Textural Variants of Iron Ore from Malmberget Characterisation, Comminution and Mineral Liberation (original) (raw)
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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.
Building a Geometallurgical Model in Iron Ores using a Mineralogical Approach with Liberation Data
A geometallurgical model is currently built in two different ways. The first and the most common way relies on geometallurgical testing, where a large number of samples are analysed for metallurgical response using small-scale laboratory tests, eg Davis tube testing. The second, mineralogical approach focuses on collecting mineralogical information over the orebody and building the metallurgical model based on mineralogy. At Luleå University of Technology, Sweden, the latter method has been adopted and taken further in four ongoing PhD studies. The geological model gives modal composition by the help of element-to-mineral conversion and Rietveld X-ray diffraction. Texturally, the orebody is divided into different archetypes, and liberation measurements for each of them are carried out in processing fineness using IncaMineral, a SEM-based technique. The grindability and liberation spectrum of any given geological unit (sample, ore block, domain) are extrapolated from the archetypes. The process model is taken into a liberation level by mass balancing selected metallurgical tests using the particle tracking technique. The approach is general and can be applied to any type of ores. Examples of ongoing studies on iron and massive sulfide ores are given.
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.
Predictive geometrization of grade indices of an iron-ore deposit
Mining of Mineral Deposits
Purposeis development of the methods to predict indices of iron-ore deposits relying upon the improvement of available techniques as well as formulation of new geometrization procedures and identification of the most adequate decision-making way to assess geological data as the basis for geometrization and prediction. Methods are to develop a self-organizing prediction algorithm based upon combination of the available techniques and formulation of new mathematical methods; consider various means to assess them in the context of iron-ore deposit; and select the most efficient one. Use of geostatistical methods makes it possible to evaluate and process output geological information. The methods help assess mineral reserves of a mining enterprise. Findings. Dependencies of magnetite ore content upon geological factors have been derived in the context of an open pit of PIVDGZK JSC. The deposit has been geometrized; predictive mining and geometric model of the deposit site has been deve-...
CHAPTER-2 Mineralogical Characterization of Iron Ores
2012
Mineralogical characterization of iron ore is a very important and basic aspect that has to get due attention before any attempt for its processing and has become almost inevitable these days because of the increasing demand of the ore. Mineral processing technology is evolved to separate and recover ore minerals from gangue in a commercially viable method and is mainly based on the process of mineral liberation and the process of mineral separation. Therefore, it is important to first get a clear understanding about oreand gangue minerals. A mineral is a natural inorganic substance having definite chemical composition and atomic structure. If the internal atomic arrangement is lacking, then it is an amorphous substance. A rock is generally composed of various minerals and if the rock contains valuable minerals frOm which metals can be extracted at a profit, it is called an 'ore'. The unwanted mineral in an ore is called gangue (i.e., generally rock forming minerals). For ex...
GEOMETALLURGICAL APPROACH FOR QUALITY CONTROL OF IRON ORES FOR AGGLOMERATION AND REDUCTION PROCESSES
The usual characterization of the intrinsic quality of iron ore fines and lumps in the metallurgical industries has been based only on chemical analysis and granulometry. A lot of typological categories of iron ores and different ore blends can be used in the industrial processes of agglomeration and reduction. The ore quality variability is continuously changing. Besides, little consideration is being today to microstructural, mineralogical and " granulochemical " characteristics. These aspects, as well as the technological tests in bench and pilot scale, are also important. Regarding geometallurgical input for agglomeration and reduction processes there is in general a lack of understanding between mining companies and metallurgical industries. Then, in the last years geometallurgical approach is gaining interest at the universities, research centers and these industries. Geometallurgy is a holistic, multidisciplinary and powerful approach to decrease variability of industrial results, to implement technological solutions and improvements, to promote innovation and add value in use in all operational phases of the steel production, from the primary ore until the end product focusing on mine, mineral processing and metallurgy. In this contribution, a geometallurgical approach for quality control of iron ores for sintering, pelletizing and reduction processes is highlighted.
2014
A preliminary characterisation of the iron ore assets of Atlantica Geologia e Mineração SA (AGEMISA) Campo Grande Project was performed on a sample composed from drill cores, to evaluate possible products and processing requirements. Magnetite largely predominates over hematite, and major gange minerals are quartz, amphibole and feldspar. Liberation spectra starting at 1⁄4” indicate there is no liberation above 1 mm, therefore no lump ore can be expected. For the sinter feed fraction (850x150 μm) an overall magnetite+hematite recovery close to 90% can be attained for a similar grade of the minerals, corresponding to 61% of Fe. The liberation is excellent for the pellet feed size range, granting recoveries better than 99% for 96% iron oxides, or a 66% Fe grade. The results are comparable to heavy liquid concentrates.