Coal Mine Gas Explosion Research Articles

Risk Assessment of Unsafe Acts in Coal Mine Gas Explosion Accidents Based on HFACS-GE and Bayesian Networks

Even in the context of smart mines, unsafe human acts are still an important cause of coal mine gas explosion accidents, but there are few models to analyze unsafe human acts in coal mine gas explosion accidents. This study tries to solve this problem through a risk assessment method of unsafe acts in coal mine gas explosion accidents based on Human Factor Analysis and Classification system (HFACS-GE) and Bayesian networks (BN). After verifying the reliability of HFACS-GE framework, a BN model of risk factors of unsafe acts was established with the Chi-square test and odds ratios analysis. After reasoning analysis, risk paths and key risk factors of unsafe acts were obtained, and preventive measures were granted. Based on the analysis of 100 coal mine gas explosion cases, the maximum probability of five kinds of unsafe acts of employees is 38%. Among the 22 risk factors, the mental state of employees has the greatest influence on the habitual violation of regulations, and the sensitivity value is 12.7%. This study can provide technical assistance for the risk management of unsafe acts in coal mine gas explosions.

Identification and Prediction of Thermodynamic Disasters During Deep Coal Mining

Researching the methods to identify and predict thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation, however, existing studies only built static models without evaluating or predicting the development trend of thermodynamic disasters, the research on dynamic modeling methods and rescue decision-making is insufficient, and they generally ignored the mechanism of mutual conversion between fire and gas explosion in deep coal mines. Thus, this paper aims to study the identification and prediction of thermodynamic disasters during deep coal mining. At first, the method for analyzing the thermal field in deep coal mining areas is introduced in detail, and the finite element thermal analysis method is adopted to study the thermodynamic disasters during deep coal mining; then, this paper establishes a thermodynamic disaster prediction model based on the improved Kernel-based Extreme Learning Machine (KELM), and introduces the improved Crow Search Algorithm (CSA) to solve the instability of prediction results caused by artificial selection of model parameters. At last, this paper uses experimental results to verify the validity of the proposed model.

Research on coal mine hidden danger analysis and risk early warning technology based on data mining in China

The development of intelligence and informatization is the inevitable trend of safety production in coal enterprises. Data mining technology plays an important role in promoting the development of coal mine safety management. This paper uses natural language processing technology and the Bi-LSTM model to automatically classify the hidden danger information and extract the theme of the coal mine. Then, taking the risk of a coal mine gas explosion as the starting point, the gas safety situation prediction model is constructed. Finally, an accurate management and control information system based on data mining technology and intelligent technology is developed, which realizes the information analysis of a large visual screen and the intelligent information processing of the APP. The system has been applied in the Luxi coal mine, and the application effect is good, which significantly improves the safety management ability and efficiency of the coal mine. This study can provide new ideas and methods for the dual pre-control construction of coal mines, strengthen the information processing capacity of the existing pre-control system, and promote the healthy development of the coal industry.

Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network

Abstract Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing machine (NTM) deep learning network algorithm is used to calculate and analyse the risk source early warning identification of coal mine gas explosion accidents. Institute with data sets of gas gas accident knowledge base matter each event to cause an (basic or intermediate events) as an example, through the study of the depth of NTM network algorithm calculation analysis shows that self-rescuer failure, personnel peccancy operation, such as downhole safety management does not reach the designated position is easy to cause important hazard of gas explosion accident, the probability to cause an 0.567. Based on the constructed NTM deep learning network algorithm, the risk factors and their weights in the early warning identification of gas explosion accidents are calculated and analysed. Through calculation analysis, it can be seen that the highest weight of risk factors is gas concentration, with a weight of 96. In the early warning identification of hazard sources, the hazard factor next to gas concentration is mine combustibles, with a weight of 75.

Modelling and analysis of unsafe acts in coal mine gas explosion accidents based on network theory

Coal mine accident prevention is a key issue affecting the safety of coal production in China. From accident analysis, it has been found that the interaction of unsafe acts of coal miners is the main cause of catastrophic accidents, and that ignoring the interrelationship between behaviors may lead to missing hidden chains of incidents as well as to a gross underestimation of behavioral risk. Therefore, to solve the above problems, it is of significant practical importance to explore unsafe acts in accidents as a collection and to analyse their evolution and development trends. The purpose of this study is to analyse the interrelationships and potential behavior patterns among unsafe acts in coal mine gas explosion accidents from the perspective of network modelling and to propose a modelling method for the mine accident unsafe acts network (MAUAN). First, by mining the causes in 86 gas explosion accident reports, a MAUAN network composed of 95 unsafe-act nodes and 681 edges was determined. Second, by calculating the topology of the MAUAN, 3 key unsafe acts and 6 key behavior paths in the gas explosion accidents were determined. The research results show that 91% of unsafe acts in gas explosion accidents were violations. The lack of safety education and skills training for miners is the most influential unsafe act in gas explosion accidents, and the absence of operating procedures or prevention measures → failure to check gas concentration → risky operation without safety safeguards was the most critical link leading to the occurrence of gas accidents. Third, illegal mining practices have become another urgent problem today—in addition to gas accumulation and ignition source generation—with 83% of gas explosion accidents involving illegal mining. This study provides a reference for accident causation modelling and the identification of key causal factors from accident report data, and the results of the analysis provide a basis for decision making in coal mine accident prevention efforts to reduce future mining accidents.

Method Construction and Evaluation: A More Comprehensive and Reliable Classification of Coal Mine Gas Explosion Causes

Coal is an important fossil fuel energy that occupies a high position in the energy use of China and even the world. However, gas explosions are still the deadliest coal mine accident in China, which has long plagued the safety of energy mining. Only through accident cause analysis can we know the exact cause of the accident so as to make targeted policies, safety trainings, etc. However, the lack of detailed accident cause classification in current coal mine gas explosion accidents affects the comprehensiveness and accuracy of energy mining safety strategies. Therefore, in this study, a classification method for coal mine gas explosion accident causes based on the sixth edition 24Model and the three-element classification of gas explosions was proposed. Then, the consistency and validity of the newly established classification system were evaluated based on the three indicators, i.e., observer consistency, content validity, and criterion validity, and the performance of the classification system was verified. The results showed that the classification method exhibits good consistency and validity, and, compared with other classification methods, it can significantly improve the comprehensiveness of accident cause analysis results so as to obtain a more scientific energy mining safety strategy. In addition, the process used in this study to construct the classification and evaluate the performance of the classification is transferable, and it can provide a reference for the construction and evaluation of accident cause classifications in other fields.

Study on the reliability of accident analysis results: Taking two groups of four accident analysis references with the 24Model as samples

Accident prevention depends on accident analysis. In order to improve the reliability of accident analysis and find the common problems related to reliability in accident analysis, two groups of four papers of master’s and doctor’s theses which applied 24Model to analysis coal mine gas explosion accident were selected as samples. The unsafe act results obtained from the analysis are compared. The results show a low consistency level, which reflects that the reliability of the output needs to be improved. Through analysis, the inconsistency of details in causes description, the difficulty in the cause description and the omission of unsafe acts are the main reasons leading to the above situation. In order to standardize the description of cause items, the components of unsafe acts were defined as “premise + action”, and seven types of premises were summarized to judge unsafe acts. In order to improve the efficiency and reliability of accident analysis, it is suggested that the current data should be taken as reference, and the taxonomy based on consequences should be established to unify unsafe act detail description level, and the corresponding computer software should be developed to assist accident analysis.

Research on causes of coal mine gas explosion accidents based on association rule

Statistical analysis of accidents is an important part of safety management and accident prevention. In this study, the case information of gas explosion accidents was analysed to deeply explore the accident characteristics. A total of 125 coal mine gas explosion accident reports in China from 2010 to 2020 were considered as the original data, and 30 causal factors of gas explosion accidents were extracted from the macro, meso, and micro levels. The Carma algorithm was used to mine the frequent itemsets of the sample data. The potential regular characteristics of gas explosion accidents were further explored by determining the association rules between the causal factors of the itemsets. The results show that the probability of gas explosion accidents caused by the violation of operating procedures, disordered ventilation systems, or illegal production organisations is high. Many strong association rules generated by human-management coupling were observed. The strong association rules of multi-factor coupling mainly include human-equipment-management coupling. This study provides new ideas for the prevention of gas explosion accidents.

Superposition risk assessment of the working position of gas explosions in chinese coal mines

The prevention of gas explosion accidents is a key factor in enhancing the safety of production of coal mines. The effective management and control of the working position risks is of great significance for the prevention of such accidents. By analyzing data from 201 typical gas explosion accidents, 21 working positions related to the accidents were identified along with the main causing factors. By employing data statistical methods, the structural importance analysis method of fault tree analysis, and the summation method, the risk associated with each working position was evaluated. Then, this work built a risk assessment matrix to categorize risk levels associated with various working positions related to gas explosions in coal mines. Also, based on the kernel density analysis, a superposition risk assessment model was established. In addition, the ArcGIS software was used to simulate and calculate the superimposed risks and draw a risk distribution map. The results show that the number of working positions of Level I is mainly affecting the fully mechanized mining team, the fully mechanized excavation team, and the ventilation team. The risk distribution map intuitively shows that the high-risk working positions involving gas explosion accidents in Chinese coal mines mainly exist in the working face and the tunneling face. It is highlighted that the working position risk and grade distribution have a significant hierarchy after risk superposition.

Dynamic Response of the Steel Plate Under the Impact of High-Speed Coal Projectile

The coal-rock projectiles induced by gas explosions in coal mines have a strong destructive effect on mine facilities and equipment, which are mostly made of steel. The LS-DYNA is used to simulate the process of coal projectile striking the steel plate. The results show that during the failure process, the morphological change of the projectile consists of three stages including plastic deformation, partial crushing, and complete crushing, and the steel plate pits at the impact point. The duration of the stress peak value of the steel plate increases as the impact velocity increases. The farther away from the center point, the smaller the stress peak value is, and the shorter the duration is. When the impact velocity is 100 m/s and 300 m/s, the axial velocity curve at the impact point of the projectile is pulsating. When the impact velocity reaches 500 m/s, the axial velocity of the projectile rapidly increases and then tends to be stable. As the impact velocity increases, the energy absorbed by the steel plate increases, and the rate of increase also increases. However, the proportion between the absorbed energy of the steel plate and the initial kinetic energy of the projectile decreases. The projectile conversion energy of the 1 mm steel plate is slightly larger than that of 1.5 mm and 2 mm steel plates, and the energy absorbed by the steel plate decreases with the increase in thickness of the steel plate. When the thickness of the steel plate is more than 2 mm, the thickness of the steel plate has little effect. The results of the study have a direct sense for the antiknock design of coal mine facilities and equipment.

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.

Instantaneous discharge characteristics and its methane ignition mechanism of coal mine rock damage.

The goaf is the main site of gas explosions in coal mines. A large number of accidents indicate that the occurrence of gas explosion in the goaf is related to the deformation and fracture of the goaf roof. An experimental system was constructed to study the instantaneous discharge characteristics and its ignition mechanism caused by rock damage. The research results reveal that different rocks produce avalanche-like dust clouds ejected outward during the fracture process, and the generation process takes an extremely short time. This dust cloud is not caused by rock fragments heated to incandescence because of friction. The electric sparks produced in the process of rock rupture and the appearance of instantaneous discharge are related not only to the quartz content of the rock, but also to the compressive strength of the rock. The piezoelectric effect of the rock becomes the key factor for the strength or the generation of an electric spark. The instantaneous discharge leaded to the collision of high-energy electrons released during the fracture process with air molecules, ionising, and breaking down the air. When the surrounding medium is gas air within the explosion limit, ionisation is formed and causes gas explosions.

An Environmentally Friendly Antioxidant Foamed Gel for Inhibiting Spontaneous Combustion of Coal

ABSTRACT Coal spontaneous combustion can cause serious coal mine fire and gas explosion. In order to overcome the problem that the effect of existing materials to inhibit coal spontaneous combustion is reduced at high temperature and some environmental protection problems of materials, this study developed an effective, green and environmental protection antioxidant foamed gel (AFG). Modified sodium polyacrylate, konjac glucomannan, sodium dodecyl sulfate, sodium alpha-olefin sulfonate, modified silicone polyether microemulsion, montmorillonite, and tert-butyl hydroquinone were selected as raw materials. A single factor experiment was conducted to determine the appropriate adding proportions for each component. Then, the optimal proportion for the five components were fitted and verified by response surface analysis. The structures of the samples were characterized, and their properties were compared and tested. The fluidity, water loss, and thermogravimetry experiments demonstrated that the effect of AFG was excellent. In the experiment of in situ diffuse reflectance Fourier transform infrared spectroscopy, the AFG inhibition reaction was verified. Finally, combined with the structural characteristics of the AFG, the following inhibiting mechanisms were confirmed: AFG adhered to the coal surface and blocked the coal–oxygen contact; the antioxidant component slowed down chemisorption and the chemical reactions; AFG interrupted the free radical chain reaction.

Experimental investigation on the effect of obstacle position on the explosion behaviors of the non-uniform methane/air mixture

Gas explosions in the underground coal mines where the obstacles exist can cause significant damage. A current knowledge gap in methane safety research concerns the effect of obstacle position on the explosion process of non-uniform mixtures. In this work, the effect of obstacle position on the explosion behavior of non-uniform methane/air mixture is investigated experimentally. The results show that the triple flame structure appears during the combustion of the non-uniform mixture, which appears delayed with increasing obstacle position. Then, broken flocculent turbulent flames occur when the obstacle is set at 400 and 600 mm, with the strongest turbulent disturbance response at 400 mm case. Moreover, the results show that the maximum flame front velocities and the maximum values of overpressures are obtained at the obstacle position of 400 mm in both diffusion times. Additionally, two obvious pressure peaks are found, which appear at increasing intervals as the distance between the obstacle and the ignition source increases. Meanwhile, the maximum overpressure values ranks as Pmax(400 mm) > Pmax(200 mm) > Pmax(600 mm). The study of gas explosion flame characteristics that meet the actual working conditions of coal mines plays an important role in the prevention of explosion accidents.

Trends and correlation characteristics of coal mine gas explosion accident factors: a case study

ABSTRACT Most statistical analyses of coal mine explosion accidents involved single-factor problem assessment. To investigate the trend and correlation of accident causes, then implement targeted prevention and control measures for gas explosion accidents, explosion accidents that occurred in China’s coal mines in 2000–2020 were statistically analyzed. The results show that working face and transportation roadway are the main sites of explosions, accounting for 80.48% of all such incidents. The causes of ventilation failure are significantly reduced, and the windless breeze has become the most prominent cause of gas accumulation in recent years. Electrical sparks remain the highest ignition source, but the proportion of gas explosion accidents induced by illegal blasting is increasing. Gas explosion accidents in the working face which are accumulated by no breeze, ventilation failure, and detonation by electric sparks or blasting flames, are the most frequent. The roadway has the strongest correlation therewith. The statistical analysis provides a reference for the prevention of coal mine explosion accidents.

Analysis of the Interaction Mechanism of the Risk Factors of Gas Explosions in Chinese Underground Coal Mines.

Gas explosion accidents easily cause severe casualties in Chinese underground coal mines. Systematic analysis of accident causation is crucial for the prevention of gas explosions. This study identifies the representative risk factors of gas explosions and determines the interrelationship among these risk factors to highlight weak links and develop countermeasures. A total of 21 representative risk factors of gas explosions were identified through 128 case studies and front-line investigations. On this basis, a five-level hierarchical structure model of gas explosions was established to explore the complex interrelationships among the representative risk factors based on a combination of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods. Moreover, the Matrix of Cross Impact Multiplications Applied to Classification (MICMAC) method was applied to achieve risk factor classification into four clusters, namely, driving factors, linkage factors, dependent factors and autonomous factors. The results indicated that the interrelationships and emergence properties among the risk factors may cause gas explosions, which should give more attention to the interrelationships among multiple factors and multiple subsystems for coal enterprises. Meanwhile, the complex geological conditions, poor safety supervision, inadequate safety education and training, incomplete execution safety regulations and poor safety technology and input are the long-term focus of risk management for coal enterprises. Finally, 10 countermeasures were proposed to control these representative risk factors and interrelationships. The results are helpful to the development of gas explosion risk management policies and to the preferential allocation of limited resources to resolve these issues.

Study on serum metabolomics of combined injury induced by gas explosion in rats

Objective: To analyze the changes of serum metabolomics in rats with combined injuries caused by gas explosion and explore its possible mechanism. Methods: In April 2018, the large coal mine gas explosion test roadway and explosion test system were used to simulate the gas explosion experiment. All 32 SD rats were randomly divided into four groups, control group (not involved in the explosion) , close range (40 m) group, medium range (160 m) group and long range (240 m) group, 8 in each group. The respiratory function at 2 hours and the neural behavior at 48 hours were detected after the explosion. The rats were anesthetized and sacrificed after 48 hours, and the serum, lung, liver and other tissues of the rats were isolated and histopathological changes of lung and liver tissues were observed by HE staining. Serum samples were detected by liquid chromatography-high resolution mass spectrometry (UPLC-Orbitrap Elite/MS) , and metabolic spectrum differences between groups were evaluated by principal component analysis. Differential metabolites were screened and identified, and metabolic pathways were analyzed. Results: Compared with control group, respiratory function indexes (respiratory frequency, minute ventilation, peak inspiratory flow rate, peak expiratory flow rate and 1/2 tidal volume expiratory flow) of rats in different explosion groups were significantly decreased (P<0.05) , but respiration pause, inspiratory time and 2/3 tidal volume required time were significantly increased (P<0.05) in 2 hours after the explosion. However, the residence times of the neurobehavioral indicators of the 40 m group and 160 m group were significantly increased (P<0.05) , and the movement distances were significantly decreased (P<0.05) in 48 hours after the explosion. HE staining results showed that the lung and liver tissues of the rats in the gas explosion group structurally damaged, and the cells were disordered, with inflammatory cell infiltration, bleeding and edema. Metabonomics analysis showed that there were significant differences in metabolic profiles between groups. A total of 18 differential metabolites were identified in serum samples, including aconitum acid, citric acid, niacinamide and pyruvate, which involved in 12 major metabolic pathways, including the glutamic acid and glutamine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glyoxylic acid and dicarboxylic acid metabolism, phenylalanine metabolism, nicotinic acid and nicotinamide metabolism, citric acid cycle (TCA cycle) . Conclusion: Gas explosion can cause multi-organ system damage in rats, the mechanism of which may be related to the biosynthesis of alanine, tyrosine and tryptophan, metabolism of niacin and niacinamide, metabolism of acetaldehyde and dicarboxylic acid, and TCA cycle, etc.

How to analyse the injury based on 24Model: a case study of coal mine gas explosion injury

BackgroundCoal mine gas explosion injury has caused mass casualties, which have resulted in widespread concern.MethodsIn order to prevent gas explosion injury in coal mine, 24Model is proposed to analyse the...

Changes and significance of autophagy in rat lung injury induced by gas explosion

Objective: To explore the changes and significance of autophagy in acute lung injury (ALI) induced by gas explosion in rats. Methods: In February 2018, the gas explosion in underground coal mine was simulated by large tunnel explosion experiment system, SD rats were randomly divided into control group and 6 distance groups (40 m, 80 m, 120 m, 160 m, 200 m, 240 m) with 18 rats in each group. The respiratory function of rats 24 h before and after explosion was detected. Post-explosion rats were anesthetized and sacrificed, histopathological changes of lung were observed by HE staining. Immunohistochemistry was performed to detect the in situ expression of autophagy marker protein microtubule-associated protein 1 light chain 3 (LC3B) . The expression levels of autophagy related gene 12 (Atg12) , LC3B, P62, lysosomal associated membrane protein 2 (Lamp2) , B-cell lymphoma/leukemia-2 (Bcl-2) and Bcl2 interaction protein (Beclin-1) were detected by Western blot. Results: After gas explosion, the rats in 80 m distance point group had the hightest mortality (n=13, 72.22%) and the most severe lung injury degree, and the histopathological scores was (4.00±0.00) point. After gas explosion, the minute ventilation volume (MVb) , maximum inspiratory flow rate (PIFb) and maximum expiratory flow rate (PEFb) of rats were lower than before the gas explosion (P<0.05) . The respiratory frequency of rats in 80 m, 200 m, and 240 m distance point groups were significantly higher than that in the control group (P<0.05) . The expression levels of LC3B in 40 m, 80 m, 120 m, 160 m, and 200 m distance point groups were higher than that in the control group (P<0.05) . The relative expression levels of Atg12 and LC3BⅡ/Ⅰ in lung tissues of rats in different distance point groups were higher than those in the control group (P<0.05) . The relative expression levels of Beclin1 in 40 m, 80 m, 120 m, and 160 m distance point groups were significantly higher than that in the control group (P<0.05) . The relative expression levels of P62 in 80 m, 160 m and 200 m distance point groups were lower than that in the control group (P<0.05) . The relative expression levels of Lamp2 and Bcl-2 in lung tissues of rats in all distance groups except 240 m distance group were lower than those in the control group (P<0.05) . Conclusion: Gas explosion could induce increased autophagy in lung tissues of ALI rats. Autophagy-related signaling pathway could be involved in the pathophysiological process of ALI in rats caused by gas explosion, then the autophagy and the severity of the lesion showed a significant positive correlation.

Removal of CO Generated by a Gas Explosion Using a Cu-Mn Elimination Agent.

Addressing the issue of suffocation and casualties caused by a large amount of poisonous CO gas generated after a gas explosion, research involving an experimental system for the removal of CO using a Cu–Mn elimination agent was studied. The influence of O2 concentration, temperature, and CO concentration on the elimination performance of the agent after a gas explosion was studied. The quantitative relationship between the amount of CO eliminated, the elimination rate, the O2 concentration, and temperature was analyzed. Further analysis was completed regarding the influence of O2 concentration, temperature, and CO concentration on the thermal effect in the elimination process. The results showed that the elimination agent had a rapid effect on the removal of CO. When the ratio of CO concentration to O2 concentration was closer to the stoichiometric ratio, the elimination and reaction were more complete, the time to complete elimination was shorter, and the peak temperature was higher. As the temperature increased, the time to reach the elimination limit became longer, the elimination rate decreased, the reaction was slower, and the peak temperature was lower. As the CO concentration increased, it was observed that the higher the peak temperature, the longer it took to reach the peak time. The results of the study provide a theoretical support for the catalytic oxidation of CO using the Cu–Mn eliminator after a coal mine gas explosion.