Predicting liner wear of ball mills using discrete element method and artificial neural network

Mill component wear is a critical issue in industrial grinding mills as it affects mill's continuous operation and performance and causes a considerable cost to replace worn parts. Understanding wear profile and its effects on mill performance would provide useful insight for process optimisation. In this work, an approach based on the Discrete Element Method (DEM) and data driven machine learning model has been proposed to predict the liner wear profile of ball mills. DEM simulations based on a small scaled experimental setup were carried out to examine the relationship between liner wear energy and the input features such as the location of the point considered on the liner, the ratio of particle size to mesh element size and energy distribution over the wear profile. Then the machine learning model for wear parameter was developed. The comparison between the actual and predicted wear parameters for the test data showed a correlation coefficient of about 0.95 and an accuracy of 93% with the desired outcomes. The predicted liner wear profiles at different times for the mill were similar to the ones obtained by the experiment. The approach proposed was finally applied to a large industrial mill with a diameter of 4.267 m. It was shown that only five intermediate simulations were needed to determine a liner wear profile of the mill close to that from field data after 57,726 h of operation. The results obtained in this study indicate that combining neural networks with the DEM provides a promising technique in developing wear models for industrial applications.