Date of Award
Master of Science (MS)
There are several stages within mine planning that utilize different block models to help predict future values. Ore reconciliation manages the variance observed between the forecasted values from these block models and actual production data. This paper primarily focuses on an ore reconciliation system that was developed for an open pit copper operation located in the Western United States. A monthly reconciliation approach has been setup in a stepwise format along the mine value chain. Mine call factors are calculated each month for each step within the operation’s mining process to measure the variance between predicted values and production data. To monitor model performance, statistical process control (SPC) has been applied utilizing the mine call factors. Run charts have been implemented to help identify any early trends in the data. Control charts have been incorporated to separate the common cause variation (capability) from the special cause variation for each model. If the common cause variation of a model exceeds the operations error tolerance, then adjustments to the predictive model may be necessary. Primary recommendations for improving ore reconciliation and managing variance are provided based upon this study.
Nielson, Bryan, "Implementing an Ore Reconciliation System Supported by Statistical Process Control" (2018). Graduate Theses & Non-Theses. 151.