E. Donskoi

Mathematical Modeling and Optimization of Iron Ore Sinter Properties

The quality of iron ore sinter is a critical factor determining the productivity of blast furnaces for iron-making. CSIRO has therefore been developing capabilities for predicting sinter characteristics, which enables sinter quality to be improved/optimized and preliminary assessments to be made of the suitability of specific ores or ore blends for sinter production. An extensive database of pilot-scale sintering experimental results has been used to create mathematical models for predicting different sinter properties. In addition to size distribution and other physical and chemical characteristics usually used for sinter quality prediction, the mineralogical and textural characteristics of iron ores intended for sintering have also been taken into account. This approach has been quite successful, the variation of sinter reduction degradation index (RDI) that is accounted for by explanatory variables (R-Sq) being 87%, for example. Optimization criteria have been developed that take into account several sinter characteristics simultaneously, and optimization of different iron ore blends to produce target sinter characteristics has been carried out. Modeling results and their preliminary validation are discussed.

Automated optical image analysis of natural and sintered iron ore

Abstract To evaluate an iron ore resource, develop processing routines for iron ore beneficiation, and understand the behavior of the ore during such processing, extensive mineralogical characterizations are required. For calculating mineral associations, mineral liberation, grain size and porosity distribution, and other textural data, reliable imaging techniques are required. Automated optical image analysis (OIA) is a relatively cheap, robust, and objective method for mineral and textural characterization of iron ores and sinters. OIA allows reliable and consistent identification of different iron oxide and oxyhydroxide minerals, e.g., hematite, kenomagnetite, hydrohematite, and vitreous and ochreous goethite, and many gangue minerals in iron ore and different ferrites and silicates in iron ore sinter. OIA also enables a distinction to be made between forms of the same mineral with differing degrees of oxidation or hydration. To reliably identify particles and minerals during OIA, a set of comprehensive procedures should be automatically applied to each processed image. Generally, this includes next stages: image improvement, particle and mineral identification, particle separation, porosity identification, identification of unidentified areas, and correction of mineral maps. This is followed by automated measurements of final mineral maps and statistical processing of results. High resolution, imaging speed, and comprehensive image analysis techniques of modern OIA systems have made it possible to significantly reduce the cost and subjectivity of iron ore and sinter characterization with a simultaneous increase in the accuracy of mineral and textural identification.

Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysis

Abstract In order to develop downstream processing routines for iron ore and to understand the behaviour of the ore during processing, extensive mineralogical characterisation is required. Microscopic analysis of polished sections is effective to determine mineral associations, mineral liberation and grain size distribution. There are two main imaging techniques used for the characterisation of iron ore, i.e. optical image analysis (OIA) and scanning electron microscopy (SEM). In this article, a QEMSCAN system is used as an example of SEM methodology and results obtained from it are compared against results obtained by the CSIRO Recognition3/Mineral3 OIA system. Both OIA and SEM systems have advantages and drawbacks. Even though the latest SEM systems can distinguish between major iron oxides and oxyhydroxides, it is still problematic for SEM systems to distinguish between iron ore minerals very close in oxygen content, e.g. hematite and hydrohematite, or between different types of goethite. Scanning electron microscopy systems also can misidentify minerals with close chemical composition, i.e. hematite as magnetite and vitreous goethite as hematite. In OIA, iron minerals with slight differences in their oxidation or hydration state are more easily and directly recognisable by correlation with their reflectivity. In both methods, the presence of microporosity can result in some misidentification, but in SEM methods misidentifications due to microporosity can be critical. Low resolution during QEMSCAN analysis can significantly affect the textural classification of particle sections. The main conclusion of this study is that, for low iron content ores or tailings, SEM systems can provide much more detailed information on the gangue minerals than OIA. However, for routine characterisation of iron ores with high iron content and containing a variety of iron oxides and oxyhydroxides, OIA is a faster, more cost effective and more reliable method of iron ore characterisation. A combined approach using both techniques will provide the most detailed understanding of iron ore samples being characterised.

Novel developments in optical image analysis for iron ore, sinter and coke characterisation

A textural approach to the geometallurgical characterisation of iron ores helps better predict ore behaviour during downstream processing. Therefore, a robust, automated, objective method for the textural characterisation of iron ores is relevant to both research and industry needs. Utilisation of an optical image analysis (OIA) technique allows reliable and consistent identification of different iron oxide and oxy-hydroxide minerals, e.g. haematite, kenomagnetite, hydrohematite, both vitreous and ochreous goethite. CSIRO Mineral4/Recognition4 OIA system automatically identifies particle sections with different textures and assigns these sections to defined textural groups. Furthermore, novel developments in the system have enabled the automatic identification of different textural forms and morphologies of the same mineral, e.g. martite and microplaty haematite in iron ore; primary and secondary haematite or different types of Silico-Ferrite of Calcium and Aluminium (SFCA) in sinter as well as segmentation of different phases with the same reflectivity like Inert Maceral Derived Components (IMDC) from Reactive Maceral Derived Components (RMDC) in coke. The high resolution and imaging speed of the OIA system makes it possible for users to significantly reduce the cost and subjectivity of iron ore characterisation with a simultaneous increase in the accuracy of mineral identification. Extra software modules have been developed to meet research and industry demands for enhanced productivity. The addition of a ‘Multiple Block Imaging’ module enables image acquisition for sets of polished blocks at a time, rather than separate imaging of individual blocks. The ‘Multiple Set Processing’ module allows the processing up to 20 groups of sets simultaneously, where every group can contain up to 20 different sets of images that share the same analysis profile. The added modules enable analyses to be performed over many hours without the need for operator intervention, thus increasing equipment utilisation and reducing operator time. These new developments, together with the improvement of previously available features, e.g. identification of non-opaque minerals, automated textural classification, automated particle separation, automated correction of mineral maps and on-line measurement, means that OIA can provide a unique, reliable, industry and research focused tool for iron ore, sinter and coke characterisation.

Mineralogical quantification of iron ore sinter

The mineralogy and microstructure of sinter play an important role in determining the physical and metallurgical properties of iron ore sinter. Characterisation of sinter phases is, therefore, a cost-effective and complementary tool to conventional physical and metallurgical testing of iron ore sinter in evaluating and predicting sinter quality. Over the years, CSIRO (Commonwealth Scientific and Industrial Research Organisation) has developed a scheme for characterising iron ore sinter which classifies primary sinter phases, such as un-reacted and partially reacted haematite, magnetite and remnant fluxes, and secondary phases including silico-ferrite of calcium and aluminium (SFCA), secondary haematite and magnetite, glass and larnite. Quantification of these phases has traditionally been carried out by manual point counting under a petrographic microscope. However, new technologies based on automated optical image analysis, quantitative X-ray diffraction and scanning electron microscopy are now available for evaluation. In this study, two sinter samples of varying chemistry were prepared and characterised using both point counting and automated optical image analysis. Quantification of sinter phases is a complementary tool for comparing the physical properties of sinter obtained from various sinter blends, and sinter phase quantification results can be used for comparing pot-grate sinter with different metallurgical properties.

Importance of textural information in mathematical modelling of iron ore fines sintering performance

ABSTRACT Predicting the sintering performance of iron ore fines and the possibility of targeted optimisation of specific sinter properties are very important for the iron ore industry and related research organisations. A comprehensive database of pilot-scale sintering experimental results was established and empirical modelling conducted to predict values for sintering performance parameters such as Tumble Index, low temperature Reduction Disintegration Index and productivity. Together with other variables, the models developed include the abundances of several different ore textures which were combined into different textural factors corresponding to different sinter properties. Coefficients for the variables within specific regression equations can provide a better understanding of the effect of the variables on the corresponding sintering performance. The modelling results were also used to predict the sintering performance of tested mixtures that were not part of the database used to establish the models, so all models were thus verified on an independent set of data.

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

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.