Hronsky, D, Mariager, S, Donaldson, S, Bobbermen, L & Eustace, C 2020, 'Simulation modelling of Carrapateena Sub Level Cave production level performance', 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. 658-667, https://doi.org/10.36487/ACG_repo/2063_44
(https://papers.acg.uwa.edu.au/p/2063_44_Hronsky/)
Abstract: Carrapateena is a copper-gold deposit hosted in a brecciated granite complex, located approximately 460 km north of Adelaide, South Australia. The deposit will be mined using the sub level cave (SLC) mining method at a rate of 4.25 Mtpa for an estimated 20 years. When entering its second phase of construction and planning prior to production commencement, the mining teams at Carrapateena were beginning to shift focus from high-level long-term business planning, into more detailed execution planning, which involved the establishment of certain operational philosophies for the production levels. These philosophies needed to consider the many varied elements that influence the production plan. Each of these elements, including both mining equipment and tasks have diverse operational rules, and with a large and complex operation to plan for, it was important to clearly understand the interactions between the elements and the effect on the production system as a whole. To understand these complexities, OZ Minerals approached Polymathian who were able to create a detailed model of all entities and processes in the caving operation using a bespoke, discrete event simulation tool. The tool allowed planners to visualise the cave’s operations over the life of a production level down to day-to-day, minute-by-minute operations. Each production process, entity and interactions between entities were modelled so planners could test various assumptions and constraints. The level of detail represented in the simulation model allowed OZ Minerals to develop an understanding of the effect of strategic and operational decisions prior to the mine going into production. The ability to validate the effect of assumptions on operational performance and test a range of scenarios enabled identification of the levers that have the biggest impact on production.  This facilitated improved operations planning decisions and saved significant time and resources in planning and during operations. New staff are also able to use the visual outputs of the simulation model to understand the operational complexities on the production level and interactions that will occur between mobile equipment. The work assisted putting Carrapateena in a better position to ‘hit the ground running’ with reduced uncertainty around operational expectations.