Bueno, Torvela, Chandramohan, Liedes, Powell, Chavez Matus,
Published in Minerals Enginering April 2021
ABSTRACT
The mining industry needs a low-cost and reliable breakage characterization test which can rapidly process a large number of samples for geometallurgical modelling. The more samples are tested, the better is the under-standing of the ore hardness variability and lower the design or production risks.
The proposed solution is a new testing device, herein named Geopy¨or¨a, which is a variation of a roll crusher with an adjustable gap and instrumentation to measure breakage forces and energy applied to rock particles during the breakage process.
The principle utilises a controlled degree of crushing, with absorbed breakage energy being a response rather than an input. The new testing device is capable of rapidly testing rocks over a wide range of sizes and accurately measured energy levels.
For a range of ores, the results were demonstrated to provide outputs that replicate the breakage modelling from full JK drop Weight tests. In addition to being suited to testing drill cores and small sample masses, the Geopy¨or¨a provides a distribution of particle strengths within every sample. This paper provides an introduction to the concept, development of the prototype device and breakage calibration results that indicate its potential to become a major player in geometallurgical ore testing.
Keywords
Geometallurgy, comminution, ore breakage characterization, variability.
ACKNOWLEDGEMENTS
This work was supported by the Business Finland public agency for research funding. The prototype machine was constructed and tested as a collaboration between the Intelligent Machines and Systems research unit and the Oulu Mining School at the University of Oulu. We thank everyone involved in providing the experimental materials and data, and our supporters for making this work possible. We express our gratitude to all our colleagues for sharing their help and expertise, and to the university for the opportunity to perform this research and publish the results.
Authors
Marcos de Paiva Bueno a,*, Janne Torvela b, Rajiv Chandramohan c, Toni Liedes b, Malcolm Powell d, Tabatha Chavez Matus e
a Geopyörä, Toppilansaarentie 3, Oulu, 90510, Finland
b Intelligent Machines and Systems, University of Oulu, P.O. Box 4200, FIN-90014 University of Oulu, Finland
c Ausenco, 855 Homer St, Vancouver, BC V6B 2W2, Canada
d JKMRC, University of Queensland, 40 Isles Rd, Indooroopilly, QLD 4068, Australia
e Oulu Mining School, University of Oulu, P.O. Box 3000, FIN-90014 University of Oulu, Finland
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