Coal Mine Safety Accidents Research Articles

Application and Research of Mine Safety Management Strategy Based on the BP Network Model

Coal mine safety production is undoubtedly the cornerstone of the healthy, table, and sustainable development of China’s coal industry, and it also profoundly affects the effective implementation of the national energy strategy. In this context, exploring the application of network models in coal mine safety management strategies not only has significant practical significance, but also has profound development value. By integrating these factors, we can have a more comprehensive understanding of the causes of coal mine safety accidents and provide a scientific basis for formulating effective preventive measures. By collecting and analyzing various data in real-time during the coal mine production process, we can promptly detect abnormal situations, predict potential safety risks, and take corresponding measures for intervention and prevention. This dynamic monitoring and early warning mechanism can not only improve the safety of coal mine production, but also enhance the efficiency and efficiency of coal mine production. In the analysis process, we particularly focused on the advantages of Back Propagation (BP) neural network models in mine safety evaluation. BP neural network models can handle uncertainty and fuzzy information and have strong learning and adaptive capabilities. By constructing a safety evaluation model based on BP neural networks, we can more accurately evaluate the safety risks in the coal mine production process, providing strong support for formulating more scientific and reasonable safety management strategies.

Research on coal mine safety risk evolution and key hidden dangers under the perspective of complex network

In order to find out the main causes of coal mine safety accidents and improve the pertinence of coal mine safety risk management and control, the identification and analysis of coal mine safety risks and hidden dangers are carried out based on the analysis of coal mine accident reports. Combing the complex network theory, a complex network model for the evolution of coal mine safety risks is constructed. The key elements that affect coal mine safety risk accidents are obtained through quantitative research on the characteristic indicators of the complex network model of coal mine safety risks. And the key nodes of coal mine safety risk spread network are obtained through network interference to the overall efficiency. The research results show that the complex network of coal mine safety risks illustrate the characteristics of a small-world network, and the spread of a certain risk is likely to cause coal mine safety accidents. Strengthening the risk management and control of hidden dangers with higher intermediate centrality can isolate the spread of coal mine safety risks and reduce the possibility of coal mine accidents.

Research on the Synergy between Economic Returns and Safety Investments in Enterprises

Coal, as a key input factor to ensure China's economic growth, plays an important role in the orderly development of its industry. The safety guarantee of production link is the only way to ensure the stable development of the industry, so it is in urgent need of the investment of safety funds and effective implementation to protect it. As a high-risk industry, the coal industry has a poor working environment, difficult working conditions, backward safety technology, and endless hidden dangers. In the process of development, the coal industry blindly pursues benefits while ignoring safety investment, which is exactly a common problem in China's coal enterprises and also the deep cause of coal mine safety accidents. This paper uses relevant theories to analyze the effect of coal enterprises' safety investment on economic benefit and determine its equilibrium point of safety and economic benefit. From micro level of building model, neural network model for coal enterprise economic benefit is explained variable, safety input as explanatory variables, core number of coal production, labor, labor productivity, and fixed assets and intangible assets investment as a intervening variable, such as inspection cause nonlinear relationship between safety investment and economic benefit are intermediary variables, measure the influence of different variables on economic benefit enterprise security mechanism, which integrated the result of theoretical analysis and empirical research, puts forward coal enterprises safety investment economic efficiency maximization of promotion strategy.

Analysis of the causes and safety countermeasures of coal mine accidents: A case study of coal mine accidents in China from 2018 to 2022

Statistical data on coal mine safety accidents in China from 2018 to 2022 were obtained. Mathematical statistical methods were used to analyze the accident level, accident type, death toll, time, space, and other factors. Grounded theory (GT), case-based reasoning (CBR), analytic hierarchy process (AHP), interpretative structural model (ISM) and other methods were used to analyze the causes of coal mine accidents in detail, and the characteristics, accident-causing attributes and importance of coal mine accidents in China were explored. The results indicate that (1) according to the level and type of accident, the incidence of general accidents in coal mines is the highest, with both the number of roof accidents and the death toll accounting for the highest values. From the time and location of the accidents, coal mine accident-prone areas are concentrated in northern and southern China. May and July are accident-prone periods every year. (2) Using GT-CBR to analyze the causes of coal mine accidents, 18 key statements were extracted, and 11 initialization concepts were summarized. Ultimately, the causes were categorized into 4 core categories: human factors (H), machine factors (M), environmental factors (E), and organizational management factors (O). (3) A hierarchical structure model of the causes of accidents according to ISM was built. The weight and proportion of accidents and deaths were combined to evaluate the importance of accident-causing attributes. Human factor-induced accidents accounted for 71.6 % of all accidents, and the number of accident death caused by organization management factors was the largest, accounting for 54.8 %. Comprehensive analysis revealed that human and organization management factors are the most important causes of coal mine accidents

Analysis of coal mining accident risk factors based on text mining

Frequently occurring coal mine safety accidents have caused great casualties and economic losses. Coal mine intelligence is the core technical support for the high-quality development of the coal industry. The deep integration of coal mining safety production management and Artificial Intelligence (AI) technology is practically significant to achieve accident prevention. To efficiently identify mining accident risk factors and explore mechanism of coupling between risk factors, this study mined 400 reported cases of mining accidents in Shanxi Province and identified 64 accident risk factors through custom, stopword, synonym dictionary construction, keyword extraction and keyword correlation analysis. Then, this study constructed association rules and a Bayesian causal network. The major risk factors are identified using a comprehensive high-frequency, sensitivity, strength and key path analysis of the Bayesian causal network. The following seven risk factors are found to play a major role in the occurrence of mine accidents: inadequate safety supervision, disordered safety management, illegal organization of production, inadequate staff safety education and training, operation against rules, command against rules and weak safety consciousness among the staff. Finally, a case study is conducted to validate the reliability of the results. This study solves the problem of incomplete extraction of key feature information in coal mine reports and the lack of analyses of coupling mechanisms between coal mine risk factors in traditional accident analysis methods, providing the methodological support for the effective use of unstructured coal mine safety production data for risk analysis.

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

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

Prediction of Coal Mine Pressure Hazard Based on Logistic Regression and Adagrad Algorithm—A Case Study of C Coal Mine

Effectively avoiding coal mine safety accidents has always been an important issue in the process of coal mining. In order to predict mine pressure hazard and reduce the occurrence of mine safety accidents, this paper innovatively combines logistic regression and mine pressure hazard prediction to establish a mine pressure hazard prediction model. By standardizing the data, the model improves the reliability of the mine pressure data and reduces the interference of the prediction effect of random errors. Based on the batch gradient descent algorithm and the Adagrad optimization algorithm, the prediction model is solved innovatively, which greatly improves the calculation speed and prediction accuracy of the model. Accuracy rate, precision rate, recall rate, and F1-score were selected as the evaluation indices to evaluate the prediction effect of the Adagrad optimization algorithm to solve the logistic regression model for mine pressure hazard. Compared with the existing classification algorithms, such as SVM and decision tree, the Adagrad optimization algorithm has the highest four indices when solving the logistic regression prediction model, and it takes the least time to predict. The results show that the model can efficiently predict mine pressure hazard. Finally, C Coal Mine was selected as the example for analysis. The prediction function was added to the mine pressure monitoring interface design. The practical application effect is similar to the theoretical verification. The establishment of this model provides a reliable guarantee for the secure and efficient production of coal mines and provides helpful research for the prediction of mine pressure.

An efficient detection of non-standard miner behavior using improved YOLOv8

To prevent coal mine safety accidents caused by non-standard mining operations, higher requirements have been proposed for identifying miners' behavior in surveillance videos. In this study, a single-stage detection model, YOLOv8, is employed, which effectively predicts the center of objects and is more suitable for recognizing miner behavior in complex environments. First, the video is segmented sampled to capture long-term information. Afterwards, deformable convolutions are integrated, applying offset learning to enhance fine-grained feature extraction and simultaneously, the introduction of the parameter-free SimAM improves the saliency mapping of miner behavior. Furthermore, an improved cooperative loss function is used for high-quality prior box regression. To validate the performance of non-standard miner behavior recognition, the proposed approach is conducted on a self-built dataset and achieves a mAP of 95.7 %, accuracy of 95.3 %, and recall of 95.1 % compared to the state-of-the-arts. It maintains an effective balance between computational cost and recognition accuracy.

Spatial and temporal convergence of provincial coal mine safety accidents and production and their influencing factors: Data from 23 coal-producing provinces in China (2005–2021)

As a high-risk industry, coal mine safety accidents can create significant loss of life and property to the nation and its people. In this work, a nonlinear time-varying factor model was adopted to investigate the spatial and temporal variation rule of coal mine safety accidents and coal production in 23 provinces of China from 2005 to 2021. The driving factors of coal mine safety accidents in 2005, 2010, 2015 and 2020 were analyzed using the geographical detector method. We further obtained the explanatory power of the primary factors and the change process of interaction. The findings showed that there are convergence clubs for both the number of coal mine safety accidents and coal production in the Chinese provinces. However, the geographical distribution of the convergence clubs varied. The geographical detector results indicated that coal mine safety accidents are affect by a combination of factors. The explanatory power of each factor for coal mine safety accidents changed over time. Notably, the explanatory power of coal production for coal mine safety accidents decreased. Industry structure, salary, security inputs, coal consumption and gross domestic product (GDP) played a significant role. The lever of impact of each factor on coal mine safety accidents was enhanced after interaction. The relationship between the factors entailed nonlinear enhanced interaction. The research findings not only contribute to a greater understanding of the macroscopic patterns of accident occurrence but also increase the awareness of the government and enterprises regarding the driving factors of coal mine safety accidents. Furthermore, such research holds crucial significance for precise prevention and control of coal mine safety accidents.

A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment.

Coal is an important resource that is closely related to people's lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.

Research on Intelligent Auxiliary Braking Technology Based on Active Safety of Mining Vehicles

Along with the continuous development of coal mining in electrification, and intelligent direction, the number of coal mine vehicles quantity has increased dramatically, and transportation safety accidents are high. The intelligent auxiliary braking system, as an effective collision avoidance or the main security technology to reduce collision damage, can be for target recognition, tracking, and measuring ahead through the millimeter wave radar and camera image sensor technology, and can warn drivers and related emergency braking measures in time through the command module for, thereby reducing the coal mine safety accident of the vehicle. The application of an intelligent auxiliary braking system to lower the speed of coal mine vehicles has a realistic significance.

Application of Multidimensional Structural Equations in the Emergency Management of Coal Mine Accidents

The use of coal as a source of energy is crucial for the growth of the national economy, but mining poses numerous risks and a potential for significant disasters. Coal mine safety is the prerequisite and guarantee for coal industry to achieve new industrialization and sustainable development. Therefore, it is crucial to predict a safety accident in the coal mine in advance. In order to facilitate the early warning of coal mine safety accidents, this study seeks to present a prediction model based on emergency management of safety accidents, which is a fusion model of principal component analysis (PCA) and long short-term memory neural network. According to the results, the correlation coefficients of risk identification and monitoring (a11), safety inspection and warning (a12), emergency planning and training (a13), material and technical support (a15), and macroenvironmental management (a21) were 0.718, 0.653, 0.628, 0.444, and 0.553, respectively, after the PCA dimensionality reduction process, demonstrating that the previous principal component analysis had a better effect. The absolute relative errors of each evaluation index of safety accident emergency management did not exceed the limit of 5%, including a15 and a21, whose values were 4.5% and −3.8%, while the relative errors of the remaining indicators were kept at a relatively low level. In conclusion, it is clear that the algorithm model suggested in this research improved the warning capabilities of safety accident emergency risk.

Epidemiology of coal miners' pneumoconiosis and its social determinants: An ecological study from 1949 to 2021 in China.

Pneumoconiosis is the most widely distributed occupational disease worldwide. China is currently the largest coal producer and consumer and the country with the most coal miners and cases of coal workers' pneumoconiosis (CWP). Despite more than 70 years of effort, the problem of CWP and silicosis remains serious. There is a lack of analysis of direct data on coal miners' pneumoconiosis from all over the country. This study aimed to describe the epidemiology of coal miners' pneumoconiosis and reveal some important clues regarding its social determinants. The annual incidence rate, 20-year prevalence rate, and incidence rate of coal miners' pneumoconiosis per million tons in China from 1949 to 2021 were calculated by using the data of annual number of coal miners' pneumoconiosis diagnosed and reported from the coal mining and dressing industry, the number of coal miners, and the raw coal production, and the relationship between the incident cases of coal miners' pneumoconiosis and the death toll from coal mine safety accidents was analyzed using Pearson correlation analysis, with the aim of exploring the relationship between the incident cases of coal miners' pneumoconiosis and its social determinants with an ecological study. From 1949 to 2021, there have been more than 462,000 patients with coal miners' pneumoconiosis in China, showing double U-shaped distributions with an increasing trend, accounting for about 50.5% (462,000/915,000) of all diagnosed pneumoconiosis in China, while the incidencet rate of coal miners' pneumoconiosis presents a large W shaped distribution with three peaks over a time span of more than 50 years. From 1949 to 1986, there was a strong correlation between the incident cases of coal miners' pneumoconiosis and raw coal production, the number of coal miners, and the number of deaths from coal mine accidents (r = 0.849, P<0.001; r = 0.817, P<0.001; r = 0.697, P<0.001, respectively), but there was no such correlation found from 1987 to 2006. It was estimated that the annual incidence rate of coal miners' pneumoconiosis in China from 2016 to 2020 was 3.4‰ (95% CI: 2.6-4.3‰), and the prevalence rate across the recent 20-year observation period was 4.8% (95% CI: 4.6-4.9%), both measured at the peak or around the peak over the 70 years. In particular, 1963, 1986, 2006, and 2009 were the four important turning points in time. There was a sustained high level of incident cases of coal miners' pneumoconiosis with double U-shaped curve in China, which may be affected by a variety of social determinants and risk factors.

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.

Construction of Emergency Culture and Safety Evaluation Index System Based on Behavioral Safety "2-4" Model

With the frequent occurrence of production safety accidents in enterprises in China, it has become a broad consensus of the whole society to attach importance to the emergency management of production safety in enterprises. Taking the coal mine as an example, the frequent safety accidents in coal mine enterprises have seriously affected human health, safety and environment, highlighting the fragility of safety management and safety culture construction in coal mine enterprises. To fundamentally reduce or eliminate the occurrence of safety accidents in coal mining enterprises, the most important and effective safety management strategy is to build a long-term safety culture. The "2-4" model of behavior safety is introduced into the construction of coal mine emergency culture, hoping to further reduce coal mine safety accidents and build an evaluation system of enterprise culture construction with the characteristics of coal mine industry. In the development stage of coal mine, a reasonable mining plan should be made, and comprehensive preparations should be made. In the tunneling stage, the construction safety and progress should be strictly managed and supervised. It will be beneficial to the standardization and scientific development of coal mine commercial enterprise culture, is the inherent requirement of building advanced enterprise culture, and promotes the safe and healthy development of coal mines.

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.

Coal Mine Safety Accidents, Environmental Regulation and Economic Development—An Empirical Study of PVAR Based on Ten Major Coal Provinces in China

Based on the PVAR model and taking the data of 10 major coal provinces in China from 2011 to 2020 as an example, the dynamic relationship between coal mine accidents, environmental regulation and economic development is analyzed at the provincial level. Research findings include: (1) From the static relationship between coal mine accidents, environmental regulation and economic development in China’s ten major coal provinces, coal mine accidents promote environmental regulation; environmental regulation inhibits coal mine accidents; economic development strongly promotes environmental regulation; environmental regulation has a weak inhibitory effect on economic development; coal mine accidents slightly inhibit economic development; economic development strongly inhibits coal mine accidents. (2) From the dynamic relationship between coal mine accidents, environmental regulation and economic development in China’s ten major coal provinces, there is a strong dynamic response relationship between environmental regulation and coal mine accidents. The impact of environmental regulation on coal mine accidents shows a decreasing volatility trend, and the impact of coal mine accidents on environmental regulation shows a rising volatility trend. There is a short-term positive interaction between economic development and environmental regulation, but the interaction response relationship between them decreases with time. Economic development has a long-term inhibitory effect on coal mine accidents, while the negative impact of coal mine accidents on economic development has gradually decreased to 0.

Design and Implementation of Coal Mine Safety Monitoring System Based on GIS

In order to further improve the safety production management capacity of coal enterprises and minimize the rate of coal mine safety accidents, based on the purpose of improving safety production efficiency, this paper constructs a coal mine safety detection system based on GIS technology. At the same time, on the basis of fixed-point positioning and remote monitoring at the PC end, this paper implements the whole process detection and monitoring of coal mine safety production with the help of mobile terminal intelligent system. The system software uses Java language to code it. In the first round of test, one function omission, three serious system errors, 14 general errors, and 21 minor errors are found. The accident passing rate of the second test is greater than 99.88%, and all performance indexes of the system meet the performance requirements, which proves the feasibility of the system.

Experimental study on the influence of mental fatigue on risk decision-making of miners

Mental fatigue increases risk-taking behavior. Using data collected between June 15 and August 6, 2020, this study investigates the impact of miners’ mental fatigue on risk decision-making to improve risk prevention and prediction abilities, and to reduce the occurrence of coal mine safety accidents. A total of 273 and 33 people participated in the preliminary and formal experiments, respectively. The participants, coal miners, visited a lab thrice to complete the pre-experiment, Balloon Analog Risk Task (BART), and Iowa Gambling Task (IGT). On the BART, mental fatigue displayed a significantly positive association with risk preference. On the IGT, as mental fatigue increased, net scores continuously decreased, while the frequency of making unfavorable decisions and the probability of taking risks increased. The BART value had no or weak correlations with the net score. Results suggest that mental fatigue leads to an increasing propensity to take risks. Therefore, regarding coal mine safety management, further attention is necessary concerning miners’ mental health, addressing mental fatigue, increasing rest time, and reducing night work. Furthermore, reasonable diet, improved working environments, and a positive attitude toward work should be promoted to reduce or eliminate mental fatigue and avoid decision-making errors that could cause accidents.

Longwall face roof disaster prediction algorithm based on data model driving

Hydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining faces. The load, location, and attitude of the hydraulic support are important sets of basis data to predict roof disasters. This paper summarized and analyzed the status of coal mine safety accidents and the primary influencing factors of roof disasters. This work also proposed monitoring characteristic parameters of roof disasters based on support posture-load changes, such as the support location and support posture. The data feature decomposition method of the additive model was used with the monitoring load data of the hydraulic support in the Yanghuopan coal mine to effectively extract the trend, cycle period, and residuals, which provided the period weighting characteristics of the longwall face. The autoregressive, long-short term memory, and support vector regression algorithms were used to model and analyze the monitoring data to realize single-point predictions. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models were adopted to predict the support cycle load of the hydraulic support. The SARIMA model is shown to be better than the ARIMA model for load predictions in one support cycle, but the prediction effect of these two algorithms over a fracture cycle is poor. Therefore, we proposed a hydraulic support load prediction method based on multiple data cutting and a hydraulic support load template library. The constructed technical framework of the roof disaster intelligent prediction platform is based on this method to perform predictions and early warnings of roof disasters based on the load and posture monitoring information from the hydraulic support.