Material tracking within a mine depends on disparate data gathered from across the site's value chain. Of particular importance are the following data sources:
- Blast Designs
- RFID insertion points (if utilising physical ore tracking)
- RFID detections at each antenna
- block models (e.g. geometallurgical, short or long term grade controlStage at which final boundaries between ore and waste are calculated. models)
- Process Data
- dispatchSystems for allocation of equipment plans (eg. Trucks, shovels etc…). and mining fleetAvailable mining equipment at a given operation (e.g. trucks, shovels etc…). Movements
Making the spatial-temporalRelating to time. connection between the blast and downstream processes, whether this is the concentratorA plant where ore is separated into concentrates and tails. or a cross belt sensor, is critical in determining GEGrade Engineering potential for a variety of levers, as well as for conducting accurate trials and evaluations.
CRC ORE has approached this data fusion in two key ways:
- Custom developed software on a case by case basis
- Developing data fusion frameworks that can be environment agnostic
Both methods have been applied multiple times to various open pit and underground mines.
Ore Tracking Example
Ore tracking in this example aimed to align the spatial properties of ore being delivered with grades being reported by a cross belt sensor and to then determine correlations over time which could be utilised to validate or improve sensor calibration. This is shown schematically below, where; x,y,z = spatial position and t = temporalRelating to time. window.
Two approaches were employed to meet these requirements. The first method utilised dispatchSystems for allocation of equipment plans (eg. Trucks, shovels etc…). data to digitally "deliver" material from the dig location to the crusher, and then compared material properties with the sensor measurements at an appropriate time offset allowing for transfer along the conveyor. Note that this approach does not consider blast induced movementDirectional translation of material as a result of blasting. where ore is displaced from its original location by the blasting process. Required data for this approach was:
| Data | Parameters |
| Equipment location data | Time, equipment ID, x, y, z |
| High Resolution block model | x, y, z, Cu (%), Fe (%) |
| Sensor Data | Time, Cu (%), Fe (%), tonnes processed |
The second methodology utilised SmartTag data to make the connection between ore characteristicsGeneral properties of rock (grade, hardness, grain size etc…). and sensor readings. As a SmartTag antenna was adjacent to the sensor installation, data from the sensor at the tag detection time could be directly compared to associated ore characteristicsGeneral properties of rock (grade, hardness, grain size etc…). from a tags original In-situContained in unbroken ground. spatial position. This approach assumes that the SmartTag remain with the material they were inserted into as it was excavated and then dumped into a crusher. It also assumed no stemmingGravel used to pack the top of blast holes to confine explosive energy. ejection occured during blasting where SmartTags could have been dislocated from the blast holeCylindrical hole used to load explosives into unbroken material. s they were was inserted into. Theoretically, this approach considers blast induced movementDirectional translation of material as a result of blasting. .
Data required for this approach was:
| Data | Parameters |
| Blast Designs | Hole ID, x, y, z |
| SmartTag installation file | Hole ID, Tag ID |
| SmartTag detection file | Detection time, Tag ID |