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Fire Size and Response Time Predictions in Underground Coal Mines Using Neural Networks

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Abstract

The prediction of the coal mine fire response time, defined as the remaining time before conditions at attack positions grow untenable for firefighters, plays a vital role in the decision-making process during a mine fire scenario. The knowledge of the response time along with the fire size, fire location, and arrival time could allow for the most suitable decision regarding direct or remote approach to the fire in the mine, mine evacuation planning, and remote attack from the surface. For this reason, this paper presents a data-driven approach to predict the response time and fire size based on available and measurable parameters during underground coal mine fires using two interconnected artificial neural networks (ANNs). A total of 300 fire dynamic simulator (FDS) and fire and smoke simulator (FSSIM) simulations of a straight and flat mine entry (replicating a belt entry) with different fire sizes, air velocities, and dimensions were used in training and testing the ANNs. The results showed that 95% of fire size and response time predictions should be within ± 29 kW and ± 4 s of true values obtained in the fire models, respectively. The approach presented in this work can provide instantaneous predictions of response time and fire size during ongoing mine fires. Additionally, this approach can be utilized in other mine fire locations as well as in different types of tunnels.

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Data Availability

All datasets and codes used for supporting the conclusions of this article are available upon request at the following website:

https://drive.google.com/drive/folders/1KEGBPD46T2B8PGabhVTkqL9CrAxHw1nR?usp=sharing

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Acknowledgements

The findings and conclusions in this report are those of the authors and do not reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the US Government.

Funding

This research was funded by the National Institute Safety and Health (NIOSH) under Contract No. 200–2014-59669.

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Authors and Affiliations

  1. Virginia Tech (Mining and Minerals Engineering), Blacksburg, VA, USA

    Barros-Daza Manuel J. & Luxbacher Kray D.

  2. Virginia Tech (Mechanical Engineering), Blacksburg, VA, 24061, USA

    Lattimer Brian Y.

  3. Jensen Hughes, 2020, Kraft Drive, Suite 3020, Blacksburg, VA, 24060, USA

    Barros-Daza Manuel J. & Hodges Jonathan L.

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  1. Barros-Daza Manuel J.
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Correspondence to Barros-Daza Manuel J..

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Barros-Daza, M.J., Luxbacher, K.D., Lattimer, B.Y. et al. Fire Size and Response Time Predictions in Underground Coal Mines Using Neural Networks. Mining, Metallurgy & Exploration 39, 1087–1098 (2022). https://doi.org/10.1007/s42461-022-00580-6

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