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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Veröffentlicht in:Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World S. 3136 - 3144
Hauptverfasser: Karagianni, Maria, Benardos, Andreas
Format: Buchkapitel
Sprache:Englisch
Veröffentlicht: United Kingdom CRC Press 2023
Taylor & Francis Group
Ausgabe:1
Schlagworte:
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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Zusammenfassung: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.
DOI:10.1201/9781003348030-379