Prediction of gas and coal spontaneous combustion coexisting disaster through the chaotic characteristic analysis of gas indexes in goaf gas extraction

Song, Yawei; Yang, Shengqiang; Hu, Xincheng; Song, Wenli; Sang, Naiwen; Cai, Jiawen; Xu, Qin

doi:10.1016/j.psep.2019.06.013

Cited by 59 publications

(20 citation statements)

References 17 publications

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“…Fig 8 shows the simulation results of the oxygen concentrations in the goaf. The oxygen concentration method is used to divide the risk area for spontaneous combustible residual coal in the goaf [27]. Fig 8 shows that the range 0~33 m from the working face is the heat dissipation zone with an oxygen concentration greater than 18%.…”

Section: Distribution Of the Air Leakage Flow Field And Spontaneous C...mentioning

confidence: 99%

Study on the air leakage characteristics of a goaf in a shallow coal seam and spontaneous combustion prevention and control strategies for residual coal

Zhang

2022

PLoS ONE

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Air leakage from surface mining-induced fissures can easily cause spontaneous combustion of residual coal in the goaf, which threatens the safe production of the underground working face. In order to study the air leakage law of the goaf under the surface air leakage and the prevention and control technology of spontaneous combustion of residual coal. Based on engineering data from the 6104 working face of the Chuancao Gedan coal mine, this study uses a combination of theoretical analyses, numerical simulations, and field observations to study the dynamic distribution characteristics of the air leakage velocity of surface mining-induced fissures in shallow coal seams, the distribution characteristics of relative pressure, the air leakage velocity, the air leakage flow field, the distribution ranges for the “three zones” of spontaneous combustion in the goaf, and a reasonable range for the pressurized ventilation of the working face. The results show that there is a quadratic relationship between the air leakage speed from the surface mining-induced fissures in shallow coal seams and the distance from the working face. The air leakage speed decreases as the distance from the working face increases, and the air leakage speed in the middle of the working face is slower than the air leakage on either side of the goaf. The pressure difference between the goaf and the surface mining-induced fissures is the root cause of air leakage into the goaf, and a change in the pressure difference has a significant impact on the air leakage flow field and the distributions of the "three zones" of spontaneous combustion in the goaf. When the pressure difference between the ground surface and the working face is maintained within the range of 200~-200 Pa, air leakage is effectively reduced, and the spontaneous combustion of residual coal is inhibited. The research results reveal the air leakage mechanism in the goaf of shallow coal seams and provide a reference for the prevention and control of spontaneous combustion of residual coal in the goaf.

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Section: Distribution Of the Air Leakage Flow Field And Spontaneous C...mentioning

confidence: 99%

Study on the air leakage characteristics of a goaf in a shallow coal seam and spontaneous combustion prevention and control strategies for residual coal

Zhang

2022

PLoS ONE

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“…Automated ventilation system adjustment software has been developed by monitoring the air volume of the minimum remainder branch in the network, and all air volumes of the branches in the network can be obtained by inverse calculation of associated branches [ 32 ]. Song et al [ 33 ] proposed a method for determining the changing trend of gas concentration in the goaf. Lyu et al [ 34 ] proposed a gas concentration prediction method based on the ARMA model, the CHAOS model, and the encoder–decoder model (single-sensor and multisensor).…”

Section: Introductionmentioning

confidence: 99%

Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network

Liu

Gao

et al. 2022

Sensors

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The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction.

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“…In recent years, researchers have found that the gas concentration generally rises abnormally before incidents of coal mine gas explosions [ 5 ]. Therefore, in order to predict gas concentration accurately and prevent gas accidents effectively, researchers have proposed some gas prediction methods.…”

Section: Introductionmentioning

confidence: 99%

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

Cai

Zhou

et al. 2021

Sensors

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Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.

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Prediction of gas and coal spontaneous combustion coexisting disaster through the chaotic characteristic analysis of gas indexes in goaf gas extraction

Cited by 59 publications

References 17 publications

Study on the air leakage characteristics of a goaf in a shallow coal seam and spontaneous combustion prevention and control strategies for residual coal

Study on the air leakage characteristics of a goaf in a shallow coal seam and spontaneous combustion prevention and control strategies for residual coal

Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

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