Working with High-Dimensional Data Part 4: Classifying Unknown Samples using Machine Learning Principles

In the previous articles in this series (part 1, part 2, and part 3) we’ve been performing analyses on an example high-dimensional […]

Working with High-Dimensional Data Part 3: Geospatial Mapping and Mine Planning

  In part 1 of this introductory series about working with high-dimensional data we looked at dimensionality reduction to allow the visualisation […]

Working with High-Dimensional Data Part 2: Classification by Cluster Analysis

In part 1 of this introductory series on working with high-dimensional data we determined that cluster analysis is a commonly used method […]

Working with High-Dimensional Data, Part 1: Dimensionality Reduction

Mineral exploration, mining, ore processing, and, more generally, earth science research, involves the collection of large and complex data sets where single […]

Evaluation of SEM-EDS Particle Count Statistics

In our last article I re-introduced the concept of using statistics to determine if enough particle sections have been measured to produce a […]


The most common approach to assessing the accuracy of QEMSCAN mineralogy results is to compare the measured assay with the mineralogy-computed chemical […]

Three areas that may affect the quality of your mineralogy data

  The complex nature of QEMSCAN mineralogy results necessitates a thorough assessment of data quality relative to ‘best practices’ values. The MinAssist […]


Do you rely on routine SEM-EDS mineral analysis to monitor or drive process development and operational optimisation? Have you ever considered the […]

A short history of automated mineralogy colours

The following article was originally posted on the Automated Mineralogy and Petrology Blog by Dr Alan Butcher and Dr David Haberlah.  The topic […]

Returning Mineral Processing to Profitability: The Cost Conscious Approach (Part 1)

As revenues from mining and mineral processing operations become largely volatile and under increased commodity price pressure, businesses and operators are re-thinking […]

Improving mining productivity: Is process mineralogy one of the keys?

Over the last few months there have been a number of reports released highlighting the declining trend in productivity for the mining […]

Flotation mineralogy: Valid and Valuable?

Following on to the conclusion of another successful MEI conference, Flotation ’13, some interesting comments and feedback have emerged that highlight the […]

Liberation and Free Surface Area in the Float Feed

Liberation measurements estimate the volumetric grade distribution of a mineral as a measure of the quality in a processing stream (Spencer and Sutherland, 2000). Put simply, it is based on the area % of the mineral grain in the particle

Whats new in Process Mineralogy Technology: The QEMSCAN EXpress

For those of us using Process Mineralogy on a regular basis, the introduction of more accessible systems, such as the QEMSCAN EXpress, opens a new door to generate meaningful mineralogical information at site, reducing turn-around times and allowing metallurgists to use mineralogy more as a predictive tool and less as a reactive post-mortem of what went wrong

Understanding what is feeding your process: How ore variability costs money!

Too often operations utilise ore type definitions that are based on geological or mining characteristics and have little relation to the processing behaviour of the material. This is perfectly valid for resource definition and mine planning but when applied to processing can be misleading. While there are situations where the relationship is valid, for the majority of operations there do remain subtle differences in how ore domains should be processed.

Process Mineralogy ’12

It is really great to see that MEI’s Process Mineralogy ’12 conference in Cape Town, South Africa was such a great success. Unfortunately MinAssist was unable to attend but Barry Will’s has provided a great summary on the MEI Blog. Congratulations to the MEI team and all the presenters.

What is a theoretical grade-recovery curve? An example.

The theoretical grade-recovery curve for an ore is a definition of the maximum expected recovery by flotation of a mineral or element at a given grade. This is defined by the surface area liberation of the value minerals and is consequently directly related to the grind size utilised in the process. The theoretical grade-recovery can be readily used to quickly identify potential recovery increases that can be gained through optimisation of flotation circuits and whether the process is running efficiently.