Abstract: In the transition towards digitalization and automation of mining processes, high vol-umes of data are generated, including data from distributed sensor and actuator networks, voice communications, videos and vehicle telemetry data. This generated data is prepro-cessed on-site and subsequently transferred not only to headquarters for detailed analysis but also between mining machines and vehicles in case of automation or autonomous mining. Stable and efficient communication in the mining industry and particularly in underground environments is essential for increasing safety and productivity, support fast and secure transportation and facilitate logistical processes, ensuring continuous and smooth operations. Several different communication technologies and protocols exist related to the mining field, each with its unique characteristics, advantages, or limitations. This article focuses on two essential technologies that, despite their shared goal of enhancing communication networks, exhibit notable contrasts in some of their characteristics: Long Range (LoRa) as an existing technology in the industry since years and the technology of the fifth genera-tion (5G) of cellular network will be presented and discussed in this article, highlighting the significant differences in their properties and applications. This provides an important basis for understanding the limits and possible trade-offs of the wireless communication technologies required in the mining industry for large scale sensor networks.

Abstract: High-level roof borehole is one of the core technologies for gas control in high-gas mines in China. However, in soft and fragmented rock strata, the influence of mining-induced stress disturbance often causes compression-torsion deformation, borehole wall collapse, or dislocation when the borehole passes through the coal-rock interface due to lithological differences. This results in blockage of the gas flow channel or even borehole failure. Existing borehole protection technologies generally suffer from issues such as heavy screen pipes, low construction efficiency, and difficulty in large-scale application. To address these problems, this study, based on the engineering background of Xin’an Coal Mine, developed a stainless-steel socket-type screen pipe with an “upper large, lower small” structure by systematically analyzing the necessity of borehole protection and the stress characteristics of protective pipes. A stepwise insertion method driven by the drill rig’s jacking system was adopted to achieve full-length borehole protection in soft rock strata. Meanwhile, the YZT-Ⅱ rock formation borehole detector was used to analyze borehole wall stability, and a comparative experiment between protected and unprotected boreholes was carried out at the 14230 working face of Xin’an Coal Mine. The results indicate that the rock formation detector identified the coal-rock interface as the high-incidence zone of borehole collapse, whereas the novel protective screen pipe effectively maintained borehole wall integrity in this zone. The gas drainage concentration of protected boreholes remained stable above 80%, with a pure extraction flow rate of ≥0.2 m3/min, and the total extracted gas volume was 8–10 times higher than that of unprotected boreholes (with extraction concentrations of 30%–45% and pure flow rates of 0.03–0.06 m3/min). Furthermore, based on a fluid-solid coupling model and field data, the optimal spacing between high-level roof boreholes under these geological conditions was determined to be 3.0–3.5 m, with an optimal number of three boreholes. The proposed novel screen pipe and corresponding construction technology effectively solve the problems of borehole collapse and blockage in high-level roof boreholes within soft and fragmented rock strata, significantly improving gas drainage efficiency and borehole utilization. This provides reliable technical support for gas control in mines with similar geological conditions and demonstrates broad application potential.

Abstract: Underground mining environments are complex and hazardous operations where emergencies continue to happen. Post-incident investigations consistently identify training gaps in human related factors such as situational awareness and decision-making under stress. Conventional mine emergency training largely relies on instruction-based approaches which provide insufficient exposure to the cognitive and behavioral demands of real underground emergency situations. There has been an identified need to train miners for knowledge, skills, abilities, and other characteristics (KSAOs). This study proposes an adaptive immersive training framework (AITF) for miner self-escape readiness integrating immersive technology, situational awareness theory, KSAOs, and cognitive task analysis (CTA). The AITF aligns NIOSH-identified self-escape competencies with immersive training scenarios designed to assess and develop cognitive readiness and decision-making. CTA of historical mine accidents is introduced as a foundational design method for translating accident investigation findings into simulation scenarios and performance metrics. CTA of 2006 Darby Mine No. 1 explosion is presented as a proof of concept. The proposed framework supports individualized assessment, iterative scenario refinement, and data-driven feedback. The AITF advances miner training toward cognitive preparedness during mine emergencies and provides a foundation for future training systems that leverage digital tools, digital twins, and artificial intelligence for the mines of the future.

Abstract:

The 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. On the basis of this assessment and the selectivity criterion, electrostatic separation was found to be the best separation method used after shredding and cryogenic grinding. Furthermore, the financial potential of recycling and other benefits that recycling can bring to the economics of the business and to the protection of the environment were presented. The possibility of using non-metallic fraction (plastic) generated during the recycling as an additive in the production of composite materials was assessed. The functional properties of the composite were assessed (static tensile, hardness, pin-on-disc, and Schopper-Schlobach abrasion tests), as well as the ecotoxicity of the powder added to polymeric materials such as polyester and epoxy resins, and silicone, used in the production of consumer goods.

Abstract:

For many years, the hard coal mining industry has been searching for engineering solutions ensuring greater reliability of the machines operating in difficult underground conditions. The foregoing applies in particular to the scraper conveyors used in longwall systems, started up very frequently and exposed to variable dynamic loads, leading to accelerated wear of powertrain components. The authors of this study have developed a longwall scraper conveyor equipped with a torsionally flexible metal clutch of novel design. The article provides a description of a mathematical model of a conveyor featuring two centrally arranged chains along with a main (discharge) and auxiliary (return) drive, as well as results of the computer simulations performed for two variants of the drive system setup analysed: one with a typical flexible clutch and the other with the innovative torsionally flexible clutch. Analysis of these results has revealed that the solution proposed significantly reduces the amplitude of dynamic loads, which contributes to increased durability and reliability of conveyors under mining conditions.

Abstract: This study investigates media wear in stirred media mills by varying key variables such as specific energy input and feed sizes. Specific energy input is considered a primary influencing factor in the grinding process, making it a central focus of the study. The investigation was carried out in two parts: experimental studies and DEM simulations. In the experimental phase, the specific energy input was varied at three levels (5, 10, and 20kWh/t), while feed sizes were varied at two levels (1150+850 μm and -600+425 μm). The data generated were used to calibrate the DEM mill program. The DEM was then used to assess energy spectra and particle probability breakage properties to predict the milling rate. The experimental findings revealed clear insights into the impact of varying specific energy and feed sizes on the grinding process, highlighting specific energy as a key driver of grinding efficiency. It is demonstrated that this simple scheme indicates good assessment capabilities without requiring the complexity of combining DEM with CFD. This approach is recommended for speedier evaluation of the material grinding rate, provided the fracture properties of the material to be ground are available.

Abstract: The growing global demand for mineral resources is challenging mining operations to maintain productivity while addressing lower-grade ore and increased extraction complexity. Despite the availability of vast datasets across mining stages, much of this information remains underused in decision-making. This study presents an integrated, knowledge-based framework that leverages artificial intelligence (AI) and high-fidelity simulation to model and optimise the full mine to mill process. Using publicly available data from the Barrick Cortez Mine in Nevada, USA, the mining chain from blasting to semi-autogenous grinding (SAG) was modelled using the Integrated Extraction Simulator (IES) from Orica. To mitigate the computational burden of full factorial simulations, three million scenarios were generated to evaluate performance sensitivity. Machine learning models, including linear regression, decision trees, random forests, and XGBoost, were trained and validated. The models achieved an accuracy of more than 90%, underscoring their reliability for predicting process outcomes. SHapley Additive exPlanations (SHAP) were applied to interpret model predictions and quantify feature importance. The findings confirm a strong alignment between simulation and real-world data and highlight key operational parameters that affect downstream process performances. This meta-model approach offers a powerful tool for real-time decision-making, enabling mining operations to improve efficiency, reduce costs, and support sustainable resource management.

Abstract: Oil agglomeration, as an efficient technique for coal slime cleaning and upgrading, was employed to separate coal slime with an ash content of 19.08% in this work. The optimum oil type, the pulp density, the oil dosage, and the agitation rate were determined at the dodecane, 12%, 24%, and 1600 r/min, respectively. The response surface methodology (RSM) was adopted to investigate the interactions between various operational factors on the response of combustible material recovery, efficiency index, and ash rejection. By considering the interactions among operational factors, the agglomeration achieved improvement, given small oil consumption, medium agitation rate, and high processing capacity, through optimized operational conditions. Moreover, a prediction model with a higher prediction accuracy for the efficiency index of coal slime oil agglomeration was established based on the artificial neural network (ANN). This work provides an experimental foundation for the operation design and process optimization in the oil agglomeration of coal slimes.

Abstract: Assessing the stress–strain state around interacting mining excavations using the finite element method (FEM) is computationally expensive for parametric studies. This study evaluates tabular machine-learning surrogate models for the rapid prediction of full stress–strain fields in jointed rock. A dataset of 1000 parametric FEM simulations using the elastoplastic Hoek–Brown constitutive model was generated to train Random For-est, LightGBM, CatBoost, and Multilayer Perceptron (MLP) models based on geometric features. The results show that the best models achieve R2 scores of 0.96–0.97 for stress components and 0.99 for total displacements. LightGBM and CatBoost provide the op-timal balance between accuracy and computational cost, offering speed-ups of 15 to 70 times compared to FEM. While Random Forest yields slightly higher accuracy, it is re-source-intensive. Conversely, MLP is the fastest but less accurate. These findings demonstrate that data-driven surrogates can effectively replace repeated FEM simula-tions, enabling efficient parametric analysis and intelligent design optimization for mine workings.

Abstract: Sluice box washing continues to face operational limitations arising from inconsistent water velocity and the need to tailor slope designs to varying particle sizes. These interrelated factors significantly diminish recovery efficiency, thereby underscoring the necessity of this study. The samples were collected each sample were homogenized into uniformly. Varied sieve size and sluice box angles were utilized. Fractional sieve analysis of a 100g sample was carried out from each site using sieves. Based on this analysis the resulted particle size distribution from the site Wolansa (30, 50, 20), Qori (25, 60, 25) and Bore (35, 47, 18) in percent is coarse, medium and fine respectively. For optimum angle 100kg of alluvial material washed at constant water pressure and mesh size. This was done for 3 different angles of inclinations (5o, 10o, and 15o). From the result, the optimum angle for artisanal gold recovery for 3 sites were 10 o since it yielded the highest amount of recovery from site Wolansa 0.43, Qori 0.46 While Bore is 0.45. Therefore, this study advice to change the sluice boxes angle from 5o to 10o for Wolansa and 15o to 10o for Bore site to increase the recovery by 85%. and 51% respectively.

Abstract: The Tala Hamza zinc–lead deposit, located in the Amizour region south of Béjaïa (Northern Algeria), is one of the largest base-metal resources in the Maghreb. This study presents an integrated geological, geostatistical, and geotechnical assessment to improve resource estimation and optimise the mining strategy. Geostatistical analyses of drilling data revealed moderate to strong spatial continuity of zinc and lead grades, with variogram ranges of 120–200 m along the main structural trends. Ordinary Kriging and conditional simulations produced three-dimensional grade and uncertainty models, confirming anisotropy consistent with the tectonic framework and identifying higher uncertainty near fault zones. These results were integrated with geotechnical investigations and FLAC3D numerical modelling to validate the Descending Backfilled Chamber (DBC) mining method. The DBC approach, combined with cemented backfilling, ensures mechanical stability, limits dilution, and maximises ore recovery. This multidisciplinary framework demonstrates the effectiveness of coupling geostatistical and geotechnical modelling for reliable resource evaluation and sustainable mine design, providing a reference for similar base-metal deposits in Algeria and beyond.

Abstract:

The growing global demand for strategic minerals such as lithium, driven primarily by the battery industry, has made rapid and effective control of mineral quality an urgent necessity. Conventional analytical methods, although accurate, often require considerable time and complex sample preparation, which can delay process control. To overcome this challenge, this work proposes the use of Fourier Transform Infrared (FTIR) spectroscopy combined with Partial Least Squares (PLS) modeling as an efficient alternative. This approach aims to provide immediate response for predicting grades in lithium-bearing ores, such as spodumene, ensuring agility and precision to meet industry demands. This study evaluated the application of FTIR spectroscopy coupled with chemometric modeling for the simultaneous prediction of lithium oxide (Li₂O) and spodumene contents in pegmatitic samples. Two independent PLS models were developed, using spectra preprocessed with first derivative and/or Standard Normal Variate (SNV). Spectral regions were selected based on the structural response of Al–O, Si–O, and OH⁻ groups, which are indirectly influenced by the presence of lithium. The spectral datasets were split into calibration and external test sets, and the models were evaluated based on statistical metrics and Principal Component Analysis (PCA). The Li₂O model achieved an R² of 0.9934 and an RMSEP of 0.185 in external validation, with a mean absolute error below 0.15%. The spodumene model achieved an R² of 0.9961, an RMSEP of 1.79, and a mean absolute error of 2.80%. The results indicate that the FTIR-PLS approach enables efficient quantitative estimation of lithium-bearing minerals, with reduced analytical time, good accuracy, and feasibility for application in process control and mineralogical sorting environments. PCA confirmed the statistical representativeness of the test sets, with no occurrence of spectral extrapolation.

Abstract: This paper examines the empirical foundations of rock engineering. We have adopted a narrative style throughout, as the philosophical nature of the questions raised in the paper is better served by discourse than conventional technical structure. In the first part of the paper, we revisit the Hoek-Brown failure criterion using synthetic rock mass models, validating the general framework while revealing critical distinctions between data-driven parameter emergence and classification-dependent derivation. The analysis reveals fundamental challenges, including the conflation of calibration with validation, the transformation of qualitative geological assessments into seemingly quantitative parameters, and the false similarity problem, where different parameter combinations yield equivalent failure envelopes. In its second part, the paper explores broader implications for professional practice, revealing a significant gap between methodological validation and professional acceptance. What satisfies the professional standard of "balance of probabilities" for establishing reasonable practice may fall short of the scientific standard of "evidence beyond a reasonable doubt" required to claim genuine predictive validity. We propose "epistemological integrity" as a framework for responsible practice: acknowledging the false similarity problem, communicating uncertainty transparently, applying validation standards consistently, and aligning professional claims with actual knowledge limitations rather than projecting false precision through computational sophistication.

Abstract: The aim of the study was to analyse the impact of carbonaceous and mineral contami-nants present in mineral oils on the tribological wear of the 42CrMo-4 steel under mixed friction conditions. A range of advanced analytical techniques was employed, including polarised light optical microscopy, SEM, XRF, EDS, XRD, as well as Raman spectroscopy and profilometry. Wear tests were conducted using a roller-on-roller tribological test rig to evaluate changes in surface topography and the extent of surface damage. The results showed no clear functional correlation between the mass loss of the samples and the con-tent of non-combustible fractions in the carbonaceous contaminants. The most favourable tribological performance was observed for the abrasive variant with low ash content and high elemental carbon content. In this case, a thin graphite-like carbon layer formed on the surface, significantly improving lubrication, reducing friction, limiting surface spalling, and reducing damage depth (to approx. 10 µm). In other variants, despite the formation of compressed carbon–mineral layers, the higher content of hard mineral particles reduced their durability and led to increased wear. Local temperature and particle size were found to significantly influence wear intensity – smaller particles contributed to lower flash temperatures and reduced surface damage. The variant with pure oil, lacking any abra-sive additives, showed no protective effects, resulting in intense microscratching and sur-face spalling due to direct asperity contact.

Abstract: Tailings reservoir is an important part of the mine. The monitoring and assessment of the stability of their key structures have always been the focus of research by many scholars. In this experiment, Interferometric Synthetic Aperture Radar (InSAR) and the Global Navigation Satellite System (GNSS) were employed to obtain 30 sets of longitudinal deformation data of 6 feature monitoring points on the surface of a tailings reservoir in Anhui Province. Considering the complementarity of InSAR and GNSS in surface monitoring, it is proposed to calculate the optimal initial parameters Q and R of Kalman filter by using Particle Swarm Optimization ( PSO ), so as to construct the optimal PSO-Kalman data assimilation model to achieve bidirectional data assimilation between InSAR and GNSS data, improve the accuracy of InSAR data, and comprehensively analyze the stability of key dams. After that, the Long Short Term Memory (LSTM) recurrent neural network is utilized to conduct time series predictions on the assimilated data and the buried depth data of the saturation line of the monitoring points of the mine dam during the same period. The experimental results show that using PSO-Kalman filter for data assimilation, compared with the original InSAR data, the overall mean absolute error ( MAE ) is reduced by 52.7 %, and the overall root mean square error ( RMSE ) is reduced by 53.6 %. By using LSTM to predict the time series data, the RMSE of the key dam deformation data test output set is less than 2.5mm, and the RMSE of the saturation line buried depth test output set is less than 1.5mm. Finally, the existing data is used to train the dam stability evaluation model. Under the condition that the correct rate of the evaluation model is 88.89 % and the AUC value of the model stability is 0.88889, the LSTM prediction data and the stability evaluation model are used to analyze the future stability of the key structure. This paper innovatively proposes to jointly evaluate the stability of the key dam structure by combining the deformation data and the saturation line height data. It also presents the first relatively complete integrated research method of ' monitoring-prediction-evaluation ' for the key dam body of the tailings reservoirs. The experimental results show that this method can provide reliable data and technical support for the monitoring, stability analysis and evaluation of the key dam body of tailings reservoirs.

Abstract: In the mines of the Karaganda Basin, in addition to methane, there are instances of intense hydrogen sulfide and sulfur dioxide emissions during coal seam extraction. Methane content in the extraction areas ranges from 25 to 38 m³/t. Consequently, degassing is applied in all extraction areas. Before degassing operations, methane is extracted from the unreleased coal seam. In seams exhibiting methane-hydrogen sulfide zones, the methane zone is prioritized for extraction, followed by the hydrogen sulfide zone. The aim of the article is to determine the specific content of hydrogen sulfide and sulfur dioxide in seams D6 and D10 during the extraction of hydrogen sulfide zones in the Karaganda Basin mines, identify the causes of gas abundance in the seams, and determine the factors influencing the intensity of sulfur-containing gas emissions. The article presents the results of research on the gas content of seams K10 and D6, based on a comparison of laboratory results obtained through coal sample analysis. It includes data on the natural gas content of seam K10, results from sampling and processing coal samples using DMT methodology, and actual gas emissions from seam K10 during its extraction at the Abai mine of ArcelorMittal Temirtau's coal department. For the first time in the Karaganda Basin, comprehensive studies of coal seam gas content and the impact of degassing on its magnitude have been conducted. Accurate knowledge of gas content is essential for designing seam extraction operations, including daily loads, ventilation parameters, degassing, and other factors dependent on methane content and its potential release during mining.

Abstract: With the increasing harsh drilling environments encountered more frequently than ever before, developing environmental benign and multifunctional additives is essential to formulate high performance drilling fluid. Herein, hydrothermal carbon/bentonite composites (HCBCs) were prepared by hydrothermal carbonization reaction using soluble starch and sodium bentonite as raw materials. A systematic investigation was conducted into the effects of HCBC concentration on the rheological, filtration, and lubricating characteristics of xanthan gum, modified starch, and high-temperature polymer slurries. These properties were evaluated before and after exposure to hot rolling at different temperatures. The hydroxyl radical scavenging properties of HCBC was evaluated. Observation showed plentiful micro- and nano-sized carbon spheres deposited on the bentonite particles, endowing the bentonite better dispersion. HCBCs could maintain the water-based drilling fluids’ rheological profile stable, decrease filtration loss and improve the lubrication with relatively low concentrations. The excellent properties were attributed to the highly efficient scavenging of free radicals and stabilization of bentonite particle dispersion.

Abstract: Optimizing beneficiation processes of iron ore industry is essential to meet the increasing demand while coping with decreasing grade deposits. As grinding largely impacts the quality and production costs, the implementation of Advanced Process Control (APC) emerges as a solution to increase efficiency and operational stability. This study evaluates the improvements obtained with the application of an APC system in the ball griding circuit of the Mineração Usiminas industrial processing plant. The adopted methodology involved the collection and analysis of operational data with the system enabled and disabled, using statistical tests and literature review to support the analyses. The results indicate significant gains in throughput and stability. This work thus contributes to the assess the benefits of Advanced Process Control in an industrial operation.

Abstract: The classification of mineral resources and reserves establishes a structured framework to quantify the contents of mineral deposits, covering continuity, grade distribution, tonnages, and the legal, technical, and economic feasibility of extraction within a defined timeframe and production rate. To reduce subjectivity and improve reliability, international reporting standards were developed on the principles of transparency, materiality, and competence. Compliance with these standards enables professionals and investors to evaluate mineral assets using consistent and comparable information across jurisdictions. Many mining operations are now seeking to align their disclosure practices with these frameworks to strengthen governance, improve credibility, and facilitate access to global capital. Within this context, the Mineral Re-sources and Reserves Readiness Index (MR³ Index) is introduced as a methodological tool to assess the degree of alignment of mining operations with international report-ing requirements. For operating mines, one of the key variables in the MR³ Index is the success in converting Inferred Mineral Resources directly into mine production, even in the absence of prior classification as Indicated or Measured Resources. This measure functions as a proxy for geological robustness and operational maturity. The applica-tion of the proposed methodology to an underground lithium mine in Brazil indicated a readiness level of 95.5%.

Abstract: Determination of the elemental composition of minerals and their derivatives at mining enterprises is important at all stages of minerals processing. This paper is devoted to the results of development and application of an online X-ray fluorescence (XRF) analysis method for monitoring mineral elements on a conveyor belt of mining enterprises. Evaluation of metrological characteristics achieved in the online analysis of lump, ore, charge feed, cake and slag materials on conveyor belt is presented. Each implementation of the online XRF analysis at mining enterprises was preceded by laboratory studies, development of measurement methods, calibration of a specific XRF analyzer using standard reference samples for a specific concentration range of the monitored elements. Typical areas of application for monitoring the concentration of elements in rocks on conveyor belts of mining enterprises and those solutions that made it possible to achieve the required measurement accuracy of an X-ray fluorescence analyzer in online mode are presented.