Experimental Study on Coal and Gas Outburst Risk under Different Water Content Rates in Strong Outburst Coal Seams

Hu, Tao; Shi, Biming; Yue, Jiwei; Liang, Yuehui; Wang, Cheng; Han, Qijun

doi:10.1021/acsomega.3c07896

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Injecting water into coal seams can enhance the water content, alter the coal’s mechanical properties, and diminish the risk of outbursts, serving as an effective strategy for preventing and controlling coal and gas outbursts. − Nonetheless, the inherent hydrophobic nature of coal leads to low water injection efficiency, making it challenging for conventional methods to guarantee the prevention of outbursts. − Consequently, enhancing the wettability of coal, thereby increasing its water absorption capacity and ultimately its water content, is crucial for the effectiveness of water injection in coal seams.…”

Section: Introductionmentioning

confidence: 99%

Influence of Nanoparticle–Surfactant Compound Solution on Coal Wettability

Zou,

Huo,

Liu

et al. 2024

Energy Fuels

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Poor coal wettability limits the effectiveness of water injection in coal seams. Nanofluids and surfactants can improve the coal wettability. In this paper, the impact of four types of composite fluids containing surfactants and SiO 2 nanoparticles on coal wettability was investigated. By analyzing the variation in contact angle and moisture content as well as functional groups before and after modification, it is found that the compound solution can effectively enhance coal surface wettability and increase the moisture content of coal samples. Compared to deionized water and SiO 2 nanofluids, the contact angle of coal modified by the compound solution does not rebound quickly with the loss of moisture. Moreover, the average strengthening rate and water absorption mass strengthening rate were utilized to quantify the modification effects of the compound solution. It can be seen that the combination of cationic surfactant CTAB with SiO 2 nanoparticles provides the most effective modification. FTIR results imply that the modification results in increased activity of free hydroxyl groups in the coal, enhancing its hydrophilicity. The synergistical interaction of nanoparticles and surfactants can occupy vacant adsorption sites on the coal surface. Additionally, surfactants adhere to the nanoparticle surface due to van der Waals forces, while hydrophilic groups attract water molecules to the coal. The research achievements provide a theoretical foundation for the potential application of nanoparticle−surfactant compound solution in coal seam water injection.

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Section: Introductionmentioning

confidence: 99%

Influence of Nanoparticle–Surfactant Compound Solution on Coal Wettability

Zou,

Huo,

Liu

et al. 2024

Energy Fuels

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Research on outburst gas pressure dynamics and water-injected coal damage

Wei,

Li,

et al. 2024

Physics of Fluids

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Coal and gas outbursts are complex dynamic disasters closely associated with gas pressure dynamics and coal damage. An experimental system was established to investigate the effects of water injection on coal particles. The experiments utilized helium (He) and carbon dioxide (CO2) gases, at initial pressures ranging from 0.25 to 1.0 MPa and water injection periods ranging from 0 to 12 days, to measure gas pressure variation, particle size distribution, equivalent diameter, and newly generated surface area. The results indicated that outburst gas pressure follows a power-law decay, with He decaying more rapidly than CO2. Elevated gas pressure and adsorptivity intensified coal particle damage: each 0.25 MPa rise in pressure increased the new surface area by 1.02 times for He and 1.28 times for CO2, with the CO2's surface area being 5.03–5.20 times larger than that of He. Water injection mitigated the damage caused by adsorptive gases; as the injection time increased, the new surface area initially decreased and then increased, with the least damage at 6 days. For every 3 day increase in the water injection time, the average surface area of He-fractured coal increases by 0.69 times. A gas pressure decay model and a coal damage model considering pressure differential tension, gas adsorption expansion, and adsorption-induced strain were developed and validated against experimental results. These findings provide theoretical insights into coal and gas outbursts.

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Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework

Yan,

Qin,

Fan

et al. 2024

Sensors

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Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

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Experimental Study on Coal and Gas Outburst Risk under Different Water Content Rates in Strong Outburst Coal Seams

Cited by 3 publications

References 43 publications

Influence of Nanoparticle–Surfactant Compound Solution on Coal Wettability

Influence of Nanoparticle–Surfactant Compound Solution on Coal Wettability

Research on outburst gas pressure dynamics and water-injected coal damage

Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework

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