Rockburst and Gas Outburst Forecasting using a Probabilistic Risk Assessment Framework in Longwall Top Coal Caving Faces

Agrawal, Harshit; Durucan, Şevket; Cao, Wenzhuo; Korre, Anna; Shi, Ji‐Quan

doi:10.1007/s00603-022-03076-3

Cited by 11 publications

(3 citation statements)

References 79 publications

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“…First, calculate the unit time coal falling quantity m l using Equation ( 19), and calculate the relative gas emission quantity q x using Equation (20). Through dimensional analysis, using Equation ( 21) obtain the final result of the real-time relative gas emission volume q r .…”

Section: Dimensional Analysis For Establishing Real-time Relative Gas...mentioning

confidence: 99%

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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Section: Dimensional Analysis For Establishing Real-time Relative Gas...mentioning

confidence: 99%

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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“…The complex interaction of various influencing factors greatly impacts the occurrence of coal and gas outbursts, rendering their prediction challenging (Black, 2019b;Amani et al, 2021;Shadrin et al, 2022). Therefore, it is indispensable to formulate an accurate approach to anticipate the crucial thresholds of indicators related to coal and gas outbursts in different mining areas (Agrawal et al, 2023;Zheng et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Experimental study of coal and gas outburst processes influenced by gas pressure, ground stress and coal properties

Ou,

Wang,

et al. 2023

Front. Earth Sci.

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With the continuous increase of mining depth, coal and gas outburst poses a significant threat to mining safety. Conducting research on the mechanisms of coal and gas outbursts contributes to understanding the evolutionary process of such incidents, thus enabling accurate prediction and prevention of coal and gas outbursts during mining operations. This paper has developed a comprehensive visual experimental system that is specifically tailored to simulate diverse coal body conditions, ground stress and gas pressures. By monitoring and analyzing the real-time progression of coal fissures during the outburst process, we can obtain valuable insights into the evolution and mechanisms of coal and gas outbursts. Additionally, this study introduces a method to determine the critical threshold for predicting coal and gas outbursts, and the critical gas pressure threshold for Jiulishan Coal Mine (Jiaozuo City, Henan Province, China) is established at 0.6 MPa.

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“…Many prominent types of simulation software have been applied for rockburst hazard analysis, such as fast Lagrangian analysis of continua [17], realistic failure process analysis [18], particle flow code [19] and so on [20,21]. Many indicators have been proposed for the simulation of rockbursts, including the absolute local energy release rate [22], energy dissipation rate [23], burst potential index [24] and so on [25,26]. The numerical simulation method can consider the effects of in situ stress, rock parameters and excavation activities simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information

Li,

Liang,

Zhao

2023

Applied Sciences

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Rockburst is a major disaster in deep mining engineering, which can cause serious injury, death and economic losses. This study aims to solve rockburst hazard evaluation problems and determine the hazard levels of mines under uncertain circumstances. To this end, a novel multicriteria decision-making (MCDM) method was proposed in an interval-valued fuzzy context. The main contributions are three-fold. First, considering the heterogeneity of rock masses and the complexity of mining engineering, interval-valued fuzzy numbers (IVFNs) were adopted to express initial indicator information. Second, accounting for the uncertainty of indicator weights, the decision-making trial and evaluation laboratory (DEMATEL) and entropy methods were extended with IVFNs to determine fuzzy indicator weights comprehensively. Third, the complex proportional assessment (COPRAS) approach was extended with IVFNs to determine the rockburst hazard level. Finally, the proposed method was applied to evaluate rockburst hazards in the Jiaojia gold mine, and the ranking results were consistent with field status. Meanwhile, sensitivity and comparison analyses were performed to reveal the stability and effectiveness of the proposed method. The results indicated that the extended COPRAS method was reliable for rockburst hazard evaluation in deep mining engineering.

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Rockburst and Gas Outburst Forecasting using a Probabilistic Risk Assessment Framework in Longwall Top Coal Caving Faces

Cited by 11 publications

References 79 publications

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

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

Experimental study of coal and gas outburst processes influenced by gas pressure, ground stress and coal properties

Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information

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