Possibilities of IPDA spaceborne lidar and neural networks for measuring methane concentration

2019 ◽  
Author(s):  
Alexander Sukhanov ◽  
Gennadii Matvienko
Keyword(s):  
Neural Networks ◽  
Methane Concentration

scholarly journals Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks

2019 ◽  
Vol 16 (8) ◽  
pp. 1406 ◽  
Author(s):  
Tutak ◽  
Brodny
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Methane Concentration ◽  
Mining Industry ◽  
Mining Area ◽  
Work Safety ◽  
Mining Operations ◽  
Mining Hazards ◽  
Artificial Neural

Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.

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