Iron ore textural information is the key for prediction of downstream process performance

Donskoi, E.; Poliakov, A.; Holmes, Ralph J.; Suthers, S. P.; Ware, N. G.; Manuel, James; Clout, J. M. F.

doi:10.1016/j.mineng.2015.11.009

Cited by 34 publications

(26 citation statements)

References 21 publications

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“…Figure 4.15c shows a multithresholding approximation of an elliptical reflectivity area achieved by combining three different individual thresholding steps. The presence of textural information is sometimes very important for the prediction of downstream processes (Donskoi et al, 2006(Donskoi et al, , 2008b(Donskoi et al, , 2009. Another capability of multithresholding allows the removal of a portion of a mineral reflectivity area where it interferes with another mineral.…”

Section: Automated Identification Of Particles and Opaque Mineralsmentioning

confidence: 99%

Automated optical image analysis of natural and sintered iron ore

2015

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Section: Automated Identification Of Particles and Opaque Mineralsmentioning

confidence: 99%

Automated optical image analysis of natural and sintered iron ore

2015

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“…These are commonly obtained from two or three dimensional imaging techniques [1,2]. Ores with similar mineralogy can behave differently during processing, as the behaviour is also determined by the spatial distribution of different minerals and porosity (that is, textural composition) of the ore [3,4]. As an example of the differences in texture possible for a chemically simple mineral such as hematite, Fig.…”

Section: mentioning

confidence: 99%

“…[9]), has better resolution for massive screening and better characterises porosity (for detailed comparison of the two techniques see Refs. [3,12]). …”

Section: mentioning

confidence: 99%

See 1 more Smart Citation

Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterization

Donskoi¹,

Poliakov²,

Vining³

et al. 2017

JEPE

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OIA (optical image analysis) has traditionally been used for reliable identification of different iron oxides and oxyhydroxides in iron ore. The automated CSIRO OIA system Mineral 4/Recognition 4 was created for rapid mineral and textural characterisation of iron ore providing identification of different minerals and different morphologies. The technique has further been applied to processed iron ore products such as iron ore sinter to determine key parameters such as porosity, different morphologies of hematite (primary and secondary), and different morphologies of SFCA (silicon ferrite of calcium and aluminium). Application of textural identification has recently been extended to coke characterisation where the software gives comprehensive characterisation of porosity, IMDC (inert material derived components), RMDC (reactive material derived components) and the boundaries between IMDC and RMDC. The software also has many unique features needed for iron ore research including characterisation of large objects like pellets and ore lumps; automated gangue (including quartz) identification; automated particle separation; multiple image set processing and on-line measurements. All these features make the Mineral 4/Recognition 4 OIA system a unique, reliable, industry/research focused tool for ore, sinter, pellet and coke characterisation.

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“…Much of the work that has been done gives emphasis to the type and amount of minerals present and relating it to processing behaviour (Alves and Hagni, 2008;Becker et al, 2008;Liang and Wang, 2008;Lotter, 2010;Rule and Schouwstra, 2011;Uliana et al, 2011). Some have looked at the texture of the ore Chetty et al, 2012;Donskoi et al, 2008;McClung and Viljoen, 2011;Meadows et al, 2012;Rozendaal and Horn, 2012;Thompson et al, 2011;Zhou and Gu, 2008). In these examples texture is classified qualitatively based on abundance of mineral, metal/mineral deportment, shape of mineral, mode of occurrence of the mineral of interest (i.e.…”

Section: Introductionmentioning

confidence: 99%