Process Mineralogy Today

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Category: Flotation


Mineralogical drivers for flotation video

 

 

At Process Mineralogy Today we like to keep an eye out for useful tools that help in gaining a better understanding of how mineralogy drives key aspects of minerals processing.  This video is a nice simple explanation of the mineralogical drivers for flotation by Stephen Gay from Midas Tech International.  Stephen has done great work around modelling for mineralogy and generating calculated mineralogical values from chemical assays and we enjoy following his work.

 

Kinetics of ore flotation

Ph.D._I.Bobins_equations_of_flotation_kinetics

The following article has been republished from Concentration of Minerals, an excellent resource compiled by Dr Natalia Petrovskaya.

 

 

Flotation kinetics (from the Greek  Kinētikós – driving) is studying the regularities of flotation process at the time, the rate and the flotation mechanism.

 

Kinetics of flotation reflects the flotation results in variable states and is characterized by dependency of the recovery R of floatable mineral in concentrate from time t, i.e. R = f (t). It allows a quantitative description of the flotation process in time.

 

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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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What is the Return on Investment of using Process Mineralogy?

In this week’s blog, we seek to respond to a common question that we hear at MinAssist: what is the return I get on investing in process mineralogy?  The short answer is it is not always easy to quantify in reality as there are often many factors at play at any given time, but an effective benchmarking study is of course always a good place to start to give you some indication of the ‘before’ picture – against which to measure changes.  The same benchmarking study can be used to also provide a good indication of where to go next with regards testwork, modelling and flowsheet design.

 

Here, by way of example, we have highlighted 3 recently published case studies that highlight how process mineralogy has been successfully integrated with geometallurgy and metallurgical testwork to provide tangible benefits.  One is from Xstrata based on the Nickel Rim South deposit, one from Rio Tinto’s Kennecott operation, and the third from the Anglo Platinum group’s operating concentrators.

 

ROI Case Studies

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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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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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