Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/52315
Title: Comparative Study of Data Mining Techniques for Predicting Explosions in Coal Mines
Contributor(s): Namazi, Samira (author); Brankovic, Ljiljana  (author)orcid ; Moghtaderi, Behdad (author); Zanganeh, Jafar (author)
Publication Date: 2020
DOI: 10.1109/Confluence47617.2020.9057921
Handle Link: https://hdl.handle.net/1959.11/52315
Abstract: 

Global warming is a long-term environmental hazard demonstrated by a gradual increase in the temperature of the Earth. It is caused by the accumulation of greenhouse gases in the atmosphere, including carbon dioxide and methane. Although, in terms of the volume, methane is considered secondary to carbon dioxide, it is about 21 times more damaging when compared over a 100-year period. Fugitive methane emissions from underground coal mines significantly contribute to global warming. Amongst all the known methods to reduce the fugitive methane, application of thermal oxidation (or, simply, burning) is deemed the most effective and practical. This process produces water vapour and carbon dioxide, which has significantly lower adverse impact on the atmosphere than methane. The thermal oxidisers operate at high temperatures, which may introduce a risk of fire and explosion to the mine. In order to mitigate such risk, a thorough understanding of the methane explosion characteristics is essential. Methane fire and explosion experiments under conditions pertinent to underground coal mines are expensive, risky and necessitate significant effort, and thus require enormous preparation and safety procedures. It is cheaper and safer to analyse existing data to discover patterns and predict explosions than to conduct new extensive experiments. In this paper, we present a comparative study of data mining and machine learning techniques used for these purposes.

Publication Type: Conference Publication
Conference Details: Confluence 2020: 10th International Conference on Cloud Computing, Data Science & Engineering, Noida, India, 29th - 31st January, 2020
Source of Publication: Proceedings of the Confluence 2020: 10th International Conference on Cloud Computing, Data Science & Engineering:
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Place of Publication: Los Alamitos, United States of America
Fields of Research (FoR) 2020: 480204 Mining, energy and natural resources law
Socio-Economic Objective (SEO) 2020: 170601 Coal mining and extraction
HERDC Category Description: E2 Non-Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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