Process Mineralogy Today

A discussion resource for process mineralogy using todays technologies

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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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How can Operational Mineralogy be used to increase the efficiency of the identify, diagnose, execute, augment (I.D.E.A) cycle for continuous improvement?

The approach in Operational Mineralogy is to focus on generating mineralogical data that is useful for particular goals.  This helps to identify when the process plant is underperforming, diagnose where that is occurring and whether the root cause can be attributed to an unexpected change in ore characteristics or an operational issue.

 

Operational Mineralogy provides a tool in increasing the efficiency of the Identify, Diagnose, Execute, Augment (I.D.E.A) cycle for continuous improvement.  This can be built using daily mineralogical analysis to identify potential issues and provide direction for diagnosis of the problem, which can then be used in more targeted projects for more detailed diagnosis and development of solutions.  Assessing mineralogical trends on daily composites can allow rapid development of a process baseline, from which any fluctuations can flag that there may be an issue.

 

I.D.E.A cycle-2

 

 

The volume of data generated in an Operational Mineralogy program can rapidly become overwhelming. An iMin Solutions program is structured around three basic principles for use of data to allow decision makers at a mineral processing operation to more quickly arrive at useful decisions that add value to the operation:

 

  1. Identify: The use of mineralogical data, in conjunction with plant operations data, to efficiently identify areas of sub-optimal performance where improvements might be made.

 

  1. Diagnose: Once areas for potential improvement have been identified more detailed analysis of mineralogical information, whether further analysis of available data or through targeted analysis programs, may be used to diagnose the cause of the issue. Mineralogy will often only be one aspect of the process for diagnosis of issues but should form the basis of explaining phenomena that are noted in other available plant data.

 

  1. Execute: By using mineralogical information on a routine basis to identify and diagnose the cause of sub-optimal process operation the program will provide sufficient data to make informed decisions about implementing recommended solutions. The goal will be to generate sufficient information that value based decisions can be made, with potential impacts on other process areas assessed in the process.

 

  1. Augment: Once changes have been implemented, maintaining routine mineralogical analysis helps to quantify the benefits and whether additional improvements can be made.  This then feeds back into identification of further improvement opportunities.

 

 

The information generated in an Operational Mineralogy program will provide an operation with the tools to react faster to process issues. As the volume of data increases and long term trends can be established this reactive approach can be extended to evaluate the mining plan and algorithms generated to predict the performance of material before it is fed to the processing circuit.

Workshop: Basics of Operational Mineralogy

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For all of you who are going to, or thinking about going to, the AusIMM Geomet 16 conference in Perth we look forward to seeing you there.  If you have been following our series on Operational Mineralogy, or are just interested in how you can begin to build a capability for mineralogy at the mineral processing plant then we strongly encourage you to stick around until Friday the 17th of June and attend our workshop on the “Basics of Operational Mineralogy“.  This workshop has been designed to provide a grounding in how to set up Operational Mineralogy on-site, what the benefits are and how it can be linked to geometallurgical modelling to support forecasting and reconciliation.  Places are limited so register here to secure your seat.

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iMin (mineral) – building operational mineralogy capability for minerals processing

160516_IMG_Kansanshi mill

The iMin(mineral) package was developed by Dr Will Goodall at MinAssist as a tool for minerals processing operations to effectively access routine mineralogical information generated on-site.  The tool has subsequently been developed through iMin Solutions to allow for more focused development and marketing.

 

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The use of trend based mineralogy in continuous process improvement

The analysis of daily composite samples forms the core component of any operational mineralogy program.  This provides a snapshot of the daily activity in the processing plant and will generate a step change in the level of understanding of the drivers for process performance as well as provide site personnel with a valuable tool for decision making.  To generate the maximum value from routine mineralogical data daily composite data can be used to create data trends over time in order to monitor process response, rather than just snapshots in time.

 

Figure 1 - Variation in copper mineralogy with time for flotation final concentrate from copper mine

Figure 1 – Variation in copper mineralogy with time for flotation final concentrate from copper mine

 

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The iMin Workbench – A framework for predictive control with mineralogy

Since the introduction of automated mineralogy almost 40 years ago the holy grail has been to bring the capability to operational sites.  Having mineralogical information at our fingertips in a form simple enough to be used in day-to-day decision making is something that is well established in bringing huge benefits to operations, but it is only now, with the introduction of ruggedised SEM systems and cloud based expert support becoming a reality.  Pioneers such as Wolfgang Baum with Phelps Dodge and Robert Schouwstra with Anglo Platinum, among a swathe of others have highlighted the positives that integrating mineralogy into process optimisation can bring when applied on a project basis.  More recently the success that we have had at Kansanshi Copper Mine is testament that this can be extended to the day-to-day operation of large complex sites.  Now that this capability is at our fingertips it is time to start thinking about how all this extra data can be useful for operations, without overwhelming the site personnel and becoming more trouble than it is worth.

 

After the introduction of iMin Solutions I wanted to introduce the framework in which we are developing not only generation of mineralogy on-site but the support structures that need to be in place to make the most value from that investment.  Today I will introduce the iMin Workbench, which is the framework in which we will tie the generation of mineralogy information on-site with how it can be integrated with existing datasets to bring true predictive analytics to our operations.

 

How the iMin Workbench can be implemented to add revenue to your operation

How the iMin Workbench can be implemented to add revenue to your operation

 

 

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