Process Mineralogy Today

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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 representative result in SEM-EDS analysis. This week I thought I’d give an example of a SEM-EDS data set to explore how the particle count impacts results and data quality. Let’s start by looking at figure 1a, which shows the cumulative quartz volume percent as a function of the number of particles measured. The data are presented in the order of particles measured. ...

Have you measured enough particles?

One of the key questions about SEM-EDS data is whether or not you’ve measured enough particle sections to produce a representative result. The critical part to remember is that SEM-EDS data are collected on particle sections exposed in a sample block, which is unlike other bulk analysis techniques such as XRF or XRD. It follows that the data from any one particle section is inherently biased and not a true representation of the character of the sample. Only a population of particle sections are able to provide accurate information. Adequate particle statistics are critical for applications such as operational mineralogy where liberation, grain size and mineral associations play a key role in mineral processing behaviour.  The question is, how many particle sections are enough? ...

The Applied Mineralogy User Community

The field of applied mineralogy has always had a basis in a strong community, this has led to to numerous developments that have benefited us all and at MinAssist we have been exploring ways to strengthen that community.

 

In the early stages of SEM-EDS commercialisation direct input from user groups was a strong driver in development of the technology.  However, over the past number of years this has fallen by the wayside. Through various conversations with former user group members it is clear that there is a continued desire for a way connecting users of mineralogy data.

 

MinAssist has developed a platform to facilitate our community and would like to invite you to help us build this by joining the beta test of the Applied Mineralogy User Community (AMUC). If you use data generated by techniques such as XRF, XRD, EPMA, SEM-EDS, ICPMS, optical microscopy, µXRF, Infra-red scanning, etc., the Applied Mineralogy User Community welcomes your membership. 

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THE IMPORTANCE OF MINERAL DEFINITIONS USED IN GENERATING QEMSCAN DATA

The most common approach to assessing the accuracy of QEMSCAN mineralogy results is to compare the measured assay with the mineralogy-computed chemical assay. In this method we assume the measured assay results are the correct values, which means we also assume that the sub-samples used for chemical assay and QEMSCAN analysis are identical, or equally representative, and that the SEM-EDS sample preparation procedure has not introduced any bias. However, the nature of QEMSCAN data means that is quite easy to achieve an acceptable reconciliation of less than 10 or 15% (depending on the element) with very different mineralogy results. ...

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 data validation service makes use of additional data that you can request from your commercial service provider or, if available, are assessable within the QEMSCAN datastores. We interrogate these data to provide an assessment of the three main aspects affecting you the quality of your mineralogy results:

 

Sample preparation,

 

Measurement setup, and

 

Reconciliation between assay and mineralogy results.

 

Sample preparation and measurement setup are critical steps that can introduce errors into a QEMSCAN analytical program. Associated errors can be identified through careful investigation of the relevant data.

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HAVE YOU VALIDATED YOUR MINERALOGY DATA?

Do you rely on routine SEM-EDS mineral analysis to monitor or drive process development and operational optimisation? Have you ever considered the reliability and consistency of your mineralogy data? The current framework for validating mineralogy results is often not visible to the end-user and in many instances inadequate to form a clear understanding of data quality. To address these shortcomings MinAssist has developed a new solution to reduce risk and give you more confidence in your SEM-EDS results so that you can focus on the interpretation and application of the data.  ...

Welcoming Pieter Botha to the MinAssist team

 

We would like to welcome Pieter Botha to the MinAssist team.  I worked closely with Pieter at Intellection on development of the QEMSCAN system.  He brings a deep knowledge of automated mineralogy applications and will be a really valuable addition to our team.  Many of you will know Pieter so don’t hesitate to get in touch and say hi.

 

Pieter holds a BSc (Hons) degree in geology, a Masters degree in igneous petrology, and he recently completed his PhD at the Department of Applied Mathematics in the Research School of Physics and Engineering at The Australian National University. From 2006-2012 Pieter worked in industry where he gained extensive experience in commercially driven consulting and research. His work focussed on the use of mineralogy and geochemistry data such as SEM-EDS (QEMSCAN), XRD, and XRF for geometallurgy and process mineralogy applications in mining, and reservoir characterisation in the oil and gas industry. He has a particularly strong background in the operation of automated mineralogy systems, data analysis, and application-specific interpretation. Some of you may remember Pieter from his days at Intellection and FEI where he was instrumental in conducting consulting services, providing customer support, applications development, and system training for new QEMSCAN users. Pieter’s recent PhD work at The ANU in Canberra was based on small scale 3D micro-CT images of core samples to develop a statistical method of predicting flow properties in large scale CT images, which capture more heterogeneity, however, because of insufficient image resolution, prevents the direct computation of fluid flow properties. The predicted flow data is ideally suited to inform larger scale geological models of reservoirs and aquifers. Apart from earth sciences Pieter enjoys running with his dog, mountain biking, graphic design, and most recently woodworking.

 

In his new role at MinAssist Pieter will be involved in a range of projects including data analysis and product development. We are keen to engage more with the automated mineralogy and geosciences community and Pieter will be in contact soon to give you more information on how you can become involved.

Upskilling the Workforce using Operational Mineralogy

Luck-vs-Skill

Increasingly mining companies are recognizing the need to take on new technology developments in order to increase the efficiency and productivity of their operations and stay ahead of their competitors. Having highly skilled staff is important in making the implementation of new technology a success; however, finding such staff is an increasingly difficult task. Mining operations employ hundreds of employees with many different skill sets and upskilling these existing staff is the best way to fill any new positions created. Providing training and further education for existing staff can have many other benefits in the longer term including increasing staff morale, longer staff retention, and higher productivity.

 

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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.

 

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How do geometallurgical models relate to operational mineralogy?

In preparation for the AusIMM Geomet ’16 conference in Perth next week we thought a brief introduction to how operational mineralogy can be used to build or enhance geometallurgical models would be of interest.  It is a question that we get asked a lot and an area where the application seems to lag behind the capability available to operations now.  Mineralogy is often viewed as too complex or too expensive to be a core aspect of a geometallurgical development program but by moving the capability to site a whole range of possibilities is opened up.  MinAssist will be presenting a workshop introducing operational mineralogy and how it can be used with production geometallurgy at the Geomet ’16 conference.  There are still places available so if you are in Perth be sure to sign up.

 

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Put simply, geometallurgy is the science of relating geochemical assay data to orebody mineralogy and orebody mineralogy to metallurgical testwork results with the ultimate aim of being able to predict metallurgical response using geochemical and mineralogical data. Operational mineralogy uses mineralogy together with geochemical assay data to understand and optimise mineral processing of an orebody. Typically, a geometallurgical model is constructed in the early stages of an operation, perhaps during prefeasibility and feasibility. Operational mineralogy is a component of sampling during active mineral processing and is undertaken once mining and processing has commenced.

 

There are obvious synergies between geometallurgical modelling and operational mineralogy. One of the first steps of an operational mineralogy program is to undertake a detailed material characterisation study to establish mineralogy and determine key textural information for typical feed material that will be processed in the short to middle term. If a geometallurgical model of the orebody already exists, domains of consistent mineralogy will already have been modelled. The geometallurgical model can be applied as a guide for early operational mineralogy sampling, resulting in a more representative operational mineralogy survey.

 

Conversely, once an operational mineralogy program is established at a site where a geometallurgical model exists, the mineralogy for feed material can be reconciled with the model. The results of the reconciliation process can be used to update the geometallurgical model. Actual processing performance of feed characterised by operational mineralogy can be used to update the geometallurgical model in order to make it more predictive for processing of future feed material.

 

For example, a geometallurgical model may have a domain of material where metal recovery is related to a combination of grade and pyrite content. Once updated with operational mineralogy data for feed from this same domain this relationship can be further refined and applied back to predict the behaviour of remaining in-ground material.

 

Geometallurgical models are based on thousands of assay data points, hundreds of mineralogy analyses and tens of metallurgical testwork results. If a geometallurgical model can be updated with operational mineralogy data and real processing performance data the amount of data in the model will increase exponentially resulting in a far more robust predictive tool.

 

If you want to find out more contact us or sign up for our workshop, Introduction to Operational Mineralogy at Geomet ’16.

 

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