2022 Vol. 48, No. 11

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Academic Column of Editorial Board Member
Coal mine rock burst and coal and gas outburst perception alarm method based on color image
SUN Jiping, CHENG Jijie, WANG Yunquan
2022, 48(11): 1-5. doi: 10.13272/j.issn.1671-251x.18042
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Abstract:
The paper analyzes the image characteristics of the thrown coal and rock when rock burst and coal and gas outburst occur. ① The coal and rock thrown out during rock burst and coal and gas outburst are mainly black, but the underground equipment of the coal mine is generally not black. Therefore, nonblack mining equipment can be used as the background and color cameras can be used to identify coal and rock. ② The normal coal falling speed, the moving speed of shearer, roadheader, and the moving speed of underground personnel and vehicles are far less than the speed of coal and rock thrown out in the event of rock burst and coal and gas outburst. Therefore, according to the speed characteristics, the interference from normal coal falling, movement of equipment such as shearer and roadheader, and movement of underground personnel and vehicles can be eliminated. ③ The explosion of gas and coal dust will also cause the objects in the roadway to have a high speed in a short time, accompanied by high brightness. But the rock burst and coal and gas outburst will not produce high brightness. Therefore, according to the average image brightness, the interference of gas and coal dust explosion can be eliminated. The paper proposes a color camera set method. The camera of the heading face should be set at the roof of the heading roadway or near the roof on both sides of the heading roadway. The camera of the working face should be set on the top of the hydraulic support. The paper puts forward a coal mine rock burst and coal and gas outburst perception alarm method based on color image. ① The color camera with fill light shall be set at the roof of the heading roadway or near the roof on both sides of the heading roadway, and at the top of the hydraulic support of the working face. The nonblack mining equipment is used as the background. ② The method monitors and identifies whether the color of the color image has changed greatly. ③ If the image color changes significantly, the average brightness of the image is identified, otherwise the monitoring of the identified image color change continues. ④ If the average brightness of the image is less than the set brightness threshold, the movement speed of the object causing a large change in the image color is identified, otherwise the monitoring of the identified image color change continues. ⑤ If the movement speed of the object is greater than the set speed threshold value, the methane concentration in the monitoring area is identified, otherwise the monitoring of the identified image color change continues. ⑥ If the methane concentration rises rapidly or reaches the alarm value, the coal and gas outburst alarm will be given. Otherwise, the rock burst alarm will be given. The method has the advantages of non-contact, wide monitoring range, low cost, convenient use and maintenance.
Analysis of 5G private network technology and coal mine 5G private network scheme
HUO Zhenlong
2022, 48(11): 6-10, 19. doi: 10.13272/j.issn.1671-251x.18040
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Abstract:
Most of the existing communication networks except 5G exist in the form of independent private networks. The performance of 5G wireless communication in bandwidth, delay, number of terminal connections, and reliability has been greatly improved. Accordingly, there are new changes in network architecture and networking mode. The private network scheme is no longer a single independent private network scheme. There are also hybrid private networks and virtual private networks. This paper introduces the key technologies of 5G private network, such as network slicing, mobile edge computing, 5G LAN, time-sensitive network and so on. This paper proposes three kinds of 5G private network schemes, namely virtual private network, hybrid private network and independent private network. The virtual private network has the characteristics of wide service scope, high flexibility, low cost, and short construction cycle. It is used for various applications which have wide coverage, have access terminals not fixed in time and space, and have certain service quality requirements and certain degree of data isolation requirements. The hybrid private network has short transmission path, high security, and low end-to-end delay. It can carry out a variety of flexible independent services, but privacy is weak. The independent private network provides a physical exclusive 5G private network to meet the needs of industrial users for high bandwidth, low delay, high security and high-reliability data transmission. This paper puts forward the general principles of 5G private network scheme selection in terms of safety, availability and reliability. This paper also proposes the special requirements of 5G private network in coal mine in terms of dispatching function, integration demand, independent operation and maintenance and intrinsic safety. The selection of 5G special network scheme for coal mine is proposed. In the early stage of intelligent construction of coal mine or there is no strict requirements on data confidentiality, system use convenience, system function expansion in the coal mine, the virtual private network or hybrid private network scheme can be selected. Otherwise, the independent private network scheme can be selected. It is pointed out that relatively more hybrid private network and virtual private network schemes are adopeted in coal mines at present. The hybrid private network and virtual private network will have some advantages in the future. With the gradual establishment of small core network ecology, the independent private network scheme will be recognized by more users. In a certain period in the future, the independent private network, virtual private network and hybrid private network schemes will give full play to their respective advantages. They serve the intelligent construction of coal mines in different periods and with different requirements.
Research on multi monitoring information fusion and linkage of intelligent mine
HE Yaoyi, GAO Wen, YANG Yao, JING Cheng, ZHU Shasha, CHEN Xing
2022, 48(11): 11-19. doi: 10.13272/j.issn.1671-251x.17962
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Abstract:
There are many types of coal mine automatic monitoring systems, and the technical routes are not unified. The system software is relatively independent, and the data is lack of correlation. At present, field data fusion and linkage control are mostly realized by the underground fusion substation or the ground fusion platform. It is difficult to realize the unified integration and linkage control of the whole mine from the bottom perception to the ground fusion. Based on the requirements of multi-system fusion of coal mine safety monitoring system and intelligent construction of coal mine, the key problems to be solved in multi monitoring information fusion of mine are analyzed. The problems include integrated acquisition and fusion of monitoring data of personnel, machine and environment, efficient and consistent sharing of safety monitoring and control data, low code and rapid secondary development of automatic monitoring system, and integrated supervision of whole life cycle of mine equipment objects. The scheme of multi monitoring information fusion and linkage for intelligent mine is proposed. The overall framework including underground data fusion and linkage control and ground multi monitoring information fusion is constructed. This paper introduces the implementation scheme of underground data fusion and linkage control based on edge fusion substation, and expounds on the key technologies of ground multi monitoring information fusion from three aspects, which include unified technology system, unified technology architecture and data processing mechanism, and deep information fusion based on the mine object information model. Therefore, an open integrated management and control platform of multi monitoring information is developed. Based on the coal industry communication driving protocol set embedded in the scheme and the basic supporting technologies such as coal mine monitoring, control, position service, 2D and 3D GIS, and workflow engine, the following platforms can be rapidly developed: the independent software platforms for automatic monitoring system of environmental safety monitoring, mobile target positioning and coal flow transportation control, the integrated safety production monitoring and control platform and integrated management and control platform of intelligent mine. The scheme forms industry-level real-time industrial configuration software.
Special of Intelligent Coal Separation Technology and Application
Development and exploration of intelligent dense medium separation technology for coal
DAI Wei, WANG Yudong, DONG Liang, ZHAO Yuemin
2022, 48(11): 20-26, 44. doi: 10.13272/j.issn.1671-251x.2022060106
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Dense medium separation, the most widely used coal preparation process, is moving from automation and informatization to intelligence. At present, the intelligent construction of dense medium coal preparation plant only realizes partial intelligent construction. It is deficient in the whole intelligent construction. The intelligent development of the core production equipment (dense medium cyclone and shallow groove) is insufficient. In order to solve the above problems, the research status of intelligent dense medium separation is described from three aspects of intelligent perception, intelligent control and intelligent optimization decision. The challenges faced by dense medium separation in the process of developing from automation to intelligence are analyzed. The challenges include the unstable operation caused by the fluctuation of raw coal quality, the high complexity of dense medium separation, and the limitations of intelligent construction of dense medium coal preparation plant. In order to promote the intelligence and greening of the dense medium separation industry, realize the autonomous control of the whole equipment, reduce the number of operators and even realize unmanned, a system is proposed. It is pointed out that the dense medium coal preparation plant should build a set of intelligent optimization production system with the integration of "intelligent perception, intelligent control and intelligent optimization decision". Intelligent perception, the basis of intelligence, is used to realize the perceptual acquisition of coal preparation process data. Intelligent optimization decision analyzes the operation state of the preparation process in the intelligent control module and adjusts the set value of the process index. Intelligent optimization decision analysis intelligent control module is used to sort process operating state, adjust the process indicators set value, so as to achieve dynamic optimization of the process indicators set value. The mutual coordination of perception, control and decision promotes the improvement of the intelligence level and production efficiency of the coal preparation plant. The coordination provides a new idea for realizing intelligent collaborative optimization control of the whole dense medium separation production process in the future.
X-ray transmission intelligent coal-gangue recognition method
WANG Wenxin, HUANG Jie, WANG Xiuyu, SHI Yulin, WU Gaochang
2022, 48(11): 27-32, 62. doi: 10.13272/j.issn.1671-251x.18037
<Abstract>(296) <HTML> (36) <PDF>(67)
Abstract:
The coal-gangue image recognition is an important part of coal-gangue separation technology based on pseudo dual energy X-ray transmission (XRT). However, it is difficult to segment the coal-gangue image due to the close proximity or occlusion of coal-gangue, and it is easy to cause classification and recognition errors of coal-gangue based on artificial threshold discrimination. Due to the above influence, existing coal-gangue recognition methods have low precision. In this paper, an X-ray transmission intelligent coal-gangue recognition method is proposed. A U-Net model combined with the receptive field block (RFB) is used to realize the effective segmentation of the pseudo dual energy X-ray coal-gangue image, which is termed as RFB + U-Net model. The problem that the recognition precision is affected by the close proximity or shielding of coal-gangue is solved. The recognition features of coal-gangue are the minimum gray value of the low-energy image in the gray level features of coal-gangue image, and the minimum value and the average difference of sharpened low-energy image in the texture features. A multi layer perceptron (MLP) model is used to realize coal-gangue recognition. Experimental results show that the RFB+U-Net model is superior to the active contour model, U-Net model and SegNet model in terms of coal-gangue segmentation accuracy, coal-gangue particle size precision, coal-gangue pixel mean intersection ratio and image segmentation effect. The reasoning time of the model is short, meeting the real-time requirements of coal-gangue image segmentation. When the number of hidden layers in the MLP model is 8, the average coal-gangue recognition accuracy under two test sets is more than 87%. Under the same data set and experimental conditions, the average recognition accuracy and gangue removal rate of the MLP model are higher than those based on Bayesian classifier, support vector machine, logic regression, decision tree, gradient boosting decision tree and K-nearest neighbor algorithm. The coal carrying rate of gangue shall not exceed 3%, meeting the requirements of actual dry coal-gangue separation.
Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model
CHEN Biao, LU Zhaolin, DAI Wei, SHAO Ming, YU Dawei, DONG Liang
2022, 48(11): 33-38. doi: 10.13272/j.issn.1671-251x.18035
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Abstract:
The existing coal-gangue separation methods based on vision technology have problems of large model parameter amount, poor feature extraction capability and low recognition precision. In order to solve the above problems, a coal-gangue recognition method based on YOLOX-S model combined lightweight Ghost-S network and hybrid parallel attention module (HPAM) named HPG-YOLOS-S model is proposed. Firstly, HPAM is added to the backbone network of YOLOX-S model. Thus the important information in an image is enhanced, and the secondary information is inhibited. The feature extraction capability of the backbone network is enhanced. Secondly, the backbone network of YOLOX-S model is replaced by Ghost-S network with smaller parameter quantity. The utilization rate and feature fusion capability are improved. Finally, in the predection layer, the SIOU loss function is used to replace the loss function of YOLOX-S model to impsrove the detection and positioning precision and enhance the extraction capability of the target. In order to verify the detection effect of the proposed method on large coal-gangue, the HPG-YOLOX-S model is compared with YOLOX-S model. The results show that the identification accuracy of the HPG-YOLOX-S model for coal and gangue is 99.53% and 99.60% respectively, which is 2.51% and 1.27% higher than those of YOLOX-S model. The results of validation show that the precision rate, recall rate and F1 value of the HPG-YOLOX-S model are all above 94%, which are 5.68%, 3.51% and 2.91% higher than those of YOLOX-S model respectively. The parameters amount of the HPG-YOLOX-S model is 7.8 MB, which is 1.2 MB lower than that of YOLOX-S model. The ablation experiment results show that the mean average precision of the HPG-YOLOX-S model is 9.17% higher than that of YOLOX-S model. The experiment result of visualization of the thermodynamic diagram shows that the HPG-YOLOX-S model focuses on the surface differences between coal and gangue, such as texture and contour. The model pays more attention to the overall target of coal-gangue.
Coal and gangue recognition research based on improved YOLOv5
ZHANG Shiru, HUANG Zongliu, ZHANG Yuanhao, ZHANG Ao, JI Liang
2022, 48(11): 39-44. doi: 10.13272/j.issn.1671-251x.2022060052
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The existing deep learning-based coal and gangue recognition methods are prone to false detection and missed detection when applied to underground complex environments. The recognition precision of small target coal and gangue is low. In order to solve this problem, an improved YOLOv5 model is proposed, and coal and gangue recognition is realized based on that model. Data enhancement is carried out on the collected coal and gangue data to enrich the data set and improve the data utilization rate. The atrous convolution and residual block are introduced into the spatial pyramid pooling (SPP) module to obtain the residual ASPP module. On the premise of not losing image information, the convolution output receptive field can be increased to enhance the extraction of deep features from the model. The AdaBelief optimization algorithm is used to replace the original Adam optimization algorithm of YOLOv5 to improve the convergence speed and recognition precision of the model. The experimental results show that the AdaBelief optimization algorithm and residual ASPP module can effectively improve the precision, recall rate and mean average precision (mAP) of the YOLOv5 model. The mAP of the improved YOLOv5 model reaches 94.43%, which is 2.27% higher than that of original YOLOv5 model. The frame rate is reduced by 0.03 frames/s. The performance of the improved YOLOv5 model is superior to SSD, Faster R-CNN, YOLOv3, YOLOv4 and other mainstream target detection models. In extremely dark environments, the improved YOLOv5 model can also accurately delineate the target boundary, and the recognition effect is better than other improved YOLOv5 models.
Application status and prospect of AI video image analysis in intelligent coal preparation plant
SHE Xiaojiang, LIU Jiang, WANG Lanhao
2022, 48(11): 45-53, 109. doi: 10.13272/j.issn.1671-251x.2022060092
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Artificial intelligence (AI) video image analysis is an important part of intelligent coal preparation plant. It can realize the intelligent monitoring of important parameters of the equipment, environment, personnel and the whole process of coal preparation. The basic structure of the intelligent coal preparation plant is proposed. It is pointed out that the existing research mostly uses AI video image analysis technology to construct the safety monitoring system of coal preparation plant for personnel, machine, environment and management. The construction process of the intelligent video image monitoring system is proposed. In view of the two goals of safe and environment-friendly production and improving product quality in the intelligent construction of coal preparation plant, the application status of AI video image analysis technology in the intelligent coal preparation plant is introduced from six aspects. The aspects include foreign object detection, intelligent separation, equipment running state monitoring, coal particle size detection, personnel behavior monitoring and environmental safety detection. The intelligent application of AI video image analysis in coal preparation plant is proposed. It is pointed out that it is necessary to build a multi-level video monitoring system based on 5G communication, the Internet of Things, AI, intelligent control theory and coal preparation industry technology from the macro architecture. It is also necessary to optimize existing general intelligent video monitoring methods or algorithms from a micro perspective, and develop intelligent video image analysis technology suitable for the coal preparation plant environment. Machine vision and computer vision should be highly integrated with deep learning. The different advantages of machine vision and computer vision should be reasonably applied in different working conditions. It is suggested to establish a multi-level integrated monitoring system framework, and deploy and optimize the algorithm model within the framework. It is suggested to establish a diversified video image database, make full use of data characteristics of different image types, and develop targeted analysis algorithms. It is suggested to deeply study the distributed data stream and real-time AI video image analysis, build a real-time AI distributed system, reasonably schedule the video image analysis model, and improve the calculation efficiency and accuracy of the real-time model.
Design of 3D visualization management platform for intelligent coal preparation plant based on coal preparation information model
GUO Qinghua, WEI Zhongkuan, ZHANG Shusen, WANG Ranfeng
2022, 48(11): 54-62. doi: 10.13272/j.issn.1671-251x.17936
<Abstract>(204) <HTML> (34) <PDF>(58)
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At present, in the intelligent construction of coal preparation plant, there is little research and development on 3D visualization management centering on the whole life cycle from design, construction to production and operation and maintenance. There is also little research on the deep integration of 3D visualization and big data cloud platform. In view of the above problems, based on the building information model (BIM), this paper improves it and puts forward coal preparation information model (CPIM). The proposed model aims at the problem that BIM only considers the information model of coal preparation buildings and structures. BIM can not meet the needs of intelligent coal preparation plant construction. Combined with the big data cloud platform technology, a 3D visualization management platform for intelligent coal preparation plant based on CPIM (the platform consists of infrastructure layer, data sources layer, basic platform, application layer and business display layer) is designed. The data standard of coal washing engineering is constructed. The data access, management, storage, analysis and sharing of the whole life cycle of coal preparation design, construction, production and operation and maintenance are realized. The key technologies of the platform implementation of BIM lightweight engine, CPIM big data sub-platform construction, CPIM whole life cycle application, standard formulation of digital construction of coal washing project and software system independent research and development and domestic hardware adaptation are analyzed. Taking a coal preparation plant of China Coal Group as the research object, based on CPIM big data sub-platform and BIM 3D engine sub-platform, the CPIM whole life cycle 3D visualization application platform is constructed. And the localization adaptation of hardware is realized. The field application shows that the platform can carry out effective data acquisition, unified processing, storage, analysis and application for the design, construction and operation of coal preparation plant. The platform opens up the data channel of the coal washing industry. The platform realizes the 3D visualization management covering the whole life cycle of coal preparation plant design, construction, and production operation and maintenance.
Prediction of overflow concentration of thickener based on ISSA-LSTM
ZHANG Yangyang, FAN Yuping, MA Xiaomin, DONG Xianshu, JIN Wei, WANG Dawei
2022, 48(11): 63-72. doi: 10.13272/j.issn.1671-251x.2022060084
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The monitoring of the overflow concentration of the thickener is the key to realize intelligent dosing of coal slurry. The overflow concentration monitoring method based on the sensor will lead to the delay of flocculant regulation. In order to solve the above problem, a prediction method of overflow concentration of thickener based on improved sparrow search algorithm (ISSA) and long-short term memory (LSTM) is proposed. Firstly, the correlation analysis and pretreatment of multi-parameter time series in the process of concentration production are carried out to obtain the input variables. Secondly, the multi-strategies are combined to improve sparrow search algorithm (SSA). Tent chaotic map is introduced to initialize the sparrow population to ensure population diversity and speed up algorithm convergence. The optimization process of SSA is improved by using the spiral predation strategy to balance both local development and global search capabilities. The firefly perturbation strategy is used to perturb the sparrow search results to improve the global search performance and avoid the algorithm falling into local optimization. Thirdly, ISSA is used to optimize the hyperparameters of the two-layer LSTM network model. Finally, the overflow concentration prediction model based on ISSA-LSTM is established for on-line monitoring. The experimental results show the following points. ① The Ackley function and Rastigin function are selected as test functions. It is concluded that ISSA's global optimization capability and convergence speed are better than those of the particle swarm optimization (PSO) algorithm, whale optimization algorithm (WOA) and standard SSA. ② Among the three improved strategies, the spiral predation strategy plays a leading role in improving the performance of ISSA. The chaotic map and the firefly perturbation strategy coordinate the convergence speed and global search capability of the algorithm to further improve the optimization performance of the algorithm. ③ ISSA is used to optimize the hyperparameters of LSTM, which solves the problem of under-fitting or over-fitting when the values are determined by subjective experience. The prediction precision of overflow concentration of the ISSA-LSTM model reaches 97.26%, which is higher than that of double-layer LSTM, SSA-LSTM, and least square support vector machine (LSSVM) models. ④ Data pretreatment can improve the precision of the model, and the prediction precision of overflow concentration after noise reduction is improved by 30.25% compared with that before noise reduction.
Automatic layout of pipeline in coal preparation plant based on optimized A* algorithm
XIAO Linjing, YAO Peixin, LIU Rui, MA Shanqing, MA Chenghan
2022, 48(11): 73-79. doi: 10.13272/j.issn.1671-251x.2022080085
<Abstract>(217) <HTML> (50) <PDF>(30)
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The pipeline design is one of the important contents of coal preparation plant design. At present, pipeline of coal preparation plant mainly depends on the manual design, which is difficult, time-consuming and difficult to guarantee the quality of pipeline layout. When A* algorithm is applied to the automatic layout of three-dimensional pipeline in coal preparation plant, the searched path does not meet the requirements of pipeline design. In order to solve the above problems, an automatic pipeline layout method for coal preparation plant based on optimized A* algorithm is proposed. Based on the pipeline layout rules of coal preparation plant, the layout space model of coal preparation plant is established. The grid and numerical processing are carried out on the layout space model. Aiming at the problem that the path searched by the A* algorithm has excessive bending, the evaluation function of the A* algorithm is optimized. To solve the problem of the slow search speed of the A* algorithm, dynamic weight are introduced into the evaluation function. Aiming at the problem that the pipeline path searched by the A* algorithm after the above optimization will bypass the required equipment, the direction-oriented strategy is introduced to improve the engineering practicability of pipeline layout. To improve the A* algorithm's operation efficiency, the Open table's array structure is replaced with the minimum binary heap structure. The simulation result shows the following points. ① After optimizing the evaluation function of the A* algorithm, the bending times of the pipeline path are reduced by about 80%. The ben is right angle, which accords with the actual situation of the pipeline layout in the coal preparation plant. After introducing the dynamic weight, the operation efficiency is improved and the path quality can be guaranteed. ② The path length of the pipeline before and after the direction-oriented strategy is introduced has no change. The lengths meet the basic constraint rule of the pipeline layout of the coal preparation plant. After the introduction of the direction-oriented strategy, the pipeline is more likely to be planned near the equipment with specific requirements for the pipeline. And the pipeline has a tendency to be arranged side by side. This indicates that the pipeline layout after the introduction of the direction-oriented strategy meets the requirements of the optimal overall layout, and is more consistent with the coal preparation engineering application. ③ The efficiency of A* algorithm after Open table optimization is improved obviously. The longer the pipeline path and the more obstacles in the middle, the more significant the efficiency improvement of the A* algorithm. The software system of automatic pipeline layout in the coal preparation plant is designed and developed. The application example of the optimized A* algorithm is verified. The results show that the optimized A* algorithm improves the efficiency and quality of piping design in the coal preparation plant, and has better visibility.
Column of Coal Mine Unmanned Transportation
Simulation system of mine unmanned vehicle based on parallel control theory
YANG Rongming, DING Zhen, YANG Jianjian, FU Jianhua, GAO Yu, WEI Ya, AI Yunfeng, ZHANG Zhiming
2022, 48(11): 80-83, 100. doi: 10.13272/j.issn.1671-251x.17999
<Abstract>(208) <HTML> (33) <PDF>(49)
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The test of mine unmanned vehicle has problems of great danger, long test time, high test cost and narrow test coverage. In order to solve the above problems, the simulation system of unmanned mine vehicle based on parallel control theory is studied. The system adopts key technologies such as mine vehicle dynamics modeling, high-fidelity scene reconstruction, and virtual sensor modeling. The system realizes the functions of comprehensive deduction of the unmanned driving algorithm, system integration reliability test, mining area production prediction simulation, and virtual and actual interactive parallel deduction. The main step of dynamic modeling of the mine vehicle is divided into two parts: vehicle model building and visual scene creation. The vehicle dynamic model is associated with the virtual scene. The simulation data generated by the vehicle model is used for driving the vehicle in the virtual scene to move in real-time. In view of the complex and irregular characteristics of the large-scale open-pit mine scene, the high-precision 3D model data of the mine is obtained by means of UAV aerial mapping and laser radar 3D scanning. Based on the virtual micro polygon geometry technology, high pixel virtual texture technology, and 3D scene real-time rendering technology, a high-fidelity virtual 3D scene is constructed. The virtual sensor mainly comprises virtual laser radar, virtual millimeter wave radar, virtual inertial navigation device and virtual vision camera. The virtual sensor is carried on the virtual mine car. It is responsible for generating virtual data information in a simulated mining area scene, and sending the data to the automatic driving controller for processing. Based on the simulation system, single-vehicle test, multi-vehicle scheduling test and intelligent scheduling algorithm test can be carried out. The dynamic virtual-reality interaction between on-site vehicles and virtual vehicles can be tested. The system is used to provide a verification platform for stable transportation of the whole mining area, deduction simulation of complex intersections and optimal decision of intelligent scheduling algorithm. The system ensures the efficiency and safety of unmanned driving test and accelerates the upgrading of unmanned driving technology in the mining area.
Analysis Research
Influence of underground radio wave on human body in coal mine
DING Xuhai, PAN Tao, PENG Ming, ZHANG Gaomin
2022, 48(11): 84-92, 144. doi: 10.13272/j.issn.1671-251x.18044
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The large-power radio waves emitted by 5G, WiFi6, UWB, ZigBee and other mine mobile communication systems and personnel and vehicle positioning systems will affect the health of underground personnel. Therefore, it is necessary to study the influence of underground radio waves on human body. The influence of electromagnetic radiation on the human body has been studied. Different parts of the human body have different absorption capacities to electromagnetic radiation, and electromagnetic radiation has the greatest impact on the human head. Radio wave emission power limits include occupational exposure limits and public exposure limits. Occupational exposure means that the exposure time of the personnel in the electromagnetic radiation environment is no more than 8 hours. The working time of coal mine underground personnel is 8 hours per shift for 3 shifts or 6 hours per shift for 4 shifts. Therefore, the minimum limit value of 6.28 W of radio wave transmission power for whole-body occupational exposure shall be selected as the limit value of radio wave transmission power in the coal mine. In order to prevent the gas explosion caused by coal mine underground radio wave emission, GB/T 3836.1-2021 Explosive Atmospheres - Part 1: Equipment-General Requirements stipulates that the radio wave transmission power in coal mines shall not be greater than 6 W. Therefore, the 5G, WiFi6, UWB, ZigBee and other mine mobile communication systems and personnel and vehicle positioning systems that have obtained the explosion-proof certificate and the permit for the use of mining safety signs will not cause harm to the underground personnel of the coal mine under the condition that the distance between the wireless transmission antenna and the fixed post personnel is greater than 1 m.
Research on radiation performance and safety performance of X-ray source for mine transmission detection
LI Zhe, WANG Wenqing
2022, 48(11): 93-100. doi: 10.13272/j.issn.1671-251x.17957
<Abstract>(108) <HTML> (28) <PDF>(18)
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The X-ray source is the core component equipment of X-ray transmission detection. The stability and reliability of the X-ray source determine the performance of X-ray transmission detection. In order to meet the performance requirements of X-ray transmission detection, the tube voltage of X-ray source should be selected between 100-160 kV, and the tube current should be controlled between 0.1-4 mA. In view of the problem that the flameproof shell made of Q235 steel plate can greatly reduce the radiation output intensity of X-ray source, the X-ray transparent window made of tempered glass is installed on the flameproof shell of mine X-ray source to increase the transmission rate of X-ray. Taking the maximum tube voltage of 160 kV and the maximum tube current of 4 mA of X-ray source applied in the field of coal mine gangue selection identification transmission detection as an example, the maximum radiation output power of the mine X-ray source is calculated to be about 50 mW through actual measurement. The result meets the requirements of GB/T 3836.22-2017 Explosive Atmospheres-Part 22: Protection of Equipment and Transmission System Using Optical Radiation which stipulates that the radiation power shall not exceed 150 mW. In order to reduce the risk of the working temperature rise of the mine X-ray source, it is proposed that the X-ray tube should be made of the ceramic shell with good thermal conductivity. The anode of the X-ray tube should be directly fixed to the metal shell to increase the heat dissipation effect. The X-ray transparent window should be used to reduce the thermal power generated by the anode current of the X-ray tube. This will ensure that the surface temperature of the flameproof shell of the mine X-ray source is less than the 150 ℃ limit specified in GB/T 3836.1-2021 Explosive Atmospheres-Part 1: Equipment-General Requirements. In order to avoid the radiation impact of mine X-ray source on the surrounding environment, it is proposed to install the X-ray tube in a lead chamber made of 3 mm thick stainless steel and 5 mm thick metallic lead. This will shield the X-ray in non-working area, so as to ensure that the dose equivalent rate of X-ray leakage in the non-working area of the mine X-ray source is less than 2.5 µSv/h limit specified in GBZ 125-2009 Radiological Protection Requirements for Gauges Containing Sealed Radioactive Source.
Analysis on deformation characteristics of surrounding rock of gob-side entry retaining with soft bottom in thick coal seam and strengthening support technology of roof and side
KANG Zhipeng, DUAN Changrui, YU Guofeng, ZHAO Jing
2022, 48(11): 101-109. doi: 10.13272/j.issn.1671-251x.2022060003
<Abstract>(170) <HTML> (26) <PDF>(19)
Abstract:
The deformation and failure mechanism of surrounding rock and the control measures under the condition of long-time high superimposed stress are the keys to gob-side entry retaining support technology in thick coal seam with soft bottom. The existing research on the deformation and failure mechanism of surrounding rock and support control of gob-side entry retaining in thick coal seam is mainly aimed at deformation of roof and side of gob-side entry with hard rock bottom, and the strength of filling body and material proportion. There are few research on retaining roadway with soft bottom in thick coal seam. The mechanical analysis of gob-side entry retaining is incomplete, and the support scheme is single. In order to solve the above problems, taking N1303 working face of Gucheng Coal Mine of Shanxi Lu'an Chemical Industry Group Co., Ltd. as the engineering background, the failure mechanics models of roof, coal wall and floor are established. The deformation and failure characteristics of the roadway surrounding rock are analyzed. The roof is in a mixed stress environment, which is prone to tensile failure. Under the action of high stress, the solid coal side suffers compression shear failure, and the anchor rod fails. The filling body intrudes into the floor under pressure, causing the floor to tilt and lose stability, which is prone to soft coal broken and swelling. According to the deformation and failure characteristics of surrounding rock, the trinity surrounding rock support control scheme is proposed, namely, controlling the roof, restricting the coal side and yielding floor. In order to ensure that the roof can balance the stress distribution above the gob-side entry retaining, the method of anchor cable + filling body top cutting is adopted. Thus the roof does not form a cantilever beam structure above the roadway, only sinking occurs, and there is no rotary deformation. Considering the roof stability of gob-side entry retaining, the way of grouting anchor cable is adopted to grout the broken roof of the roadway to form a whole for better controlling the roof. In order to improve the support strength of the solid coal side, short anchor cables are added to connect the coal seam in the limit equilibrium area with the deep elastic bearing layer, and reduce the support resistance of the filling body beside the roadway. The proper yielding of the floor is beneficial to the flexible support of the whole roadway. The floor is reinforced by digging grooves and pouring strip foundations under the filling body wall. The original gob-side entry retaining support scheme is optimized by using the trinity surrounding rock support control scheme. The field test results show that after using the optimized support scheme, the roof movement subsidence is reduced from 337 mm to 142 mm, and the coal side movement is reduced from 305 mm to 70 mm. The floor movement is reduced from 675 mm to 162 mm, and the roadway convergence rate is reduced from 34.1% to 10.73%. The working resistance of the anchor rod (cable) is stable, the filling body is free of damage and inclination, and the support effect is good.
Experimental Research
Research on intelligent control of air volume of mine ventilation network
REN Zihui, LI Ang, WU Xinzhong, XU Jialin, CHEN Zepeng
2022, 48(11): 110-118. doi: 10.13272/j.issn.1671-251x.2022040020
<Abstract>(250) <HTML> (34) <PDF>(32)
Abstract:
The existing intelligent optimization algorithm of air volume of mine ventilation network has the defects of complex model, slow convergence speed, easy falling into local optimum when solving the air adjustment parameters. There is a lack of research on the combination of optimal selection of air adjustment branches. To solve the above problems, an intelligent control method of air volume of mine ventilation network based on improved beetle antennae search (BAS) algorithm is proposed. Firstly, the mathematical model of air volume optimal adjustment is established by taking the air volume demand of the air consumption branch as the optimization objective. In view of the air volume adjustment constraint conditions in the model, the non-differentiable exact penalty function and the simulated annealing algorithm are adopted to optimize the penalty term, so that the model is unconstrained. Secondly, by solving the sensitivity matrix and combining the theory of air volume sensitivity and branch dominance, the optimal adjustment branch set is selected. The air resistance adjustment range is determined as the initial solution set of the model. Finally, based on the improved BAS algorithm, the optimal air adjustment parameters are solved. The corresponding air adjustment facilities are controlled to realize air volume adjustment. The reliability of the method is verified by experiments based on the mine ventilation experimental platform. The results show that compared with the standard BAS algorithm and particle swarm optimization (PSO) algorithm, the improved BAS algorithm has superior comprehensive optimization performance. The average value and optimal solution of air volume are higher than those of the PSO algorithm and standard BAS algorithm. Although the average running time is slightly longer than the standard BAS algorithm, it is far shorter than the PSO algorithm. The average convergence algebra is the most, the precision is the highest, and it is easy to jump out of the local loop to get the optimal solution. After setting the air volume adjustment target, the intelligent control method of air volume of the mine ventilation network based on the improved BAS algorithm can quickly and accurately solve the optimal value of the air volume of the branch to be adjusted. The adjusted branch air volume meets the air volume adjustment requirements of mine safety production, and the air volume is increased up to 46.5%.
Hydraulic support straightening method based on maximum correntropy Kalman filtering algorithm
SONG Danyang, LU Chungui, TAO Xinya, YANG Jinheng, WANG Pei'en, ZHENG Wenqiang, SONG Jiancheng
2022, 48(11): 119-124. doi: 10.13272/j.issn.1671-251x.2022020030
<Abstract>(102) <HTML> (17) <PDF>(14)
Abstract:
The existing hydraulic support straightening method is affected by the sensor measurement error and the hydraulic support moving error, which make the straightening error larger. In the non-Gaussian measurement noise environment, the traditional Kalman filter (KF) straightening method has low accuracy in predicting the trajectory of the hydraulic support, and cannot achieve the ideal straightening effect. In order to solve the above problems, a hydraulic support straightening method based on maximum correntropy Kalman filtering (MCKF) algorithm is proposed. Firstly, the straightening reference line is determined according to the position coordinates of the hydraulic support and the advancing direction of the working face. Secondly, the state equation and observation equation of the linear moving system of hydraulic support is constructed according to the straightening principle of hydraulic support. After MCKF algorithm processing, the predicted trajectory of hydraulic support after moving is obtained. Finally, the moving distance compensation amount of each hydraulic support is calculated according to the predicted trajectory of the hydraulic support and the straightening reference line, so as to achieve the purpose of straightening. The simulation results show that the hydraulic support straightening method based on the MCKF algorithm can effectively reduce the influence of measurement noise and process noise on the straightness of the hydraulic support compared with the existing straightening method based on the KF algorithm. When the measurement noise obeys non-Gaussian distribution, the average of mean square error of the method is only 4.76 mm, which is far less than the mean square error of the straightening method based on the KF algorithm. The real trajectory of the hydraulic support can be predicted more accurately, which reduces the straightening error of the hydraulic support by 36% after straightening. The method thus effectively improves the straightening precision. The straightening error of the hydraulic support is only related to this straightening process, which effectively avoids the accumulated error.
Experience Exchange
Design of intelligent control software for whole mine gas extraction based on cross-platform architecture
WU Kejie, HUANG Qiang, XU Jin, CHEN Yunqi
2022, 48(11): 125-132. doi: 10.13272/j.issn.1671-251x.17987
<Abstract>(181) <HTML> (30) <PDF>(25)
Abstract:
The existing coal mine gas extraction anomaly recognition needs to rely on manual assistance to view related data analysis, lacking autonomous and intelligent analysis means. The capability of timely handling of abnormal extraction is weak, and the multi-system linkage control function in case of gas disaster is lacking. The software deployment environment is single, and cross-platform and cross-end application access cannot be realized. In view of the above problems, a set of intelligent control software for whole mine gas extraction based on cross-platform architecture is designed. The software consists of two parts: company side and mine side gas extraction intelligent control software. By adopting technologies such as extraction multi-source data acquisition, extraction drill hole aided design, extraction data analysis, extraction equipment operation and maintenance management, extraction GIS display, extraction fusion control, the global visualization of an extraction system is realized. A comprehensive data display of mine up and down integrated extraction has been formed, and a multi-level data traceability access mechanism has been established. The comprehensive analysis of mathematical and physical characteristics of extraction data, extraction evaluation characteristics, equipment fault characteristics, extraction anomaly characteristics, and borehole trajectory characteristics is realized by constructing thematic data characteristic graph. In case of abnormal extraction, the software will automatically link with the broadcasting system to inform the evacuation personnel in the dangerous area of gas emission. At the same time, the software will link the ventilation monitoring system to strengthen the ventilation in the abnormal area. The software automatically pushes the abnormal alarm message to the relevant person in charge through the message push strategy, so as to solve the extraction problem in a timely and rapid manner. The Docker technology is adopted to realize the cross-platform design of the software. The SQL Server database and Web terminal application are deployed in the Docker environment. Native HTML technology combined with responsive layout style is adopted to realize multi-terminal access such as HTML webpage, mobile APP and WeChat applet. The field application results show that the software can meet the access needs of two levels of users of the group company and mine. The software effectively improves the efficiency of mine extraction, and enhances the capability of mine in gas extraction evaluation, extraction abnormal disposal and extraction utilization. The software reduces the occurrence rate of mine gas abnormal accidents.
Research on fully mechanized mining equipment removal planning during sequencing working face
SHI Menghan, ZHU Weibing, REN Haibing
2022, 48(11): 133-138. doi: 10.13272/j.issn.1671-251x.18025
<Abstract>(122) <HTML> (26) <PDF>(20)
Abstract:
The current fully mechanized mining equipment removal plan during sequencing working face mainly depends on manual preparation. The large workload and low efficiency lead to the extension of the construction period. The quick removal mainly depends on a high degree of mechanized operations. There is little research on optimizing the fully mechanized mining equipment removal plan during sequencing working face between different mines or different working faces in the same mine. In order to solve this problem, by investigating the mining conditions of Shendong Group's fully mechanized mining equipment in recent three years, the key parameters such as working face, equipment, personnel, and time are defined, which characterize the fully mechanized mining equipment removal during sequencing working face. Taking minimizing the maximum completion time as the objective function, a mathematical model for the fully mechanized mining equipment removal planning during sequencing working face is established. A genetic algorithm is designed to solve the mathematical model. The three-segment coding method considering the selection of working face, fully mechanized mining equipment and construction team is adopted, and the fitness function is built. The chromosomes of working face, fully mechanized mining equipment and construction team are selected, crossed and mutated. Considering the latest mining time, the legitimacy of chromosomes is judged and adjusted. By setting the number of iterations, search process of the algorithm is terminated and outputs the results. Based on the genetic algorithm for the fully mechanized mining equipment removal planning during sequencing working face, a management system of the fully mechanized mining equipment removal plan during sequencing working face based on B/S architecture is developed. It has realized the functions of basic information management of fully mechanized working face removal during sequence working face, and fully mechanized mining equipment removal planning during sequencing working face. The example shows that the application of genetic algorithm can shorten the construction period of fully mechanized mining equipment removal of 11 fully mechanized working faces in Shendong Group in 2021 from 103 days to 91 days. The method effectively improves the fully mechanized mining equipment removal planning efficiency and engineering efficiency.
Real-time 3D mapping method of fully mechanized working face based on laser SLAM
QI Yuhao, GUAN Shiyuan
2022, 48(11): 139-144. doi: 10.13272/j.issn.1671-251x.2022060047
<Abstract>(205) <HTML> (43) <PDF>(45)
Abstract:
The mobile mapping method relies on fiber optic inertial navigation with high precision and odometer to calculate the position and attitude. But in the actual engineering practice, the precision of odometer is difficult to meet the application requirements, resulting in incomplete 3D laser point cloud of working face. In order to solve this problem, a real-time 3D mapping method of fully mechanized working face based on laser SLAM is proposed. The method mainly comprises the steps of distortion removal of laser point cloud, feature extraction , position and attitude estimation and optimization mapping. The distortion of laser point cloud is eliminated through the inertial navigation data. The inertial navigation data is retrieved according to the time stamp of each point in the point cloud to obtain the attitude angle corresponding to each point. If the corresponding attitude angle is not retrieved, the quaternion method is adopted for interpolation. The geometric tensor feature of the point cloud is extracted by principal component analysis. Firstly, the covariance matrix of the point set is solved. Secondly, the eigenvalue decomposition is performed to obtain the geometric tensor feature. The distance between the feature points in two adjacent frames is calculated to construct an objective function. The Levenberg-Marquardt algorithm is used to solve the objective function and obtain the transformation matrix, so as to realize position and attitude estimation. The incremental optimization algorithm is adopted. The GTSAM optimization library is used for carrying out joint optimization on the historical keyframe and the current keyframe. All obtained keyframe point clouds are superposed together to obtain the global 3D real-time map. The results of the underground industrial test show that this method can construct the 3D map of the whole working face in real-time, completely and accurately. The maximum mean absolute error is 0.19 m, which meets the precision requirements of monitoring of fully mechanized working face and straightening of the scraper conveyor.

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