New population balance model for predicting particle size evolution in compression grinding. (15th March 2015)

• Record Type: Journal Article
• Authors: Esnault, V.P.B.; Zhou, H.; Heitzmann, D.
• Is Part Of: Minerals engineering. Volume 73(2015)
• Page Start: 7
• Page End: 15
• Publication Date: 2015-03-15
• Subjects: Grinding -- Comminution -- Process modeling -- HPGR -- PBM; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2014.12.036
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: We model particle size evolution during compression grinding, using PBM. Model parameters vary with density to account of evolving conditions in the bed. Model is calibrated using a large set of piston-die tests on monodisperse samples. Model can then predict the evolution of any starting particle size distribution. Predictive power of the model is tested on three different mineral materials. Abstract: Population Balance Models (PBM) are widely used to predict the evolution of the particle size distribution during various grinding processes, such as ball milling. They represent breakage through the definition of particle destruction and fragments generation rates. Their application to compression grinding (HPGR, vertical mills…) has been limited, due to the complexity of interactions between particles of different sizes. In this work, we present a new PBM approach for compression grinding. Complex interactions between size classes are represented in a simplified manner by making particle destruction and fragment generation depend on the bed porosity. Model is tested by confrontation to an extensive collection of experimental results on a piston-die cell, on three different materials (cement clinker, limestone, and quartz). When properly calibrated with preliminary tests, the model is able to predict the evolution of the particle size with accuracy, for any starting grain size distribution and any load.