scholarly journals A New Method for Predicting Coal and Gas Outbursts

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guowei Dong ◽  
Xuanming Liang ◽  
Qixiang Wang

In view of the fact that coal and gas outbursts are difficult to predict, a new method for predicting coal and gas outbursts was proposed based on occurrence mechanisms of coal and gas outbursts relating to coal mass strength, gas pressure, and in situ stress. The method revealed that the rate of occurrence of coal and gas outbursts in mines was 5% to 10% and gas pressures for coal and gas outbursts in shallow and deep mines in China were greater than 0.74 and 0.6 MPa, respectively. The prediction index for coal and gas outbursts based on the gas factor was the gas desorption index of drilling cuttings (K1), which is referred to the gas content desorbed from the coal mass in the first minute of drilling. The prediction index for coal and gas outbursts based on coal mass strength was the thickness of a soft layer that could be twisted into powder by hand. Based on many cases of coal and gas outbursts, the critical thickness of the soft layer was found to have been 0.2 m. The prediction index for coal and gas outbursts based on in situ stress was the weight of drilling cuttings, which represented the mass of drilling cuttings per linear metre of boreholes with diameters of 42 or 75 mm. Finally, the new prediction method and prediction index critical values for coal and gas outbursts were verified based on industrial application tests. This method has been widely applied on-site and obtained good prediction results.

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tianjun Zhang ◽  
Jiaokun Wu ◽  
Yong Chen ◽  
Hong Ding ◽  
Hongyu Ma ◽  
...  

Stress is one of the main factors influencing coal and gas outbursts. The apparent effects of the crustal stress, the structural stress, and the mining-induced stress increase as the depth of mining increases. At present, there have been few studies of the relationship between the comprehensive analyses of the crustal stress, mining-induced stress, and coal gas. The in situ measurement of the relationship between stress-related behaviors and coal gas under the influence of mining was conducted through experimental analysis of surrounding rock support and coal and gas outburst control and optimization of surrounding rock support materials and system construction. The results showed that the mining-induced stress first increased to a peak value, then gradually decreased, and tended to stabilize as the footage progresses. Stress appears at 96 m ahead due to mining; after 57 m of advancing, there is a large increase until it passes through this area. The stress in front of the working face increases linearly, and the increase range is obviously larger than that of the coal body in a certain range on both sides. The support anchoring force gradually decreased and tended to be stable after rapidly increasing to a maximum value. The deep displacement of the roof increased linearly and tended to be stable after reaching an accumulated displacement which can reach 16-28 mm. The residual gas pressure in front of mining operations decreased rapidly, and beyond 15 m on each side of the roadway, it decreased significantly. The residual gas pressure and gas content were consistent with the gas desorption index of drill cuttings due to the influences of gas predrainage and mining. The stress along the direction of the roadway and the residual gas content, the residual gas pressure, and the gas desorption index of drill cuttings conform to the logarithmic functional relationship. The research results provide a basis for the comprehensive prevention and control of coal and gas outbursts from multiple angles considering stress, coal, and gas.


2011 ◽  
Vol 21 (3) ◽  
pp. 439-443 ◽  
Author(s):  
Dingqi Li ◽  
Yuanping Cheng ◽  
Lei Wang ◽  
Haifeng Wang ◽  
Liang Wang ◽  
...  

Processes ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 223 ◽  
Author(s):  
Fakai Wang ◽  
Xusheng Zhao ◽  
Yunpei Liang ◽  
Xuelong Li ◽  
Yulong Chen

Coalbed gas content is the most important parameter for forecasting and preventing the occurrence of coal and gas outburst. However, existing methods have difficulty obtaining the coalbed gas content accurately. In this study, a numerical calculation model for the rapid estimation of coal seam gas content was established based on the characteristic values of gas desorption at specific exposure times. Combined with technical verification, a new method which avoids the calculation of gas loss for the rapid estimation of gas content in the coal seam was investigated. Study results show that the balanced adsorption gas pressure and coal gas desorption characteristic coefficient (Kt) satisfy the exponential equation, and the gas content and Kt are linear equations. The correlation coefficient of the fitting equation gradually decreases as the exposure time of the coal sample increases. Using the new method to measure and calculate the gas content of coal samples at two different working faces of the Lubanshan North mine (LBS), the deviation of the calculated coal sample gas content ranged from 0.32% to 8.84%, with an average of only 4.49%. Therefore, the new method meets the needs of field engineering technology.


2019 ◽  
Vol 43 (24) ◽  
pp. 9458-9465
Author(s):  
Xiquan Yue ◽  
Lihong Su ◽  
Xu Chen ◽  
Junfeng Liu ◽  
Longpo Zheng ◽  
...  

The strategy is based on small molecule-mediated hybridization chain reaction.


2021 ◽  
Vol 22 (12) ◽  
pp. 6598
Author(s):  
Cheng Wang ◽  
Jun Zhang ◽  
Peng Chen ◽  
Bing Wang

Backgroud: The prediction of drug–target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The prediction method based on the Ensemble model of Multiple Feature Pairs (Ensemble-MFP) is introduced. Firstly, three negative sets are generated according to the Euclidean distance of three feature pairs. Then, the negative samples of the validation set/test set are randomly selected from the union set of the three negative sets in the validation set/test set. At the same time, the ensemble model with weight is optimized and applied to the test set. Results: The area under the receiver operating characteristic curve (area under ROC, AUC) in three out of four sub-datasets in gold standard datasets was more than 94.0% in the prediction of new drugs. The effectiveness of the proposed method is also shown with the comparison of state-of-the-art methods and demonstration of predicted drug–target pairs. Conclusion: The Ensemble-MFP can weigh the existing feature pairs and has a good prediction effect for general prediction on new drugs.


2014 ◽  
Vol 53 (11) ◽  
pp. 4147-4155 ◽  
Author(s):  
Anna Bacciarelli-Ulacha ◽  
Edward Rybicki ◽  
Edyta Matyjas-Zgondek ◽  
Aleksandra Pawlaczyk ◽  
Malgorzata I. Szynkowska

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