Mining & Mineral Engineering

Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.

1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Geostatistical analysis of resource data

   Grade data are spatially correlated and often skewed. Variography, kriging and conditional simulation describe spatial continuity and uncertainty. Declustering corrects for preferential sampling in high-grade areas, and outlier treatment, such as top-cutting, must be justified. Simulation produces multiple equally likely realisations that show the uncertainty in tonnage and grade above a cut-off, which a single kriged model hides.

2. Rock mechanics and slope-stability modelling

   Rock mass strength depends on intact rock, discontinuities and groundwater. Classification systems and empirical criteria provide estimates, and limit-equilibrium or numerical models assess stability of slopes and underground openings. Parameters are uncertain, discontinuity geometry is only partly known, and failure often follows structures. We emphasise sensitivity analysis and probabilistic treatment, and we separate research modelling from the geotechnical design required for operations.

3. Mineral processing data analysis

   Plant data include flows, grades, particle size and power draw over time. Mass balancing and data reconciliation produce consistent estimates of recovery and circulating loads. Comminution models relate energy to size reduction, while flotation, gravity and leaching are characterised by kinetics. Analysis must account for ore-type changes, sensor drift and sampling error, since small errors in assays produce large errors in recovery.

4. Optimisation of process operation

   Operating strategies can be explored by designed plant trials or by simulation. Set-point optimisation aims to maximise recovery or throughput subject to quality and equipment limits. Trials in an operating plant are constrained by production needs and by variability in feed, so designs must be robust, with blocking by ore type. Benefits claimed from optimisation should be tested against control periods.

5. Sampling theory and measurement error

   Gy's sampling theory describes errors from fundamental particle size, segregation and sample mass. Poorly designed sampling creates errors that no analysis can correct. We review sampling protocols, estimate their precision with duplicate samples and propagate sampling error to resource and recovery estimates.

6. Environmental and sustainability aspects

   Mining research increasingly addresses tailings, water use, energy and rehabilitation. Studies of tailings behaviour, acid drainage and water balance require geotechnical, chemical and hydrological analysis. We support analysis of such data and discuss limitations, noting that environmental compliance decisions belong to licensed professionals and regulators.

7. Data management in mining research

   Mining datasets span drill-hole databases, survey files, plant historians and laboratory assays, often in inconsistent formats. Before analysis, collar coordinates, down-hole surveys and assay units are validated, duplicates are located and lithological codes standardised. Version control of the database and a log of corrections let results be reproduced. Confidentiality of company data is respected by agreeing in advance on anonymisation and what can be published.

8. Linking geometallurgy and spatial models

   Geometallurgical studies connect ore properties, such as hardness, mineralogy and liberation, with processing performance across the deposit. Spatial models of these properties allow plant response to be forecast block by block. The link is only as good as the sampling of the process-relevant properties, which are measured on far fewer samples than grade. We state this limitation and show how forecast uncertainty grows where such data are sparse.

Common pitfalls this service helps avoid

• Treating a single kriged model as truth. It smooths variability and understates uncertainty in tonnage and grade.
• Ignoring preferential sampling. High-grade clustering biases means and variograms.
• Recovery estimates from poorly sampled streams. Assay and sampling errors can dominate the result.
• Claiming plant improvement without controls. Feed variability can mimic or hide optimisation effects.

What good reporting looks like

A mining research report describes the data and sampling, the spatial and statistical methods with parameters, the rock-mechanics or process models, the uncertainty analysis, the plant-trial design and results and the limitations. It is not a mineral resource or reserve estimate under reporting codes, nor a mine design or geotechnical certification, which must be prepared by qualified persons under applicable standards.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Suppose a researcher studies flotation recovery across ore types. A statistical analysis of plant data shows recovery correlates with feed grade, but when ore type is included, the relationship weakens. A designed trial varying collector dose by ore type, blocked by shift, shows a modest benefit for one ore type only. The report presents recovery with confidence intervals and the sampling error of assays, and notes that results apply to the tested ore types.

Questions to settle before you begin

• Has my sampling been preferential or representative?
• How do I propagate sampling error?
• Which ore types or domains must I treat separately?
• What control will show that an optimisation really helped?
• Which results would need qualified-person sign-off?

Key terms

Variography

The analysis of spatial continuity of a variable.

Top-cutting

Capping extreme high values to limit their influence.

Comminution

Size reduction of ore by crushing and grinding.

Recovery

The proportion of valuable material extracted.

Conditional simulation

Generation of multiple realisations honouring data and spatial structure.

Working together on Specialised Applied Sciences projects

Specialised applied-science engagements start with the field-specific question, data and methods, and with an honest check of whether the necessary domain expertise is available for your topic. We ask for protocols, datasets, instrument descriptions and relevant literature. Where a request is highly specialised, feasibility is confirmed before a proposal is issued. Analyses are documented and reproducible, results are reported with uncertainty, and any interpretation beyond the data is labelled. The service does not provide clinical, regulatory or safety advice.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Geology & Geophysics

  Analysis and interpretation support for geological and geophysical datasets, including seismic, gravity, magnetic and electrical methods, with attention to processing steps and non-uniqueness.

• Geotechnical Engineering

  Analysis of soil and rock behaviour for foundations, slopes, retaining structures and ground improvement, using analytical methods and numerical modelling with documented parameter selection.

• Chemical & Process Engineering

  Research and analysis support for chemical processes: reactor and separation modelling, heat and mass transfer, process optimisation and experimental data analysis.

• Engineering Optimisation

  Formulation and solution of optimisation problems in engineering design and operation: defining objectives, variables and constraints carefully, then choosing an algorithm suited to the problem structure.