This paper describes the application of modern data-analysis tools, such as data mining, to determine a representative cost driver for haulage activity. This cost driver was subsequently used to trace incurred haul costs by bench for openpit optimization. For this purpose, large amount of previously unused cost and production data from an open pit mine was extracted, loaded, transformed and integrated into a data warehouse. Predictive modeling was performed using Microsoft Decision Trees algorithm to compute expected unit haul costs by bench for future mine expansions. Finally, a sensitivity analysis was carried out to determine the effect of two cost drivers (if any) for incremental haul costs in the final pit outline process.
|Original language||English (US)|
|Number of pages||6|
|Specialist publication||Mining Engineering|
|State||Published - Oct 2008|
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology