Coal Mine Accidents Research Articles

Study on the Influence of Geological Structure on Gas Occurrence and Gas Outburst

Gas is a gaseous geological body and a product of geological genesis. The geological structure and the historical evolution of coal bearing strata control the generation conditions of coal-bed gas and the preservation conditions of coal-bed gas subjected to depression, uplift, compression and tension in previous tectonic movements. The geological conditions that control the occurrence of gas disasters determine the state of gas occurrence, gas content, gas pressure, gas emission and the risk of coal and gas outburst in coal seams. There are obvious characteristics of zoning and zoning. Gas occurrence and gas outburst are controlled by various geological factors, and geological structure plays a decisive role in coal and gas outburst. Studying the influence of geological structure on gas occurrence law and gas outburst and mastering gas geological law can achieve the purpose of gas prediction and gas control. This paper discusses the influence of fold, fault and sliding structure on gas occurrence, and discusses the influence of geological structure on gas outburst from three aspects of coal seam gas pressure, in-situ stress and coal body structure. It can provide a basis for the prediction of gas outburst and has important significance for the prevention and control of gas accidents in coal mines.

Problems Existing in The Management of Coal Mine Safety Production Under The New Situation and Solutions

Four major coal mine accidents, for example, one of coal mine safety production management problems and way to tackle, based on the analysis of the current trend of the development of coal industry in China, on the basis of the objective factors and subjective factors of lead to safety hazard analysis, and the problems existing in the safety management and safety management measures are summarized, do management concept, technology, equipment, creative corporate culture and so on.

Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China

This study aims to accurately identify mine water sources and reduce the hazards caused by water inrush accidents in coal mines. Taking the Gubei coal mine as an example, the water quality results of the water samples from the Cenozoic unconsolidated aquifer, Permian sandstone fracture aquifer, and Carboniferous Taiyuan Formation limestone karst fracture aquifer in the mine area were tested, and K++Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3−, TDS (Total Dissolved Solids), and pH were selected as the main indicators to study the water chemistry characteristics of the aquifer through water chemistry component analysis, major ion content analysis, Piper trilinear analysis, and correlation analysis. Thirty-five groups of water samples were randomly selected and imported into SPSS software for factor analysis (FA) and downsized to three main factors as the input variables of the artificial neural network model. The particle swarm optimization (PSO) code was written based on the MATLAB platform to improve the self-adjustment weights and acceleration factors for optimizing the initial weights and thresholds of the Back-Propagation (BP) neural network. The training and prediction samples were learned in the ratio of 8:2, and the recognition results were compared with the traditional BP neural network model. Results showed that the groundwater of the Gubei coal mine demonstrated a water quality vertical zoning pattern, and the chemical composition was dominated by cation K++Na+ and anion Cl−. The FA-PSO-BP neural network model has a higher accuracy of water source discrimination compared with the cluster analysis and the FA-BP neural network model. The FA-PSO-BP neural network model is worthy of further application in the problem of water source identification in mine water inrush.

Spatial and Temporal Characterization of Mine Water Inrush Accidents in China, 2014–2022

Currently, water inrush accidents in China’s coal mines are mainly under control, but occasionally, grave water inrush accidents still occur, causing significant casualties and economic losses. Existing studies have primarily focused on accident statistics, and the research on the trend of accident evolution is becoming obsolete to match the current context of coal resource development. This study analyzes the water inrush accidents in China’s coal mines between 2014 and 2022. It investigated the spatial and temporal distribution of accidents, the level of accidents, and the extent to which water prevention and control measures vary by zone in coal mines. The study results showed that from 2014 to 2022, water inrush accidents in coal mines exhibited a “decline-stability-fluctuation” stage change pattern. Additionally, the location of water inrush accidents has shifted westward. Paying particular attention to preventing and controlling water disasters in coal mines within the west development zone is crucial. The water disaster accidents in China’s coal mines have been effectively controlled, but the problem of unequal levels of preventing and controlling water disasters in coal mines persists. This study can provide a reference for the safe and efficient production of coal mines and the control of the number of deaths in mine water hazard accidents.

Correlational analysis of occupational accidents and the safety policies in the Chinese coal mining industry from 2008 to 2021

This study investigates the correlation between previous coal mine safety policies and accidents in China. Data on coal mine accidents and government regulatory information from 2008 to 2021 are collected. The characteristics of coal mine accidents are analyzed, and safety policy indexes are identified. An ordinary least squares (OLS) regression model is established to quantitatively analyze the correlation between accidents and safety policy. The study finds that safety policies have some impact on accident occurrence in coal mines. Although there has been a decrease in accidents and deaths over time, higher mortality rates are observed during periods of increased production intensity and on weekends. Gas accidents are the most common, followed by roof and flood accidents. The study concludes that national safety policies with wider coverage and a stronger system are effective in preventing accidents, but caution should be exercised to avoid reduced vigilance with decreasing death rates.

Study on the Influence of Safety Culture Level on The Unsafe Behavior of Coal Miners

In order to explore the influence of safety culture level on the unsafe behavior of coal miners, the structural equation model method is used to study. Summary combing the internal and external research results, put forward the measure of safety culture level of safety atmosphere, safety attitude, safety awareness, safety training four influencing factors, construct structure equation model, proposed model analysis hypothesis, the introduction of unsafe psychology as a intermediary variable, according to Henan, Shanxi coal mine 226 effective sample data, using AMOS software verification analysis. The results show that the safety atmosphere, safety attitude, safety awareness and safety training all have negative effects on the unsafe behaviors, and the unsafe psychology is positively related to the unsafe behaviors. According to the results of the model analysis, the corresponding intervention countermeasures will have a positive effect on preventing accidents in coal mine.

Statistical analysis of coal mine accidents from 2012 to 2023 and trend prediction in the future in China

Statistical analysis of coal mine accidents from 2012 to 2023 and trend prediction in the future in China

Analysis on accident types of coal mine in global major coal producing countries

Coal plays an extremely important role in the global primary energy consumption structure; It is of great significance to analyze the types of coal mine accidents in major coal producing countries. This paper makes a comparative analysis of the types of coal mine accidents in the USA, Australia, Russia, India, and China, which has a significance for further improving coal mine safety in various countries.

Monitoring of aerological risks of accidents in coal mines

The assessment and management of aerological risks in coal mine accidents are based on the development of a data analytics system that hosts design values for various parameters and subsystems related to coal mines, as well as the real-time monitoring of operational parameters through various sensors and devices. This study presents the methodology for monitoring aerological risks. It utilizes mining, geological, and geotechnical conditions for seam extraction, along with statistical data concerning elements of coal mine ventilation and gas drainage systems, to assess aerological risks at individual coal mine functionality levels and individual risk factors. Eight coal mines have been ranked according to their aerological risk level. For rank I, the minimum aerological risk is 0.0769, while the maximum is 0.5698. Rank II is associated with category II mines. Aerological risk for this rank is the lowest and ranges from 0,1135 to 0,3873. In the case of rank III, the minimum aerological risk is 0.057, with a maximum of 0.595. This ranking of coal mines by aerological risk level allows to identify potentially unsafe mines in terms of aerology, and enables us to determine aerological risk mitigation measures (technical, technological, and organizational) for each mine to enhance aerological safety.

Study of a Small Robot for Mine Hole Detection

China is rich in coal resources, but complex hydrogeological conditions lead to difficulties in coal mining, including coal mine collapses, roof and water damage, and other accidents that occur frequently, resulting in many casualties and property losses. The use of coal mine hole detection technology to detect and analyze the internal environment of the coal mines in advance helps to reduce safety hazards and prevent coal mine accidents; however, the operation of existing coal mine hole detection technology is cumbersome, difficult to control, and encounters problems due to an insufficient depth of jacking. This paper designs a new type of small robot for mine hole detection. Firstly, we analyzed the function and structural design of the mine hole detection robot, designed a variable diameter function according to the characteristics of narrow and uneven mine holes in coal mines, and analyzed the mechanics of the critical parts using theoretical calculations. Secondly, using three-dimensional modeling software (Solidworks 2019), we established a structural model of the small robot for mine hole detection. After that, we designed a hardware circuit and control program for the robot and emphasized the safety design of the circuit, considering the presence of water and gas inside the coal mine. Finally, to verify the feasibility of the design program, the basic parameters and function tests of the mine hole-detection small robot were carried out. The experimental results show that the developed mine hole-detection small robot can adapt to working hole diameters from 65 mm to 100 mm and has a maximum working power of only 12 W and a maximum crawling speed of 3.96 m/min. The maximum crawling slope reaches 90°, which can meet existing mine hole inspection needs. This research provides theoretical and design guidance for developing mine hole-detection robots with substantial engineering practical reference values.

Knowledge graph construction and visualization analysis of coal mine accident safety management research based on CiteSpace and VOSviewer

Although the coal mining industry has implemented mechanization and automation, frequent coal mine accidents still occur, making coal mine safety a primary concern. To gain a comprehensive understanding of global progress in preventing coal mine accidents and managing safety, we utilized the Web of Science database (WOS) as our sample source. With the support of CiteSpace and VOSviewer software, we constructed a knowledge graph depicting publication numbers, research institutions, and keyword clustering to visually analyze coal mine accident prevention and safety management. Our research reveals that this field has undergone three stages of development: initial growth, steady progress, and rapid advancement. Particularly noteworthy is the current golden age of rapid development from 2018 to 2023 which has garnered significant attention from researchers. In terms of author collaboration networks, notable contributors with high publication rates and citation counts include Wu Qiang, Liu Quanlong, et al. Regarding institutional cooperation networks, major research institutions such as China University of Mining & Technology, Shandong University of Science & Technology, and Anhui University are actively engaged in important scientific research related to coal mining. Through keyword clustering analysis based on relevant literature review, it is evident that research on coal mine safety management primarily focuses on high-frequency keywords such as "coal and gas outburst","risk assessment" and "coal miners" By extracting the top nine emergent keywords with high-intensity levels and arranging them chronologically according to their emergence year, the evolution pattern over time was obtained for emergent words. Furthermore, it was observed that emerging keywords like "China", "subsidence" and "workers" have also gained increasing attention during this period.

Analysis on the Causes and Countermeasures of Mechanical and Electrical Transportation Accidents in Coal Mines

There are many types of mechanical and electrical equipment involved in current coal mine mechanical and electrical transportation, and many workers are not familiar with the operation of mechanical and electrical equipment. Improper operation often leads to operational failures of coal mine mechanical and electrical equipment. The important reason for this phenomenon is that the transportation management of underground mechanical and electrical equipment is not in place, and there may also be incorrect and unreasonable management methods. This situation will directly hinder coal mining work and exacerbate the threat to the safety of workers' lives. Therefore, coal mining enterprises must develop good management measures for underground mechanical and electrical equipment based on the current operating status of the equipment and the company's own development status, reduce operational accidents of mechanical and electrical equipment, and improve normal and stable production in the coal mine.

A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 × 10−4, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference between the results and the actual value is small, which verifies the reliability of the model. According to the results of the water abundance division, strong water abundance areas are mainly concentrated in the third-partition area. This study provides a new method for the prediction of aquifer water abundance, improves the prediction accuracy of aquifer water abundance, reduces the cost of coal mine production, and provides a scientific evaluation method and a theoretical basis for the prevention and control of water disasters in coal seam roofs.

Accident case-driven study on the causal modeling and prevention strategies of coal-mine gas-explosion accidents: A systematic analysis of coal-mine accidents in China

Post-accident prevention measures in Chinese coal mining enterprises were based on single accident case training, and the learning of accident information is limited to the violation in the accident or incident. The nature of accidents proves that accident prevention encompasses not only visible violations and hazardous materials, but also deep-rooted organizational factors. How to obtain valuable and learnable safety information from limited accident date remains a key issue. This study investigated the use of a modeled accident-prevention framework for combining accident case data to construct an accident causation network for targeted data mining, which in turn could help obtain accident-prevention measures with more control values. The results of the study showed that (1) A framework for categorizing the factors influencing coal mine accidents, including four layers and 42 indicators, was identified through a literature review and factor extraction from 86 coal mine accident reports. (2) A causation network model of gas-explosion accidents comprising 163 accident causation nodes and 681 connected edges was established, and SP02(failure to implement the stop production instruction), SA01 (poor awareness of system compliance), PB03(fluke psychology) and SH01(habitual violation of procedures and rules) were the most influential accidental causes in the accidental network. (3) An F-Score-based attack strategy for accident-causing networks was proposed, which is superior to generalized attack strategies for reducing accident network connectivity and more conducive to rapidly obtaining accident prevention priorities. (4) A gas explosion accident prevention strategy based on the combination of accident network model and dynamic Bayesian network was constructed, and inference simulation was performed for four cases such as IA01 (unchecked gas concentration), verifying that it has the possibility of predicting accident hazards in the workplace. This study provided accident data-driven prevention interventions to reduce future mining accidents.

Analysis of characteristics and causes of gas explosion accidents: a historical review of coal mine accidents in China

Objectives This study aimed to provide greater insight into the characteristics of severe and extraordinarily severe gas explosion accidents (SESGEAs). Methods. The study analyzed the accident characteristics and causes of SESGEAs. As an example, we conducted a specialized case analysis using the 24Model (fourth edition) on the recent Baoma coal mine gas explosion. Results. SESGEA data are characterized by greater volatility, with significant differences in the geographical distribution, temporal distribution and attributed characteristics of the accidents. From the accident analysis: chaotic ventilation management was the most serious accident cause of SESGEAs; unsafe acts related to ventilation operations accounted for 18.51% of all unsafe acts; coal miners lack professional safety knowledge and have a serious fluke mentality in mining work; enterprises have insufficient enforcement of safety procedure documents, and lack of attention to the allocation of underground human resources and safety training systems; and the importance of safety, the role of the safety department and satisfaction with safety facilities have become the most serious missing items of safety culture. Conclusion. This study can provide important data support and management basis to assist mine operators in developing more targeted accident prevention strategies.

Gas-dynamic processes and causes of accidents at mines

Geodynamic phenomena that occur during the exploitation of a coal-bearing massif lead to human casualties, reduce the speed of penetration, and significantly complicate the operation of the mine.The article analyzes existing views on geodynamic processes occurring within mining fields located in coal-bearing formations. A safe technology for identifying potential outburst locations during coal mining is characterized. The technology has been tested during prospecting and environmental studies within the mining workings at active mines and abandoned mining fields. The results of profile and planar investigations in he areas exhibiting geodynamic outbursts in coal deposits and mines are presented.When sampling subsoil air using the technology of structural-thermo-atmospheric-hydrogeochemical research (STAHGS), which is carried out on the surface of the earth in profile and planar versions, the authors came to the conclusion about the possibility of predicting fault zones of increased permeability and zones of low fluid permeability. The first affect the rate of destruction of rocks under pressure, and in the second case, the possibility of gas emissions into the mine, which can be accompanied by an explosion and fire. Such zones are dangerous, because the most destructive events occur in places of collision. Early identification of places of geodynamic stress of rocks, fault zones of increased permeability, accumulation of gases – all this will allow to make a decision in advance about changing the conditions of conducting mining operations and degassing of the coal massif.In addition, the authors draw attention to hydrogen gas as one of the causes of initiation of explosions and fires during underground mining operations in mines, which is not taken into account at the current stage of safety control of mining operations. The possible cause of a hydrogen explosion is not taken into account in coal mine accidents. The appearance of hydrogen and helium in coal samples taken from the mine may indicate the deformation of the massif, which leads to the opening of cracks and the outflow of gas from deeper horizons. And then an increase in the concentration of hydrogen, not methane, creates the conditions for explosions and fires.

Fractal Characteristics of Overburden Rock Fractures and Their Impact on Ground Fissures in Longwall Coal Mining

Ground fissures are generated during the coal mining process due to overlying strata migration, which provides gas and water seepage channels and usually contributes to coal mining accidents in shallow buried coal seams with larger mining height working faces. Thus, the evolution features of ground fissures in large mining height working faces and shallow buried coal seams were explored by considering field observation data, similar simulation, and numerical simulations. The results show that the weathered rock layer above the thin bedrock inhibits fracture growth caused by coal mining. Overlying strata fracture expansion can be quantitatively divided into three stages based on fractal dimension: the overburden fracture formation stage, the overburden deformation fracture expansion stage, and the overburden fracture stable development stage. The movement deformation region of the ground fissure can be described by three characteristic zones: the boundary tension zone, the central compression zone, and the central dynamic tension and compression zone. Central dynamic fissures usually have 3–5 fissure counts with 2–5 m intervals between each group of fissures; central fissures develop twice from open to closed widths; the time period for two central fissures to reach their with maximum widths is 11–20 days; and border fissures expand rapidly with maximum values in a time period of 5–6 days.

Explosion and explosion suppression of gas/deposited coal dust in a realistic environment

Gas/deposited coal dust explosions are always one of the major accidents in coal mines. In this paper, gas/deposited coal dust explosion and explosion suppression experiments are conducted in a large tunnel to clarify the propagation law of gas/deposited coal dust explosion and the effect of ultra-fine dry powder on it in a real environment, and the evaluation method for the explosion wave energy in the roadway is also listed, which provides new ideas for the analysis of explosion accidents in the roadway. The results show that the pressure propagation velocity is always faster than the flame propagation velocity and the initial state of gas accumulation mainly influences the initial explosion propagation process. The increasing concentration of coal dust increases the flame propagation velocity. The maximum explosion (without explosion suppression) is achieved in the 100–120 m range. After the initial accumulation of gas has exploded, the explosion wave energy gradually increases, but the growth rate is slow. After 40 m, as the amount of deposited coal dust raised to participate in the explosion reaction increases, the growth rate of the explosion wave energy accelerates, and after 80 m, the explosion wave energy increases sharply, and then decreases as the fuel is consumed. The growth rate of the explosion wave energy is greatest in the 80–100 position interval. The explosion suppressing powder forms an inert cloud barrier in the cross section of the roadway. This barrier prevents the coal dust from continuing to move to the rear of the roadway, creating a region of high coal dust concentration in front of the barrier, resulting in an early maximum of explosion pressure and flame propagation velocity in this region. When the explosion flame passes through the explosion-suppressing cloud barrier, it is suppressed by a combination of surface cooling, gas-phase inerting and dilution, chemical synergy and physical isolation mechanisms.

Risk Assessment of Coal Mine Gas Explosion Based on Fault Tree Analysis and Fuzzy Polymorphic Bayesian Network: A Case Study of Wangzhuang Coal Mine

The prevention and control of gas explosion accidents are important means to improving the level of coal mine safety, and risk assessment has a positive effect on eliminating the risk of gas explosions. Aiming at the shortcomings of current risk assessment methods in dynamic control, state expression and handling uncertainty, this study proposes a method combining fault tree analysis and fuzzy polymorphic Bayesian networks. The risk factors are divided into multiple states, the concept of accuracy is proposed to correct the subjectivity of fuzzy theory and Bayesian networks are relied on to calculate the risk probability and risk distribution in real time and to propose targeted prevention and control measures. The results show that the current risk probability of a gas explosion accident in Wangzhuang coal mine is as high as 35%, and among the risk factors, excessive ventilation resistance and spontaneous combustion of coal are sources of induced risk, and the sensitivity value of electric sparks is the largest, and the prevention and control of the key factors can significantly reduce the risk. This study can provide technical support to coal mine gas explosion risk management.

Dynamic simulation and control strategy exploration of the unsafe behavior of coal mine employees

Approximately 80% of production safety accidents in Chinese coal mining industry companies are caused by unsafe behaviors. Therefore, in-depth study of employees unsafe behavior and its control technology have great practical and theoretical value for the prevention and control of coal mine accidents. In this paper, a dynamic simulation model of the unsafe behavior of coal mine workers is established, and 28 factors that affect the unsafe behavior of coal mine workers are summarized. Combined with agent and system dynamics, a dynamic simulation of the change law of unsafe behavior of coal mine workers is carried out, and unsafe behavior is found. The simulation results between different groups are consistent. Through the study of interventions and control measures for the unsafe behavior of coal mine employees, a control strategy for the unsafe behavior of coal mine employees is proposed, and a control strategy base is established. According to the situation, this kind of result will strengthen the early-warning control of unsafe behaviors and provide a practical method for the guidance of safety management. Research on unsafe behavior can potentially solve the safety management of coal mining enterprises.