Tracing High-Temperature Points in Goaf Based on CO Gas Concentration Distribution at the Working Face

The extensive area of goaf makes high-temperature points highly concealed, and prolonged heating can easily trigger spontaneous coal combustion. Traditional temperature monitoring methods are limited in spatial coverage and thus fail to detect high-temperature points in a timely manner. To address this issue, this study proposes an integrated analytical method combining numerical simulation and intelligent inversion, with Taihe Coal Mine as the research object. First, A coupled flow–temperature–gas field model of the goaf was established in COMSOL Multiphysics 6.3 to simulate working-face CO concentration distributions corresponding to high-temperature points at different locations, thereby constructing a comprehensive dataset. Then, a BP neural network prediction model improved by the dung beetle optimization algorithm (DBO-BP) was trained to infer the spatial location of high-temperature points based on CO concentration distributions. Finally, a geometric prediction method was introduced to guide precise drilling within the predicted high-risk areas for field verification. The results demonstrate that the proposed DBO-BP model can effectively trace the locations of high-temperature points from CO concentration data. When combined with the geometric prediction method, it provides an efficient and reliable technical solution for the early prevention of spontaneous coal combustion in goaf.