Prediction of methane emissions in coalmine - Soko
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Date
2023
Authors
Ivaz, Jelena
Petrović, Dejan
Radovanović, Mladen
Zlatanović, Dragan
Stojadinović, Saša
Stojković, Pavle
Journal Title
Journal ISSN
Volume Title
Publisher
University of Belgrade, Technical Faculty in Bor
Source
Proceedings - 54th International October Conference on Mining and Metallurgy - IOC 2023, 18-21 October 2023, Bor Lake, Serbia
Volume
Issue
Abstract
Methane in underground mines represents a high level of risk from the aspect of safety and health at work, namely the explosion of this gas is one of the most common causes of major disasters in coal underground mines. The consequences of these accidents are a large number of injured workers as well as material damage, and very often the shutdown of the mines themselves. Methane also has a negative effect on the atmosphere because it creates a greenhouse effect. However, methane, in addition to its negative effects, is also used in the chemical industry as an energy source for mass consumption. The paper presents a method for predicting methane emissions based on an artificial neural network (ANN). The model was created for ten years of data on the amount of methane in the exhaust air for ventilation in the underground coal mine - Soko. This method can very accurately predict methane emissions in coal mines. Data on methane missions have multiple benefits for mines because they enable the prevention of major disasters in mines, as well as the potential exploitation of methane, which would reduce the negative impact on the environment.
Description
Keywords
neural network, methane, coal mine, prediction
Citation
DOI
Scopus
ISSN
ISBN
978-86-6305-140-9
License
CC-BY-NC-ND