2021 Vol. 47, No. 3

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Status of intelligent coal technology research and policy development
HU Qingsong, QIAN Jiansheng, LI Shiyin, SUN Yanjing
2021, 47(3): 1-8. doi: 10.13272/j.issn.1671-251x.17708
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Abstract:
The current research on intelligent coal mines focuses on top-level design, theoretical architecture, core technologies, control platforms, construction routes, standard development and evaluation systems, etc. There is no research on the technical research before and after the release of The guiding opinions on accelerating the development of intelligent coal mines. Intelligent coal mines are technology-intensive and capital-intensive, and need to be guided by policies to form a joint force which brings the government, academia, industry, finance and users together. Using CNKI (China National Knowledge Infrastructure) as a tool, this paper analyzes the current status of intelligent coal technology research, hot spots of concern and major research institutions. This paper summarizes the current status of intelligent coal mine policy development at the national level and in major coal-producing provinces such as Guizhou, Shandong and Shanxi. Although there are slight differences in the starting point and implementation progress of intelligent coal mines, all provinces have issued guiding documents in terms of implementation methods and acceptance and grading, and have made attempts to develop standards and establish innovative R&D centers to accelerate the pace of intelligent coal mine research and construction.
Safety technical requirements and inspection methods of coal mine 5G communication system
ZHENG Xiaolei, LIANG Hong
2021, 47(3): 9-13. doi: 10.13272/j.issn.1671-251x.2021010066
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Abstract:
The 5G communication system used in coal mines must obtain a safety mark certificate. In order to better promote the intelligent construction of coal mines and the development of 5G technology in coal mines, help the companies to understand the technical requirements of coal mine 5G communication system and inspection requirements in advance, shorten the review period, this paper studies the safety technical requirements and inspection methods of coal mine 5G communication system. The safety technical requirements of coal mine 5G communication systems are discussed in terms of basic requirements, networking requirements, 5G communication technology requirements, explosion-proof safety requirements and anti-interference requirements. Moreover, relevant suggestions are made for the issues that need to be focused on. In terms of the coal mine 5G communication system, this paper focuses on the system networking mode discrimination, core network inspection and anti-interference performance inspection. In terms of 5G base stations and terminals, this paper focuses on the inspection of 5G technical indicators and explosion-proof safety requirements. The issues such as coal mine 5G communication system management issues, 5G base station multi-antenna power superposition threshold power calculation issues, anti-interference technical difficulties, the technical requirements and inspection methods of transmission rate and delay, the inspection of antenna-integrated base stations, and the problems of 5G upstream and downstream bandwidth in mines are also discussed in this paper.
The stress distribution of coal and rock mass and the risk evaluation of rock burst in the composite structure area
GAO Jiaming, XIA Yongxue, YANG Guangyu, CHEN Xuehui
2021, 47(3): 14-19. doi: 10.13272/j.issn.1671-251x.17686
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Abstract:
The abnormal concentration of stress in the structure area of the working face is closely related to the rock burst. Based on the good correlation between the wave velocity and stress of the coal and rock mass, the seismic wave CT detection technology is widely used in the evaluation and warning of the risk of rock burst. However, the existing researches are mainly focused on simple conditions or single structure area. The composite structure area formed by multiple geological structures has not been discussed. Taking the 2305 fully mechanized working face of a mine in Shandong as an example, in the context of the concentration of microseismic events in the composite structure area and the occurrence of rock bursts, the seismic wave CT detection technology is used to obtain the stress distribution of coal and rock mass in the structure area and the characteristic index c including speed and speed gradient of seismic wave is introduced to analyze the stress distribution characteristics and the risk of rock burst. The research results show that: ① The concentration of microseismic events is related to adjacent structures. The concentrated areas can be divided into single or composite areas according to the number of structures, and can be divided into strongly or weakly disturbed areas according to the degree of disturbance. ② The strongly disturbed area composite structure area has the highest static stress concentration, and the weakly disturbed single structure area has the lowest stress concentration. The stress increase level of the fault single structure area is positively correlated with the degree of disturbance. The syncline in the weakly disturbed composite structure area has a significantly higher stress than the fault area. ③ The static stress concentration of the surrounding rock in the structure area is the main cause of intense surrounding rock activity. Based on the static load of surrounding rock and the dynamic load increment under the influence of mining, the risk of rock burst in the concentrated area of different microseismic events can be obtained.
Research on mining damage characteristics of roadway support structure in high stress area
CHANG Lizong, SU Xuegui, DU Xianjie, YANG Pengbo, GUO Maomao
2021, 47(3): 20-26. doi: 10.13272/j.issn.1671-251x.2020100043
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Abstract:
There are fissures in the surrounding rock of the roadway in the high stress area, and the support structure is more likely to develop instability and damage due to mining. At present, there are few researches on the damage evolution characteristics of the roadway support structure during mining. In the context of the geological characteristics of the 3316 working face in Shuangliu Coal Mine, the dynamic evolution characteristics of the support structure of the 3316 extraction roadway under the influence of mining are studied using the methods of mine dynamic load measurement, in-situ detection and numerical simulation. The results show that: ① Mining has significant influence on the dynamic load of the roadway support structure in the high stress area, and the mining influence enhancement factor reaches 2.1-5.8, resulting in some anchors (cables) reaching the yield limit or even breaking. Therefore, there are risks of instability and damage of the roadway surrounding rock. ②The increase of mining stress causes the expansion of secondary fissures inside the surrounding rock, which are concentrated in the range of 0-2.44 m. The coefficient of fissure expansion of the surrounding rock affected by mining is 1.92-2.54. The cohesion of the surrounding rock is reduced, which accelerates the damage of support structure and leads to the increase of roadway deformation. ③The influence of mining on the damage of the support structure has significant time-dependent characteristics. The force of the support structure at the working face over 10-70 m from the measurement point is most influenced by mining, and the force of the two sides of the support structure is asymmetric. By optimizing the key support parameters such as anchor strength, anchor cable diameter and inter-row spacing, the support strength can be effectively improved. The deformation value of the roadway roof is 146 mm, which is 71.7% less than the deformation before optimization, and the stability control of the roadway surrounding rock is obtained.
Research on the variation law of coal electrical parameters under different temperatures and detection frequencies
ZHENG Xuezhao, XU Chengyu, WANG Baoyuan, YANG Wei, CHEN Yineng, ZHANG Duo
2021, 47(3): 27-33. doi: 10.13272/j.issn.1671-251x.17689
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Abstract:
Most of the existing research on the influencing factors of coal electrical parameters are carried out under fixed frequency or low frequency and high temperature conditions. There is a lack of research on the influencing factors of coal electrical parameters under ultra-wideband frequency and normal temperature conditions. Therefore, there are certain limitations in the research on the influencing factors of coal electrical parameters. To address this problem, in order to study the influence of coal electrical parameters on the selection of ultra-wideband radar wave mine detection frequency band under different conditions, the Concept-80 ultra-wideband dielectric spectrum test system is applied. The system is set to detect the relative permittivity, electrical conductivity and loss angle tangent values of coals with different metamorphic degrees (lignite, long-flame coal and lean coal) at 0-80 ℃ and 500-1 000 MHz detection frequency and analyze their variation laws. The analysis results show that the relative permittivity of coal increases with the increase of coal metamorphic degrees, and the electrical conductivity and loss angle tangent values of coals decrease with the increase of coal metamorphic degrees. The relative permittivity of coal samples with different metamorphic degrees decreases, then increases and decreases again with the increase of detection frequency, and the relative permittivity is positively correlated with temperature. The electrical conductivity is positively correlated with both detection frequency and temperature. The loss angle tangent value is positively correlated with temperature. However, the detection frequency does not reflect the variation of loss angle tangent value well. Based on the variation law of three electrical parameters at different temperatures and detection frequencies, and the comprehensive comparison of influence of three electrical parameters on the transmission of ultra-wideband radar waves in coal, it is pointed out that the frequency of 550-650 MHz performs best when using 500-1 000 MHz ultra-wideband radar waves to penetrate the coal body for detection at 0-80 ℃, and the detection frequency should decrease as the temperature increases. The conclusion of this study can provide a reference for the selection of ultra-wideband radar wave mine detection frequency band under different conditions.
Research on gas sensitive mechanism of low concentration methane threshold based on micro-nano ionization sensor
LIU Changyi, ZHANG Jingyuan, HUANG Xiangdong, ZHANG Ni, LI Jingbo, LIU Jie
2021, 47(3): 34-40. doi: 10.13272/j.issn.1671-251x.2020110067
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When the concentration of CH4 is low, the current detection methods for CH4 in coal mines have problems such as low sensitivity and slow response. The existing research on micro-nano ionization gas sensors mainly focuses on the detection of high concentration gas, and the simulation model used is a one-dimensional simplified discharge model, ignoring the lateral drift and diffusion of N2 and CH4 molecules and ions generated by ionization. For low-concentration CH4 gas, the accurate detection of the sensors needs to be further verified. In order to solve the above problems, the lateral drift and diffusion of ions are considered on the basis of existing studies and a plasma module is added. A two-dimensional discharge model of CH4-N2 mixed gas at room temperature and pressure in the micro-nano field domain is established by using a fluid-chemical dynamics hybrid method. The model analyzes the safe discharge voltage, gas sensitivity and the relationship between the discharge current density and the CH4 concentration of CH4-N2 mixed gas at room temperature and pressure. The analysis results show that the safe discharge voltage of the ionization sensor is 200 V and the signal-to-noise ratio is high. In CH4-N2 mixed gas, CH4 inhibits the ionization process of N2. The output current density of the ionization sensor decreases linearly with the increase of CH4 concentration (0.25%-1.5%), which reflects the sensitive characteristics of the ionization sensor to low concentration of impurity gas. Hence, the detection of low concentration CH4 can be achieved by using the monotonously decreasing linear relationship between CH4 concentration and the current density.
Construction and application of knowledge graph for coal mine equipment maintenance
CAO Xiangang, ZHANG Mengyuan, LEI Zhuo, DUAN Xinyu, CHEN Ruihao
2021, 47(3): 41-45. doi: 10.13272/j.issn.1671-251x.2020090013
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Abstract:
Using big data management system to manage coal mine equipment maintenance information lacks the ability to express coal mine equipment maintenance knowledge, has not formed a relatively complete coal mine equipment maintenance knowledge management system, and cannot realize knowledge mining and inter-knowledge relationship links, resulting in a large number of information with in-depthmining value cannot be usedeffectively.In order to solve the above problems, a coal mine equipment maintenance knowledge graph is constructed. Firstly, ontology-based knowledge modeling is carried out by defining key concepts, relationships and attributes of coal mine equipment maintenance. Secondly, obtain knowledge from structured, semi-structured and unstructured data sources, and coal mine equipment maintenance knowledge extraction is completed through named entity identification, relationship extraction and event extraction.Finally, the graph databaseNeo4jis used to store coal mine equipment maintenance knowledge and form a coal mine equipment maintenance knowledge graph.The application of coal mine equipment maintenance knowledge graph in intelligent semantic search, intelligent question and answer and visualization decision support can improve the efficiency of coal mine equipment maintenance knowledge management and provide strong support for the realization of intelligent dynamic management of coal mine equipment.
Coal mine mobile robot positioning method based on fusion of vision and inertial navigatio
ZHANG Yufei, MA Hongwei, MAO Qinghua, HUA Hongtao, SHI Jinlong
2021, 47(3): 46-52. doi: 10.13272/j.issn.1671-251x.2020110049
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Abstract:
The existing mobile robot monocular vision positioning algorithm performs poorly in illumination changing and weak illumination areas, and cannot be applied to dark scenes in coal mines. In order to solve these problems, the oriented FAST and rotated BRIEF (ORB) algorithm is improved by non-maximal value suppression processing and adaptive threshold adjustment. The random sample consensus (RANSAC) algorithm is used for feature point matching, which improves the efficiency of feature point extraction and matching in weak illumination areas of coal mines. In order to solve the problem that the distance between the robot and the object and the size of the object cannot be determined by monocular vision positioning alone, the epipolar geometry method is used to visually calculate the matched feature points, and the inertial navigation data is used to provide scale information for monocular visual positioning. Based on the tight coupling principle, the graph optimization method is applied to fuse, optimize and solve the inertial navigation data and monocular visual data so as to obtain the robot pose information. The experimental results show that: ① Although the number of feature points extracted is small, the ORB algorithm takes less time. The feature points, which are evenly distributed, can accurately describe the object features. ② Compared with the original ORB algorithm, the improved ORB algorithm has a certain increase in extraction time. However, the number of available feature points extracted is also greatly increased. ③ The RANSAC algorithm eliminates the mismatched points and improves the accuracy of feature point matching, thus improving the accuracy of monocular vision positioning. ④ The accuracy of the improved fusion positioning method is greatly improved, the relative error is reduced from 0.6 m to less than 0.4 m, the average error is reduced from 0.20 m to 0.15 m, and the root mean square error is reduced from 0.24 m to 0.18 m.
Hybrid solution method for ultra-wideband positioning in coal mines
CHEN Meirong, WANG Kai, ZHANG Jiachun, XU Pengyuan
2021, 47(3): 53-59. doi: 10.13272/j.issn.1671-251x.17710
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Ultra-wideband positioning is based on the marked point distance measured by the base station and a set of non-linear positioning equations to obtain the precise device position by applying Taylor series expansion algorithm, Chan algorithm or least square solution. Among these algorithms, the Taylor series expansion algorithm has high solution accuracy, but has a strong dependence on the initial value. If the initial value is not selected properly, the algorithm will not converge. In order to solve the above problems, a hybrid solution (BSO-Taylor) method combining brain storm optimization (BSO) and Taylor series expansion is proposed. The BSO algorithm is used to solve the optimal solution for minimizing the error function from the mobile station to the base station. The time different of arrival(TDOA) value of the optimal individual is used as the initial value of the Taylor series expansion algorithm to carry out the Taylor expansion solution to obtain the positioning information.This method solves the problem that the Taylor series expansion algorithm requires better initial value. Moreover, the results of Chan algorithm, Taylor series expansion algorithm and the BSO-Taylor hybrid solution method are compared. The results show that the BSO-Taylor hybrid solution method obtains the iterative initial value close to the true position through the global search strategy. The method not only obtains the positioning performance close to the true value, but also solves the sensitivity of the Taylor series expansion algorithm to the bad initial values. Compared with the Chan algorithm, the solution of the BSO-Taylor hybrid solution method is more stable and more accurate. Compared with the Taylor series expansion algorithm, the initial position of which is the true position, the solution error of the BSO-Taylor hybrid solution is slightly larger. The effects of the variation of the positioning distance and the variation of the standard deviation of the TDOA measurements value on the Taylor series expansion algorithm and the hybrid BSO-Taylor solution method are basically the same. However, the effect on the Chan algorithm is greater.
Coal and gangue identification method based on EMD feature extraction and random forest
DOU Xijie, WANG Shibo, LIU Houguang, CHEN Qianyou, ZOU Wencai, LU Zhaodong
2021, 47(3): 60-65. doi: 10.13272/j.issn.1671-251x.2020100038
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Identification based on vibration signals is an effective method to realize coal and gangue identification in fully mechanized mining. The existing method needs to be further studied in terms of identification accuracy and effectiveness. A coal and gangue identification method based on empirical mode decomposition(EMD) feature extraction and random forest(RF) is proposed in this study. The acceleration sensor and data acquisition instrument are used to collect the vibration signals generated by the impact of coal and gangue on the tail beam of the hydraulic support in a fully mechanized working face. Then the two signals are processed by EMD respectively so as to obtain a series of intrinsic mode functions(IMF). The effective IMFs are selected according to the EMD results, and the IMF energy, kurtosis, matrix singular values and corresponding entropy are extracted as feature vectors. Each feature vector is used to train the RF model independently. The feature vectors are filtered according to the identification results of each RF model on the test samples, and the feature data set is established. The feature data set is used to train the RF model, and the trained RF model is applied to realize the coal and gangue identification. The test results show that the identification accuracy of the method reaches 96.5% for 200 sets of coal and gangue test samples, and the highest identification accuracy is achieved when the number of decision trees in the RF model is set to 100 or 150. Furthermore, the time consumed for feature extraction and identification of test samples is less than 0.2 s, which meets the requirements of accuracy and real time of coal and gangue identification in fully mechanized working face.
Research on face recognition method in working environment of coal preparation plant
GAO Hongjie, CONG Haoran, GUO Xiucai
2021, 47(3): 66-70. doi: 10.13272/j.issn.1671-251x.2021010047
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The face image information of coal preparation plant is easily affected by complex environmental factors, which makes the recognition difficult. In order to solve this problem, a face recognition method in working environment of coal preparation plant is proposed. The Gabor wavelet transform is applied to the normalized original face image of coal preparation plant to obtain characteristic maps of 8 directions and 5 scales. The method encodes with the improved AR-LGC coding algorithm, and characteristic fusion is performed on the encoded maps of different directions at the same scale to obtain the fused characteristic map of the image. The fused characteristic map is divided into multiple sub-blocks, and the histogram characteristic vector H is obtained by counting the block histogram and weighting cascade. Then H is trained in residual neural network to realize the face recognition of the personnel in the coal preparation plant. The improved AR-LGC coding algorithm enhances the texture correlation of face images in coal preparation plant, solves the problem of insufficient image texture correlation, retains more important characteristics in face images while weakening the interference characteristic, and alleviates the problem of personnel faces being polluted by coal ash. The experimental results show that when the faces of coal preparation plant are polluted by coal ash, the characteristics extracted by the improved AR-LGC coding algorithm retain the local characteristics coarse granularity and have better noise immunity. The recognition rate of the improved AR-LGC coding algorithm is 94.5% and the average time consumed is 0.933 0 s. Compared with similar algorithms, the recognition rate of this algorithm has improved under the condition of sacrificing part of the time performance, and the sacrificed time performance is acceptable.
Fault diagnosis of shearer rocker gear based on deep residual network
LI Changwen, CHENG Zeyin, ZHANG Xiaogang, DING Hua
2021, 47(3): 71-78. doi: 10.13272/j.issn.1671-251x.2020110043
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The traditional shearer rocker gear fault diagnosis methods cannot extract features autonomously, resulting in poor gear fault diagnosis accuracy and efficiency. In order to solve the above problems, a fault diagnosis model of shearer rocker gear based on deep residual network (ResNet) is constructed. By pre-activating the residual unit module, the complexity of the model is reduced so as to make the model converge faster. By reorganizing the vibration signal data, the data input method is optimized so as to improve the model's ability to identify the fault of shearer rocker gear. Model verification tests are carried out on the rocker gear loading test bench of shearer to collect vibration signals of the rocker spur gears under five states of normal, worn, fractured, pitting and cracked. It is concluded that there are significant differences in their characteristics. The visual analysis of the confusion matrix of the test set verifies that the ResNet model can realizeshearer rocker gear fault classification well. Moreover, the comparison results with the DNN model and LeNet-5 model show that the ResNet model has higher fault diagnosis accuracy and efficiency. The comprehensive recognition rate and F-score reach 99.19% and 99.05% respectively. The t-SNE technology is used to reducedimension and visualize the high-dimensional features output from the maximum pooling layer, the pre-activated residual unit module and the fully connected layer of the ResNet model, which verifies that the ResNet model has strong feature extraction capability.
Intelligent safety monitoring and predictive maintenance system for mining equipment
XU Chang, WANG Daoyuan, LI Jingzhao, CHEN Zihua
2021, 47(3): 79-82. doi: 10.13272/j.issn.1671-251x.17688
<Abstract>(102) <HTML> (13) <PDF>(12)
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In order to solve the problems of low accuracy of monitoring data, weak data analysis capabilities, and low intelligenceof existing mining equipment, an intelligent safety monitoring and predictive maintenance system for mining equipment is proposed.The system preprocesses mining equipment safety monitoring data through an intelligent data pre-selection model based on the simulated annealing algorithm, filters out the data within the threshold range so as to eliminate outliers in the data. The intelligent safety predictive maintenance model based on convolutional neural network is used to perform dual-channel convolution using convolutional kernels of different sizes to diversify the extracted filtered data characteristics. By comparing them with the mining equipment historical status sample data, it is able to judge the mining equipment operation status and then provide corresponding equipment predictive maintenance solutions.The experimental results show that the system has high accuracy in judging the operation status and predictive maintenance of mining equipment.
Damage identification of broken wires inside and outside the wire rope
DOU Liancheng, ZHAN Weixia, BAI Xiaorui
2021, 47(3): 83-88. doi: 10.13272/j.issn.1671-251x.2020090025
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The wire rope broken wire damage detection is mainly focused on the outside broken wire damage detection, not on the inside broken wire damage detection. Moreover, the inside and outside broken wire identification accuracy is not high. In order to solve the above problems, this paper proposes a wire rope inside and outside broken wire damage identification method. The magnetic flux leakage signal generated by the wire rope broken wire damage is collected by the wire rope damage radial magnetic flux leakage detector. The double-density dual-tree complex wavelet transform is used to reduce the noise of the magnetic flux leakage signal. By setting an adaptive threshold, it is able to extract the time domain characteristics of the noise reduction signal and extract the frequency domain characteristics of the original magnetic flux leakage signal at the same time. A method based on the distance between classes and mutual information is used for characteristics selection. Firstly, all characteristics are normalized to eliminate characteristics with large standard deviation and small distance between classes. Secondly, the mutual information between characteristics is calculated to exclude characteristics with similar damage information. Thirdly, the two damage types with the worst discrimination among the characteristics are calculated, and the characteristics with the largest distance between these two types are retrieved from the eliminated characteristics. The retained characteristics are fused as the optimal characteristic subset and input into the BP neural network for classification and recognition. The test results show that the method can identify broken wire damage inside and outside the wire rope with a recognition accuracy of 97.8%.
Movement law of overburden in upward fully mechanized working face and determination of support working resistance
GUO Yufeng, PU Shijiang, FU Wei, LIANG Wenxu
2021, 47(3): 89-94. doi: 10.13272/j.issn.1671-251x.2020060048
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The overburden movement law of the upward fully mechanized working face is different from that of the horizontal working face. The roof collapses frequently, which brings a great test to the stability of the working face support. The existing research on the safe mining of the upward fully mechanized working face mainly focuses on the influence of the change of the mining angle on the overburden movement law, and there is a lack of systematic research on the force characteristics of the roof and floor. In order to solve the above problems, taking the 8102 upward fully mechanized working face in Ruilong Mine as the background, the UDEC numerical simulation software is used to analyze the overburden movement law and roof fracture characteristics at different advancing distances of the upward fully mechanized working face. In the upward working face, affected by the inclination angle and mining method, the bottom coal stress is most concentrated, and the working face has obvious characteristics of initial pressure and periodic pressure. Compared with the nearly horizontal fully mechanized working face, the periodic pressure step is obviously reduced, the peak strength of the overburden is relatively low, the roof is not easy to form a structure, the pressure is more frequent, and the mine pressure appears more intense. The step distance of the first collapse of the direct roof of 8102 working face is 25 m, the step distance of the initial pressure of the basic roof is 40 m, and the step distance of the periodic pressure is 10-15 m. The existing calculation methods of using the roof-support mechanical relationship to determine the working resistance of support are relatively cumbersome. Many methods are not practical when applied to the engineering site. By comparing the advantages, disadvantages and practicality of several commonly used support working resistance calculation methods, it is concluded that the dynamic load calculation method is the most suitable method for calculating the working resistance of the support according to the actual working conditions of 8102 working face. The method has the characteristics of accurate calculation results and easy selection of parameters. It is determined that the maximum working resistance of the support of this working face is 6 359 kN/frame, and the support of this working face with a working resistance greater than 7 066 kN can meet the support requirements. The results of engineering application have proved the correctness of using dynamic load calculation method to calculate the working resistance of the support of 8102 working face.
Research on frequency stability of magnetic coupling wireless power transfer system
ZHANG Lian, JING Tingwei, ZHANG Lu, LI Mengtian, YANG Kai
2021, 47(3): 95-100. doi: 10.13272/j.issn.1671-251x.2020080090
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The natural oscillation frequency of the magnetic coupling wireless power transfer system is easily affected by circuit parameters and frequency bifurcation phenomenon often occurs. In order to solve the above problems, the equivalent circuit model of magnetic coupling wireless energy transfer system is used to derive the load resistance and transfer distance range that can maintain the frequency stability of the system. Furthermore, the load resistance range under which the system frequency stability is not affected by the transfer distance is obtained. In this range, the system always maintains a constant frequency and the natural oscillation frequency is not affected by the transfer distance. Moreover, the theoretical analysis is verified by simulation and experiment. The results show that the increase of load resistance and transfer distance will reduce the output power and transfer efficiency of the system to a certain extent. Therefore, in the context of maintaining the frequency stability of the system, the output power and transfer efficiency should be considered, and the appropriate load resistance and transfer distance should be selected so as to prevent the output power and transfer efficiency of the system from being too low.
Two-dimensional laser scanner based coal mine shaft guide deformation detection device
NIU Weifeng, ZONG Liangliang, WU Feng, TU Shiyu, AI Lingyun, HU Wenbin, GAN Weibing
2021, 47(3): 101-104. doi: 10.13272/j.issn.1671-251x.2021010018
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In order to solve the problems of low accuracy and cumbersome operation of traditional coal mine shaft guide deformation detection methods, a coal mine shaft guide deformation detection device based on 2D laser scanner is designed. When the cage moves linearly along the shaft guide, the pulley that connected to the cage through the support arm and closely attached to the shaft guide rotates, driving the coaxial mileage encoder that closely connected with the pulley to rotate and generate a trigger signal. Then the controller receives the trigger signal and starts the 2D laser scanner fixed to the cage. The 2D laser scanner emits laser to scan the shaft guide, and the laser projected on the surface of the shaft guide is diffusely reflected and then received by the 2D laser scanner to obtain the scan data of the shaft guide. The scan data of the shaft guide is transmitted to the controller, and the host computer calls the scan data in the controller and processes it. It can draw the shaft guide outline online and the 3D model of the shaft guide offline to obtain the gap width between the two shaft guides, the misalignment value in each direction and the wear amount of a single shaft guide. Therefore, the deformation of the shaft guide is determined. The experimental results show that the maximum absolute error of the device is 0.6 mm, and the maximum relative error is 10%. The device has the characteristics of high measurement accuracy, simple operation and simple structure.
Research and application of intelligent caving technology in fully mechanized working face
WU Tong, YU Rui, LIU Qing, WEI Wenyan
2021, 47(3): 105-111. doi: 10.13272/j.issn.1671-251x.2020100020
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The traditional top coal caving control mainly relies on manual coal caving control, which uses single-round of sequential coal caving. The working face equipped with electro-hydraulic control system mainly adopts the two-round sequential coal caving method combining program control and manual supplementary caving. If the implementation of coal releasing control is not sufficient, the coal recovery rate will be greatly reduced. If a large amount of gangue is mixed in the coal caving process, the coal mining quality will be significantly reduced. In order to solve the above problems, the intelligent coal caving technology in fully mechanized working face is studied. By analyzing the automatic caving process of fully mechanized working face, it is pointed out that in order to realize the intelligent caving process, it is necessary to upgrade shearer, hydraulic support, scraper conveyor and other equipment of the fully mechanized working face on the basis of the automatic caving process. By installing audio and video monitoring system on the fully mechanized caving support, it is able to monitor whether there are large pieces of coal blocking the coal caving opening, affecting the top coal caving. By installing motor current monitoring system at the rear scraper conveyor, it is able to realize automatic control of coal flow. At the same time, the system includes manual intervention functions, such as supplement and parking functions. By installing ash detection system at the end of the belt conveyor, it is able to analyze online whether the ash is increasing. By installing the vibration sensor-based coal and gangue identification device on the fully mechanized caving support, it is able to identify whether there is serious mixed gangue according to the amount of gangue falling based on the vibration sensor data. Combined with the intelligent coal caving process, the intelligent coal caving scheme is customized for the Wangjialing Coal Mine 12309 fully mechanized working face. Based on the automatic sequential coal caving and interval coal caving process, the gangue identification control of vibration signal and the manual coal caving flow control technology, the intelligent coal caving of this working face is obtained. Moreover, the actual application results have verified the effectiveness of the intelligent coal caving technology.
Discussion on prevention and control experience of coal spontaneous combustion in 8.8 m super large mining height working face
LI Yufu
2021, 47(3): 112-118. doi: 10.13272/j.issn.1671-251x.2020110051
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The monitoring data of coal spontaneous combustion in super large mining height working face includes gas concentration, temperature, etc. The research methods include experiments, numerical simulations, on site measurements, etc. Most of the existing studies have not considered the relationship between the indicators, and the research methods and data analysis methods are single. In order to solve this problem, taking the working face with a mining height of 8.8 m in Shangwan Coal Mine as an example, this paper analyzes the correlation between gas concentration and temperature in the process of coal spontaneous combustion and summarizes the law and characteristics of fire in the goaf area through a combination method of coal spontaneous combustion experiment, on-site "three zone" measurement and numerical simulation. A surface borehole nitrogen injection model is established, and the changes of O2 concentration field, CO concentration field, temperature field and "three zone" distribution before and after the nitrogen injection measures are inverted. For the high temperature abnormal area, the nitrogen injection location is selected according to the numerical simulation results. And the combined method of surface nitrogen injection and mine nitrogen injection is adopted to reduce the fire hazard. The research results show that CO can be used as an indicator gas for predicting coal spontaneous combustion, O2 and CH4 cannot be used as indicator gases, and C2H6, C2H4, C2H2 and H2 can be used as auxiliary indicator gases. After the nitrogen injection measures are taken, the width of the oxidation heating zone is greatly reduced, and the CO volume fraction is significantly reduced. The highest temperature drops rapidly and the inerting effect is significant. The CO volume fraction and temperature in the high temperature abnormal area show a gradual decrease trend, which verifies the rationality of the nitrogen injection location and the effectiveness of nitrogen injection measures.

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