Process Mineralogy Today

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Category: Characterisation


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. ...

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. ...

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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ZEISS Mineralogic-Mining: a new automated mineralogy system on the market

This week, ZEISS released the latest automated mineralogy system to hit the market; Mineralogic-Mining.  Mineralogic-Mining combines a scanning electron microscope with one or more EDS detectors, a mineral analysis engine and the Mining software plug-in, and is available on a range of ZEISS SEM platforms including tungsten and FEG options.  ZEISS have a long-running history in the automated mineralogy field, with many instruments around the world based on ZEISS platforms, and this latest release represents one of several new products coming to the market from their expanding natural resources group.

 

 

SIGMA-HD_system

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Value losses due to poor liberation and classification

Ball MillThe crushing and grinding circuit in any process flowsheet is a major contributor to cost and should be a major focus of any continuous process improvement program. While the direct costs (i.e. power and maintenance) within the crushing and grinding circuits are generally the primary consideration for optimisation, the indirect costs associated with insufficient liberation or over grinding can have as profound an impact on downstream processes. Care should be taken when evaluating comminution circuit optimisation that efforts to increase throughput or reduce energy requirements don’t have a negative impact on the liberation characteristics of the material and result in reduced downstream recovery.

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Understanding what is feeding your process: How ore variability costs money!

Feed conveyorFollowing our article looking at areas in which minerals processing operations typically lose money we have selected the most important topics and will be running a series of articles exploring ways to avoid value loss in more detail.  We believe that as times become tougher through the minerals industry consideration of where additional value can be achieved and how costs can be reduced is of paramount importance.

 

Over the years, MinAssist has been involved in many optimisation programs and when asked to examine areas for optimisation in a processing plant invariably we will begin by looking at the feed material, specifically it’s variability and the impact that is having on stable operation of the process circuit.  Often this area is the major cause of lost value in an operation and the impacts of its effects continue right through the process flowsheet.

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