Abstract: With the production of electric vehicles grows, the rare earth elements Pr and Nd, become increasingly significant, which are key to apply in the magnetic materials production. In order to achieve the green and efficient recovery of rare earth elements Pr and Nd from the rare earth leachate, this paper selected kaolinite and halloysite as adsorbents to conduct rare earth solution adsorption experiments for exploring the effects of the initial leachate concentration, the solution pH and the adsorption temperature on the adsorption process. The adsorption characteristics of Pr and Nd by typical clay minerals were analyzed by quantum chemical calculation. The results showed that the adsorption effects of clay minerals on Pr and Nd decreased with the rise of leachate concentration. When leachate pH increased, the adsorption efficiency of kaolinite and halloysite for Pr firstly increased and then decreased, and the optimal adsorption efficiency was 13.33% and 24.778% at pH 6, respectively. The adsorption effects of kaolinite and halloysite on Nd enhanced gradually with the increase of pH, which increased to 15.925% and 30.482% at pH 7, respectively. With temperature increased, the adsorption of Pr and Nd by kaolinite and halloysite was positively correlated. The isothermal adsorption model was fitted to the experimental data, and it was found that the adsorption of Pr and Nd by kaolinite and halloysite was consistent with the Langmuir model, with R2 above 0.96, indicating that the adsorption process was a single molecular layer adsorption. The results provide theoretical support for the effective recycling of Pr and Nd, which is of great significance for the utilization of rare earth permanent magnets.

Abstract: Gold price and production output are the primary drivers of value in the mining industry. They stimulate mining companies to invest in exploration or acquire exploration projects from junior exploration companies. This study aimed to evaluate the effect of gold price and gold production on exploration budgets in South Africa between 1997 and 2021. The results were compared with the results from Ghana, Mexico and Russia. The Spearman and Kendall rank correlation coefficients were used to measure the relationship between the dependent variables and the independent variables. The study found that South African grassroots, late-stage and feasibility exploration budgets were not correlated to the gold price and annual production. The minesite exploration budget had a positive correlation with the gold price and an inverse correlation with annual production.

Abstract: This paper presents a thorough review of path-planning algorithms employed for the navigation of Unmanned Ground Vehicles (UGVs) in underground mining environments. It outlines the key components and requirements that are essential for an effective path planning framework, including sensors and the Robot Operating System (ROS). The review examines both global and local path-planning techniques, encompassing traditional graph-based methods, sampling-based approaches, nature-inspired algorithms and Reinforcement learning strategies. Through the analysis of the extant literatures on the subject, the review paper highlighted the strengths of the employed techniques, the application scenarios, the testing environments and the optimization strategies. The most favorable and relevant algorithms were identified. The paper acknowledges a significant limitation: the over-reliance on simulation testing for path-planning algorithms and the computational difficulties in implementing some of them in real mining condition. It concludes by emphasizing the necessity for full-scale research on path planning in real mining conditions.

Abstract: Unsupervised anomaly detection algorithms have gained significant attention in the field of Mineral Prospectivity Mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships between exploration data and mineral deposits. This study utilizes two tree-based anomaly detection algorithms namely Isolation Forest (IForest) and Extended Isolation Forest (EIF) to enhance MPM and exploration targeting. In accordance with the conceptual model of porphyry copper deposits, key evidence layers were generated, including fault density, multi-element geochemical signatures, proximity to various alteration types (phyllic, argillic, propylitic, and iron oxide), as well as proximity to intrusive rocks. These layers were integrated using IForest and EIF algorithms, and their results were subsequently compared with a geological map of the study area. The comparison revealed a high degree of overlap between identified anomalous zones and geological features such as andesitic rocks, tuffs, rhyolites, pyroclastics, and intrusions. Additionally, quantitative assessments through prediction-area plots validated the efficacy of both models in generating prospective targets. The results highlight the significant influence of hyperparameter tuning on the accuracy of prospectivity models. Furthermore, the study demonstrates that hyperparameter tuning is more intuitive and straightforward in IForest, as it provides a clear and distinct tuning pattern, whereas EIF lacks such clarity, complicating the optimization process.

Abstract: In this study, we model a solution surface with each point having nine components using artificial intelligence (AI) in optimising the effects of ground vibration during blasting operations in an open-pit diamond mine. This model has eight input parameters that can be adjusted by blasting engineers to arrive at a desired output value of ground vibration. It is built using the best performing artificial neural network architecture that best fits the blasting data from 100 blasting events provided by the Debswana diamond mine. Other AI algorithms used to compare the model performance were k-nearest neighbour, support vector machine, and random forest – together with more traditional statistical approach, i.e., multivariate and regression analysis. The inputs parameters were burden, spacing, stemming length, hole depth, hole diameter, distance from the blast face to the monitoring point, maximum charge per delay, and powder factor. The optimised model allows variations in the inputs values, given constraints, such that the output ground vibration will be within the minimum acceptable value. Through unconstrained optimisation, the minimum value of ground vibration is around 0.1 mm/s that is within the range caused by a passing vehicle.

Abstract: In close-distance coal seam mining, the concentrated stress from overlying obliquely in-tersected remnant coal pillars significantly impacts the mining of underlying coal seams. Taking the mining of Panel 22208 in the 2-2 coal seam beneath the Huojitu Mine of Da-liuta Coal Mine as the engineering case, this study established an elastic thin plate me-chanical model with two elastically clamped boundaries and two elastically simply sup-ported boundaries under the concentrated stress of overlying obliquely intersected rem-nant coal pillars (nonlinear load Q), based on theoretical analysis and elastic thin plate mechanics theory. The mechanical and fracture characteristics of key rock strata were in-vestigated when the working face was located at 0 m, 20 m, 40 m, and 60 m within the in-fluence range of remnant coal pillars. The results indicate: (1) The spatial sequence of ini-tial fracture in key rock strata is: coal wall side ahead of the working face → coal wall side behind the working face → solid coal side → central area of the working face → coal pillar side. (2) Under nonlinear load Q, a critical transition occurs at 40 m within the influence range of remnant coal pillars. The absolute value of maximum principal bending moment shifts from dual-peak maxima at both sides to a single-peak maximum at one side, with an overall increase of 12%. (3) The concentrated stress from overlying obliquely intersect-ed remnant coal pillars (nonlinear load Q) is the primary factor altering the fracture se-quence and angles of key rock strata, demonstrating significant influence on their fracture characteristics.

Abstract: The progressive depletion of high-grade sulfide ores necessitates the development of effective enrichment methods for oxidized lead–zinc ores. This study investigates the application of sulfidization flotation for oxidized ores from the Koskuduk deposit, focusing on the use of calcium polysulfide (CaSₓ) as an alternative to conventional sodium sulfide. Laboratory tests demonstrated that a CaSₓ dosage of 700 g/t led to superior recovery rates for lead (53.56%) and silver (66.1%) compared to sodium sulfide. Mineralogical and chemical analyses confirmed the dominance of oxidized forms of lead and zinc in the ore, emphasizing the importance of efficient sulfidization. The study establishes optimal flotation parameters to improve the recovery of valuable metals, highlighting calcium polysulfide as a more sustainable and effective sulfidizing agent.

Abstract: Pre-mining grouting is an effective means to prevent mine water inrush, while the microseismicity information induced by pre-mining grouting is often ignored. This paper proposes a novel method to predict the danger of mine floor water inrush based on pre-mining grouting-induced microseismicity (PMGIM). The mechanical mechanism and characteristics of PMGIM are explored through mechanical analysis and numerical simulation. Taking 182602 working face in Wutongzhuang coal mine as a case study, the temporal and spatial distribution law of PMGIM is analyzed, and the connection between the grouting process and microseismic energy is established. Based on the PMGIM information, Moran's index is used for the prediction of water inrush possibility, and the validity of the method is verified by electric monitoring.

Abstract: In this study, flotation tests were conducted on a laboratory scale using sample of microcrystalline graphite ore from the Canindé region, Ceará, Brazil. The objective was to investigate the grinding time, reagent dosage, and purification process for obtaining graphene-based nanomaterials. Natural graphite has stacked planar structure and exhibits polymorphism with rhombohedral, hexagonal, and turbostratic geometries, characteristics that directly influence its properties and technological applications. The results demonstrated that it was possible to obtain rougher concentrate with a graphite carbon content of 23.4% and a recovery of 86.4%, using grinding time of 7.5 minutes and reagent dosages of 150 g/t of kerosene and 100 g/t of Flotanol D-25. This flotation process resulted in a graphite concentrate with 76.6% graphite carbon content. To increase the purity of the concentrate and expand its industrial applications, the graphite was purified in an alkaline autoclave using the hydrothermal method. In the next stage, acid leaching was performed, and this chemical treatment destabilized the regular stacking of the graphite layers, promoting the formation of graphene-like nanoplates, including monolayer graphene. Thus, the nanomaterials obtained through the process developed in this study have potential for various innovative applications, such as lithium-ion batteries, electric vehicles, and two-dimensional graphene-based materials.

Abstract: This paper presents an analysis of the current state of processing lead-zinc ores from the Koskudyk deposit (Kazakhstan). At present, polymetallic ores are being extracted from the Ridder-Sokolnoye, Zyryanovskoye, Maleevskoye, and Achisai deposits. However, the reserves of rich and easily beneficiable ores are being depleted, and the supply of raw materials from the developed deposits does not exceed 25 years. As a result, more complex and difficult-to-enrich oxidized and mixed ores are being involved in production, and the extraction of non-ferrous metals from these ores presents a significant technological challenge. The most effective method for enriching oxidized polymetallic ores is flotation with preliminary sulfidization. Laboratory studies were conducted on a sample of oxidized lead-zinc ore from the Koskudyk deposit, which contains 79.69% oxidized lead compounds and 84.72% oxidized zinc compounds. This study examines the effect of sulfidization using sodium sulfide and determines the oxidative-reductive potential (ORP) levels for various reagent dosages. The results showed that increasing the sodium sulfide dosage to 700 g/t and achieving an ORP of −200 mV led to a 50.07% lead extraction, while the quality and quantity of zinc extraction remained stable. Determining the optimal ORP level significantly improves the efficiency of the enrichment process, enhancing both qualitative and quantitative indicators and simplifying the regulation of the technological process.

Abstract: This study examines the influence of average monthly load variations on the unit energy consumption indicator (ZE) of belt conveyors, defined as the energy required to transport 1 Mg of bulk material (coal) over a distance of 1 km. The analysis is based on energy consumption data from twin belt conveyors operating in a Polish lignite mine over four years. Monthly records included transported mass, conveyor operating time, energy consumed, and atmospheric conditions (temperature and humidity). The results indicate statistically significant differences in ZE between the analyzed conveyors. A relative increase in average load by 35% (from 2000 Mg/h) corresponded to a 26% reduction in ZE, with a specific decrease of 90 Wh/Mg/km. The findings highlight that optimizing conveyor load distribution can significantly reduce energy consumption—by up to 30%. These insights underscore the potential for energy savings through better conveyor load management without requiring significant infrastructural investments.

Abstract: The electrode materials from spent lithium-ion batteries consist of graphite and lithium cobalt oxides (LCO), which cannot be efficiently separated by conventional flotation technique due to fine size distributions of graphite and LCO. In this work, nanobubbles were introduced to the flotation system of electrode materials. Nanobubbles were produced with the method of temperature difference. The different degrees of gas over-saturation in water/slurry were created by increasing temperature of cold water stored in a 4℃ environment at least 72 h to the terminal 20℃, 25℃, 30℃ respectively. It was founded that height and lateral distance of nanobubbles increased with the degree of gas oversaturation of water. Besides, the larger graphite agglomerations were observed to form in the presence of nanobubbles. The D50 (chord length) of graphite agglomerations increased by 8 μm, 11 μm and 21 μm respectively compared with the D50 of graphite in natural water. More graphite agglomerations adhered to a captive bubble with aid of nanobubbles than the case of no nanobubbles, which was indicated by increased wrapping angles of graphite (agglomerations) adhering to a captive bubble. Furthermore, the maximum adhesion force between a captive bubble and substrate increases to 220, 270, 300 μN as cold water temperature increases to 20, 25 and 30℃. The frost of nanobubbles on graphite surface and thus resulting in graphite agglomerations by bridging effect of nanobubbles together should be responsible for the improved flotation performance of electrode materials. The present results indicate that flotation performance of fine minerals can be regulated by regulating gas oversaturation degree of slurry.

Abstract: Scaling is a critical activity in tunnel construction that significantly affects both safety and maintenance costs. Recognized as a high-risk mining operation, it poses substantial dangers to operators, with a notable incidence of accidents and fatalities in the tunneling industry. Large-scale projects such as hydroelectric power plants (HPP) present significant challenges, including substantial vertical elevations and the constraint of a single heavily trafficked main road. These conditions often necessitate semi-mechanized work methods for tunnel construction, which can be labor-intensive, time-consuming, and pose significant safety risks. To address these challenges and improve efficiency, an alternative mechanized method was evaluated and implemented. This approach involved deploying a fleet of excavators equipped with hydraulic hammers and advanced protection systems. Considering local regulations, the Caterpillar M313D wheel excavator and Atlas Copco SB452SC hydraulic hammer were selected for their superior operational performance and ability to mitigate risks associated with scaling activities. The standard scalers and their subsystems were evaluated for their reliability and availability, using data from underground mining operations to guide the configuration and decision-making process. This approach aimed to address operational challenges while being constrained by investment costs, high depreciation expenses due to the shorter project duration, and demanding work conditions. The proposed solution was implemented in tunnel construction for an HPP in Chaglla project over three years. A fleet of six-wheel excavator systems demonstrated effective performance, successfully adhering to the project's safety policies and operational goals pursuing zero fatalities, and high availability and reliability.

Abstract: This study presents an application of Boruta-SHapley Additive ExPlanations (Boruta-SHAP) for geotechnical characterization using Measure-While-Drilling (MWD) data, enabling a more interpretable and statistically rigorous assessment of feature importance. Pit-scale Measure-While-Drilling (MWD) data was used to characterize geotechnical properties via regression-based algorithms. In contrast to previous studies using MWD data to recognize rock type using Principal Component Analysis (PCA), which only identifies the directions of maximum variance, Boruta-SHAP quantifies the individual contribution of each MWD parameter. This method ensures interpretable and reliable geotechnical characterization as well as robust feature selection by comparing predictors against randomized ‘shadow’ features. The Boruta-SHAP analysis revealed that bit air pressure and torque-to-penetration ratio were the most significant predictors of rock strength, contradicting previous assumptions that rate of penetration was the dominant factor. Moreover, feature importance was conducted for fracture frequency and Geological Strength Index (GSI), a rock mass classification system. Boruta-SHAP A comparative analysis of prediction performance was also performed using a range of different machine learning algorithms that resulted in strong coefficient of determinations of actual field or laboratory results versus predicted values. The results are plausible, confirming that MWD data could provide a high-resolution description of geotechnical conditions prior to mining leading to a more confident prediction of subsurface geotechnical properties. Therefore, the fragmentation from blasting as well as downstream operational phases, such as digging, hauling, and crushing, could be improved effectively.

Abstract: In recent years, rock burst accidents have frequently occurred in deep mining operations in China's coal mines. With the gradual increase in mining depth of inclined coal seams in many mining areas, the threat of rock burst disasters in working faces has intensified. This paper employs a comprehensive approach, including theoretical analysis, numerical simulation, and field measurements, to analyze the movement of overlying strata and the distribution laws of abutment pressure in inclined coal seam working faces. A computational model for stress superposition on the working face and the solid coal side of the gate road is established. Based on stress control theory, a method for assessing rock burst risk in inclined coal seam working faces is proposed. By analyzing the calculated results of abutment pressure and numerical simulation results, the rock burst risk level of the working face is comprehensively determined. Building on theoretical analysis and field measurements, the relationship between deep mining pressure and rock burst in inclined coal seam working faces is further investigated. The results indicate that the theoretical model calculates a medium rock burst risk level during the square period of the working face, with abutment pressure reaching its maximum value. The microseismic events and energy release also peak, validating the rationality of the theoretical model. The numerical simulation results and field monitoring data collectively demonstrate that mining pressure manifestations are most pronounced during the square period of the working face. By combining the established model to assess the rock burst risk level, the research confirms the mining pressure manifestation laws during the square period. The calculated rock burst risk level aligns with field conditions, providing guidance for subsequent mining operations and rock burst prevention during the square period of the working face.

Abstract: Due to limited exploration drilling and analogue mapping, bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis. The Measure-While-Drilling (MWD) system uses sensors to collect drilling data from mining blast hole drill rigs. Historically, MWD studies have focused on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including Stratigraphic Unit, Rock/Soil Strength, Rock Type, Geological Strength Index, and Weathering properties. Feature selection algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Random Forests (RFs), Linear Discriminant Analysis, and Naive Bayes. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI in real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis.

Abstract:

Rock fracturing by blasting is the most common and efficient method of rock fragmentation in mining operations. The fragmentation size affects the productivity and costs of downstream op-erations, which is influenced by the encountered rock mass and blast design. The encountered rock mass is the unmodifiable parameter in blasting. Therefore, blasting improvements can be achieved by blast design, which includes explosive selection, geometrical design, and initiation sequencing and delays. Stress wave interaction between blastholes can improve or diminish fracturing. The analysis conducted in this study through numerical modelling demonstrated the improvement in blast outcomes with appropriate delay and sequencing in some cases. The optimum delay ensures the formation of fractures on the succeeding blasthole and constructive interaction with the stress wave from the preceding blasthole, increasing the stress pulse and fracturing. While it is insig-nificant in intact rock blasting, the firing sequence is vital when blasting through the contacts of soft and hard rocks or joints, depending on the infill material. Sequential initiation and firing direction do not improve fracturing in all cases; for example, when blasting through an empty joint, the joint acts as a free face with minimum to no interaction of stress wave from adjacent charges. In such cases, simultaneous initiation can be used.

Abstract: The automation of mining mobile equipment is a topic of considerable interest, as it has the potential to significantly reduce the number of accidents and implement the so-called zero-entry mining concept, which would eliminate the need for any human presence on the mine site. Nevertheless, the current state of robotics and automation technology does not yet meet the requirements for the implementation of fully autonomous operations in mines. Autonomous mining equipment continues to operate under the supervision of humans, and a considerable number of mining equipment have not yet been automated. This indicates the necessity of identifying novel strategies to increase the safety of mining operations through the utilization of robotics and automation technologies. One potential solution to address this challenge is to increase the involvement of humans in autonomous mining operations. This could entail integrating human decision-makers into the decision-making loops of autonomous mining equipment. To this end, we propose the paradigm of autonomous collaborative mining, wherein humans and autonomous machines work together in a collaborative manner to increase the safety and efficiency of mining operations. We analyze the enabling factors required to implement this paradigm and present the case of autonomous loading using LHDs based on the autonomous collaborative mining paradigm.

Abstract: Bench-scale geological modeling is often uncertain due to limited exploration drilling and geophysical wireline measurements, reducing production efficiency. Measure-While-Drilling (MWD) systems collect drilling data to analyze mining blast hole drill rig performance. Early MWD studies focused on penetration rates to identify rock types. This paper investigates Artificial Intelligence (AI)-based regression models to predict geophysical signatures like density, gamma, magnetic susceptibility, resistivity, and hole diameter using MWD data. Machine Learning (ML) models evaluated include Linear Regression (LR), Decision Trees (DTs), Support Vector Machines (SVMs), Random Forests (RFs), Gaussian Processes (GP), and Neural Networks (NNs). An analytical method was validated for accuracy, and a three-tier experimental method assessed the importance of MWD features, revealing no performance loss when excluding features with less than 2% importance. RF, DTs, and GPs outperformed others, achieving R² values up to 0.98 with low RMSE, while LR and SVMs showed lower accuracy. NN performance improved with larger datasets. The study concludes DT, RF, and GP models excel in predicting geophysical signatures. Model selection depends on computational resources and application needs, offering valuable insights for real-time orebody analysis using AI. These findings could be invaluable to geologists who wish to utilize AI techniques for real-time orebody analysis and prediction.

Abstract: This study examines the conversion of an overflow ball mill into a new energy-efficient discharge system via Discrete Element Method (DEM) and Smoothed Particle Hydro-dynamics (SPH) simulations. The research evaluates milling charge dynamics, empha-sizing the impact of liner geometry and operational variables such as charge filling and ball size. Our methodology integrates SPH to assess effects of the slurry on energy dissipation, power loss, breakage rates, and material transport. Our findings highlight significant operational inefficiencies in the overflow setup, notably extensive dead zones and excessive charge volume that hinder milling efficiency by limiting slurry in-teraction and reducing energy for comminution. Additionally, slurry pooling shifts the center of gravity, causing torque losses and direct material bypass to the discharge zone. Our simulations replicate these challenges and benchmark them against indus-trial-scale operations, identifying critical charge excesses that constrain throughput and elevate power consumption. The new discharge system decouples the filling charge from the evacuation mechanism, further than tackling common issues in the traditional grate discharge setups like backflow and carry-over. This approach substantially improves grinding efficiency, as demonstrated by enhanced breakage rates and diminished specific energy consumption. The results provide a robust framework for mill design and operational optimization, underscoring the value of integrated slurry behavior analysis in mill performance enhancement.