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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Management of methane emission in coal mines using artificial neural networks: A systematic review

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  • Management of methane emission in coal mines using artificial neural networks: A systematic review

Sundas Matloob 1 *, Li Yang 1, Sumaiya Bashiru Danwana 1, Ikram Ullah 2, Marcel Merimee Bakala Mboungou 3 and Iqra Yamin 3

1 School of Economics and Management, Anhui University of Science and Technology, Huainan, China.
2 School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huainan, China.
3 School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China.

Review Article
 
International Journal of Science and Research Archive, 2024, 11(02), 1387–1404.
Article DOI: 10.30574/ijsra.2024.11.2.0579
DOI url: https://doi.org/10.30574/ijsra.2024.11.2.0579

Received on 25 February 2024; revised on 06 April 2024; accepted on 09 April 2024

Underground mines are responsible for a large number of the released methane worldwide. Our study paper can contribute to mitigating methane emissions, hence reducing the concentration about greenhouse emissions within the environment and mitigating the associated hazardous risks. In this study, more than 45 recent journals on Methane emission in Coal Mines were gathered from Web of Science, IEEE Xplore, ScienceDirect, and ResearchGate. A systematic review is accomplished of the past four years of various parts of Methane gas emission such as anthropogenic emission sources, gas emissions detection, prediction of methane using different technology and the Artificial intelligence projection model for methane emission. The outcomes reveal that since the methane emission management has obtained increasing attention over the past four years. This study also shows that big countries are using technology to control and utilize the methane emission to reduce the energy crisis. To decrease the coal mine injuries, academic understanding of underground methane management has increased, different technologies are integrated and support from various IT departments has amplified for the forecasting. In the future, the most critical task for coal mines risk assessment is to restore the worker's trust in mine safety, and the primary solution is to give more awareness to the underground management and workers through utilizing Artificial Intelligence (AI) mainly Artificial Neural Networks (ANN).

Coal mining; Methane emission; ANN; Risk assessment; Energy waste; Artificial intelligence

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2024-0579.pdf

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Sundas Matloob, Li Yang, Sumaiya Bashiru Danwana, Ikram Ullah, Marcel Merimee Bakala Mboungou and Iqra Yamin. Management of methane emission in coal mines using artificial neural networks: A systematic review. International Journal of Science and Research Archive, 2024, 11(02), 1387–1404. Article DOI: https://doi.org/10.30574/ijsra.2024.11.2.0579

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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