Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model

Yongkang Yang,Chenlong Wang,Qiaoyi Du,Yu Bai

doi:10.3390/en13226112

Abstract

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

Highlights

Throughout the years, coal mines have played a crucial role in energy resources around the world.Ranking first in coal production, China is the largest consumer of coal all over the world—consuming roughly half of the world’s coal consumption
Coal still plays a dominant role in the total primary energy, even though coal production is declining in China [1,2]
An radial basis function (RBF) neural network is a model based on Cover which can use primary function approximating arbitrary function

Summary

Introduction

Throughout the years, coal mines have played a crucial role in energy resources around the world. Ranking first in coal production, China is the largest consumer of coal all over the world—consuming roughly half of the world’s coal consumption. Coal exploitation has made an important contribution to developing China’s economy by providing employment opportunities and generating purchasing power. Coal still plays a dominant role in the total primary energy, even though coal production is declining in China [1,2]. The production system in underground coal mines is a peculiarly complex disaster system, which causes coal mine accidents to frequently occur [3]. Safe production in underground coal mines is threatened by a wide variety of coal mine disasters

Methods

Results

Conclusion