High pressure grinding rolls modelling and parameters dependency
Chair / Department / Institute
Universitat Politècnica de Catalunya. Departament d'Enginyeria Minera, Industrial i TIC
PublisherUniversitat Politècnica de Catalunya
RequiresMicrosoft office, Matlab2016a
Rights accessOpen Access
European Commisision's projectOptimOre - Increasing yield on Tungsten and Tantalum ore production by means of advanced and flexible control on crushing, milling and separation process (EC-H2020-642201)
A model for High Pressure Grinding Rolls (HPGR) was developed in this work based on the widely used Population Balance Model (PBM). This approach uses a variety of different functions one of which is the breakage distribution function. The methodology to determine the function parameters is presented and using these values, the model was compared with real processed materials from an HPGR pilot plant, with tungsten ore as the test material. The results of the model parameter determination, and the product of the comminution in the HPGR, showed the dependency of material breakage on the material characteristics, and on the operative and process conditions. The model presented is reasonably robust, showing less error than the 3.0 Root Mean Square Error when compared with a heterogeneous feed particle size distribution material. The operational gap was also studied, and its dependency on the feed particle size, porosity, moisture, and specific pressing force was proven.
The data base is related to the High Pressure Grinding Rolls (HPGR) modelling. This data was generated in a HPGR manufacturer pilot plant in Freiberg, Germany, and represents the particle size distribution measurement of the feed and the product of the milling reactor, and the calculation of some material features, as the moisture or the density. This data is necessary for the correct modelization of the comminution in a HPGR, and it can be used for different mathematical models.
CitationAnticoi Sudzuki, H. F. (2019). High pressure grinding rolls modelling and parameters dependency [Dataset]. 1 v. Universitat Politècnica de Catalunya.