Process Mineralogy Today

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Category: Data Validation


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.

...

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.  ...