WT-KPCA-SVR coupling model based gas emission quantity prediction method

A WT-KPCA-SVR, gas emission technology, applied in the direction of forecasting, instrumentation, data processing applications, etc.

Inactive Publication Date: 2015-07-22
SHANDONG UNIV OF SCI & TECH
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Problems solved by technology

However, in the mining process, the factors affecting the gas emission are constantly changing, which makes the gas emission of the working face very uncertain. Therefore, it is necessary to propose a new prediction method that can predict the gas emission. The long-term development trend and changing fluctuation intensity of the gushing volume can also improve the prediction accuracy and generalization ability

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  • WT-KPCA-SVR coupling model based gas emission quantity prediction method
  • WT-KPCA-SVR coupling model based gas emission quantity prediction method
  • WT-KPCA-SVR coupling model based gas emission quantity prediction method

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Embodiment 2

[0062] Example 2: Prediction of the amount of gas emission in a mining face of a certain mine. The prediction steps are carried out according to Example 1. The specific prediction process and results are as follows:

[0063] 17 groups of gas emission monitoring data and corresponding factors were collected in the 2074 mining face. The specific data are shown in Table 1.

[0064] Table 1 Original sample data table

[0065]

[0066] Firstly, wavelet transform is used to extract the quantum sequence of gas gushing, and the Mallat wavelet decomposition is preferably selected twice. After reconstruction, it is found that the high-frequency components of the second layer fluctuate less around zero, so a Mallat wavelet decomposition is performed to decompose the gas gushing output monitoring sequence ( figure 2 ) is decomposed into a subsequence of trend items ( image 3 ) and subsequences of fluctuation items ( Figure 4 ).

[0067] Select the sub-sequence of the trend item ...

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Abstract

The invention discloses a WT-KPCA-SVR coupling model based gas emission quantity prediction method. The method comprises the following steps: firstly, performing data preparation, namely collecting gas emission quantity monitoring data and corresponding factors, extracting gas emission quantity subsequences according to wavelet transform, and separating out a trend item subsequence and a fluctuation subsequence; determining the influence factors of each subsequence according to a gray relative analysis method, performing kernel principal component dimensionality reduction on the influence factors of each subsequence to reconstitute the principal component of each subsequence; combining the reconstituted principal components of all the subsequences and values of all gas emission quantity subsequences into a sample set; respectively establishing support vector machine regression models of the trend item subsequence and the fluctuation subsequence according to a training sample; synthesizing the two models to obtain a final gas emission quantity prediction model; performing model precision detection by using a detection sample, wherein the model can be used if passing the detection. The model is reliable in design principle, simple in prediction method, high in prediction accuracy and friendly in prediction environment.

Description

technical field [0001] The invention relates to a method for predicting gas emission in coal mines, in particular to a method for predicting gas emission based on wavelet transform (WT)-kernel principal component analysis (KPCA)-support vector machine regression (SVR) coupling model. Background technique [0002] Gas disaster is one of the major hidden dangers of coal mine safety production. With the increasing depth and intensity of mine excavation in my country, the gas problem is becoming more and more serious. Predicting the amount of gas emission in the mining face is of great significance to ensure the safe and efficient production and economic benefits of the mine. [0003] At present, many scholars at home and abroad have proposed many methods in the research process of gas emission prediction, which can be roughly divided into two categories: one is linear models, such as separate source prediction methods, principal component regression analysis methods, statistical...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/02
Inventor 施龙青邱梅滕超韩进
Owner SHANDONG UNIV OF SCI & TECH
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