Using gray model with fractional order accumulation to predict gas emission

Wu, Lifeng; Liu, Sifeng; Yao, Ligen; Cui, Wei

doi:10.1007/s11069-013-0960-z

Cited by 40 publications

(18 citation statements)

References 11 publications

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“…Yang et al [4] mapped the time series of gas emission on the polar coordinates and predicted gas concentration in the mining face in an intuitive and concise manner. In addition, other models such as the partial correlation analysis and support vector regression model proposed by Yang [17] and the gray prediction model with fractional order accumulation proposed by Wu et al [18] also obtained good prediction results. Tutak et al [19] presented the methodology of using artificial neural networks for predicting methane concentration in one mining area.…”

Section: Introductionmentioning

confidence: 90%

Prediction of Gas Emission from Floor Coalbed of Steeply Inclined and Extremely Thick Coal Seams Mined Using the Horizontal Sublevel Top-Coal Caving Method

Hu¹,

Wang²,

Chen³

et al. 2019

Preprint

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In the steeply inclined and extremely thick coal seams (SIETCS) mined using the horizontal sublevel top-coal caving (HSTCC) method, the uncertainty of gas emission is a safety threat to the mining operations. In order to reduce the occurrence of accidents, the determination of gas emission is crucial. In this paper, we first proposed a prediction model for workers at the floor coalbed to calculate gas emission on site. We then put forward a finite element numerical simulation for researchers to predict gas emission from the floor coalbed. At last, we measured gas emitted from the floor coalbed of SIETCS in Wudong Coal Mine in a specific mining period and used the data to verify the applicability and accuracy of these two gas-emission prediction methods. The results showed that the gas emission from Wudong Coal Mine was 1.08 m3/min calculated based on the prediction model and 1.07 m3/min obtained using the user-defined integration method. Both methods have their own advantages, disadvantages and applicable objects, and are important in predicting gas emission from SIETCS mined using HSTCC method.

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Section: Introductionmentioning

confidence: 90%

Prediction of Gas Emission from Floor Coalbed of Steeply Inclined and Extremely Thick Coal Seams Mined Using the Horizontal Sublevel Top-Coal Caving Method

Hu¹,

Wang²,

Chen³

et al. 2019

Preprint

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show abstract

“…The GM(1, 1) model has advantage not only in calculation speed but also in prediction accuracy. Also, building GM(1, 1) model needs less data [11][12][13][14]. GM(1, 1) model can be finally expressed aŝ…”

Section: Gm(1 1) Model Improved By Ipsomentioning

confidence: 99%

High Precision Clock Bias Prediction Model in Clock Synchronization System

Liu

Chen

Liu

et al. 2016

Mathematical Problems in Engineering

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Time synchronization is a fundamental requirement for many services provided by a distributed system. Clock calibration through the time signal is the usual way to realize the synchronization among the clocks used in the distributed system. The interference to time signal transmission or equipment failures may bring about failure to synchronize the time. To solve this problem, a clock bias prediction module is paralleled in the clock calibration system. And for improving the precision of clock bias prediction, the firstorder grey model with one variable (GM(1, 1)) model is proposed. In the traditional GM(1, 1) model, the combination of parameters determined by least squares criterion is not optimal; therefore, the particle swarm optimization (PSO) is used to optimize GM(1, 1) model. At the same time, in order to avoid PSO getting stuck at local optimization and improve its efficiency, the mechanisms that double subgroups and nonlinear decreasing inertia weight are proposed. In order to test the precision of the improved model, we design clock calibration experiments, where time signal is transferred via radio and wired channel, respectively. The improved model is built on the basis of clock bias acquired in the experiments. The results show that the improved model is superior to other models both in precision and in stability. The precision of improved model increased by 66.4%∼76.7%.

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“…Being different to the use of nonlinear structures, the FOA was used as an nonlinear data preprocessing method to describe the nonlinear series for the grey models. With high performance of improving the grey models and innovative methodology, the FOA and FGM soon appealed considerable interest of research in nearly 5 years, and have been widely used in the realworld applications, such as the weapon system costs [38], gas emission [39], etc. According to Wu's results, the FOA can perform as an error reducer to the grey models [40], and the FGM is also effective in time series forecasting with small samples.…”

Section: Introductionmentioning

confidence: 99%

The conformable fractional grey system model

et al. 2020

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The fractional order grey models (FGM) have appealed considerable interest of research in recent years due to its higher effectiveness and flexibility than the conventional grey models and other prediction models. However, the definitions of the fractional order accumulation (FOA) and difference (FOD) is computationally complex, which leads to difficulties for the theoretical analysis and applications. In this paper, the new definition of the FOA are proposed based on the definitions of Conformable Fractional Derivative, which is called the Conformable Fractional Accumulation (CFA), along with its inverse operation, the Conformable Fractional Difference (CFD). Then the new Conformable Fractional Grey Model (CFGM) based on CFA and CFD is introduced with detailed modelling procedures. The feasibility and simplicity and the CFGM are shown in the numerical example. And the at last the comprehensive real-world case studies of natural gas production forecasting in 11 countries are presented, and results show that the CFGM is much more effective than the existing FGM model in the 165 subcases.

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Using gray model with fractional order accumulation to predict gas emission

Cited by 40 publications

References 11 publications

Prediction of Gas Emission from Floor Coalbed of Steeply Inclined and Extremely Thick Coal Seams Mined Using the Horizontal Sublevel Top-Coal Caving Method

Prediction of Gas Emission from Floor Coalbed of Steeply Inclined and Extremely Thick Coal Seams Mined Using the Horizontal Sublevel Top-Coal Caving Method

High Precision Clock Bias Prediction Model in Clock Synchronization System

The conformable fractional grey system model

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