Impact energy of particles in ball mills based on DEM simulations and data-driven approach

Abstract

The discrete element method has been used to simulate the particle flow in a ball mill under different operating conditions. The model was validated by comparing the simulated results of the flow pattern and input power with those measured from a same-scale laboratory mill. The impact energy of the particles under different operating conditions was analysed in detail. The results showed that the impact energy was affected by the operating conditions of the mill and can be linked to the grinding rate for a given material. The correlation between impact energy and grinding rate follows first-order grinding kinetics. Mill performance decreases with increasing mill size. Furthermore, a data-driven machine learning framework has been proposed to predict the impact energy for different operating conditions. It was found that the prediction for mills with diameters of 2000 and 3000 mm based on the training model developed by mills with diameter less than 254 mm could be achieved with an accuracy of 80% and a correlation coefficient of 0.9. Through the combination of DEM simulation and data-driven approach, the computing time required in the determination of impact energy for large scale mills can be dramatically reduced.