Making Use of Online Production Data: Sequential Updating of Mineral Resource Models

Abstract

The information flow along the mining value chain from exploration through resource/reserve estimation, mine planning, operations management and processing generally occurs discontinuously over long time spans. To react to deviations between produced ore and model based expectations, reconciliation exercises are performed adjusting resource models and mine planning assumptions. However, there is often a lag of weeks, months or even years. Developments over the last decade have created a flood of online data about different aspects during the production process. For example, sensor technology enables online characterization of geochemical, mineralogical and physical material characteristics. The ability to fully exploit this additional information and feed it back into resource models will open up new opportunities for improved decision making in short-term planning and operational control. This contribution introduces a new approach for sequential resource model updating utilizing online sensor data. The updating approach is based on differences between model-based predictions and sensor measurements of raw material properties. In this context, one major challenge has to be solved. Raw material streams at sensor stations often occur as a blend of material originating from multiple different extraction locations. A direct allocation of the source of differences between the model-based prediction and sensor measurements is difficult. To overcome this issue, this contribution proposes an adaption of a data assimilation-based approach for sequential model updating. The theoretical description of the method is provided followed by a demonstrative case study that investigates the performance with respect to different mine configurations.