Research on Temperature Variation during Coal and Gas Outbursts: Implications for Outburst Prediction in Coal Mines

Zhang, Chaolin; Wang, Enyuan; Xu, Jiang; Peng, Shoujian

doi:10.3390/s20195526

Cited by 17 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although deep coal and gas outbursts are intuitively closely related to the mining depth, they actually occur because the mining environment significantly changes with the increase in mining depth. In the deep mining environment, nonlinear changes in single vital indexes, such as the coal seam gas pressure, gas content, initial gas release velocity of coal, and coal firmness coefficient, are aggravated, and the degree of interaction between indexes increases, significantly aggravating the risk of deep coal and gas outbursts [37].…”

Section: Mining Depthmentioning

confidence: 99%

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Zhu

Yang

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

show abstract

Section: Mining Depthmentioning

confidence: 99%

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Zhu

Yang

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Song and Zhang [27,28] predicted and visualized the coal-gas outburst area based on geographical information system technology. Zhang et al [29][30][31] studied a new prediction method of coal-gas outburst based on the η parameter that was put forward on the basis of experimental study, and its dangerous level was divided into five categories; the method was compared with other traditional methods at the same time. Liu et al's physical simulation system for large coal-gas outburst had been developed, and the occurrence mechanism of coal-gas outburst was analyzed from the perspective of energy, which laid a theoretical foundation for the accurate prediction of coal-gas outburst disasters.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Predict Shock-Type Coal-Gas Outburst Disaster and Its Application

Pengfei

Chen

2023

Geofluids

View full text Add to dashboard Cite

Coal-gas outburst is the one of most serious coal mine dynamic disasters which affects safety mining, with 39 deaths reported during 2019 in China. The mechanism of coal-gas outbursts is complex, and the prediction methods are immature at present. This article was based on previous research results. Firstly, the occurrence mechanism of coal-gas outburst disasters was summarized. It is clear that the occurrence of coal-gas outburst disasters is jointly affected based on the stability of the static structure of coal-rock, the gas parameters, and the release intensity of shock stress. Secondly, the stability evaluation models of coal-rock static structure, gas parameters, and shock stress release intensity were built, respectively, based on the influence factors. Then they were coupled and supposed to form a new prediction model. Finally, the prediction method was applied to the Ji15-17200 working face of No.12 coal mine in Pingmei company. The working face was divided into three danger levels including weak outburst danger area, medium outburst danger area, and strong outburst danger area. The accuracy of the predicted results was analyzed based on the actual mining condition of the Ji15-17200 working face. The results show that prediction accuracy is high, and it can be used for actual applications. The research results are of guiding significance to better prevent and control coal-gas outburst accidents and ensure safe production in coal mines.

show abstract

“…14,15 This change is more obvious especially in the case of coal and gas outburst. 16 When the temperature of the coal seam rises, the adsorption capacity of CBM changes. 17,18 Killingley and co-workers 19 showed that the methane adsorption capacity decreased by 0.12 cm 3 /g when the temperature increased by 1 °C at 5 MPa.…”

Section: Introductionmentioning

confidence: 99%

Principle and Application for Thermal Exploitation of Coalbed Methane Recovery

Wang,

Feng,

Zhou

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

Coalbed methane (CBM) is strongly adsorbed to coal, with over 95% existing in an adsorbed state, making it difficult to effectively extract using traditional borehole drainage or drainage gas methods, particularly in low permeability and under pressure CBM reservoirs. This study proposes thermal-enhanced CBM extraction by superheated water injection. Laboratory experiments were first conducted to examine deformation and temperature change during coal adsorption/desorption, to quantify adsorption heat at low (room temperature−150 °C) and high (150−240 °C) temperature ranges, and to elucidate the potential physical mechanisms of temperature modulation on coal adsorption of methane. Further experiments studied two-phase methane−water flow under elevated temperature and thermal-induced methane drainage affected by the water lock effect. Pilot field tests then validated the feasibility and effectiveness of this thermal extraction technology based on the theoretical findings. The results demonstrate that (1) energy conversion occurs during coal adsorption/desorption of methane, absorbing heat and causing matrix shrinkage during desorption. (2) Heterogeneous coal adsorption potential requires increased temperature to desorb methane from deep wells into the free state. Under isobaric conditions, adsorption heat is significantly higher at 150−240 °C versus room temperature−150 °C due to potential well depth effects. (3) The gas-driving-water process involves liquid flow, gas−liquid twophase flow, and gas flow stages. Elevated temperature facilitates water and gas flows, accelerates gas breakthrough, and improves the production rate. (4) High temperature eliminates the water lock effect and increases the desorption rate to around 80% regardless of adsorption pressure, greatly shortening equilibrium time. (5) Further field tests show that the drainage period after thermal injection is only 1/46−1/31 that of the conventional method given the same production volume. Thus, thermal-enhanced CBM extraction is disruptive, with this study providing theoretical and engineering basis.

show abstract

Research on Temperature Variation during Coal and Gas Outbursts: Implications for Outburst Prediction in Coal Mines

Cited by 17 publications

References 35 publications

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

A New Method to Predict Shock-Type Coal-Gas Outburst Disaster and Its Application

Principle and Application for Thermal Exploitation of Coalbed Methane Recovery

Contact Info

Product

Resources

About