Sensor-based ore sorting can reduce the rock sent to grinding and wet treatment. The savings depend on mineral response, sorting speed and how much valuable material stays in the processing stream.
A grinding mill uses energy to break rock down, whether that rock contains valuable minerals or very little worth recovering. Sensor-based ore sorting gives operators a chance to remove low-value material before it reaches that stage.
The attraction is straightforward: put less unwanted rock through the plant. The difficulty is deciding what can safely be left out, at a speed that keeps up with production.
How Does Ore Sorting Reduce Processing Demand?
Ore sorting reduces processing demand by identifying and diverting low-value material before grinding and mineral recovery. With less rock entering those circuits, a plant can reduce the work required to grind solids, pump slurry and treat material in water-based processes such as flotation.
Rejecting coarse material early can also reduce the solids entering fine tailings. A review in Minerals describes the potential to lower energy, water and reagent use per ton of product through this approach.
Consider an illustrative 100-ton feed. If sorting diverts 20 tons, 80 tons reach the mill. That is a 20% reduction in mill-feed mass; the engineering implication is not an automatic 20% reduction in mine-wide energy or water use. Extraction and primary crushing may already have happened. Sorting adds its own demand. Ore hardness and the required grind size affect energy use, while water savings depend on recycling and losses.
A mine may also use the freed capacity to process more valuable ore. Resource use per ton of metal could improve while total site consumption stays similar.
Reading Minerals with Radio Waves
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) developed magnetic-resonance sensing technology, commercialized through NextOre, that uses short radio-frequency pulses tuned to a target mineral. The resulting signal allows operators to estimate its concentration within material moving on a conveyor.
Bulk sorting then directs successive batches into different streams. It makes a decision about a section of belt load, rather than ejecting each rock individually.
For copper, an important target is chalcopyrite, a copper-bearing sulphide. Because the measurement is mineral-specific, operators must establish how the detected mineral relates to total valuable metal in their particular ore.
Throughput Changes the Sorting Decision
Commercial copper applications already exist. CSIRO reported in December 2022 that a commissioned system at Zambia’s Kansanshi mine operated at 2,800 tons per hour.
Speed affects selectivity. At that throughput, a four-second measurement represents roughly 3.1 tons of rock, calculated from the flow rate. A longer interval combines more material into one decision, potentially averaging valuable and low-grade rock together.
The controls must also track that measured batch to the diverter. An accurate reading has limited value if the gate switches too early or too late.
Mineral Suitability and Sorting Errors
A responsive mineral is only part of the requirement. The feed must contain enough variation between the batches being sorted. If valuable minerals are distributed almost evenly, there may be little low-value material to separate. Mixing during mining, stockpiling and handling can further blur the differences.
NextOre’s technical guidance therefore identifies mineralogy and grade variability as central to assessing suitability.
Errors have two practical consequences. Sending low-value material to the plant leaves unnecessary processing demand in place. Sending valuable material to the reject stream loses potential revenue. A more aggressive rejection threshold can improve feed grade while sacrificing metal recovery.
Trials should compare sensor readings with laboratory assays and measure both streams. Operators need the tons rejected, the valuable metal retained and the downstream recovery. Plant trials should then track kilowatt-hours and make-up water per ton of recovered metal. Reject percentage alone cannot establish whether sorting has improved the operation.
Lithium Sensing Is a Separate Development
CSIRO’s September 2025 research update described work to detect spodumene, a lithium-bearing mineral in pegmatite rocks. That project faced a different engineering requirement: integrating a large electromagnet into a mine environment. The copper sensing described in the report does not require that large static magnetic field.
The update still identified questions about operation at scale. It should therefore be read as developing lithium-sensing work, rather than evidence of a commercially operating lithium sorter. Commercial copper experience supports the sensing approach, but does not establish lithium performance.
Connecting the Sensor to the Processing Plant
These decisions connect measurement with plant design. At the Engineering Institute of Technology (EIT), the Master of Engineering (Industrial Automation) covers industrial instrumentation, programmable logic controllers and process control. The Master of Engineering (Mechanical) includes process engineering and fluid machinery, relevant to evaluating the equipment and treatment loads that follow sorting.
EIT’s article on recovering critical minerals from mine waste considers material already discarded. Ore sorting asks an earlier question: which material needs intensive processing in the first place? Rejected rock still needs suitable storage and environmental assessment; it has simply avoided part of the processing route.
References
CSIRO: Inside the Lab — Recent Sensing and Sorting Commercialisation Success
CSIRO: MRI for Rapid Copper Ore Sorting
CSIRO: NextOre Wins Major Mining Technology Solution Award
Robben and Wotruba: Sensor-Based Ore Sorting Technology in Mining — Past, Present and Future
NextOre: Bulk Ore Sorting Whitepaper
NextOre: Frequently Asked Questions
Australian Government: Mining Energy Efficiency Guide
This article was published October 22nd, 2026 and the content is current as at the date of publication.
