Application of Grey Relation Clustering and CGNN in Gas Concentration Prediction in Top Corner of Coal Mine

2009 ◽  
Author(s):  
Qu Zhiming ◽  
Liang Xiaoying
Keyword(s):  
Coal Mine ◽  
Gas Concentration ◽  
Grey Relation ◽  
Concentration Prediction

Distributed gas concentration prediction with intelligent edge devices in coal mine

2020 ◽  
Vol 92 ◽  
pp. 103643
Author(s):  
Yiwen Zhang ◽  
Haishuai Guo ◽  
Zhihui Lu ◽  
Lu Zhan ◽  
Patrick C.K. Hung
Keyword(s):  
Coal Mine ◽  
Gas Concentration ◽  
Concentration Prediction

scholarly journals Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiliang Ma ◽  
Hua Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Prediction Error ◽  
Prediction Method ◽  
Gas Concentration ◽  
Quantum Genetic Algorithm ◽  
Rescue Robot ◽  
Concentration Prediction ◽  
Mine Rescue

The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway.

scholarly journals Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 161 ◽  
Author(s):  
Tianjun Zhang ◽  
Shuang Song ◽  
Shugang Li ◽  
Li Ma ◽  
Shaobo Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Coal Mine ◽  
Time Series Prediction ◽  
Mean Square ◽  
Gas Concentration ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Concentration Prediction

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

scholarly journals Research on time series characteristics of the gas drainage evaluation index based on lasso regression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuang Song ◽  
Shugang Li ◽  
Tianjun Zhang ◽  
Li Ma ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Coal Mine ◽  
Experimental Comparison ◽  
Lasso Regression ◽  
Gas Drainage ◽  
Gas Concentration ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Concentration Prediction

AbstractThe evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal α value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.

Study on Coal Mine Gas Monitoring System Based on Arduino

2014 ◽  
Vol 1073-1076 ◽  
pp. 2173-2176 ◽  
Author(s):  
Hui Chun Gao ◽  
Chao Jun Fan ◽  
Jun Wen Li ◽  
Ming Kun Luo
Keyword(s):  
Monitoring System ◽  
Coal Mine ◽  
Low Cost ◽  
Gas Monitoring ◽  
Underground Coal Mine ◽  
Gas Concentration ◽  
Coal Mine Gas ◽  
Good Market ◽  
Mine Gas ◽  
Sd Card

Aimed at the frequency gas accident of coal mine, we designed a coal mine gas monitoring system based on Arduino microcontroller. The MQ-4 gas sensor was used to collect gas concentration, wireless ZigBee was used to transfer data of gas concentration to PC. The system can display gas concentration real-timely by LCD and use SD card to store the data. The system will send out sound and light alarm when the gas concentration overruns. Industrial tests have been carried out in Wuyang coal mine. Results show that gas monitoring system can well adapt to environment of underground coal mine and the measurement is accurate. The system is real-time monitoring and early warning. It has the characteristics of low power consumption, low cost, wireless, good market prospect.

Hardware Design of Coal Mine Safety Monitoring System Based on WSN

2012 ◽  
Vol 546-547 ◽  
pp. 1483-1488
Author(s):  
Shu Ren Han ◽  
Jun Wang ◽  
Ling Liang ◽  
Xian Peng Liu
Keyword(s):  
Monitoring System ◽  
Coal Mine ◽  
Hardware Design ◽  
Mine Safety ◽  
Sink Node ◽  
Function Structure ◽  
Gas Concentration ◽  
Coal Mine Safety ◽  
Safety Monitoring System ◽  
Safety Production

In the safety production of coal mine, monitoring exact and real-time mine parameter is very important and key problem. The monitoring system of mine environment with wireless is designed, which is based on the structure of wireless sensor network (WSN).The system includes sensor node, Sink node and monitoring center. In the paper, the function structure and hardware design of sensor are introduced for the monitoring of temperature, humidity and gas concentration, and the function structure and hardware design of sink node is designed. The system has low power, rapid real-timing, stable running. Etc. This can satisfy with the requirement of WSN and suit the monitoring of bad environments. It will have wide application prospect.

scholarly journals Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Keke Gao ◽  
Wenbin Feng ◽  
Xia Zhao ◽  
Chongchong Yu ◽  
Weijun Su ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Normal Mode ◽  
Coal Mine ◽  
Spontaneous Combustion ◽  
Sample Entropy ◽  
Generative Adversarial Networks ◽  
Detection Accuracy ◽  
Gas Concentration ◽  
Adversarial Networks

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.

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