FAQ

Ore characterisation for ore sorting is the systematic measurement of particle-scale properties - size distribution, grade variability, mineralogy, density, and surface condition - that determine whether sensor-based ore sorting is technically viable for a given ore body. Pre-concentration-specific characterisation goes beyond standard bulk assay to examine how these properties vary at the particle scale, which is the scale at which ore sorting operates. It is the first and most critical step in any ore sorting evaluation.

Standard metallurgical test work typically characterises ore in bulk - head grades, bulk mineralogy, average physical properties. Ore characterisation for ore sorting examines how grade, mineralogy, density, and surface properties vary at the individual particle scale, across size fractions, and between ore domains. This particle-scale understanding is essential because an ore sorter classifies and separates individual particles, not bulk material.

Characterisation covers particle size distribution including F95, F50 and F5 anchor points; grade distribution and compositional contrast between ore and waste; lithological and mineralogical heterogeneity including mineral phases and liberation; physical and mechanical properties including density, shape factors and breakage behaviour; and surface condition including coatings, oxidation and moisture that affect sensor detectability.

The sensor principle best suited to an ore sorting application is determined by the specific physical and compositional contrast between ore and waste in the material. Ore characterisation identifies these contrast drivers before any sensor testing begins, enabling a structured, evidence-based sensor selection rather than one driven by availability or assumption. Choosing a sensor before characterising the material is one of the most common causes of misdirected ore sorting investment.

Characterisation data provides the inputs for every subsequent stage. Particle size distribution, grade variability and density feed directly into intrinsic sortability modelling - establishing the theoretical grade/recovery relationship before physical testing. Mineralogical and surface data inform sensor selection. Breakage data informs feed preparation design. At pre-feasibility level, characterisation data can support preliminary mass balances and sorter sizing without costly pilot trials.