Coal Mine Safety Research Articles

Initial Desorption Characteristics of Gas in Tectonic Coal Under Vibration and Its Impact on Coal and Gas Outbursts

The rapid desorption of gas in coal is an important cause of gas over-limit and outbursts. In order to explain the causes of coal and gas outbursts induced by vibration, this paper studies the gas desorption experiments of tectonic coal with different particle sizes and different adsorption equilibrium pressures under 0~50 Hz vibration. High-pressure mercury intrusion experiments were used to measure the changes in pore volume and specific surface area of tectonic coal before and after vibration, revealing the control of pore structure changes on the initial desorption capacity of gas. Additionally, from the perspective of energy transformation during coal and gas outbursts, the effect of vibration on the process of coal and gas outbursts in tectonic coal was analyzed. The results showed that tectonic coal has strong initial desorption capacity, desorbing 29.58% to 54.51% of the ultimate desorption volume within 10 min. Vibration with frequencies of 0~50 Hz increased both the gas desorption ratios and desorption volume as the frequency increased. The initial desorption rate also increased with the vibration frequency, and vibration can enhance the initial desorption capacity of tectonic coal and delay the attenuation of desorption rate. Vibration affected the changes in the initial gas desorption rate and desorption rate attenuation coefficient by increasing the pore volume and specific surface area, with the changes in macropores and mesopores primarily affecting the initial desorption rate and 0~10 min desorption ratios, while the changes in micropores and minipores mainly influenced the attenuation rate of the desorption rate. Vibration increased the free gas expansion energy of tectonic coal as the frequency increased. During the incubation and triggering processes of coal and gas outbursts, vibration has been observed to accelerate the fragmentation and destabilisation of the coal body, while simultaneously increasing the gas expansion energy to a point where it reaches the threshold energy necessary for coal transportation, thus inducing and triggering the coal and gas protrusion. The study results elucidate, from an energy perspective, the underlying mechanisms that facilitate the occurrence of coal and gas outbursts, providing theoretical guidance for coal and gas outburst prevention and mine safety production.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Deformation and Fracture Mechanisms of Thick Hard Roofs in Upward Mining Coalfaces: A Mechanical Model and Its Validation

The safety and efficiency of underground coal mining are threatened by thick hard roofs characterized by large overhang areas, problematic spontaneous caving, and high dynamic load upon their breakage. In this study, a mechanical model of the bearing capacity of thick hard roofs in upward mining coalfaces associated with mining activities is built based on bending theories for beams with single generalized displacement and the elastic foundation beam theory. Using this method, we analyze the deformation and fracture mechanisms of a thick hard roof during upward mining. We further derive the mechanical equations of rotational angle, bending moment, shear force, and deflection of the free overhang and coal-bearing zone in the thick hard roof and an equation for calculating the limiting span. The mechanical behaviors of the thick hard roof bearing state are analyzed under different parameters. The results show that the foundation coefficient, roof thickness, and angle of upward mining have little influence on the roof bending moment but are positively correlated to the limiting span. Roof load and overhang length have a significant influence on the roof bending moment. They are negatively and positively correlated with the limiting span, respectively. Finally, a case study is performed on the Ш601 upward mining coalface in the Zhuzhuang Coal Mine. The distribution characteristics of the bending moment of the thick hard roof at different extraction stages are analyzed. At each stage, the limiting spans of the thick hard roof upon breaking were calculated as 13.18, 18.82, and 22.50 m, respectively, being close to the on-site measured periodic weighting lengths of 13.33, 19.33 m, and 22.67 m. This close fit proves the feasibility and accuracy of the developed mechanical model. The present study offers theoretical guidance for estimating the weighting length of thick hard roofs in coalfaces and for engineering technology control in similar scenarios.

Monitoring and Analysis of Surface Deformation in the Buzhaoba Open-Pit Mine Based on SBAS-InSAR Technology

The Buzhaoba open-pit mine is an important lignite production base in Yunnan Province, China. As mining activities have continued to progress, varying degrees of deformation have occurred in different areas of the Buzhaoba open-pit mine, threatening normal coal production and mine safety. To comprehensively investigate the characteristics of surface deformation and its influencing factors at the Buzhaoba open-pit mine, this study employed the following methods: first, the SBAS-InSAR technique was used to process 86 Sentinel-1A ascending and descending orbit remote sensing images from 2020 to 2023, obtaining LOS surface deformation information for the mining area; second, leveling observation data were used to validate the accuracy of the SBAS-InSAR results, and based on the principle of two-dimensional deformation decomposition, the east–west and vertical surface deformation information of the mining area was obtained; finally, the temporal variation characteristics and influencing factors of the Buzhaoba open-pit mine were analyzed. The study results indicate that (1) the maximum LOS surface deformation rates in the ascending and descending orbits of the mining area were −42.1 mm/a and −114.0 mm/a, respectively; (2) the correlation coefficient between the SBAS-InSAR monitoring results and the leveling observation results was 0.938, confirming the reliability of the SBAS-InSAR monitoring results; (3) the maximum east–west and vertical deformation rates obtained from the two-dimensional deformation decomposition were −103.4 mm/a and −189.2 mm/a, respectively, with the surface deformation in the east–west direction being more pronounced; (4) internal factors such as stratigraphic lithology and geological structures, as well as atmospheric rainfall, have a certain degree of influence on the surface deformation of the Buzhaoba open-pit mine. Therefore, the research results of this study can provide important data support and theoretical references for safety management and disaster prevention in the mining area.

IoT-Enabled Methane Monitoring and LSTM-Based Forecasting System for Enhanced Safety in Underground Coal Mining

IoT-Enabled Methane Monitoring and LSTM-Based Forecasting System for Enhanced Safety in Underground Coal Mining

Research on Occurrence Law and the Prevention of Rockbursts in Main Roadways Affected by Mining Activities: Two Case Studies from Gaojiapu and Cuimu Coal Mines, Shaanxi, China

Rockburst, one of the leading types of disaster in mining and rock engineering causing serious injuries and the loss of property, frequently occurs, involving various features and complex evolutionary mechanisms. Compared to rockbursts occurring at mining faces, those occurring in main roadways cause more serious problems for mine production. This paper first analyzes the characteristics of rockbursts in main roadways using two case studies involving the Gaojiapu and Cuimu coal mines. The causes of rockbursts in main roadways were studied using microseismic monitoring, energy density cloud maps, and seismic velocity tomography. During the mining of the 22306 working face in the Cuimu coal mine, targeted measures, such as deep-hole blasting of the roof strata and deep-hole blasting of the coal seam, were implemented to prevent rockbursts in the main roadways. The effectiveness of these measures was verified through long-term analysis of tremor activities. The study found that the influence of mining at two working faces on both sides of main roadways was significantly greater than that from a single-sided working face. The intensity of the tremor activities occurring near the main roadways was correlated with the distance from the working face to the main roadways. The closer the working face was to the main roadways, the stronger the tremor activities were near the main roadways. According to the distribution range of the tremors, the influence area of working face mining exceeded 800 m, with tremors distributed linearly along the main roadways. Even five months after the completion of working face mining, there were still a large number of tremors near the main roadways, which gradually disappeared after another five months. Mining activities were the main reason for the occurrence of main roadway rockbursts and the stress concentration within the main roadways themselves was another reason for the occurrence of rockbursts. The influence of working face mining could be reduced by deep-hole blasting of roof strata and the stress concentration within main roadways themselves could be reduced by large-diameter drilling. Those joint preventive measures effectively prevented the occurrence of rockbursts in main roadways. This study is of important theoretical and practical significance for further studies of rockburst mechanisms and prevention in regard to main roadways in coal mines, and the findings are significant in terms of the enhancement of safety in coal mines.

Study on the “Two-Zone” Heights in Lower Slice Mining Under Thick Alluvium and Thin Bedrock

The extraction of thin bedrock coal seams with thick alluvium poses a challenging issue in the realm of coal safety production in China. Especially for mining under aquifers, knowing the development height of water-conducting fracture zones above the goaf is crucial for coal mine safety and production. Taking the 11092 working face of lower slice mining in Zhaogu No. 1 Mine as an example, the failure transfer process of the overlying strata is analyzed first. On this basis, the development height of the water-conducting fracture zone is predicted using empirical formulas and the BP neural network. According to the empirical formula, the height of the roof caving zone ranges from 6.93 m to 27.72 m, while the height of the water-conducting fracture zone ranges from 22.17 m to 71.73 m. The BP neural network predicts that the development height of the water-conducting fracture zone in the working face after mining is 56.83 m. CDEM numerical simulation is employed to analyze the development height of two zones of overburden rock. The findings indicate that with a mining height of 2.5 m and a cumulative mining height of 6 m, the maximum caving ratio is 2.61. It is observed that for a cumulative mining thickness of less than 6 m, a bedrock thickness of not less than 30 m, and a clay layer thickness exceeding 5 m, the clay layer effectively obstructs the upward development of the water-conducting fracture zone. Finally, the prediction results of the development height of the two zones of overlying strata in the working face are verified by using the height observation method on the underground water-conducting fracture zone and the borehole peeping method. In conclusion, the height of the overlying strata after mining the lower slice working face in the first panel of the east can be used as a basis for determining the thickness of coal (rock) pillars for waterproofing and sand control safety during the mining of lower slice working faces in mines.

Air leakage mechanism and hole sealing technology in directional long-drilled perimeter rock-borehole composite fissures: for gas enrichment in varying coal-seam thickness change areas

The issue of gas enrichment in the tectonic zone of coal seams, which poses significant threats to coal mine safety and leads to gas disasters, is primarily addressed through borehole extraction. The quality of this extraction is notably influenced by air leakage and sealing technologies used during drilling. To tackle the problem of gas leakage in directional long boreholes within the floor, a composite fissure leakage model for the surrounding rock and borehole is developed. This model inverts the characteristics of coal seam thickness variations and abnormal gas enrichment through heterogeneous geostatistical modeling. It enables the quantitative characterization of the air leakage mechanism in directional long boreholes of the bottom plate and proposes an effective sealing process. Due to damage and disturbance, fissures and irregular plastic zones form around the excavated roadway, which are major contributors to air leakage. Over time, the air content in the coal and rock seams gradually increases while the gas content and concentration decrease continuously. Compared to the polyurethane "two plugs and one injection" sealing process, the multi-stage grouting and sealing process provides a more stable gas extraction concentration and flow rate in the sealed drill holes. This process effectively seals the fissure zones of the roadway, reduces air leakage from the boreholes, and improves gas extraction concentration.

The Re-Crushing Spatiotemporal Evolution Law of Broken Coal in the Goaf for Sustainable Utilization of Abandoned Mines

The broken coal samples’ (BCS) re-crushing characteristics in the goaf during roof compaction directly affect the mechanics and seepage characteristics of the caving zone. This will further affect the safety of coal mining and the sustainable utilization of abandoned mines. Thus, the experiment of BCS compaction is carried out with the help of an acoustic emission (AE) monitoring system. The Hurst exponent changes of the AE counts at different stages were obtained using the R/S analysis method. The results indicate that the compaction and re-crushing of the BCS at the laboratory scale have long-term memory. When providing sufficient stress, the AE activity of BCS will continue to develop according to the current trend. Based on the AE breakage location technology, the spatial distribution re-crushing characteristics of the BCS are obtained. Re-crushing of the BCS demonstrates uniform breakage in the horizontal direction and layered breakage in the vertical direction. In the horizontal direction, the boundary area first began to break, and the damage gradually spread evenly to the central area. In the vertical direction, the upper layer was the first to be broken, and then the damage began to shift to the middle and lower layers.

Advanced modeling of seepage dynamics and control strategies in thick coal seams under high-confined aquifer conditions: A case study

The hydraulic behavior of the connection between the floor failure area and the aquifer water-conductive zone is considered to be the root cause of mine water inrush disasters. Therefore, unraveling the floor failure mechanism is particularly important for safe coal mining above the high-confined aquifer. This paper estimates the depth of the baseplate failure to be 18.4–27.3 m by combining network parallel electrical methods with drilling visualization technology. The FLAC3D-based numerical model considering the strain hardening of caved rock was established with rigorous calibration and verification. The results showed that the depth of damage to the floor is 23.1 m, and the dominating floor failure mechanism is shear failure caused by the vertical stress exceeding the rock bearing capacity. Moreover, the stress recovery process of the baseplate does not alter the failure morphology of the baseplate. Based on the above research findings, the in-situ floor control technique of the working face No. 4305 is proposed and practiced in the field. Field measurements show that floor control performance is satisfactory with water inflow in the goaf being roughly stable at 50 m3/h. Our results can provide useful reference for safe mining above confined aquifer and prevention and mitigation of water-related hazards.

Research on the surface subsidence characteristics and prediction models caused by coal mining under the reverse fault

Predicting and understanding the phenomenon of surface subsidence caused by coal mining in working faces with faults are important issues for safe coal mining and efficient production. In numerical simulation experiments, it was found that the phenomenon of surface subsidence manifests when faults exist, and the degree of influence of faults with different dip angles on surface subsidence varies. This phenomenon is attributed to fault activation. According to the experimental results, the impact of faults with different dip angles on surface subsidence falls into three levels: level I for 35° faults, level II for 45° and 55° faults, and level III for 65° and 75° faults. Similarly, the relationship between the difficulty of fault activation and the dip angle of faults can be categorized as 35° faults prone to activation, 45° and 55° faults difficult to activate, and 65° and 75° faults not prone to activation. The probability integral correction model for fault mining, which integrates the surface subsidence values caused by fault-induced attenuation and the subsidence arising from separation spaces, was introduced, thereby constructing a surface subsidence prediction model. This proposed prediction model can accurately predict surface subsidence, with a root mean square error of 10.74 mm between the predicted and measured values, as validated using DInSAR results from the III 6301 working face in the Jincheng mining area.

Review of Major Influencing Factors Contributing to Persisting Safety Problems in Coal Mines: Addressing Systemic Challenges

The coal mining industry in Pakistan faces recurring fatal accidents due to data scarcity, lack of research, and technology adoption. This paper reviews the current status of coal mining, the ongoing energy crisis, the utilization of indigenous coal resources, and the prevailing safety challenges. By comparing coal mining safety standards in Pakistan with global benchmarks, this study proposes advanced mining technologies to improve productivity and safety. This study emphasizes the importance of investigating the underlying factors causing mining accidents in order to devise effective strategies to mitigate them. The lack of relevant data and the reluctance to adopt technology in the industry are identified as major obstacles to improving safety conditions. The proposed strategies for overcoming safety issues include improving data collection and analysis, increasing research efforts, and promoting the adoption of advanced technology. Thus, this paper highlights the urgent need to address safety concerns in Pakistan’s coal mining industry to avoid further loss of lives and resources.

Research on the Evolutionary Law of Fracture Formation in Loose Seams Under High-Intensity Mining with Shallow Depth

The western mining regions of China, known for shallow-buried and high-intensity mining activities, face significant ecological threats due to damage to loose strata and the surface. The evolution of fissures within the loose layer is a critical issue for surface ecological environment protection in coal mining areas. The study employed field measurements, mechanical experiments, numerical simulations, and theoretical analysis, using the ‘triaxial consolidation without drainage’ experiment to assess the physical and mechanical properties of various strata in the loose layer. Additionally, the PFC2D numerical simulation software was employed to construct a numerical model that elucidates the damage mechanisms and reveals the evolution of loose layer fissures and the development of ground cracks. The research findings indicate that during shallow-buried high-intensity mining loose layer fissures undergo a dynamic evolution process characterized by “vertical extension-continuous penetration-lateral expansion”. As the working face advances, these fissures eventually propagate to the surface, forming ground cracks. The strong force chains within the overlying rock (or soil) layers develop in the form of an “inverted catenary arch”. As the arch foot and the middle of the arch overlap, fissures propagate along these strong force chains to the surface, resulting in ground cracks. The study elucidates the surface damage patterns in shallow-buried, high-intensity mining, offering theoretical insights for harmonizing coal mining safety with ecological conservation in fragile regions.

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction

As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equipment, coupled with the high costs associated with manual annotation. Another challenge stems from the complex structure of the mining machines, making it difficult to reflect the overall operational state through local anomaly detection. Consequently, the application of decoupled local anomaly detection for mining machines in practical production remains challenging. This paper presents an unsupervised learning-based method for detecting anomalies in shearer. The method includes a module for constructing a Multi-scale Correlation Matrix (MSCM) of mining machine operating conditions, as well as the CNN-ConvLSTM Autoencoder (C-CLA) network. The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales, thereby enhancing the model’s perceptual capabilities towards changes in equipment status. Finally, shearer anomaly detection is achieved through the analysis of reconstructed residual matrices. The rationality and practicality of the proposed method have been validated on our dataset, and the model’s generalization capability has been verified through repeated experiments in similar scenarios. However, due to variations in the working environment of different mining faces and differences in equipment models, implementing detection on other mining faces often requires retraining the model with new data. Furthermore, we compared our method with other anomaly detection techniques, and our detection efficiency was superior by approximately 3%. This method effectively detects anomalies in the shearer.

Investigation of Load Characteristics and Stress-Energy Evolution Laws of Gob-Side Roadways Under Thick and Hard Roofs

The stress environments of gob-side roadways (GSRs) are becoming increasingly complex during deep coal mining under thick and hard roofs. This leads to strong strata behaviors, including roadway floor heave, roof subsidence, and even coal bursts. Among them, coal bursts pose the greatest threat to production safety in coal mines. Coal bursts in a GSR strongly correlate with the load characteristics and stress-energy evolution laws of the roadway. This study analyzes the roof structures of double working faces (DWFs) during the initial weighting stage (IWS) and full mining stage (FMS) of gob-side working faces (GSWFs). This study also explores how varying roof structures affect the stability of GSRs. Three-dimensional roof structure models of DWFs and mechanical models of dynamic and static loads superposition on a GSR throughout the IWS and FMS of a GSWF were developed. An analysis identified the primary stress sources affecting the GSR throughout various mining stages of the GSWF. Subsequently, the principle of “three-load” superposition was developed. A novel method was proposed to quantify the stress state in the GSR surrounding rock across different mining stages of the GSWF. The method quantitatively characterizes the load of the GSR surrounding rock. Based on this, the criterion for judging the burst failure of the roadway was established. Numerical simulations are used to analyze the stress-energy evolution laws of the working face, coal pillar, and GSR surrounding rock during the mining process of the GSWF. These findings offer valuable references for studying and preventing coal bursts in GSRs under equivalent geological situations.

Effect of Primary/Artificial Interface on Dynamic Failure Behavior of Combined Coal and Rock: Monitoring by High-Speed Imaging and Scanning Electron Microscopy.

The instability of the primary coal and rock structure significantly impacts the safety of coal mining and construction. The complex coal-rock interface cannot be simplified as a smooth surface. To investigate the dynamic response of primary composite coal and rock (primary-CCR), we studied the impact of load, hydrostatic pressure, and interface type on the mechanical behavior and macro/micro failure characteristics using a separate Hopkinson bar, a high-speed camera, and a scanning electron microscope. The results indicate a linear relationship between the composite strength, impact toughness, and impact velocity of the two types of composite coal and rock. The mechanical behavior of the primary-CCR initially increases and then decreases with the rise of hydrostatic pressure (turning point: 10 MPa). There is a positive correlation between artificial combined coal-rock (artificial-CCR) and hydrostatic pressure. The dissipative energy of combined coal and rock increases linearly with impact velocity. Initially, the dissipative energy per unit fracture area increases with hydrostatic pressure, then decreases as pressure continues to rise. Additionally, an increase in impact load causes the energy dissipation inflection point to shift forward. The primary interface significantly reduces the energy threshold for instability failure, resulting in a transition from energy transfer to energy dissipation in rock components. This transition manifests as the failure of artificial combined coal and rock cracks, which develop into rock fragments at an impact velocity of 14 m/s. Furthermore, the change in section roughness of coal and rock correlates with the degree of macroscopic crack growth, following the order: coal > primary-CCR > artificial-CCR > rock.

Influence of roof confined water drainage on stress distribution in coal seam

Coal and gas outbursts, along with rock bursts, are common dynamic disasters in coal mining, threatening the safety and green production of coal mines. The distribution of coal seam stress is an external factor contributing to coal mine power disasters. The drainage of confined water in coal seam roof is the primary factor of coal seam stress changes. Using theoretical analysis and numerical simulations, the influence of roof confined water on coal seam stress by looking at the law of stress and stress conservation transfer in the drainage layer is investigated. Our results show that: (1) after drainage, there is an increase in the local stress within the coal seam, with a stress concentration factor of 1.35. The influence range of theoretically calculated stress is consistent with the measured results. (2) the maximum value of drainage boundary stress correlated positively with the angle of the water-rich abnormal area, with a linear fitting of R2 = 0.98. These findings hold significant theoretical and practical implications for preventing and controlling dynamic disasters during the mining of water-rich coal seams.

Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Research on methane Hazard interval prediction method based on hybrid “model-data”driven strategy

Safe mining of coal has an important impact on energy security, while effective control of methane hazard is the key to ensuring safe coal mining. Methane concentration is the main factor determining the hazard of methane in coal mines, and in order to limit the impact of methane on coal mine safety, this study proposes a methane concentration interval prediction method based on a hybrid “model-data” driven idea. Firstly, by analyzing the data and constructing a methane concentration prediction method based on model-driven, which reduces the influence of multicollinearity in the methane concentration series on the prediction effect, and then, in combination with the deep learning technique, a method based on the Wasserstein distance to improve the Informer model is proposed, and finally a hybrid-driven methane concentration interval prediction model is established by introducing the IOWGA operator and the statistical method. After an example analysis of a coal mine in Guizhou Province, China, the hybrid-driven model proposed in this study has better applicability and prediction accuracy in the methane concentration prediction task, which can effectively prevent the occurrence of coal mine accidents and is more in line with the needs of coal mine safety production.