Understanding iron ore analytical tools.

July 3rd, 2017

Keith ViningBy Keith Vining

The 7 analytical tools you can use to improve mine performance

The utilisation of increasingly goethitic ore deposits in Australia has created a greater need for the iron ore supply chain to adopt the use of new tools and analysis techniques in order to understand goethite properties, and continue to drive operational efficiencies.

There are a number of iron ore analytical tools and techniques that you can leverage to better understand your ore bodies. These techniques provide you with detailed analysis of a number of ore properties to improve performance across the entire value chain. The table below outlines the seven key tools that you can use to maximise process efficiency and outputs in your organisation.

Table: Overview of Iron Ore Analytical Tools

Overview of iron ore analytical tools


Iron Ore Analytical Tools in Application

Each of the seven tools listed in the above table can be used for improving mine performance and after Iron Ore 2017, CSIRO will be conducting a two day workshop: Advanced Characterisation and Geometallurgy of Iron Ores to show how you can leverage each tool to improve performance across your value chain.

In the meantime, the section below provides a detailed breakdown of how the XRD, EPMA, and HyLogger™ analysis tools can work in your operation.

XRD: Sinter characterisation – effects of alumina source on sinter quality

In situ X-ray diffraction may be used to examine the relative effects of alumina sources gibbsite, kaolinite, and aluminous goethite on the formation and stability of key iron ore sinter phases, SFCA and SFCA-I. Iron ores containing aluminous goethite and kaolinite maximise the formation of these important sinter bonding phases. This explains why sintering investigations using Australian ores containing kaolinite and aluminous goethite produce higher quality sinter compared with ores containing alumina in the form of gibbsite.

Figure 1 shows sample results from an in situ X-ray diffraction experiment using an Al-goethite rich iron ore. Compared to iron ores containing gibbsite, bonding phase formation occurs at lower temperatures and over a broader temperature range for Al-goethite (and kaolinitic) ores.

Graph showing smears of bright green and red on a blue background which denote X-ray diffraction results showing formation of SFCA and SFCA-I bonding phases at ~1100-1200°C in a mixture containing Aluminium-rich goethite

Figure 1: X-ray diffraction results showing formation of SFCA and SFCA-I bonding phases at ~1100-1200°C in a mixture containing Al-rich goethite. Further details: Webster, N.A.S., O’Dea, D.P., Ellis, B.G. and Pownceby, M.I. (2017) Effects of gibbsite, kaolinite and Al-rich goethite as alumina sources on silico-ferrite of calcium and aluminium (SFCA) and SFCA-I iron ore sinter bonding phase formation. ISIJ International, 57(1), 41-47.

XRD: Rapid prediction of sinter quality parameters

Partial least squares regression (PLSR) analysis of X-ray diffraction data  enables prediction of sinter quality parameters such as optimum basicity (CaO:SiO2 ratio) and FeO content. PLSR can also be used as a method for prediction of sinter tumble index (TI), a measure of sinter strength, from powder XRD data (Figure 2). This can be used as a key marker of sinter quality and used for process control – without the need for rigorous mineralogical analysis.

Graph showing pointds scattered around a dotted line increasing diagonally from left to right

Figure 2: Partial least squares regression (PLSR) analysis of X-ray diffraction results. Further details: Webster, N.A.S., Pownceby, M.I., Ware, N. and Pattel, R. (2017) Predicting iron ore sinter strength through X-ray diffraction analysis. Proceedings Iron Ore 2017.

EPMA: Goethite characterisation – distribution of impurities in ores

EPMA X-ray mapping techniques can offer unique insights into how goethites with different impurity levels are distributed in an iron ore sample. Figure 3 shows a sample where alumina is present within high-Al (>2.4wt% Al2O3) goethite, low-Al (<2.4wt% Al2O3) goethite and gibbsite phases. This analysis technique can also be used to track the success of beneficiation processes applied to lower alumina.

Brightly coloured yellow, green and orange iiregular shapes scattered across a black background

Figure 3: EPMA phase map showing the distribution and texture of key alumina-containing minerals in a fine grained iron ore. Further details: Pownceby, M.I., MacRae, C.M. and Torpy, A. (2017) A comparison of X-ray and electron-based analysis techniques for characterising the mineralogy and alumina distribution in iron ores. Proceedings Iron Ore 2017.


In the iron ore industry, the CSIRO-developed HyLogging™ system uses visible and infrared spectroscopic reflectance data to identify hematite, goethite and its subtypes (ie vitreous and ochreous goethite) and waste minerals including AlOH-clays (kaolinite, Al-smectite), carbonates, gibbsite, chlorite and amphibole (aluminosilicates) and hydrated silica as well as water content. Advantages of HyLogging systems include:

  • Delineation of different ore zones by mapping vitreous and ochreous goethite. This has a potentially large impact in mine planning and operations, as hard, vitreous goethite and soft, powdery, ochreous goethite have different processing characteristics that can affect plant throughput if not carefully managed.
  • A potentially significant impact on waste rock characterisation, as many of the waste materials can be identified and quantified with hyperspectral data (eg clay type, hydration state).
  • Can provide accurate three-dimensional (3D) mineralogy to iron ore companies that will empower them to improve future exploration and resource planning.

For more details see the CSIRO blog on the hype about HyLogger.

Which iron ore analytical tool should I invest in?

When implemented in your operation, these tools provide complementary information. As such, your application of these techniques needs to consider the specific improvements you are trying to drive across your value chain.

Identifying the gaps that exists in process and planning, and recognising the appropriate tool to mitigate these gaps is often a challenging task. To help you action these changes, in conjunction with Iron Ore 2017, CSIRO is conducting a two day workshop, Advanced Characterisation and Geometallurgy of Iron Ores to optimise production processing.

The workshop will provide you with a deeper understanding of some of the iron ore analytical tools that you can implement in your organisation, so that you are able identify and engage the tools that will allow you to improve your processes across the value chain.

If you aren’t attending Iron Ore 2017, but still want to drive efficiencies in your value chain, improve mine planning, and reduce processing costs and losses, contact the Carbon Steel Futures’ Geometallurgy team on +61 07 3327 4761 or email Research Group Leader Keith Vining (keith.vining@csiro.au).

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