Mine Accidents Research Articles

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.

Přeshraniční znečištění v případu úniku kyanidu z těžebního provozu v Baia Mare: mezinárodněprávní aspekty

The following paper traces the circumstances of the 2000 mining accident in the Romanian town of Baia Mare, which has been described as one of the worst environmental disasters since Chornobyl. Following this, the authors analyze the international legislation in force at the time of this accident and mention two types of obligations resulting from international treaties in force at the time, to which Romania was also a contracting party. At the turn of the millennium, there was a lack of legislation at the international and European Commission level to regulate this waste management process, making the whole process riskier and, in some cases, was also the reason for mining accidents associated with cyanide leaks into the environment. The authors describe the changes in international law adopted in response to the mining accidents, cite the case-law of the ECtHR related to the Baia Mare accident case (case Tǎtar v Romania). The authors leave aside the European and Czech aspects of the issue, which they will focus on in a separate article.

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.

Regional Risk Assessment for Urban Major Hazards Using Hybrid Method of Information Diffusion Theory and Entropy

Urban regional risk is a complex nonlinear problem that encounters insufficient information, randomness, and uncertainty. To accurately assess the overall urban risk, a regional risk assessment model for urban public safety was proposed by using the information diffusion theory. The entropy theory was employed to optimize the information diffusion model to reduce the uncertainty. A framework of urban regional risk assessment model based on information diffusion and entropy was constructed. Finally, a case study of Hangzhou city in China was presented to demonstrate the performance of the proposed method. Results showed that the proposed method could successfully estimate the urban regional risk of Hangzhou city. The risk levels and probabilities of different hazard indicators were basically consistent with reality. The hazards with respect to industrial and mining accidents and road traffic accidents were extremely serious. More than 80 deaths from industrial and mining accidents would occur almost every 3 years, and more than 400 deaths of RTA would occur almost every 2.6 years. Moreover, centralized intervals of the risk level associated with five hazards were found, where urban risks were more likely to happen and had higher vulnerability. It could provide guidance for the government’s urban safety management and policy-making.

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.

An Investigation of Learning Needs in the Mining Industry

Mining operations are risky and often dangerous, with a high potential for accidents. Many of these accidents can be prevented by implementing safety measures. It is essential that mining companies take these measures seriously to protect the safety and wellbeing of their workers and ensure the sustainability of the industry. Among these measures, those related to training are addressed in this paper in relation to the ERASMUS+ project entitled DigiRescueMe, which aims at developing courses to increase the knowledge and level of awareness of miners, rescue members, and mining engineers and, consequently, reduce the death rate in mine accidents. For this goal, semi-structured interviews and surveys were implemented, and the collected data were analyzed. The mining industry is a wide domain connected to other sectors like universities, vocational schools, rescue centers, and agencies. For this reason, the investigations carried out herein engaged people from all these sectors to identify firstly the themes, secondly, the topics, and finally the knowledge levels corresponding to those themes and topics in order to determine the learning needs and translate them into requirements for the courses that will be developed during the project activities.

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.

A dynamic combination algorithm based scenario construction theory for mine water-inrush accident multi-objective optimization

Mine safety is a crucial aspect of national infrastructure. In recent years, there have been frequent mine safety accidents in China, and the proportion of mine water-inrush accidents (MWAs) is high among them. It causes significant challenges for governments and mine managers. The emergency response of MWAs is a multi-scale expert decision-making system that involves considerable cost, time, and energy consumption. The Mine Accidents Central Control Office is constituted of mine managers and government officials, and it lacks scientific validity in analyzing the risk correlation only based on sensory-historical experience. It can lead to deviations in emergency response capability and cause unnecessary waste of costs, time, and energy. This study introduces scenario construction theory and multi-attribute dynamic combination algorithms to address this systemic challenges based on the data-driven model. Firstly, the dataset is structured by the induction method based on historical MWAs from 2003 to 2023 in China, and it is divided into nine accident attributes and three calculated categories. Secondly, the scenario parameters are selected with bottom-line thinking and correlation analysis of accident attributes. Thirdly, the scenario queue model and scenario disposal model are constructed based on the scenario construction theory with the dynamic combination algorithm; a systemic mathematical model is established to optimize the multi-objective (cost, time, and energy consumption) in emergency response. Finally, a real metal mine is selected for model simulation, based on data results to analyze algorithm variable reflected handle measure. This study provides a novel approach and scientific reference to improve emergency plans in mine safety management. It enhances the effectiveness and timeliness of emergency response in the MWAs.

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.

Rancang Bangun Alat Pengisian Laddle Nikel Matte Otomatis dengan Sensor HC- SR 04 dan Buzzer Menggunakan Mikrokontroller Atmega

As we already know that the majority of work accidents in mining in Indonesia are fatal. Advances in technology and shifting work practices can increase the number of work accidents due to a lack of understanding of workers at work locations. As a result, we need tools that can reduce the number of work accidents and the number of deaths due to work accidents. Microcontrollers are now very widely used because they are very useful and very helpful in solving problems. Microcontrollers are widely used in everyday life, for example traffic lights. Therefore, the author has the idea to create an automatic tool based on the Atmega microcontroller using the Research and Development (R&D) method. The design of this tool uses the Arduino Atmega microcontroller as a controller and the HC-SR04 sensor to detect the fill level in the laddle and a servo motor to open and close the filling valve. With this tool, it can overcome the problems of the mining industry in the form of human labor in very dangerous areas in the mining industry. The results of this study are that this tool is capable of filling, lifting, carrying and pouring matte nickel laddles automatically.

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.

Mining security assessment in an underground environment using a novel face recognition method with improved multiscale neural network

Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise separable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to construct a new multiscale model. The miniers can be recognized in the harsh underground environment during the life rescue. Experimental results show that the accuracy, recall and F1-score indexes of the proposed method for the miner face recognition in the underground mining environment are 97.26%, 94.17% and 95.42%, respectively. Transfer model with joint supervised loss can effectively improve the recognition accuracy by about 0.5–1.5%. In addition, the average recognition accuracy of the proposed face recognition method achieves to 91.34% and the miss detection rate is less than 5% in the dugout tunnel of coal mine.

Effects of dust concentration, particle size, and crude oil concentration on the explosion characteristics of oil-immersed coal dust

To investigate the explosion hazards of oil-immersed coal dust in co-existing mine areas, the explosion characteristic parameters of coal dust under different particle dimensions, coal dust concentration, and oil-immersed concentration were studied in a 20-L explosion chamber. The maximum explosion pressure of oil-immersed coal dust first decreases and then increases as coal dust particle size increases. For oil-immersed concentration samples between 2 and10 wt.%, the Kst value, and maximum explosion pressure of the oil-immersed samples were higher than those of the raw coal counterpart. The maximum explosion pressure of oil-immersed samples was 7.49%, 4.10%, 5.7%, 7.13%, and 4.10% higher than the raw samples. Moreover, when the oil-immersed concentration was 6 wt%, the minimum explosion concentration (MEC) of finer particles was the lowest, while the MEC for large particles was the highest. The impact mechanism of coal dust concentration on maximum explosion pressure is that the number of coal dust particles affects the distance between particles and heat transfer efficiency. The influence mechanism of particle size on the explosion of oil-immersed coal is that the particle size can reflect the specific surface area size, which affects the diffusion of oxygen in particles and the rate of combustible volatiles released by particles. Hydrocarbons released from the crude oil via heating and the complex organic molecules generated during high-temperature pyrolysis produced simpler hydrocarbons, generating hydrogen and higher temperatures that accelerated the onset of explosions and increased coal dust explosion intensity. These experimental results were of use to process safety working and can help prevent coal dust explosion accidents in underground coal mines.

Relationships among causal factors influencing mine accidents using structural equation modelling

Accidents occur due to a series of interactions between deficiencies within the various levels of a sociotechnical system. Quantifying the relationship between upper and lower levels helps develop accident countermeasures focusing on significant organisational latent conditions. This study explores the relationship between the causal factors of accidents within Ghanaian mines using SEM. Data obtained from the analysis of incident reports using HFACS-GMI were quantified to enable its use in the SEM software, as SEM calculations cannot be done using a 0/1 description. The study also tests five hypotheses, including the basic assumption of the HFACS model. The case study results showed that organisational factors significantly influence workplace/individual conditions; upper causal categories do not only influence adjacent immediate lower causal categories, and partial correlations exist between causal categories with a particular level. Based on the SEM model from LISERL, an accident causation path diagram was developed. The diagram reveals that leadership flaws, the technological environment and adverse physiological/mental states were the mediating factors in accident causation within the mines. The operational process has a prominent position in the organisational factors tier and is an essential factor in the entire accident system. Therefore, accident countermeasures should be directed to addressing operational deficiencies.