Application of Artificial Intelligence to the Alert of Explosions in Colombian Underground Mines

• Published in: Mining, metallurgy & exploration Vol. 41; no. 4; pp. 2129 - 2142
• Main Authors: Vallejo-Molina, Luis, Blandon-Montes, Astrid, Lopez, Sebastian, Molina-Escobar, Jorge, Ortiz, Andres, Soto, David, Torero, Jose, Toro, Alejandro, Molina, Alejandro
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2024
• Subjects: Engineering; Materials Engineering; Metallic Materials; Mineral Resources; CFD simulation; Methane explosion; Coal mining safety; Artificial Intelligence (AI); Incident investigation; Artificial Neural Network (ANN)
• ISSN: 2524-3462; 2524-3470
• DOI: 10.1007/s42461-024-01008-z

The use of Artificial Intelligence (AI), particularly of Artificial Neural Networks (ANN), in alerting possible scenarios of methane explosions in Colombian underground mines is illustrated by the analysis of an explosion that killed twelve miners. A combination of geological analysis, a detailed characterization of samples of coal dust and scene evidence, and an analysis with physical modeling tools supported the hypothesis of the existence of an initial methane explosion ignited by an unprotected tool that was followed by a coal dust explosion. The fact that one victim had a portable methane detector at the moment of the methane explosion suggested that the ubiquitous use of these systems in Colombian mines could be used to alert regulatory agencies of a possible methane explosion. This fact was illustrated with the generation of a database of possible readouts of methane concentration based on the recreation of the mine atmosphere before the explosion with Computational Fluid Dynamics (CFD). This database was used to train and test an ANN that included an input layer with two nodes, two hidden layers, each with eight nodes, and an output layer with one node. The inner layers applied a rectified linear unit activation function and the output layer a Sigmoid function. The performance of the ANN algorithm was considered acceptable as it correctly predicted the need for an explosion alert in 971.9 per thousand cases and illustrated how AI can process data that is currently discarded but that can be of importance to alert about methane explosions.