Impact of Gas Control Policy on the Gas Accidents in Coal Mine

Experimental Study on Emergency Psychophysiological and Behavioral Reactions to Coal Mining Accidents

Effective emergency responses are crucial for preventing coal mine accidents and mitigating injuries. This paper aims to investigate the characteristics of emergency psychophysiological reactions to coal mine accidents and to explore the potential of key indicators for identifying emergency behavioral patterns. Initially, virtual reality technology facilitated a simulation experiment for emergency escape during coal mine accidents. Subsequently, the characteristics of emergency reactions were analyzed through correlation analysis, hypothesis testing, and analysis of variance. The significant changes in physiological indicators were then taken as input features and fed into the three classifiers of machine learning algorithms. These classifications ultimately led to the identification of behavioral patterns, including agility, defensiveness, panic, and rigidity, that individuals may exhibit during a coal mine accident emergency. The study results revealed an intricate relationship between the mental activities induced by accident stimuli and the resulting physiological changes and behavioral performances. During the virtual reality simulation of a coal mine accident, subjects were observed to experience significant physiological changes in electrodermal activity, heart rate variability, electromyogram, respiration, and skin temperature. The random forest classification model, based on SCR + RANGE + IBI + SDNN + LF/HF, outperformed all other models, achieving accuracies of up to 92%. These findings hold promising implications for early warning systems targeting abnormal psychophysiological and behavioral reactions to emergency accidents, potentially serving as a life-saving measure in perilous situations and fostering the sustainable growth of the coal mining industry.

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.

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.

Determination of Gas Extraction Borehole Parameters in Fractured Zone on ‘Borehole in Place of Roadway’ Based on RSM-GRA-GA

Large-diameter gas extraction borehole is considered an effective method by which to realize coal mine methane exploitation and outburst prevention. Efficient gas extraction can be achieved by selecting the right borehole parameters. In this paper, by comparing several conventional objective weighting methods, the PCA was used to assign the weights to the research indices, the optimization objective was reduced from multi-dimensional to one-dimensional with the help of the gray correlation analysis. The study of gas extraction effect under different borehole parameters based on the response surface model. Numerical simulations were used to analyze the mixed volume of gas extraction, the pure volume of extraction and the concentration in the upper corner after extraction under different schemes. Finally, a genetic algorithm degree model was used to solve the solution and determine the optimal arrangement of borehole parameters. The study shows that (1) the weight shares of borehole stratum, borehole diameter and borehole spacing were 0.385, 0.285 and 0.33, respectively, in the reduced dimensional analysis of the PCA. (2) Using the results of improved gray correlation analysis as a comprehensive evaluation value to measure the effect of gas extraction, the optimal range of the model was 28–30 m borehole level, 190–210 mm borehole diameter and 5.5–6.5 m borehole spacing. (3) Using the genetic algorithm to solve the model, we obtained the borehole layer 28.79 m, borehole diameter 199.89 mm, borehole spacing 5.76 m. The borehole gas extraction effect was good under this parameter. The extraction mixed volume was 129.8 m3/min, the extraction pure volume was 9.16 m3/min, the upper corner concentration was 0.52%, and the prediction accuracy of the model was 97.8%.

Autonomous Instrumentation for Measuring Electromagnetic Radiation from Rocks in Mine Conditions-A Functional Analysis

This paper analyses the function of an innovative integrated receiver for the measurement of electromagnetic field emissions. The autonomous receiver measures and registers the elevated emission levels of both components of the EM field originating from rocks subjected to increased mechanical stress. The receiver’s sensitivity of 60 µV/m, its dynamic range of 98 dB, and its impulse response of 0.23 V/µs were determined in laboratory conditions. Real EM field signals from hard coal samples subjected to crushing force were recorded using an autonomous receiver. The observed and recorded results confirm that the receiver operates in the full range of amplitudes of the EM field signal emitted from the rock. The results determine the band of characteristic signals for EM field emission from hard coal. The system created on the basis of autonomous EM receivers can support the existing seismic safety systems in real mine conditions by predicting the possibility of mine collapse hazards.

Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

In order to achieve the accuracy of gas emission prediction for different workplaces in coal mines, three coal mining workings and four intake and return air roadway of working face in Nantun coal mine were selected for the study. A prediction model of gas emission volume based on the grey prediction model GM (1,1) was established. By comparing the predicted and actual values of gas emission rate at different working face locations, the prediction error of the gray prediction model was calculated, and the applicability and accuracy of the gray prediction method in the prediction of gas gushing out from working faces in coal mines were determined. The results show that the maximum error between the predicted and actual measured values of the gray model is 2.41%, and the minimum value is only 0.07%. There is no significant prediction error over a larger time scale; the overall prediction accuracy is high. It achieves the purpose of accurately predicting the amount of gas gushing from the working face within a short period of time. Consequently, the grey prediction model is of great significance in ensuring the safety production of coal mine working face and promote the safety management of coal mine.