Coal Mine Safety Production Research Articles

Suppression effect of CaCo3, NaCl, and NH4H2PO4 on gas-fat coal dust explosion pressure and flame characteristics

Coal dust explosion is a major accident affecting the safety of coal mine production. In order to control coal dust explosion, using inert dust to suppress explosion is one of the effective methods. Taking the gas-fat coal as the research object and CaCO3, NaCl, and NH4H2PO4 as the inert dust, the suppression effect of inert dust on gas-fat coal dust explosion pressure and flame is studied. It is found that when the gas-fat coal dust particle size is 48~58 ?m, both the maximum pressure and the farthest distance of flame reach the maximum value. Among the three inert dusts, NH4H2PO4 has the best suppression effect, followed by NaCl, and CaCO3 has the worst suppression effect on explosion. The smaller the particle size of

Application of Dynamic Reactive-power Compensator Based on SVG in Coal Mines

With the improvement of automation degree of coal mine mechanical and electrical equipments, such as conveyors, crushers, mining motors, inverters, and a large number of non-linear loads, cause large reactive power consumption, power factor of coal mine power grid, power supply quality and stability, and bring greater influence to coal mine safety production and economic benefits. As a new generation of high voltage dynamic reactive power compensator, SVG (Static Var Generator) has fast response and compound functions, such as compensating reactive power, suppressing harmonic and imbalance with FC(Filter Capacitor), which plays a good role in improving the power quality of coal mine. This paper designs the system parameters of SVG in coal mine and introduces its application.

Research on the Prediction Method of Monthly Hidden Danger Quantity in Coal Mine Based on BP Neural Network Periodic Combination Model

To better prevent the occurrence of hidden dangers of coal mine accidents and ensure the safety production of coal mine enterprises. This paper mines and analyses the pattern of historical monthly hidden danger quantity in the coal mine and constructs three models: the traditional backpropagation (BP) neural network model, the BP neural network based on the adaptive moment estimation optimization algorithm (Adam-BP) model, and the BP neural network prediction model with the introduction of monthly moderators (Month-Adam-BP). The experimental results show that the Adam-BP model can improve the prediction accuracy, in which the mean absolute percentage error (MAPE) improves by 8.93%, the root mean square error (RMSE) improves by 8.15%, the postdifference ratio C improves by 0.04, and the small error probability P improves by 0.12; the Month-Adam-BP model with the introduction of the monthly adjustment factor further improves the prediction accuracy, in which MAPE improves by 2.61%, RMSE improves by 5.41%, the postdifference ratio C improves by 0.06, and the small error probability P improves by 0.03. And the Month-Adam-BP model prediction accuracy reaches the level 2 standard with credible prediction effect; it can also be used to predict coal mines with periodic characteristics of hidden hazard data. Our prediction results show that the predicted number of hidden hazards in this coal mine for the next month is 29, which is an increase compared to the number of hidden dangers in the previous month. Thus, the coal mine safety managers need to strengthen the management of hidden hazards further to prevent accidents, which can better serve the standardization of coal mine safety production and ensure the smooth production of the coal mine.

Case Study on Dynamic Identification of Overburden Fracture and Strong Mine Pressure Mechanism of Isolated Working Face Based on Microseismic Clustering

Strong mine pressure has a great impact upon the safety production of coal mines. Microseismic information provides a more advanced technical means for overburden fracture dynamic identification and mine pressure mechanism research, since it contains rich information on rock fracture sources. In this study, the isolated LW8102 working face in Tongxin Mine was investigated in order to propose a spatio-temporal microseismic event data analysis method based on the k-means clustering algorithm. This algorithm can handle dynamic identification of overburden fractures constrained by spatiotemporally discrete distributions of microseismic events. This provided the dynamic extension process and the fracture distribution pattern of the overburden: eight fracture extensions were formed in the overburden. In each extension, vertical fractures connected the low and high rock layers in the LW8102 and LW8103 goafs, and through fractures connected the LW8102 and LW8103 goafs in their high, middle, and low levels. Some extensions had fractures that were connected to form a closed loop structure. In the vertical fracture, there was a tendency for one or two layers of the stratum to fail first, and then extend to one or both sides. The process of through and vertical fracture propagation followed a certain temporal sequence, reflected primarily in two forms: firstly, as the vertical fracture extended to a certain layer, it provided the initial rupture space for through fracture spreading; secondly, the through fracture first broke, and then extended to the vertical fracture until it intersected with the vertical fracture or provided the initial rupture space for the expansion of the vertical fracture. By matching the overburden fracture to the mine pressure that responded to the support resistance, we analyzed the mechanism of mine pressure at the working face. Through fracture at the high level was found to be the primary cause of the occurrence of mining pressure. It was precisely placed that the formation of multiple adjacent high through fractures 110 m from the floor, triggering simultaneous instability motion of the lower multi-layer level rock; this was the main reason for the phenomenon of strong mine pressure at the working face. Meanwhile, high through fracture at 80 m from the floor was the main reason for the phenomenon of large mine pressure at the working face.

Discussion on the Significance of Constructing the Standardization Management System of Coal Mine Safety Production

Discussion on the Significance of Constructing the Standardization Management System of Coal Mine Safety Production

Design of Test System for Arcing Time of Air-Type Switch for Explosion-Proof Electricity in Coal Mine

In order to improve the accuracy of testing the arcing time of air-type switch for explosion-proof electrical use in coal mine, this paper puts forward the testing technology of coexistence characteristics of voltage and current. On the basis of analyzing the leading position of coal industry in energy structure and the situation of coal mine safety production, this paper expounds the generation mechanism and the theory of arc suppression of air-type switch used in coal mine explosion-proof electricity, and designs a test system for the arcing time of air-type switch used in coal mine explosion-proof electricity, which can effectively test the arcing time.

Evaluation Cloud Model of Spontaneous Combustion Fire Risk in Coal Mines by Fusing Interval Gray Number and DEMATEL

Coal still occupies a key position in China’s energy consumption structure, and ensuring safe production in coal mines is a key focus for ensuring energy security. Spontaneous combustion fires in coal mines are a serious threat to the sustainability of safe production in coal mines. In order to prevent coal mine fire risk scientifically and effectively and to assess the level of disaster risk effectively and rationally, a study was conducted on the risk of spontaneous combustion fires in underground coal mines. An evaluation cloud model of spontaneous combustion fire risk in coal mines integrating the interval gray number with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was established. Seventeen representative risk evaluation indicators were selected, and a coal mine spontaneous combustion fire risk evaluation index system was constructed based on four aspects: personnel, machinery, environment, and management. The interval gray number theory was introduced to improve the classical DEMATEL analysis method, which fully expresses the expert empirical knowledge and solves the problem of ambiguity and randomness in the semantic expression of expert evaluation. The relative importance of each indicator was determined by analyzing the influence relationships between risk evaluation indicators through the improved DEMATEL. A cloud model capable of transforming quantitative descriptions and qualitative concepts was used for comprehensive evaluation of risk, and based on the results of DEMATEL analysis, a comprehensive evaluation cloud model of coal mine spontaneous combustion fire risk was formed. Finally, the validity and practicality of the model were verified by using a mine in Shenmu City, Shaanxi Province, China as an example. This study provides a powerful tool to prevent spontaneous combustion fires in coal mines and makes a positive contribution to the sustainable development of coal mine safety management.

Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis

Under the background of the intelligent construction of a coal mine, how to efficiently extract effective information from the massive monitoring data of mine earthquakes, and improve prediction accuracy, is a research hotspot in the field of coal mine safety production. In view of this problem, more and more machine learning methods are being applied to the prediction on mine earthquakes. Considering that clustering analysis can enhance the correlation between microseism data, we propose a method whose main idea is to cluster microseism data before establishing the prediction model, and then train the model, so as to improve prediction accuracy. Specifically, microseism events on a working face are divided into clusters in advance by the Spatial Temporal-DBSCAN(ST-DBSCAN) algorithm, then a prediction model is established with Support Vector Regression (SVR) to predict the occurrence location and daily frequency of high-energy mine earthquake events. A set of engineering experiments were conducted in H Coal Mine, and the results show that the spatial-temporal clustering analysis of microseism events can indeed improve the prediction accuracy of machine learning methods on mine earthquakes.

The Application of the Foam Technology to the Dust Control for Reducing the Dust Injury of Coal Mines

Mine dust is one of the main disasters that threatens the safety of coal mine production, deteriorates the underground working environment and induces various occupational diseases. Based on the harm of mine dust identified and the current domestic and foreign dust-proof technology, the technical research of the foam dust reduction is reviewed; the advantages and disadvantages of foam dust reduction are also discussed. According to the experimental exploration and the study of foam flow characteristics, the process of foam device and foam nozzle separation was adopted, and some site tests were conducted in fully mechanized mining and excavation working faces in Zhangcun, Shanjiaoshu and Jinjia coal mines. The foam technology led to a 59.5% dust removal efficiency in Zhangcun coal mine, which is much higher than the dust removal efficiency of the water spray dust reduction. The efficiency of total mine dust in Shanjiaoshu coal mine reached as high as 72%, and the efficiency of respirable mine dust reached 67%. Compared with the circumstance with no dust-control measure, the average density of the total mine dust in Jinjia coal mine decreased by 22.75~32.95% using foam technology. The results show that the use of the foam to inhibit the transformation of mine dust from the source of dust production to the floating state and the efficiency of dust control is greatly improved. This method can effectively reduce the mine dust density in underground mining and excavation working faces, clean the working environments, and guarantee the health of miners. The research results are of great significance for improving the production conditions of mining and excavating face and building green mines and standardized mines. Moreover, foam technology for dust control has been widely used in coal mines.

Evaluation of coal seam floor water bursting in multi-aquifer Gequan coal mine, China

A water-bursting evaluation of the coal seam floor is critical to ensure safety of coal mine production. The vulnerability index method based on AHP is selected for this study’s evaluation method. Water pressure, measured specific yield, equivalent thickness of effective aquiclude, brittle rock thickness under mining pressure damage zone, distribution of faults, distribution of collapse column, distribution of endpoints, and the intersection of fault are taken as the evaluation index based on the analysis of geological data in the study area. The authors assessed the threat posed by the two aquifers in the lower portion of the coal seam. Separate evaluations were conducted on the Benxi and Ordovician limestone aquifers. The results’ veracity was confirmed by comparing the obtained results to the water bursting point and a few boreholes. The evaluation results provide recommendations for the safe operation of coal mines.

Evaluation of Slope Stability within the Influence of Mining Based on Combined Weighting and Finite Cloud Model

Coal mining under slopes often leads to slope instability, resulting in substantial economic losses and human casualties. Therefore, constructing a scientific and practical slope stability evaluation model for slope disaster prevention and control is of great significance. In this study, seven evaluation indexes were selected, and their influence mechanisms were analyzed to establish a system of evaluation indexes for the stability of mining slopes in Xing County, Shanxi Province, China. A new evaluation model for the stability of mining slopes was established based on the interval analytic hierarchy process (IAHP) to determine the subjective weights of the indexes and the improved CRITIC method to determine the objective weights of the indexes, which circumvented the limitations of subjective or objective weights by combining the minimum discriminating information. The model was applied to the mining slopes in Xing County, Shanxi Province, and the results showed that slopes A, C, D and E were in an unstable state, slope B was in an understable state after mining. Finally, the accuracy of the evaluation results was verified through field surveys. The model has engineering application value in predicting the stability of mining slopes and can provide theoretical suggestions for later slope management to ensure the safe production of coal mines.

Realizing Smart Safety Management in the Era of Safety 4.0: A New Method towards Sustainable Safety

Safety 4.0 is a new stage of safety science coincident with the development of Industry 4.0. In Safety 4.0, safety researchers and professionals attach importance to the perspective of safety information and emerging technologies in safety management, and thus promote a new concept: smart safety management (SSM). However, there are still many gaps in its fundamental theory, and there are few fundamental studies on the concept and essence of SSM. In order to fill these gaps, this paper introduces a theoretical study on the method of SSM. Firstly, in order to clarify the concept of smartness in the era of information, we elaborate the smartness performance of artificial entities and the essence of smart safety capability on the basis of analyzing the smartness performance of smart safety entities (SSEs). Then, we review the new characteristics and requirements of organizational safety management research and practice in the era of Safety 4.0; on this basis, we propose the definition and connotation of SSM in the era of Safety 4.0, and elaborate the specific content of the SSM method. Specifically, we divide SSM into four modules, safety information processing, safety action, inspiring awareness of safety and internal optimization, and thus build the content model of SSM. By expounding the contents and steps of the four modules, we further elaborate how to conduct SSM in industrial organizations. Then, we propose a SSM ecosystem for realizing sustainable safety in industrial organizations and analyze the approaches to realizing SSM in coal mine safety production. Finally, we analyze the significance of SSM in supporting sustainable safety and discuss the practical challenges that SSM may encounter in the future. The results show that SSM is a method based on safety intelligence, and it can support sustainable safety through the four aspects of comprehensive function, safety predictability, safety awareness and continuous optimalization.

Safety monitoring method of moving target in underground coal mine based on computer vision processing

Coal is one of the main energy sources in China. The country attaches great importance to the development of coal mining industry, and coal production is on the rise. At the same time, mine safety accidents are becoming more and more frequent, and the country is paying more and more attention to mine safety accidents. The underground environment of coal mine is complex, noisy and uneven, and there will be problems such as occlusion and high false detection rate during video monitoring. In order to ensure the safety of underground personnel, moving target detection and tracking based on video monitoring information is of great significance for coal mine safety production. The purpose of this paper is to study how to analyze and study the monitoring of moving targets in coal mines based on computer vision processing, and describe the image processing methods. This paper puts forward the problem of target monitoring, which is based on image processing, and then elaborates on the concept of image enhancement and related algorithms. From the average gradient, the algorithm in this paper is 56.60% higher than the histogram equalization algorithm, and 68.26% higher than the dark primary color prior dehazing algorithm. and designs and analyzes cases of image enhancement in coal mines. The experimental results show that the information entropy of the algorithm in this paper is 31.10% higher than that of the dark primary color prior dehazing algorithm, and 18.72% higher than that of the histogram equalization algorithm. It can be seen that the algorithm in this paper can achieve better enhancement effect.

Application of Transmission Slot Wave Exploration Technology in Structural Exploration

The geological conditions of coal mining in my country are complex, and the difficulty of coal mining is becoming more and more difficult. Among them, the problem of coal mine structure also appears frequently. The existence of structures will not only greatly increase the cost of coal mining, but also pose a great threat to the safe production of coal mines. Conventional exploration methods cannot accurately predict structural development areas. At present, trough wave seismic exploration technology has become a mine geophysical method to detect coal seam structure. Because trough wave seismic exploration technology has the advantages of large exploration distance, high precision, strong anti-interference ability, easy identification of waveform characteristics and intuitive results, it is widely used in engineering widely used in practice. This time, the transmission slot wave exploration method was used to detect the structural abnormal area of the working face. The results show that the attenuation coefficient inversion results can intuitively reflect the abnormal area with structural development, and the interpretation accuracy of the structure is greatly improved, which provides a guarantee for the safe production of coal mines.

Microseismic response difference and failure analysis of roof and floor strata under dynamic load impact

The disaster induced by dynamic loads in the coal mining process seriously affects the safe production of coal mines. A new method for failure evaluation of roof and floor strata under dynamic load impact is proposed in this paper. The key roof strata breaking and periodic weighting experienced by the 9212 working face of the Dongpang Coal Mine in China while passing through the abandoned roadway is taken as the research background. Firstly, the research stages are divided through theoretical analysis and microseismic monitoring, and then the plane, profile distribution and spatial position relationship of microseismic events in different stages are analyzed. After that, the energy variation coefficient (CVE) and two spatial factors (S and α) are introduced to analyse the difference in the migration characteristics and damage degree of roof and floor strata under dynamic load impact. Then, the comprehensive weight is determined by principal component analysis and entropy weight method, and the failure evaluation model is established based on vulnerability index method; Finally, the risk grade zoning map of three stages is drawn by using ArcGIS technology. The research shows that the angle factor α can better describe the development direction of roof and floor fissures. The area factor S combined with the energy variation coefficient CVE can explain the development speed of fissures and the damage degree of coal and rock mass. According to the failure evaluation model, the corresponding risk degree of roof and floor in different stages can be comprehensively evaluated. The research results can be used as a reference for targeted disaster prevention and control in similar situations, such as large-area roof collapse and floor water inrush.

Nonlinear seepage erosion model of water inrush considering particle size distribution of karst collapse column and its engineering applications

Water inrush through karst collapse column is one of the great disasters which threaten coal mine safety production. The particle size distribution of karst collapse column is one of its most basic physical properties, which has a strong correlation with particle migration, and is an important basis for evaluating the water inrush risk of collapse column. The nonlinear flow tests of broken rock under different gradation conditions were carried out by a custom-built apparatus, and the relationship equation between nonlinear flow parameters (permeability and non-Darcy factor) and Talbol power exponent n were constructed. A nonlinear flow model with variable mass of water inrush from karst collapse column was established. The spatio-temporal evolution law of pressure, velocity, porosity and concentration under particle loss and the influence of particle gradation on the water inrush risk of karst collapse column at Fan gezhuang mine were discussed. During the water inrush, the flow state of fluids in karst collapse column gradually transitions from a weak inertial flow to a strong one, eventually becoming a turbulent flow. The flow model based on single flow state cannot reflect the essence of flow regime transition in water inrush. The larger n is, the stronger the water permeability of the karst collapse column, the faster the particles migrate and are lost, the faster the flow channel with high porosity develops, the shorter the time for the water inflow to reach its peak value, and the greater the risk of water inrush.

Self-Derived Wavelet Compression and Self Matching Reconstruction Algorithm for Environmental Data in Complex Space of Coal Mine Roadway

A crucial assurance for coal mine safety production, prevention and control, and rescue, which is the fundamental tenet of implementing intelligent coal mining, is the safety, stability, and quick transmission of coal mine roadways. However, because of the complex structure of the roadway environment, such as limited and variable space and numerous pieces of equipment, the wireless communication network is affected by the environment, the data transmission channel characteristics are complex and variable, and the existing data transmission methods are weak in adapting to the changing channel. These factors result in poor stability of the transmission of coal mine roadway environment detection data in the wireless communicative network. As a result, this article investigates the wireless communication systems’ real-time transmission in the intricate environmental setting of a coal mine. Based on the application of multiscale wavelet theory in data compression and reconstruction, an adaptive multiscale wavelet compression model based on the wireless data transmission channel is proposed, with an improved Huffman data compression coding algorithm derived from the multiscale wavelet, so that the environmental data meet the wireless communication channel transmission capability. The proposed algorithm boosts the compression ratio and adaptability of environmental data. A self-matching wavelet reconstruction algorithm is developed to achieve real-time and accurate data reconstruction following self-driven wavelet decomposition. The compression and reconstruction experiment performed during real-time wireless transmission of gas concentration data reveals that the original signal’s compression ratio reaches 74% with minor error and high fidelity. The algorithm provides the theoretical foundation for compression and reconstruction in complex coal mine environments for accurate, stable, and real-time data transmission. It is critical for ensuring reliable data transmission in safe production, prevention and control, rescue, and other operations, and it provides theoretical and technical support for intelligent coal mining.

Thermodynamic variation law and influence mechanism of low-temperature oxidation of lignite samples with different moisture contents

Coal spontaneous combustion (CSC) is one of the main disasters that threaten the safe production in coal mines. Moisture, which is the main factor affecting coal oxidation, has a considerable impact on CSC. In this paper, the variation characteristics of TG-DTG and DSC-DDSC curves of two kinds of lignite during low-temperature oxidation were investigated. The following results were obtained: Oxidation-induced heat production (OHP) starts to play a leading role in the temperature range of 50–75 °C; pyrolysis-induced heat absorption (PHA) grows faster than OHP in the temperature range of 70–130 °C; and OHP is dominant in the temperature range of 130–260 °C. Furthermore, for coal samples from Shengli (SL) Coal Mine and Yuanbaoshan (YB) Coal Mine, the initial heat release temperature and the end temperature both conform to the Lorentz function T=T0+2A(B4(M−Mc)2+B2). Thermodynamic parameters reach the maximum under the moisture content of 12%–17% where the CSC is the most likely to occur. In the oxygen adsorption and weight gain stage, the activation energy decreases and gradually stabilizes with the increase in moisture content. In the accelerated oxidation stage, moisture in coal plays a chemical catalytic role in the processes of oxygen chemical adsorption and intermediate complex formation and decomposition. However, excessive moisture cannot accelerate the coal-oxygen composite reaction.

Design of gas drainage pipe network regulation and control system based on PLC

In order to improve the regulation efficiency of the coal mine gas drainage pipe network, reduce the labor intensity of workers, and ensure the safe production of coal mines, a gas drainage pipe network regulation and control system is designed. The system adopts Siemens S7-1200 series PLC as the core control unit of the gas drainage pipe network. Through the explosion-proof treatment of the PLC control box, it meets the special requirements of PLC used in the coal mine environment. Through the design and selection of flow sensors and methane sensors it improves the flow and methane sampling accuracy of the gas drainage regulation system. By writing local control strategies and remote control strategies in PLC, the automatic regulation of the gas drainage pipeline network is realized.

Study on a coalbed methane liquefaction system based on thermoacoustic refrigeration method

Recovery of coalbed methane (CBM) is significant for the safe production of coal mines, environmental protection, and clean energy supply. A novel CBM liquefaction process composed of a looped three-stage thermoacoustic engine and three pulse tube coolers (PTCs) is proposed. Each PTC is connected to one thermoacoustic engine unit by bypass. Potential liquefaction processes using thermoacoustically driven PTCs are proposed and studied numerically. The operating parameters of the system are globally optimized by performing genetic algorithm procedures, using specific power consumption (SPC) as the objective function. Additionally, exergy analyses are conducted on the main components of the system to reveal the potential directions for improvement. It is found that a three-stage thermoacoustically driven PTCs is an appropriate configuration considering the overall efficiency and geometric compactness of the system. The optimized SPC of the system is 0.58 kWh·Nm−3, which is comparable to that of the traditional liquefiers. The overall exergy efficiency of the system is 36.25%. Total exergy loss of the three PTCs is 4.978 kW, accounting for 80% of that of the system. Having the advantages of being driven by heat instead of electricity, safe operation, compact structure, and high efficiency, the proposed liquefaction process is promising for small-scale gas reserves, especially for CBM.