Analysing the large volumes of data being produced in modern mines can identify opportunities to optimise mine operations and maximise value.
The mining industry is awash with data from many sources, including from exploration, mining, and mobile and fixed equipment through process control systems. The challenge is to use this data to make informed decisions that enhance safety and maximise value.
This research program aims to develop and deploy new techniques for analysing the large volumes of data that will be produced in the future and use this analysis to improve mine operations.
Mine sites are increasingly moving to remote and autonomous operation with remote sensing, robotics and autonomous infrastructure. In this setting, more data will be available than ever, from the increased monitoring of a material’s characteristics as it moves from pit to plant and real-time knowledge of the location and status of equipment and stockpiled ore.
Next-generation digital mining techniques have the capacity to:
- facilitate reconciliation between expected and realised outcomes;
- communicate the overall state, and predicted future state, of operations to miners in real time, given large volumes of data from varying sources; and
- continually revise ore body models in terms of composition and processing characteristics as new data arises.
- Data mining
- Machine learning
- Simulation and modelling
- Remote sensing
- Stream data mining
- Decision analytics
- Real options analysis
Dr Ben Rubenstein