Hybrid method for analyzing air thermal conditions in underground mines

Thermal prediction of underground mine air is required to develop control measures against heat problems. Researchers have extensively studied this topic using numerical simulations; however, these require long processing times. Recently, researchers have begun using artificial intelligence algorithms; however, the predictive capabilities of the model are still limited because an intelligent system is formed from field measurement data. This study presents a fast and accurate prediction of the Wet Bulb Globe Temperature (WBGT) in underground mines using a hybrid integrated numerical method and an adaptive neuro fuzzy inference system (ANFIS) method. The mine air thermal conditions in various scenarios were analyzed by numerical simulation, and the results were utilized to develop intelligence for ANFIS model -based predictors. A case study was conducted in two underground gold mine areas to demonstrate the effectiveness of this method. The ANFIS model was trained and tested with 81 scenarios generated from numerical simulations. Accurate predictors were obtained, with a coefficient of determination (R2) of 0.98 and 0.97. In addition to predicting the WBGT, the developed ANFIS model optimized the selection of the auxiliary fan power, minimizing the power consumption while simultaneously providing a comfortable WBGT.