Search Results (47)

The behavior of gas emissions at the heading face of the coal mine is a key indicator of potentially harmful gas disaster risk, necessitating in-depth study via analytical and statistical methods. However, conventional prediction and evaluation methods depend on long-interval statistical data, which are too coarse for and lack the immediacy required for real-time applications. Based on the physical laws of gas storage and flow, a refined computational model has been developed to compute dynamic gas emission rates that vary with geology and excavating process. Furthermore, by comparing the computed outputs with actual monitoring data, it becomes possible to assess whether abnormal gas emissions are occurring. Methodologically, this model first applies the finite difference method to compute the dynamic gas flux and the dynamic residual gas content. It then determines the exposure duration of each segment of the roadway wall at any given moment, as well as the mass of newly dislodged coal. The total gas emission rate at a specific sensor location is obtained by aggregating the contributions from all of the exposed wall and the freshly dislodged coal. Owing to some simplifications, the model’s applicability is currently restricted to single, medium-thick coal seams. The model was preliminarily implemented in Python (3.13.2) and validated against a case study of an active heading face. The results demonstrate a strong concordance between model predictions and field measurements. The model notably captures the significant variance in emission rates resulting from different mining activities, the characteristic emission surges from dislodged coal and newly exposed coal walls, and the influence of sensor placement on monitoring outcomes. Full article

In response to the challenges of controlling surrounding rock deformation in gob-side entry driving towards the advancing working face, a systematic study on the stability of the headgate# 15107 and coal pillar section was conducted, using a combination of theoretical analysis, numerical simulation, and field testing. First, based on the theory of internal and external stress fields, the range of the internal stress field was determined to be 9.83~11.43 m, and combined with the limit equilibrium theory, the most reasonable width of the narrow coal pillar was found to be 6 m. Secondly, the stability of the surrounding rock and coal pillars of the headgate# 15107 under different coal pillar widths during roadway excavation and working face mining was simulated, respectively. The simulation results show that during the head-on mining and driving period, when the coal pillar width is 4 m or 5 m, the plastic zone in the coal pillar is completely damaged and loses its bearing capacity; when the coal pillar width is 6 m, an elastic zone appears in the coal pillar, and the area of the elastic zone increases with the increase in the coal pillar width. During the excavation along the goaf, when the coal pillar width is 4, 5, 6, 8, or 10 m, the stress curve inside the coal pillar shows a single-peak distribution, and the stress peak of the coal pillar increases with the increase in the coal pillar width, with the stress peaks being 7.66, 9.74, 12.32, 16.02, and 27.05 MPa, respectively. When the coal pillar width is 25 m, the stress curve inside the coal pillar shows a double-peak distribution. During the advancement of the 15107 working face, the stress peaks corresponding to the 4, 5, 6, 8, 10, and 25 m coal pillars are 29.8, 27.5, 26.8, 27.2, 33.7, and 24.3 MPa, respectively. Throughout the entire simulation process, when the coal pillar width is 6 m, the coal pillar has good bearing capacity and a low degree of stress concentration. Finally, based on this, the support scheme for the headgate# 15107 was optimized, and industrial experiments were conducted. Field testing showed that a 6 m narrow coal pillar for roadway protection and an optimized roadway support can effectively control the deformation of the surrounding rock of the roadway. Full article

Accurate carbon market price prediction is crucial for promoting a low-carbon economy and sustainable engineering. Traditional models often face challenges in effectively capturing the multifractality inherent in carbon market prices. Inspired by the self-similarity and scale invariance inherent in fractal structures, this study proposes a novel multifractal-aware model, MF-Transformer-DEC, for carbon market price prediction. The multi-scale convolution (MSC) module employs multi-layer dilated convolutions constrained by shared convolution kernel weights to construct a scale-invariant convolutional network. By projecting and reconstructing time series data within a multi-scale fractal space, MSC enhances the model’s ability to adapt to complex nonlinear fluctuations while significantly suppressing noise interference. The fractal attention (FA) module calculates similarity matrices within a multi-scale feature space through multi-head attention, adaptively integrating multifractal market dynamics and implicit associations. The dynamic error correction (DEC) module models error commonality through variational autoencoder (VAE), and uncertainty-guided dynamic weighting achieves robust error correction. The proposed model achieved an average R² of 0.9777 and 0.9942 for 7-step ahead predictions on the Shanghai and Guangdong carbon price datasets, respectively. This study pioneers the interdisciplinary integration of fractal theory and artificial intelligence methods for complex engineering analysis, enhancing the accuracy of carbon market price prediction. The proposed technical pathway of “multi-scale deconstruction and similarity mining” offers a valuable reference for AI-driven fractal modeling. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

In the Karaganda coal basin, deteriorating geomechanical conditions have been observed, including seam disturbances, diminished strength of argillite–aleurolite strata, water ingress, and pronounced floor heave, all of which markedly increase the labor intensity of maintaining developmental headings. The maintenance and operation of these entries for a reference coal yield of 1000 t necessitate 72–75 man-shifts, of which 90–95% are expended on mitigating ground pressure effects and restoring support integrity. Conventional heave control measures—such as relief drifts, slotting, drainage, secondary blasting, and the application of concrete or rock–bolt systems—deliver either transient efficacy or incur prohibitive labor and material expenditures while lacking unified methodologies for predictive forecasting and support parameter design. This study therefore advocates for an integrated framework that synergizes geomechanical characterization, deformation prognosis, and the tailored selection of reinforcement schemes (incorporating both sidewall and floor-anchoring systems with directed preloading), calibrated to seam depth, geometry, and lithological properties. Employing deformation state assessments to optimize reinforcement layouts for floor stabilization in coal mine workings is projected to curtail repair volumes by 30–40% whilst significantly enhancing operational safety, efficiency, and the punctuality of face preparation. Full article

To address the challenges posed by significant parallax, dynamic changes in monitoring camera positions, and the need for rapid wide-field image stitching in underground coal mine tunneling faces, this paper proposes a fast image stitching method for tunneling face images based on navigation and positioning data. First, using a pixel-based calculation approach, the tunneling face scene is partitioned into the cutting section and the ground, enhancing the reliability of scene segmentation. Then, the spatial distance between the camera and the cutting plane is computed based on the tunneling machine’s navigation and positioning data, and a plane-induced homography model is employed to efficiently determine the dynamic transformation matrix of the cutting section. Finally, the Dual-Homography Warping (DHW) method is applied to achieve fast panoramic image stitching of the tunneling face. Comparative experiments with three classical stitching methods, SURF, SIFT, and BRISK, demonstrate that the proposed method reduces stitching time by 60%. Field experiments in underground environments verify that this method can generate a complete panoramic stitched image of the tunneling face, providing an unobstructed perspective beyond the machine body and cutting head to clearly observe the shovel plate and surrounding ground conditions, significantly enhancing the visibility and convenience of remote operation. Full article

To address ventilation challenges in the working face of metal mine excavation, an equal-scale physical model was established with a mine section as the test site, combined with field-measured data and relevant parameters of spent air reuse equipment. Numerical simulations were carried out using Fluent 2020 R2 software to analyse the characteristics of the airflow field, temperature field, and dust distribution in the excavation roadway. The results show that when the cold air outlet temperature (T₀) is 22 °C, the temperature within the cooling zone does not exceed 26.3 °C, thereby demonstrating effective cooling. The equipment parameters significantly impacted cooling and dust removal. When the distance from the cold air outlet to the heading face was set to Z_m = 8 m, the air outlet temperature was T₀ = 22 °C, and the ventilation circulation rate was F = 40%, the working area achieved better cooling and dust removal effects. On-site application showed that within 15 m of the working face, temperatures dropped by 3–3.5 °C, reaching a low of 25.1 °C. The relative humidity at a point 1 m away from the working face decreased from 90.6% to 70.2%, and the average dust removal efficiency was 44.9%, which significantly improved the comfort and safety of the working environment at the heading face. Full article

The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent services. However, existing POI recommendation systems face three major challenges: (1) oversimplification of user preference modeling, limiting adaptability to dynamic user needs, (2) lack of explicit arrival time modeling, leading to reduced accuracy in time-sensitive scenarios, and (3) complexity in trajectory representation and spatiotemporal mining, posing difficulties in handling large-scale geographic data. This paper proposes NextMove, a novel POI recommendation model that integrates four key modules to address these issues. Specifically, the Probabilistic User Preference Generation Module first employs Latent Dirichlet Allocation (LDA) and a user preference network to model user personalized interests dynamically by capturing latent geographical topics. Secondly, the Self-Attention-based Arrival Time Prediction Module utilizes a Multi-Head Attention Mechanism to extract time-varying features, improving the precision of arrival time estimation. Thirdly, the Transformer-based Trajectory Representation Module encodes sequential dependencies in user behavior, effectively capturing contextual relationships and long-range dependencies for accurate future location forecasting. Finally, the Next Location Feature-Aggregation Module integrates the extracted representation features through an FC-based nonlinear fusion mechanism to generate the final POI recommendation. Extensive experiments conducted on real-world datasets demonstrate the superiority of the proposed NextMove over state-of-the-art methods. These results validate the effectiveness of NextMove in modeling dynamic user preferences, enhancing arrival time prediction, and improving POI recommendation accuracy. Full article

The current methods for detecting coal gangue face several challenges, including low detection accuracy, a high probability of missed detections, and inadequate real-time performance. These issues stem from the complexities associated with diverse industrial environments and mining conditions, such as the mixing of coal gangue and insufficient illumination within coal mines. A detection model, referred to as EBD-YOLO, is proposed based on YOLOv11n. First, the C3k2-EMA module is integrated with the EMA attention mechanism within the C3k2 module of the backbone network, thereby enhancing the model’s feature extraction capabilities. Second, the introduction of the BiFPN module reduces computational complexity while enriching both semantic information and detail within the model. Finally, the incorporation of the DyHead detector head further enhances the model’s ability to express features in complex environments. The experimental results indicate that the precision (P) and recall (R) of the EBD-YOLO model are 88.7% and 83.9%, respectively, while the mean average precision (mAP@0.5) is 91.7%. These metrics represent increases of 3.4%, 3.7%, and 3.9% compared to those of the original model, respectively. Additionally, the frames per second (FPS) improved by 10.01%. Compared to the mainstream YOLO target detection algorithms, the EBD-YOLO detection model achieves the highest mAP@0.5 while maintaining superior detection speed. It exhibits a slight increase in computational load, despite an almost unchanged number of parameters, and demonstrates the best overall detection performance. The EBD-YOLO detection model effectively addresses the challenges of missed detections, false detections, and real-time detection in the complex environment of coal mines. Full article

As a critical task in underground coal mining, personnel identification and positioning in fully mechanized mining faces are essential for safety. Yet, complex environmental factors—such as narrow tunnels, heavy dust, and uneven lighting—pose significant challenges to accurate detection. In this paper, we propose a personnel detection network, MSS-YOLO, for fully mechanized mining faces based on YOLOv8. By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. Meanwhile, by designing a Spatial Pyramid Shared Conv (SPSC) module, the redundancy of the model is reduced, which effectively compensates for the problem of the max pooling being prone to losing the characteristics of the personnel at long distances. Finally, the lightweight Shared Convolutional Detection Head (SCDH) ensures real-time detection under limited computational resources. The experimental results show that compared to Faster-RCNN, SSD, YOLOv5s6, YOLOv7-tiny, YOLOv8n, and YOLOv11n, MSS-YOLO achieves AP50 improvements of 4.464%, 10.484%, 3.751%, 4.433%, 3.655%, and 2.188%, respectively, while reducing the inference time by 50.4 ms, 11.9 ms, 3.7 ms, 2.0 ms, 1.2 ms, and 2.3 ms. In addition, MSS-YOLO is combined with the SGBM binocular stereo vision matching algorithm to provide a personnel 3D spatial position solution by using disparity results. The personnel location results show that in the measurement range of 10 m, the position errors in the x-, y-, and z-directions are within 0.170 m, 0.160 m, and 0.200 m, respectively, which proves that MSS-YOLO is able to accurately detect underground personnel in real time and can meet the underground personnel detection and localization requirements. The current limitations lie in the reliance on a calibrated binocular camera and the performance degradation beyond 15 m. Future work will focus on multi-sensor fusion and adaptive distance scaling to enhance practical deployment. Full article

Deep mining in metal mines faces more and more complex geological conditions, such as “three highs and one disturbance” (high ground stress, high permeability, high temperature, and mining-induced disturbance), which can easily trigger water inrush disasters and seriously affect the safety and efficiency of deep mining. This paper focuses on the deep hydrogeological structural characteristics of the Huize lead–zinc mine. Firstly, two main factors affecting the production safety of the mining area, namely the water source and water channel of the mine, were analyzed. Based on this analysis, nine factors were determined as indicators for the risk assessment of water inrush, including the water head difference, water-bearing capacity, permeability coefficient, aquifer thickness, water pressure, fault type, fault scale, fault water conductivity, and karst zoning characteristics. Then, a water inrush risk assessment model for the deep mine was constructed, and the weights of the individual factors were determined using the analytic hierarchy process (AHP) and entropy weight method (EWM). Combined with the multi-factor spatial fitting function of the GIS, a zoning map of the risk assessment of water inrush was developed. The results showed that the aquifer groups of the Permian Liangshan Formation and the Carboniferous Maping Formation (P₁l + C₃m) were relatively safe, whereas the karst fissure aquifer of the Qixia–Maokou Formation (P₁q + m) posed a high risk of water inrush, necessitating advanced exploration and water drainage in the area. These findings provide guidance for water control measures in the Huize lead–zinc mine and offer valuable insights into the prediction and prevention of mine water hazards associated with ore body mining in karst aquifers. Full article

Gas explosions are the most serious type of accident in coal mines in China. This study analyzed 125 gas explosion accidents that occurred between 2010 and 2020. The results showed that the number of gas explosion accidents and deaths in 2010–2020 was stable and decreasing. The number of larger gas explosion accidents in 2010–2020 is the largest, but the death toll from major accidents was much greater. Coal faces, headings, and roadways are the main locations where gas explosions are initiated. The coal mines in which gas explosions occur in coal faces and headings are mainly “township” enterprises and private mines, all of which engage in illegal operations. The main cause of gas accumulations in roadways is ventilation system failure; these failures can be reduced with improved ventilations system management. The number of gas explosion accidents and related deaths in the Sichuan, Guizhou, and Heilongjiang provinces are very high. The annual change in the frequency of gas explosion accidents, the quarterly distribution of gas explosion accidents, and time during a mining shift when gas explosion accidents occur are closely related to national policies and regulations, company annual production goals, and the mental status of miners, respectively. Full article

This work proposes a novel multi-camera system-based method for relative pose estimation and virtual–physical collision detection for anchor digging equipment. It is dedicated to addressing the critical challenges of achieving accurate relative pose estimation and reliable collision detection between multiple devices during collaborative operations in coal mines. The key innovation is that the multi-camera multi-target system is established to collect images, and the relative pose estimation is completed by the EPNP (Efficient Perspective N-Point) algorithm based on multiple infrared LED targets. At the same time, combined with the characteristics of a roadheader and anchor drilling machine, AABB (Axis Alignment Bounding Box) with a simple structure and convex hull with a strong wrapping are selected to create the mixed hierarchical bounding box, and the collision detection is carried out by combining SAT (Split Axis Theorem) and GJK (Gilbert–Johnson–Keerthi) algorithms. The experimental results show that the relative pose estimation error of the multi-camera system is within 20 mm, with an angular error within 1.002°. The position error in the X-axis direction is within 1.160 mm, and the maximum deviation in the Y-axis direction is within 0.957 mm in the virtual–physical space. Compared with the existing methods, our method integrates digital twin technology, and has a simple system structure, which can meet the requirements of relative attitude estimation and collision detection between equipment in the process of heading face operation, and at the same time improve the system performance. Full article

The formation of a coal mine roadway cross-section is a primary task of the boom-type roadheader. This paper proposes an intelligent robust control scheme for the cutting head trajectory of a coal mine tunneling robot, which is susceptible to unknown external disturbances, system nonlinearity, and parameter uncertainties. First, the working conditions of the cutting section were analyzed, and a mathematical model was established. Then, a high-gain disturbance observer was designed based on the system model to analyze cutting loads and compensate for uncertainties and disturbances. A sliding mode controller was proposed using the backstepping design method, incorporating a saturation function control term to avoid chattering. The eel foraging optimization algorithm was also improved and used to tune the controller parameters. A simulation model of the system was developed for performance comparison tests. Finally, experimental verification was conducted under actual working conditions in a tunnel face, and the results demonstrated the effectiveness of the proposed control method. Full article