This study assessed the feasibility of cultivating Lavandula dentata in Technosols produced from fine and coarse coal mining waste, focusing on plant development, substrate functionality, essential oil production, and post-mining ecosystem restoration. The Technosols were formulated using coal waste from the Moatize Coal Mine, Mozambique, combined or not in different configurations with agricultural soil and amended with sewage sludge (3% organic matter) and chemical fertilizer to ensure adequate nutrient availability. The experiments were conducted in 30 L containers, performed in triplicate for each experimental group. All settings allowed good plant growth, although the treatment that used only fine waste presented the closest performance to agricultural soil in terms of the production of aerial biomass. In this case, the dried biomass production of the shoots reached an average of 165 g per pot over 8 months (with a standard deviation of 20.3). The study showed a positive correlation between plant development and the available water capacity of the substrates. The plant tissue of L. dentata, in all the Technosols configurations studied, presented a similar composition to the control, with a biomass composition within the standard range established by the literature. The essential oil production ranged from 0.3 to 0.7% (m/m), averaging 0.5% (m/m), with chemical characteristics also alike the control trial. Technosols composed of coal waste from Moatize appear to be an alternative, both to provide a suitable destination for mining waste and to provide conditions for the revegetation and recovery of degraded areas by coal mining. This avoids the commissioning of nearby areas to supply soil for the restoration process. L. dentata, in addition to its various medical, ornamental, and aromatic uses, has potential as an “ecological trigger” in the restoration process with environmental and socioeconomic benefits.

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R2 = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments.

Background: Bucket-wheel excavators are critical assets in surface mining operations, where structural modifications to increase productivity must be validated through rigorous stress analysis to ensure operational safety. Following modification of an ERc 1400-30/7 excavator’s bucket wheel from 18 to 20 buckets, increased operational loads necessitated experimental verification of structural integrity. Methods: A custom 10-channel strain-gauge data acquisition system with 0–10 kHz bandwidth measured stresses in cable anchoring lugs and H-type diagonal members under operational conditions at the Jilț lignite mine, Romania. Measurements were performed during both left and right bucket-wheel rotation. Finite element analysis validated experimental results. Results: Maximum equivalent stresses of 210.0 MPa and 167.1 MPa were measured in the left and right anchoring lugs, respectively, during left bucket-wheel rotation, representing 59% and 47% of material yield strength with safety factors of 1.69 and 2.12. Significant load asymmetry was observed, with left rotation inducing 220–284% higher stresses than right rotation. FEA validation showed <15% agreement with measurements. Dynamic stress amplification of 15–32% above quasi-static values was attributed to bucket–soil interaction and structural vibration. Conclusions: Despite increased operational loads, measured stresses remain below yield strength, confirming structural adequacy. Both anchoring lugs require prioritized monitoring due to elevated stress levels and load asymmetry. The validated methodology provides a framework for post-modification verification of large mining equipment.

The mining industry still faces major environmental and socioeconomic problems as a result of tailings dam failures, which highlights the urgent need for improved monitoring and early-warning systems. This research offers practical recommendations for improved monitoring and safer design practices, in addition to investigating the use of digital image processing (DIP) as a non-invasive technique for tracking slope deformation in tailings dam models subjected to incremental pore water pressure increases. To replicate real-world conditions as closely as possible, a scaled laboratory embankment was built using coarse and fine tailings. During controlled pore-pressure loading, more than 500 high-resolution photos were taken, recording the entire deformation sequence from initial displacement to slope failure. The images were processed using Mathematica to generate pixel-by-pixel displacement fields and vector plots, providing a detailed visualization of deformation mechanisms. The findings demonstrated that DIP accurately detects and measures surface displacement, revealing the mechanisms, direction, and intensity of deformation. This study illustrates the extensive potential of DIP for real-time monitoring by directly connecting slope instability triggered by incremental pore water pressure with visual indications of slope deformation. While the results confirm the strong potential of DIP for deformation monitoring with a minimum detectable displacement of approximately 1.0 mm under controlled laboratory conditions, its field application may be affected by scale effects, variable lighting, and environmental occlusion. The mining industry benefits greatly from the insights gained through in-depth image analysis, which promotes safer tailings dam design and management. Overall, DIP can provide a reliable, scalable foundation for real-time deformation monitoring in operational tailings dams, where continuous image-based measurements can help identify early signs of instability and support proactive risk management.

Close-distance multi-seam mining frequently induces secondary surface deformation and subsidence. Extracting a lower coal seam beneath an existing goaf repeatedly disturbs the overburden, often leading to roof collapse and the expansion of vertical water-conducting fractures that connect the working face to aquifers. Furthermore, the overlying goaf increases the risk of water inrush into active lower workings. This study investigates the mechanisms of strata reactivation and fracturing within an overlying goaf during lower seam extraction at a mine in Northwest China. Using theoretical analysis, numerical simulation, and microseismic monitoring, the research examines the secondary fracture mechanisms of the goaf roof and the resulting water-inrush potential. Research Findings: Strata Instability: Analysis of the key sandstone strata indicates that subsidence (W) of the key rock blocks satisfies 3.17 < W1 = 4.61 m < 18 m for the lower seam and 3.17 m < W2 = 5.31 m < 69.6 m for the 3-1# seam. These values confirm that key rock blocks in the basic roof undergo “reactivated” instability following fracture during lower seam mining. Pressure Relief and Fluid Dynamics: Mining-induced fracture initiation and propagation trigger strata reactivation. As the distance to the center of the goaf decreases, the subsidence of the overburden increases, ultimately resulting in a “trapezoidal” bending deformation pattern. Due to secondary activation, the roof subsidence 30 m above the 221 coal seam increased from 1.89 m to 5.475 m. The layers of high-strength, medium-grained sandstone and siltstone overlying the 317 coal seam and beneath the 221 goaf serve as high-strength material for the overlying rock formations. This suppresses the development of the caving zone and fracture zone, leading to subsidence failing to reach the sum of the heights of the two coal seams (6.8 m) and only reaching a value of 5.475 m. During extraction, the stress field undergoes a distinct evolution: it transitions from an initial “regular triangular” pressure-relief zone into a tripartite “weak–strong–strong” distribution. Furthermore, fluid discharge in the overlapping zone between the 317 working face and the 221 goaf increased sequentially, displaying an “alternating” pattern of peak vector variations as the face advanced. Microseismic Activity: Monitoring within the 300–500 m range identified frequent low-energy events and high-magnitude events (104 J, 105 J). These findings demonstrate that secondary excavation directly impacts the aquifer, creating a significant water-inrush hazard for the active working face.

Mining conveyor belts are critical components in bulk material transportation, but their operational safety is frequently threatened by diverse damages such as blocks, cracks, foreign objects, and holes. Existing detection methods, including traditional computer vision and conventional neural networks, struggle to balance accuracy and efficiency in harsh mining environments—marked by high levels of dust, uneven lighting, and extreme scale variability (5–300 pixels). Our study proposes WTConv-YOLO, an improved model based on YOLOv11, integrating two core modules: (1) wavelet transform convolution (WTConv), which achieves a logarithmically expanding receptive field with linearly growing parameters, allowing for the concurrent capture of high-frequency local details and low-frequency global context; (2) Scale-based Dynamic Loss (SD Loss), which dynamically adjusts bounding box similarity and localization loss weights according to target scale, mitigating IoU fluctuation interference and enhancing small-target detection stability. Experiments on the Mining Industrial Conveyor Belt Dataset show that WTConv-YOLOv11 achieves a mean Average Precision (mAP@0.5) of 73.8%—a 3.5% improvement over the baseline YOLOv11. A Python-based software system is developed for end-to-end detection. This work provides a practical solution for reliable conveyor belt damage detection in mining scenarios.

The mining industry places high priority on occupational safety, process quality and operational efficiency. Roadheaders are widely deployed in coal mines to support fully automated excavation, reducing workers’ physical strain and improving overall safety. This article examines an automatic control system for a roadheader cutting head designed to increase mining efficiency, reduce energy consumption and maintain stable performance under varying coal and rock conditions. The system integrates advanced control algorithms with geological strength index (GSI) analysis and asynchronous motor control strategies. GSI-based adaptive speed control conserves energy and increases cutting efficiency compared to manual control. By reducing dynamic load fluctuations, transitions between different cutting zones become smoother, which decreases equipment wear. The proposed control system incorporates speed feedback loops that use a proportional–integral (PI) controller with field-oriented control (FOC), as well as super-twisted sliding mode control (STSMC) with FOC. FOC with STSMC improves roadheader productivity by applying advanced control strategies, adaptive speed regulation and precise geological strength analysis. It is also better able to handle disturbances and sudden loads thanks to STSMC’s nonlinear control robustness. The result is safer, more efficient, and more cost-effective mining that can be implemented across a wide range of underground mining scenarios.

Fires in underground mines pose significant risks to worker safety. In this study, a digital twin of an underground mine was created, and the heat, gas distribution, and airflow dynamics were investigated during and after the fire using computational fluid dynamics (CFDs) methods at three different locations. While traditional methods did not indicate any problems, the results from the CFDs analyses revealed some important findings. One of the key findings of the study was the change in airflow direction caused by the changing thermodynamic conditions caused by the fire. The digital twin allows us to demonstrate how a fire at any point within the mine can affect the entire mine under these changing thermodynamic conditions. The digital twin enables the real-time monitoring of underground events. Additionally, it facilitates strategic planning to anticipate potential incidents during a fire in an underground mine, allowing for necessary precautions to be implemented.

Underground pumped hydro storage (UPHS) in solution-mined salt caverns offers a promising approach to address the intermittency of renewable energy in flat geological regions such as Southern Ontario, Canada. This work presents the first fully coupled thermo-hydro-mechanical (THM) numerical model of a two-cavern UPHS system in Southern Ontario, providing a foundational assessment of long-term cavern stability and brine leakage behavior under cyclic operation. The model captures the key interactions among deformation, leakage, and temperature effects governing cavern stability, evaluating cyclic brine injection–withdrawal at operating temperatures of 10 °C, 15 °C, and 20 °C over a five-year period. Results show that plastic deformation is constrained to localized zones at cavern–shale interfaces, with negligible risk of tensile failure. Creep deformation accelerates with temperature, yielding maximum strains of 2.6–3.2% and cumulative cavern closure of 1.8–2.6%, all within engineering safety thresholds. Leakage predominantly migrates through limestone interlayers, while shale contributes only local discharge pathways. Elevated temperature enhances leakage due to reduced brine viscosity, but cumulative volumes remain very low, confirming the sealing capacity of bedded salt. Overall, lower operating temperatures minimize both convergence and leakage, ensuring greater stability margins, indicating that UPHS operation should preferentially adopt lower brine temperatures to balance storage efficiency with long-term cavern stability. These findings highlight the feasibility of UPHS in Ontario’s salt formations and provide design guidance for balancing storage performance with geomechanical safety.

Aluminum’s unique properties have led to its widespread use across multiple industries, including transportation, aviation, power generation, construction, and food packaging. In recent years, global aluminum consumption has risen significantly, with China experiencing particularly sharp growth in both production and demand. In Russia, the aluminum industry is dominated by UC RUSAL, which consolidates all Russian aluminum and alumina production facilities, along with several international operations and mining assets. Despite its global presence, the company remains heavily reliant on imported raw materials (approximately 50%) for alumina production, resulting in reduced operational efficiency and declining output. This dependency has necessitated the exploration of strategies to diversify raw material sources across different stages of the aluminum production value chain. This study identifies and classifies key diversification options for global aluminum companies, focusing on secondary aluminum production, primary aluminum production, and alumina extraction from mined minerals, industrial waste, and by-products. The options were evaluated based on predefined criteria (feasibility, cost per Mg of alumina, logistics, alumina output, and economic security), and two options were selected. The research substantiates the feasibility of diversifying production through nepheline utilization. For the medium term, an economic efficiency assessment was conducted for a proposed 30% capacity expansion at the Pikalevo Alumina Refinery. Additionally, long-term opportunities for increasing aluminum output were identified, including leveraging foreign assets while accounting for associated risks.