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Automatic extraction of coal flow region of coal mine belt conveyor plays an important role in coal flow monitoring, and real-time control of belt speed through real-time accurate monitoring of coal flow, which realizes the purpose of energy saving and consumption reduction of belt conveyor. In this paper, a real-time semantic segmentation network with detail enhancement for pixel-level coal flow monitoring, called DENet, is proposed. First, to ensure the strong real-time performance of the network, a two-branch coding structure is used to extract the semantic information and spatial detail information. Second, to improve the feature representation of spatial detail information, we design the Parameter-free Attention-Guided Enhancement Module (PF-AGEM) and the detail enhancement module (DEM), which fully integrate the semantic information features in the semantic branch into the detail branch and further enhance the detail features. Third, we design the multi-scale channel attention (MSCA) module in the semantic branch to extract the semantic information features of small targets earlier in the high-resolution feature maps, which solves the problem that the semantic information features of small targets are easily lost in the low-resolution feature maps. Finally, we propose a selective feature fusion module (FFM) to better realize the fusion of semantic information and spatial detail information. Experimental results show that the proposed DENet achieves a mean intersection over union (mIoU) of 96.23% at 87.1 frames per second (FPS) on the Coal Flow Segmentation (CFS) dataset and 74.9% mIoU at 207 FPS on the Camvid dataset, which is competitive with the state-of-the-art real-time semantic segmentation models.

Aiming at the problem of serious shutdowns of conveyors caused by abnormal coal flow of scraper conveyors, a coal flow monitoring method based on speckle structured light is proposed. The point cloud data of the coal body on the scraper conveyor is collected through the speckle structured light acquisition system. Then, the proposed PDS-Algorithm (Planar Density Simplification Algorithm) is used to complete the simplification and differentiation of the collected point cloud data, which provides a basis for constructing geometric characteristics of coal flow lineament. This paper uses the processed point cloud data to calculate the volume of the coal mass and monitor the coal flow of the scraper conveyor. Finally, this method is used in the detection of abnormal coal flow of a coal mine scraper conveyor, and the results show that the proposed abnormal flow monitoring method can meet the accuracy and real-time requirements of coal mine abnormal alarms.

Methane is a powerful greenhouse gas responsible for around 20 % of radiative forcing (relative to the pre-industrial era) caused by all long-lived greenhouse gases (WMO, 2021). About 60 % of the global emissions are from anthropogenic sources, and coal mining is one of the largest contributors. Emissions are either estimated by bottom-up approaches (based on inventories) or top-down approaches (based on atmospheric measurements). Combining those with an accurate error estimation allows us to better characterise model errors e.g. caused by transport mechanisms. Here we provide a detailed description of factors influencing the coal mine methane emission variability. We use high-frequency (up to hourly) temporal data from seven coal mines in the Upper Silesian Coal Basin during the Carbon dioxide and Methane (CoMet 1.0) mission from 14 May to 13 June 2018. Knowledge of these factors for the individual ventilation shaft is essential for linking the observations achieved during the CoMet 1.0 mission with models, as most publicly available data in the bottom-up worldwide inventories provide annual emissions only. The methane concentrations in examined shafts ranged from 0.10 % to 0.55 %±0.1 % during the study period. Due to the changing scope of mining works performed underground, they were subjected to a significant variation on a day-to-day basis. The yearly methane average emission rate calculated based on 1 month's set of temporal data of the analysed subset of mines was of the order of 142.68 kt yr−1 (σ=18.63 kt yr−1), an estimate 27 % lower than the officially published State Mining Authority (WUG) data and 36 % lower than reported to the European Pollutant Release and Transfer Register (E-PRTR). We also found that emissions from individual coal mine facilities were over- and underestimated by between 4 % to 60 %, compared to the E-PRTR, when short-term records were analysed. We show that the observed discrepancies between annual emissions based on temporal data and public inventories result from (1) the incorrect assumption that the methane emissions are time-invariant, (2) the methodology of measurements, and lastly, (3) the frequency and timing of measurements. From the emission monitoring perspective, we recommend using a standardised emission measurement system for all coal mines, similar to the Methane Fire Teletransmission Monitoring System (SMP-NT/A). Legal safety requirements require all coal mines to implement this system. After an adaptation, the system could allow for gas flow quantification, necessary for accurate and precise estimations of methane emissions at a high temporal resolution. Using this system will also reduce the emission uncertainty due to factors like frequency and timing of measurements. In addition, it would be beneficial to separately identify the emissions from individual ventilation shafts and methane drainage stations. That would bridge the gap between bottom-up and top-down approaches for coal mine emissions. The intermittent releases of unutilised methane from the drainage stations are currently not considered when constructing regional methane budgets.

Efficient gas drainage is crucial for safe coal mine production and the clean utilization of gas resources. Despite recent advances, complex geological conditions and unstable system operation limit the effectiveness of traditional monitoring in underground borehole-pipe-pump systems. This study conducts controlled experiments to analyze the operational behavior of the gas drainage network under various leakage scenarios, quantitatively revealing characteristic patterns in negative pressure and flow rate. Based on these insights, an intelligent gas-drainage performance evaluation model using a Convolutional Neural Network (CNN) is developed to automate classification of drainage effectiveness. Experimental results using 10,000 samples from Xinfa Coal Mine show that the CNN model achieves optimal performance with a learning rate of 0.1 and batch size of 256, reaching classification accuracies of 100% for Classes I–III, 93% for Class IV, and 50% for Class V. The proposed approach integrates experimental simulation, leakage characterization, and deep-learning-based evaluation into a unified framework, providing an effective solution for real-time monitoring and intelligent assessment of gas drainage systems. This study offers technical support for improving gas extraction efficiency, enhancing mine safety, and promoting the clean and efficient utilization of coal-mine gas.

Constructed wetlands (CWs) are engineered systems that use the natural functions of vegetation, substrate and microorganisms to treat wastewater. In coal mining regions, low calorific coals are dumped as discard. Left unattended, discard and slurry ponds contaminate surface and groundwater, cause erosion and sedimentation of particulates into nearby rivers and dams and contribute to atmospheric pollution and landslides. This study sought to investigate the use of South African bituminous discard as filter bed material for CW. A laboratory-scale horizontal subsurface flow (HSF) CW was supplied either nutrient-poor tap water (TW) or nutrient-rich advanced facultative pond (AFP) effluent, and quality of the treated water monitored over 6 months. Additionally, residual material from the discard coal filter bed was assayed after 6 months to establish substrate stability and to assess the contribution of phyto-biodegradation. Results showed successful establishment of P. australis on discard coal, better plant performance (measured as PSII quantum yield and biomass accumulation) and greater nutrient removal when fed AFP effluent. Discard coal filter bed material had greater ash content, sustained fixed carbon and C/N ratio with unchanged electrical conductivity (EC) and sulphate and phosphate concentration, indicative of balanced ion exchange. This, along with a > 70% reduction in NH4+-N concentration, yielded a final effluent within the general limit set by the South African authority for either irrigation or discharge, into a water resource that is not a listed water resource, for volumes up to 2000 m3 on any given day.

As a typical disaster-causing geological structure, karst collapse pillar (KCP) is widely distributed in coalfields of northern China. The interior of KCP is filled with loose and weakly cemented rock masses. Fine particles can be eroded under the hydraulic pressure and the disturbance of the coal mining operation. Then, water inrush pathway can be formed easily, resulting in water inrush hazard. The release of untreated coal mine water can pollute the environment and waste the limited water resource in China. To investigate the particle erosion effect on the water inrush mechanism of KCP, FLAC 3D numerical investigations were conducted to simulate the water flow process of KCP in the mining floor during the coal seam excavation, according to the stress-seepage coupling model with the consideration of the particle erosion. Besides, the evolution of shear stress field, seepage field, and plastic zone along was obtained as working plane advances. Meanwhile, the influence of the thickness of a waterproof rock floor and the hydraulic pressure of aquifer on the formation of water inrush pathway was analyzed. Numerical results indicated that: (1) Shear failure of the KCP near the side of the working plane occurs under the effect of mining excavation; then, the KCP connects with the damage area around the working plane; finally, the water inrush pathway is formed. (2) Water inrush disaster will not occur immediately when the KCP is connected with the damaged area around the working plane; it only occurs when the KCP is completely exposed in the mining. (3) With the mining advances, the thinner the waterproof rock floor and the greater the hydraulic pressure of the aquifer, the easier the groundwater can lead up, and the KCP tends to be damaged with the formation of water inrush pathway.

The present study investigates dust generated from the unloading process in a high-rise building for the mine hoist system and analyzes dust dispersion with the moving conveyance in the building. First, the gas-solid two-phase flow in the building was investigated based on the CFD-DPM method. In particular, the moving conveyance was considered in detail and treated via the dynamic mesh technology. Then, the airflow and dust distribution were investigated in the building. The airflow and the dust concentration at selected points show good agreement with the relative results of field measurements by ourselves. It is found that the descending conveyance significantly influences the surrounding flow field and the spatial and temporal distribution of dust. Dust concentration before the dust source (2 m × 2 m) is high, which extends downward with the conveyance. Dust concentration of the lower floors increases obviously when compared with that of the condition without the movement of the conveyance. The descending velocity of the conveyance also affects the amount of PM 2.5 discharged from the return air outlet. The fitting functions are provided to predict PM 2.5 emissions to the surrounding atmosphere. The research results are of great significance for the improvement of the dust control system for cleaner production technology. Graphical Abstract

To solve the hidden danger of high methane and low permeability gas in the coal mining process, potentially affecting the safety production in an orderly way, we propose the use of deep hole blasting technology to improve the permeability of the coal seam gas drainage, increase the quantity and rate of extraction, and reduce methane output. Taking the geological conditions of the 201 working surface of Tingnan Coal Mine as an example, it is calculated that the single drilled fracture crack extension range is 3.11~5.24 m according to the coal seam deep-hole pre-splitting blasting joint mechanism and fracture propagation mechanics model, providing a theoretical basis for choosing the appropriate hole spacing. Using COMSOL simulation software to simulate the effective gas drainage radius of a coal seam from a two-dimensional perspective on a single borehole radial, the least squares fitting method was used to analyze the simulated data, and obtained the effective drilling extraction radius after pre-split blasting in a deep hole that is 3.6 m, which is in accordance with the theoretical calculations. In order to obtain accurate and scientific calculations, Fast lagrangian analysis of continua (FLAC3D) numerical simulation software was used. After simulating the distribution of plastic zone between two blast holes at different intervals from a three-dimensional angle, and evaluating the development of cracks in the blasting hole, the white zone of the blasting space was completely eliminated when the interval between blasting holes was 7 m, and the cracks could be propagated throughout the surroundings. Therefore, a blasting hole spacing of 7 m is optimal. On-site monitoring in the Nanting coal mine showed that the quantity and rate of extraction of the single hole after pre-splitting blasting were 2.36 times and 1.62 times as much as before. By integrating the borehole drainage amount and the optimized calculation equation, it could be concluded that the permeability coefficient of the coal seam after blasting was 7.78 times as much as before. The function of time-variated drilling methane emission was obtained using multivariate statistical regressions based on the on-site monitored borehole methane emission (qt), and the drilling limit after pre-splitting blasting revealed that the limitation of methane extraction volume was 5.27 times as much as before.

Underground coal mining destroys overlying strata, and phreatic aquifer above the panel is destabilized, causing phreatic water level (PWL) variation. On-site monitoring of the PWL variation throughout the mining period is of great significance to the management and conservation of groundwater resources in arid and semiarid mining areas. The research on the decline of the PWL when phreatic aquifer leakage is concentrated, but there is little research on the fluctuation characteristics of PWL under the condition of phreatic aquifer without leakage. Therefore, using the #108 coalface in the Jinjitan colliery of the Yushenfu mining area as a case study to carry out the research on PWL fluctuation induced by underground coal mining. First, phreatic water without leakage throughout the coal mining period in the #108 coalface was determined. Second, considering surface subsidence induced by mining activities and PWL in fluviograph comprehensively, true PWL fluctuation characteristics were analyzed throughout the whole coal seam mining period. It is concluded that the buried depth of PWL presented a trend of “decreasing sharply—increasing sharply—decreasing slowly—increasing slowly—becoming stable” in the monitoring period of 1 year. Furthermore, a well flow model was established to simulate the PWL variation process before and after coal mining and to predict the PWL recovery time after coal mining. On these bases, the error analysis of the measured and predicted PWL recovery time, the relationship between the PWL fluctuation and the residual aquiclude thickness, the impact of rainfall on PWL recovery, the response of surface vegetation eco-environment, and phreatic water resources management and conservation were discussed. These research results are important for achieving a win–win situation strategy that balances the exploitation of coal resources and the conservation of phreatic water resources, promoting the sustainable development of coal mining.

Coal mining is one of the core industries that contribute to the economic development of a country but deteriorate the environment. Being the primary source of energy, coal has become essential to meet the energy demand of a country. It is excavated by both opencast and underground mining methods and affects the environment, especially hydrological cycle, by discharging huge amounts of mine water. Natural hydrological processes have been well known to be vulnerable to human activities, especially large scale mining activities, which inevitably generate surface cracks and subsidence. It is therefore valuable to assess the impact of mining on river runoff for the sustainable development of regional economy. In this paper, the impact of coal mining on river runoff is assessed in one of the national key coal mining sites, Gujiao mining area, Shanxi Province, China. The characteristics of water cycle are described, the similarities and differences of runoff formation are analyzed in both coal mining and pre-mining periods. The integrated distributed hydrological model named MIKE SHE is employed to simulate and evaluate the influence of coal mining on river runoff. The study shows that mining one ton of raw coal leads to the reduction of river runoff by 2.87 m3 between 1981 and 2008, of which the surface runoff decreases by 0.24 m3 and the baseflow by 2.63 m3. The reduction degree of river runoff for mining one ton of raw coal shows an increasing trend over years. The current study also reveals that large scale coal mining initiates the formation of surface cracks and subsidence, which intercepts overland flow and enhances precipitation infiltration. Together with mine drainage, the natural hydrological processes and the stream flows have been altered and the river run off has been greatly reduced.