Search Results (102)

Coal and gas outbursts are one of the major hidden hazards in coal mine production safety. To achieve the effective prevention and control of this type of disaster, detailed measurements of relevant parameters are conducted based on the No. 9 coal seam in Longfeng Coal Mine, Guizhou Province. Using the obtained data and combining it with the Kriging algorithm, the gas content in the coal seam is accurately predicted and analyzed, taking into account the spatial location of the prediction points and the prediction level. This investigation reveals the regional occurrence characteristics of gas under complex geological conditions and enables the early identification of regional gas hazards. The main findings are as follows: (i) There is a significant relationship between gas content, elevation, and burial depth in the studied coal seam. The relationship between gas content and elevation can be expressed by the following formula: y = −0.0406x + 54.845, R² = 0.9202. The relationship between gas content and burial depth can be expressed by the following formula: y = 0.0269x + 5.1801, R² = 0.8925. (ii) The gas content reaches a critical value of 8 m/t when the coal seam burial depth reaches 105 m, and the area below 105 m is identified as the outburst hazard zone. (iii) The gas content prediction function formula for coal seam No. 9 based on the Kriging algorithm is derived as y = 0.84x + 1.840, with an average prediction accuracy of 90.44%. Full article

Coal and gas outburst remains a critical and persistent challenge in coal extraction, posing a profound threat for mine safety. The underlying mechanisms of such disaster, particularly the gas-driven coal fragmentation, continue to elude comprehensive understanding. To explore this problem, in this paper, gas seepage regularity in different structural bituminous coal and its influence on outburst-coal breaking were investigated through strength tests, isothermal adsorption tests, and gas seepage tests of stressed coal under various conditions. The results indicated that coal permeability decreased as axial stress, confining pressure, and gas kinetic diameter increased. That meant outburst-induced abrupt stress unloading and coal matrix destabilization changed gas seepage characteristics. As a result, a self-reinforcing cycle effect where outburst-coal breaking and gas seepage are mutually stimulated was formed in a short time period when outbursts initiated, which further promoted outburst-coal breaking and outburst initiation. The findings of this study enhance our understanding of the mechanism of gas participating in coal fragmentation during outbursts, which are significantly conducive to gas disaster prevention, sustainable coal production, and efficient CBM development, further ensuring global energy security. Full article

Sudden coal and gas outbursts pose a significant hazard in deep-seated coal seam extraction, necessitating reliable risk assessment methods. Traditionally, assessments focus on gas-dynamic parameters, but experience shows they must be supplemented with tectonic factors such as fault-related disturbances, weak interlayers, and increased fracturing. Even minor faults in the Karaganda Basin can weaken the coal massif and trigger outbursts. The integration of 3D modeling enhances risk evaluation by incorporating both dynamic (gas-related) and static (tectonic) parameters. Based on exploratory drilling and geophysical studies, these models map coal seam geometry, fault positioning, and high-risk structural zones. In weakened coal areas, stress distribution changes can lead to avalanche-like gas releases, even under normal gas-dynamic conditions. An expert scoring system was used to convert geological and gas-dynamic data into a comprehensive risk index guiding preventive measures. An analysis of Karaganda Basin incidents (1959–2021) shows all outbursts occurred in geological disturbance zones, with 43% linked to fault proximity, 30% to minor tectonic shifts, and 21% to sudden coal seam changes. Advancing 3D modeling, geomechanical analysis, and microseismic monitoring will improve predictive accuracy, ensuring safer coal mining operations. Full article

Gas extraction is the main method to reduce the gas content of a coal seam and prevent coal and gas outburst. The sealing depth is one of the key parameters affecting the sealing effect. The principle of the high-density direct current method is to lay electrodes underground, and by injecting a stable DC current into the underground medium, the potential difference is measured to calculate the apparent resistivity, which reflects the difference in electrical conductivity of the underground rock or coal body, and then inferring the physical characteristics, such as its structure, water content, or stress state. Based on the basic principle of the high-density direct current method, this study analyzed the change rule of resistivity after the secondary stress of the roadway; tested the distribution of the roadway stress field in Juji Mine; and finally, determined the sealing depth of this coal seam. The main conclusions were as follows: The resistivity of the loose crushing zone after the roadway disturbance stress corresponded to the plasticity and destruction stage of the coal body, and the resistivity was larger compared with that of the original rock stress area. The stress concentration zone corresponded to the compression stage, where the destruction of the coal and rock state was smaller, and the resistivity was smaller compared with that of the original rock stress area. The range of the loose circle of the roadway of the coal seam was 6 m, and the range of the stress concentration zone was 6–17.5 m. The range of resistivity changes of the loose crushing zone was larger, and it had a large range of resistance, which had a good effect. The resistivity of the loose broken zone varied widely and was random, while the visual resistivity of the stress concentration zone was basically the same and was stable. Full article

The ability to predict the volume of methane outbursts in coal mines is critical for the prevention of methane outburst accidents and the assurance of coal-mine safety. This paper’s central argument is that existing prediction models are limited in several ways. These limitations include the complexity of the models and their poor ability to generalize. The paper proposes a methane outburst volume-prediction and early-warning method. This method is based on a secondary decomposition and improved TSMixer model. First, data smoothing is achieved through an STL decomposition–adaptive Savitzky–Golay filtering–reconstruction framework to reduce temporal complexity. Second, a CEEMDAN-Kmeans-VMD secondary decomposition strategy is adopted to integrate intrinsic mode functions (IMFs) using K-means clustering. Variational mode decomposition (VMD) parameters are optimized via a novel exponential triangular optimization (ETO) algorithm to extract multi-scale features. Additionally, a refined TSMixer model is proposed, integrating reversible instance normalization (RevIn) to bolster the model’s generalizability and employing ETO to fine-tune model hyperparameters. This approach enables multi-component joint modeling, thereby averting error accumulation. The experimental results demonstrate that the enhanced model attains RMSE, MAE, and R² values of 0.0151, 0.0117, and 0.9878 on the test set, respectively, thereby exhibiting a substantial improvement in performance when compared to the reference models. Furthermore, we propose an anomaly detection framework based on STL decomposition and dual lonely forests. This framework improves sensitivity to sudden feature changes and detection robustness through a weighted fusion strategy of global trends and residual anomalies. This method provides efficient and reliable dynamic early-warning technology support for coal-mine gas disaster prevention and control, demonstrating significant engineering application value. Full article

During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of a large and potentially dangerous glacial lake. Our resultant study of the issue included informal interviews with local informants, comparison of time series satellite composite images acquired by Sentinel-2 Multispectral Instrument, and modeling of different lake development, outburst flood scenarios, and prospective downstream impacts. Assuming that the future glacial lake will be formed by the merging of present-day supraglacial ponds, filling the low-gradient area beneath the present-day glacier terminal complex, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 10⁶ m³. Potential mass movement-triggered outburst floods would travel downstream distances of almost 120 km even under the small magnitude scenario, and under the worst-case scenario would reach the Indo-Gangetic Plain and cross the border into India, exposing up to 90 buildings and 44 bridges. In response, we suggest that the lower Kanchenjunga glacier region be regularly monitored by both local communities and Kathmandu-based research entities over the next decade. The development of user-friendly early warning systems, hazard mapping and zoning programs, cryospheric hazards awareness building programs, and construction of locally appropriate flood mitigation measures are recommended. Finally, the continued development and refinement of the models presented here could provide governments and remote communities with a set of inexpensive and reliable tools capable of providing the basic information needed for communities to make informed decisions regarding hazard mitigation, adaptive, and/or preventive measures related to changing glaciers. Full article

Gas accumulation triggered by coal and gas outbursts is the core cause of secondary disasters in coal mines. This study focuses on the risk assessment of gas accumulation during disaster scenarios, proposing a multidimensional evaluation method integrating the analytic hierarchy process (AHP), information entropy theory, kernel density estimation, and dynamic risk propagation modeling. A unified intelligent prevention system encompassing “monitoring–prediction–decision making” is established. Leveraging the TFIM3D simulation platform and case studies from the Qunli Coal Mine accident, this research reveals spatiotemporal evolution patterns of gas concentration and explosion risk thresholds. A ventilation optimization strategy based on risk classification is proposed. The results demonstrate that the dynamic risk index (DRI), derived from the coupling of the roadway air volume stability coefficient and gas concentration information entropy, can accurately identify high-risk zones. The findings provide theoretical foundations and practical pathways for dynamic risk management in ventilation systems during coal and gas outburst disasters. Full article

Outbreaks involving carbapenemase-producing enterobacteria (CPE) have become a common occurrence in healthcare settings. While clonal dissemination is firmly established as a cause for these outbreaks, horizontal gene transfers (HGTs) between different species of Enterobacterales found in clinical and environmental isolates are less so. To gather evidence backing up this hypothesis, a review covering the 2013–2024 period was performed. HGTs between different species of clinical and environmental Enterobacterales were identified in thirteen papers, half of those published within the last three years. A combination of short- and long-read whole genome sequencing (WGS) was predominantly used to identify mobile genetic elements and plasmids. The more frequently reported carbapenemases were KPCs, followed by NDMs and IMPs. Predictably, broad-host-range plasmids were responsible for over 50% of HGTs, with the IncA/C group being in the lead. Klebsiella pneumoniae and Enterobacter cloacae complexes were the most frequent species identified in clinical samples, while Citrobacter freundii dominated environmental ones. Drains and pipework frequently constituted CPE reservoirs in protracted outbreaks, alternating epidemic outbursts with silent phases. Including WGS in a systematic environmental surveillance would help in swiftly identifying those CPE reservoirs and possibly help better control plasmid outbursts by allowing the implementation of adequate infection prevention and control measures. Full article

Coal seam gas drainage serves as an effective engineering measure to mitigate compound disasters of the rockburst and outburst in deep mining, and its efficacy is fundamentally governed by the permeability of coal around the gas drainage borehole. To systematically study the permeability law of broken coal body around borehole under different stress states and particle size distribution, the coal particle samples were prepared for the triaxial permeability tests by the gradation theory whose Talbot power exponents n are 0.1 to 1.0. Several valuable findings have been obtained through meticulous research and analysis, according to Darcy’s law and the Forchheimer equation. The seepage velocity is affected by the Talbot power exponent, pressure gradient, confining pressure, and axial pressure, among which the pressure gradient has the most prominent influence. The larger the Talbot power exponent of the sample composition and the larger of the pressure gradient inside the sample, the larger is the seepage velocity obtained by the sample. The axial pressure has a notable influence on permeability by modifying the pore structure of broken coal. As the axial pressure increases, the permeability decays exponentially until it reaches a stable state at a specific limit. The permeability decreases exponentially with the increase of effective stress, while the power exponent (a) decreases gradually with the increase of Talbot power exponent, and the coefficient (b) increases gradually with the increase of Talbot power exponent (index), in the effective stress-permeability equation, which implies that the inhibition and amplitude effects of effective stress on permeability become more intense. The permeability shows three stages of growth, namely the slow growth stage, the linear growth stage, and the exponential growth stage, which are influenced by small-sized coal particles, particle-size ratio, and large-sized coal particles respectively, when the Talbot power exponent (n) of the broken coal increases from 0.1 to 1.0. These findings advance understanding of the permeability of broken coal around boreholes, providing theoretical foundations for optimizing gas drainage parameters and preventing the compound disaster of the rockburst and outburst. Full article

The influence of coal and gas outbursts from a coal seam adjacent to the working face is crucial for determining its automatic gas discharge width, which is an important basis for the roadway position design of the adjacent working face. This study focuses on determining the automatic gas discharge width of the coal body in the neighboring goaf, especially examining the working face of the E₁₀-32040 air mining area and the E₁₀-32060 wind tunnel of the No. 1 Mine operated by Pingmei Company. Theoretical analysis, strain-softening simulation, and field testing were adopted to study the automatic gas discharge width under the current mining conditions, and the results are as follows: (1) Back mining at the working face has a greater impact on the coal body of the neighboring goaf than roadway excavation, and the compression deformation at 50 m from the goaf after back mining is 6.18 times that during roadway excavation. (2) The gas content of the coal body of the neighboring goaf is linearly distributed, and the coefficient of determination (R²) is 0.98024. (3) The extent of compression and deformation of the neighboring coal body follows an exponential distribution, and the coefficient of determination (R²) is 0.99482. (4) Under the current mining conditions, the risk of protrusion can be considered eliminated when the residual gas content is below 4.45 m³/t. The compression deformation is 0.96‰ when the automatic gas discharge width is 30.11 m. The research results can provide theoretical reference and data support for adjacent roadway location design and the selection of gas prevention and control measures in coal seams. Full article

Research on the deformation and failure behavior of coal is a key scientific issue in the study of coal–rock dynamic disaster prevention technology. It is a critical means to grasp the structural effect of coal–rock deformation and failure behavior to explore the effects of fracture structure on coal–rock deformation and failure behavior. Our experiment on the failure characteristics of coal–rock and the evolution of deformation–fracture structures before the peak stress of coal–rock primarily investigates the influence of fracture structures on its deformation and failure behavior under loading, with a focus on analyzing the size of the primary fractures. The results indicate that the influence of the primary fracture structure on the physical and mechanical properties of coal–rock varies, and the sensitivity of different properties to these structures also differs. Compared to coal–rock without outburst proneness, the fracture structure evolution of coal–rock with strong outburst proneness before failure is more intense and exhibits significant geometric nonlinearity. The size of the fracture that plays the main role in the pre-peak deformation of coal–rock with strong outburst proneness is about one-third of the size of the specimen, and it is about one-fifth of the size of the specimen for coal–rock without outburst proneness. The fracture structure affects the whole deformation process before the failure of coal–rock with strong outburst proneness, but its influence on coal–rock without outburst proneness is gradually reduced with the loading. Full article

Coal seam gas drainage is a primary measure for mitigating coal and gas outburst hazards. Grouting sealing can form coal-slurry consolidated bodies, significantly improving the sealing quality of gas drainage boreholes and alleviating coal and gas outburst risks. Therefore, this study conducts triaxial loading and seepage experiments to analyze the mechanical failure characteristics and permeability variation of coal-slurry consolidated bodies under loading conditions following grouting sealing of gas drainage boreholes. Based on the “cube” model, a permeability model for the damaged coal-slurry consolidated body under loading conditions is established. The findings provide guidance for evaluating the leakage prevention performance of sealing materials in field engineering and optimizing the sealing efficiency of grouting materials. Future research may explore the damage and seepage evolution of coal-slurry consolidated bodies under various loading conditions and sealing material types. Full article

The weathered bedrock aquifer in the Jurassic coalfield of northern Shaanxi Province is a direct water-bearing aquifer, and accurately predicting its water-bearing properties is essential for preventing and controlling water hazards in mining operations. Traditional Bayes discriminant methods have limitations in predicting water-bearing properties, particularly because not all primary factors influencing water-bearing properties meet the criteria for multivariate normal distribution. In this paper, the southern flank of the Ningtiaota Minefield is taken as an example, with the weathered bedrock aquifer as the research object. Six main controlling factors are selected: weathered bedrock thickness, core recovery rate, degree of weathering, lithological combination, elevation of the weathered bedrock surface, and sand-to-base ratio. A kernel density estimator–Bayes (KDE–Bayes) discriminant method for predicting water-bearing properties is presented. The kernel density estimation was carried out on the three main controlling factors that do not conform to a normal distribution—weathered bedrock thickness, core recovery rate, and sand-to-base ratio—and, in conjunction with other primary factors, a KDE–Bayes model was constructed for predicting the water-bearing properties in the southern flank of the Ningtiaota Minefield, based on which a detailed prediction of the water-bearing properties of the south flank of the Ningtiaota Minefield was conducted. By analyzing the actual dewatering data from the S1231 working face and past water inrush (or outburst) incidents, the feasibility and accuracy of this prediction method are demonstrated, providing valuable insights for predicting the water-bearing properties of weathered bedrock aquifers in the Ningtiaota Coal Mine and similar mining conditions. Full article

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations. Full article

Deep coal mining is essential for energy use and sustainable development. In a situation where coal–rock–gas dynamic disasters are prone to occur in coal seam variation areas affected by different degrees of roof angle during deep coal seam mining, a disaster energy equation considering the influence of roof elastic energy is established, and the disaster energy criterion considering the influence of roof elastic energy is derived and introduced into COMSOL6.1 software for numerical simulation. The results show that, compared with the simple change of coal thickness and coal strength, the stress concentration degree of a thick coal belt with small structure is higher, and the maximum horizontal stress can reach 47.6 MPa. There is a short rise area of gas pressure in front of the working face, and the maximum gas pressure reaches 0.82 MPa. The plastic deformation of the coal body in a small-structure thick coal belt is the largest, and the maximum value is 18.04 m³. The simulated elastic energy of rock mass is about one third of that of coal mass, and the influence of the elastic energy of roof rock on a disaster cannot be ignored. When the coal seam is excavated from thin to thick with a small-structural thick coal belt, the peak value of the energy criterion in front of the excavation face is the largest, and the maximum value is 1.42, indicating that a dynamic disaster can occur and the harm degree will be the greatest. It is easy to cause a coal and gas outburst accident when the excavation face enters a soft coal seam from a hard coal seam and a small-structural thick coal belt from a thin coal belt. Practice shows that holistic prevention and control measures based on high-pressure water jet slit drilling technology make it possible to increase the average pure volume of gas extracted from the drilled holes by 4.5 times, and the stress peak is shifted to the deeper part of the coal wall. At the same time, the use of encrypted drilling in local small tectonic thick coal zones can effectively attenuate the concentrated stress in the coal seam and reduce the expansion energy of gas. This study enriches our understanding of the mechanism of coal–rock–gas dynamic disaster, provides methods and a basis for the prevention and control of dynamic disaster in deep coal seam variation areas, and promotes the sustainable development of energy. Full article