Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory

Geng, Jiabo; Xu, Jiang; Nie, Wen; Peng, Shoujian; Zhang, Chaolin; Luo, Xiaohang

doi:10.1016/j.ijmst.2017.01.004

Cited by 45 publications

(15 citation statements)

References 12 publications

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“…In the D-S theory of evidence [24][25][26][27], the set of all possible propositions is defined as a universe X={x1,x2,…,xn}, and the basic belief assignment function is defined as m:…”

Section: Decision-level Fusion Based On D-s Theory Of Evidencementioning

confidence: 99%

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

He¹

2019

RIA

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The gas outburst in coalmines is influenced by multiple factors. These influencing factors are highly uncertain and have complex nonlinear relationships. Considering these features, this paper puts forward a gas outburst prediction model based on data mining and information fusion. On the feature level, the backpropagation neural network (BPNN) was selected to set up a gas outburst identification model, thanks to its strong self-learning ability, and then optimized by the improved particle swarm optimization (IPSO); then, the outputs of the optimized BPNN were taken as the identification results, and used to establish a feature database. On the decision level, the Dempster-Shafter (D-S) theory of evidence was introduced to fuse the identification results in the time domain and the spatial domain, and make decisions on the gas state of the coalmine based on the fused data. Finally, the proposed model was applied the predict the gas outburst in a coalmining area of a coalmine in Shanxi Province, China, using the data collected from the workface, intake airway, return airway and transport roadway. Our model fuses the data on two layers, namely, the time domain and the spatial domain, and reduces the prediction uncertainty to zero. The results show that our model can optimize the prediction parameters, enhance the accuracy of gas monitoring information, and improve the correctness of decisions concerning gas outburst in the coalmine.

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“…In the D-S theory of evidence [24][25][26][27], the set of all possible propositions is defined as a universe X={x1,x2,…,xn}, and the basic belief assignment function is defined as m:…”

Section: Decision-level Fusion Based On D-s Theory Of Evidencementioning

confidence: 99%

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

He¹

2019

RIA

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“…Coal and gas dynamic disasters, such as coal and gas outbursts, show an increasing trend. 3,4 Advanced technical means are used to obtain the precursor characteristics of coal and gas outbursts, to achieve effective monitoring and early warning of outburst disasters, which is the premise for coal and gas outburst prevention and control.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission monitoring technology for coal and gas outburst

et al. 2019

Energy Science & Engineering

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In recent years, with the increase in mining depth and strength, coal‐rock gas dynamic disasters, such as coal and gas outburst, have shown an increasing trend. Acoustic emission (AE) technology has been viewed as a promising method that can effectively forecast coal and gas dynamic disasters. This paper first tests the AE characteristics of coal and rock samples during loading. Then, self‐developed AE continuous monitoring and early warning equipment is used to monitor and predict the coal and gas outburst dynamic disasters on the working face. And it is found that the coal samples primarily show ductile failure, and the AE exhibits the evolutionary characteristics of “rise‐peak‐fall”. The rock samples primarily exhibit brittle failure, and the AE evolution mode is almost no falling stage. Coal and gas outbursts occur after the stress peak. Before coal and gas outbursts occur, there is a clear increasing trend in the AE ahead of the gas concentration variation. When the gas‐bearing coal is damaged by the load, the coal body first breaks due to the stress, and the AE value increases. Then, due to the fracture of the coal body, the crack penetrates, gas rushes out, and the gas concentration increases. The research results can provide an advanced technical method for the monitoring and early warning of coal–rock gas dynamic disasters, and improve the prediction accuracy for dynamic disasters.

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“…Sobczyk et al constructed a self-development two-dimensional load test equipment, then carried out a series of experiments to study the relationship between the uniaxial compressive strength, gas pressure and the outburst risk [12][13][14]. Peng et al used outburst test equipment to analyze the influence of permeability and non-uniform loading, gas pressure and moisture content [15][16][17]. In the recent two years, scholars such as Tu [18], Zhao [19] and Xu [20] studied the effects of gas enrichment and adsorption on the outburst by simulation tests.…”

Section: Introductionmentioning

confidence: 99%

Experimental Research on the Impactive Dynamic Effect of Gas-Pulverized Coal of Coal and Gas Outburst

Sun

Cao

et al. 2018

Energies

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Coal and gas outburst is one of the major serious natural disasters during underground coal, and the shock air flow produced by outburst has a huge threat on the mine safety. In order to study the two-phase flow of a mixture of pulverized coal and gas of a mixture of pulverized coal and gas migration properties and its shock effect during the process of coal and gas outburst, the coal samples of the outburst coal seam in Yuyang Coal Mine, Chongqing, China were selected as the experimental subjects. By using the self-developed coal and gas outburst simulation test device, we simulated the law of two-phase flow of a mixture of pulverized coal and gas in the roadway network where outburst happened. The results showed that the air in the roadway around the outburst port is disturbed by the shock wave, where the pressure and temperature are abruptly changed. For the initial gas pressure of 0.35 MPa, the air pressure in different locations of the roadway fluctuated and eventually remain stable, and the overpressure of the outburst shock wave was about 20~35 kPa. The overpressure in the main roadway and the distance from the outburst port showed a decreasing trend. The highest value of temperature in the roadway increased by 0.25 • C and the highest value of gas concentration reached 38.12% during the experiment. With the action of shock air flow, the pulverized coal transportation in the roadway could be roughly divided into three stages, which are the accelerated movement stage, decelerated movement stage and the particle settling stage respectively. Total of 180.7 kg pulverized coal of outburst in this experiment were erupted, and most of them were accumulated in the main roadway. Through the analysis of the law of outburst shock wave propagation, a shock wave propagation model considering gas desorption efficiency was established. The relationships of shock wave overpressure and outburst intensity, gas desorption rate, initial gas pressure, cross section and distance of the roadway were obtained, which can provide a reference for the protection of coal and gas outburst and control of catastrophic ventilation.Keywords: coal and gas outburst; laboratory simulation experiment; two-phase flow of a mixture of pulverized coal and gas; shock wave; propagation model

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Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory

Cited by 45 publications

References 12 publications

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

Acoustic emission monitoring technology for coal and gas outburst

Experimental Research on the Impactive Dynamic Effect of Gas-Pulverized Coal of Coal and Gas Outburst

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