Process Mineralogy Today

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Category: Liberation


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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MINERALOGICAL MODELLING: RULE OF THUMB VS PROBABILITY METHODS (PART 2)

This is the second of two blog posts by Stephen Gay exploring mineralogical modelling (Part 1 published 20th March 2014).  Part 2 here develops the theme by comparing the value of using rule-of-thumb and probability -based particle modelling principals.

 

Probability methods

Mineralogical analysis is often approached from a pragmatic viewpoint (referred to here as the ‘rule of thumb’ approach), however the power of mineralogical analysis is greatly increased when linked with probabilistic models.  The stereology problem (Figure 2) is an example of a probability problem. There is some probability a linear intercept will appear liberated, barren or composite.  The particular subbranch of mathematics dealing with such problems is called geometric probability.  In this case the probability that a section will appear composite is much larger because the grain size has decreased.

Fig 3. Linear Intercepts through a particle where the grain size is smaller than in Figure 2 (in Part 1)

 

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Mineralogical Modelling: Rule of Thumb vs Probability Methods (Part 1)

This is the first of two blog posts on mineralogical modelling where we highlight some of the terminology used and misconceptions made.  Part 2 will further develop the theme by comparing the value of using rule-of-thumb and probability -based particle modelling principals.

 

Liberation

Many users of mineralogical analysis focus only on elementary concepts such as grain size.  Yet there are many levels of depth available to analyse liberation data.  In this section we focus on using liberation data in a simulation model.  Critical to understanding mineralogy is the concept of ‘liberation’.  A particle is considered liberated if it consists of only one mineral.  Conversely a particle is considered ‘barren’ if it contains no valuable mineral.  There are a few issues with ‘liberation’ defined in this way.  For a start is it really important if a particle is say 99% valuable mineral rather than 100% mineral?  Because the purest definition (particles consisting of only valuable mineral) is of limited value, the word ‘liberation’ has become generalised, and to some extent over-used.  Yet when discussing ‘liberation’ generally we are simply referring to how distinct the valuable mineral is from the associated gangue.

Various definitions of particle types (Liberated, Binary, MultiMineral)

Fig 1. Various definitions of particle types (Liberated, Binary, MultiMineral)

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Flotation mineralogy: Valid and Valuable?

Following on to the conclusion of another successful MEI conference, Flotation ’13, some interesting comments and feedback have emerged that highlight the continuing interest in and need for mineralogical data in understanding flotation response – and some of the challenges that emerge from trying to obtain that.

 

Barry Wills’ blog of 18th November 2013 refers back to the prediction made by Professor Dee Bradshaw at Flotation ’11 that chemistry would dominate discussions; and how she has seen that shift to a point where mineralogy dominates at Flotation ’13.  This point is further underlined by Dr Chris Greet (30th November 2013), who also makes the essential connection between the realisation of the value of mineralogy, and the hurdles encountered in generating and utilising valid mineralogical data correctly.  Chris sights three commonly encountered hurdles:

 

1) Turn-around time
2) Expense
3) Validity

 

Flotation_Valid and valuable Time to Result AND Validity of Result – does it have to be a trade-off?

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Operational Health Check Suite

Over the last few months, MinAssist has progressively launched a series of “Operational Health Checks” that have been developed as suite of off-the-shelf process mineralogy studies targeted at giving rapid performance gains for a minimum of fuss.  Each of these fit in to a Suite of programs that are focused on bringing cost savings, recovery improvements and general risk reduction through improved understanding of ore types.

 

Key points within the processing circuit have been identified, and a mineralogical testwork program developed to:

     – target the typical challenges encountered

     – indicate overall circuit efficiency

     – identify possible areas for improvement

 

The sample points have been pre-determined, the analytical testwork process developed, and the critical information to examine identified.  This removes much of the hassle for a busy plant metallurgist looking to undertake a process mineralogical study.  It also reduces the overall time-to-result: providing a concise, metallurgically focussed report of the mineralogy in a meaningful time frame.

 

HC Benefits

The Health Check suite is ideal to for:

     – the busy process metallurgist looking to get the best from a circuit

     – taking a quick look at the health of a circuit to make sure things are running as they should be

     – as a prelude to a more in-depth study based on the findings of the health check

 

A Health Check can be run as a one-off study, or on a routine basis to build up a complete picture over time.

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Using mineralogical understanding as a building block for plant process improvement

Developers and operators of mining and mineral processing operations face constant challenges to become more efficient, whilst at the same time being faced with increasingly complex ore bodies.  This complexity is characterised by multiple mineralisation events and complex formation histories, leading to variation in ore mineralogy.  This inconsistency can often be explained by changes in the ore texture, or the relationship between minerals present in the ore.  Understanding the ore texture can be a very useful tool in developing a process flowsheet or optimising an existing circuit.  For complex ore bodies with multiple ore textures this understanding is not only useful, but is essential to manage variability, reduce risk and optimise the operation.

Ore Feed Recovery

MinAssist has complied a short white paper discussing some of the mineralogical influences on feed ore quality and subsequent recovery: The Influence of Rock Texture on Processing

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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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Liberation and Free Surface Area in the Float Feed

The importance of the liberation of target minerals in the feed to a flotation circuit is well recognised and understood by process metallurgists.  This blog seeks to introduce some of the concepts around:

 

– how liberation is defined

  • – what the important parameters to understand are

– how liberation is defined by process mineralogists

 

Liberation measurements estimate the volumetric grade distribution of a mineral as a measure of the quality in a processing stream (Spencer and Sutherland, 2000).  Put simply, it is based on the area % of the mineral grain in the particle:  which brings us to the first key question – what is the difference between a “grain” and a “particle”?

 

The second critical question is to ask whether area % alone is enough to help predict how a particle will behave in a flotation cell – what about free surface area?  A grain may be defined as 90% liberated, but have no free surface area… so will this recover more quickly or more slowly to the flotation concentrate than say a grain that is 60% liberated but has a high free surface area?

 

What is the difference between a “Grain” and a “Particle”?

 

Typically, a “grain” is classed as a single mineral, whilst a “particle” is made up of one or more mineral grains.  The figure below provides an example of a single “particle” that contains four mineral “grains”.

 

Example of a “particle” containing five mineral “grains” (4 black and 1 white) Example of a “particle” containing five mineral “grains” (4 black and 1 white)

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Rock and Mineral Texture: Controls on Processing

The texture of an ore will define: the grain size distribution(s) and P80 target grind size; the grindability of the ore; the degree of liberation of the target mineral(s); the phase specific free surface area of the target mineral(s); the amount of fines; and the number of coarse composite particles.  These factors will play a major influence on the process flowsheet developed for an ore, from mining strategy through to blending, processing, target grade and recovery, and tailings management.  Understanding these will aid the processing engineer when trying to unlock the maximum value from the rocks, with the minimum of effort, cost and environmental impact.

 

Texture, in the context of geometallurgy, simply refers to the relationship between the minerals of which a rock is composed (Wikipedia definition).  It includes the size, shape, distribution and association of the minerals in the rock.  All textures, including crystallinity, grain boundary relations, grain orientations, fractures, veinlets etc have a bearing on processing ores, but the sizes of the mineral grains, and the bonding between the grains are the main characteristics that influence ore breakage and mineral liberation (Petruk, 2000).  Understanding the geology and history of an ore will help unravel the complex nature of the textures that may be encountered during processing.

 

Example of textural changes due to oxidation and deformation (Butcher 2010).

Example of textural changes due to oxidation and deformation (Butcher 2010).

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Flotation Circuit: Concentrate Grade and Recovery

The texture of particles within a flotation cell play a pivotal role in both mineral recovery, and the grade, in the flotation concentrate.  Theoretical curves can be generated based on particle mineralogy and texture to indicate the maximum grade-recovery possible for a given feed ore.  Comparing this ‘theoretical’ curve to actual grade recovery will provide insight in to the efficiency of the flotation circuit.  Inevitably the ‘actual’ curve will plot below the ‘theoretical’; the question is how far below and can that gap be reduced (Figure 1)?

 

During day-to-day plant operation, deviation of the actual grade/recovery curve from this theoretical curve can be considered to be the result of either a change in the feed texture and mineralogy, or less than optimal operating conditions.  A comprehensive understanding of the controls on this will feed decision-making and reduce operational risk.  MinAssist has therefore added the Flotation Health Check to its suite of off-the-shelf process mineralogy studies; making it quick, simple and cost effective to use the theoretical grade recovery to help identify potential circuit optimisation.

Figure 1.  (A) Ore texture defines the theoretical grade recovery curve.  Particle images are used to show how high target mineral recovery will typically also mean recovery of gangue, reducing the grade. (B) If actual grade/recovery is less than the theoretical, then operational conditions may be changed to improve this (1).  If grade/recovery above the theoretical curve is targeted, then the texture of the feed will need to change (2). Figure 1. (A) Ore texture defines the theoretical grade recovery curve. Particle images are used to show how high target mineral recovery will typically also mean recovery of gangue, reducing the grade. (B) If actual grade/recovery is less than the theoretical, then operational conditions may be changed to improve this (1). If grade/recovery above the theoretical curve is targeted, then the texture of the feed will need to change (2).  Developed in conjunction with Professor Dee Bradshaw of JKMRC.

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