Charumporn, B., Yoshioka, M., Fujinaka, T., & Omatu, S. (2003). Early Stage Fire Detection Using Reliable Metal Oxide Gas Sensors and Artificial Neural Networks. In The Proceedings of the International Joint Conference on Neural Networks (pp. 3185-3188). Institute of Electrical and Electronics Engineers.
https://doi.org/10.1109/IJCNN.2003.1224082

has been cited by the following article:

• TITLE: A Review on Mine Fire Disasters and Assessment of Fire Detection Using a Dual-Cab Suppression System

  AUTHORS: Idongesit Bassey Utip, Yulong Zhang, Li Ren, Appiah Augustine, Junfeng Wang

  KEYWORDS: Coal Spontaneous Combustion, Mine Fire, Fire Detection, Suppression System, Dual-Cab

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.10 No.12, December 7, 2022

  ABSTRACT: The health and productivity of mining operations are negatively impacted by coal mine fires, making them dangerous. It happened everywhere, in both working and abandoned coal mines. This study seeks to review and provide technical analytics of potential mine fires and fire detection in a Dual-Cab suppression system. Analysis was done on potential mine fires like spontaneous combustion, flammable gas explosions, and cab vehicle fires. Additionally, a review of the NIOSH experiment was conducted to assess the performance of smoke and flame detectors in a dual-cab suppression system. This study guides both open-pit and underground mining operations. Additionally, a few ideas and suggestions are presented to assist with on-the-job safety analysis, ensuing creative alterations, and technology advancement for the mining industry’s overall safety.