Search Results (3,067)

In recent years, controlling the integrity of shallow-buried natural gas pipelines within surface subsidence zones caused by high-intensity underground longwall mining in the Daniudi Gas Field of China’s Ordos Basin has emerged as a critical challenge impacting both mine planning and the safe, efficient co-exploitation of coal and deep natural gas resources. This study included field measurements and an analysis of surface subsidence data from high-intensity longwall mining operations at the Xiaobaodang No. 2 Coal Mine, revealing characteristic ground movement patterns under intensive extraction conditions. The subsidence basin was systematically divided into pipeline hazard zones using three key deformation indicators: horizontal strain, tilt, and curvature. Through ABAQUS-based 3D numerical modeling of coupled pipeline–coal seam mining systems, this research elucidated the spatiotemporal evolution of pipeline Von Mises stress under varying mining parameters, including working face advance rates, mining thicknesses, and pipeline orientation angles relative to the advance direction. The simulations further uncovered non-synchronous deformation behavior between the pipeline and its surrounding sand and soil, identifying two distinct evolutionary phases and three characteristic response patterns. Based on these findings, targeted pipeline integrity preservation measures were developed, with numerical validation demonstrating that maintaining advance rates below 10 m/d, restricting mining heights to under 2.5 m within the 260 m pre-mining influence zone, and where geotechnically feasible, the maximum stress of the pipeline laid perpendicular to the propulsion direction (90°) can be controlled below 480 MPa, and the separation amount between the pipe and the sand and soil can be controlled below 8.69 mm, which can effectively reduce the interference caused by mining. These results provide significant engineering guidance for optimizing longwall mining parameters while ensuring the structural integrity of shallow-buried pipelines in high-intensity extraction environments. Full article

This study systematically investigates the effects of welding current on the macro-morphology, microstructure, mechanical properties, and corrosion resistance of Cu-Mn-Al alloy coatings deposited on 27SiMn steel substrates using Cold Metal Transfer (CMT) technology. The 27SiMn steel is widely applied in coal mining, geology, and engineering equipment due to its high strength and toughness, but its poor corrosion and wear resistance significantly limits service life. To address this issue, a Cu-Mn-Al alloy (high-manganese aluminum bronze) was selected as a cladding material because of its superior combination of mechanical strength, toughness, and excellent corrosion resistance in saline and marine environments. Compared with conventional cladding processes, CMT technology enables low-heat-input deposition, reduces dilution from the substrate, and promotes defect-free coating formation. To the best of our knowledge, this is the first report on the fabrication of Cu-Mn-Al coatings on 27SiMn steel using CMT, aiming to optimize process parameters and establish the relationship between welding current, phase evolution, and coating performance. The experimental results demonstrate that the cladding layer width increases progressively with welding current, whereas the layer height remains relatively stable at approximately 3 mm. At welding currents of 120 A and 150 A, the cladding layer primarily consists of α-Cu, κII, β-Cu₃Al, and α-Cu + κIII phases. At higher welding currents (180 A and 210 A), the α-Cu + κIII phase disappears, accompanied by the formation of petal-shaped κI phase. The peak shear strength (509.49 MPa) is achieved at 120 A, while the maximum average hardness (253 HV) is obtained at 150 A. The 120 A cladding layer demonstrates optimal corrosion resistance. These findings provide new insights into the application of CMT in fabricating Cu-Mn-Al protective coatings on steel and offer theoretical guidance for extending the service life of 27SiMn steel components in aggressive environments. Full article

To optimize the location optimization of the coalbed methane (CBM) extraction target area in abandoned mines, based on the background of the Songzao mining area in Chongqing, theoretical analysis and numerical simulation research methods were comprehensively used to systematically evaluate the potential of residual CBM resources in the goaf of the Songzao mining area. The stress-fracture evolution law and permeability enhancement characteristics of overlying strata under repeated mining of inclined multi-coal seams were deeply revealed, and the location optimization of the residual CBM extraction borehole target area was carried out. The results show that the amount of CBM resources in Songzao Coal Mine is 5.248 × 10⁷ m³, accounting for 26.57% of the total resources, which is suitable for the extraction of CBM left in goaf. The maximum height of the overburden fracture zone caused by repeated mining of K2b, K1, and K3b coal seams in Songzao Coal Mine is 72.3 m, which is basically consistent with the results of the numerical simulation (69.76 m). The fracture development of overlying strata is in the distribution form of a symmetrical trapezoid and inclined asymmetrical trapezoid, and its development height increases with an increase in coal seam mining times, and finally forms a three-dimensional ‘O’-ring fracture area, which provides a channel and enrichment area for the effective migration of CBM. The significant permeability-increasing zone of overburden rock is stable in the range of 10~40 m above the roof of the K3b coal seam and is nearly trapezoidal. According to the calculation of the height prediction model of the fracture zone in the abandoned goaf, the fracture height of the long-term compaction of the Songzao Coal Mine is reduced to 63.74 m. Based on the stress-fracture evolution characteristics of the overburden rock, combined with the permeability-increasing characteristics of the overburden rock and the migration law of the remaining CBM, it is determined that the preferred position of the remaining CBM extraction target area of the Songzao Coal Mine should be in the upper corner of the fracture development area within the range of 10~32.47 m above the K36 coal seam. Full article

Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction based on Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) models optimized by the Improved Sparrow Search Algorithm (ISSA). Nine conventional hydrochemical indicators are selected, including Ca²⁺, Mg²⁺, Na⁺+K⁺, HCO₃⁻, Cl⁻, SO₄²⁻, total hardness, alkalinity, and pH. KPCA can realize the dimensionality reduction to eliminate the redundancy of information between discriminant indices, simplify the model structure, and enhance the calculation speed of the predicted model. The penalty factor C and kernel parameter g of the SVM model are optimized by the Sparrow Search Algorithm (SSA). In addition, comparative analysis with the SVM, SSA-SVM, and ISSA-SVM models demonstrates that the KPCA and ISSA significantly enhance the classification performance of the SVM model. The KPCA-ISSA-SVM model outperforms three contrastive models in terms of accuracy, precision, recall, Kappa coefficient, Matthews Correlation Coefficient, and geometric mean values of 90.75%, 0.90, 0.88, 0.89, 0.87, and 0.89, respectively. These outcomes underscore the superior performance of the KPCA-ISSA-SVM hybrid model and its potential for effectively identifying mine water sources. This research can serve to identify the mine water sources. Full article

With the rapid development of social media, short-text data have become increasingly important in fields such as public opinion monitoring, user feedback analysis, and intelligent recommendation systems. However, existing short-text sentiment analysis models often suffer from limited cross-domain adaptability and poor generalization performance. To address these challenges, this study proposes a novel short-text sentiment classification model based on the Bidirectional Encoder Representations from Transformers (BERTs) and a dual-stream Transformer gated attention mechanism. This model first employs Bidirectional Encoder Representations from Transformers (BERTs) and the Chinese Robustly Optimized BERT Pretraining Approach (Chinese-RoBERTa) to achieve data augmentation and multilevel semantic mining, thereby expanding the training corpus and enhancing minority class coverage. Second, a dual-stream Transformer gated attention mechanism was developed to dynamically adjust feature fusion weights, enhancing adaptability to heterogeneous texts. Finally, the model integrates a Bidirectional Gated Recurrent Unit (BiGRU) with Multi-Head Self-Attention (MHSA) to strengthen sequence information modeling and global context capture, enabling the precise identification of key sentiment dependencies. The model’s superior performance in handling data imbalance and complex textual sentiment logic scenarios is demonstrated by the experimental results, achieving significant improvements in accuracy and F1 score. The F1 score reached 92.4%, representing an average increase of 8.7% over the baseline models. This provides an effective solution for enhancing the performance and expanding the application scenarios of short-text sentiment analysis models. Full article

The massive volumes of data and the intensification of digital transformation are reshaping recruitment practices within organizations, particularly for specialized information technology (IT) profiles. However, existing studies have often remained conceptual, focused on developed economies, or limited to a narrow set of factors, thereby leaving important gaps in emerging contexts. Moreover, there are few studies that critically assess how Data Mining is impacting the IT recruitment process, and none that assess this in the context of Morocco. This study employs an extensive literature review to explore the role of Data Mining in facilitating the recruitment of top IT candidates, focusing on its ability to improve selection quality, reduce costs, and optimize decision-making procedures. The study provides empirical evidence from the Moroccan aeronautical and digital services sectors, an underexplored context where IT talent scarcity and rapid technological change pose critical challenges. Primary data comes from a survey of 200 IT recruitment professionals operating in these sectors in Morocco, allowing an assessment of the impact of Data Mining on IT talent acquisition initiatives. The findings reveal that a range of capabilities resulting from the application of Data Mining significantly and positively influences the success of IT recruitment processes. The novelty of the article lies in integrating six key determinants of algorithmic recruitment into a unified framework and demonstrating their empirical significance through binary logistic regression. The focus on the Moroccan context adds value to the international discussion and extends the literature on HR analytics beyond its conventional geographical and theoretical boundaries. The article thus contributes to the emerging literature on the role of digital technologies in IT recruitment that will be of interest to industry practitioners and other researchers in this field. Full article

Mine fire is caused by external heat source or coal seam spontaneous combustion, and there are serious hidden dangers in mining operation. The existing detection methods have high cost, limited coverage and delayed response. An edge intelligent fire detection system based on multi-source information fusion is proposed. We enhance the YOLOv5s backbone network by (1) optimized small-target detection and (2) adaptive attention mechanism to improve recognition accuracy. In order to overcome the limitation of video only, a dynamic weighting algorithm combining video and multi-sensor data is proposed, which adjusts the strategy according to the real-time fire risk index. Deploying quantitative models on edge devices can improve underground intelligence and response speed. The experimental results show that the improved YOLOv5s is 7.2% higher than the baseline, the detection accuracy of the edge system in the simulated environment is 8.28% higher, and the detection speed is 26% higher than that of cloud computing. Full article

Proof-of-Work (PoW) blockchains with symmetric consensus face threats such as selfish mining, bribery mining, block withholding, and replay attacks. This paper introduces a hybrid attack, Bribery–Stubborn Mining (BSbM), which integrates stubborn mining’s delayed chain publication with bribery incentives to recruit miners during forks. Simulation experiments confirm that BSbM yields additional revenue. To obtain even higher revenue, we propose Leading Hidden Bribery–Stubborn Mining (LHBSbM) based on BSbM. By concealing and delaying broadcasts, LHBSbM constructs a triple fork, maintaining three chains at the same height. Upon revealing the private chain, two public blocks can be isolated, breaking the single-block limit of double-fork attacks. Theoretical analysis shows that LHBSbM raises the attacker’s maximum effective block rate from $\alpha /(1-\alpha )$ to $\alpha /(1-\alpha -\beta )$. Experimental results indicate that, under ideal conditions ($r=0$), BSbM becomes profitable once the attacker’s hash rate ($\alpha$) exceeds approximately $34\%$ and further confirm that, under certain conditions, LHBSbM nearly doubles isolated blocks compared to BSbM, yielding greater profits. Finally, potential defenses against such hybrid attacks are discussed, offering new insights for blockchain security. Full article

In the context of global sustainable development, the construction of green mining facilities has emerged as a pivotal strategy for advancing sustainable mining practices. As a substantial mineral resource base in China, the Qinghai-Xizang Plateau (QXP) is of significant concern due to its importance for mineral exploitation. However, the natural conditions of the region, such as freezing temperatures, low oxygen levels, frequent freeze–thaw cycles, and fragile ecology, pose substantial challenges to mining activities, making green mine construction an inevitable choice for mining development on the QXP. This study uses SWOT analysis to macroscopically evaluate the strengths, weaknesses, opportunities, and threats of green mine construction on the QXP. This study adopts SWOT analysis to sort out, from a macro and systematic perspective, the internal resource endowments, technical reserves, external policy and market opportunities, as well as multiple challenges such as ecological vulnerability, harsh climate, regulation, and public opinion in the construction of green mining on the QXP. Furthermore, four typical cases, namely the Julong Copper Mine, Zhaxikang Lead–Zinc Mine, Zaozigou Gold Mine, and Duolong Copper Mine, are selected for analysis, and their differentiated paths in ecological restoration, digital mines, tailings disposal, and community-benefit sharing are summarized. International comparisons reveal the similarities and differences in policies, technologies, and other aspects between the QXP and other high-altitude regions. The study holds that it is necessary to promote the coordinated development of resource exploitation and ecological protection in green mining on the QXP through technological innovation, policy optimization, community collaboration, and the construction of a full-life-cycle environmental-monitoring system. At the same time, it points out the limitations of the current research in quantitative analysis and future research directions. Full article

This study investigates underground heat sources and develops effective strategies for mitigating heat hazards in coal mines, with a focus on the design and optimization of cooling systems. Using the 3107 fully mechanized mining face of Menkeqing Coal Mine as a case study, geological survey data and in situ measurements were combined to evaluate the severity of thermal hazards. Thermodynamic and heat transfer models were applied to quantify heat dissipation from multiple sources. Computational fluid dynamics (CFD) simulations, based on data-driven modeling and geometric reconstruction, tested different equipment layouts and spacing configurations to identify optimal cooling schemes. Field implementation of the designed cooling system confirmed its effectiveness, offering practical guidance for improving heat hazard control and cooling system optimization in deep coal mines. Full article

Gas monitoring and dust control in coal mine goafs are critical for ensuring safe and efficient production. To address the challenges posed by dust accumulation from mechanized mining and ventilation systems, this study designs a spiral-driven gas purification pipeline robot integrating a wet dust removal mechanism. The robot features a modular structure, including a spiral drive, a plugging and extraction system, and a wet dust removal unit, to enhance pipeline adaptability and dust removal performance. Dynamic modeling reveals that the robot’s speed increases with the deflection angle of the driving wheel, with optimal performance observed at a 45° angle. The analysis of the rolling friction, medium resistance, and deflection angle indicates that reducing the angle improves the obstacle-crossing ability. Numerical simulations of gas migration in the goaf identify a high dust concentration at the air outlet and show that flow velocity significantly affects dust removal efficiency. Simulation and prototype testing confirm stable robot operation at deflection angles of between 30° and 90° and effective crossing of 5 mm barriers. Optimal dust removal is achieved with a 5 m/s flow velocity, 0.6 MPa water mist pressure, and 400 mm chord grid spacing, providing both theoretical and practical guidance for gas monitoring and dust control in coal mine goafs. Full article

Ensuring the stability of surrounding rock in underground excavations is a critical prerequisite for safe mining operations. This study examines the mechanisms of wedge failure formation and determines the performance of a combined support system (rock bolts + shotcrete) through probabilistic analysis. Field investigations in the Zhylandy ore field (Kazakhstan) included fracture mapping, rock mass quality assessment (RQD), fracture frequency (FF), and in situ stress measurements, which confirmed a thrust-faulting regime. Numerical modeling with Dips ver.8 and UnWedge ver.6 software (Rocscience) identified critical excavation orientations of 120° and 141° associated with maximum-volume wedge formation, as well as a “safe orientation window” of 70° ± 10°. The probabilistic analysis showed that rock bolts alone yield a factor of safety (FS) < 1.2, whereas the combined support system increases FS to 2.4–3.5, significantly reducing the likelihood of wedge failures. An adaptive framework integrating numerical modeling with intelligent monitoring (“monitor → update model → adjust support”) is proposed, allowing real-time adjustment of support parameters and optimization of material consumption. The practical significance of this work lies in providing design-ready recommendations for support selection and excavation orientation, contributing to accident prevention and sustainable mining operations. Full article

Moisture in coal seams significantly impacts methane adsorption/desorption, yet its microscopic mechanism in intact coal remains poorly characterized due to methodological limitations. This study introduces a novel approach that integrates low-field nuclear magnetic resonance (LF-NMR) with volumetric analysis to quantify, in real-time, the effect of moisture on methane dynamics in intact coal samples. The results quantitatively demonstrate that micropores (relative specific surface area > 700 m²/cm³) are the primary adsorption sites, accounting for over 95% of the stored gas. Moisture drastically reduces the adsorption capacity (by ~72% at 0.29 MPa and ~57% at 1.83 MPa) and inhibits the desorption process, evidenced by a strong linear decrease in desorption ratio (DR) (R² = 0.906) and a sharp exponential drop in the initial desorption rate (R² = 0.999) with increasing moisture content. The findings provide a mechanistic understanding that is crucial for optimizing coalbed methane (CBM) recovery and enhancing strategies for outburst prevention and methane emission mitigation. The results reveal distinct adsorption and desorption features of intact coal compared with coal powder, which can be useful in total methane utilization and mining safety enhancement. Full article

The stability of internal dumps in open-pit coal mines is critical for the safe production and economic performance of the entire mine. To further enhance slope stability and ensure safe production, a new method for constructing trenches (referred to as an anti-sliding trench) on the sloped basal bed of the dump slope in open-pit mines was proposed to improve slope stability. The internal dump slope at the Luzigou anticline of the Anjialing Open-Pit Mine was studied. The slope failure modes of the dumping steps were studied experimentally and by numerical simulations at different widths of anti-slide trenches at the slope’s toe in a staged loading state. Without anti-slide trenches, shear-layer and along-layer failure modes occurred, while the failure modes with anti-slide trenches included shear-layer, along-layer, and squeeze-out failure. Based on the limit equilibrium theory and the determined failure modes, the preset anti-slide trenches at the toe of the dumping steps were theoretically analyzed. The relationships between the slope stability coefficient and the width and depth of anti-slide trenches, as well as the physical and mechanical parameters of the slope body, were derived. Given the physical and mechanical parameters of the slope body and targeted improvement in the slope stability coefficient, the size parameters of anti-slide trenches were designed and optimized through the derived relationships. At the Anjialing Coal Mine, presetting anti-slide trenches with a depth of 1.5 m and a width of 22.68 m at the toe of the dumping steps increased the slope stability coefficient from 1.3095 to 1.6. The proposed method provides a guiding reference for designing similar internal dump slopes in open-pit coal mines and for disaster prevention. Full article

This study evaluates the use of seawater and continental water in tailings thickening and copper flotation at laboratory scale, focusing on water reuse in mining operations in arid regions. The tailings had a mean particle size of 10 µm, with 75% < 50 µm, and a specific weight of 2.64 g/cm³. Seawater contained significantly higher ion concentrations Na⁺ 10,741 ppm, Mg²⁺ 1245 ppm, and Ca²⁺ 556 ppm compared with continental water (187, 32, and 127 ppm, respectively), which negatively affected polymer performance. Sedimentation tests showed that the anionic polymer (A3) increased settling rates by 33 times with continental water at 40 g/t, while with seawater the increase was 31 times at 60 g/t. In column thickener tests, discharge solids reached 65% with continental water and 62% with seawater, representing an annual reduction of ~17,000 m³ of recovered water when seawater is used. Consistency tests indicated that achieving slump <20% required 75% solids with continental water and 77.5% with seawater. With dewatering polymers, doses of 200 g/t achieved ~70% solids and slump values near 50%, surpassing column thickener performance. Primary flotation results showed that recirculated and filtered seawater improved copper recovery by 3–5% compared with fresh seawater, due to partial removal of interfering ions. In contrast, recirculated and filtered continental water reduced recovery by 2–4%, likely because of residual polymer effects on mineral surfaces. These findings highlight the importance of polymer selection and dosage optimization to ensure efficient water recovery and sustainable flotation performance under varying water chemistries. Full article