Geometallurgy relates to the practice of combining
geological understanding with
metallurgical test work and / or real time processing plant data (for extractive metallurgy), to create a geological based three-dimensional
predictive model of mineral processing response. It is used in the hard rock
mining industry for risk management and mitigation during mineral processing plant design. It is also used for production mine planning to optimize the ore feed to the processing plant.
There are four important components or steps to developing a geometallurgical program,:[Bulled, D., and McInnes, C: Flotation plant design and production planning through geometallurgical modeling. Centenary of Flotation Symposium, Brisbane, QLD, 6-9. June 2005.]
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the geologically informed selection of a number of ore samples
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laboratory-scale test work to determine the ore's response to mineral processing
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the distribution of these parameters throughout the orebody using an accepted geostatistical technique
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the application of a mining sequence plan and mineral processing models to generate a prediction of the process plant behavior
Sample selection
The sample mass and size distribution requirements are dictated by the kind of mathematical model that will be used to simulate the process plant, and the test work required to provide the appropriate model parameters. Flotation testing usually requires several kg of sample and grinding/hardness testing can required between 2 and 300 kg.
[McKen, A., and Williams, S.: An overview of the small-scale tests required to characterize ore grindability. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006]
The sample selection procedure is performed to optimize granularity, sample support, and cost. Samples are usually composited over the height of the mining bench.[Amelunxen, P. et al: Use of geostatistics to generate an orebody hardness dataset and to quantify the relationship between sample spacing and the precision of the throughput estimate. Autogenous and Semi-Autogenous Grinding Technology 2001, Vancouver, Canada, 2006] For hardness parameters, the variogram often increases rapidly near the origin and can reach the sill at distances significantly smaller than the typical drill hole collar spacing. For this reason the incremental model precision due to additional test work is often simply a consequence of the central limit theorem, and secondary correlations are sought to increase the precision without incurring additional sampling and testing costs. These secondary correlations can involve multi-variable regression analysis with other, non-metallurgical, ore parameters and/or domaining by rock type, lithology, alteration, mineralogy, or structural domains.[Amelunxen, P.: The application of the SAG Power Index to ore body hardness characterization for the design and optimization of comminution circuits, M. Eng. Thesis, Department of Mining, Metals and Materials Engineering, McGill University, Montreal, Canada, Oct. 2003. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006][Preece, Richard. Use of point samples to estimate the spatial distribution of hardness in the Escondida porphyry copper deposit, Chile. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006]
Test work
Geometallurgical test work is broken into those that impact
comminution and those that impact recovery of the valuable component.
Comminution test work
The following tests are commonly used to generate inputs for geometallurgical modelling of comminution parameters, that is crushing, grinding and their associated energy use:
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Bond ball mill work index test
[Allis Chalmers. Crushing, Screening and Grinding Equipment, Metallic Ore Mining Industry. Rock Grinding - Training Session. White Paper, Undated.]
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Modified or comparative Bond ball mill index
[Smith, R.W., and Lee, K.H.. A comparison of data from Bond type simulated closed-circuit and Batch type grindability tests. Transactions of the SME. March 1961 - 91.][Berry, T.F., and Bruce, R.W., A simple method of determining the grindability of ores. Canadian Gold Metallurgists, July 1966. pp 63]
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Bond rod mill work index and Bond low energy impact crushing work index
[Barratt, D.J., and Doll, A.G., Testwork Programs that Deliver Multiple Data Sets of Comminution Parameters for Use in Mine Planning and Project Engineering, Procemin 2008, Santiago, Chile, 2008]
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SAGDesign test
[Starkey, J.H., Hindstrom, S., and Orser, T., “SAGDesign Testing –What It Is and Why It Works”; Proceedings of the SAG Conference, September 2006, Vancouver, B.C.]
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SMC test
[Morrell, S. Design of AG/SAG mill circuits using the SMC test. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006]
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JK drop-weight test
[Mineral Comminution Circuits: Their Operation and Optimisation. ed. Napier-Munn, T.J., Morrell, S., Morrison, R.D., and Kojovic, T. JKMRC, The University of Queensland, 1996.]
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Point load index test
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SAG Power Index test (SPI(R))
[Kosick, G., and Bennett, C. The value of orebody power requirement profiles for SAG circuit design. Proceedings of the 31st Annual Canadian Mineral Processors Conference. Ottawa, Canada, 1999.]
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MFT test
[Dobby, G., Kosick, G., and Amelunxen, R. A focus on variability within the orebody for improved design of flotation plants. Proceedings of the Canadian Mineral Processors Meeting, Ottawa, Canada, 2002]
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FKT, SKT, and SKT-WS tests
[http://www.aminpro.com. Aminpro - FKT, SKT and SKT-WS flotation kinetic testwork procedures. 2009.]
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de Paiva Buena, M., Almeida, T., Lara, L., and Powell, M. The Geopyörä Index: A New Instrument for Assessing Comminution and Rock Strength Parameters. IMPC-International Mineral Processing Congress 2024. Washington D.C.
Geostatistics
Block
kriging is the most common geostatistical method used for
interpolate metallurgical index parameters and it is often applied on a domain basis.
[Dagbert, M., and Bennett, C., Domaining for geomet modelling: a statistical/geostatical approach. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006.] Classical geostatistics require that the estimation variable be additive, and there is currently some debate on the additive nature of the metallurgical index parameters measured by the above tests. The Bond ball mill work index test is thought to be additive because of its units of energy;
nevertheless, experimental blending results show a non-additive behavior.
[Yan, D., Eaton, R. Breakage Properties of Ore Blends, Minerals Engineering 7 (1994) pp. 185–199] The SPI(R) value is known not to be an additive parameter, however errors introduced by block kriging are not thought to be significant .
[Amelunxen, P.: The application of the SAG Power Index to ore body hardness characterization for the design and optimization of comminution circuits, M. Eng. Thesis, Department of Mining, Metals and Materials Engineering, McGill University, Montreal, Canada, Oct. 2003.][Walters, S., and Kojovic, T., Geometallurgical Mapping and Mine Modeling (GEM3) - the way of the future. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006] These issues, among others, are being investigated as part of the Amira P843 research program on Geometallurgical mapping and mine modelling.
Mine plan and process models
The following process models are commonly applied to geometallurgy:
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The Bond equation
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The SPI calibration equation, CEET
[Dobby, G. et al., Advances in SAG circuit design and simulation applied to the mine block model. Autogenous and Semi-Autogenous Grinding Technology 2001, Vancouver, Canada, 2006]
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FLEET
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SMC model
[Morrell, S., A new autogenous and semi-autogenous mill model for scale-up, design, and optimisation. Minerals Engineering 17 (2004) 437-445.]
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Aminpro-Grind, Aminpro-Flot models
[http://www.aminpro.com, 2009]
See also
Notes
General references
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Isaaks, Edward H., and Srivastava, R. Mohan. An Introduction to Applied Geostatistics. Oxford University Press, Oxford, NY, USA, 1989.
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David, M., Handbook of Applied Advanced Geostatistical Ore Reserve Estimation. Elsevier, Amsterdam, 1988.
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Mineral Processing Plant Design, Practice, and Control - Proceedings. Ed. Mular, A., Halbe, D., and Barratt, D. Society for Mining, Metallurgy, and Exploration, Inc. 2002.
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Mineral Comminution Circuits - Their Operation and Optimisation. Ed. Napier-Munn, T.J., Morrell, S., Morrison, R.D., and Kojovic, T. JKMRC, The University of Queensland, 1996
