Process Mineralogy Today

A discussion resource for process mineralogy using todays technologies


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Category: Process Mineralogy

Direct Measurement of Submicroscopic Gold: Methods and Applications

The ability to recover gold effectively is largely dependent on the nature/carriers of gold and the mineral processing techniques utilized. Gold ores are often classified into two major ore types: free-milling and refractory. Note that many gold deposits contain proportions of both ore types. Free-milling ores refers to those where the majority of gold is liberated through crushing/milling and recoverable by cyanide leaching. Refractory ores are defined as those which yield low gold recoveries, requiring complex and costly chemical pre-treatment prior to cyanidation. Refractoriness is directly controlled by ore mineralogy, generally resulting from the presence of either naturally occurring ‘preg-robbing’ carbonaceous minerals or submicroscopic gold.


Figure courtesy of David Holder: SIMS element distribution maps of a colloform pyrite grain showing Au and As in solid solution. Note the pyrite has been stained using KMnO, which can indicate compositional variations within and between pyrite morphologies.

Figure courtesy of David Holder: SIMS element distribution maps of a colloform pyrite grain showing Au and As in solid solution. Note the pyrite has been stained using KMnO, which can indicate compositional variations within and between pyrite morphologies.



Why we should consider operational mineralogy


Operational mineralogy infographicIn most operations we understand that minerals are being processed but we tend to almost exclusively rely on chemical assays to monitor the health of the process and make day-to-day decisions.  This often means that we are making decisions based on only one part of the puzzle and the ability to accurately identify issues and efficiently address them is compromised.  With lower grade and more complex resources becoming the norm it is becoming even more important to utilise all the tools available to maximise productivity and ensure that avoidable metal losses aren’t occurring.


Petrolab adds new automated mineralogy capability


Image of concrete samples mapped using the new Petrolab system for evidence of thaumasite sulphate attack (red in the image) (Image courtesy of

Image of concrete samples mapped using the new Petrolab system for evidence of thaumasite sulphate attack (red in the image) (Image courtesy of

Petrolab, our great friends in Cornwall, UK have been busy over the last few months building up their capability in automated mineralogy.  It is great to see that they have added a new Zeiss automated mineralogy system with Mineralogic Mining to their already strong suite of process mineralogy services.  The team at Petrolab has always offered great service and we’ll be looking forward to seeing some of the great things they can do with the Zeiss system.



5 common misconceptions about process mineralogy


Process mineralogy is a term that is used in a lot of contexts from process optimisation to Geometallurgy but it’s usefulness and application is often clouded by misconceptions that it is too hard or “our ore body is homogeneous and simple”.  For many of you the term has probably come up in conversations about ore types but have you ever stopped to think what getting a better understanding of mineralogy at could mean for your operation and whether it might actually make your job easier?


Why use screened fractions in automated mineralogy?

Blog diagrams - front

In the years since automated mineralogy has become a mainstream tool there has been debate over whether it is necessary to used screened fractions.  I often get asked whether we can reduce costs on a project by analysing unsized material, effectively reducing the total number of blocks required to generate an answer. The general question is that there is capability in all the analysis software for automated mineralogy to produce a synthetic particle or grain size distribution so why not use it?  My response is always that it is fine for analysis of modal mineralogy or elemental deportment but to generate a meaningful grain size distribution or locking/liberation analysis screened fractions should be a requirement. This isn’t the answer that we generally want to hear but I will attempt to explain why it adds significant risk to cut corners by eliminating screened fractions.


Zeiss release Mineralogic Mining 1.1 update

Nickel sulphide ore by Mineralogic Mining. Sample courtesy of The University of Leicester, UK

Nickel sulphide ore by Mineralogic Mining. Sample courtesy of The University of Leicester, UK

Zeiss microscopy have recently announced the release of version 1.1 of their Mineralogic Mining software.  After releasing Mineralogic Mining in July 2014 Zeiss have been working hard to expand the capabilities and provide a tool that can be targeted at on-site mineralogy as well as the laboratory.  MinAssist have been fortunate enough to work on a project over the past 6 months using the Mineralogic Mining software and have been pleased with what it offers. 



MinAssist sponsorship of Process Mineralogy ’14


MinAssist is proud to announce sponsorship of Minerals Engineering International’s (MEI) 3rd International Symposium on Process Mineralogy (Process Mineralogy ’14) to be held in Cape Town on November 17-19, 2014.  The 2 years since Process Mineralogy ’12 has seen a dramatic change in the landscape for process mineralogy tools and we are looking forward to being part of the new developments that are taking us into the future.  The rapid development of new automated mineralogy systems, such as the TESCAN TIMA and Zeiss Mineralogic Mining means that process mineralogy is more accessible than ever to research institutions, laboratories and operating sites and we are looking forward to hearing about how that is translating into everyday use of mineralogical information.


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 sector.  This comes amid a scramble by many organisations to cut costs to compete in a market with both declining commodity prices and declining ore grades.   The question that should be being asked is how we can be smarter about processing to reverse the declining productivity trend and be ready to maximise gains when the inevitable recovery arrives.


ZEISS Mineralogic-Mining: a new automated mineralogy system on the market

This week, ZEISS released the latest automated mineralogy system to hit the market; Mineralogic-Mining.  Mineralogic-Mining combines a scanning electron microscope with one or more EDS detectors, a mineral analysis engine and the Mining software plug-in, and is available on a range of ZEISS SEM platforms including tungsten and FEG options.  ZEISS have a long-running history in the automated mineralogy field, with many instruments around the world based on ZEISS platforms, and this latest release represents one of several new products coming to the market from their expanding natural resources group.






This is the second of two blog posts by Stephen Gay exploring mineralogical modelling (Part 1 published 20th March 2014).  Part 2 here develops the theme by comparing the value of using rule-of-thumb and probability -based particle modelling principals.


Probability methods

Mineralogical analysis is often approached from a pragmatic viewpoint (referred to here as the ‘rule of thumb’ approach), however the power of mineralogical analysis is greatly increased when linked with probabilistic models.  The stereology problem (Figure 2) is an example of a probability problem. There is some probability a linear intercept will appear liberated, barren or composite.  The particular subbranch of mathematics dealing with such problems is called geometric probability.  In this case the probability that a section will appear composite is much larger because the grain size has decreased.

Fig 3. Linear Intercepts through a particle where the grain size is smaller than in Figure 2 (in Part 1)