Process Mineralogy Today

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Category: Automated Mineralogy


Considerations with QEMSCAN Grain Size Estimation

Most SEM-EDS systems have the ability to compute grain size. Not all systems use the same computational methods and we advise that you familiarise yourself with the details of your system of choice. In the case of QEMSCAN the presence of fine-grained inclusions in the mineral of interest (e.g.: a major ore-forming mineral like chalcopyrite, or key minerals in sedimentary systems such as quartz) has an affect on the computed mineral grain size, therefore, care must be taken when using automated grain size calculations.   ...

Linking Sample Prep with Particle Stats and Data Quality

 

 

In last week’s article we spent some time evaluating particle statistics using SEM-EDS data to demonstrate the importance of the number of particles measured. As I analysed and plotted the data it dawned on me just how critical sample preparation really is. Of course we know that sample preparation is important. It is fairly intuitive that the particle population has to be distributed and oriented randomly within the sample block volume and the exposed block section to achieve a representative result, but this was the first time I could visualise why. In this article we will spend some time evaluating the implications of non-random particle distributions and the practical implications of the number of particles measured. ...

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 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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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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Introducing iMin Solutions – a new way to build mineralogy capability in operations

iMin-on-White_1592x1000

We are pleased to announce that MinAssist, along with Petrolab have teamed up to establish iMin Solutions, a company focused on bringing greater mineralogical capability directly to the mine site and mineral processing plant.  The driving influence of iMin Solutions is to provide accessible mineralogy solutions for operations, with a focus on data integration and remote support to generate more value from an improved understanding of mineralogy.

 

iMin Solutions provides tools to mineral processing operations to improve profitability through better use of processing data. In doing this iMin Solutions focuses on facilitating on-site personnel to make use of data through accessible expert support. This is achieved with the use advanced technologies to enable generation, communication and analysis of data in minerals processing operations.

 

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