Authors: Pierce, ME; Campbell, R; Llewelyn, K; Fuenzalida, M; Simanjuntak, K; Kurniawan, A; Haflil, D; Meiriyanto, F; Rogers, S

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This paper is hosted with the kind permission of the Universidad de Chile, Eighth International Conference & Exhibition on Mass Mining, 2020.


DOI https://doi.org/10.36487/ACG_repo/2063_35

Cite As:
Pierce, ME, Campbell, R, Llewelyn, K, Fuenzalida, M, Simanjuntak, K, Kurniawan, A, Haflil, D, Meiriyanto, F & Rogers, S 2020, 'Cave propagation factor for caving rate and drawpoint productivity forecasting at PTFI', in R Castro, F Báez & K Suzuki (eds), MassMin 2020: Proceedings of the Eighth International Conference & Exhibition on Mass Mining, University of Chile, Santiago, pp. 519-534, https://doi.org/10.36487/ACG_repo/2063_35

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Abstract:
Monitoring of fracture limits, cave backs and drawpoint fragmentation at both the Grasberg Block Cave (GBC) and Deep Mill Level Zone (DMLZ) caves at PT Freeport Indonesia’s operations suggest that Cave Propagation Factor (CPF), weak and persistent vein intensity and hydrofracture spacing exert a strong control on the caving rate and primary fragmentation within the cave back. CPF in this case is defined as the ratio of cave back stress to defected rock strength and can vary significantly across the cave back, both due to variability in vein mineralogy and intensity and complex cave back geometries. Cave back stress is understood through 3D elastic modelling of anticipated cave shapes (fracture limits) while point load indices, weak and persistent vein intensity and Synthetic Rock Mass (SRM) testing are used to understand the variability in strength and fragmentation potential of the sparsely jointed, massive to heavily veined rock mass domains. When coupled with predictive relations for secondary fragmentation, these ultimately led to the development of a methodology for forecasting of caving rate and primary fragmentation in the cave back, the fragmentation (including oversize and fines) reporting to the drawpoints and the associated hangup frequency and productivity. These forecasts are presented in the form of block models, forecast maps and drawpoint-based charts. When calibrated against monitored cave back and drawpoint performance these can be used to assist in more local cave back shaping and preconditioning (function of caving rate), recovery estimation and secondary breakage planning (function of productivity) and management of wet muck and mill throughput (function of fines entry).

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