Abstract
The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.
Highlights
With the increase of mining intensity and mining depth of China’s coal resources, when the coal under high ground stress and high gas pressure is disturbed by mining, the coupling effect of rockburst and coal-gas outburst becomes intense [1,2,3]
In the GRA-principal component analysis (PCA)-BP model, firstly, the influencing factors with high weight are screened by grey relational analysis
The prediction of coal-gas compound dynamic disaster is of great significance for the safe and efficient mining of coal mine
Summary
With the increase of mining intensity and mining depth of China’s coal resources, when the coal under high ground stress and high gas pressure is disturbed by mining, the coupling effect of rockburst and coal-gas outburst becomes intense [1,2,3]. They include zoning and grading monitoring and early warning, microseism monitoring method, electromagnetic radiation, elastic wave CT and vibration wave CT in the aspect of rock burst monitoring, drilling gas gushing initial velocity method, R-index method, and electromagnetic radiation method in the aspect of coal and gas outburst monitoring They pointed out that for the monitoring and early warning of coalgas compound dynamic disaster, we should focus on the establishment of multiparameter normalized dimensionless monitoring and early warning model and criteria for dynamic disaster risk, build the monitoring and early warning index system suitable for coal-gas compound dynamic disaster, and develop the corresponding monitoring technology and equipment. Combined with the application of mathematical method in the prediction of coal-gas outburst and rockburst [26,27,28,29], a method predicting the coal-gas compound dynamic disaster based on the GRA-PCA-BP model is put forward, and the model is verified, and good results are achieved
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