工矿自动化

Research on the safe transmission power of mine radio wave explosion prevention

SUN Jiping, PENG Ming

2024, 50(3): 1-5. doi: 10.13272/j.issn.1671-251x.18184

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Abstract:
High power radio waves emitted by mobile communication systems such as 5G, 5.5G, WiFi6, WiFi7, UWB, ZigBee, as well as personnel and vehicle positioning systems in mines, pose a risk of igniting gas and coal dust. Therefore, it is necessary to set a reasonable threshold for the explosion-proof safe power of radio waves emitted by explosion-proof radio equipment, and limit the power of radio waves emitted by explosion-proof radio equipment. The European standard CLC/TR 50427:2004 Assessment of inadvertent ignition of flammable atmospheres by radio-frequency radiation-Guide specifies a threshold for the safe reception and ignition power of radio waves in explosive gas environments. But it lacks content on the threshold for the safe transmission power of radio waves. Although the national standard GB/T 3836.1-2021 Explosive atmospheres-Part 1:Equipment-General requirements and the international standard IEC 60079-0:2017 Explosive atmospheres-Part 0:Equipment-General requirements have relevant provisions on the safe transmission power threshold for radio wave explosion protection, they mistakenly modify the safe reception ignition power threshold for radio wave explosion protection in the European standard CLC/TR 50427:2004 to the safe transmission power threshold for radio wave explosion protection. It greatly reduces the maximum transmission power allowed by radio equipment in explosive atmospheres. There’s a lack of slender structural objects such as cranes that can serve as receiving antennas in coal mines. The existing radio communication and positioning systems in mines operate at frequencies far greater than 30 MHz. Therefore, the threshold for the safe reception and ignition power of radio waves should be 8 W, instead of the radio wave explosion-proof safe transmission power threshold of 6 W specified in the national standard GB/T 3836.1-2021 and the international standard IEC 60079-0:2017. When the energy of the radio waves emitted by the transmitting antenna is fully absorbed by the equivalent antenna, which is the most unfavorable for wireless explosion-proof transmission and coupling, and the operating frequency of the radio equipment is the equivalent antenna resonance frequency, the reception and ignition power reaches its maximum. It is half of the total power received by the equivalent antenna, that is, half of the transmission power. In practical engineering, both radio transmission efficiency and coupling efficiency are not equal to 1. Therefore, the threshold for safe transmission power of radio waves should be more than twice the threshold for safe reception and ignition power of radio waves. The threshold for the safe reception and ignition power of underground radio waves in coal mines is 8 W. Therefore, the threshold for the safe transmission power of underground radio waves in coal mines should be greater than 16 W.

Design of automatic control system for underground hydraulic fracturing in coal mine

LIU Bo

2024, 50(3): 6-13. doi: 10.13272/j.issn.1671-251x.2023060078

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Abstract:
The analysis points out that the current hydraulic pressure technology in coal mines faces challenges such as the inability to quickly and accurately adjust the output pressure and flow rate of fracturing pumps, the need to improve the remote safety monitoring effect, and the level of automation. An underground hydraulic fracturing automatic control system in coal mine has been designed. Based on the hydraulic fracturing technology and system composition in coal mines, it is clear that the key technologies of the control system include rapid and precise adjustment of the output flow and pressure of high-pressure and high flow fracturing pumps, remote high reliability and safety, high-speed real-time monitoring, one click start stop, and graphical analysis. Based on the KXH12 intrinsic safety controller as the core, combined with frequency converters, combination switches, monitoring hosts, electric valves and other equipment, as well as a dual line redundant communication scheme with fiber optic and CAN bus, the control system hardware has been developed. The fracturing pump controller, water tank controller, and central controller software have been developed. It achieves functions such as fast variable flow water injection, stable pressure maintenance, remote high-speed real-time monitoring and alarm of the fracturing system. Industrial tests are conducted on the hydraulic fracturing automatic control system at the coal mine site, and the results show that the pressure control precision of the system is 0.1 MPa, and the flow control precision is 0.1 m³/h. During 14 consecutive fracturing processes, good pressure retention effects are achieved after each coal layer crack. The operation is simple with high safety, meeting the requirements of hydraulic fracturing technology in coal mines underground.

Research on mine network security system based on boundary isolation and system protection

HE Yinjie, LI Chenxin, WEI Chunxian

2024, 50(3): 14-21. doi: 10.13272/j.issn.1671-251x.2023100008

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Abstract:
With the continuous construction and promotion of intelligent mining information infrastructure, the switching of mine terminal equipment between private and public networks has introduced information security risks to the mine network. It is necessary to study the isolation boundaries of the mine network and build system protection measures. The study analyzes the main risks faced by the mine network, and points out that the key to dealing with risks is to define isolation boundaries, strengthen system protection measures, and develop specific underground equipments. In response to the needs of mine network security protection, three major isolation boundaries have been defined: business management network and industrial control network, transmission network and server area, and underground industrial control network and industrial control network on the ground. A mine network security system protection architecture based on boundary isolation and system protection is proposed. A mine network security system based on network, host, application, and data subsystems protection is designed, along with corresponding security transmission processes and protection ideas. In response to the current situation where mine network security protection mainly focuses on networks on the ground and lacks underground network security protection measures, a mine explosion-proof and intrinsically safety network interface has been developed as underground network security protection equipment. Corresponding protection rules have been formulated for industrial protocols commonly used in underground terminals such as Modbus, Profibus, IEC 61850, RTSP, etc. The test results show that the average recognition rate of the interface device against network attacks is 98.8%, the average protection rate is 98.0%, and the throughput of the gigabit interface is not less than 95% of the line speed. It achieves underground information security protection function and ensures data transmission performance.

Research progress on digital twin technology for intelligent mines

XING Zhen

2024, 50(3): 22-34, 41. doi: 10.13272/j.issn.1671-251x.2024010079

<Abstract>(89) <HTML> (22) <PDF>(34)

Abstract:
The application of digital twin technology in the field of intelligent mining needs to face many complex and special technological breakthroughs. This article elaborates on the applicability of digital twins in the field of intelligent mining, and summarizes the research and application status of digital twin technology in coal mine safety, production, and operation management. In terms of coal mine safety management, digital twin technology is mainly applied in disaster warning, risk control, disaster rescue, etc. In terms of coal mine production, digital twin technology is mainly applied in the overall mining working face area, monitoring and control of single machine mechanical equipment status, and predictive maintenance of mechanical equipment. Starting from five dimensions: physical entity, virtual entity, connection interaction, digital twin data, and functional services, this paper explores the key common problems that urgently need to be solved in the field of intelligent mining. The physical entity dimension needs to focus on breaking through the research and development of comprehensive perception and control equipment. The virtual entity dimension needs to conduct in-depth research on physical, behavioral, and rule models. The connection interaction dimension needs to tackle key technologies for underground 5G network transmission in coal mines. The twin data dimension needs to solve problems such as high-performance computing. The functional service dimension needs to develop simulation software and artificial intelligence algorithms to better adapt to the on-site environment. This article looks forward to the development trend of digital twin technology in the field of intelligent mining from the aspects of disaster prevention design, production system design, geological environment prediction in the planning, development, and construction stages of mines, disaster warning and prevention, optimization of production scheduling decisions, and full life cycle management of production equipment in the production and operation stage of mines. It is believed that fine twinning should be carried out for key components or equipment, core links, important or dangerous places, areas, etc.

A small object detection method for coal mine underground scene based on YOLOv7-SE

CAO Shuai, DONG Lihong, DENG Fan, GAO Feng

2024, 50(3): 35-41. doi: 10.13272/j.issn.1671-251x.2023090088

<Abstract>(106) <HTML> (39) <PDF>(21)

Abstract:
Although current small object detection methods have improved the detection performance, they are mostly objected at conventional scenarios. In harsh underground environments in coal mines, there are difficulties in extracting small object feature information during the underground small object detection process. In order to solve the problem. a small object detection method for coal mine underground scenes based on YOLOv7-SE has been proposed. Firstly, the simulated annealing (SA) algorithm is integrated with the k-means++clustering algorithm to accurately capture small underground objects by optimizing the estimation of initial anchor box values in the YOLOv7 model. Secondly, a new detection layer is added to the YOLOv7 backbone network to obtain high-resolution feature maps of underground small objects, reducing the interference of a large amount of coal dust on the feature representation of underground small objects. Finally, a dual layer attention mechanism is introduced after the aggregation network module in the backbone network to enhance the feature representation of small underground objects. The experimental results show the following points. ① The loss function of the YOLOv7-SE network model after training is stable around 0.05, indicating that the parameter settings of the YOLOv7-SE network model are reasonable. ② The average precision (AP) of helmet detection based on the YOLOv7-SE network model has improved by 13.86%, 25.3%, 16.13%, 12.71%, 15.53%, 11.59% and 12.20% compared to Faster R-CNN, RetinaNet, CenterNet, FCOS, SSD, YOLOv5 and YOLOv7, respectively. The self rescue device detection AP based on the YOLOv7-SE network model has improved by 12.37%, 20.16%, 15.22%, 8.35%, 19.42%, 9.64% and 7.38% compared to Faster R-CNN, RetinaNet, CenterNet, FCOS, SSD, YOLOv5 and YOLOv7, respectively.The frames per second (FPS) of the YOLOv7-SE network model has increased by 42.56, 44.43, 31.74, 39.84, 22.74 and 23.34 frames/s compared to Faster R-CNN, RetinaNe, CenterNet, FCOS, SSD and YOLOv5, respectively, and decreased by 9.36 frames/s compared to YOLOv7. The YOLOv7-SE network model effectively enhances the feature extraction capability of the YOLOv7-SE network model for small underground objects while ensuring detection speed. ③ In the detection of safety helmets and self rescue devices, the YOLOv7-SE network model effectively improves missed and false detection, and improves detection precision.

Large block coal detection algorithm for fully mechanized working face based on MES-YOLOv5s

XU Ciqiang, JIA Yunhong, TIAN Yuan

2024, 50(3): 42-47, 141. doi: 10.13272/j.issn.1671-251x.2024030009

<Abstract>(63) <HTML> (17) <PDF>(23)

Abstract:
The objects in the fully mechanized working face have the features of high-speed motion, multi-scale, occlusion, etc. The existing object detection algorithms have problems such as low precision, large memory of models, and strong hardware dependence. In order to solve the above problems, a large block coal detection algorithm based on MES-YOLOv5s is proposed in fully mechanized working face. The method adopts a lightweight design, uses MobileNetV3 as the backbone network to reduce the memory occupied by the model and improve the detection speed on the CPU side. The method adds an efficient multi-scale attention (EMA) module to the neck network, fuses contextual information of different scales, and further reduces computational overhead. The method uses SIoU loss function instead of CIoU loss function to improve training speed and inference accuracy. The ablation experiment results show that MobileNetV3 significantly reduces the memory and detection time occupied by the model, but the mAP loss is severe. The EMA module and SIoU loss function can restore the precision of the loss to a certain extent, while ensuring that the model has a high detection speed on the CPU, meeting the real-time detection needs of coal mine underground objects. The comparative experimental results show that compared with DETR, YOLOv5n, YOLOv5s, and YOLOv7 models, the MES-YOLOv5s model has the best overall performance, with an mAP of 84.6%. The model occupies 11.2 MiB of memory and has a detection time of 31.8 ms on the CPU side. It can maintain high recall and precision in high-speed motion, multi-scale, occlusion, and multi-object working environments.

A point cloud denoising method for unstructured roadways based on regional growth

LIAN Zhongwen, REN Zhuli, HAO Yinghao, YANG Fan, BAI Gang, FANG Cheng, YUAN Ruifu

2024, 50(3): 48-55. doi: 10.13272/j.issn.1671-251x.2024010037

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Abstract:
Currently, research on point cloud denoising in underground roadways has not fully met the special denoising needs of roadway point clouds. Especially in narrow, enclosed, and complex underground roadway environments, the research has not fully addressed the challenges caused by pipe wall attachments, dust, and human noise. By analyzing the unstructured scenes and sensor errors underground, considering the noise caused by personnel, mobile devices, and pipeline networks, a point cloud denoising method for unstructured roadways based on region growth is proposed. The method uses 3D laser scanning technology to obtain 3D point cloud information of underground roadway scenes, and analyzes the abnormal points caused by unstructured underground scenes and sensor errors, as well as the noise features formed by personnel, mobile devices, and air and water pipelines. The method uses k-dimensional trees (kd-tree) to construct the topological relationship of point clouds, selects appropriate seed nodes and growth criteria, and sets appropriate curvature and angle thresholds. The method implements effective segmentation of roadway point clouds through region growth algorithms, and removes outlier point clouds that have not been added to the segmentation area. Based on the features of noise, further denoising optimization is carried out based on the segmentation results of the roadway point cloud region. The experimental results indicate that for situations where there are features such as personnel and equipment in the roadway, it is recommended to set the angle threshold of the region growth algorithm to around 10° and the curvature threshold to around 3. In practical applications, it is necessary to balance the reduction of data volume with the denoising effect to ensure the effectiveness of data processing and improve data quality. When using a region growth based unstructured roadway point cloud denoising method for denoising, the reduction in point cloud quantity is between SOR filter and low-pass filter, which can effectively remove noise such as personnel and equipment.

A mine image enhancement method based on structural texture decomposition

ZHANG Hong, SUO Tingfeng, SONG Wanying

2024, 50(3): 56-64. doi: 10.13272/j.issn.1671-251x.2023100005

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Abstract:
There is a phenomenon of low lighting and excessive dust in underground mines, which leads to uneven lighting, blurriness, and loss of details in the images captured by monitoring videos. It affects subsequent intelligent image recognition. Existing mine image enhancement methods generally suffer from unclear texture details and poor visual effects. A method for image enhancement based on structural texture decomposition is proposed. Firstly, the maxRGB algorithm is used to extract the initial lighting component from the original image. Then, an optimization objective function is constructed to sequentially optimize and solve the structural component, texture component, and noise component in the initial lighting component. The weighted guided filtering is applied to the initial lighting component as a prior constraint, and the structural component with clear edges is obtained iteratively. Combined with the maximum neighborhood difference method and weighted average local variation, a local variation deviation function is constructed as constraint weights. The texture component with rich details is obtained iteratively. Secondly, the original image is transformed into the HSV color space, and the lighting component of the original image is extracted. Combined with the structural component, texture component, and noise component, Retinex theory is used for reconstruction to obtain the enhanced initial lighting component. To avoid excessive lighting enhancement, adaptive Gamma correction with weight distribution (AGCWD) is introduced to process the initial lighting information of the image and obtain the final lighting component. Finally, the image is converted to RGB color space to obtain an enhanced image. The experimental results show the following points. ① The image enhancement algorithm based on structural texture decomposition can ensure clearer texture details at the edges of the image, reduce halo artifacts during the image enhancement process, and achieve a more balanced grayscale histogram of the enhanced image. ② Compared with five image enhancement algorithms, including the Retinex algorithm based on structure and texture aware Retinex(STAR), the joint intrinsic-extrinsic prior model (JieP), the weighted variational mode (WVM), the semi-decoupled decomposition (SDD), and multi-scale Retinex with color restoration (MSRCR), the natural image quality evaluator (NIQE) of the image enhancement algorithm based on structural texture decomposition is reduced by 8.69%, 29.05%, 11.2%, 29.53%, and 33.54%, respectively. The visual information fidelity (VIF) increases by 91.17%, 117.86%, 59.38%, 48.78%, 183.12%, and the entropy index (Entropy) increases by 3.20%, 8.02%, 4.07%, 3.49%, and 22.68%, respectively. ③ The image enhancement algorithm based on structural texture decomposition has a running time only longer than the MSRCR algorithm. But the enhancement effect is better, which can meet the needs of image enhancement in underground mines.

Transformer based time series prediction method for mine internal caused fire

WANG Shubin, WANG Xu, YAN Shiping, WANG Ke

2024, 50(3): 65-70, 91. doi: 10.13272/j.issn.1671-251x.2023100084

<Abstract>(39) <HTML> (12) <PDF>(8)

Abstract:
Although traditional machine learning based methods for predicting mine internal caused fire have certain predictive capabilities, they cannot effectively capture global dependencies between complex multivariate data, resulting in low prediction precision. In order to solve the above problems, a transformer based time series prediction method for mine internal caused fire is proposed. Firstly, the Hampel filter and Lagrange interpolation method are used to detect outliers and fill in missing values in the data. Secondly, the self attention mechanism of Transformer is utilized to extract features and predict trends from time series data. Finally, by adjusting the size and step size of the sliding window, the model is trained in different time dimensions at different time steps and prediction lengths. Combining gas analysis method, the iconic gases generated by mine fires (CO, O₂, N₂, CO₂, C₂H₂, C₂H₄, C₂H₆) are used as input variables for the model, with CO as the target variable for model output and O₂, N₂, CO₂, C₂H₂, C₂H₄, C₂H₆ as covariates for model input. Selecting the bundle data of S1206 return air corner fire warning in Ningtiaota Coal Mine of Shanmei Coal Group for experimental verification, the results show the following points. ① Univariate prediction and multivariate prediction of CO show that multivariate prediction has higher prediction precision than univariate prediction, indicating that multivariate prediction can improve the prediction precision of the model by capturing the correlation between sequences. ② When the time step is fixed, the prediction precision of the Transformer based mine internal caused fire prediction model decreases with the increase of prediction length. When the prediction length is fixed, the prediction precision of the model improves with the increase of time step. ③ The prediction accuracy of the Transformer algorithm is improved by 7.1%-12.6% and 20.9%-24.9% over the long short-term memory (LSTM) algorithm and recurrent neural network (RNN) algorithm, respectively.

Research on transparency of hidden disaster causing factors in coal mines based on 3D geological modeling technology

WANG Jiawei, WANG Haijun, WU Hanning, WU Yan, HAN Ke, CHENG Xin, DONG Mintao

2024, 50(3): 71-81, 121. doi: 10.13272/j.issn.1671-251x.2023110030

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Abstract:
Hidden disaster causing factor is the key issue that restricts the construction of intelligent coal mining. The 3D geological modeling is the main technical means to achieve transparency of hidden disaster causing factors. At present, the 3D geological modeling technology of coal mines mainly relies on geometric modeling and attribute modeling as a supplement, lacking disaster attribute modeling for hidden disaster causing factors. In order to solve the above problems, taking a coal mine in northern Shaanxi as the research object, the 3D geological modeling is conducted on hidden disaster causing factors such as coal seam thickness, roof and floor structural undulations, waterlogged areas, and shallow coal seam topography. Firstly, the digitization of geological data, geophysical exploration, drilling and other achievements are completed. The coal mine geological database is established. Secondly, the DepthInsight modeling software is used to carry out modeling work from two scales: the entire mine and the working face. The drilling layer data is used as the stratigraphic control point, and the stratigraphic sequence is jointly controlled through coal seam and surface contour lines, virtual drilling, and other data. The layer crossing anomalies in the initial layer model is processed. The ground level model and geological body model are constructed. The digital elevation model is used to construct the surface model of the working face. Thirdly, rock mass modeling is used to construct models of goaf and waterlogging areas, and temperature, gas and other information are annotated. The actual mining measurement data of the working face is used to construct a mining measurement model. Finally, the truncated grid model is created. The permeability and water-rich coefficient model of the aquifer is generated through sequential Gaussian simulation to achieve transparent display of hidden hydrological disaster causing factors in the area. Based on a 3D geological model, the distribution and impact of hidden disaster causing factors are analyzed from multiple perspectives such as strata, coal seams and working faces, goaf and its waterlogged areas, and hydrological attributes. The research results can provide a target area for the precise management of hidden disaster causing factors in coal mines, and assist in the construction of intelligent mining in coal mines.

Recognition of unsafe behaviors of key position personnel in coal mines based on improved YOLOv7 and ByteTrack

HAN Kang, LI Jingzhao, TAO Rongying

2024, 50(3): 82-91. doi: 10.13272/j.issn.1671-251x.2024030015

<Abstract>(51) <HTML> (15) <PDF>(14)

Abstract:
The application of artificial intelligence technology can real-time recognize the behavior of key position personnel in coal mines, such as mine hoist drivers, to prevent dangerous situations such as equipment misoperation. It is of great significance for ensuring coal mine safety production. The personnel behavior recognition method based on image features has problems of poor resistance to background interference and insufficient real-time performance. In order to solve the above problems, a coal mine key position personnel unsafe behavior recognition method based on improved YOLOv7 and ByteTrack is proposed. Firstly, based on MobileOne and C3, lightweight improvements are made to the backbone and head network of the YOLOv7 object detection model to improve the inference speed of the model. Secondly, integrating ByteTrack tracking algorithm, to achieve the tracking and locking of personnel is achieved, and the capability to resist background interference is improved. Thirdly, MobileNetV2 is used to optimize the network structure of OpenPose and improve the efficiency of skeleton feature extraction. Finally, the spatial temporal graph convolutional networks (ST−GCN) is used to analyze the spatial structure and dynamic changes of the key points of the human skeleton in the time series, achieving the analysis and recognition of unsafe behaviors. The experimental results show that the precision of the MobileOneC3−YOLO model reaches 93.7%, and the inference speed is improved by 52% compared to the YOLOv7 model. The success rate of personnel locking model integrating ByteTrack reaches 97.1%. The improved OpenPose model reduces memory requirements by 170.3 MiB. The inference speed on CPU and GPU is improved by 74.7% and 54.9%, respectively; The recognition precision of the unsafe behavior recognition model for four types of unsafe behaviors, including fatigue sleeping on duty, leaving work, side talking, and playing with mobile phones, reaches 93.5%, and the inference speed reaches 18.6 frames per second.

Real time identification of microseismic events based on ridge regression improved normative variable analysis

CHENG Jian, SHI Linsong, LUO Yi, ZHOU Tianbai, YANG Lingkai

2024, 50(3): 92-98. doi: 10.13272/j.issn.1671-251x.18170

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Abstract:
The identification of microseismic events is the foundation of microseismic monitoring in coal mines. Most existing microseismic monitoring technologies are developed based on the variation law of single physical quantities, which can easily lead to misjudgment when processing coal mine microseismic data containing a large amount of noise and interference signals. In order to solve the above problem, the loss function of canonical variate analysis (CVA) is optimized and improved using ridge regression algorithm to achieve sparse modeling and enhance the model's generalization capability. The ridge regression improved CVA is used for fusion analysis of multi-channel coal mine microseismic monitoring data, and then real-time identification of complex microseismic monitoring data status is achieved. The simulated data and actual coal mine microseismic monitoring data are used for experimental verification of ridge regression improved CVA. In experiments based on simulated data, as the noise variance increases from 5×10⁻⁶ to 5×10⁻², the recognition accuracy of ridge regression improved CVA increases by 0.6%-5.4% compared to CVA, and the sum of false alarm rate and omission rate decreases by 4.8%-17.3% compared to CVA. In experiments based on actual microseismic monitoring data, ridge regression improved CVA can reflect the fluctuation of microseismic signals in the fusion analysis results of 20 channels of microseismic monitoring data. It verifies that this method has the capability to identify microseismic events in real-time. The average identification accuracy is 97.14%, which is 2.39% higher than CVA. The sum of false alarm rate and omission rate is 31.06%, which is 0.07% lower than CVA. The error rate is 2.86%, which is 2.4% lower than CVA.

Recognition model of IIoT equipment in coal mine

HAO Qinxia, LI Huimin

2024, 50(3): 99-107. doi: 10.13272/j.issn.1671-251x.2023100092

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Abstract:
The computing and storage resources of the industrial Internet of things (IIoT) equipment in the coal mine are limited, making it vulnerable to illegal network intrusion, causing sensitive data leakage or malicious tampering, and threatening the safety of coal mine production. Precise recognition of coal mine IIoT equipment can achieve effective management and maintenance of equipment operation, improve equipment safety and protection capabilities. However, existing equipment recognition algorithms suffer from complex feature construction, high memory and computing requirements, making it difficult to deploy in resource limited coal mine IIoT equipment. In order to solve the above problems, a coal mine IIoT equipment recognition model is proposed. Firstly, the model performs traffic segmentation, irrelevant field removal, deduplication, and fixed length field truncation operations on traffic data that supports TCP/IP protocol transmission. The model then converts it to IDX format for storage. Secondly, the model uses convolutional block attention module (CBAM) to optimize depthwise separable convolu-tion(DSC). A lightweight DSC-CBAM model is constructed to filter Non-IIoT equipment. Thirdly, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is used to expand the data of coal mine IIoT equipment with less traffic, achieving the goal of balancing offset traffic data. Finally, multi-scale feature fusion (MFF) technology is introduced on the basis of DSC-CBAM to capture shallow global feature information, and Mish activation function is added to improve model training stability. The MFF-DSC-CBAM-Mish (MDCM) model is established to achieve precise recognition of coal mine IIoT equipment. The experimental results show that the model has a fast convergence speed, with accuracy, recall, precision, and F1 score all reaching 99.98%. The model has a small number of parameters, which can accurately and efficiently recognize IIoT equipment in coal mines.

Research on safety of electromagnetic wave thermal effect in explosive environment of underground coal mine

GUO Bochao, TIAN Zijian, HOU Mingshuo, SHI Yangming, YANG Wei

2024, 50(3): 108-113. doi: 10.13272/j.issn.1671-251x.2023120069

<Abstract>(22) <HTML> (3) <PDF>(4)

Abstract:
GB/T 3836.1-2021 Explosive atmospheres-Part 1: Equipment-General requirements stipulates that the threshold power of RF equipment in explosive environments shall not exceed 6 W. This regulation limits the application of high-power RF equipment in coal mines. However, existing research on electromagnetic safety in explosive environments lacks comprehensive theoretical analysis and experimental verification. In order to solve the above problems, the electromagnetic wave thermal effect equation is derived. It is analyzed that the controllable parameters affecting the generation of thermal energy from the mixture of gas and coal dust coupled by electromagnetic waves are the electromagnetic wave coupling time, the electric field strength and the electromagnetic wave frequency. Based on the regulation in GB/T 3836.1-2021 that the maximum surface temperature of electrical equipment that may accumulate coal dust cannot exceed 150 ℃, simulation experiments are conducted using the multi physics field simulation software COMSOL to evaluate the thermal safety of gas and coal dust mixtures coupled with electromagnetic waves of different emission powers. The results show that the emission power that meets the safety threshold of electromagnetic wave thermal effect with a temperature not exceeding 150 ℃ is 16.48 W. With the increase of electromagnetic wave emission power, the safe duration of electromagnetic wave thermal effect (the corresponding time period that the thermal energy generated by the mixed gas of electromagnetic wave coupling gas and coal dust not causing the ambient temperature to exceed 150 ℃) gradually decreases. However, as long as the safe duration is maintained, the emission power of electromagnetic waves is not limited.

A calculation model for the associated position interference between the boom and shovel table mechanisms of intelligent roadheader

LIU Ruohan, LIU Yongli, LIU Shuang

2024, 50(3): 114-121. doi: 10.13272/j.issn.1671-251x.2023090014

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Abstract:
In the process of working state perception and control, the intelligent boom-type roadheader interferes with the position of the boom and shovel table mechanism. However, the existing research on the prevention of interference and collision in boom-type roadheader is mainly based on a single control method. There are relatively few studies that integrate the prevention of interference and collision conditions into the control. In order to solve the above problems, a calculation model for associated position interference between the boom and shovel table mechanism is proposed. Based on the relative spatial position relationship between the boom and shovel table mechanism of a multi degree of freedom boom-type roadheader during the movement process, the boom mechanism is simplified as a segmented spatial straight line, and the shovel table mechanism is simplified as a spatial plane. Based on the distance between the specific points on the equivalent segmented spatial straight line of the boom boundary and the boundary point of the cutting tool to the equivalent spatial plane of the shovel table, the interference between the boom and shovel table is determined. The application example of the interference calculation model for the relationship between the boom and the shovel table mechanism show the following points. When the shovel platform is in the middle position, under the condition that the main boom and auxiliary boom have no relative swing, the ultimate constraint condition for the boom and shovel table to not interfere and collide is that the swing position angle of the main boom does not exceed 29.5°, and the cutting tool's bottom depth does not exceed 42 mm. The ultimate constraint condition for the boom and shovel table to not interfere and collide under the coordinated swing of the main boom and auxiliary boom is that cutting tool's bottom depth does not exceed 163 mm. The interference calculation model for the position interference between the boom and the shovel table mechanism lays the foundation for the digital and intelligent autonomous control of the full domain prediction and warning of the interference and collision between the boom and the shovel table.

The influence of coal pore structure on gas desorption-diffusion-seepage process

JIA Nan

2024, 50(3): 122-130. doi: 10.13272/j.issn.1671-251x.2023110076

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Abstract:
Fully understanding the mechanism of coal seam gas migration is the fundamental prerequisite for improving extraction efficiency. At present, research on the micro migration features of coal gas mostly focuses on the micro pore gas migration features of coal, ignoring the gas desorption-diffusion process. Taking coking coal as an example, the pore space structure of coal is accurately reconstructed and quantitatively characterized using mercury intrusion testing, nanoscale industrial CT scanning, and numerical simulation. The evolution process of gas desorption-diffusion-seepage is analyzed from a microscopic perspective, and the influence of coal pore space structure on gas migration is preliminarily explored. The results show the following points. ① The gas pressure is relatively high at the center of the pore, and desorption-diffusion proceeds from the center of the pore to the edge. The distribution of gas pressure varies significantly at different times and positions. The reason for the difference in gas pressure distribution is that the radius, length, shape, and connectivity of pores and throats in each representative elementary volume (REV) unit are different. ② The pore structure and topological advantages expand the range of gas desorption-diffusion-seepage. The large-sized pore structure can provide diversified movement space for gas molecules, weaken the influence of size effect on diffusion breadth, and promote the rate of gas desorption-diffusion. ③ In the strongly heterogeneous connected pore structure, gas seepage is dispersed and efficient, and the transformation of gas from diffusion to seepage can be achieved through extensive communication with the coal matrix, improving the efficiency of gas mass transfer. In weakly heterogeneous connected pore structures, the gas seepage path is single, the seepage lines are concentrated, the mass transfer resistance of the seepage is high, and the transformation efficiency of gas molecules from diffusion to seepage is low. It is not conducive to efficient gas migration. The research results enrich the theory of coal gas migration from a microscopic perspective and provide a theoretical basis for gas extraction engineering practice.

Study on the overburden failure features and microseismic measurements in non-pillar gob-side entry retaining by roof cutting

ZHANG Yingyi, WANG Tong

2024, 50(3): 131-141. doi: 10.13272/j.issn.1671-251x.2023100062

<Abstract>(34) <HTML> (14) <PDF>(6)

Abstract:
In order to further study the failure law of overburden after the mining of non-pillar gob-side entry retaining by roof cutting technology, taking the S1201-II working face of Ningtiaota Coal Mine as the engineering background, physical similarity simulation and numerical simulation research methods are used. Combined with on-site microseismic monitoring technology, a microseismic waveform data library is established. With continuous mining of the working face, the evolution of overburden mining induced cracks and stress spatial distribution features at different stages of non-pillar gob-side entry retaining by roof cutting are studied. The periodic crack law of the overburden in the working face has been obtained. The research results show that the height of the overburden cracks during the initial pressure on the working face is about 57.6 m, the height of the middle crack zone before cutting is 95.5-96.1 m, the crack mining ratio is 23.8-24.0, the height of the edge side cracks is 105.9-106.4 m, and the crack mining ratio is 26.4-26.6 m. After the roof cutting, the final development height of the crack zone on both sides of the working face is about 104.3-105.2 m, with a crack mining ratio of 26.1-26.3. Due to the continuous compaction and closure of the overburden layer, the final development height of the crack zone in the middle of the working face is 94.3-95.2 m, with a crack mining ratio of 23.6-23.8. When the roadways are in the excavation and cutting stages respectively, there is basically no change in the displacement of the roof. When it enters the sinking and roadway formation stage, the displacement value of the roof continuously increases. After the completion of roof cutting and pressure relief, the peak support pressure on the side of the roadway increases, indicating that the span of the working face further increases after the cutting seam, and the inclined support pressure continues to increase. The pressure relief effect of the working face roof is significant, and the roof produces a large-scale stress release phenomenon. A microseismic monitoring system is installed in the working face, and it is found that there is a strong correlation between the periodic occurrence of microseismic events and the periodic pressure of the working face. The development process can be divided into the budding stage, development stage, and climax stage. Further comprehensive analysis can be conducted to obtain the periodic crack evolution law of the overburden.

Study on the stress distribution of surrounding rock and the inclination effect of gangue filling features in steeply dipping mining sites

GAO Lijun, JIN Fadong, LIANG Dongyu, YANG Wenbin, TANG Yepeng, WANG Tong

2024, 50(3): 142-150. doi: 10.13272/j.issn.1671-251x.2023100064

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Abstract:
The dip angle of coal seam is one of the important factors that cause the complexity and particularity of the mining dynamic behavior of the large dip angle stope and induce many disasters and accidents. In order to reveal the influence law of the dip angle of coal seam on the control of surrounding rock and the characteristics of mine pressure in the large dip angle stope, the research method of physical similarity simulation and numerical calculation is adopted. Based on a comprehensive analysis of the features of roof crack and gangue sliding and rolling filling in steeply dipping working faces, a finite element discrete element (FLAC2D-PFC2D) coupling algorithm is used to establish a coupled numerical model of high angle mining areas with different inclinations. The stress distribution of surrounding rock and the inclination effect of gangue filling features in steeply dipping mining areas are studied. The results show the following points. ① Under the action of mining, the stress distribution of the surrounding rock in the roof and floor of the high angle mining area is asymmetric arched. As the dip angle of the coal seam increases, the range of the arched vertical stress release zone and the degree of upward displacement gradually increase. But the range and force value of the horizontal stress release zone gradually decrease. Both vertical and horizontal stresses are prone to stress concentration at the top and bottom of the working face. But the maximum concentrated stress value will decrease with the increase of coal seam inclination angle. The stress magnitude and transmission direction inside the roof and floor of the steeply dipping working face exhibit asymmetric features. As the coal seam dip angle increases, the stress arch height of the working face roof and floor gradually decreases. The transmission direction of surrounding rock stress is mainly towards the mining space, and gradually deviates from the initial approximate vertical direction towards the vertical direction of the working face. ② The roof crack of the working face and the sliding and filling of gangue exhibit temporal and regional evolution features, and exhibit a certain dip angle effect with the change of coal seam dip angle. As the inclination angle of the coal seam increases, the initial breaking position of the direct roof will gradually shift towards the upper area of the working face. At the same time, due to the increased force of gravity along the inclination direction of the working face, the filling degree of the gangue along the inclination direction will be more dense. But the filling length will decrease. As the inclination angle of the coal seam increases, the degree of crack and the range of voids in the high-level rock layers in the upper and middle areas of the working face will also increase. ③ The mechanism of the action of gangue on the surrounding rock in the goaf is mainly reflected in providing lateral stress and vertical support, and is greatly affected by the gravity of gangue, which will show a strong inclination effect with the change of coal seam inclination angle.

Research on optimization of coal roadway support parameters and equipment technology in Tianshuibao Coal Mine

MENG Jian, ZHU Changhua, NIU Zhijun, WANG Xufeng, LYU Hao

2024, 50(3): 151-159. doi: 10.13272/j.issn.1671-251x.2024010016

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Abstract:
Currently, research on rapid excavation technology mainly focuses on the influencing factors and equipment optimization of rapid excavation. There is relatively little research on the joint optimization of roadway empty roof distance, support parameters, and construction technology. In order to solve the above problem, the study focuses on the return air roadway of the 1309 working face in the No.2 of Tianshuibao Coal Mine in Huanxian County, Gansu Province. The study investigates the optimization methods of coal roadway support parameters and equipment technology. The study analyzes the time features of each process of roadway excavation. It is found that excavation, permanent support, and temporary support take the most time, accounting for 25.3%, 49.9%, and 6.2% respectively. Focusing on the three most time-consuming processes as the optimization direction, a mechanical model of the roof in the goaf area of the excavation face is constructed. The theoretical maximum empty roof distance of the excavation face is obtained to be 2.32 meters. Considering the influence of equipment, geology, technology and other factors on site, the empty roof distance is determined to be 2.0 meters. Based on the distribution features of stress, deformation, and plastic zone in the surrounding rock of the roadway under different support schemes, combined with the efficient excavation requirements of the roadway, the optimal spacing between anchor rods is determined to be 800 mm × 1000 mm. Based on the actual geological conditions of the roadway, the excavation equipment, temporary support technology, and construction technology are optimized and matched. The on-site test results show that after optimization, the maximum daily footage has been increased from 8 meters to 10 meters, and the roadway excavation speed has been increased by 25%. The deformation of the surrounding rock in the roadway is basically in a stable state, with a maximum deformation of 226 mm. The optimization plan not only ensures the safety and stability of the roadway, but also significantly improves the excavation speed of the roadway.

Hydraulic fracturing weakening roof borehole protection technology

XUE Jiangda, SUN Yongkang, WANG Jun, ZHANG Geng

2024, 50(3): 160-166. doi: 10.13272/j.issn.1671-251x.2023080114

<Abstract>(17) <HTML> (3) <PDF>(3)

Abstract:
In the case of sequential mining with single wing arrangement in coal mine working face, the drilling along the working face is easily affected by the support stress of adjacent working faces, leading to drilling failure. At present, research on borehole protection focuses on enhancing the strength of the borehole itself, without proposing solutions to the fundamental factors that affect borehole stability. In order to solve the above problems, a hydraulic fracturing weakening roof borehole protection technology has been proposed. By using hydraulic fracturing to weaken the roof, the peak mining support stress acting on adjacent coal working faces is reduced, and the transmission of high support stress to the surrounding coal bodies in the bedding boreholes is blocked. The entire process of screening is carried out in the bedding boreholes to ensure that the gas escaping from the coal body can enter the bedding boreholes. Numerical simulation is used to analyze the changes in vertical stress and plastic zone of the coal body around the borehole before and after hydraulic fracturing weakening the roof. The results show that by weakening the roof through hydraulic fracturing, the peak vertical stress of the coal body around the borehole decreases from 21.2 MPa to 9.1 MPa, and the plastic zone range of the coal body decreases from 19 m to 11 m. According to the numerical simulation results, hydraulic fracturing parameters are determined and tested on site. The results show that after using hydraulic fracturing weakening roof borehole protection technology, the average volume fraction of gas extraction from boreholes increases from 3.6% to 14.1%. The average mixed flow rate of gas extraction decreases from 1.28 m³/min to 0.464 m³/min. There is no occurrence of coal oxidation and CO production in a large area of bedding boreholes. Therefore, hydraulic fracturing weakening roof borehole protection technology can effectively avoid drilling failure and gas leakage, improve drilling and extraction efficiency, and ensure drilling and extraction safety.

Current Issue
2024, Volume 50, Issue 3

Submission System

Latest AcceptedMore>

Current Issue 2024, Volume 50, Issue 3

Submission System

Latest AcceptedMore>

Current Issue
2024, Volume 50, Issue 3