To understand and optimise downstream processing of ores, reliable information about mineral abundance, association, liberation and textural characteristics is needed. Such information can be obtained by using Optical Image Analysis (OIA) in reflected light, which can achieve good discrimination for the majority of minerals. However, reliable automated segmentation of non-opaque minerals, such as quartz, which have reflectivity close to that of the epoxy they are embedded in, has always been problematic. Application of standard thresholding techniques for that purpose typically results in significant misidentifications. This paper presents a sophisticated segmentation mechanism, based on enhanced thresholding of non-opaque minerals developed for Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) Mineral5/Recognition5 OIA software, which significantly improves segmentation in many applications. The method utilises an enhanced image view using an adjusted reflectivity scale for more precise initial thresholding, and comprehensive clean-up procedures for further segmentation improvement. For more complex cases, the method also employs specific particle border thresholding with subsequent selective erosion-based “reduction to borders”, while “particle restoration” prevents the detachment of non-opaque grains from larger particles. This method can be combined with “relief-based discrimination of non-opaque minerals” to achieve improved overall segmentation of non-opaque minerals.

The trends and efficacy of ultrasonic pre-treatment before desliming high phosphorus Australian iron ore fines was investigated in the presented fundamental study. The-1 mm iron ore fines (57.5 wt% Fe, 2.47 wt% Si, 2.15 wt% Al, 0.132 wt% P) were subjected to sonication while the ultrasonic power, duration and pumping direction were varied. Sonication increased the iron grade and decreased the Si and Al assay in all size fractions except the ultrafines, where the opposite occurred. Ultrasonic treatment was found to depend on the total sonication energy input (up to 68 kJ) with no pref-erence for either power or duration alone. The direction of pulp circulation was important and best results were obtained by pumping the pulp downwards past the ultrasonic probe rather than upwards. Sonication of iron ore fines suspended in water promotes deagglomeration, surface cleaning or disintegration, resulting in substantial deportment of mass from the coarser size fractions to the ultrafines (less than9.9 mu m). The iron grade of ultrafines generated by sonication was lower than that of the ultrafines in the head sample, while the corresponding grades of Si, Al and P were higher. Increasing the sonication energy increased the proportion of hematite and decreased the proportion of kaolinite in material that deported to the ultrafines, which was most probably due to the surface action of ultrasonic treatment. A substantial portion of kaolinite (similar to 33 %) deported to the ultrafines during sonication using 68 kJ, compared with only 8.8 % of the goethite. Desliming of the unsonicated and sonicated samples was evaluated by calculated size classification. The sonication pre-treatments substantially improved the predicted grades of Fe, Si and Al in the deslimed products compared with unsonicated deslimed sample. For experiment using the highest sonication energy (68 kJ) and calculated desliming of < CS5 fraction, the predicted product iron grade improved by 2.8 wt% (1.6 wt% more than unsonicated) after desliming, while the silicon grade decreased relatively by 36.0 % (22.7 % more than unsonicated), the aluminium grade decreased relatively by 39.5 % (25.1 % more than unsonicated) and the predicted phosphorus content decreased relatively by 3.0 % (1.5 % more than unsonicated). The difference in sonication effect between Al, Si and P was due to the association with different minerals. Aluminium and silicon were mainly associated with kaolinite and soft goethite, which were easily disintegrated by ultrasound and deported to the ultrafines, while phosphorus was mainly associated with goethite (mostly of the hard types) present at around 40-48 wt% in most size fractions before and after sonication.

To optimise processing/beneficiation procedures a detailed characterisation of goethitic ores is needed, including mineral liberation, association and textural classification. The identification of different iron oxides and oxyhydroxides is already reliably performed by optical image analysis (OIA). Automated OIA identification of different gangue materials, particularly quartz, can be problematic, however. The article demonstrates the capability of OIA software Mineral4/Recognition4 to characterise goethitic iron ores. Characterisation includes identification of the different types of goethite, hydrohematite and gangue materials such as quartz and kaolinite. XRD and XRF analysis results are compared with those from OIA. Correlation of these results and visual comparison shows that optical image analysis can be an effective tool for characterisation of low and medium grade iron ores. The work highlights issues regarding discrimination of aluminous goethite and gangue, micro and nano-porosity and effective density, for further study.

Sinter quality is a key element for stable blast furnace operation. Sinter strength and reducibility depend considerably on the mineral composition and associated textural features. During sinter optical image analysis (OIA), it is important to distinguish different morphologies of the same mineral such as primary/secondary hematite, and types of silico-ferrite of calcium and aluminum (SFCA). Standard red, green and blue (RGB) thresholding cannot effectively segment such morphologies one from another. The Commonwealth Scientific Industrial Research Organization's (CSIRO) OIA software Mineral4/Recognition4 incorporates a unique textural identification module allowing various textures/morphologies of the same mineral to be discriminated. Together with other capabilities of the software, this feature was used for the examination of iron ore sinters where the ability to segment different types of hematite (primary versus secondary), different morphological sub-types of SFCA (platy and prismatic), and other common sinter phases such as magnetite, larnite, glass and remnant aluminosilicates is crucial for quantifying sinter petrology. Three different sinter samples were examined. Visual comparison showed very high correlation between manual and automated phase identification. The OIA results also gave high correlations with manual point counting, X-ray Diffraction (XRD) and X-ray Fluorescence (XRF) analysis results. Sinter textural classification performed by Recognition4 showed a high potential for deep understanding of sinter properties and the changes of such properties under different sintering conditions.

Optical image analysis is commonly used to characterize different feedstock material for ironmaking, such as iron ore, iron ore sinter, coal and coke. Information is often needed for phases which have the same reflectivity and chemical composition, but different morphology. Such information is usually obtained by manual point counting, which is quite expensive and may not provide consistent results between different petrologists. To perform accurate segmentation of such phases using automated optical image analysis, the software must be able to identify specific textures. CSIRO’s Carbon Steel Futures group has developed an optical image analysis software package called Mineral4/Recognition4, which incorporates a dedicated textural identification module allowing segmentation of such phases. The article discusses the problems associated with segmentation of similar phases in different ironmaking feedstock material using automated optical image analysis and demonstrates successful algorithms for textural identification. The examples cover segmentation of three different coke phases: two types of Inert Maceral Derived Components (IMDC), non-reacted and partially reacted, and Reacted Maceral Derived Components (RMDC); primary and secondary hematite in iron ore sinter; and minerals difficult to distinguish with traditional thresholding in iron ore.

Many different approaches have been used in the past to characterise iron ore sinter mineralogy to predict sinter quality and elucidate the impacts of iron ore characteristics and process variables on the mechanisms of sintering. This paper compares the mineralogy of three sinter samples with binary basicities (mass ratio of CaO/SiO2) between 1.7 and 2.0. The measurement techniques used were optical image analysis and point counting (PC), quantitative X-ray diffraction (QXRD) and two different scanning electron microscopy systems, namely, Quantitative Evaluation of Materials by Scanning Electron Microscopy (QEMSCAN) and TESCAN Integrated Mineral Analyser (TIMA). Each technique has its advantages and disadvantages depending on the objectives of the measurement, with the quantification of crystalline phases, textural relationships between minerals and chemical compositions of the phases covered by the combined results. Some key differences were found between QXRD and the microscopy techniques. QXRD results imply that not all of the silico-ferrite of calcium and aluminium (SFCA types) are being identified on the basis of morphology in the microscopy results. The amorphous concentration determined by QXRD was higher than the glass content identified in the microscopy results, whereas the magnetite and total SFCA concentration was lower. The scanning electron microscopy techniques were able to provide chemical analysis of the phases; however, exact correspondence with textural types was not always possible and future work is required in this area, particularly for differentiation of SFCA and SFCA-I phases. The results from the various techniques are compared and the relationships between the measurement results are discussed.

Mineral liberation, association and textural information about processed iron ore is vital for understanding and predicting iron-ore downstream processing performance. Such information is usually obtained by two major imaging techniques - Optical Image Analysis and Scanning Electron Microscopy. Both techniques use polished epoxy resin blocks with embedded sized particles suitable for imaging. Each particle section can be classified to a certain liberation or textural class. If the amount of analyzed particle sections is sufficient for statistical validity, the abundance of particles in each class gives an objective description of the whole sample. Different particle sections in the block can touch each other and the processing software may consider such particles as just one particle. If the touching particles belong to different liberation or textural classes this will most probably result in distortion of the liberation, association and textural data of the sample. CSIRO developed the new Mineral4/Recognition4 software for textural characterization of different ores, sinters and coke with a novel image analysis processing routine for separation of touching particle sections. This processing is based on two major methods: modified watershed separation and modified binary erosion separation. Depending on the ore type, particle size, imaging magnification and other factors, both methods can be adjusted to obtain a better final result. The article highlights the negative effect of particle touching, the importance of separation, and improvements made by distinct methods to incorrect separation by comparing different cases, including those when touching particles are not separated at all, separated manually and when they are separated automatically by each method, or their combination, with different options. The article demonstrates the effects that touching particle sections have on liberation and textural iron-ore characterization and shows that the proper use of automated separation developed within the Mineral4/Recognition4 system significantly reduces errors introduced by touching particle sections.c

•Vertical sections of epoxy blocks should be used during particle characterisation.•Difference between layers in epoxy block due to SG segregation can be unacceptably high.•Equidistant imaging of vertical section cannot prevent characterisation errors.•The whole vertical span of the sample section should be analysed.•Textural characterisation gives the deepest understanding of the segregation effect.

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.

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.

To better understand the connection between parent coal blend, coke structure, and coke strength novel structural identification and characterisation techniques for processing high resolution optical photomicrographs of coke were developed. Automated segmentation of inert maceral derived components (IMDC) and reactive maceral derived components (RMDC) allowed measurement of a broad range of parameters characterising coke structure. The characterisation included separate characterisation of IMDC and RMDC, IMDC boundaries including calculation of parameters characterising connections between IMDC and RMDC, coke porosity and, separately, IMDC porosity.Several other novel approaches using image analysis software were investigated, such as:Separation of large porosity objects into individual pores (Separated Pores),Removal of weak walls and hence potential identification and measurement of Weak Areas,Identification of nodes (relatively thick agglomerations in the coke structure) and walls (part of the coke matrix connecting nodes),Measurement of Modified Wall Thickness (when only walls are taken into account), Wall Neck Thickness (weakest points in walls) and Specific Neck Thickness (sum of all necks per unit area).Application of these novel characterisation techniques to a set of coke samples produced a substantial amount of data, which revealed many strong correlations between novel parameters characterising coke structure, and also between these parameters, coke strength indices and parent coal composition. This demonstrated the usefulness of the newly developed characterisation methodologies and gave a significantly improved understanding of coke structure and its connection with coke strength and parent coal blend. It should also be noted that these novel approaches to structural/textural characterisation can be applied to carbonaceous materials other than coke or other structural materials such as sinter, ore, and ceramics. (C) 2017 Elsevier Ltd. All rights reserved.

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.

Textural information or information about the presence of porosity, different material or mineral types and their structural arrangement in iron ore is crucial for understanding, predicting and optimising downstream processing performance. Ores with the same chemical and mineral composition may behave very differently during downstream processing due to differences in textural components.To produce a textural description of iron ore, it is preferable to use an automated system to avoid subjectivity and to collect additional information about mineral abundance, liberation and association. CSIRO created a unique dedicated optical image analysis software package for automated textural classification and characterisation of different minerals, sinters and coke. This software, called Mineral4/Recognition4, has been used extensively to collect data for this article.Four case studies of CSIRO research are presented to demonstrate the importance of textural information.The first example shows that iron ore samples with different texture but similar mineralogy undergo different degrees of assimilation in compact sintering.The second example shows that empirical modelling of sinter properties was improved considerably after introducing textural information.The third example demonstrates the application of classification by ore texture to model and optimise hydrocyclone performance.The last example is an experimental study of ultrasonic treatment of hematitic-goethitic iron ore fines. It demonstrates how the resulting breakdown or deagglomeration of different particles, and the mineral deportment, can be better understood when textural information is also considered.In all cases, the availability of textural information was critical, providing a better prediction of process performance or a deeper understanding of the unit process. (C) 2015 Elsevier Ltd. All rights reserved.

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.

Ore characterisation is important in order to understand the quality of ores and their behaviour during downstream processing. Many significant ore characteristics can only be determined through the use of various imaging techniques. Optical Image Analysis (OIA) is one such technique and is particularly attractive for many applications due to its low cost and high resolution. However OIA also has some limitations, one of which is the difficulty with discriminating non-opaque minerals. Some non-opaque minerals, such as quartz, are typical gangue minerals in certain types of iron ores. Even though in many cases quartz particles can be easily seen and attributed by mineralogists in polished sections, their automated discrimination has always been an issue, the reasons for which are discussed in this article. The ability to automatically discriminate quartz and other non-opaque minerals would significantly increase the value of OIA for the mineral industry.This paper describes a novel method of discriminating non-opaque minerals in the sample by their optical relief, which results in visible borders between the mineral and the epoxy resin mounting medium. An algorithm for such discrimination that has been developed for the CSIRO Mineral4/Recognition4 OIA software package is described. The algorithm is based on dynamic thresholding of the image with subsequent cleanup and enhancement to reliably determine borders between non-opaque particles and epoxy and on subsequent attribution of image areas created by these borders to either the non-opaque mineral or the epoxy resin. Further, this article discusses difficulties that may arise when applying this algorithm due to sample peculiarities and describes algorithm enhancements incorporated in Mineral-4 in order to overcome these issues. The resulting software is capable of reliably discriminating non-opaque minerals in a variety of samples, including iron and manganese ores. (C) 2013 Elsevier Ltd. All rights reserved.

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 oreminerals 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 bothmethods, 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.

Ultrasonic waves in pulps containing iron ore fines can start, or significantly intensify, particle cleaning, de-agglomeration or disintegration. Some softer minerals, often gangue minerals with lower iron contents such as kaolinite or ochreous goethite, disintegrate several orders of magnitude faster than the valuable iron-bearing minerals such as magnetite or hematite. This facilitates selective disintegration of the gangue minerals leaving the valuable minerals mostly unchanged.A set of experiments involving ultrasonic treatment of four Australian iron ore fine samples was undertaken using three different ultrasonic experimental setups. The effect of ultrasound duration, power and pulp density on the recoveries and grades of iron, alumina and silica was studied.The results showed that for hematitic/goethitic ores, the application of ultrasound enabled soft material of relatively low iron grade to de-agglomerate from the larger size fractions and report to the ultrafine size fractions. Modelled de-sliming of the ultrasonically treated ores showed that de-sliming following ultrasonic treatment could significantly improve the product iron grade, while de-sliming with a finer cut size could also improve the iron recovery compared with de-sliming identical ore that had not been pre-treated with ultrasound. It has been shown mathematically that in some scenarios it may be possible to simultaneously increase the iron grade and iron recovery in the de-slimed product if the ore has been treated with ultrasound before de-sliming. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.