Mine Accidents Research Articles

Research on coal mine safety management based on digital twin

Coal mine safety management is the foundation and decisive factor of coal mining. The manual detection model is the main way for traditional coal mine safety management, which has problems such as inefficient identification of safety risks in coal mines, poor control accuracy and slow response measures and so on. Therefore, to make up for the shortcomings in the traditional coal mine safety management model, this paper introduces digital twin technology into coal mine safety management to achieve intelligent and efficient management of coal mine safety accidents. Firstly, we introduce the digital twin technology, select the five-dimensional model as the modeling basis, based on the existing twin model architecture, analyze the types of coal mine accidents and disasters, select the most destructive gas accidents as the research object, construct a twin safety management model for coal mine gas accidents using the digital twin five-dimensional model. Secondly, analyses of the actual operation mechanism of the digital twin model, and the advantages of the twin model in achieving prior prevention, rapid response and accurate control of gas incidents are pointed out. Finally, the house of quality of the gas accident digital twin model is established through the quality functional deployment tool, and key technical requirements for the construction of the twin model are given to accelerate the application of the gas accident twin model in the field.This study innovatively introduces digital twin technology into the field of coal mine safety management, proposes the application scenarios of emerging technologies such as digital twins in the coal mine field, and provides the possibility of multi-scene application of smart mine construction and technologies such as digital twins.

Study on flame propagation characteristics of coal dust explosion in horizontal straight pipe

AbstractCoal dust explosion is one of the common accidents in mines. The flame generated in the explosion process spreads rapidly, and the high-temperature flame will cause personal injury and equipment damage in the working environment. In order to study the flame propagation characteristics of coal dust explosion, four kinds of coal dust explosion flame propagation experiments were carried out by using the intelligent coal dust explosion identification device, and the flame propagation process was simulated by computational fluid dynamics (CFD) simulation. The results show that the flame length of lignite dust explosion is obviously longer than that of long flame coal, non-caking coal and gas coal. From the ignition of lignite coal dust, the flame length increases continuously, reaches the maximum of 52 cm at a certain time and then gradually decreases to 0. While the flame length increases, the flame propagation rate also experiences the propagation process of accelerating and decelerating. By comparing the explosion flame length obtained by CFD numerical simulation with the experimental results, it is found that the simulation error is within the acceptable range. Further statistical analysis is made on the simulation results of overpressure generated by explosion, and it is found that the change law is consistent with the theoretical formula, so as to further verify the feasibility of numerical simulation for explosion research. The research results lay a theoretical foundation for predicting the disaster scope of coal dust explosion accidents, and have practical significance for taking explosion suppression and flameproof measures in mines.

Construction and Application of an Intelligent Roof Stability Evaluation System for the Roof-Cutting Non-Pillar Mining Method

In order to sustainably use coal resources and reduce coal mine accidents, the stability evaluation of roadway roofs is particularly important. The existing methods of roof stability evaluation and control application are greatly disjointed, the relationship between roof stability evaluation and early warning control is ignored, and an intelligent evaluation and calculation control system is lacking. Based on the successful application of the roof-cutting non-pillar mining method in various engineering geology and mining conditions, the roof stability evaluation system, mobile intelligent computing system, and engineering application research are carried out. An evaluation index system for roof stability in the roof-cutting non-pillar mining method is established, including the roof rock integrity and the roof-surrounding rock displacement. A comprehensive evaluation method for roof stability grades is proposed based on the coupling of evaluation index grading criteria and improved analytic hierarchy process (AHP) weight assignment. A handheld mobile intelligent platform for roof stability evaluation, roof hazard zone, and control suggestion is developed. The research results have been applied in the coal mine of Hecaogou with good outcomes. This intelligent stability evaluation system will provide an economical and effective approach to achieving sustainable use of coal resources.

Grenzen der Sicherheit. Unfälle, Medien und Politik im deutschen Kaiserreich ed. by Amerigo Caruso and Birgit Metzger

Reviewed by: Grenzen der Sicherheit. Unfälle, Medien und Politik im deutschen Kaiserreich ed. by Amerigo Caruso and Birgit Metzger Jens-Uwe Guettel Grenzen der Sicherheit. Unfälle, Medien und Politik im deutschen Kaiserreich. Edited by Amerigo Caruso and Birgit Metzger. Göttingen: Wallstein, 2022. Pp. 293. Hardcover €36.90. ISBN 978-3835339064. This edited volume is a much-needed intervention in newly invigorated debates about the German Empire. The volume's concrete focus on accidents, their reception, politicization, and utilization in the Kaiserreich before 1914 provides depth and details to discussions that, after the critique of Hedwig Richter's Demokratie. Eine deutsche Affäre, have tended to unfold within, at times, rather lofty historiographical meta-levels. Grenzen der Sicherheit brings us back down to earth (even below the earth's surface level with respect to mining accidents) as the book covers everything from non-military shooting accidents (Dagmar Ellerbrock) to collisions of naval ships (Sebastian Rojek) and zeppelin accidents (Jürgen Bleibler, Rüdiger Haude), to accidents within the army (Birgit Metzger), to mining and industrial disasters (Michael Farrenkopf, Fabian Trinkaus), to the accidental deaths of prominent individuals such as Friedrich August II of Saxony (Amerigo Caruso) and the pioneer of aviation Otto Lilienthal (Peter Busse/Bernd Lukasch). Grenzen der Sicherheit provides us with the history of accidents in the German Empire and thus combines two separate debates: inquiries into the handling of physical risks and the changing attitudes towards and demands for individual and collective security on the one hand, and on the other, longstanding questions about the character of the first German nation state and the ambiguities of modernity (7–8). The book covers these issues with great verve and clarity, and the individual [End Page 157] chapters show repeatedly how significantly perceptions about risk, risk management, and related social controlling and policing mechanisms changed between the middle years of the nineteenth century and the early twentieth century. Dagmar Ellerbrock's chapter on gun-related accidents highlights this development, as it traces both the changing public attitudes toward private shooting accidents and deaths and the correspondingly shifting policy responses, thereby clearly demonstrating that in the early years of the twentieth century these events were seen as a security problem in need of policy solutions, while the same issues had been perceived as natural risks half a century earlier. Other chapters of the book sketch out similar developments, among them Michael Farrenkopf's contribution on mining disasters and Fabian Trinkaus's piece on accidents in the iron and steel industry. Yet Grenzen der Sicherheit also shows that social and political responses did not always sync up with the growing knowledge and awareness of certain risks. A partial exception to this "norm" is presented in the book's chapters on aircraft accidents. Especially with respect to the frequent and disastrous accidents of Count Zeppelin's airships, Farrenkopf and Trinkaus both show that public reaction nevertheless remained favorable to these machines, as they were seen as symbols of German technological prowess and imperial aspirations. With respect to the book's intervention in debates about "the character of the first German nation state," Grenzen der Sicherheit answers as many queries as it raises new questions. Given this edited volume's breadth and depth, this is entirely as it should be. The book's contributions demonstrate convincingly how significantly attitudes towards what constituted (acceptable) social and individual risk(s) changed during the covered period. However, this process is at least at times described as an essentially naturally occurring one, rather than as a development driven by human actors. For example, the increasing public awareness (and fear) of gun-related accidents during the years before the beginning of war in 1914 was not just fueled by independently changing attitudes toward privately owned firearms and accidents caused by them, but also by the increased use of such weapons in unruly and densely populated working-class neighborhoods. Beginning with the massive suffrage demonstrations in the spring of 1910 and the Moabit unrests later during the same year, the increased use of privately owned weapons resulted in something of an arms race between the Prussian police and working-class demonstrators. Changing perceptions of what risks were...

Potential of CBM as an Energy Vector in Active Mines and Abandoned Mines in Russia and Europe

The energy sector is in the spotlight today for its contribution to global warming and its dependence on global geopolitics. Even though many countries have reduced their use of coal, the COVID-19 crisis, the drop in temperatures in Central Asia, and the war between Russia and Ukraine have shown that coal continues to play an important role in this sector today. As long as we continue to depend energetically on coal, it is necessary to create the basis for the successful extraction and industrial use of coal mine methane (CMM), for example, as an unconventional energy resource. Early degassing technology is a technique that allows for the extraction of the methane contained within the coal seams. The application of this technology would reduce emissions, improve mine safety, and even increase their profitability. However, this technology has been understudied and is still not implemented on a large scale today. Moreover, mines with this technology generally burn the extracted methane in flares, losing a potential unconventional fuel. This study, therefore, presents different scenarios of the use of coalbed methane (CBM), with the aim of generating an impact on pollutant emissions from coal mines. To this end, a model has been designed to evaluate the economic efficiency of degasification. In addition, an emissions analysis was carried out. The results showed that the use of this technology has a negative impact on the economy of mines, which can be completely reversed with the use of CBM as fuel. Furthermore, it is observed that degasification, in addition to reducing the number of accidents in coal mining, reduces emissions by 30–40%.

Structural equation model of the influence of noise annoyance on miners’ unsafe behavior in coal mines

Miners' unsafe behavior (UB) is the main cause of coal mine accidents. Previous research has suggested that excessive noise in tunnels impacts miners' temperaments and safe production behavior. To explore the influence of noise on UB, four different dimensions of noise annoyance were identified: cognitive, emotional, communication, and physical mechanism annoyance. The coal mine noise annoyance scale, miners' unsafe behavior intention (UBI) scale, and miners’ UB scale were developed according to different dimensions. In the first survey, a total of 200 questionnaires were distributed in the two coal mines, and 193 valid questionnaires were recovered. Exploratory factor analysis was conducted, and invalid items were removed according to the analysis results to form the final scale. Then, 500 final scales were distributed in two Chinese coal mines, and 482 valid questionnaires were collected. Confirmatory factor analysis was conducted by collating the data of the second questionnaire. Based upon the structural equation model (SEM), the SEM of the influence of noise annoyance degree on UB and the path analysis with latent variable path analysis with the latent variables (PA-LVs) mediating model of the effect of UBI on UB were established, and the path coefficients with fitting data of the model were analyzed. The results indicated that the four dimensions of coal mine noise annoyance had a strong positive impact on UB, which was reduced by less noise annoyance. In addition, the Bootstrap method was used to verify the mediating method among the four dimensions of noise annoyance, cognitive annoyance, emotional annoyance, with communication annoyance indirectly affecting UB through UBI. The research results provide theoretical support for reducing the UB of miners and improving coal mine safety levels.

Hazardous Area Reconstruction and Law Analysis of Coal Spontaneous Combustion and Gas Coupling Disasters in Goaf Based on DEM-CFD.

Coal spontaneous combustion and gas coupling disasters are the highest percentage of serious accidents in coal mines, causing the most serious disasters. China is one of the countries with the most serious coal spontaneous combustion and gas coupling disasters in goaf, and it is of great significance to explore the evolution law of coal spontaneous combustion and gas coupling disasters in goaf for disaster prevention and control. To study the three-dimensional spatial characteristics of the hazardous area of the coupling coal spontaneous combustion and gas disaster in goaf, a discrete element method-computational fluid dynamics (DEM-CFD)-based hazardous area reconstruction method was proposed. Taking a fully mechanized caving face of a coal mine in Shandong, China, as an example, first, the working face mining model was established by PFC3D, and the porosity of different horizontal and vertical positions of goaf after mining was extracted. Second, the porosity extracted was imported into computational fluid simulation software FLUENT by UDF. Finally, the distribution laws of oxygen and gas concentration during the real goaf mining process were simulated and analyzed. The results showed that the oxygen concentration in the intake roadway at a depth of 50.7 m in goaf decreased to 12%, and the gas concentration at a depth of 42.0 m in goaf increased to 16%. The oxygen concentration in the return airway roadway was reduced to 12% at 17.3 m depth in goaf, and the gas concentration increased to 16% at 6 m in the direction of goaf. The gas concentration was higher at the return air corner. The three-dimensional shapes of the hazardous area in goaf were constructed to satisfy the coupling of O2 concentration field, CH4 explosion limit concentration field, and fracture field and so were the laws of hazardous area analyzed qualitatively and quantitatively. It has important research significance for the rapid identification and determination of the coal spontaneous combustion and gas coupling disasters hazardous area.

Advanced detection for faults in front of coal mine roadways using seismic scattered waves

An unsuspected fault in front of a roadway is a key hidden disaster-causing factor that can result in major accidents in coal mines; therefore, the accurate advanced detection of faults is important for the safe coal mining. However, existing seismic advanced detection methods are difficult to execute because of the narrow space limits of roadways. Therefore, on the basis of the generalized theory of scattered waves and the scenes of advanced detections in roadways, we construct a new and superior advanced detection method using scattered waves. In this method, a virtual equivalent survey line perpendicular to the roadway strike is established to reconstruct scattered wave records and a polarization filter function is built to dynamically and accurately extract different types of scattered waves in the target area. An analysis of numerical simulations indicates the following. 1) The true reflection position of a fault in front of a rock roadway is closely related to its dip, and the proposed method can accurately extract and image the scattered multiwaves generated at the true reflection position to identify the inclination of the fault, which can avoid the arc-shaped illusion problem caused by the false reflection signal. 2) Under the condition that the scattered waves at the breakpoint of a broken coal seam are significantly disturbed by the coal seam waveguide effect, the fault inclination of the coal roadway can be determined by the joint imaging and interpretation on the reflected channel wave of the hanging wall breakpoint and the scattered wave of the footwall breakpoint. A field study was performed to verify the effectiveness of the proposed method. The results indicate that this method can effectively identify the position and inclination of faults and provide an effective new means for the accurate detection of faults in front of coal mine roadways.

Determination of underground hazard location using machine learning techniques

ABSTRACT Underground mine accidents, such as mine fires, remain a concern for mine operators, posing a health and safety risk to mine workers. Dealing with an unknown location of an accident underground can be a challenging task, creating hazardous conditions for miners during an evacuation and rescue operation. Timely determination of an underground fire event location and size is of great importance in reducing the risk of injuries. Machine learning (ML) has made its way into mining, enabling the development of data-driven predictive models that can be applied to miner health and safety problems. A new methodology has been developed using the application of an ML technique to characterize underground accidents such as the size and location of an underground mine fire using the post-fire airflow data. This paper describes the methodology and its verification through examples. The National Institute for Occupational Safety and Health (NIOSH) is endeavoring to develop workplace solutions to improve the detection of and reduce the risk of hazardous conditions. The results demonstrate a promising application of ML-based models and provide a useful tool for solving the problem of unknown fire locations and reducing the risk of hazardous conditions.

Optimization of escape routes during mine fire using GIS

ABSTRACT One of the essential factors in underground mining operations is safety during potential mine accidents, like fires, for example. We used Geographical Information System (GIS) for network analysis to identify the best escape routes from different locations inside an underground mine during a fire to a safe place inside or outside the mine. We created an effective evacuation system with a more accurate 2D model based on fire dynamics, the actual dimensions of the mine, and its related elements. We applied our system to the Maghara coal mine to predict the possible fire outbreak scenarios. Our results could be used as a guide to evacuation and rescue plans during harmful events in underground mines, especially fires. Our system is an integrated system that analyses potential fire scenarios and elects the best evacuation plan using GIS technology.

Risk assessment for vehicle injury accidents in non-coal mines based on bow-tie model

In order to reduce the incidence of vehicle injury accidents in non-coal mines, a quantitative risk assessment method based on fuzzy bow-tie model is proposed. First, the bow-tie model of vehicle injury accidents in non-coal mines is established. Then, the fuzzy failure probability of vehicle injury accidents is calculated by using fuzzy set theory and expert evaluation method. Finally, the risk value of non-coal mines vehicle injury accident consequence is obtained based on fuzzy analytic hierarchy process. Taking a mine vehicle injury accident as an example, the results show that the probability of vehicle injury accident is 2.104×10−2, and the risk value is 4.319×10−6. Based on the above risk assessment, the risk prevention and control measures for vehicle injury accidents in non-coal mines are proposed, which provides theoretical support for emergency management of vehicle injury accidents in mines.

Risk assessment for vehicle injury accidents in non-coal mines based on bow-tie model

Risk assessment for vehicle injury accidents in non-coal mines based on bow-tie model

Research on Safety Evaluation and Early Warning of Emergency Management Capability for Major Disaster Accidents in Coal Mines Based on Complexity System Theory in the Context of Digitization

Research on Safety Evaluation and Early Warning of Emergency Management Capability for Major Disaster Accidents in Coal Mines Based on Complexity System Theory in the Context of Digitization

Simulation Study on Risk Warning and Control Mechanism and Transmission Path of Major Disasters and Accidents in Coal Mine Based on Method of Structural Equation

Simulation Study on Risk Warning and Control Mechanism and Transmission Path of Major Disasters and Accidents in Coal Mine Based on Method of Structural Equation

Microbiological indicators as sensitive indicators in the assessment of areas contaminated by heavy metals

Context As one of the world’s largest mining spills, the Aznalcóllar pyrite mine accident in Spain in 1998 resulted in ~45 km2 of agricultural soils polluted by arsenic and heavy metals. The Guadiamar Green Corridor (GGC) helped with soil remediation but residual pollution is still detected 20 years later. Aims Several methodologies based on chemical indicators have been used to assess the recovery of these areas. However, simple microbiological indicators (e.g. microbial activity and biomass, and metabolic quotient (qCO2)) are yet to be uesed. The aim of this study was to evaluate areas contaminated by arsenic and heavy metals using microbiological indicators of soil quality. Methods We used a systematic random sampling design to collect soil samples from two soil groups with different recovery trajectories. We analysed the total and water-soluble concentrations of arsenic (As), lead (Pb), copper (Cu), and zinc (Zn), the main soil properties, and bioassays including microbial activity and biomass, and metabolic quotient or microbial stress (qCO2). Key results Twenty years after the accident, soils with As and Pb concentrations that consistently exceeded regulatory levels had altered soil microbial biomass and functioning. Although overall rates of microbial respiration were not significantly different between polluted soils, microbial biomass was lower and qCO2 was higher in the more polluted than in less polluted soils. Conclusions The metabolic quotient and microbial biomass are sensitive indicators in the monitoring over time of soil polluted by arsenic and heavy metals. Implications Microbial indicators must be considered in the assessment of potential ecotoxicity and in the evaluation of soil biological properties influencing soil recovery in the long term.

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

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

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.

Distributed Fuzzy Clustering Analysis of Time-Lapse Electrical Resistivity Tomography for Water Inrush Monitoring in Coal Mines

The majority of water inrush accidents in coal mines are caused by mining engineering activities. To avoid water inrush accidents, the Time-lapse Electrical Resistivity Tomography (TLERT) is applied to monitor water migration in fractured zone. A great challenge for the application of TLERT monitoring is the huge and numerous time series data sets generated by monitoring systems, which are difficult to process manually. This research proposed a distributed fuzzy clustering algorithm based on kernel function estimation to analyze TLERT images automatically. The resistivity date can be classified with different cluster centroids. The fuzzy c-means algorithm was chosen to display resistivity change. The algorithm was validated using a floor water inrush model. The results indicate that the water migration in the fractured zone can be monitored automatically and the edge of the resistivity changing area can be shown clearly.

Coal mine electrical safety management and accident prevention based on neural network and signal processing

Based on the upgrading of electronic components, communication and computer technology have made rapid progress. Neural network technology which mainly relies on the simulation of animal nerves, realize large-scale node information processing functions. Signal processing can complete the processing of various types of signals to obtain key data. With the further shortage of global energy, countries have begun to pay attention to the development of energy. As one of the traditional energy supply methods, coal mines have received a lot of financial support from the government. However, coal mine safety accidents have always been the focus of many enterprises and governments. In the process of coal mine development, there are many electrical equipment, and there are many changes in the underground environment. The problems of flooding, subsidence, and rodents biting off the line cause the paralysis of the electrical system and eventually lead to safety accidents in coal mine development. The neural network technology, can add all the electrical equipment in the mine operation, and to the network for analysis, and the signal processing technology can centrally process the signals of a variety of electrical equipment, and analyze the abnormal signals sent by the electrical equipment, and finally realize the prediction of electrical accidents. This paper firstly introduces the neural network and signal processing technology used in the text. Using the examples of hidden safety hazards in mining areas discovered during the team’s inspection as a basis, and combining with the actual situation in our country, these cases are classified. Next, using literature analysis and comparative research methods to analyze the problems existing in the current coal mine electrical safety management and accident prevention. A programme of safety management and accident prevention. The O-Net monitoring system constructed by the institute has its own unique advantage that it has more inputs than other networks, and its network structure is more complex and diverse, which of course also shows that it has more functions than other networks. The results of the scheme test show that the scheme can effectively reduce the probability of electrical safety accidents in coal mines, and at the same time effectively improve the efficiency of electrical safety management in coal mines. After multiple cycles of verification, the algorithm designed in this study has a very good effect in improving the safety of electrical equipment in coal mines, and the risk factors of each work have been effectively controlled, which is worthy of promotion in coal mines with similar environments.

Risk Level Assessment and CO Prediction of Underground Mines for Poisoning and Asphyxiation Accidents

To effectively prevent the occurrence of poisoning and asphyxiation accidents in underground mines, this paper establishes an evaluation index system for the factors influencing accidents, constructs a combined assignment model to solve the problem of low accuracy of assignment results caused by a single algorithm, predicts the CO concentration after blasting because CO poisoning is the main cause of accidents, explores the accuracy of different time series prediction methods, and projects the required ventilation after blasting to ensure the safe operation of personnel. Firstly, starting from “man-machine-environment-management”, social factors are introduced to build an evaluation index system. Secondly, three combinatorial allocation models were compared, namely rough set theory–G1 method (RS-G1), entropy method–G1 method (Entropy-G1), and CRITIC method–G1 method (CRITIC-G1). The best model was selected and the allocation rating model was constructed in combination with the cloud model, and the mine risk level was evaluated by using the model. Thirdly, the GM(1,1) model, the quadratic exponential smoothing method, and the ARIMA model were compared by calculating posterior differences and errors, and the method with the highest accuracy was selected for predicting CO concentration. The results show that the inclusion of social assessment indexes in the assessment index system makes the consideration of assessment indexes more comprehensive. The RS-G1 combined assignment model achieved higher accuracy than other combined assignment models, and the GM(1,1) model had the highest accuracy and the best prediction effect. The results of the study can help provide targeted prevention and management measures for poisoning and asphyxiation accidents in underground mines.