Advances in Optical Image Analysis Textural Segmentation in Ironmaking (original) (raw)
Related papers
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...
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.
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.
Iron ore textural information is the key for prediction of downstream process performance
2016
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.
Minerals Engineering, 2015
The present study focused on overcoming the primary problem faced by any quantitative mineralogical study involving iron ore characterisation using a reflected light optical microscope; distinguishing the quartz mineral from the epoxy resin in digital images taken from mounted polished sections. Difficulties arise in this case because both phases reflect in the same colour intensity range. To overcome this problem, a digital image analysis system denominated Opt-Lib was developed. In order to evaluate the system responsivity, a characterisation study and modal and liberation analyses were performed using typical Brazilian iron ore containing quartz and the main iron oxide/hydroxide minerals: magnetite, hematite, and goethite. For the system performance evaluation, these results were compared with those generated by the scanning electron microscopy (SEM)-based Mineral Liberation Analyzer for the same sample. The results show that the main advantage of the Opt-Lib system over the SEM-based system is that it facilitates differentiation, classification, and quantification of not only the quartz mineral, but also the iron oxide/hydroxide minerals within the sample, thus providing a more precise qualitative response.
The application of image analysis techniques to mineral processing
Pattern Recognition Letters, 1983
With the increasing demand for minerals, the development of efficient techniques for mineral recovery is important. The application of image analysis techniques to mineral beneficiation studies is described in this paper. We carry out ore identification not pixel-by-pixel but rather by considering the average reflectance of grains. This is accomplished by first carrying out segmentation, a process in which a facet model based edge operator is used to delineate the boundaries of grains.
Minerals
Natural or artificial light allows us to see and analyze matter with our eyes, which are the first tools used in several experiments. In geosciences, particularly in mineralogy, light is used for optical microscopy observations. Reflected and transmitted light applied to the study of ore deposits can be useful to discriminate between gangue from precious phases. Knowledge of the structural and morphological characteristics, combined with the quantitative evaluation of mineral abundance, is fundamental for determining the grade of ore deposits. The accuracy and reliability of the information are closely linked to the ability of the mineralogist, who more and more often uses Scanning Electron technology and automated mineralogy systems to validate the observations or solve complex mineralogy. While highly accurate, these methods are often prohibitively expensive. The use of image analysis using standard algorithms and artificial intelligence, available as open source, and commercial 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.
Traditional identification of ore minerals with reflected light microscopy relies heavily on the experience of the observer. Qualified observers have become a rarity, as ore microscopy is often neglected in today's university training, but since it furnishes necessary and inexpensive information, innovative alternatives are needed, especially for quantification. Many of the diagnostic optical properties of ores defy quantification, but recent developments in electronics and optics allow new insights into the reflectance and colour properties of ores. Preliminary results for the development of an expert system aimed at the automatic identification of ores based on their reflectance properties are presented. The discriminatory capacity of the system is enhanced by near IR reflectance measures, while UV filters tested to date are unreliable. Interaction with image analysis software through a wholly automated microscope, to furnish quantitative and morphological information for geometallurgy, relies on automated identification of the ores based on the measured spectra. This methodology increases enormously the performance of the microscopist, nevertheless supervision by an expert is always needed.