Search Results (72)

To satisfy the industrial standards for phosphate products, phosphate ore must undergo beneficiation for the removal of gangue minerals such as carbonate minerals, silicate minerals, and clay minerals. Reverse flotation is commonly used to remove carbonate minerals from apatite. However, reverse flotation removes carbonate gangue minerals but fails to eliminate silicate and clay minerals. The use of pre-concentration (such as dense-medium cyclones and photoelectric sorting) in phosphate ore beneficiation enables the early rejection of gangue before the ore enters the fine-grinding and complex flotation circuits, thereby providing a range of technical and economic benefits. In addition, pre-concentration holds the potential to reject silicate and clay minerals in the beneficiation of phosphate ore. The phosphate ore investigated in this study was obtained from Hubei, China. It is characterized by well-defined gangue banding, which facilitates the liberation of some gangue minerals at relatively coarse comminution sizes (−15 + 0.5 mm). Based on these characteristics, photoelectric separation and dense-medium cyclone separation were employed as pre-concentration methods prior to reverse flotation, with the aim of achieving economically viable recoveries and marketable product grades. The results indicate that the combined dense-medium cyclone separation–reverse flotation process was the most effective for this phosphate ore, producing a final concentrate with a P₂O₅ grade of 31.08% and a recovery of 81.91%. Comparative evaluation reveals notable differences in gangue removal efficiency among the tested processes. While both reverse flotation and the combined photoelectric separation–reverse flotation process effectively removed dolomite, the dense-medium cyclone separation–reverse flotation process demonstrated superior overall performance by enabling the simultaneous removal of both silicate and dolomite impurities. Full article

As an important part in the mining crushing and grinding circuit, the high-pressure grinding roll (HPGR) is essential for energy efficiency, cost reduction, quality improvement and efficiency gains; therefore, it plays a key role in sustaining stable production and intelligent control of mining operations. Its instantaneous throughput and processing capacity directly influence the efficiency of the comminution system, the compatibility of production scheduling, and overall energy consumption. Therefore, these metrics serve as important indicators for intelligent optimization and stable operation. However, it is a difficult task to predict the throughput of HPGRs by traditional purely data-driven models. The process is characterized by strong nonlinearity, significant time-lag effects, and limited physical consistency. To address these challenges, this work develops a tailored throughput prediction framework for HPGRs by combining Gated Recurrent Units with Physics-Informed Neural Networks (GRU-PINN), which integrates an HPGR-specific volumetric throughput mechanism as a dedicated physical constraint. This approach first exploits the GRU network’s “reset” and “update” gates to selectively filter historical operating information while adaptively updating the current process features. This allows the model to better capture complex long-term temporal dependencies in the production data. Additionally, based on volumetric analysis and the principles of bed comminution, the HPGR throughput formula is incorporated into the loss function as a physical constraint. Together, these components establish a joint optimization framework that combines data-driven learning with physical constraints to correct prediction biases generated by the neural network during abrupt changes in operating conditions. The proposed GRU-PINN model was validated using real operational data collected from an industrial mining site. The results show that the proposed framework significantly improves the physical consistency of HPGR throughput predictions and, at the same time, effectively corrects prediction biases, which are commonly observed in conventional purely data-driven models under complex operating conditions. It also exhibits improved robustness and lower inference latency. Compared with other benchmark models, the proposed method displays superior overall performance across key evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), prediction accuracy, and inference speed. These findings confirm that the proposed method greatly enhances the physical interpretability of the prediction model without compromising accuracy. Consequently, it lays a solid foundation for process parameter optimization and intelligent control of HPGR system. Full article

Comminution–classification circuits are difficult to optimize because hydraulic, granulometric, energy, and economic responses are nonlinearly coupled, while circuit simulation, equipment sizing, simulator benchmarking, and operating optimization are often treated separately. This study aimed to develop an open Python framework for steady-state simulation and five-objective optimization of a rod mill–hydrocyclone–ball mill circuit processing a gold ore. The framework integrates solid and water balances, Rosin–Rammler particle-size reconstruction, comminution and hydrocyclone models, preliminary equipment sizing, explicit feasibility constraints, and Multiobjective Particle Swarm Optimization (MOPSO). Its novelty lies in coupling complete-circuit simulation, simulator-to-simulator benchmarking against USIM PAC^®, model-based sizing, convergence diagnostics, and Pareto optimization within one transparent workflow. The benchmark produced zero or below ${10}^{-3}\%$ errors in solid balances and sizing differences of 1.07%, 8.21%, and 0.00% for the rod mill, ball mill, and hydrocyclone, respectively. Relative to the base case, the joint minimum-water, minimum-energy, and minimum-cost solution reduced specific water consumption by 24.50%, specific grinding energy by 4.24%, specific operating cost by 10.19%, and mass recirculation by 8.80%, while useful recovery decreased slightly from 82.67% to 81.78%. The maximum-recovery solution increased useful recovery to 84.45%, with higher water, energy, and operating-cost requirements. The framework supports reproducible evaluation of resource–recovery trade-offs in grinding–classification circuits. Full article

Anatase-bearing lateritic ores from Qinglong, Guizhou Province, China, are characterized by extremely low TiO₂ grade, high clay content, fine-grained dissemination, and complex intergrowths with iron oxides, which severely hinder efficient beneficiation. In particular, anatase commonly occurs as ultra-fine particles encapsulated by clay minerals or closely associated with iron oxides, and its surface is often covered by nanoscale goethite films, resulting in surface passivation and pseudo-magnetic behavior. These characteristics lead to a pronounced contradiction between mineral liberation and excessive slime generation during conventional grinding processes. To address these challenges, a high-efficiency pre-concentration flowsheet was developed based on selective desliming, stage grinding, intensive scrubbing, flotation, and weak magnetic separation. Selective desliming via hydrocyclones was adopted, which is inferred to preferentially discard true slimes finer than 10 μm while potentially retaining most fine anatase particles within the underflow. Stage grinding was then applied, which may promote the improved liberation of anatase and early rejection of coarse gangue, and may help reduce overgrinding. Intensive scrubbing was introduced, which is expected to weaken or partially remove iron oxide coatings from the anatase surface, thereby potentially restoring surface activity and reducing pseudo-magnetic interference. Subsequent flotation and low-intensity magnetic separation were optimized to increase the concentrate TiO₂ grade and cut iron impurities, which may be associated with improved surface selectivity and weakened pseudo-magnetic responses. Closed-circuit beneficiation tests demonstrated that a TiO₂ concentrate with a grade of 29.62% and a recovery of 65.4% could be obtained from an ore with an initial TiO₂ grade of only 4.39%. Moreover, approximately 40% of the feed mass was rejected at the pre-concentration stage, significantly reducing the load on downstream separation processes. The proposed process demonstrates promising potential as a technical route for the beneficiation of similar refractory anatase-bearing lateritic ores. Full article

Pebbles are competent ore fragments that are difficult to further reduce in size in conventional comminution circuits, and their efficient treatment is essential for improving circuit stability and lowering downstream grinding energy consumption. In this study, pebbles from the Julong Copper Mine were used to optimize the key operating parameters for high-pressure grinding roll (HPGR)-based pebble treatment. A uniaxial piston compression device was employed to simulate the confined particle-bed breakage process in HPGR, and the effects of feed volume, moisture content, applied pressure, loading speed, and roll surface profile on pebble compression performance were systematically investigated. The compressed products were characterized by particle size distribution, fine fraction yields, and grinding energy indices. The results indicated that the optimal compression conditions were a feed volume of 240 cm³, a moisture content of 6%, a loading speed of 0.2 mm/s, and an applied pressure of 1000 kN. Under these conditions, the products exhibited higher fine fraction yields and lower grinding energy indices, indicating improved subsequent grindability. Moreover, among the tested roll surface profiles, the cylindrical studded platen with 60% coverage produced the best compression performance. The findings provide a useful basis for optimizing HPGR operating parameters for copper ore pebble treatment. Full article

This article presents a technology for the physical recycling of printed circuit boards (PCBs) that is consistent with the principles of circular economy and sustainable production. A life cycle assessment (LCA) was performed for PCB recycling using shredding, grinding, and physical and physicochemical processes such as electrostatic separation, gravity separation, and flotation for the separation of metals and plastics. Based on this assessment and the selectivity criterion, electrostatic separation was found to be the best separation method, followed by shredding and cryogenic grinding. For this option, the yield of metals and plastics was 25.1% and 72.5% of feed, respectively, while the yield of the middling’s product (mixture of metals and plastics) was only 2.4%. Furthermore, the financial benefits of recycling, including economics of the business case and the environmental benefits are presented. The possibility of using non-metallic fraction (plastic) generated during recycling as an additive in the production of composite materials was also assessed. The results suggest that low filler contents (2.5–5%) provide a compromise between maintaining mechanical performance and improving hardness and tribological properties. Physical recycling technology is a pretreatment method for WPCB, complementing conventional chemical recycling methods. The global warming potential for the entire physical and chemical process is then lowered by about 70%, due to the smaller mass of input material going to the downstream metallurgical processes. Full article

SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables. This procedure allowed for the identification of the modeling approaches that provide the most robust performance across varying operational regimes. The results show that XGB achieved the best predictive performance, with test RMSE and R² values of 87.98 and 97.35% for SAG Production, and 431.11 and 95.11% for SAG Power, respectively. Stochastic approaches provided complementary uncertainty quantification, supporting risk-informed decision making under variable operating conditions. The analysis by operational regime indicates that XGB presents better fit in the Thick hydraulic regime, for both responses’ variables, which could be explained why a dense pulp operation provides more predictable grinding dynamics. The comparative analysis reveals trade-offs between model complexity, interpretability, computational requirements, and predictive performance, offering practical guidance for selecting appropriate modeling frameworks based on specific operational objectives and data availability in mineral processing applications. Full article

Hydrocyclones are widely applied devices in mineral processing due to their simple design, high capacity and low operational costs. Some of the main applications are classification in closed grinding circuits and desliming, as well as dewatering. However, hydrocyclones have an inherent inefficiency known as the fine particles bypass to the underflow stream, often associated with entrainment by water flow. Several approaches have been proposed to mitigate fine particle bypass, such as optimizing hydrocyclone design, adjusting apex and vortex finder diameters, water injection systems and improved inlet design. The objective of the present work was to assess hydrocyclone performance on different apex and vortex diameter combinations, seeking the reduction in fine particles bypass to underflow on the Paragominas bauxite processing industrial desliming circuit. Two different bauxite samples were used in a hydrocyclone classification test work, carried out on a specially built pilot plant. Six different combinations of apex and vortex were evaluated in a 254 mm diameter hydrocyclone, covering a range of apex-to-vortex diameters from 0.38 to 0.57. The results indicate operating conditions that significantly reduce fine particles bypass to underflow, increasing classification efficiency with minor effects in overflow selected size distribution parameter—d₉₅. Accordingly, smaller apex-to-vortex ratios result in overall better performances, reducing fine particles bypass to underflow from 33% to 7%, as well as reducing the partition curve slope from 0.52 to 0.21 for one of the tested samples. Significant benefits are also obtained in terms of reducing the contents of reactive silica in the underflow of the optimized desliming hydrocyclone. Full article

Flotation circuits typically incorporate grinding stages, yet mathematical models for these processes often operate on different principles, leading to misalignment in circuit design. Building on a previously established grinding model for flotation performance, this research introduces significant advances to develop a more comprehensive and industrially relevant framework. The primary innovation is the integration of mechanical entrainment and gangue recovery into the kinetic model, distinguishing between species captured by true flotation and those carried to the surface despite being non-hydrophobic. We developed a robust set of grinding-mill equations based on first-order kinetics to describe the mass-fraction transformation of both true-flotation and entrainment species. To ensure practical applicability, a systematic experimental and modeling methodology for parameter adjustment is introduced, providing a clear sequence for identifying breakage rate constants and flotation kinetic parameters. The proposed strategy was validated using two distinct case studies: an expanded analysis of a copper sulfide ore (ore A) and a new case involving significant gangue entrainment (ore B). The results demonstrate that the model accurately predicts species kinetics, providing a high-fidelity, cost-effective tool to optimize mineral recovery and prevent economic losses from overgrinding in industrial processing plants. Full article

Grinding wheel conditioning is critical for maintaining cutting efficiency and surface quality, yet conventional mechanical dressers struggle with metal-bonded superabrasive wheels. In this study, wire electrical discharge machining (WEDM) dressing was evaluated on metal-bond diamond wheels of two grit sizes (D54 and D91) and compared to standard mechanical dressing. Dressing was performed on a WEDM machine using varied discharge currents, open-circuit voltages, and duty factors; subsequently, each wheel ground twelve grooves in tungsten carbide under identical parameters. Performance was assessed via maximum spindle power, tangential and normal forces, surface roughness (Ra), radial wheel wear, and edge radius. WEDM-dressed wheels exhibited up to 56% lower peak spindle power and 40–50% lower forces than mechanically dressed wheels. Compared to mechanically dressed wheels, WEDM-conditioned wheels exhibited markedly lower radial wear and maintained substantially sharper, more stable edge radii throughout the grinding cycles. Surface roughness converged after an initial break-in, matching mechanical methods. By selectively eroding the bond without damaging grains, WEDM dressing extends dressing intervals by approximately fivefold and reduces maintenance. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

Microplastic (MP) pollution poses an emerging environmental concern, yet current methods for isolation and quantification are often time-consuming, costly, and poorly adapted to real-world variability. In this study, a workflow for the preparation, filtration, and quantification of MP standards, emphasizing environmental relevance and methodological efficiency, was developed and evaluated. To address the scarcity of irregularly shaped MP standards, low-cost, environmentally representative standards were lab-prepared by grinding and sieving plastic sheets. These MPs were successfully categorized according to sizes up to ~250 μm and dyed for enhanced visibility. The filtration efficiency for two systems, a long-circuit pump (LC-pump) and a short-circuit vacuum (SC-vacuum), was compared. The SC-vacuum method demonstrated a more than 11-fold increase in filtration speed and higher MP recovery rates for both polystyrene and polypropylene standards. Ethanol-based solvents significantly improved MP dispersion and recovery for irregular shapes of the MPs, including polystyrene and polypropylene. Finally, a user-guided machine learning tool (Ilastik) was implemented for automated MP quantification. Ilastik showed a strong correlation with manual counting (r = 0.824) and reduced variability, offering a reproducible and time-efficient alternative. By cutting down cost, time, and technical complexity relative to existing MP analysis techniques, this workflow provides a more accessible path toward consistent and scalable environmental MP assessments. Full article

Optimizing beneficiation processes in the iron ore industry is essential to meet increasing demand while dealing with declining ore grade. Since grinding has a major impact on product quality and production costs, the implementation of Advanced Process Control (APC) emerges as an effective strategy to enhance efficiency and operational stability. This study quantifies the operational improvements achieved after the implementation of an APC system in the ball grinding circuit of the Mineração Usiminas industrial processing plant. The assessment was based on an ON/OFF test conducted over 67 days, during which operational data were collected for periods with the APC system enabled and disabled, supported by statistical tests and a literature review. The results show significant improvements in both stability and throughput under the unconstrained operating scenario. The standard deviation of the hydrocyclone feed pulp density setpoint decreased by 63%, while the circuit throughput increased from 541 to 571 tph. Moreover, the specific energy consumption was reduced by more than 5% in the same scenario. These findings demonstrate the tangible benefits of implementing Advanced Process Control in industrial grinding operations. Full article

Precision dicing with diamond wheels is a key technology in semiconductor dicing, integrated circuit manufacturing, aerospace, and other fields, owing to its high precision, high efficiency, and broad material applicability. As a critical processing stage, a comprehensive analysis of dicing technologies is essential for improving the machining quality of hard-and-brittle optoelectronic materials. This paper reviews the core principles of precision diamond wheel dicing, including dicing processes and blade preparation methods. Specifically, it examines the dicing mechanisms of composite and multi-mode dicing processes, demonstrating their efficacy in reducing defects inherent to single-mode approaches. The review also examines diverse preparation methods for dicing blades, such as metal binder sintering and roll forming. Furthermore, the roles of machine vision and servo control systems are detailed, illustrating how advanced algorithms facilitate precise feature recognition and scribe line control. A systematic analysis of key components in grinding wheel dicer is also conducted to reduce dicing deviation. Additionally, the review introduces models for tool wear detection and discusses material removal mechanisms. The influence of critical process parameters—such as spindle speed, feed rate, and dicing depth—on dicing quality and kerf width is also analyzed. Finally, the paper outlines future prospects and provides recommendations for advancing key technologies in precision dicing, offering a valuable reference for subsequent research. Full article

The integration of high-pressure grinding roller (HPGR) with pre-concentration techniques and stirred mills is recognized for its energy efficiency. Studies have suggested that the feed with a P₈₀ around 1 mm is acceptable for stirred mills or coarse particle flotation. Nonetheless, published experimental data characterizing the comminution behavior of single-stage HPGR circuits configured with a 1 mm screen aperture remain scarce. Moreover, extant research remains confined to laboratory scale. Consequently, critical performance metrics, including production capacity, screening efficiency, and process continuity, have not been substantively documented in the literature. In this paper, the HPGR performance in an industrial-scale HPGR/tower mill comminution circuit was assessed and optimized by laboratory and industrial tests. The research meticulously analyzed the impact of feed rate on the industrial-scale flip-flow screen and HPGR performance and found that the HPGR featuring two studded rolls with a diameter of 800 mm and a width of 400 mm, operating in a reverse classification circuit with a scalped feed by a 14.64 m² flip-flow screen while running continuously 24 h per day, is capable of producing a −1 mm comminution product suitable for tower mill feed. Under the optimal operating conditions identified, it achieved a specific energy consumption of 4.57 kWh/t with a feed rate of 27.08 t/h. Full article