An integrated ore analysis software package is providing industry with a more detailed understanding of their orebodies in real time. Adam Courtenay investigates.
Rapid resource characterisation article from resourceful: Issue 6, March 2014
Optical image analysis of mineral clusters, ores and sinters has been around for years, but now there is an instrument that boasts all the major functions needed to process images and classify particles automatically and with unerring accuracy.
Mineral4/Recognition4 is the latest version of CSIRO’s integrated ore analysis package.
The package not only fully identifies and analyses the mineral and chemical composition of an ore sample, it assesses the sample’s density, porosity, textural characteristics and particle dimensions.
The accuracy of Mineral4 in processing ore sample images allows it to go a step further than other technologies.
It allows not only for minerals to be ‘mapped’ but also for ore textural classes to be automatically determined and measured, which has numerous commercial benefits.
In various iterations since the early 2000s, Mineral4 has been used mostly on iron and manganese ore, ore sinters and, in recent years, on coke and coal characterisation.
Using Zeiss optics it measures reflectivity and colour shades of sample features, interpreting the data to correspond with different minerals and ore textures.
Eugene Donskoi, Project Leader at CSIRO, explains that an ore is never homogenous. In the iron ore industry, for example, any lump or collection of ore particles tends to bear an array of different mineral types, textures and agglomerations, some of which are valuable and others not.
An iron ore may contain, for example, 60 per cent iron and 40 per cent of other elements, and iron comes in a variety of oxides and hydroxides that are of unequal value and must be processed differently.
Mineral4 can reliably distinguish between different iron and non-iron minerals in the sample and therefore provide insight into the most beneficial downstream processing routine. The result may also reveal that an ore is not suitable for processing as it contains too many contaminants, or conversely that contaminants can be removed by various means.
To achieve this, Mineral4 uses a rapid-fire imaging system. There is no longer a need for an expert mineralogist and/or geologist to either work the equipment or interfere with the process in real time.
‘The operator simply hits the button and leaves it for a while to automatically complete the acquisition of the images and then – for their automated analysis,’ Andrei Poliakov, researcher at CSIRO, says.
Simon Campbell Hardwick, Principal Geometallurgist at Fortescue Metals Group, says his company currently uses Mineral3/Recognition3 but is looking at upgrading.
‘Mineral3 has proven to be most useful for fine samples of ore at less than one millimetre,’ he says.
‘With this software there is less margin for error for analysis of very fine particles.’
For coarser particles, a geologist is able to make decisions with greater speed than the technology.
‘However, the full set of data provided by the system will be missing in that case,’ Dr Donskoi adds.
Mr Campbell Hardwick says Mineral3 has not only been useful in analysing iron ore texture to predict sintering performance, it also comes into its own for technical marketing to customers.
The physical characteristics of the company’s products can be compared to those of its competitors, which gives it an advantage.
‘The technology’s greatest asset is its accuracy – it can look at fine size fractions and determine mineral and porosity proportions of particles with far greater accuracy and speed than a geologist’s visual estimation and you don’t need an expert to use it. The technology does all the work,’ he says.
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