工矿自动化

Research and formulation of coal mine information comprehensive bearer network standards

SUN Jiping, PENG Ming

2024, 50(4): 1-8. doi: 10.13272/j.issn.1671-251x.18185

<Abstract>(206) <HTML> (45) <PDF>(46)

Abstract:
In order to meet the different requirements of coal mine monitoring, positioning, video, audio, remote control, 5G and other services for latency, reliability, bandwidth and other indicators, the coal mine information comprehensive bearer network should have the following functions. ① The network slicing function supports FlexE interface technology or channelized sub interface technology, and divides port bandwidth resources into different network slices. The services between different network slices are isolated and carried by each other without affecting each other. ② The online bandwidth expansion function of network slicing ensures that there is no packet loss during the bandwidth adjustment process. ③ Network slices set the power-off protection function. ④ The network complies with IEEE 802.3 and TCP/IP protocols, supports IPv6 protocol and IPv6 service bearer, and supports both IPv4 and IPv6 services simultaneously. ⑤ The network supports 10GE optical interface, 1GE optical interface, 10/100/1000 Mbit/s adaptive interface. The core and aggregation nodes should support optical interfaces of 50GE or above. ⑥ It is advisable to use a circular or double ring structure. ⑦ The real time monitoring function for business quality, monitors the delay, jitter, and packet loss rate of specified services in real time. ⑧ 1588v2 clock synchronization function supports 5G base station service access. The main technical indicators of the coal mine information comprehensive bearer network should meet the following requirements. ① The optical port transmission distance should be ≥ 20 km. The transmission distance of the electrical port should be ≥ 100 meters. ② The transmission rate of the backbone network should be ≥ 10 Gbit/s. The transmission rate of the access network should be ≥ 1 Gbit/s. ③ The packet loss rate for different frame lengths is ≤ 0.01% (under 70% network traffic load conditions). ④ The single node transmission delay should be ≤ 1 ms (when the Ethernet frame length is 1518 bytes). ⑤ Node forwarding jitter should be ≤100 μs. ⑥ The number of slices supported by a single interface should be ≥ 5. ⑦ The minimum bandwidth of FlexE interface technology should be ≤1 Gbit/s. The minimum bandwidth of channelized sub interface technology should be ≤ 2 Mbit/s. ⑧ The self-healing time of network reconstruction should be ≤ 50 ms. ⑨ After a power outage in the power grid, the continuous working time of the bearer network equipment under standby power supply should be ≥ 4 hours.

A mine image stitching method based on improved best seam-line

ZHANG Xuhui, WANG Yue, YANG Wenjuan, CHEN Xin, ZHANG Chao, HUANG Mengyao, LIU Yanhui, YANG Junhao

2024, 50(4): 9-17. doi: 10.13272/j.issn.1671-251x.2023120003

<Abstract>(151) <HTML> (70) <PDF>(29)

Abstract:
The harsh environment of high dust and low lighting in the coal mine underground excavation working face results in low signal-to-noise ratio of the image, and a serious reduction in the number of effective feature points. The processed image has significant color difference and noise. When using the best seam-line algorithm for image stitching, there are problems such as fine section misalignment, unnatural transitions at the seam line, or obvious stitching traces. In order to solve the above problems, a mine image stitching method based on improved best seam-line is proposed. Firstly, the original image is subjected to HSV spatial transformation, and an improved Retinex algorithm is used for enhancement on the luminance component. Bilateral filtering is used instead of the center surround function to solve the halo problem caused by large brightness differences. The number of feature points extracted is effectively increased through the enhancement algorithm. Secondly, the SIFT algorithm is used to extract feature points, and cosine distance is used as the matching degree indicator. The method introduces pixel cosine similarity as a constraint, and uses morphological operations to improve color difference intensity, uses dynamic programming to search for the best seam-line to avoid misalignment at image stitching. Finally, combined with the gradual in and out algorithm, the image transition is smooth to achieve image fusion of the underground excavation working face. Experimental verification is conducted by simulating the actual working environment underground. The results show that the mine image stitching method based on the improved best seam-line avoids the phenomenon of misalignment stitching caused by color differences and noise compared to the traditional best seam-line algorithm. The image transition at the stitching seam is more natural, avoiding the generation of 'ghosts' and obvious stitching seams. The average gradient of the image is increased by about 2.38%, and the stitching time is increased by about 32.5%, making the fusion area smoother and more natural, improving the stitching quality.

Research progress on monitoring, early warning, and prevention and control technologies for coal-rock-gas composite dynamic disasters

YANG Ke, LI Caiqing, LIU Wenjie, ZHANG Zhainan

2024, 50(4): 18-32. doi: 10.13272/j.issn.1671-251x.18187

<Abstract>(290) <HTML> (65) <PDF>(54)

Abstract:
Coal-rock-gas composite dynamic disaster is a major safety hazard in deep coal mining. Exploring its disaster mechanism and developing monitoring, early warning, prevention and control technologies are key to prevention and control. Therefore, the 'tetrahedral' theory for the prevention and control of coal-rock-gas composite dynamic disasters is proposed. The theory summarizes the research progress of coal-rock-gas composite dynamic disasters from four levels: disaster classification, disaster mechanism, disaster warning, and disaster prevention and control. The study summarizes the basis for classifying the type of composite dynamic hazard based on the main body of energy release, initial gas pressure, and loading conditions. The study reviews the research progress on composite dynamic disaster mechanisms at both theoretical and laboratory scales. It is found that stress paths, dynamic evolution of microcracks, and critical indicators of geological factors associated with coal and rock occurrence are key to the study of disaster mechanisms. The paper summarizes the research progress of composite dynamic disaster monitoring and early warning technology, with the main focus on early disaster precursor information identification, mid-term disaster precursor information collection, and integrated monitoring and early warning of later disasters. The study reveals the scientific connotation of integrated prevention and control technology for energy dissipation and disaster reduction in composite power disasters, as well as key technologies for multi-scale and multi-source prevention and control. On this basis, based on the features of disasters in the Lianghuai Mining Areas, intelligent identification and warning methods for composite dynamic disasters under deep strong dynamic load conditions and zoning collaborative prevention and control methods are proposed. Finally, based on current research progress, urgent issues in the study of coal-rock-gas composite dynamic disasters are proposed to help achieve safe, precise, and efficient mining of deep coal.

UWB based measurement system for pushing progress of fully mechanized working face

LIU Qing, LIU Junfeng

2024, 50(4): 33-40. doi: 10.13272/j.issn.1671-251x.2023120024

<Abstract>(119) <HTML> (26) <PDF>(34)

Abstract:
A real-time measurement system for the pushing progress of fully mechanized working face based on UWB ranging technology is proposed to address the problems of existing measurement and calculation methods, such as time-consuming, labor-intensive, large cumulative errors, and inability to recalculate after sensor damage. The system adopts a combination of mining intrinsic safety distance measurement substation and distance measurement marker card, and achieves real-time measurement of the progress of roadway pushing in the fully mechanized working face through wireless communication. At the end of the fully mechanized working face, a distance measuring sub station is arranged on the hydraulic support, and a distance measuring mark card is hung at the fixed marking point of the mining roadway. The distance is measured through UWB wireless signal in the roadway. When the mining is about to reach the nearest distance measuring mark card position, the distance measuring mark card is removed. The subsequent distance measuring mark card is replaced to measure and calculate the progress of the roadway pushing, so as to continuously replace the measurement. Based on the coal mining technology, a limited amplitude median average filtering model is established based on the position of the shearer and the action of the hydraulic support. This model deeply integrates limited amplitude filtering, median filtering, and arithmetic mean filtering to eliminate invalid data with large measurement deviations caused by measurement and occlusion in massive data. At the same time, the maximum and minimum deviation data in the effective data are eliminated, further ensuring the accuracy and effectiveness of the measurement values obtained through arithmetic mean operation. The continuous measurement of the progress of the fully mechanized working face is achieved. The ground test results show that the maximum error of ranging substation 1 is 0.32 m, and the proportion of errors less than 0.2 m is 84.62%. The maximum error of distance measurement substation 2 is 0.48 m, and the proportion of errors less than 0.2 m is 76.92%. The industrial underground test results show that the difference between the daily average advance degree of the system and the measured data of the coal mine is 0.13 m. The result proves the feasibility of UWB ranging technology in underground roadway conditions and the accuracy of the pushing progress measurement model based on coal mining technology.

Measurement system for key attitude parameters of hydraulic support

LIU Xiangtong, LI Man, SHEN Siyi, CAO Xiangang, LIU Junqi

2024, 50(4): 41-49. doi: 10.13272/j.issn.1671-251x.2023120006

<Abstract>(186) <HTML> (44) <PDF>(28)

Abstract:
The existing hydraulic support attitude monitoring methods have the problems of incomplete measurement parameters, low precision and reliability, and poor adaptability to working conditions. In order to solve the above problems, a key attitude parameter measurement system for hydraulic supports is proposed. The system combines direct and indirect measurement. An attitude sensor with DSP as the core, MEMS inertial navigation as the measurement element and with LoRa wireless function, is developed. The key parameters affecting the support attitude of hydraulic supports are analyzed. The parameters include the angle between the base, front connecting rod, cover beam, and top beam and the horizontal plane, as well as the displacement distance, which are directly measured. The support height, column, and balance jack length are indirectly measured. The system includes four attitude sensors installed on the base, front connecting rod, cover beam, and top beam, as well as one infrared laser ranging sensor installed on the base. The system is networked using LoRa wireless communication. The attitude sensor at the base serves is used as a gateway (i.e. gateway sensor) to measure the angle between the base and the horizontal plane, control the infrared laser ranging sensor to measure the displacement distance, and calculate the support height, column length, and balance jack length. The other three attitude sensors are served as nodes (i.e. node sensors) to measure the angle between the front connecting rod, cover beam, and top beam and the horizontal plane, and report the obtained angle information to the gateway sensor. The test results show that the maximum absolute error of attitude angle measurement is 0.2°. The maximum percentage relative errors of support height, column length, and balance jack length measurement are 0.78%, 0.72%, and 0.83%, respectively. The maximum absolute error of displacement step measurement is 1.9 mm. Taking the ZY9000/22/45D hydraulic support as an example, the error distribution under different attitude angle ranges is analyzed. The maximum measurement error of the support height is 27.4 mm, and the maximum measurement error of the column length is 16.6 mm.

Design of mine opposed wind speed and direction sensor

AN Sai, ZHAO Zhonghui, ZHANG Lang, LI Wei, PENG Ran

2024, 50(4): 50-54. doi: 10.13272/j.issn.1671-251x.2024010055

<Abstract>(140) <HTML> (31) <PDF>(23)

Abstract:
In response to the current problems of high startup wind speed, complex design schemes, and inability to accurately measure the average wind speed of the entire section of the roadway using wind speed sensors, based on the principle of ultrasonic opposed wind measurement, a mine opposed wind speed and direction sensor with STM32 as the core is designed. The overall structure of the sensor, the design of the transmitting and receiving circuit, the filtering algorithm, and the software process are introduced. This sensor has changed the wind measurement method from point to surface, using a single ARM core and measuring the wind speed at the centerline of the roadway through long-distance (5-12 m) ultrasonic wind measurement technology. This wind speed represents the average wind speed of the entire roadway. It greatly improves the accuracy and real-time performance of roadway wind speed measurement. A test prototype is developed based on the design scheme, and the test results in a circular wind tunnel show that the measured values of the sensor has good consistency with the standard wind speed values in the range of 0.1-15 m/s, with a measurement error of less than 0.1 m/s. It can meet the precision requirements of intelligent mines for roadway wind speed measurement.

A method for detecting dust particles in excavation working face based on image analysis

GONG Xiaoyan, FENG Hao, FU Haoran, CHEN Long, CHANG Huqiang, LIU Zhuangzhuang, HE Zilun, PEI Xiaoze, XUE He

2024, 50(4): 55-62, 77. doi: 10.13272/j.issn.1671-251x.2023100074

<Abstract>(185) <HTML> (44) <PDF>(47)

Abstract:
Based on the principle of light scattering, measuring dust concentration can only be done manually at fixed times and locations, with poor real-time performance. It can only detect dust mass concentration and cannot provide a range of particle size distribution. At present, research on dust particle detection based on image analysis mainly focuses on unilateral research on dust mass concentration or particle size distribution. It cannot achieve simultaneous detection of dust mass concentration and particle size distribution range. In order to solve the above problems, a method for detecting dust particles in excavation working face based on image analysis is proposed. It explores the relationship between image features and dust mass concentration and particle size distribution. By using a dust sample collection and image acquisition device, dust particle images are collected and the dust mass concentration at the time of image acquisition is obtained. An image processing algorithm for dust samples, is developed to extract parameters related to grayscale features, texture features, and geometric features of the image. The correlation analysis between the extracted image features and the measured dust mass concentration is performed, and the image features with high correlation is selected as parameters to establish a regression mathematical model. The method extracts the number of pixels in the dust particle object. Combining with the conversion coefficient, the method calculates the size and distribution range of the dust particle based on the geometric equivalent area diameter. The experimental results show that the average relative error between the measured dust mass concentration and the calculated values of the established image feature multiple nonlinear regression model mathematical model is 12.37%. The maximum relative error between the standard measured particle size and the geometric equivalent area size obtained from the particle size distribution is 8.63%, and the average relative error is 6.37%. This verifies the accuracy of the image feature based dust mass concentration regression mathematical model and the geometric equivalent area diameter distribution mathematical model.

A mine image denoising algorithm based on improved trimmed mean

XIONG Zengju, YAO Chenggui, ZHANG Dehua

2024, 50(4): 63-68. doi: 10.13272/j.issn.1671-251x.2024010063

<Abstract>(133) <HTML> (49) <PDF>(18)

Abstract:
The existing mine image denoising algorithms have limited effectiveness in removing complex noise, and their processing speed cannot meet the requirements of real-time monitoring. In order to solve the above problems, a mine image denoising algorithm based on improved trimmed mean is proposed. Firstly, a trimmed mean filter is used to preliminarily filter out image noise, and a secondary inspection mechanism is introduced to handle residual noise points. By introducing discrete coefficients, the algorithm's capability to distinguish different pixels is improved, enhancing the denoising performance. Secondly, a classification processing and retesting mechanism based on the number of extreme values is adopted to effectively reduce the problem of residual noise. Thirdly, new control variables are introduced into the wavelet function to optimize the soft threshold function and hard threshold function, and a dual threshold function is constructed. The method combines with Radon transform to enhance the processing of linear features and enhance the detection capability of mine images. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for image quality evaluation. The experimental results show that compared to the trimmed mean algorithm, hard threshold algorithm, and soft threshold algorithm, the MSE growth of the mine image denoising algorithm based on the improved trimmed mean is relatively slow, with the smallest MSE and the best image denoising effect. After introducing the discrete coefficient, the MSE of the model is about 300 dB lower than before, and the PSNR is about 20 dB higher than before. Introducing the discrete coefficient can effectively reduce the impact of noise points on the algorithm. Compared with Kalman genetic optimization algorithm, transform domain image denoising algorithm, and cross branch convolutional denoising network, the MSE of the proposed algorithm is reduced by 27, 21, and 13 dB respectively. The PSNR is improved by 8, 6, and 3 dB respectively. The time consumption is shortened by 0.20, 0.16, and 0.14 seconds, respectively.

Research on coal gangue recognition method based on infrared thermal imaging

CHENG Gang, PAN Zeye, WEI Yifan, CHEN Jie

2024, 50(4): 69-77. doi: 10.13272/j.issn.1671-251x.2023100086

<Abstract>(160) <HTML> (34) <PDF>(34)

Abstract:
Coal and gangue sorting methods based on heavy-medium coal selection technology, jigging technology, flotation technology, dry coal selection technology and γ-ray detection method have high investment costs, low sorting efficiency and serious environmental pollution. The accuracy of the coal gangue sorting method based on CCD cameras is not high, and the X-ray based coal gangue sorting technology can harm the health of personnel. Infrared thermal imaging technology has the advantage of being unaffected by light and dust, and will not cause harm to the human body. A coal gangue recognition method based on infrared thermal imaging has been proposed. Firstly, coal and gangue pass through the heating area under the conveyor belt, and the temperature of the center point of coal and gangue is monitred through an infrared thermal imager to obtain the temperature of the heated coal and gangue. The infrared thermal imager is used to capture the uniformly heated coal and gangue in the heating area, obtaining infrared grayscale and color images of the coal and gangue. Secondly, Gaussian filtering is used to preprocess and extract features from the infrared grayscale images and infrared color images of coal and gangue. The grayscale mean of the infrared grayscale image, the grayscale value feature corresponding to the maximum frequency, and the G-channel first-order moment and G-channel second-order moment features of the infrared color image are used as sorting features. The above four features are used as inputs for the classification model. Finally, support vector machine (SVM) is used for classification and recognition to achieve the goal of recognizing coal and gangue. The experimental results show that the coal gangue recognition method based on infrared thermal imaging has achieved an accuracy rate of over 98% for the sorting of bituminous coal, anthracite, and lignite, and has a good classification effect.

Experimental study on coal rock recognition based on infrared thermal imaging and vibration signals

LIU Zhixiang, SUN Zhan, YIN Jiakuo, ZOU Kang

2024, 50(4): 78-83, 152. doi: 10.13272/j.issn.1671-251x.2023110029

<Abstract>(134) <HTML> (30) <PDF>(16)

Abstract:
In response to the difficulties in practical application, susceptibility to signal interference, high cost, and complex implementation of existing coal rock recognition technologies, this paper theoretically analyzes the relationship between coal rock cutting heat production and coal rock hardness. The paper proves the rationality of using infrared thermal imaging to obtain cutting temperature changes for coal rock recognition. A coal rock cutting test bench is built for roadheader. Long-term cutting tests are conducted on ordinary coal seams, coal rock interfaces, and sandstone layers with different hardness. The cutting temperature and vibration signals of the cutting head are obtained through infrared thermal imaging and vibration sensors, and their change patterns are analyzed. The research results indicate the following points. ① As the cutting time increases, the cutting temperature gradually increases. The higher the hardness of coal rock, the higher the cutting temperature, and the faster the rate of increase in cutting temperature. At the initial stage of cutting, coal and rock cannot be recognized by cutting temperature, but during stable cutting, coal and rock can be recognized based on cutting temperature features. ② The vibration intensity of the cutting head increases with the increase of coal rock hardness, but does not show a significant change with the increase of cutting time. Therefore, it can compensate for the lack of recognition of coal rock through cutting temperature at the beginning stage of cutting.③ Accurate recognition of coal and rock cannot be achieved through a single cutting temperature or vibration intensity. Therefore, coal and rock can be recognized through vibration intensity during the initial cutting stage and frequent flash temperatures. In the stable cutting stage, coal and rock can be recognized through temperature obtained from infrared thermal imaging.

Construction of knowledge graph for fully mechanized coal mining equipment based on joint coding

HAN Yibo, DONG Lihong, YE Ou

2024, 50(4): 84-93. doi: 10.13272/j.issn.1671-251x.2023100009

<Abstract>(138) <HTML> (32) <PDF>(18)

Abstract:
Using knowledge graph technology for data management can achieve effective representation of fully mechanized coal mining equipment. The information with deep mining value can be obtained. The imbalanced data of fully mechanized coal mining equipment and the limited number of entities in certain categories of equipment affect the precision of entity recognition models. In order to solve the above problems, a knowledge graph construction method for fully mechanized coal mining equipment based on joint coding is proposed. Firstly, the fully mechanized coal mining equipment ontology model is constructed, determining the concepts and relationships. Secondly, the entity recognition model is designed. The model uses Token Embedding, Position Embedding, Sentence Embedding, and Task Embedding 4-layer Embedding structures and Transformer Encoder to encode fully mechanized coal mining equipment data, extract dependency relationships and contextual information features between words. The model introduces a Chinese character library, using the Word2vec model for encoding, extracting semantic rules between characters, and solving the problem of rare characters in fully mechanized coal mining equipment data. The model uses the GRU model to jointly encode the data of fully mechanized coal mining equipment and the character vectors encoded in the font library, and fuse vector features. The model uses the Lattice-LSTM model for character decoding to obtain entity recognition results. Finally, the model uses graph database technology to store and organize extracted knowledge in the form of graphs, completing the construction of knowledge graphs. Experimental verification is conducted on the dataset of fully mechanized coal mining equipment. The results show that the method improves the recognition accuracy of fully mechanized coal mining equipment entities by more than 1.26% compared to existing methods, which to some extent alleviates the low accuracy problem caused by insufficient data when constructing a knowledge graph of fully mechanized coal mining equipment in a small sample situation.

Autonomous pose estimation of underground disaster rescue drones based on visual and laser fusion

HE Yijing, YANG Wei

2024, 50(4): 94-102. doi: 10.13272/j.issn.1671-251x.2023080124

<Abstract>(83) <HTML> (21) <PDF>(9)

Abstract:
The autonomous navigation capability of drones in post disaster mines is a prerequisite for their capability to perform rescue and disaster relief tasks. The autonomous pose estimation technology in unknown three-dimensional space is one of the key technologies for autonomous navigation of drones. At present, vision based pose estimation algorithms are prone to blurred scale and poor positioning performance due to the inability of monocular cameras to directly obtain depth information in three-dimensional space and the susceptibility to underground dim light. However, laser based pose estimation algorithms are prone to errors due to the small viewing angle, uneven scanning patterns, and constraints on the structural characteristics of mining scenes caused by LiDAR. In order to solve the above problems, an autonomous pose estimation algorithm of underground disaster rescue drones based on visual and laser fusion is proposed. Firstly, the monocular camera and LiDAR carried by the underground drone are used to obtain the image data and laser point cloud data of the mine. The ORB feature points are uniformly extracted from each frame of the mine image data. The depth information of the laser point cloud is used to recover the ORB feature points. The visual based drone pose estimation is achieved through inter frame matching of the feature points. Secondly, feature corner points and feature plane points are extracted from each frame of underground laser point cloud data, and laser based drone pose estimation is achieved through inter frame matching of feature points. Thirdly, the visual matching error function and the laser matching error function are placed under the same pose optimization function, and the pose of the underground drone is estimated based on vision and laser fusion. Finally, historical frame data is introduced through visual sliding windows and laser local maps to construct an error function between the historical frame data and the latest estimated pose. The optimization and correction of the drone pose under local constraints are completed through nonlinear optimization of the error function, avoiding the accumulation of estimated pose errors that may lead to trajectory deviation of the drone. The simulation experiments that simulating the complex environment after a mine disaster are conducted. The results show that the average relative translation error and relative rotation error of the pose estimation algorithm based on visual and laser fusion are 0.001 1 m and 0.000 8°, respectively. The average processing time of one frame of data is less than 100 ms. The algorithm does not experience trajectory drift during long-term operation underground. Compared to pose estimation algorithms based solely on vision or laser, the accuracy and stability of this fusion algorithm have been improved, and the real-time performance meets the requirements.

Automatic recognition method of ventilator wind pressure performance curve for mine ventilation network calculation

WU Fengliang, KOU Lu

2024, 50(4): 103-111. doi: 10.13272/j.issn.1671-251x.2023100036

<Abstract>(92) <HTML> (31) <PDF>(15)

Abstract:
Sampling and recognizing the wind pressure performance curve from the wind performance curve image, and then fitting the wind pressure performance function, is a key technology for solving the mine ventilation network. Currently, manual methods are commonly used to recognize wind pressure performance curves, which have low efficiency and poor accuracy. This study proposes an automatic recognition method for the wind pressure performance curve of ventilator based on image processing technology. The method uses bilateral filtering, image sharpening, and binarization techniques to preprocess the original ventilator wind pressure performance curve image, in order to improve image quality. The method extracts grid lines and coordinate text from the performance curve image of the ventilator based on corrosion algorithm and contour detection algorithm. The method uses logical operation, median filtering, contour detection, and K3M algorithm to extract the wind pressure performance curve. The pixel coordinates of the wind pressure performance curve are recognized using a row by row pixel recognition method. The method uses template matching algorithm to recognize coordinate numbers, and then complete the conversion from pixel coordinates to physical coordinates, achieving wind pressure performance curve recognition. The automatic recognition method for the wind pressure performance curve of the ventilator is integrated into the ventilation network calculation software. The recognition experiment is conducted on the wind pressure performance curve of the ventilator. The results show that the sampling speed of the method for a wind pressure performance curve is 24 Samples/s. The recognized wind pressure performance curve has a high overlap with the original curve. The maximum error between the wind pressure fitting value and the original value is only 0.88%. Compared to manual recognition methods, the method greatly improves the efficiency and accuracy of the ventilation network calculation .

Numerical simulation study on the coal-bed methane displacement effect of different gas injection components

XU Xiaoma, YAN Xiangxiang, HUANG Shuyu

2024, 50(4): 112-120. doi: 10.13272/j.issn.1671-251x.2023080027

<Abstract>(85) <HTML> (33) <PDF>(7)

Abstract:
The main components of gas injection to promote methane extraction are N₂, CO₂, and air, but there is currently limited research on the comparison of displacement effects for different injection components. In order to solve the above problems, a mathematical model for gas injection displacement considering fracture gas seepage and matrix pore gas diffusion is established. Based on the validation of the model, the process of injecting gas into coal samples for methane displacement is simulated. The effects of different injection components (N₂, CO₂, and air) on methane displacement are compared and studied under the same injection pressure and coal permeability conditions. The results show the following points. ① Under the same injection time, the volume fraction of injected gas gradually decreases from the injection end to the exhaust end, with the highest injected gas volume fraction near the injection end. The methane volume fraction gradually increases, with the highest methane volume fraction near the exhaust end. As the injection time increases, the area with an increase in injected gas volume fraction gradually moves towards the exhaust end until it covers the entire coal sample. The area with a decrease in methane volume fraction also gradually moves towards the exhaust end until it covers the entire coal sample. It indicates that the methane in the coal sample is gradually displaced and driven out of the whole sample. ② Within the same injection time, from the injection end to the exhaust end, the volume fractions of N₂, CO₂, and air injected gases and methane have similar changes. That is, from the injection end to the exhaust end, the volume fraction of injected gas gradually decreases and the methane volume fraction gradually increases. With the increase of injection time, the area of increase in injected gas volume fraction increases. The volume fraction of injected gas and methane at the same injection time and at the same position of the coal sample complement each other, that is, they add up to 100%. ③ The ranking of the methane displacement effects of three types of injected gases is CO₂>air> N₂. ④ The analysis of the gas volume fraction at the exhaust end shows that the gas volume fraction at the exhaust end can be divided into breakthrough stage, equilibrium stage, and displacement completion stage over time. The duration of the three stages of injecting different gases varies, with N₂ breakthrough time and displacement completion time of 30 and 90 minutes, respectively. The breakthrough time for CO₂ injection and the completion time for displacement are 20 and 80 minutes, respectively. The breakthrough time for air injection and the completion time for displacement are 28 and 87 minutes, respectively. ⑤ When applied on site, appropriate injection gases should be selected based on the adsorption and desorption capacity of specific coal seams, as well as the spontaneous combustion features of coal seams.

Research on the device and method for measuring the initial velocity of in-situ gas emission from coal

XUE Weichao

2024, 50(4): 121-127. doi: 10.13272/j.issn.1671-251x.2023100059

<Abstract>(80) <HTML> (67) <PDF>(7)

Abstract:
The initial velocity of gas emission is one of the important indicators for identifying the risk of coal and gas outburst. The existing research has not organically combined the testing of the initial gas emission rate index of coal with the testing of the other three indicators for outburst identification (coal seam gas pressure, coal failure type, and coal solidity coefficient). The current method for measuring the initial velocity of gas emission is based on AQ 1080-2009 "Method for Measuring the Initial Velocity Index (∆p) of Coal Gas Emission". The measurement results only reflect the difficulty of gas emission through coal particles under standard experimental conditions, without considering the in-situ environment of coal seam gas occurrence. The results cannot accurately reflect the severity of the disaster of gas emission inside the coal rock mass on site. In order to solve the above problems, the device and method for measuring the initial velocity of in-situ gas emission are proposed. The method replaces coal particles with original coal blocks, replaces methane with original gas components, increases the gas pressure, stress, and temperature environment in which the coal body is located. The method restores the in-situ environment for measurement. A in-situ gas emission initial velocity measurement experiment is conducted by use of coal samples from coal and gas outburst coal seams in a certain coal mine. The conclusions are listed as follows. ① With the simulation of gas emission process, the gas emission flow rate gradually decreases and shows a negative exponential change law with time. The gas flow rate is used to characterize the initial velocity of in-situ gas emission, then Δp_QA=7.1 mmHg, Δp_QI=2.9 mmHg. ② As the simulation of the gas emission process progresses, the gas pressure in the emission space gradually increases, and the rate of gas pressure increase gradually decreases. The gas pressure changes roughly with time in a logarithmic function relationship. The emission gas pressure is used to characterize the initial velocity of in-situ gas emission, then Δp_PA=25 mmHg, Δp_PI=26.6 mmHg, Δp_PD=11 mmHg. The measurement results can comprehensively reflect the dual pore structure of coal seams, the mechanical properties of coal bodies, the energy of gas occurrence in coal bodies, the in-situ environment of stress and temperature in coal seams, and truly reflect the degree of outburst danger in coal mines underground.

Prediction of gas concentration in coal mine excavation working face

CHEN Xianzhan, SHEN Yicheng, HONG Feiyang, SHI Shen

2024, 50(4): 128-132. doi: 10.13272/j.issn.1671-251x.18122

<Abstract>(136) <HTML> (44) <PDF>(15)

Abstract:
In current gas concentration prediction methods, there are problems of data processing uncertainty, feature extraction limitations, and prediction bias caused by subjective factors. In order to solve the above problems, a gas concentration prediction method for coal mine excavation working face is proposed. Firstly, laser gas sensors are installed every 1 meter in the return airway of the coal mine excavation working face, forming a sensor network to collect real-time gas concentration data. Secondly, the method searches and removes outliers in the gas concentration data according to the Laida criterion, and uses the Lagrange interpolation polynomial to fill in the missing values in the gas concentration data. Finally, based on removing outliers and filling in missing values in the gas concentration data, the empirical mode decomposition algorithm is used to decompose the gas concentration data into intrinsic mode functions and trend terms. The Hilbert transform is then used to process the intrinsic mode functions to obtain the high-frequency and low-frequency terms of the data, which are then input into the least squares support vector machine for weighted processing to output the gas concentration prediction results. The gas concentration prediction simulation experiment is conducted using a simulation device for the excavation working face, and an on-site test is conducted on a certain coal mine excavation working face. The results show that the predicted gas concentration by this method is very close to the actual measurement value, with a small mean square error, indicating a high accuracy of the prediction results. The small fluctuation of mean square error indicates good adaptability and strong stability of prediction results. Short prediction time indicates high prediction efficiency.

Research on optimization of working performance of shearer drum

WANG Hongwei, GUO Junjun, LIANG Wei, GENG Yide, TAO Lei, LI Jin

2024, 50(4): 133-143. doi: 10.13272/j.issn.1671-251x.2023100095

<Abstract>(128) <HTML> (19) <PDF>(14)

Abstract:
In actual production, the cutting and crushing process is the result of multi action coupling. The bidirectional coupling technology of discrete element method (DEM) and multi bodydynamics (MBD) can achieve information exchange between coal mining equipment and coal wall. It is in line with actual production situations and has significant advantages. In order to improve the working performance of the shearer drum, based on the DEM-MBD bidirectional coupling mechanism, combined with mechanical performance experiments and simulation experiments to obtain actual operating parameters, a bidirectional coupling model of the shearer drum cutting coal wall is established using simulation software EDEM and RecurDyn. The torque and cutting force experienced by the drum during the simulation process are analyzed, and it is proved that the coupling effect and cutting effect are good. Single factor experiments and orthogonal experiments are designed to analyze the influence of drum operating parameters on working performance. SPSS software is used to obtain the degree of influence of drum speed, cutting depth, and traction speed on cutting specific energy consumption, coal loading rate, and load fluctuation coefficient. The feasibility of the model is verified through on-site experiments. A multi-objective optimization model is constructed with drum speed, cutting depth, and traction speed as decision variables, and cutting specific energy consumption, coal loading rate, and load fluctuation coefficient as objectives. The improved multi-objective gray wolf optimization (MOGWO) algorithm and technique for order preference by similarity to ideal solution (TOPSIS) method are used to solve the model. It is found that when the drum speed is 31.12 r/min, the cutting depth is 639.4 mm, and the traction speed is 5.58 m/min, the working performance of the shearer drum is optimal. At this time, the cutting specific energy consumption is 0.467 7 kW·h/m³, the coal loading rate is 43.01%, and the load fluctuation coefficient is 0.327 8.

A generation method for the cutting height template of the shearer drum based on working condition triggering

LI Zhongzhong, YAO Yupeng

2024, 50(4): 144-152. doi: 10.13272/j.issn.1671-251x.2024010097

<Abstract>(81) <HTML> (31) <PDF>(6)

Abstract:
In order to solve the problem of low precision in drum height adjustment caused by different working conditions during the working process of the shearer, a generation method for cutting height template of the shearer drums based on working condition triggering is proposed. The method preprocesses and extracts features from historical sensor data of the shearer, selects 5-dimensional feature data that affect the adjustment of drum height, including cutting motor current, cutting motor temperature, pitch angle, roll angle, and traction speed. The method constructs a compensated echo state network (C-ESN) model for generating drum cutting height templates. The method establishes a working condition triggering mechanism, inputs real-time data from the shearer sensors into the C-ESN model. The method uses testing error as the judgment criterion to recognize the current working condition of the shearer as normal area, triangular coal area, or abnormal working condition. Finally, the C-ESN model generates the corresponding drum cutting height template. When the testing errors in both the triangular coal area and the normal area are greater than the threshold, transfer learning method is used to correct the parameters of the cutting height template with small testing errors to ensure the precision of the cutting height template under abnormal working conditions. The experimental results based on actual data of on-site coal mining machines show that compared with the actual cutting height, the maximum errors of the left and right drum cutting height templates in the normal area are 11.47 cm and 9.96 cm, respectively, and in the triangular coal area are 12.91 cm and 7.94 cm, respectively.The results can meet the practical requirements of engineering. Compared with traditional echo state network and radial basis function network models, the precision of the C-ESN model has been improved by 54% and 57% in the normal region, and by 10% and 69% in the triangular coal region, respectively.

Cutting control of boom-type roadheader considering coal rock hardness

XU Xiangqian, JIAN Kuo, WANG Ning, LI Shengli

2024, 50(4): 153-158. doi: 10.13272/j.issn.1671-251x.18171

<Abstract>(113) <HTML> (34) <PDF>(22)

Abstract:
The coal rock hardness significantly affects the spatial operation status of boom-type roadheader. Analyzing the correlation between the spatial operation status of roadheader and changes in coal rock hardness can help better achieve automatic cutting control of boom-type roadheader. To improve the cutting control precision, a boom-type roadheader cutting control method considering coal rock hardness is proposed. Based on the principles of dynamics, the relationship between the spatial operation status of the boom-type roadheader and the changes of coal rock hardness is obtained. It is found that as the distance between the cutting head and the target point, the radius of the motion range, and the dynamic angle increase, the operational stability of the cutting head will correspondingly improve. The automation control parameters are determined using a weighted balance method, and PID control and closed-loop fuzzy control methods are used to achieve automatic cutting control of the roadheader. The experimental results show that the method exhibits good performance in both horizontal and vertical control. The cutting head swing speed of the roadheader reaches a stable value within 2 seconds, and the dynamic working stability is good. The alignment between the trajectory of the rotation and lifting angle change of the boom-type roadheader's cutting head and the expected trajectory is high, and the overall angle deviation is small, resulting in high control precision.

Research on decoupling control method for single-phase cascade H-bridge rectifier in coal mine scenarios

LIU Shiyuan

2024, 50(4): 159-168. doi: 10.13272/j.issn.1671-251x.2023090089

<Abstract>(68) <HTML> (45) <PDF>(8)

Abstract:
In response to the problems of secondary voltage ripple on the DC side of single-phase cascade H-bridge rectifiers during operation in coal mine scenarios, such as grid side current distortion and capacitance drift, this paper analyzes the causes of secondary voltage ripple on the DC side of single-phase cascade H-bridge rectifiers and proposes an optimization control method based on an independent decoupling topology with unequal split capacitors. This method effectively suppresses the secondary voltage ripple on the DC side by overlaying twice the power frequency voltage on both ends of the capacitor to counteract the secondary voltage ripple. A study is conducted on parameter design and control strategies for three decoupling methods based on constructing secondary voltage (DC split capacitor with unequal capacitance values and equal DC voltage components; DC split capacitor with unequal capacitance values and unequal DC voltage components; DC split capacitor with equal capacitance values and unequal DC voltage components). By analyzing the influence of parameters on the amplitude of secondary voltage, the optimal parameter range is determined to achieve effective power decoupling, reduce capacitance values, and lower equipment volume and cost. The simulation results show the following points. ① The split capacitor IAPD (SC-IAPD) is added at 0.2 s, SC-IAPD circuit control method based on decoupling method 2, SC-IAPD circuit optimization control method based on decoupling method 2, and SC-IAPD circuit control method based on decoupling method 1 all control the DC side output voltage ripple at 1-1.5 V. This indicates that the symmetrical half bridge decoupling circuit can effectively suppress DC voltage fluctuations and has good decoupling performance when load changes. ② In the case of light load switching to heavy load, the optimized control method of SC-IAPD circuit based on decoupling method 2 can quickly follow the changes in load, achieve ripple suppression, and have stronger load carrying capacity and better decoupling effect. In the case of heavy load switching to light load, the SC-IAPD circuit control method based on decoupling method 1 can better achieve decoupling performance, controlling voltage ripple within 1 V. If we consider minimizing the capacitance value, the control method of SC-IAPD circuit based on decoupling method 2 is more advantageous. The experimental results show the following points. ① Before the sudden change of load, both traditional control methods and decoupling control methods based on secondary voltage can effectively suppress the voltage ripple on the DC side. However, decoupling control methods based on secondary voltage have better effects in suppressing voltage ripple, resulting in smaller voltage ripple on the DC side. ② After a sudden change in load, traditional control methods cannot maintain the stability of the DC side voltage, resulting in significant oscillations and loss of stability.

2024 Vol. 50, No. 4

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