scholarly journals Real-Time Prediction Model of Coal and Gas Outburst

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Ru Yandong ◽  
Lv Xingfeng ◽  
Guo Jikun ◽  
Zhang Hongquan ◽  
Chen Lijuan
Keyword(s):  
Missing Data ◽  
Prediction Model ◽  
Real Time ◽  
Prediction Performance ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Gas Outburst ◽  
Coal Mine Safety ◽  
Data Value ◽  
First Time

Coal and gas outburst has been one of the main threats to coal mine safety. Accurate coal and gas outburst prediction is the key to avoid accidents. The data is actual and complete by default in the existing prediction model. However, in fact, data missing and abnormal data value often occur, which results in poor prediction performance. Therefore, this paper proposes to use the correlation coefficient to complete the missing data filling in real time for the first time. The abnormal data identification is completed based on the Pauta criterion. Random forest model is used to realize the prediction model. The prediction performance of sensitivity 100%, accuracy 97.5%, and specificity 84.6% were obtained. Experiments show that the model can complete the prediction of coal and gas outburst in real time under the condition of missing data and abnormal data value, which can be used as a new prediction model of coal and gas outburst.

scholarly journals Construction and application of comprehensive early warning system for coal and gas outburst in Xinjing Coal Mine

2021 ◽  
Vol 329 ◽  
pp. 01016
Author(s):  
Yunlong Zou
Keyword(s):  
Real Time ◽  
Coal Mine ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coal Industry ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Time Dynamic ◽  
Gas Outburst

In order to further strengthen the prevention and control of coal and gas outburst in Xinjing Coal Mine, the online comprehensive analysis and early warning index system and rules of coal and gas outburst suitable for Xinjing Coal Mine were studied. Based on the corresponding early warning computer system and guarantee mechanism, a comprehensive early warning system for coal and gas outbursts in Xinjing Coal Mine was established, realizing real-time intelligent early warning of outburst dangers in working faces. The system realizes the standardization and dynamic management of outburst prevention information at the working face, as well as the real-time dynamic update and sharing of outburst prevention information, which improves the efficiency of mine outburst prevention management and the level of mine safety. The actual application of the system in Xinjing Coal Mine shows that the system can provide an effective reference for the comprehensive early warning of outbursts in other outburst mines of Yangquan Coal Industry Group.

Intensity of Coal and Gas Outburst Prediction Model Based on the Improved BP Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 1190-1193
Author(s):  
Long Kong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Forecast Accuracy ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Simulation Test ◽  
Gas Outburst ◽  
Major Disaster

Coal and gas outburst has become one of the major disaster hazard of coal mine safety, Staff on gas outburst disaster prevention is now important research project. The gas outburst prediction work, different degrees of factors has some impact on forecast accuracy, such as logical reasoning efficiency is low. This paper, by using the BP neural network combined with gas outburst samples a prediction model is established, According to the data of a certain coal mine as a sample, Using MATLAB software to simulation test, have been predicted and actual values fitting degree is higher, Can reflect the realities of the coal and gas outburst.

Risk Assessment on Coal and Gas Outburst Based on TOPSIS Model

2012 ◽  
Vol 608-609 ◽  
pp. 1483-1486
Author(s):  
Yu Zhong Yang ◽  
Li Yun Wu
Keyword(s):  
Risk Assessment ◽  
Safety Management ◽  
Mining Area ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Entropy Weight ◽  
Gas Outburst ◽  
Coal Mine Safety ◽  
Order Preference ◽  
Topsis Technique

Risk assessment on coal and gas outburst was necessary to Coal mine safety management. The model of TOPSIS(Technique for Order Preference by Similarity to Ideal Solution), which is based on entropy weight, was constructed to evaluate outburst risk. The subjectivity which lies in ascertaining factors’ weights was avoided in this model. So the evaluation result is more objective than other evaluation methods. The model was applied in the safety assessment of coal and gas outburst for four mining faces from east area of Ping Dingshan mining area. The order preference of outburst risk was gained. At the same time, the risk differences among four faces were attained. The evaluation results indicate that TOPSIS method be more reasonable and objective. It is easier to use in Coal mines.

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

Coal Mine Safety Monitoring System Based on ZigBee and GPRS

2013 ◽  
Vol 422 ◽  
pp. 215-220 ◽  
Author(s):  
Jie Cheng ◽  
Dian Wu Gao ◽  
Jun Fei Wang ◽  
Dong Ge Wen
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Coal Mine ◽  
Low Cost ◽  
Safety Monitoring ◽  
Mine Safety ◽  
Remote Transmission ◽  
Time Treatment ◽  
Coal Mine Safety ◽  
Safety Monitoring System

A wireless coal mine safety monitoring system based on ZigBee wireless sensor network and GPRS wireless remote transmission was established, which enjoys the characteristics of ZigBee technology, including quick networking, low cost, less power consumption, simple network structure and real-time parameter monitoring. With mature GPRS technology, remote data transmission was achieved and associated director can be informed through short message sent to his cellphone, which contributes to the early identification of serious accidents and real-time treatment, thus increasing the safety of coal mining.

Application of Radar Velocity Tomography for Detecting Coal and Gas Outburst in Deep Coal Mining

2014 ◽  
Vol 513-517 ◽  
pp. 3659-3662
Author(s):  
Cui Du ◽  
Xin Jun Xu ◽  
Xing Yu Li
Keyword(s):  
Geophysical Methods ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Gas Outburst ◽  
Safety Production ◽  
Geological Factors ◽  
Radar Velocity ◽  
Mining Coal ◽  
Ground Penetrating ◽  
Velocity Tomography

Among the main geological factors to cause the disasters of mine safety production in deep mining, coal and gas outburst is the first major calamity that restricts colliery exploiting. Aiming at this problem, current geophysical methods were reviewed and compared, and the velocity tomography technology using ground penetrating radar was studied. Two models with ground stress anomaly and collapse columns were built and inverted using LSQR algorithm, respectively. The results show that the proposed method gives very consistent results with respect to the models information, and uncertain features of inverted models were identified accurately. This verified radar velocity tomography is effective and practical.

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