The focus of our research is on developing, facilitating and implementing key enabling technologies to enhance the performance and competitiveness of the European mineral resource industry. Our research is focussed on the development of innovative “Real-Time Mining” methods for on-line sensor-based mineral characterization and real-time resource-model updating and extraction optimization (in analogy to the CLRM approach in petroleum engineering). The approach combines sensor-based material characterization, geostatistical resource modelling and data integration to achieve risk-based mine-planning optimization, to create one holistic closed-loop framework.
To be able to better utilize such unconventional mineral resources of the future, smart autonomous mining systems are needed, which integrate local exploration of spatially varying material characteristics in–situ and have the ability to follow and extract the pay zones in the ore body while optimizing the sustainable value of extraction. The main barriers to overcome for the successful economic exploitation are:
- Effective grade control, which will maximize resource potential along the whole value chain
- Minimization of handling of zero-value material introduced by dilution, thus reducing unnecessary expenditure of energy and financial resources
- Management and control of the geological uncertainty due to limited information available, thus optimising resource utilisation.
The key concept of the research promotes the change in paradigm from discontinuous, intermittent process monitoring and control to a continuous closed-loop process-management system.
The development of such an integrated framework in the context of mineral resource management is novel and involves significant scientific challenges, since it has to integrate the following distinct scientific disciplines into one coherent process optimisation framework:
- Underground equipment positioning
- Sensor-based material characterization
- Sensor-based machine control monitoring
- Methods of spatial grade prediction using geostatistical approaches and rapid updating
- Optimization of short-term planning.