Evolving neural network using genetic algorithm for mining method evaluation in thin coal seam working face

2018 ◽  
Vol 9 (3) ◽  
pp. 228
Author(s):  
Tian Shixiang ◽  
Wang Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Seam ◽  
Mining Method ◽  
Method Evaluation ◽  
Thin Coal Seam ◽  
Working Face

Recent Patents on Thin Coal Seam Mining Equipment

2020 ◽  
Vol 13 (2) ◽  
pp. 99-108
Author(s):  
Yanxiang Wang ◽  
Daolong Yang ◽  
Bangsheng Xing ◽  
Tingting Zhao ◽  
Zhiyi Sun ◽  
...  
Keyword(s):  
Coal Seam ◽  
Underground Space ◽  
Mining Method ◽  
Mining Equipment ◽  
Coal Seams ◽  
Thin Coal Seam ◽  
Recoverable Reserves ◽  
Working Face ◽  
Geological Conditions ◽  
Seam Mining

Background:: China's thin and extremely thin coal seam resources are widely distributed and rich in reserves. These coal seams account for 20% of the recoverable reserves, with 9.83 billion tons of industrial reserves and 6.15 billion tons of recoverable reserves. Objective: Due to the complex geological conditions of the thin coal seam, the plow mining method cannot be effectively popularized, and the drum mining method is difficult to be popularized and applied in small and medium-sized coal mines, so it is necessary to find other more advantageous alternative mining methods. Methods: The equipment integrates mining operations, conveying operations, and supporting operations, and is suitable for mining short and extremely thin coal seam with a height of 0.35m-0.8m and width of 2m-20m. It has the advantages of the low body of the shearer, no additional support on the working face, and small underground space. The mining efficiency of thin coal seam and very thin coal seam can be improved and the mining cost can be reduced. Results: Thin coal seam shear mining combines mining, conveying, and supporting processes together and has the advantages of a low fuselage, no extra support required for the working face, and feasibility in a small underground space. Conclusion: The summarized mining method can improve the mining efficiency of thin and extremely thin coal seams, reduce mining costs, and incorporate green mining practices, which take both mining economy and safety into account.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

Application of Thick Coal Seam Mining Method Prediction Model Based on Artificial Neural Network

2014 ◽  
Vol 962-965 ◽  
pp. 242-246
Author(s):  
Wen Yu Lv ◽  
Zhi Hui Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Seam ◽  
Prediction Model ◽  
Coal Mining ◽  
Mining Method ◽  
Thick Coal Seam ◽  
Method Selection ◽  
Mining Method Selection ◽  
Seam Mining

Because of thick coal seam mining method selection is not only affected by coal seam geological conditions, but also limited by workers, and not fully utilization of experts` experience, the effect of tradition coal mining method selection methods are not ideal. The thick coal seam mining method prediction model based on artificial neural network (TCSMMPM-ANN) was established through the analysis of thick coal seam mining by using Levenberg – Marquardt (L-M) improved algorithm to train network, the simulation results of network test show that this model can provide a new research idea for thick coal seam mining method optimal selection and face economic and technical index prediction, it will have a broad prospect in thick coal mining.

Analysis of LED Light Source Energy Consumption for Lighting System Optimized by BP Neural Network Based on Genetic Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2509-2514
Author(s):  
De Qiang Wei ◽  
Hu Cheng Chen ◽  
Jun Wei Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Energy Consumption ◽  
Light Source ◽  
Bp Neural Network ◽  
Electricity Consumption ◽  
Analysis Model ◽  
Led Light ◽  
Working Face ◽  
Accuracy Of Prediction

To study influence of LED light source lifetime on electricity consumption, optimization of BP neural network is adopted to establish analysis model of energy consumption for neural network, regarding environmental illumination, LED working face illumination, attenuation rates of LED lifetime as input parameters and PWM as output parameters. Under future lifetime of LED, energy consumption is predicted through the model. Results show BP neural network based on genetic algorithm can calculate energy consumption of LED light source quickly and accuracy of prediction is high. The method can be well used to predict energy consumption of short-time LED.

The Current Situation and Developing Trend of Extremely Thin Coal Seam Mining of China

2013 ◽  
Vol 295-298 ◽  
pp. 2918-2923 ◽  
Author(s):  
Li Ming Zhang
Keyword(s):  
Coal Seam ◽  
Automatic Monitoring ◽  
Development Trend ◽  
Mining Method ◽  
Mining Equipment ◽  
Coal Seams ◽  
Existing Problems ◽  
Thin Coal Seam ◽  
Equipment Development ◽  
Seam Mining

Degree of mechanization of extremely thin coal seam mining is growing, with introducing the main mining method currently used by the different inclination of the extremely thin coal seams in China, focusing on the different mining technology and related ancillary mining equipment development status and existing problems, and discuss the extremely thin coal seam in the future to realize the remote control, automatic monitoring and unmanned mining development trend.

Risk Evaluation of Water Inrush from Coal Floor Based on BP Neural Network

2015 ◽  
Vol 744-746 ◽  
pp. 1728-1732 ◽  
Author(s):  
Wei Tao Liu ◽  
Shi Liang Liu ◽  
Yan Shuang Sun
Keyword(s):  
Neural Network ◽  
Coal Seam ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Evaluation Method ◽  
Water Inrush ◽  
Test Collection ◽  
Working Face ◽  
Floor Water Inrush

According to the nonlinear dynamic characteristic of coal seam floor water inrush, coal seam floor water inrush risk evaluation which includes 4 first level indicators,14 level two indexes was built based BP neural network. According to the test collection of engineering data, coal seam floor water inrush risk evaluation system based VB and MATLAB is reliable. Application to a mine coal seam No.2 working face was verified. The results show that, the evaluation method in water inrush is feasible, reasonable.

Sign in / Sign up

Export Citation Format

Share Document