Geometallurgy

Geometallurgy relates to the practice of combining geology or geostatistics with metallurgy, or, more specifically, extractive metallurgy, to create a spatially or geologically based predictive model for mineral processing plants. It is used in the hard rock mining industry for risk management and mitigation during mineral processing plant design. It is also used, to a lesser extent, for production planning in more variable ore deposits.

There are four important components or steps to developing a geometallurgical program,:^[1]

• the geologically informed selection of a number of ore samples
• laboratory-scale test work to determine the ore's response to mineral processing unit operations
• the distribution of these parameters throughout the orebody using an accepted geostatistical technique
• 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.^[2]

The sample selection procedure is performed to optimize granularity, sample support, and cost. Samples are usually core samples composited over the height of the mining bench.^[3] 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.^[4][5]

Test work

The following tests are commonly used for geometallurgical modeling:

• Bond ball mill work index test^[6]
• Modified or comparative Bond ball mill index^[7][8]
• Bond rod mill work index and Bond low energy impact crushing work index ^[9]
• SAGDesign test^[10]
• SMC test^[11]
• JK drop-weight test^[12]
• Point load index test
• Sag Power Index test (SPI(R)) ^[13]
• MFT test ^[14]
• FKT, SKT, and SKT-WS tests ^[15]

Geostatistics

Block kriging is the most common geostatistical method used for interpolating metallurgical index parameters and it is often applied on a domain basis.^[16] 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 SPI(r) value is known not to be an additive parameter, however errors introduced by block kriging are not thought to be significant .^[17][18] 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:

• The Bond equation
• The SPI calibration equation, CEET ^[19]
• FLEET^[14]*
• SMC model^[20]
• Aminpro-Grind, Aminpro-Flot models ^[21]

See also

• Extractive metallurgy
• Geostatistics
• Mining
• Mineral Processing

Notes

1. Bulled, D., and McInnes, C: Flotation plant design and production planning through geometallurgical modeling. Centenary of Flotation Symposium, Brisbane, QLD, 6-9. June 2005.
2. 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
3. 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
4. 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
5. 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
6. Allis Chalmers. Crushing, Screening and Grinding Equipment, Metallic Ore Mining Industry. Rock Grinding - Training Session. White Paper, Undated.
7. 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.
8. Berry, T.F., and Bruce, R.W., A simple method of determining the grindability of ores. Canadian Gold Metallurgists, July 1966. pp 63
9. 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
10. 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.
11. Morrell, S. Design of AG/SAG mill circuits using the SMC test. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006
12. 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.
13. 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.
14. 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
15. http://www.aminpro.com. Aminpro - FKT, SKT and SKT-WS flotation kinetic testwork procedures. 2009.
16. Dagbert, M., and Bennett, C., Domaining for geomet modelling: a statistical/geostatical approach. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006.
17. 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.
18. 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
19. 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
20. Morrell, S.,A new autogenous and semi-autogenous mill model for scale-up, design, and optimisation. Minerals Engineering 17 (2004) 437-445.
21. http://www.aminpro.com, 2009

General references

• Isaaks, Edward H., and Srivastava, R. Mohan. An Introduction to Applied Geostatistics. Oxford University Press, Oxford, NY, USA, 1989.
• David, M., Handbook of Applied Advanced Geostatistical Ore Reserve Estimation. Elsevier, Amsterdam, 1988.
• Mineral Processing Plant Design, Practice, and Control - Proceedings. Ed. Mular, A., Halbe, D., and Barratt, D. Society for Mining, Metallurgy, and Exploration, Inc. 2002.
• 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