Abstract
The paper describes the stages of primary study and preprocessing of data obtained from air-gas monitoring sensors for their further analysis using machine learning methods.
Highlights
Methane release during the development of gas-bearing coal seams significantly limits possible loads on the mining and tunneling equipment
When predicting the state of methane in a mine taking into account a large number of measured indicators, it is advisable to use the apparatus of neural networks
Scaling the data is an important step before training a neural network. different values, that vary in different ranges, can be obtained by the inputs and outputs of neural network after information coding
Summary
Methane release during the development of gas-bearing coal seams significantly limits possible loads on the mining and tunneling equipment. The sensors allow the volume fraction of methane in the atmosphere to be recorded and a decision on further work to be made based on this information. Continuous measurement of these and other variable parameters of production makes it possible to form a database, on the basis of which mathematical methods can be applied for analysis of mutual influence of factors, and to build predictive models. When predicting the state of methane in a mine taking into account a large number of measured indicators, it is advisable to use the apparatus of neural networks. An important condition for ensuring effective control of gas release is the correct prediction of methane release at the extraction area and the permissible load on the working face by the gas factor [4, 5]
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