- Why Pre-concentration Specific Ore Characterization Matters
Ore characterisation is the first and most critical step in any ore sorting evaluation. Before a single particle passes a sensor, the fundamental question must be answered: does the material contain enough inherent contrast to justify sorting at all?
Pre-concentration-specific ore characterisation is fundamentally different from standard metallurgical test work. It goes beyond bulk assay to examine how grade, mineralogy, density and surface properties vary at the particle scale - the scale at which sorting actually operates. The data generated determines not only whether sorting is technically plausible, but which sensor technology is best aligned with your material, what performance is theoretically achievable and where the boundaries of economic recovery lie.
Without this foundation, sensor testing and pilot trials risk being misdirected - optimising the wrong technology for the wrong material properties. ROKKSTA's ore characterisation services provide the evidence base that ensures every downstream decision is grounded in material reality.
- What We Characterise
Particle Size Distribution
Understanding the size distribution of your feed material is the starting point for any sorting evaluation. We define the key anchor points - F95, F50 and F5 - and establish how grade, density and mineralogy vary across size fractions. This directly determines which size classes are sortable, informs circuit design and provides the input data required for sorter capacity and performance modelling.
Grade Distribution & Compositional Contrast
The degree of grade separation between ore and waste at the particle scale sets the theoretical upper limit of sorting performance. We quantify the grade variability within and between material classes, identify the degree of compositional overlap and establish particle-scale grade distributions that go beyond bulk averages. This is the data that defines whether binary separation is technically achievable and what recovery losses are acceptable within a given classification boundary.
Material Heterogeneity
Sorting requires heterogeneity - meaningful contrast in composition, mineralogy or physical properties between particles. We characterise lithological and mineralogical variability, examine the association between valuable minerals and gangue phases and assess whether sorting-relevant contrasts are consistent across ore domains, particle sizes and spatial positions within the deposit. Materials that appear homogeneous in bulk assay often show strong particle-scale heterogeneity and vice versa.
Physical & Mechanical Particle Properties
Sorting performance depends not just on what particles contain, but how they behave on the belt/slide. We measure particle shape factors (volume and area), bulk and particle density, hardness and breakage behaviour. These parameters determine sorter capacity, influence ejection mechanics and are essential inputs for performance prediction and plant sizing. Breakage characterisation also reveals whether preferential liberation of valuable minerals occurs during crushing - a key value driver in pre-concentration circuit design.
Mineralogy & Liberation
We examine mineral phases, liberation state and textural heterogeneity to understand how valuable and gangue minerals are distributed within and across particles. Poorly liberated ores may show weak sorting potential at coarse fractions but strong potential at finer sizes - or may require a different pre-processing route entirely. Mineralogical analysis also identifies which sensor principles are best aligned with the specific contrast drivers in your material.
Surface Condition & Sensor Detectability
Surface coatings, oxidation, moisture and contamination can significantly affect the ability of optical, NIR and XRT sensors to detect the contrasts that drive sorting decisions. We evaluate surface condition systematically, identify detection constraints attributable to material rather than sensor limitations and define any pre-conditioning requirements - such as washing or drying - before sortability testing proceeds.
- Our Methodology
ROKKSTA's ore characterisation follows a structured, phase-aligned workflow that is independent of any sensor manufacturer or equipment vendor. Our approach ensures that the data generated is directly relevant to sorting decisions - not generic metallurgical reporting that requires reinterpretation.
1. Sampling and Subdivision
Representative sampling across ore types, mineralised domains and size fractions. Sample preparation follows the Theory of Sampling to ensure that heterogeneity is correctly captured and that subsequent test work is not invalidated by non-representative feed.
2. Physical Characterisation
Systematic measurement of particle size distribution, shape, density, hardness and moisture content. Breakage testing where appropriate to assess preferential liberation and fines generation during crushing.
3. Geochemical Assay by Size
Elemental composition across size and density fractions, complemented by Principal Component Analysis (PCA) to identify geochemical proxies linked to ore type, acid consumption, mineralogy or other sorting-relevant attributes.
4. Mineralogical Analysis
Identification of mineral phases, mineral associations, liberation and textural heterogeneity using appropriate analytical techniques (XRD, optical microscopy, SEM-EDS, LIBS where required).
5. Screening Response Evaluation
Assessment of natural grade upgrading potential through size classification alone - establishing whether screening ahead of sorting can reduce mass to the sorter while retaining recoverable material.
6. Surface Condition Evaluation
Evaluation of coatings, oxidation state and moisture effects that may influence sensor detectability, feed handling or the performance of downstream processing stages.
7. Feed Definition for Sortability Testing
Clear definition of representative size and material fractions to be used in sortability testing, together with baseline parameters required for intrinsic grade–recovery modelling and sorting simulation.
- The Foundation of Every Sorting Decision
The most common mistake in ore sorting evaluations is moving directly from limited test results to equipment selection - optimising the wrong sensor for the wrong material properties or discovering fundamental sorting limitations only at the pilot stage, when redesign is costly.
ROKKSTA's approach inverts this sequence. Before any sensor is selected and before any trial is contracted, we establish a clear, quantified understanding of what your material can and cannot do. This means:
• Sensor selection is driven by material physics, not equipment availability or vendor relationships.
• Theoretical recovery boundaries are defined before testing begins, so pilot results can be assessed against a credible baseline rather than in isolation.
• Gaps and risks are identified early, when the cost of addressing them is a fraction of what it would be in a later project phase.
• Downstream engineering and costing are grounded in material reality - not upscaled from convenience samples under non-representative conditions.
This is particularly critical for projects at scoping and pre-feasibility stage, where characterisation data directly informs whether sorting should be shortlisted at all, which sensor principles merit evaluation and what level of recovery risk is acceptable to the project.
- What You Receive
Every ROKKSTA ore characterisation engagement delivers a structured technical report alongside the underlying datasets, providing both an immediate decision-support tool and a durable baseline for subsequent project phases.
DELIVERABLE | DESCRIPTION |
|---|---|
Feed Definition Document | Representative fractions and baseline parameters for sortability testing and simulation input |
Surface Condition Evaluation | Coatings, oxidation or contamination findings affecting sensor detectability |
Breakage Behaviour Assessment | Response to crushing and preferential mineral release |
Grade Distribution Summary | Grade variability across fractions and size classes, including overlap analysis between ore classes |
Mineralogical Analysis | Mineral phases, liberation state and textural heterogeneity |
Geochemical Assays | Elemental composition by size and density fraction, including PCA for proxy identification |
Particle Properties Dataset | Size, shape, density, hardness and moisture data by fraction |
Characterisation Report | Summary of physical, geochemical and mineralogical results with interpretation in the context of sorting potential |
- From Characterisation to Confident Decision-Making
Ore characterisation data feeds directly into each subsequent stage of the ROKKSTA workflow:
Characterisation results define the theoretical grade–recovery envelope and identify which sensor principles are physically capable of detecting the material contrasts present. Sortability testing then validates this potential against real sensor response.
Particle size distribution, shape factors, density and grade distribution data are the direct inputs to sorter capacity and performance modelling - generating reliable mass balance projections at pre-feasibility level without requiring costly pilot equipment.
Breakage behaviour, fines generation and size-fraction performance data inform crushing and screening circuit design ahead of sorting, ensuring that the sorter receives correctly prepared feed.
For operations where sorting is already installed, characterisation establishes whether feed material properties match the design basis - often the first diagnostic step when sorting performance falls below expectations.
- Benefits to Your Operation
By integrating pre-concentration specific ore characterisation into your exploration and operational workflows, you can:
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Accelerate PFS - characterisation provides direct inputs to simulation, reducing the need for physical trial campaigns
Select the right sensor, not the available one - ground sensor selection in material physics rather than vendor relationships
Reduce investment risk - understand whether sorting is viable before committing to trials or plant design
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Avoid misdirected test work - identify material classes, size fractions and ore domains that drive sorting potential before designing test programmes
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Define theoretical limits before testing - establish what performance is intrinsically possible, so test outcomes can be evaluated critically
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Strengthen project documentation - provide defensible, traceable technical evidence for investment decisions and study reports
- Proven Expertise Across Commodities and Applications
From pre-concentration of low-grade sulphide ores to waste rejection ahead of heap leach, from diamond recovery to battery mineral beneficiation, ROKKSTA's ore sorting services cover a broad range of commodities and processing objectives.
Sensor-based ore sorting is applicable wherever meaningful compositional, mineralogical or physical contrast exists between valuable and waste fractions at the particle scale - and establishing whether that contrast exists and whether it is practically exploitable, is exactly what we do.
Commodities We Work With
• Calcite & Dolomite
• Chromite Ore
• Coal
• Copper Ore
• Diamonds
• Fluorspar
• Gold Ore
• Iron Ore
• Lead, Zinc & Silver Ore
• Limestone
• Lithium / Spodumene Ore • Magnesite
• Manganese Ore
• Nickel Ore
• Phosphate Ore
• Platinum Group Metals
• Quartz
• Tin & Tungsten Ore
Unit Process Applications
• Particle Sorting
• Bulk Sorting
• Pre-concentration
• Final Recovery
• Waste Rejection
• Contaminant Removal
• Grade Control
- Get in Touch With ROKKSTA
Ready to optimise your ore sorting process? Our team of experts is here to provide tailored solutions and independent insights.
