Study on early temperature inversion identification of coal spontaneous combustion based on acoustic signals

The spontaneous combustion of coal stockpiles leads to resource depletion, safety hazards, and atmospheric pollution. Hence, reliable identification of ignition sources within loose coal beds is critical for prevention. In this study, sound waves were selected for fire-source detection. Using four coal ranks long-flame, non-caking, coking, and lean coals as experimental materials. Based on acoustic theory and wave propagation in porous media, a temperature measurement model for loose coal is established by introducing the tortuosity parameter and the conversion factor. An acoustic testing system was established to collect and analyze signals from the four coal samples during oxidative heating. The relative humidity surrounding all coal samples increased during heating, ranging from 54.84% to 92.78%. Humidity had a greater influence on sound-velocity measurement than atmospheric pressure. At ambient temperatures above 30 °C, humidity-induced temperature errors reached 8 °C, whereas errors caused by atmospheric pressure remained below 1 °C. Using the proposed model, acoustic temperatures of the four coal samples were calculated. The acoustic temperatures deviated from the measured values by at most 1.78, 1.73, −2.36, and −2.78 °C for the four coals, corresponding to maximum relative errors of −6.62%, −5.19%, −7.32%, and 8.85%, respectively. These errors mainly resulted from deviations of gas composition from the air assumption in the semi-enclosed environment, sound propagation-time measurement errors, and residuals in conversion-factor fitting. The study results provide a theoretical basis and technical approach for the early warning and spatial positioning of concealed combustion areas in coal piles.