Iron ore textural information is the key for prediction of downstream process performance (original) (raw)
Related papers
2007
Optical image analysis is a very convenient tool for obtaining comprehensive information about fine iron ore size fractions. Data can be obtained on mineral abundances, porosity, particle shape and ore textures with a high level of accuracy. A range of techniques has been used to characterise iron ore samples on a particle-by-particle basis. Automatic textural classification of iron ore particles was used to establish classes containing particles with very similar mineral composition and texture. Image analysis coupled with probe analysis and mineral density measurements provided information about the chemical composition and density of each particle class. The combination of these results enabled a ''virtual feed'' to be created, which can be a key input into a beneficiation unit model for predicting its performance. Identification and classification of the textural type of each particle was performed according to the CSIRO-Hamersley Iron Ore Group Classification Scheme. If more detailed classification is needed, further classification can be performed based on dimensional, chemical or mineral criteria, such as the presence of certain minerals in particles or total iron content. Some deficiencies of the current image analysis procedures and their further improvement and automation are also discussed.
Automated Optical Image Analysis of Iron Ore Sinter
2021
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 p...
Textural Descriptors for Multiphasic Ore Particles
Image Analysis & Stereology, 2012
Monitoring of mineral processing circuits by means of particle liberation analysis through quantitative image analysis has become a routine technique within the last decades. Usually, liberation indices are computed as weight proportions, which is not informative enough when complex texture ores are treated by flotation. In these cases, liberation has to be computed as phase surface exposed to reactants, and textural relationships between minerals have to be characterized to determine the possibility of increasing exposure. In this paper, some indices to achieve a complete texture characterization have been developed in terms of 2D phase contact and mineral surfaces exposure. Indices suggested by other authors are also compared. The response of this set of parameters against textural changes has been explored on simple synthetic textures ranging from single to multiple inclusions and single to multiple veins and their ability to discriminate between different textural features is analyzed over real mineral particles with known internal structure.
Advances in Optical Image Analysis Textural Segmentation in Ironmaking
Applied Sciences
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 algo...
Development of a geometallurgical framework to quantify mineral textures for process prediction
Minerals Engineering, 2015
A geometallurgical framework was developed in three steps using the Malmberget iron ore deposit, northern Sweden, as a case study. It is based on a mineralogical-particle approach which means that the mineralogical information is the main focus. Firstly, the geological model describes quantitatively the variation in modal composition and mineral textures within the ore body. Traditional geological textural descriptions are qualitative and therefore a quantitative method that distinguishes different mineral textures that can be categorised into textural archetypes was developed. The second step of the geometallurgical framework is a particle breakage model which forecasts how ore will break in comminution and which kind of particles will be generated. A simple algorithm was developed to estimate the liberation distribution for the progenies of each textural archetype. The model enables numerical prediction of the liberation spectrum as modal mineralogy varies. The third step includes a process model describing quantitatively how particles with varying particle size and composition behave in each unit process stage. As a whole the geometallurgical framework considers the geological model in terms of modal composition and textural type. The particle breakage model forecasts the liberation distribution of the corresponding feed to the concentration process and the process model returns the metallurgical response in terms of product quality (grade) and efficacy (recovery).
Comparison of the Mineralogy of Iron Ore Sinters Using a Range of Techniques
Minerals
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 te...
Automatic characterization of iron ore by digital microscopy and image analysis
Journal of Materials Research and Technology, 2018
This paper presents an automatic system for mineralogical and textural characterization of iron ores based on digital microscopy and image analysis. It employs a motorized and computer-controlled reflected light microscope in a correlative approach that combines bright field and circular polarization modes. Mosaic images covering large areas of polished sections are acquired to image thousands of particles. Different classifiers discriminate compact and non-compact hematite, polycrystalline and monocrystalline particles, and identify particles as granular, lamellar, and lobular. The entire process is automatic and produces a full pdf report containing typical images and the quantification of mineral and textural phases.
Geometallurgy, 2018
Increasing competition in the minerais industry and fluctuating coramodity prices require new ways of saving energy, lime, and general operational costs. A good understanding of physical processing or pre-processing streams that can potentially cut these costs requires detailed analyses of chemical and physical behaviours and processing responses during rainera]. processing. It is very useful to perform a detailed mineralogical and micro-textural characterization of materials (ore, tailings, and waste) that addresses, among other parameters, particle and grain sizes, as well as particle densifies. The choice and/or corabination of the 'best' processing approaches is crucial for processing efficiencies, and can be established and verified by using automated mineralogy with the associated software. A sample of low-grade iron ore from El Volcan, Mexico, serves as an example to demonstrate in a step-by-step approach how QEMSCAN® analyses provide processing information. Elements under consideration include iron, phosphorus, and sulphur.
Image processing applications for customized mining and ore classification
2011
During the mining operation, ore sorting and directing different grade ores to different processing circuits is a manual task in most of working mines, but this work puts a step forward toward automation of this process. The radical development in the area of image and data processing allows speedy processing of the full color digital images for the preferred investigations. In this paper, an approach has been proposed to classify the ores for blast furnace feed, based on the visual texture of the ore particles. The visual texture of ore particles vary with the mineral contents, for example, blue dust, hard ore, soft ore, etc. This information can be quantified by using image processing technique in red, green, and blue color space and first-and second-order statistical analysis. Commonly used Hartlics textural features were calculated along with red, green, and blue color values for 5×5-pixel size windowpanes extracted from five separate images. Results obtained show encouraging accuracy to apply the approach to develop an expert system for online ore quality monitoring to control the ore blending in the feed ore circuits as well as separating gangue minerals present in the feed ores. Matlab 6.5 was used for visual textural analysis and classification.