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

Jun Zeng,Qinsheng Li

doi:10.3390/pr9071147

Abstract

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.

Highlights

Coal mine safety production management is an important research content related to the stable development of energy in China
In underground coal mining face and working face, the monitoring and prediction of gas is the focus of coal mine safety management [10,11,12]
By analyzing the effective stress, temperature and gas pressure of coal, Yin et al [27] constructed a BP neural network of gas concentration, and established a gas prediction model based on a large number of sample data, with a maximum prediction error of 4.298%

Summary

Introduction

By analyzing the effective stress, temperature and gas pressure of coal, Yin et al [27] constructed a BP neural network of gas concentration, and established a gas prediction model based on a large number of sample data, with a maximum prediction error of 4.298%. The grey theory model can summarize and integrate the limited data, generate a group of regular and effective series, summarize the data development law through data processing and other means, so as to realize the prediction function of the data in the future. At present, it has been applied in a variety of industries [33,34,35]. The research content hopes to provide some reference for the prevention and control of coal mine gas disasters

Brief Introduction of Grey Prediction Method

Calculation Process of Grey Prediction Model

General of CoalCoal

Geographical

Comparison the Predicted

Conclusions