Process Mineralogy Today

A discussion resource for process mineralogy using todays technologies


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Virtual Mineral Processing Assistance with MinAssist

The rapid developments with COVID-19 mean that access to experts for dealing with process issues has become much harder.  This doesn’t mean that below target performance or process issues need to impact your productivity.  MinAssist has developed a digital framework to ensure you continue to have access to the best mineral processing expertise.

Keep on reading!

Analytics in Mineral Processing


Mineral processing plants are one of the most data rich parts of any mining operation.  Data is generated and stored for practically every step of the process but do we get the most value from this data?

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Social License – The Key to Successful ISR Operations


In the previous article we wrote an introductory overview of In-Situ Recovery (ISR) and recent interest from industry and research organisations for its application in the recovery of metal ores such as copper. With numerous successful ISR operations around the world, especially in uranium deposits, there is considerable information available about the technical challenges of ISR, but none of them are as critical as the challenge of gaining Social License to Operate. Positive engagement and honest science communication with local and regional communities hold the key to long term success of future ISR operations. In this article we briefly look at some of the aspects that impact on social license and how the industry can work with communities to build trust and gain support. ...

A Brief Overview of In-situ Recovery (ISR)


Over the past few years there has been a renewed interest in in-situ recovery methods for metals (ISR). The concept of ISR is by no means new and has been used successfully in the past, especially in the recovery of uranium. CSIRO Waterford in Perth has been leading ISR research efforts in Australia. Along with international collaborators the industry is working towards more extensive application of ISR methods for commodities such as copper. This article is a brief overview of ISR methods and practical considerations for its application. ...

Working with High-Dimensional Data Part 4: Classifying Unknown Samples using Machine Learning Principles

In the previous articles in this series (part 1, part 2, and part 3) we’ve been performing analyses on an example high-dimensional geochemical data set from a resource feasibility study with the goal of developing a geometallurgical domain model, which could be used for sample selection for more detailed characterisation work and practical mine planning. In this article we continue with this example and take a closer look at how the geometallurgical domains from part 3 guide the grouping of unclassified samples collected from the same resource. ...

Working with High-Dimensional Data Part 3: Geospatial Mapping and Mine Planning


In part 1 of this introductory series about working with high-dimensional data we looked at dimensionality reduction to allow the visualisation of complex data. Part 2 of the series explored the K-means clustering method as a technique to classify samples according to their geochemical characteristics. In this article we place the classified samples back into their geospatial context and use the spatial relationships between the clusters to define a behaviour profile, which is aimed at guiding critical mine planning decisions. ...

Working with High-Dimensional Data Part 2: Classification by Cluster Analysis

In part 1 of this introductory series on working with high-dimensional data we determined that cluster analysis is a commonly used method to perform more reliable and scalable classification of large data sets. In the case of high-dimensional data such as geochemical assays or SEM-EDS mineralogy and texture analyses, dimensionality reduction enables visualisation of the raw data and provides a framework to assess the quality of the cluster analysis results. In this article we take a closer look at the K-means clustering method to form a better understanding of its strengths and weaknesses. ...

Working with High-Dimensional Data, Part 1: Dimensionality Reduction

Mineral exploration, mining, ore processing, and, more generally, earth science research, involves the collection of large and complex data sets where single sampling points often consist of multiple dimensions. For example, the analysis results of a single field specimen collected during exploration, or a sample from a mineral processing stream, contains the proportions of multiple elements and minerals. A common data analysis goal is to group samples according to their chemical and mineralogical characteristics or according to the manner in which they behave during metallurgical testing. In this series of articles we briefly look at the techniques that enable the grouping and assessment of high-dimensionsional data. ...

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. ...