emission source and health risk assessment.

Ore particle size is a critical indicator of ore fragmentation and a key parameter for automated mineral processing. However, traditional ore image segmentation methods suffer from low accuracy, severe under-segmentation, and weak adaptability in complex mining scenarios, while few approaches effectively balance precision and efficiency. To fill this gap, this paper proposes a novel segmentation method that combines an improved distance transform with the watershed algorithm. After image preprocessing, seed regions are optimized using a self-developed local-maximum strategy, and watershed segmentation is further refined with morphological operations. Experimental results on seven test images show that the proposed method achieves a segmentation accuracy of 0.932, an over-segmentation rate of 0.109, and an under-segmentation rate of 0.091, which are significantly better than those of traditional watershed and UNet methods.The proposed method thus exhibits superior performance and stability in practical ore particle analysis.

This paper analyzes Indonesia’s downstreami ndustrialization policy as a form of second-generation resource nationalism aimed at repositioning the country within global value chains, particularly in the nickel sector. Grounded in political economy frameworks— such as resource curse theory, resource nationalism, Hirschmanian linkage theory, and the developmental state—it frames downstreaming as a long-term structural transformation rather than a mere value-added strategy. Using a qualitative-analytical approach, the study combines historical policy analysis (1945–2025), secondary data review, and international comparisons. The findings show that downstream industrialization has significantly altered export structures, attracted major investment, and built a domestic mineral processing base. However, progress in technological and functional upgrading remains limited. Key challenges include reliance on foreign technology, carbon-intensive energy systems, vulnerability to commodity price fluctuations, and risks of resource-driven industrial populism. The study argues that the policy’s success depends on sustained political commitment, disciplined industrial policy, investment in human capital and innovation, and sound fiscal and energy governance. Indonesia’s downstreaming strategy is thus at a critical juncture: it can either evolve into a competitive, innovation-driven mineral-based development model or remain constrained at a midstream stage with limited value addition. The trajectory will depend on the country’s ability to deepen institutional capacity and technological capabilities in the coming decade.

ABSTRACT China has become a prominent external actor in downstream and midstream infrastructure projects, including oil refining and mineral processing, in Africa. This raises the question whether such investment alters the structural patterns described by dependency theory. Through a comparative analysis of Angola, the Democratic Republic of the Congo, and Uganda, this article examines whether the localisation of refining and processing infrastructure enables greater domestic retention of value and reconfigures the geography of surplus production. Situated within critical political economy and historical materialism, the study conceptualises infrastructure as a contested site of industrial strategy and political sovereignty. The three cases are interpreted as operational, transitional, and anticipatory moments along a continuum of infrastructural development. The findings suggest that Chinese capital, under specific political and institutional conditions, can expand the scope for partial delinking by supporting the onshoring of value-added activity. These engagements reshape the terrain on which dependency is negotiated, with outcomes contingent upon state capacity, regulatory coherence, and the strategic appropriation of surplus.

Introduction. Magnetic separation is one of the most effective and widely used techniques for the purification and enrichment of materials. It plays a crucial role in mineral processing, recycling, and environmental applications, where the separation efficiency depends on both the magnetic field characteristics and the physical properties of the treated materials. Problem. A major limitation of existing studies is that the frictional drag force is often neglected in magnetic separation, although it can sometimes completely prevent the separation process. Goal. To estimate and experimentally verify the effect of frictional drag force on the performance and operational limits of open gradient magnetic separation (OGMS) under dry conditions. Methodology. An integrated analytical, numerical, and experimental approach was used. The granular medium was modeled as a complex fluid where friction acted as a drag force. The coupled magnetic and dynamic equations were solved using Finite Element (FE) – Runge–Kutta (RK4) methods, and results were validated experimentally with a permanent magnet drum separator. Results. To verify the obtained results experiments were carried out on samples of a mixture of sand and iron particles with different components sizes (iron particles and sand grains) in a permanent magnet drum separator. Limited to fine granulometries, the experiments carried out confirmed the results obtained theoretically. Scientific novelty. The study introduces a coupled FE–RK4 model that explicitly integrates the frictional drag force into the particle dynamic equations, enabling accurate prediction of trajectories and operational thresholds. This provides a realistic description of dry magnetic separation behavior, which has been largely overlooked in previous models of dry magnetic separation. Practical value. The findings provide engineers with a framework for optimizing dry OGMS performance. The developed model defines the threshold separating efficient from inhibited particle capture and clarifies how frictional drag controls the operational range of magnetic separators. These insights support improved design, process adjustment, and greater reliability in dry magnetic separation. References 41, tables 7, figures 12.

Abstract Ore image segmentation plays a vital role in mineral processing, directly affecting the accuracy of crushing quality assessment and particle size analysis. However, the accuracy of traditional segmentation techniques is severely compromised by three inherent challenges in ore images: the wide size range of particles, significant color variations within individual ores, and indistinct boundaries between agglomerated particles, leading to imprecise segmentation results. To overcome these challenges, this study introduces LAES-UNet, an integrated network based on EfficientSAM, which combines a pre-trained EfficientSAM encoder with a multi-level decoder. The proposed architecture incorporates three tailored modules: the Local-Global Hierarchical Interaction (LGHI) for multi-scale feature enhancement, the Adaptive Spatial Feature Refinement (ASFR) for adaptive weighting across color-variable regions, and the Edge Focusing Module (EFM) for explicit edge and fine-detail perception. Experiments were conducted on a self-built conveyor belt ore image dataset containing 148 manually annotated images, which were cropped into 296 non-overlapping samples, as well as on a public mineral image benchmark. The results show that LAES-UNet achieves the best overall segmentation performance among the compared methods, with up to 2.8% higher IoU and consistently improved boundary delineation. Furthermore, a kernel density estimation (KDE)-based particle size distribution fitting method verifies the practical value of the segmentation results in quantitative ore particle size analysis. Overall, LAES-UNet delivers a generalizable solution for automated, high-precision particle size measurement within intelligent mineral processing systems.

Fenton-like catalysis of single-atom Co-N4 for polymeric transformation and recovery of benzohydroxamic acid in mineral processing wastewater

The microstructural evolution and the tensile strength of in-situ eutectic composite in slightly hypo-eutectic Fe–27Cr-2.7C alloy

Reverse cationic flotation remains the principal technique for upgrading hematite from silicate minerals. Micaceous phyllosilicates, such as biotite and phlogopite, pose significant challenges because their surface properties often resemble those of hematite. Limited investigations address the potential interactions between these silicates and hematite depressants. To address this gap, this study comprehensively evaluated two biodegradable depressants, corn starch (CS) and tannin (TA), assessing their effects on the floatability of biotite and phlogopite. An integrated approach was employed, combining mineral characterization (XRD and XRF), surface chemistry analyses (contact angle, zeta potential, turbidity, adsorption, and FTIR), and micro-flotation experiments (single and mixed minerals) to reveal the molecular interactions of these green reagents with mineral surfaces under industrially relevant alkaline conditions. Experimental results revealed distinct adsorption mechanisms of CS and TA on the mineral surfaces studied, leading to different flotation responses. At an optimum depressant dosage of 100 mg/L, hematite flotation recovery decreased to ∼1.9-8.8% with TA and ∼2.5-12.7% with CS over the examined EDA range (0-30 mg/L). TA exhibited broader depression, affecting both hematite and micaceous gangue minerals. It forms strong chemical bonds with Fe-rich surfaces via complexation, making it suitable for bulk gangue rejection but limiting selective separation. CS was selective because it preferentially adsorbed onto hematite via acid-base/H-bond interactions with hydroxylated Fe sites, thereby limiting amine adsorption, whereas its weaker interaction with micaceous minerals (limited accessible metal sites; siloxane/silanol-dominated surfaces) preserved biotite/phlogopite floatability. Its adsorption occurred primarily via acid-base interactions and hydrogen bonding, yielding higher-grade iron concentrates with lower SiO 2 contamination (SiO 2 reduced from 16.74% to 4.09% in hematite concentrate). Flotation tests on single and mixed minerals confirmed that selecting an appropriate depressant can significantly improve hematite recovery while reducing silica in the concentrate. Overall, CS proved more selective and effective for high-quality separation. The mechanistic understanding and development of sustainable depressants are thus essential for enhancing beneficiation efficiency, meeting quality standards in steel production, and promoting mineral processing.

Research on hazard factor identification and safety risk assessment of mineral processing operations.

This research addresses the need for improved operational efficiency, equipment reliability, and mineral quality assessment in the mining industry, where traditional methods often treat maintenance and quality control separately. In mineral processing, especially in iron ore flotation, accurate information identification is critical. Flotation is a widely used technique for separating valuable minerals from waste, but its efficiency depends on complex physicochemical properties and multiple interrelated subprocesses, making control and optimization challenging. To tackle this, the study proposes a Deep Probabilistic Neural Network Model for predicting % Iron Concentrate and % Silica Concentrate in flotation systems. The model uses a comprehensive flotation dataset that includes feed composition, reagent dosages, pulp characteristics, and column operating conditions. Several machine learning approaches are implemented, including Restricted Boltzmann Machine (RBM) regressor for probabilistic feature extraction, Extreme Gradient Boosting (XGB) regressor for capturing nonlinear relationships, and Adaptive Boosting (AdaBoost) regressor for sequential ensemble learning. A novel hybrid model, Probabilistic Pattern Neural Network with Extra Trees (PPNN-XT) regressor, is introduced, combining probabilistic learning with ensemble tree-based methods to improve prediction accuracy and robustness. The system includes data preprocessing, feature scaling, model training, and evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) score. Additionally, a Flask-based web application is developed for dataset upload, model training, prediction, and visualization. Experimental results show that the PPNN-XT model achieves superior performance with minimal error, making it an effective decision-support tool for optimizing flotation processes and enhancing mining efficiency.

Gas bubbles are ideal hydrophobic structures that underpin technologies ranging from mineral processing to chemical analysis. The technological value of bubbles lies in their ability to create hydrophobic-hydrophilic phase boundaries, simply and efficiently. However, bubbles remain largely incompatible with electrochemical processes: they block charge transfer reactions by interrupting solution-electrode contact. We demonstrate a path to integrate bubbles with electrode reactions. For micrometer-sized electrodes and surface-active reactants (<60 mN/m), a nanoscale disjoining liquid film forms under bubbles that visually appear as surface-adherent. Gas-solution-electrode junctions sustained by repulsive van der Waals (vdW) forces allow the oil-like properties of bubbles to be harnessed in aqueous electrolytes. Through vdW-stabilized junctions, bubbles are redefined from detrimental dielectric blocks to facilitators of electrode processes. This is demonstrated by 10-fold rate enhancements, improved reaction reversibility and ionic conductivity, and the redox cycling of enzymes stabilized by confinement between bubbles and electrodes.

Historical thorium bioassay data collected from Australian mine workers in the 1990s, including in vivo lung counting and thoron-in-breath (TIB) measurements, were re-evaluated using current dosimetric and biokinetic models. Revised daily thorium intakes from bioassay were compared with previous estimates to assess changes in the dose profile and potential implications for contemporary operations. Reanalysis revealed lower estimates of annual dose, including the number of workers assessed as exceeding 20mSv. However, it was found that intakes derived from industry personal air sampling (PAS) underestimated intake by up to three-fold. This study determined that sensitive bioassay techniques, such as TIB using an electrostatic collection chamber, are feasible for detecting low thorium lung burdens in longer-term workers at current mineral processing operations involving naturally occurring radioactive materials (NORM). This study improves the accuracy of historical exposures and highlights that PAS protocols require improvement to enhance radiation protection practices in industries handling NORM.

This study investigates the comminution behavior and beneficiation potential of lithium-bearing ores, zinnwaldite from Cínovec (Czech-Germany border) and lepidolite from Villasrubias (Spain) by integrating mineralogical analysis and mechanical characterization. The research is driven by Europe’s need for secure lithium supply chains. In particular, it focuses on the challenges associated with low-grade, fine-grained lithium micas found in hard-rock ores, which offer significant potential to supply in Europe but also pose substantial processing challenges. QMA (Quantitative Microstructural Analysis) revealed distinct differences in the textural and structural characteristics of the studied ores. Zinnwaldite-bearing rocks are coarser-grained with high interlocking and roughness, while lepidolite-bearing samples showed finer grains, lower roughness, and more disseminated mica distribution, indicated by their low clustering degree. In terms of mechanical characterization, zinnwaldite-rich ores have the lowest compressive strength, while lepidolite-rich samples showed the highest values, attributed to their finer grain size and more cohesive structure. This suggests that lepidolite may require higher energy input and finer crushing stages to achieve the target liberation size. These features influenced the breakage behavior observed during mechanical testing and comminution and are essential for enabling selective comminution, separating mica from gangue material. This study contributes to analyzing the potential of European hard-rock lithium resources from the perspective of upstream comminution, which is an essential step influencing downstream energy consumption, reagent use, and overall recovery efficiency. The results of this research emphasize that selective comminution should not rely solely on mineral hardness contrasts but must incorporate microstructural parameters such as clustering, grain size distribution, and orientation.

Bubble rising dynamics in a transverse ultrasonic standing wave field: Role of acoustic-induced viscous dissipation.

Unravelling the pH-driven multiscale cascade of hematite flocculation: From interfacial tuning to structural assembly and sedimentation dynamics