Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations
Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2025.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1869483750915571712 |
|---|---|
| access_status_str | Open Access |
| author2 | Ayomoh, Michael |
| author_browse | Ayomoh, Michael |
| author_facet | Ayomoh, Michael |
| collection | Thesis |
| dc_rights_str_mv | © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2025. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/108418 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-07-01T04:03:58.187Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/108418 Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations Ayomoh, Michael u20751495@tuks.co,za Mooroogen, Rubeshen UCTD Sustainable Development Goals (SDGs) Coal mine methane Machine learning Underground mining Sensors Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2025. Despite underground mining being a major economic stay for several nations across the globe as a result of the need to explore diverse geo-resources ranging from coal, through oil and gas, to precious metals amongst others, for economic sustainability, emissions from the underground mining activities have posed a major challenge to the health of mine workers over the years. In a bid to narrow down, by focusing on coal mining, it could be said of the latter that it is a dangerous activity which has been notably responsible for large amounts of accidents resulting in the death of several mine workers globally. A key element that is responsible for the fatal aspect of underground coal mining is the presence and accumulation of toxic gases such as carbon monoxide, Sulphur, carbon dioxide and methane. The decision as well as the choice of coal mine methane (CMM) for this research is as a result of its susceptibility to explosions, fires and asphyxia. This is rendered possible as methane is a highly flammable gas as well as possessing the ability of displacing oxygen. This paper focused its investigation specifically on Coal Mine Methane (CMM) which is released as a result of the extraction of coal and the disturbance inflicted to surrounding rocks’ formation during deep mining operations. This research was based on the use of machine learning models to successfully predict dangerous concentrations of methane over the authorized threshold. Five machine learning classification models were implemented and compared with the objective towards finding the best model to predict and detect dangerous level of CMM. The models that were investigated include: Naïve-Bayes, logistic regression, Decision Trees, XG-Boost and artificial neural networks (ANN). Those predictions were made from a dataset containing information on the temperature, airflow, humidity, pressure and methane concentration at an underground coal mine. The temperature, airflow, humidity and pressure measurements were recorded by a series of sensors namely anemometers and component sensors. Furthermore, a vital research deliverable reached was the ability to evaluate and infer the most effective machine learning model for the research premised on a comparative investigation of five ML models. The results obtained using generated metrics such as the classification reports and the confusion matrices, resulted in the Artificial Neural Network (ANN) selection as the most effective ML technique. The summarized f1 scores for each model are described as follows: 0.28 for Naïve Bayes, 0.66 for the logistic regression, 0.42 for the decision tree, 0.23 for XG-Boost and 0.82 for the ANN. Furthermore, recommendation for building more robust machine learning models towards successfully predicting and detecting dangerous levels of CMM from an artificial intelligence’s perspective were provided. Industrial and Systems Engineering MEng (Industrial Engineering) Restricted Faculty of Engineering, Built Environment and Information Technology SDG-09: Industry, innovation and infrastructure SDG-08: Decent work and economic growth 2026-02-18T13:43:46Z 2026-02-18T13:43:46Z 2026-04 2025-12-03 Dissertation * A2026 http://hdl.handle.net/2263/108418 Disclaimer Letter sent to RDM en © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Coal mine methane Machine learning Underground mining Sensors Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations |
| title | Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations |
| title_full | Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations |
| title_fullStr | Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations |
| title_full_unstemmed | Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations |
| title_short | Machine learning predictive model development for Coal Mine Methane (CMM) concentration detection in underground mining operations |
| title_sort | machine learning predictive model development for coal mine methane cmm concentration detection in underground mining operations |
| topic | UCTD Sustainable Development Goals (SDGs) Coal mine methane Machine learning Underground mining Sensors |
| url | http://hdl.handle.net/2263/108418 |