Classification technique for danger classes of coal and gas outburst in deep coal mines |
| |
Authors: | Xueqiu He Wenxue Chen Baisheng Nie Ming Zhang |
| |
Affiliation: | 1. Department of Resource and Safety Engineering, China University of Mining and Technology, Beijing 100083, China;2. Department of Mining and Materials Engineering, McGill University, Canada H3A2A7 |
| |
Abstract: | In this investigation a new classification technique based on artificial neural network (ANN) and exponent evaluation method (EEM) has been developed to classify the danger classes of coal and gas outburst in deep mines. A weight computing model of mutual affecting factors is derived from backward algorithm of ANN (BA-ANN), which diminishes the influence of factitious factor, the environment factor and the time factor to the weight. The BA-ANN model is used for modeling the correlation between danger class and 12 affecting factors of coal and gas outburst and calculating weights of interconnection factors, which performs very well. In order to classify danger classes in a daily routine, the EEM with the well trained weights which are from BA-ANN, is performed in a deep mine. The case study shows that this new technique is useful to classify danger classes with quick and accurate computation. Moreover, the weight computing model of BA-ANN can be extended to other safety issue in different fields as well. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|