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Research on rapid prediction of mine fire in full ventilation network based on BP neural network
Abstract Mine fires pose a serious threat to underground safety, and rapid prediction of fire-induced hazards is critical for timely rescue operations. Traditional numerical simulations using Computational Fluid Dynamics are accurate but time-consuming and limited to single or local tunnels. This study proposes a rapid prediction method for mine fires in a full ventilation network based on a backpropagation (BP) neural network.
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