Machine Learning Techniques to Model and Predict Airflow Requirements in Underground Mining
This paper analyzes the airflow requirements of underground operations and the accurate assessment of future conditions so as to effectively adjust ventilation parameters. More particularly, ML techniques are utilized to capture patterns or prevailing conditions and to be able to generalize/predict...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Materials Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4605/15/1/17 |
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| Summary: | This paper analyzes the airflow requirements of underground operations and the accurate assessment of future conditions so as to effectively adjust ventilation parameters. More particularly, ML techniques are utilized to capture patterns or prevailing conditions and to be able to generalize/predict future conditions managed via the ventilation system. The case examined is about underground bauxite mining operations, the ventilation characteristics and requirements of which have been firstly developed and modelled into a validated digital twin. With this twin model, several scenarios are developed and evaluated and more importantly data are gathered, allowing for the training of the ML algorithms used to assess and predict the required ventilation airflow, taking into account air quality data, the number of workers, and machine fleet. |
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| ISSN: | 2673-4605 |