Response Characteristics of Gas Concentration Level in Mining Process and Intelligent Recognition Method Based on BI-LSTM

The change of gas emission or concentration level at the working face is one of the main precursor characteristics of coal and gas outburst. At present, coal and gas outburst monitoring and early warning are mainly based on whether it exceeds the limit and its change law. However, the gas concentration level is affected by factors such as coal seam gas content, permeability, and mining process, and the change law is complex to recognize manually. In this paper, the response characteristics of gas concentration level in the mining process are analyzed and revealed, and a bidirectional long short-term memory model is established. The change characteristics of the gas concentration level in the mining and non-mining processes are studied and recognized. The results show that the change law of gas concentration in the mining process has apparent periodicity and trapezoidal volatility. The proposed intelligent recognition method based on the bidirectional long short-term memory neural network can automatically recognize the underground mining and non-mining processes, and the recognition accuracy achieves 97.7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.7\mathrm{\%}$$\end{document}. The research can significantly help improve the level of coal mine safety management and the accuracy of early warning of coal and gas outburst.