Sensor Based Real-Time Resource Model Reconciliation for Improved Mine Production Control—A Conceptual Framework

The flow of information and consequently the decision making along the chain of mining from exploration to beneficiation typically occurs in a discontinuous fashion over long time spans. In addition, due to the uncertain nature of the knowledge about the deposit and its inherent spatial distribution of material characteristics actual production performance in terms of produced ore grades and quantity and extraction process efficiency often deviate from expectations. Reconciliation exercises to adjust mineral reserve models and planning assumptions are performed with timely lags of weeks, months or even years. With the development of modern Information and Communication Technology over the last decade, literally a flood of data about different aspects of the production process is available in a real-time manner. For example, sensor technology enables online characterisation of geochemical, mineralogical and physical material characteristics on conveyor belts or at working faces. The ability to utilise the value of this additional information and feed it back into reserve block models and planning assumptions opens up new opportunities to continuously control the decisions made in production planning to increase resource recovery and process efficiency. This leads to a change in paradigm from a discontinuous to a near real-time reserve reconciliation and model updating, which calls for suitable modelling and optimisation methodologies to quantify prior knowledge in the reserve model, to process and integrate information from different sensor-sources and accuracy, back propagate the gain in information into reserve models and efficiently optimise operational decisions real-time. This contribution introduces the concept of an integrated closed-loop framework for Real-Time Reserve management (RTRM) incorporating sensor based material characterisation, geostatistical modelling under uncertainty, modern data assimilation methods for a sequential model updating and mining system simulation and optimisation. The effectiveness of the framework and the value added will be demonstrated in an illustrative case study.