Skip to main content

Optical image analysis for textural ore classification

Posted by: Keirissa Lawson

November 22, 2017

Keith Vining By Keith Vining

Using CSIRO’s Mineral4/Recognition4 to predict ore behaviour

Ore classification by mineral and textural type is a critical method for predicting the behaviour of iron ore fines during beneficiation and in sintering and pelletising agglomeration processes.

In current industry practice, chemistry (or ore grade) is the primary measure of ore quality. However, classifying ores through grade alone doesn’t guarantee product quality or consistency later in the process.

We’ve found that it is vital to characterise ore texture i.e. the proportions of mineral phases and the amount and distribution of porosity.

Incorporating texture into ore classification enables correlation with ore physical performance and provides the link between chemistry, mineralogy and process performance through the value chain.

Pitfalls in current ore classification methods

Current ore classification approaches present several potential drawbacks:

  1. Unpredictable behaviour in downstream processes — solely classifying ore through chemistry and mineralogy means that the behaviour of iron ore fines in beneficiation and agglomeration processes is difficult to predict.
  2. Subjectivity in ore classification — ore needs to be classified at the mine site and throughout product handling stages. Currently ore type logging relies on the subjective opinion of the logger.
  3. No awareness of the effects of texture on ore behaviour — particles with the same mineralogy but different textures can behave very differently during downstream processing. Potential issues include poor flowability—causing bottlenecks and downtime in processing plants—and differing hardness resulting in the generation of high levels of ultrafines, representing a loss of potentially recoverable iron units.

Ultimately, improved understanding of the understanding of ore texture and monitoring of problematic ore types may assist in the management of ore handling issues and minimisation of the operational and financial consequences.

The benefits of ore classification by texture

In order to optimise production, characterising ore by texture — in addition to chemistry and mineralogy — provides more accurate insights into ore behaviour. This drives improvement in transport and materials handling. It also aids the generation of a fines product that both meets chemical grade, and is characterised well enough to ensure its properties allow for the production of a blast furnace feed of consistent quality.

Texture-based ore classification provides higher resolution data than can be obtained on-site through visual inspection at a macroscopic scale. The value of being able to more accurately and objectively predict the behaviour of ore in beneficiation and agglomeration processes is huge.

Optical Image Analysis is changing the landscape of ore classification

Building on our scientific work to improve ore classification, CSIRO has developed Mineral4/Recognition4 Optical Image Analysis (OIA) software to achieve textural ore classification.

Using the latest Zeiss microscopy technology as a platform, this OIA software is unique in the world. It allows for on- or off-site classification of ores and can provide you with an accurate break down of ore components.

OIA improves ore characterisation and drives process improvements. This solution has many benefits:

  • Comprehensive analysis of mineralogy, porosity, mineral association, texture, size distribution, mineral liberation, particle textural classification with calculated class abundances, densities and mineral composition. The image below illustrates how OIA can be used to automatically identify minerals in iron ore.
Two panal panels showing patches of colour against a black background and a third panel showing a colour key for the images
Automated identification of minerals in iron ore using Optical Image Analysis
  • Automated mineral identification and textural classification which removes any subjectivity that may be present during manual mineral characterisation by a visual estimation or point counting.
  • Adjustment for any particle size from ultrafines up to individual lump-sized material.
  • Identification of micro-porosity and discrimination of a full range of iron oxides and oxyhydroxides, including goethite sub-types.
  • It can be customised to look at process products and examine the mineralogy of iron ore sinter and pellets. The image below shows a sample output using Optical Image Analysis to classify minerals in sinter.
Automated identification of minerals in sinter using Optical Image Analysis
  • Adapted to work for coke and materials with similar porous structures, giving you a very comprehensive structural characterisation.

Using Mineral4/Recognition4 OIA, preliminary on-site analysis, for example, from blast cones on-site or from selected points throughout the crushing and screening process, will provide the information needed for understanding how an ore is likely to behave during processing.

Microscopic analysis of polished sections are effective not only to determine mineral associations, mineral liberation and grain size distribution, but also to provide comprehensive textural classification of ore.

Using OIA will give you operational benefits and improved business processes resulting in operational efficiency, productivity and cost savings.

The Carbon Steel Futures team at CSIRO, through our research and breakthrough science, is uniquely positioned to add value to your operation. Through ongoing analytical support, training, or product development, we can help you improve your agglomeration and beneficiation processes by applying proven characterisation techniques based on Mineral4/Recognition4 OIA.

Give me, Research Group Leader Keith Vining, a call on +61 07 3327 4761 or email me directly on Keith.Vining@csiro, to discuss how Optical Image Analysis can be used to drive process improvements in your organisation.

Subscribe and receive the latest updates on our iron-ore and carbon steel research.