No abstract

Miners’ unsafe behavior is the main cause of accidents in coal mines, and unsafe state have an important influence on unsafe behavior among miners. To minimize accidents from the source of accident chain, we evaluated the impact of workload on miners’ unsafe state. It is important for coal enterprises to monitor miners’ unsafe state and to prevent unsafe accidents. Workload is divided into two dimensions: work time and work demand. Meanwhile, we introduced job satisfaction as a moderating variable. Through empirical research methods, first-line employees from two coal mines in China were enrolled in the questionnaire survey. Regression analysis was used to verify the impact of workload and its various dimensions, job satisfaction, and miners’ unsafe state. We found that workload, work time and work demand have significant positive effects on miners’ unsafe state. Job satisfaction plays a moderating effect in the relationship between workload and miners’ unsafe state. To some extent, a higher job satisfaction was associated with reduced workload, reduced occurrence of miners’ unsafe state and minimal incidences of unsafe accidents. On this basis, measures were proposed to improve miners’ unsafe state in terms of workload and job satisfaction. This study informs the establishment of effective intervention measures to monitor miners’ unsafe state and is also beneficial to the improvement of coal mine safety.

Gas disasters threaten the safe operation of coal mines. Improving the accuracy of gas concentration predictions can effectively prevent gas disasters and reduce disaster losses. Traditional gas concentration prediction methods poorly couple the gas concentration and its influencing factors when dealing with a great number of features and data, which results in low prediction accuracy and poor efficiency. To solve this problem, we used an innovative Pearson-LSTM prediction model, which uses the Pearson coefficient to select features of gas concentration data. It then uses long short-term memory (LSTM) that has been optimized using adaptive moment estimation (Adam) to predict a time series. In the process of model establishment, the optimal prediction model was obtained by constantly adjusting the number of network layers and batch size based on the fitting effect, performance issues, and result errors. Taking monitoring data from the 2407 working face at Yuhua Coal Mine as the sample, we compared our method with the traditional Bi-RNN and GRU machine learning methods. The results show that, compared with the Bi-RNN and GRU models, the mean square error of the Pearson-LSTM model can be reduced to 0.015 with an error range of 0.005 to 0.04, which has higher prediction accuracy. This method has excellent precision and robustness for forecasting gas concentration time series. The model was able to make predictions 15 min in advance for the 2409 working face of the Yuhua Coal Mine, and the mean square error could be lowered to 0.008, which verifies the applicability and reliability of the model and provides a reference for ensuring the safety of coal mine operations. In summary, Pearson-LSTM models have higher accuracy and robustness and can effectively predict changes in gas concentration, thus allowing for more response time for accidents, which is important for coal mine production safety.