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Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks

Author

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  • Magdalena Tutak

    (Faculty of Mining and Geology, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Jarosław Brodny

    (Faculty of Organization and Management, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.

Suggested Citation

  • Magdalena Tutak & Jarosław Brodny, 2019. "Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks," IJERPH, MDPI, vol. 16(8), pages 1-21, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:8:p:1406-:d:224142
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    References listed on IDEAS

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    1. Papagera, A. & Ioannou, K. & Zaimes, G. & Iakovoglou, V. & Simeonidou, M., 2014. "Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(4), pages 1-11, December.
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    Cited by:

    1. Lluís Sanmiquel-Pera & Marc Bascompta & Hernán Francisco Anticoi, 2019. "Analysis of a Historical Accident in a Spanish Coal Mine," IJERPH, MDPI, vol. 16(19), pages 1-11, September.
    2. Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2022. "A Gas Concentration Prediction Method Driven by a Spark Streaming Framework," Energies, MDPI, vol. 15(15), pages 1-13, July.

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