Stewart, CA, Allman, A & Hall, BE 2010, 'Block cave optimisation — a value driven approach', in Y Potvin (ed.), Caving 2010: Proceedings of the Second International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 219-230, https://doi.org/10.36487/ACG_rep/1002_13_Stewart
(https://papers.acg.uwa.edu.au/p/1002_13_Stewart/)
Abstract: Optimising the footprint, production rate and block height of a block caving operation is a trade-off between technical and value parameters. After the technical constraints have been quantified (e.g. fragmentation, drawbell spacing, minimum hydraulic radius and footprint span for cave initiation) the key parameters to be assessed are:
extraction level elevation (and ultimately, block height)
cutoff and shut-off (footprint size and draw limits)
cave initiation location
production rate (and ultimately draw rate).
Because the extraction level position, drawbell layout and cave footprint are set early in the planning process with little or no chance for subsequent change based on mining outcomes, hill-of-value (HOV) techniques provide a powerful tool for optimisation. Value, defined by the corporate goals, may be assessed against any combination of design options. Techniques to rapidly generate reserves for the different cutoff and extraction level combinations are employed. A range of production rates and cave initiation locations with appropriate capital and operating costs applied can be modelled.
An example of this technique is presented. An additional advantage of the technique is the ability to analyse and quantify the risks associated with the optimal cave chosen on a value basis. The method can also be modified to analyse panel caving or front caving. Sensitivity to external factors, such as metal prices and discount rates, can easily be quantified. A value driven approach allows revenue, costs and productivity to be assessed to produce optimised design criteria. This approach should be undertaken early in a project’s life both to optimise the value of the resource and to highlight any bottlenecks or constraints to achieving the optimal result.