Executive Summary

Milling is commonly deployed in many industrial sectors for intended particle size reduction. In this project, we aim to develop a robust methodology to link material grindability with particle dynamics in a mill in order to provide an innovative step- change in mill fingerprinting and optimization. This involves characterizing the stressing events that prevail in a milling operation and establishing material grindability in the context of the stressing events. The material grindability will require a detailed study of the fundamental fracture and breakage mechanisms of individual particles under different loading regimes, and how they relate to the mechanical properties and the final size distribution. This will provide the fundamental scientific basis for developing appropriate grindability measure capable of analysing particle breakage subjected to particle impact, compression, and shear etc. pertaining to a milling process, which in turn will provide the basis for an improved particle breakage model calibrated against the defined grindability.

The centrifugal impact pin mill has been selected to be studied for this project, in collaboration with Hosokawa Micron Ltd. UPZ100 pin mill experiments with varying rotary speeds and feed rates were reported and analyzed in the past reports. The work performed in Year 5 of the project is to develop a coupling framework between discrete element method (DEM) and population balance model (PBM) to predict the product size distribution of milling experiments. At the particle scale, DEM simulations were performed to understand the fundamentals of the particle dynamic and stressing conditions inside the mills. Variables in the PBM kernel were classified into material dependent parameters and mill operation dependent parameters. The impact velocity distributions obtained through DEM simulations were utilized to inform the mill operation dependent parameters of PBM at the process scale. The remaining parameters of PBM, i.e. material dependent parameters, were estimated based on the milling test at 12000 RPM. The resulting DEM-PBM coupled model is then used to predict the milling results for the other three rotary speeds to validate the proposed DEM-PBM model. A good agreement between the tests and the predictions of product size distribution has  been achieved, which indicates the potential application of the proposed DEM-PBM