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Assessment and modelling of quaternary and quinary HPGR performance for iron ore applications

University of British Columbia

The complexity degree of a mining plant’s comminution circuit is critical for allowing the processing of low-grade iron ore deposits. Considering that the comminution stage is responsible for most of the energy consumption in a mineral processing plant, the pursuit of energy-efficient technologies is a major challenge of the mining industry. The High Pressure Grinding Rolls (HPGR) have been applied for over 20 years and is widely recognized in terms of energy savings. However, the limited access to industry and academic studies about the HPGR performance and the lack of a widely industry accepted bench-scale laboratory test for sizing and modelling HPGRs are major hindrances that must be addressed in order to promote the machine acceptance and implementation. The research's main objectives were to evaluate the HPGR amenability to comminute iron ore in a two-stage HPGR circuit and extend the applicability of Davaanyam’s (2015) Direct Calibration and Database-Calibrated methodologies for predicting the energy consumption and size reduction of HPGRs through laboratory-scale piston-press tests. The HPGR performance and modelling evaluation for quaternary and quinary applications were supported by a combination of laboratory-scale testing, pilot-scale testing and modelling work. Results obtained from the research showed that the HPGR is suitable for comminuting iron ore in open circuit quaternary and closed circuit quinary applications. The performance evaluation revealed a strong linear relationship between the machine’s specific pressing force and net specific energy consumption. The size reduction also increased linearly with the increase of the pressing force. The ore moisture content revealed to be detrimental to the HPGR’s throughput at high concentrations but did not impact the performance in terms of size reduction. The Direct Calibration methodology was successfully applied to iron ore for quaternary applications, but the current Database-Calibrated regression models resulted in poor energy-size reduction predictions. because the variable levels for the quaternary application extended beyond the ones used for developing the current regression models. The results indicate that the database needs to be extended to finer sizes, higher moisture levels and possibly ore types.

Text

2020-12-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/76889

Mining Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/