Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input (NARX) algorithm
Loading...
Authors
Roghanchi, Pedram
Kocsis, Karoly C.
Issue Date
2019
Type
Article
Language
en_US
Keywords
Artificial neural network , Nonlinear autoregressive with external input (NARX) , Thermal damping effect , Underground mining , Vertical openings
Alternative Title
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 (T-d) 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 T-d at the bottom of intake shafts. (C) 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
Description
Citation
Roghanchi, P., & Kocsis, K. C. (2019). Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input (NARX) algorithm. International Journal of Mining Science and Technology, 29(2), 255–262. doi:10.1016/j.ijmst.2018.06.002
Publisher
International Journal of Mining Science and Technology
Journal
Volume
Issue
PubMed ID
ISSN
2095-2686