A soft-sensor approach to impact intensity prediction in stirred mills guided by DEM models

Abstract

Stirred mills are used as part of the comminution process on a mine site to reduce ore particles to very fine sizes. Optimization of stirred mills largely depends upon operator experience and trial and error tests due to a lack of technology available to monitor the internal state of the mill online. The internal state of interest in this work is the particle-particle impact intensity which is related to comminution efficiency of the mill but cannot be directly measured. In this paper, a soft-sensor approach is proposed for estimation of particle-particle impact intensity in a dry stirred mill based on the measurement of particle-wall impact energy. Unlike many existing developments which are based on experimental data, the proposed soft-sensor development is guided by offline simulations based on the discrete element method (DEM) models. An accurate DEM simulation is often computationally complex and as such cannot be directly used for online prediction. We obtained impact intensities of a stirred mill from DEM simulations across a range of operating conditions. These sets of data were used to train soft-sensor models for online prediction of particle impact intensity. This work, although only a proof of concept, shows that it is possible to estimate the particle-particle impact intensity in a stirred mill from particle-wall impact measurement. © 2011 Elsevier B.V.