Modelling underground mine ventilation characteristics using artificial neural networks
Underground bauxite mining exploitations is a challenging environment for ventilation. A controlled underground ventilation system can significantly improve the environmental and working conditions at the mines. In this paper, the modelling of a section of an existing complex underground ventilation...
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| Vydáno v: | Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World s. 3136 - 3144 |
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| Hlavní autoři: | , |
| Médium: | Kapitola |
| Jazyk: | angličtina |
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United Kingdom
CRC Press
2023
Taylor & Francis Group |
| Vydání: | 1 |
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| Abstract | Underground bauxite mining exploitations is a challenging environment for ventilation. A controlled underground ventilation system can significantly improve the environmental and working conditions at the mines. In this paper, the modelling of a section of an existing complex underground ventilation network assisted by machine learning (ML) techniques and more particularly by the use of Artificial Neural Network (ANN). The developed ANN is focusing in the prediction of NOx concentration at a selected mine site in order to model its operating characteristics so that they can be automatically adjusted to the existing conditions, ensuring better working conditions and creating a safer and controlled underground environment. The above model can make prediction that are accurate and respond to actual conditions and can be the basis for a further improvement of the Ventilation on Demand (VoD) technology. |
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| AbstractList | Underground bauxite mining exploitations is a challenging environment for ventilation. A controlled underground ventilation system can significantly improve the environmental and working conditions at the mines. In this paper, the modelling of a section of an existing complex underground ventilation network assisted by machine learning (ML) techniques and more particularly by the use of Artificial Neural Network (ANN). The developed ANN is focusing in the prediction of NOx concentration at a selected mine site in order to model its operating characteristics so that they can be automatically adjusted to the existing conditions, ensuring better working conditions and creating a safer and controlled underground environment. The above model can make prediction that are accurate and respond to actual conditions and can be the basis for a further improvement of the Ventilation on Demand (VoD) technology. |
| Author | Benardos, Andreas Karagianni, Maria |
| Author_xml | – sequence: 1 givenname: Maria surname: Karagianni fullname: Karagianni, Maria organization: School of Mining & Metallurgical Engineering, NTUA, Athens, Greece – sequence: 2 givenname: Andreas surname: Benardos fullname: Benardos, Andreas organization: School of Mining & Metallurgical Engineering, NTUA, Athens, Greece |
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| Copyright | 2023 Taylor & Francis Group, London, UK |
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| Editor | Marinos, Vassilis P. Anagnostou, Georgios Benardos, Andreas |
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| Keywords | Above Ground Mine Ventilation Digital Twin Unlabelled Training Data BIM Model Ventilation Network Geological Strength Index Productive Tunnels FFN Model Flexible Ducts Ann Model Overburden Minimum Relative Error NOx Measurement Actual Output Result NOx Concentration SEM Diesel Equipment Airflow Quantity Requirement Airflow SCL Airflow Parameters Min Max Normalization Optimum Topology |
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| PublicationSubtitle | Proceedings of the ITA-AITES World Tunnel Congress 2023 (WTC 2023), 12-18 May 2023, Athens, Greece |
| PublicationTitle | Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World |
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| Snippet | Underground bauxite mining exploitations is a challenging environment for ventilation. A controlled underground ventilation system can significantly improve... |
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| Title | Modelling underground mine ventilation characteristics using artificial neural networks |
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