Research on Forecasting Model of Gas Emission in Coal Mining and Heading Face Based on ARIMA-GM Method

2015 ◽  
Author(s):  
Bing Wu ◽  
Wensheng Zhang ◽  
Zhiguo Guo ◽  
Ziwei Wang
Keyword(s):  
Coal Mining ◽  
Forecasting Model ◽  
Gas Emission

Application of GM (1, 1) Prediction Model in Coal Consumption Market

2013 ◽  
Vol 869-870 ◽  
pp. 554-558
Author(s):  
Hong Ying Lu
Keyword(s):  
Prediction Model ◽  
Coal Mining ◽  
Forecasting Model ◽  
Excellent Performance ◽  
Coal Consumption ◽  
Practical Applications ◽  
Government Department

With the constant development of economy in China, the total coal consumption is gradually increasing. It is a vital task for government department to rationally and scientifically develop coal mining plans. A forecasting model GM (1, 1) is used in prediction of coal consumption-market. The design of this model is analyzed in detail. The performance of this method is evaluated and the result indicates that the model has excellent performance. This method has obtained favorable effects in practical applications.

scholarly journals Prediction of Coal Mine Gas Emission Quantity Based on Grey-Gas Geologic Method

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Wang Wei ◽  
Peng Lei ◽  
Wang Xiaochao
Keyword(s):  
Forecast Accuracy ◽  
High Accuracy ◽  
Forecasting Model ◽  
Test Results ◽  
Gas Emission ◽  
Factors Affecting ◽  
In Situ Data ◽  
Coal Mine Gas ◽  
Main Factor

To improve the accuracy and reliability of gas emission prediction, the various factors affecting the amount of gas emission were researched and the main factor determining the amount of gas emission was determined by the gas geology theory. In this paper, we adopted grey-gas geologic method and grey relevancy analysis separately to estimate forecast accuracy and to establish the grey systematic forecasting model; meanwhile, two residual tests were carried out. Combined with the concurrent in situ data, the result of the grey systematic prediction model is verified. The later residual test results indicated that the model is of a high accuracy and the prediction result is reliable, manifesting the method of grey-gas geologic method is a better way to forecast the gas emission.

Predication of Gas Emission Based on Wavelet Multi-Resolution Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 3662-3666
Author(s):  
Bao Ming Qiao ◽  
Qiao Kang
Keyword(s):  
Time Series ◽  
Stationary Time Series ◽  
Ar Model ◽  
Forecasting Model ◽  
Gas Emission ◽  
Emission Data ◽  
Stationary Time ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Single Branch

Gas emission is basically non-stationary time series, Based on this view, wavelet multi-resolution analysis was applied to the predication of gas emission. Firstly, the gas emission data was decomposed by wavelet multi-resolution analysis. Secondly, the single branch reconstruction of each layer was predicted by establishing AR forecasting model. Synthesized all of the results from every layers, the forecasting result was obtained. The simulation showed that predication for mine gas emission with the method of this paper has much higher accuracy than AR model forecasting model.

Prediction and Governance of Mine Gas Emission

2013 ◽  
Vol 419 ◽  
pp. 500-504
Author(s):  
Yi Wen Liu ◽  
Yi Cao ◽  
Lin Zhang ◽  
Ming Chuan Meng
Keyword(s):  
Neural Network ◽  
Coal Mining ◽  
Bp Neural Network ◽  
Emission Control ◽  
Control Measures ◽  
Gas Emission ◽  
Emission Data ◽  
Main Factors ◽  
Mine Gas ◽  
Emission Control Measures

Coal mining gas emission constrained by many factors, considering the eight main factors of gas emission. The first gas emission data are normalized, avoid data overflow to improve the training speed of neural network. Then use BP neural network to predict the amount of mine gas emission, finally proposed gas emission control measures.

A Forecasting Model of RBF Neural Network Based on Particle Swarm Optimization

2011 ◽  
Vol 65 ◽  
pp. 605-612
Author(s):  
Yu Min Pan ◽  
Cheng Yu Huang ◽  
Quan Zhu Zhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Optimization Method ◽  
Forecasting Model ◽  
Emission Model ◽  
Gas Emission ◽  
Swarm Optimization ◽  
Random Optimization

In order to improve the precision of gas emission forecasting,this paper proposes a new forecasting model based on Particle Swarm Optimization (PSO).PSO is a novel random optimization method which has extensive capability of global optimization.In the model, PSO is used to optimize the weight,width and center of RBF neural network and the optimal model is applied to forecast gas emission.The diversified factors analysised with grey correlation,MATLAB is employed to implement the model for gas emission forecasting.The simulation results show that the gas emission model optimized by PSO is more accurate than the traditional RBF model．

Sign in / Sign up

Export Citation Format

Share Document