Search Results (236)

With the development of the mining industry, safety issues in underground operations are becoming increasingly relevant. Complex gas conditions in mines, including the presence of explosive and toxic gases, pose a serious threat to the lives of miners and the stability of production processes. This paper describes the development and modeling of an integrated fire monitoring and automatic extinguishing system that combines gas collection, concentration analysis, and rapid response to emergencies. The main components of the system include the following: a gas collection module that uses an array of highly sensitive sensors to continuously measure the concentrations of methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S) with an accuracy of up to 95%; a gas analysis module that uses data processing algorithms to identify gas concentration threshold exceedances (e.g., CH₄ > 5% vol. or CO > 20 ppm); and an automatic fire extinguishing module that activates nitrogen supply, ventilation, and aerosol/powder fire extinguishers when a threat is detected. Simulation results in MATLAB/Simulink showed that the system reduces the concentration of hazardous gases by 30% within the first 2 s after activation, which significantly increases safety. Additionally, scenarios with various types of fires were analyzed, confirming the effectiveness of the extinguishing modules in mines up to 500 m deep. The integrated system achieves 95% gas detection accuracy, 90 ms response latency, and 40% hazard reduction within 3 s of activation, verified in 500 m deep mine simulations. Quantitative comparison shows a 75% faster response time and 10% higher detection accuracy than conventional systems. The proposed system demonstrates high reliability in difficult conditions, reducing the risk of fires by 75% compared to traditional methods. This work opens up prospects for practical application in the coal industry, especially in regions with a high risk of spontaneous coal combustion, such as India and Germany. Full article

A methane-air premixed gas explosion is one of the most destructive disasters in the process of coal mining, and the dynamic coupling between the shock wave triggered by the explosion and the surrounding rock of the roadway can lead to the destabilization of the surrounding rock structure, the destruction of equipment, and casualties. The aim of this study is to systematically reveal the propagation characteristics of the blast wave, the spatial and temporal evolution of the wall load, and the damage mechanism of the surrounding rock by establishing a two-way fluid-solid coupling numerical model. Based on the Ansys Fluent fluid solver and Transient Structure module, a framework for the co-simulation of the fluid and solid domains has been constructed by adopting the standard $k-\epsilon$ turbulence model, finite-rate/eddy-dissipation (FR/ED) reaction model, and nonlinear finite-element theory, and by introducing a dynamic damage threshold criterion based on the Drucker–Prager and Mohr–Coulomb criteria. It is shown that methane concentration significantly affects the kinetic behavior of explosive shock wave propagation. Under chemical equivalence ratio conditions (9.5% methane), an ideal Chapman–Jouguet blast wave structure was formed, exhibiting the highest energy release efficiency. In contrast, lean ignition (7%) and rich ignition (12%) conditions resulted in lower efficiencies due to incomplete combustion or complex combustion patterns. In addition, the pressure time-history evolution of the tunnel enclosure wall after ignition triggering exhibits significant nonlinear dynamics, which can be divided into three phases: the initiation and turbulence development phase, the quasi-steady propagation phase, and the expansion and dissipation phase. Further analysis reveals that the closed end produces significant stress aggregation due to the interference of multiple reflected waves, while the open end increases the stress fluctuation due to turbulence effects. The spatial and temporal evolution of the strain field also follows a three-stage dynamic pattern: an initial strain-induced stage, a strain accumulation propagation stage, and a residual strain stabilization stage and the displacement is characterized by an initial phase of concentration followed by gradual expansion. This study not only deepens the understanding of methane-air premixed gas explosion and its interaction with the roadway’s surrounding rock, but also provides an important scientific basis and technical support for coal mine safety production. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

The multi-branch horizontal well for coalbed methane (CBM) is a core technical means to achieve efficient CBM extraction, and its productivity is jointly restricted by geological and engineering factors. To accurately grasp the main controlling factors of the productivity of multi-branch horizontal wells and provide a scientific basis for the optimized design of CBM development, this study takes Well W1 in the Wenjiaba Coal Mine of the Zhina Coalfield in Guizhou, China, as an engineering example and comprehensively uses three-dimensional geological modeling and reservoir numerical simulation methods to systematically explore the key influencing factors of the productivity of multi-branch horizontal wells for CBM. This study shows that coal seam thickness, permeability, gas content, and branch borehole size are positively correlated with the productivity of multi-branch horizontal wells. With the simulation time set to 1500 days, when the coal seam thickness increases from 1.5 m to 4 m, the cumulative gas production increases by 166%; when the permeability increases from 0.2 mD to 0.8 mD, the cumulative gas production increases by 123%; when the coal seam gas content increases from 8 m³/t to 18 m³/t, the cumulative gas production increases by 543%; and when the wellbore size increases from 114.3 mm to 177.8 mm, the cumulative gas production increases by 8%. However, the impact of branch angle and spacing on productivity exhibits complex nonlinear trends: when the branch angle is in the range of 15–30°, the cumulative gas production shows an upward trend during the simulation period, while in the range of 30–75°, the cumulative gas production decreases during the simulation period; the cumulative gas production with branch spacing of 100 m and 150 m is significantly higher than that with spacing of 50 m and 200 m. Quantitative analysis through sensitivity coefficients reveals that the coal seam gas content is the most important geological influencing factor, with a sensitivity coefficient of 2.5952; a branch angle of 30° and a branch spacing of 100 m are the optimal engineering conditions for improving productivity, with sensitivity coefficients of 0.2875 and 0.273, respectively. The research results clarify the action mechanism of geological and engineering factors on the productivity of multi-branch horizontal wells for CBM, providing a theoretical basis for the optimized deployment of well locations, wellbore structure, and drilling trajectory design of multi-branch horizontal wells for CBM in areas with similar geological conditions. Full article

The accurate estimation of methane reserves in closed coal mines is crucial for supporting clean energy recovery and reducing greenhouse gas emissions. This study addresses the technical challenges associated with complex geological conditions and limited post-closure data in China’s closed mines. Three mainstream estimation methods—the material balance, resource composition, and decline curve—are systematically reviewed and applied to a case study in the Huoxi Coalfield. Results indicate that the material balance method provides upper-bound estimates but is highly sensitive to incomplete historical data, whereas the resource composition method yields more conservative and geologically realistic values. Although the decline curve method is not applied in this case, it offers potential for forecasting when long-term monitoring data are available. A multi-method integration approach, supported by enhanced data archiving and uncertainty assessments, is recommended to improve the accuracy and reliability of methane reserve evaluations in post-mining environments. Full article

This study addresses gas drainage challenges in the Pingdingshan NO.10 mine JI_15-16 coal seam through coupled COMSOL-FLAC^3D numerical simulations. The research evaluates the effectiveness of a cross-measure borehole drainage system. It analyzes the failure mechanisms of the surrounding rock in both the machine roadway and floor roadway of the 24130 working face under the influence of boreholes. The results demonstrate that extended drainage duration progressively reduces both gas content and pressure within the borehole-affected zone of the coal seam while enhancing the effective permeability of the JI_15-16 coal stratum. The operational system extracted 1,527,357 m³ of methane, achieving a pre-drainage efficiency of 59.18% through cross-measure boreholes. The measured gas content aligns with simulated predictions, though field-recorded gas pressure registered slightly higher than modeled values. This validated drainage design complies with the Pingmei Group’s regulations for coal and gas outburst prevention. Critically, cross-measure boreholes alter stress distribution around both coal and floor roadways, promoting plastic zone expansion. Consequently, during the development of the 24130 working face’s machine roadway, intensified ground pressure monitoring is essential near borehole locations in the roof, floor, and rib strata. Supplementary support reinforcement should be implemented when required to prevent rib spalling and roof collapse incidents. Full article

This study suggests a method for analyzing the risk of methane explosions using the N-K model and Social Network Analysis (SNA) to understand how different risk factors related to coal-mine methane explosions are connected and change over time, aiming to prevent these accidents effectively. We identified 41 secondary risk factors and four fundamental risk factors—human, equipment, environment, and management—based on the 4M accident causation theory. The SNA model was utilized to determine the main risk factors and their evolutionary routes, while the N-K model was utilized to quantify the degree of risk coupling. The findings show that the number of risk variables engaged in the methane-explosion risk system closely correlates with the number of accidents that occur and the maximum coupling level among the four elements. The primary control factors in the methane-explosion risk system are poor equipment management, broken safety monitoring and control systems, inadequate safety education and training, safety regulation violations, and poor safety production responsibility system implementation. We utilized the primary evolution paths and key elements to propose risk control approaches. A reference for ensuring safety in coal-mine operations can be found in the research findings. Full article

This paper presents the procedures and results of the numerical modelling and simulations performed to analyse an innovative method of advanced methane drainage employing underground long-reach directional drilling (LRDD) technology. The analysis involved the implementation of geomechanical and dynamic reservoir models to simulate processes in coal seams and the surrounding rocks during coal mining and concurrent methane drainage, in accordance with the proposed technology. The analysis aimed to quantitatively assess the effectiveness of the technology, evaluate its sensitivity to the geological and geomechanical properties of the rocks, and identify the potential for optimisation of its technological and operational parameters in the proposed strategy. The works presented in this paper include the following key tasks: the construction of a system of geological, geomechanical, and dynamic simulation models; the analysis of the geomechanical effects of various types and regions of occurrence; the implementation of the correlation between the geomechanical states of the rocks and their transport properties; and the performance of the effectively coupled geomechanical and reservoir fluid flow simulations. The proposed approach was applied to the specific conditions of the multi-seam Murcki–Staszic Coal Mine operated by Jastrzębska Spółka Węglowa, Poland. Full article

The efficiency of coalbed methane (CBM) extraction is closely related to the drilling response of coal seams, which is significantly influenced by natural fracture development of coal seams. This work investigated 11 coal samples from the Baode, Xinyuan, and Huolinhe mines, employing quantitative fracture characterization, acoustic wave testing, drilling experiments, and cuttings analysis to systematically reveal the relationships and mechanisms between fracture parameters and coal drilling response characteristics. The result found that acoustic parameters (average wave velocity v and drilling surface wave velocity v₀) exhibit significant negative correlations with fracture line density (ρ₁) and area ratio (ρ₂) (|r| > 0.7), while the geological strength index (GSI) positively correlates with acoustic parameters, confirming their utility as indirect indicators of fracture development. Fracture area ratio (ρ₂) strongly correlates with drilling cuttings rate q (r = 0.82), whereas GSI negatively correlates with drilling rate w, indicating that highly fractured coal is more friable but structural stability constrains drilling efficiency, while fracture parameters show limited influence on drill cuttings quantity Q. Cuttings characteristics vary with fracture types and density. Type I coal (low-density coexisting exogenous fractures and cleats) produces cuttings dominated by fine particles with concentrated size distribution (average particle size d ≈ 0.52 mm, crushability index n = 0.46–0.61). Type II coal (exogenous-fracture-dominant) exhibits coarser particle sizes in cuttings (d ≈ 0.8 mm, n = 0.43–0.53). Type III coal (dense-cleat-dominant) drill cuttings are mainly coarse particles and are concentrated in distribution (d ≈ 1.53 mm, n = 0.72–0.98). Additionally, drilling response differences are governed by the coupling effects of vitrinite reflectance (R_o), density, and firmness coefficient (f), with Huolinhe coal being easier to drill due to its lower R_o, f, and density. This study elucidates the mechanism by which fracture development affects coal drilling response through multi-parameter correlation analysis, while also providing novel insights into the optimization of fracturing sweet spot selection for CBM development. Full article

Gas extraction is an important means to reduce coalbed gas and ensure safe coal production. Injecting N₂/CO₂ into a coalbed can enhance coal seam gas extraction, but problems with N₂/CO₂ sources underground have prevented the wide application of this technology in coal mines. The air contains a large amount of N₂, but only a few studies have investigated the injection of air into coalbeds to facilitate gas extraction. In this study, a thermal–hydraulic–solid coupling model for air-enhanced coalbed gas extraction (Air-ECGE) was established. Additionally, the impact of air injection on coalbed methane extraction was simulated, and field experiments were conducted on air injection to enhance gas extraction. The results showed that injecting high-pressure air into a coalbed can effectively facilitate gas desorption and gas migration within the coalbed, greatly improving the efficiency of gas extraction in the coalbed. In addition, owing to the large pressure gradient that can lead to fast coalbed gas seepage, the gas production rate of the extraction borehole is directly proportional to the gas injection pressure. Further, the spacing of the boreholes limits the influence range of the gas injection: the larger the spacing, the larger the influence range, and the higher the gas extraction rate of the extraction borehole. After injecting air into the coalbed of the Liuzhuang coal mine, the extraction flow rate and concentration of gas from the extraction boreholes both increased significantly. A certain delay effect was also observed in the gas injection effect, and the gas extraction flow rate only decreased after a period of time after the gas injection had stopped. Full article

To elucidate the gas adsorption characteristics of medium-rank coal, this study collected samples from fresh mining faces in the Qinshui Basin. A series of experiments were conducted, including low-temperature carbon dioxide adsorption, low-temperature liquid nitrogen adsorption, mercury intrusion, and methane isothermal adsorption experiments, which clarify the pore structure characteristics of medium-rank coals, reveal the gas adsorption behavior in medium-rank coal, and identify the control mechanism. The results demonstrate that the modified Dubinin–Radushkevich (D-R) isothermal adsorption model accurately describes the gas adsorption in medium-rank coal, with fitting errors remaining below 1%. Comprehensive pore structure analysis reveals that the coal pore volume consists primarily of absorption pores (<2 nm), transitional pores (10–100 nm), and seepage pores (>100 nm), while the specific surface area is predominantly contributed by absorption pores (<2 nm). At low pressures, gas molecules form monolayer adsorption on absorption pore (<2 nm) and adsorption pore (2–10 nm) surfaces. With increasing pressure, multilayer adsorption dominates. As pore filling approaches the maximum capacity, the adsorption rate decreases progressively until reaching an equilibrium, at which point the adsorption capacity attains its saturation limit. The adsorption data of the gas in medium-rank coal can be explained by the improved D-R isothermal adsorption model. The priority of gas filling in pores is different, and the absorption pore is normally better than the adsorption pore. The results provide a new idea and understanding for the further study of the coalbed gas adsorption mechanism. 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

As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

The acidification modification of coal seams is a significant technical measure for transforming coalbed methane reservoirs and enhancing the permeability of coal seams, thereby improving the extractability of coalbed methane. However, the acids currently used in fracturing fluids are predominantly inorganic acids, which are highly corrosive and can contaminate groundwater reservoirs. In contrast, organic acids are not only significantly less corrosive than inorganic acids but also readily bind with the coal matrix. Some organic acids even exhibit complexing and flocculating effects, thus avoiding groundwater contamination. This study focuses on the 1/3 coking coal from the Guqiao Coal Mine of Huainan Mining Group Co., Ltd., in China. It systematically investigates the fractal characteristics and chemical structure of coal samples before and after pore modification using four organic acids (acetic acid, glycolic acid, oxalic acid, and citric acid) and compares their effects with those of hydrochloric acid solutions at the same concentration. Following treatment with organic acids, the coal samples exhibit an increase in surface fractal dimension, a reduction in spatial fractal dimension, a decline in micropore volume proportion, and a rise in the proportions of transitional and mesopore volumes, and the structure of the hydroxyl group and oxygen-containing functional group decreased. This indicates that treating coal samples with organic acids enhances their pore structure and chemical structure. A comparative analysis reveals that hydrochloric acid is more effective than acetic acid in modifying coal pores, while oxalic acid and citric acid outperform hydrochloric acid, and citric acid shows the best results. The findings provide essential theoretical support for organic acidification modification technology in coalbed methane reservoirs and hydraulic fracturing techniques for coalbed methane extraction. Full article