Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input (NARX) algorithm

Abstract

As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the “thermal damping effect” will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input (NARX) algorithm was used as a novel method to predict the dry-bulb temperature (Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network (ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical “damping coefficient” for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Td at the bottom of intake shafts. Keywords: Underground mining, Vertical openings, Thermal damping effect, Artificial neural network, Nonlinear autoregressive with external input (NARX)