The AusIMM and CSIRO are co-hosting the Iron Ore 2017 conference, which is being held in Perth from 24-26 July. Visit the Iron Ore 2017 website for more details on the conference program and registration.
The ability to accurately predict the downstream processing performance of any given orebody is crucial for resource evaluation, development decision-making, and maximising output.
However, without an ore textural classification scheme and an appropriately characterised resource, modelling will be inaccurate, reducing a mine’s ability to implement cost reductions and productivity gains across the iron ore value chain.
Chemical composition data is used predominately for resource evaluation, planning, and product quality control. However, this approach fails to account for the significant business impact that the textural composition of an orebody can have.
Orebodies contain a wide range of ore textural types in differing proportions, which can vary significantly even at a local scale within the orebody.
The different textures have distinct physical properties and behaviours that will impact grade, crushing, processing, bulk handling, sintering (fine ore) and blast furnace (lump ore) behaviour.
The increased prevalence of goethitic ore coming from Australia’s mines means the need for a robust textural classification framework is growing.
Understanding goethite in Australian iron ores
After preferentially mining high-grade, low-phosphorus Brockman ore for some decades, Pilbara iron ore producers have now made the transition to increasing outputs of Marra Mamba (MM), Channel Iron Deposit (CID) and high-phosphorus Brockman ores. Increasing proportions of these ore types are resulting in increasingly goethitic products, which has had a noticeable impact on the iron ore supply chain. In Figure 1, the balance of ore textural components is shifting to the right with time, from hematite-dominated (blue, red) to goethitic (brown, yellow).
Compared with hematite and magnetite, goethite has a more diverse range of textural forms and occurrences, and its influence on iron ore processing is less well understood. Currently, most analysis of the effects of goethite on mine performance considers only the chemistry of the ore and not its textural components. Through improved textural classification of goethitic ores, we can reframe the perceptions the industry has of goethite, and optimise overall processing performance.
Types of goethite
A greater understanding of the textural differences between types of goethite is the first step towards optimising a mine’s ability to process goethitic iron ores. Table 1 shows the three basic types of goethite.
In bedded hematite-goethite ores, ochreous goethite is more abundant in the lower part of the orebody, including below water table zones and hard brown goethite tends to be prevalent in the upper (hydrated) zone. Vitreous goethite occurs largely in deposit hardcap and is associated with localised and relatively high levels of Al and Si, whereas chemical analysis typically shows low impurity levels associated with brown and ochreous goethite in Pilbara deposits, contrary to common belief.
The importance of textural classification when managing goethite
Failing to leverage a textural classification scheme for managing goethite ore can impact a mining operation in numerous ways.
Effective mine planning relies on an understanding of your deposit and its distribution of ore types.
Accounting for the chemical and textural composition of goethite in a mine allows for optimised planning and processing to account for the varying effects that the types of goethite can have on mine performance.
Plant downtime and underutilisation
Goethite present in iron ore deposits affects how your mine manages the handling and processing of ore. Failing to understand how the different textural composition of goethite impacts processing performance can lead to downtime and reduced mine efficiency.
Textural classification allows mines to better predict where different types of goethite are present, allowing blending processes to be adjusted to address differences between ore types.
Fine (less than 1 mm) goethite is very reactive during the sintering process and can be leveraged to enhance melting and matrix strength. Fine, ochreous goethite also plays an important role in enhancing granulation. Conversely, coarse (more than 2 mm) goethite particles often show surprising resistance to reaction forming relatively stable nuclei, and increasing the overall efficiency of the process. Figure 2 shows that a high grade ochreous goethite matrix (Ore G) can achieve high strength (expressed as matrix TI), compared with dense hematite (Ore H) and porous hematite-goethite (Ore H-G) samples, at a moderate sintering temperature.
However, if a producer fails to acknowledge the textural composition of fine goethitic ore during sintering, they’re unable to capitalise on this additional efficiency. If the goethite types are unbalanced in a sinter blend, the result can be higher sintering fuel rate, due to the energy required to drive off excess moisture – as well as reduced productivity, due to excessive melt formation – so the key is knowing what goethite textural types are present and in what proportions.
Without understanding the textural composition of goethite in ore bodies, consistency throughout the supply chain cannot be maintained. Therefore, with the increased presence of goethite in Australian ore, it is important for supply processes to account for differences between goethite types. Failure to address these differences may lead to damaged commercial relationships due to inconsistent outputs.
There is a common perception that ochreous goethite, in particular, is associated with both chemical impurities and problems in processing (leading to disruption in plant throughput) and utilisation in sinter fines blends. While it is true that excessive ultrafines can cause problems in plant operation and ochreous goethite can carry a high level of moisture, the real challenge is in controlling the proportions of ore components to maintain consistent product quality and anticipating problems before they occur.
Nature and deportment of impurities
The distribution and mineralogical association of impurities in goethite is critical in determining the feasibility of upgrading an ore and the optimum processing route. In the case of goethite, impurities may be present either substituted in the crystallographic lattice of the mineral, or as discrete minerals, usually kaolinite clay, quartz and gibbsite. The form and occurrence of phosphorus is not yet fully resolved, with suggested mechanisms including adsorption and coupled Al-P substitution.
Loss of iron units to tailings
A growing issue for Australian producers is maximising recovery of iron units, with the increasing need to beneficiate lower grade ores to meet customer requirements. At present, high-grade, valuable hematite and goethite are being lost to waste streams during wet processing, leading to inefficient plant operation as well as inefficient resource utilisation. From a producer’s perspective, the simplest solutions to this issue appear to involve improved flexibility in selection of processing routes to cope with feed variation. This is an area where improved and higher resolution ore classification data would be of great potential benefit.
The broader need for textural classification schemes
The increased prevalence of goethite in ore from Australian mines highlights the growing need for textural classification to account for differences between goethite types. However, there are several other key areas where mines benefit more broadly by leveraging textural classification.
Prediction of downstream processing performance
With a texture-based iron ore classification, you can understand the porosity, physical properties, mineral proportions and mineral associations of ores, increasing the efficiency of downstream processing and allowing a proactive response to changing feed type.
Textural classification, and an understanding of gangue deportment, also allows a more accurate prediction of the grind size required for beneficiation and maximising the energy efficiency of comminution circuits.
Below is an example of how characterising the texture of an ore feed to a hydrocycle significantly improves the predictability of product and waste streams.
Figure 3 shows a simplistic visualisation of how feed material can be characterised on the basis of higher density (black) and lower density (white) ore particles with different sizes. Figure 4 shows a more advanced classification scheme may account for association of the two minerals in a single particle. Figure 5 shows how textural classification allows for a more comprehensive classification of mineral association and introduces the dimension of porosity.
Understanding textural characteristics allows a mine to account for how porosity, mineral proportions, and associations of the particles within different size fractions can impact their output. By calculating ore density and the probability of ore particles reporting to either the product or waste stream, a model can be developed that allows grade and recovery values to be calculated within certain sets of operating parameters.
Bulk handling and transport
Variability of ore texture impacts handling and transportation options. For example, knowing which components of ore are associated with clay minerals or are inherently ‘sticky’ allows mine operators to adjust their processing to account for more complex ore transportation by mitigating the risk of screens, chutes, transfer points, and rail cars getting blocked throughout the supply chain.
Similarly, an understanding of an ore’s moisture carrying capacity through textural classification can mitigate the risks associated with transportation. Mines with this information can develop blending strategies and adjust their handling systems to proactively manage moisture issues, instead of reactively adjusting to minimise output losses.
Implementing textural classification schemes
At CSIRO, the core of our geometallurgical philosophy is built around improving processing and performance. Subsequently, CSIRO textural classification methods allow mines to understand how different orebodies will process downstream, improving the predictability of their value chain.
Similarly, textural classification can reduce a mine’s production and energy costs, by improving decision-making by enhancing ore prediction accuracy and handling modelling.
The scheme also creates a new means of communication for the various stakeholders in your business.
Currently, the language used to discuss resource extraction is focused primarily on chemical composition. However, this does not adequately describe the likely processing behaviour that stakeholders further down the value chain can expect. By reframing the conversation around texture and downstream utilisation, you can shift the language to provide a common reference frame for geologists, metallurgists, technical marketers and the end product user.
Finally, as we see increasingly goethitic ore mined and on-sold in the Australian market, there is an opportunity for mines to implement a textural classification scheme and adapt their processes to deal with the behavioural characteristic of goethitic ore – ultimately improving their output.
As part of CSIRO’s continued contribution to the iron ore industry, we are proud to announce we will be cohosting Iron Ore 2017 at the Perth Convention and Exhibition Centre, 24-26 July. At the event, the Carbon Steel Futures team will present recent work including papers on goethite classification, mineralogical characterisation, and microwave treatment of low-grade goethite ores.
Manuel J R and Clout J M F, 2017. Goethite classification, distribution and properties with reference to Australian Iron Deposits. To be presented at Iron Ore 2017, 24-26 July, Perth.
Ware N A, Manuel J R and Raynlyn T D, 2013. Fundamental Melting Behaviour of Hematite and Goethite Fine Ores in the Sintering Process, in Proceedings Iron Ore 2013 (The Australasian Institute of Mining and Metallurgy: Melbourne).