Mine Accidents Research Articles

The Impacts of Workplace Environment on Coal Miners' Emotion and Cognition Depicted in a Mouse Model.

Most coal mine accidents are caused by the unsafe behavior of employees. Previous studies have shown that there is a significant connection among the working environment, the psychological state of employees, and unsafe behaviors. However, the internal biological mechanism has not been revealed. To explore the physiological and psychological alterations of coal mine workers and the underlying mechanisms that cause unsafe behaviors, the current study established a novel coal mine environment biological simulation (CEBS) model in mice. This model recreated the underground workplace environment facts in coal mines such as temperature, humidity, and noise, and mice were employed to receive these conditioning stresses according to the 8-h work. Animal behavior tests were performed to evaluate the evolution of the mental state including anxiety and depression, as well as the abilities of learning and memory during the 4-week environmental simulation. CEBS mice showed the adaptation process of anxiety from occurrence to stability in the process of environmental simulation, and also suffered from severe depression compared to the control mice. In addition, impaired spatial memory was also implicated in mice after 4-week CEBS. The behavior results of CEBS mice were consistent with the previous psychological investigation of coal workers. In summary, a novel mouse model was established in this study to depict the occurrence of negative emotions and impaired cognition in coal miners by simulating the underground workplace environment, which provided a basis for further exploring the biological mechanism of miners’ unsafe behavior.

Analysis of Factors Influencing Miners' Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model.

Coal mine accidents seriously affect people’s safety and social development, and intelligent mines have improved the production safety environment. However, safety management and miners’ work in intelligent mines face new changes and higher requirements, and the safety situation remains challenging. Therefore, exploring the key influencing factors of miners’ unsafe behaviors in intelligent mines is important. Our work focuses on (1) investigating the relationship and hierarchy of 20 factors, (2) using fuzzy theory to improve the decision-making trial and evaluation laboratory (DEMATEL) method and introducing the maximum mean de-entropy (MMDE) method to determine the unique threshold scientifically, and (3) developing a novel multi-criteria decision-making (MCDM) model to provide theoretical basis and methods for managers. The main conclusions are as follows: (1) the influence degree of government regulation, leadership attention, safety input level, safety system standardization, and dynamic supervision intensity exert the most significant influence on the others; (2) the causality of government regulation, which is the deep factor, is the highest, and self-efficacy displays the smallest causality, and it is the most sensitive compared to various other factors; (3) knowledge accumulation ability, man–machine compatibility, emergency management capability, and organizational safety culture has the highest centrality among the individual factors, device factors, management factors, and environmental factors, respectively. Thus, corresponding management measures are proposed to improve coal mine safety and miners’ occupational health.

Norwegian and Russian Mining and Mining Communities in Monica Kristensen’s Oeuvre

Monica Kristensen is a Norwegian glaciologist, meteorologist, polar explorer and author of fiction (crime novels) and nonfiction (about expeditions, Svalbard, The Kings Bay Affair, and Roald Amundsen). In her altogether five crime novels, all of which take place in Svalbard, the plot is inextricably bound to place, i.e. to the Arctic. In two of her five crime novels, Kullunge and Den døde i Barentsburg, crucial parts of the action take place in the coal mines of Longyearbyen and Barentsburg. In her documentary book about the Kings Bay Affair she investigates the terrible mining accident in 1962 at Kings Bay mines that killed 21 miners. In this article I examine the literary construction of these specific places – the Arctic and the coal mines, or, the coal mines in the Arctic – in these three books, as well as the related political, ethical and existential questions of settlement and living conditions.

Cause Analysis of Coal Mine Gas Explosion Based on Bayesian Network

As one of the types of coal mine disasters and accidents, gas explosion is an important resistance affecting coal mine safety production. How to effectively identify gas explosion, find out the causes of gas explosion accidents, and make timely prediction and rectification is an urgent problem in coal mine industry. In this paper, the cause model of coal mine gas explosion is constructed based on the Bayesian network, and the cause chain of gas explosion accident is established by SPSS correlation analysis. The main factors leading to gas explosion accidents are found by parameter learning, reverse reasoning, sensitivity analysis, and key cause path. The results show that the main causes of mine gas explosion are gas accumulation and fire source explosion. The main causes of gas accumulation are no wind or breeze caused by local ventilator problems. The generation of electric spark is the main cause of fire source explosion. Cable damage, short circuit, mechanical and electrical equipment explosion, illegal operations, and lack of safety skills are also important factors leading to gas explosion.

Effect of opening blockage ratio on the characteristics of methane/air explosion suppressed by porous media

Gas explosion is a common serious accident in underground coal mines and industrial production processes. Porous media, due to its special cellular structure, has a significant effect on stopping the propagation of pressure and flame, which can effectively reduce the explosion hazard. In this study, the explosion suppression effect of Fe-Ni and Cu foams with different pore sizes (20 and 40 holes pores per inch (ppi)) was experimentally investigated and comparatively analyzed at opening blockage ratios (OBR) equal to 0.36, 0.64 and 0.84, respectively. The results demonstrate that Fe-Ni foam with 20ppi quenches the flame only under the OBR of 0.84, which indicates that the larger the OBR is, the better the explosion suppression effect is. However, under the OBR of 0.64, the porous media achieve an enhanced quenching efficiency and the shortest quenching time. In addition, when the vent area is relatively large, the obstacle effect of porous media is stronger than its pressure absorption capacity, thus leading to an increased pressure in the explosion area. The lowest peak overpressure attenuation rate, − 25.9%, is observed for 40ppi Fe-Ni foam under the OBR of 0.36. In general, the OBR is positively correlated with the depressurization capacity. Among all the cases used in this study, the 20ppi Cu foam boasts the highest upstream and downstream peak overpressure attenuation rates under three OBRs, 0.86%, and 6.33%, respectively.

Study on Safety Management Assessment of Coal Mine Roofs Based on the DEMATEL-ANP Method

Coal mine roof accidents are one of the main single risks faced by coal miners. According to the statistical data of coal mine accidents in China, there were 40 roof accidents and 55 deaths in 2020 alone, accounting for 32.8 and 24.4% of the total, respectively. Therefore, we can see its danger. To realize the comprehensive scientific assessment of coal mine roof accidents, first, through the collation and analysis of relevant literature reviews and accident investigation reports, combined with the expert investigation method, an assessment index system of coal mine roof accidents is constructed. Then, based on the analysis of the characteristics of the influencing factors of coal mine roof accidents, the assessment model of coal mine roof accidents is established by using the DEMATEL-ANP method. Finally, the established assessment model is applied to a coal mine to verify the rationality of the model.

Reform and development of coal mine safety in China: An analysis from government supervision, technical equipment, and miner education

Coal mine safety management has been one of the most successful public governance cases in China in the last 20 years. This paper systematically analyzes the reform measures and experience effects of the Chinese government in coal mine safety governance. It does so from three aspects: government supervision, technical equipment, and miner training. The results show that the number of coal mine accidents in China has fluctuated greatly, which can be divided into five stages. Small and medium-sized coal mines, dominated by township and village coal mines, are the key targets of coal mine safety management in China. Two policies —"shutting down small coal mines” and “integrating resources"—have strengthened the government's safety management of small coal mines. Secondly, the establishment of the State Administration of Coal Mine Safety has solved the supervision conflict caused by the multiple identities of agents and represents a turning point in China's coal mine safety management. Third, coal mine mechanization has a significant negative correlation with the number of accidental deaths and is a crucial measure for improving the intrinsic safety of coal mines. Fourth, the improvement of miners' overall education level has an obvious effect on enhancing coal mine safety. Following long-term safety education and skills training, the number of unsafe actions caused by knowledge deficits and cognitive errors gradually fell. More accidents are caused by the coal mine management giving wrong orders and illegal commands. Finally, this paper discusses the potential deficiencies of coal mine safety management in China at present, and makes some policy suggestions.

Physical Risks in terms of Occupational Health and Safety in Underground Coal Mines

Mining is one of the oldest professions in history. Along with the development of human life, mines also give direction to civilization and the economy. Mining activities carry various risks in terms of occupational health and safety (OHS). These risks can be examined under the main headings of work accidents and occupational diseases. As a result, thousands of workers face illness and death every year. Additionally, economic losses occur as a result of production interruption and stopping. Despite all the efforts to reduce mining accidents, statistics show that underground mining is one of the riskiest among all working industries. It is seen that the physical risk factors encountered in the mining sector line are remarkable in terms of risk factors. Physical risk factors that may be encountered in mines are dust, noise, vibration, lighting problems, and thermal conditions. It is essential to take measures to assess these risks and ensure the comfort of employees where each physical risk factor needs to be examined separately. In this study, physical risks being frequently encountered in underground coal mines are taken into account with recommendations for the risks.

Evaluation Method of Roof Damage Degree and a Case Study in Maoping Lead-Zinc Mine, Yunnan

With the continuous increase in the mining depth of underground mineral resources, the geological conditions encountered in mining have become more complex. The complex geological conditions have led to varying degrees of roof damage, especially the frequent occurrence of roof collapse accidents in metal mines, causing huge losses to mining enterprises. How to evaluate the risk of roof subsidence, falling, and even collapse under different geological conditions has become the primary issue. This article first selects the main evaluation indicators in the domestic and foreign roadway roof failure research literature for statistical analysis. Then, according to the statistical results, a classification approach of roof damage degree using the fuzzy comprehensive clustering method is established with roof rock strength, broken degree, roadway section size, buried depth, and roof sinking amount as evaluation indicators. The damage of the top plate is divided into five grades: minor damage, obvious damage, serious damage, extremely serious damage, and devastating damage. Finally, the established evaluation method was applied to the project site-supporting work of the 760 m main transport roadway in Yunnan Maoping Lead-Zinc Mine. The evaluation result is consistent with the actual situation on-site. The research results can provide a reference for the roof stability analysis under the background of this project and similar projects and at the same time help the next step in the classification control of different levels of roof stability and the design of the overall roadway support.

Review of preventive and constructive measures for coal mine explosions: An Indian perspective

Firedamp and coal dust explosion constitute a lion’s share in mine accidents in a global mining scenario. This paper reports a list of mine explosion disasters since last two decades, a critical review of the different prevention and constructive measures, and its recent development to avoid firedamp and coal dust explosion. Preventive legislation in core coal-producing countries, viz. China, USA, Australia, South Africa, and India related to firedamp and coal dust explosion are critically analysed. Accidents occurred due to explosion after Nationalisation of Coal Mines (1973) in India are listed. Prevention and constructive measures adopted in India are critically analysed with respect to the global mining scenario. Measures like methane credit concept, classification of mines/seams with respect to explosion risk zone, deflagration index; installation of automatic fire warning devices, canopy air curtain technology, explosion-prevention measures, such as fire-retardant materials, inhibitors, extinguishing agent, dust suppressor, and active explosion barrier are discussed in detail to avoid explosion and thereby adhering to zero accident policy due to coal mine explosion.

Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques

Coal industry is a typical high-risk industry with frequent accidents. In an effort to ensure workers’ safety and health, and reduce the probability of productivity decrease, it is essential to identify the contributing factors of coal mine safety production risks through certain technical means. Accident cases, as a concentrated display of accident hazard source, are of great value in extracting key risk factors that may induce coal mine disasters. Therefore, this study creatively proposed an effective method combining text mining, association rule mining and Bayesian network to deeply mine and use the massive coal mine safety accident case text data, so as to achieve effective identification of coal mine safety risk factors and explore the mechanism of interaction between risk factors and their importance. The research main included three steps. First, due to the high uncertainty and difference in the way of expression of the coal mine accident report texts, the conventional text mining process cannot effectively identify the risk factors, resulting in the incompleteness and deviation of the risk factors list. This study improved the text mining process, through Chinese word segmentation, keyword extraction, related word mining, semantic analysis, etc. to mine the collected 726 reports, and identify 78 safety risk factors. Then, the Apriori algorithm was used to obtain the extremely frequent itemset of risk factors and 362 strong association rules, and constructed the Bayesian network model on this basis. Finally, six main risk factors of coal mine safety production and their associated-factors were clarified through sensitivity and critical path analysis. The study shows that compared with the risks caused by the environment and equipment, the lack of management, education, and supervision are the root cause of coal mine accidents. This research provides a new way of thinking for effectively extracting using information from unstructured and non-standardized texts, as well as a new perspective for data-driven safety risk factors identification and complex interaction mechanisms research, having a great significance for coal mine safety risk pre-control management.

A Smart Helmet for Secure Monitoring of Miner Data to Improve Safety

Abstract: In recent times, mining has been a dangerous activity taking a severe toll on the lives of miners. Underground mining hazards include gas poisoning, suffocation, roof collapse, and gas explosion. This project aims to develop a smart helmet based on Zigbee wireless technology to build wireless sensor networks for real-time surveillance and monitoring hazardous gases and abnormal levels of temperature and humidity in the mine. These three parameters are continuously being detected using the gas sensor(s), temperature and humidity sensor, and if the predefined limit is crossed, a buzzer and LED will be initiated. Two infrared sensors, one inside and one on the surface of the helmet, are placed to check if the helmet is properly placed and to detect collision from falling debris, respectively. These values are continuously being transmitted to the control room for monitoring. In case of any hazardous situation, the led and buzzer are activated in the control room as well as on the miner’s helmet. The helmet is powered by the energy generated from piezoelectric sensors placed in the miner’s shoes, which work on the principle of vibration detected from the miner’s movement. The safety measures included in our project can drastically help us avoid mining accidents and thus saving invaluable lives. Keywords: Smart helmet, Zigbee Technology, Gas sensor, Temperature and Humidity sensor, Infrared Sensor, Coal mines, Safety

Molecular characteristics of coals at different coal seams in the same mine obtained by high performance separation methods

Two coal samples in the same mine but different coal seams, soft coal and hard coal, were subjected to a parallel thermal dissolution process using two organic solvents to obtain four soluble portions (SPs). A preparative high performance liquid chromatography (prep-HPLC) with a fraction collector was introduced to obtain twenty-eight fractions from the SPs. Both gas chromatograph/mass spectrometry (GC/MS) and Orbitrap-MS with a collision cell were employed to elucidate the molecular structure of soluble species. Some heavy aromatic compounds in SPs not identified by GC/MS were detected in fractions, indicating that the off-line prep-HPLC is of perfect separation and enrichment ability, and can improve the reliability of analytical data. In addition, to preferably elucidate the difference in composition of compounds between the two coals, the possible fragmentation pathways in the collision cell were proposed. Such accurate identification will provide theoretical guidance for the efficient utilization of coals in the same mine but different coal seams, and the safety guidance to avoid mine accidents generated by the inconsistence in coal seams.

Coal mining accident causes classification using voting-based hybrid classifier (VHC)

Coal mining accident causes classification using voting-based hybrid classifier (VHC)

Modeling the causes of accidental gas explosions from the perspective of safety information loss

AbstractTo effectively prevent gas explosion accidents in coal mines, this study analyzes the flow of safety information in complex systems and subsequently reveals the influence of information loss on accident evolution. Safety information in the context of gas explosion accidents is defined and combined with related theories of informatics to analyze the process and the loss of safety information flow in 30 gas explosion incidents. Thereby, a chain of safety information loss is established. Further, based on the basic theories of cognitive science, organizational behavior, and the cause of safety information loss, an accident‐cause model for gas explosions is constructed. The model is applied to a gas explosion incident to determine the causes of the accident, thus verifying its applicability. In addition, the corresponding preventive measures are proposed. The results indicate that the loss of safety information affects gas explosion accidents in coal mines, and the proposed accident‐cause model can provide theoretical support for the prevention and further research of gas explosion accidents.

Investigation on gas drainage effect under different borehole layout via 3D monitoring of gas pressure

Gas drainage using underground boreholes is an important method for the prevention of coal gas mine accidents. To study the influence of the increase in the number of boreholes on the gas drainage effect using a self-developed device for gas drainage, pressure sensors and branch boreholes were installed in a coal body. Four groups of physical simulation experiments of gas drainage were conducted. The results revealed that with an increase in the number of boreholes, the attenuation rate of the gas pressure is accelerated; the decrease in the gas pressure accelerates in the form of a natural logarithmic function. The effective drainage area, with characteristics of dynamic expansion and variations, has an approximately circular expansion with the borehole as the center. It gradually increases with the advancement of drainage. The relationship between the effective drainage area and the extraction time is in the form of a power function. With an increase in the number of boreholes, the effective drainage area increases more rapidly, and the regional outburst elimination occurs earlier. The effective stress and matrix shrinkage effect exhibit a coupled influence on the permeability, which causes the permeability evolution to exhibit periodic variations. At the early stage of drainage, the effective stress plays a major role, which reduces the permeability. At the middle and late stages of drainage, the matrix shrinkage effect plays a major role, which increases the permeability. With an increase in the number of boreholes, the attenuation rate and recovery amplitude of the permeability increase.

Evaluation on the Risk of Water Inrush Due to Roof Bed Separation Based on Improved Set Pair Analysis-Variable Fuzzy Sets.

To study and prevent bed separation water inrush accidents in coal mines, it is necessary to evaluate the risk according to the limited geological data correctly. In this work, based on hydrogeological and mining conditions, we established a risk evaluation model and selected seven important factors, including the aquifer thickness, aquifer water abundance, hydraulic pressure of the aquifer, effective aquifuge thickness, mining failure ratio, mining height of the working face, and advancing distance as evaluation indexes. The intuitionistic fuzzy analytic hierarchy process (IFAHP) and entropy weight method (EWM) were used to analyze the weights of the original data, and the minimum information entropy principle was used to further integrate the abovementioned calculation results. With the weight results, set pair analysis–variable fuzzy set (SPA-VFS) theory was applied to determine the risk grade of each working face, which provided scientific guidance for the safe mining of coal mines. For the working face where water inrush may occur, the risk of bed separation water inrush can be reduced by optimizing the parameters or changing the mining conditions through the model analysis.

Spherical Robots for Special Purposes: A Review on Current Possibilities.

The review discusses the possibilities of different driving mechanisms and sensors of spherical robots, and a special kind of mobile robots is introduced and discussed. The sensors discussed can expand robots’ sensing capabilities which are typically very limited. Most spherical robots have holonomic characteristics and protect the inner environment using a shell. Today, there are a diversity of driving mechanisms. Therefore, this article provides a review of all of them and identifies their basic properties. Accordingly, many spherical robots have only inner sensors for moving, balancing, driving, etc. However, a few of them are also equipped with sensors that can measure environmental properties. Therefore, in this paper, we propose the possibility of using such sensors as cameras, LiDARs, thermocouples, and gas sensors, which can be used for special purposes underground, for example, in mines, underground tunnels, or road tunnels. After combining all components are combined, it is possible to design a special type of spherical robot designed for underground exploration, such as accidents in mines or road tunnels.

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

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

An analysis of gas accidents in Chinese coal mines, 2009 – 2019

Underground coal mining has long been a dangerous occupation, particularly in China, where the incidence of gas-related accidents is high. This paper analyzes the frequency and the temporal and spatial distribution of major gas accidents in China that resulted in three or more deaths during the period 2009–2019. The analysis reveals a downward trend between 2009 and 2014, followed by relative stability from 2015 to 2019. The number of gas-related accidents was higher in some months than others, and most accidents occurred between 9 a.m. and 12 noon. There is also a clear geographical distribution pattern, with the majority of accidents occurring in provinces in the southwest region of China. While the immediate cause of these accidents is unsafe conditions or unsafe behavior, the study finds that the root cause is the lack of a safety culture. The article ends by making some suggestions for accident prevention, under the categories of technical measures, safety management, and safety culture.