Many mining operations are trialling novel sensing technologies to support potential Grade Engineering strategies and it is important to quantify how these measurements can enhance the mining value chain; from the draw pointUnderground mining term for access points to broken material. or dig faceActive area of shovel/truck loading operations., to final concentratorA plant where ore is separated into concentrates and tails. product. These technologies vary in the attributes being measured as well as granularity of measurement required in order to achieve acceptable results. One difficulty that is often experienced is the shift of the ore characteristicsGeneral properties of rock (grade, hardness, grain size etc…). from a spatially defined value, for example a grade in a block model, to a time based trend transmitted from a cross belt sensor. This spatial-temporalRelating to time. transition is a constant challenge for mines and data alignment and fusion is critical to understanding the value sensor technologies can deliver.
CRC ORE in collaboration with its Mining Essential Participants has conducted several studies fusing new sensor technologies and existing routine operational data with the primary aim of understanding how the new technologies fit into the current business. The schematic below demonstrates the potential complexity of a large open cut mining system with numerous variable data sources and routes for the ore to travel upon.
When conducting these investigations, CRC ORE will analyse data collected over a three-month or longer period to determine whether the sensor measurements being evaluated at the site can be used for bulk ore sorting, where uneconomic ore can be removed from the feedMaterial entering a predetermined system. to the downstream processes. In addition, correlations between rock mass properties from the block model, and key concentratorA plant where ore is separated into concentrates and tails. performance metrics such as throughputMetric for overall tonnage moving through a system within a specified time. and recovery, can also be explored at the same time.
Data integration enables operations to determine the value of new sensor technologies from both business improvement and Grade Engineering perspectives. CRC ORE has developed and is continuing to develop several prototype data fusion, analysis and visualisation tools that are being utilised at operations globally to understand the opportunities for new sensor technologies and their fusion into existing operational systems.
The data from these point solutions, in conjunction with block model grade estimates, physical assays, on-line flotation feedMaterial entering a predetermined system. assays, ore delivery schedules and/or live material flow information (from ore tracking systems for example) can be fused to determine which technology stacks could potentially be used to implement bulk ore sorting strategies at site.
The studies attempt to reconcile sensor readings with block model estimations and potential physical assays by integrating and time shifting the captured data to align required. Apart from conducting physical sampling of significant amounts of ore, this is the only pragmatic way to perform this reconciliation. Once the readings from the sensors (or a fusion of them) can be proven to be of a sufficient accuracy, an opportunity assessment is undertaken to evaluate the value that could be gained by implementing a diversion strategy to remove uneconomic material from the mill feedMaterial entering a predetermined system.. In addition, process data around the comminution circuitGeneral term for a collection of comminution/flotation equipment. (such as mill throughputMetric for overall tonnage moving through a system within a specified time., power draw, etc.) can also be included in this analysis to map comminution circuitGeneral term for a collection of comminution/flotation equipment. performance back to the block model. This information can be used to enhance any geometallurgical estimations at site.