Machine-Learning Model for Predicting Shock Loss due to Buntons in a Shaft

Shafts are critical components of the mine ventilation systems and contribute significantly to the mine ventilation pressure. Estimation of shaft pressure losses is an important aspect of mine ventilation planning and shaft frictional pressure losses are extensively studies. Shock losses contributed by shaft buntons are estimated using the interference factor model developed half a century ago. This study aims to develop a better performing prediction model for shaft buntons using state of the art experiment and numerical data, and machine learning techniques. Results show that the present prediction model is performing poorly, and the prediction performance can be improved by incorporating additional predictor variables. The Artificial Neural Network standardized Machine Learning model developed in this study is able to predict the shaft bunton shock loss with high accuracy (R2 = 0.956).