Ore texture breakage characterization and fragmentation into multiphase particles (original) (raw)
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Comminution modeling using mineralogical properties of iron ores
Minerals Engineering, 2017
Comminution modeling aims to predict the size and liberation distribution of mineral particles and the required comminution energy. The current state-of-the-art comminution models provide a calculation of neither particle size distribution, grinding energy and throughput dependency with neither a broad understanding of how the mineral grade varies by size nor the liberation distribution of the product. The underlying breakage mechanisms affect the liberation of mineral grains and are dependent on modal mineralogy and mineral texture (micro structure). It has also been a challenge to model comminution systems to predict the optimal energy and size for better mineral liberation because of the variability of the mineral particle properties i.e. grains arrangement and composition. A detailed mineralogical study was carried out in order to broaden the understanding of the nature and distribution of comminuted particles in a ball mill product. Focusing on iron ore samples the study showed how the particle breakage rate decreases when the particles reach the grain size of the main mineral component. Below that size, comminution does not increase mineral liberation and therefore in most of the cases passing over that boundary is only a waste of energy. The study involving iron ores from Malmberget and Kiruna, Northern Sweden, showed that certain shortcuts can be applied to empirically model the mineral liberation distribution of the particles in a ball mill based on the mineral grade-by-size pattern from a geometallurgical program. In Malmberget and Kiruna the mineral grade-by-size pattern is depending on the mineral distribution and grain size of gangue as well as magnetite or hematite minerals. A significant difference between mineral breakage of the same grade and gangue minerals can be observed due to texture differences.
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).
E3S Web of Conferences, 2020
The complexity of deep processing of fine-grained and refractory mineral raw materials is determined by the difficulty of disclosing aggregates of ore components during disintegration and extracting them into commercial products of standard quality. The main task of the disintegration of such ores is to destroy the object along the phase boundaries without overgrinding while minimizing energy costs. To implement selective disintegration, a precise study of the properties of the mineral components of the ore is necessary. However, there are no systematic data on the effect and relationship of the mineralogical-technological, structural-textural and physical-technical properties of minerals, rocks and ores with the processes of selective disintegration. The article presents the results of computer microtomographic and optical-microscopic studies of the structural and textural characteristics of typical sulfide copper-nickel ores using a SkyScan-1173 microtomograph from Bruker (Belgium...
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.
Minerals
The study evaluated the milling kinetics of three copper ores, from a multi-mineralised deposit, which were identified as sulphide 1 (with bornite as a dominant copper mineral), sulphide 2 (mainly composed of chalcopyrite) and oxide (with malachite as a dominant copper mineral) and related the breakage parameters to the mineral composition data. Five mono-size fractions between 1000 µm and 212 µm were dry milled for short grinding times in the laboratory ball mill in order to obtain data for predicting breakage rate parameters. The analytical and mineralogical characterisation of the ores were performed using X-ray fluorescence (XRF) analysis, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) analysis, optical microscopy analysis and X-ray diffractometer (XRD). The mineralogy data showed that quartz was the abundant gangue mineral (average for each ore was above 60% (w/w)), followed by K-feldspar minerals (orthoclase and microcline) which constituted between 4% (...
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.
The study validates how the original grain size of the ore influences the propensity of a mineral to be liberated. An Australian zinc ore was investigated for the variation in composition with respect to grain size using QEMSCAN. The breakage patterns of the ore showed that the liberated phases were influenced by the association of various phases in the original ore affecting the size distribution of different species. The combined breakage and the QEMSCAN data were used to predict the effect of grinding which affects the grain size and hence the distribution of grain phases in different size fractions, and relates the latter to the proportions of binary and ternary composite grains in the original ore sample. The study provides a direct correlation between the grain size distribution resulting from grinding to the liberated free minerals, binary and ternary composites. The liberation model parameters determined in the present study are used to predict the liberation of different mineral compositions directly from the size distribution analysis as well as from the t 10 parameter. The study has implications for optimizing grinding practice for improved beneficiation, and liberation as over-grinding produces undesirable fines difficult to process as well as incur additional energy input.
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.
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.
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.