Early integration of sustainability decisions and mineralogical attributes into the design of minerals processing units offers potential for reducing environmental impacts at mining and processing sites. The objective of this study is to demonstrate how the integration of sustainability indicators and mineralogical attributes could be achieved in developing an integrated modelling framework of a magnetic separator. A magnetic separator unit model based on existing literature was developed to include process stream mineralogical data and to output sustainability indicators. The overall sustainability of processing three ore types (low, medium and high grade iron ore) was evaluated using the developed model. Novel measures for evaluating magnetic separation (Grade Recovery Deviation Index (GRDI)) and energy efficiency (Rotational Energy Transfer Efficiency (RETE)) that incorporate the use of ore characteristics were developed in this study. These measures were used to calculate the separation and energy efficiency sustainability indicator ratings. In total eleven magnetic separator sustainability indicators were identified. Each indicator was assigned a weighting value out of 10 based on its importance. Of the 11 sustainability indicators identified; safety, reliability, Carbon dioxide (CO2) emissions, water use, noise and job creation ratings did not vary with changing mineralogical attributes of the feed ore. GRDI, RETE, electricity cost, particle emissions and waste generation ratings were observed to be dependent on the ore characteristics and therefore their values varied with different feed ore grades. The Analytic Hierarchy Process (AHP) and Weighted Sum Method (WSM) methods were applied to the sustainability indicator ratings and weightings to evaluate an overall sustainability cardinal score of processing a particular ore feed. Results of this study demonstrate the dependence of overall process sustainability indicators on feed ore mineralogical attributes. The results also provide an indication of the effect of ore variability (typical within a single deposit) on sustainability indicators.
HydroCopperTM technology comprises a chloride-leaching method for copper sulfi de concentrates and copper production up to semi-products. As compared with the commonly used sulfate solutions, brine solutions offer aggressiveness and stability of the copper(I) ion and, consequently, a lower energy consumption in leaching. Copper(II) ions and oxygen are used as oxidants. Iron reports to the leaching residue as oxide and sulfur as elemental sulfur. Gold is dissolved and recovered in the third stage of the counter-current leaching when the redox potential reaches higher levels.
This work presents a literature review on ways to acquire relevant experimental data for the process model of a geometallurgical program. It identifies the needs in several unit models and proposes ideas for future developments
The Geometallurgy combines geology and mineralogy, processing techniques and metallurgy into spatially-based predictive model and is useful tool that can be used in production management of a mineral processing plant. This work presents characterisation of different textural variants of the breccia iron ore from Malmberget, Northern Sweden, an iron mine operated by Luossavaara-Kiirunavaara Aktiebolag (LKAB). Experimental work includes point load tests, compressive tests, and laboratory grinding and liberation measurements of the products. The motivation of this work is based on the need of the industry to predict the throughput, particle size distribution, modal mineralogy, mineral textures and specific energy of the material.Around Magnetite orebodies, a wide range of feldspar-rich iron ore breccia has been described and classified by Lund, Lamberg (2013a) into eight different classes according to their feldspar content. The two end-members exhibit, respectively, a high iron grade (low feldspar content) and low iron grade (high feldspar content). A definition for the micro texture (micro fabrics) used in this study has been developed by Lund, Lamberg and Lindberg (2013b): two sample are texturally different if their modally refined liberation distribution is different in a given particle size. The hypothesis of the study is that it should be possible to quantitatively describe the textures of Malmberget feldspar type with two textural archetypes: high-graded massive and low-graded disseminated. The sample used were named after the earlier classification from CF1 (low iron grade) to CF8 (high iron grade) and compared per class. After simple rock mechanics tests, the second step involved comminution with a jaw crusher with 5 mm opening followed by 20 minutes in a ball mill with 1L of water for 1.2 kg of material. The 53 to 75 µm size fraction was selected for further characterisation and liberation analysis with x-ray fluorescence (XRF) and scanning electron microscope (SEM).The point load tests and compressive tests showed good agreement. When combined with specific gravity measurements, these properties allowed grouping of the eight classes into three clusters instead of the two end-members proposed initially. The jaw crusher and ball mill sieving curves further more supported this classification.The study with a SEM using INCA Mineral software and HSC Geo provided information on modal mineralogy, particles and degree of liberation in the 53 to 75 µm size fraction. Using these results, an association index (AI), describing how gangue minerals are associated with magnetite in the ore breccia, was calculated. Preliminary linear models predicting a relative work index for ball mill, the reduction ratio for jaw crusher and the degree of liberation of magnetite-based on modal mineralogy or mechanical properties were developed.While further work is required, this study shown the relevance and practical utility of the method used and indicates a relation between mechanical strength, iron ore grade, physical properties and textural information. The main hypothesis of two end-members had to be rejected and three classes were introduced instead, due to the large variability of breccia. Additional studies could include a higher number of samples, evaluate which mechanical tests are relevant in a context of geometallurgy, find a way to directly measure the AI and describe the effect of mixing different kinds of ore breccia before comminution in terms of particles, energy consumption and liberation
Geometallurgy as a link connecting geological features of deposit with metallurgical performance of a concentrator have found broad utilization in metals mining as well as for industrial minerals and black sands mining. However, coal industry yet stays uncovered by successful applications of geometallurgical approach due to certain specifics of a commodity. Production of coal preparation plant in terms of quality and quantity can be forecasted knowing behavior of coal bearing particles in process that is controlled by petrological and mineralogical properties. Application of process mineralogical tools together with comprehensive metallurgical testwork helps to acquire essential information for a simulation of coal preparation operations. Being combined with geological, geochemical and geotechnical data available for a deposit, outcome of process simulation will form holistic geometallurgical model. Once implemented, such models will become a powerful instrument for efficient utilization of resources and proper risk management, e.g. adaptation of the process to variations in run-of-mine coal quality, "what-if" analysis of alternative production strategies, forecasting of financial results, assessment of environmental impact.
Geometallurgy combines geological and metallurgical information to create spatially-based predictive model for mineral processing plants. In the mines geometallurgy is executed through a geometallurgical program. The program goes through various stages and the most critical one is where samples are selected for the metallurgical testing. Paradoxically, the sample set shouldinclude the full variation within the ore body in terms of metallurgical response but this data is not available when samples are collected since it will be measuredonly afterwards. To overcome this problem mineralogical analyses or geometallurgical tests are recommended for every tenth ore sample.As an alternative a particle-based approach is presented. The particle-based approach uses minerals and particles to link the geological model with the process models. In the approach geological model contains quantitativeinformation on modal mineralogy and mineral textures. This information is adequate for the process models to forecast what kind of particles will be generated in the comminution and how these particles will behave in the concentration unit processes. The particlebased approach is still a concept which needs further development for example in how the textural informationis collected and used in the process model. Even it is regarded that Canada and Australia are the forerunners of geometallurgy Nordic Countries can show some tradition and recent investments in the area.
Geometallurgy combines geological and metallurgical information to create spatially-based predictive model for mineral processing plants. A review of how geometallurgy is currently applied in mining industry shows that the linkage of geological information and metallurgical response relies on small number of samples tested in laboratory. Therefore a holistic particle-based approach is proposed. The particle-based approach uses minerals and particles as a common parameters going through the geometallurgical program from the collecting of the geological data to the process simulations. The approach consists of three quantitative models: 1) geological model, 2) particle breakage model and 3) unit process models. The geological model describes quantitatively and spatially modal composition and texture of the ore. The particle breakage model that describes quantitatively what kind of particles will be produced as the rocks given by the geological model are broken. The unit process models quantify how particles behave in different unit operations. For developing and managing the models some practical techniques are described and proposed. Finally the models are combined in a simulator which is used to run the process simulation and derive process performance parameters for each ore block individually. The process performance parameters include figures like throughput; energy consumption; concentrate recovery and grade; and tailing properties. Finally a practical example from Kemi chromite mine is given.
Geometallurgical model aims to capture geological and metallurgical variation of an ore in a 3D map. This is an important part of production planning and management. In creating such a model geological and metallurgical information is combined but current practices almost solely ignore the liberation information. This paper describes a technique how this important data can be taken into a geometallurgical model. Malmberget, the case study iron ore in Northern Sweden, consists of several ore lenses with variable mineralogy illustrated e.g. in magnetite-hematite ratio. Modal mineralogy does not fully explain the metallurgical response. To include ore texture and mineral liberation factors two sub-models were created based on liberation analyses of metallurgical testing and the Particle Tracking technique. Consequently, for each ore block, the particle breakage model gives the liberation distribution. Thereafter the process model, consisting of unit operations with property based models, forecasts the metallurgical response.
Mineral resource estimates of metallic ores use traditionally elemental grades when describing the quality of the deposit. This information is very defective as it does not take into account how much of metal is recoverable. When considering the processing properties of an ore, i.e. geometallurgy, more comprehensive picture could be received if the resource model would report mineral grades rather than elemental grades. This is because minerals define the value and possible processing options of the deposit. Techniques commonly used for an analysis of mineral composition, i.e. modal analysis, are either tedious or poor in quality. This paper shows with few examples how reliable modal analysis can be done by combining diagnostic analysis methods with element to mineral conversion.
Mineralogical information forms a vital basis for designing, diagnosing and optimizing mineral processing circuits. Often modal mineralogical mass balance (i.e. mass balance on mineral grades) is adequate; i.e. liberation data is not required. In analysing mineral grades in process samples automated mineralogy (SEM based image analysis) is mostly used. As this method is tedious, slow, and costly, and has some limitation, an alternative technique was developed by combining quantitative X-ray diffraction (XRD) and chemical assays by X-ray fluorescence (XRF). A case study on magnetic separation test is presented. Method has potential for an automatized off-line technique for providing mineralogical mass balance in majority of mineral processing plants.
Operators of industrial flotation circuits experience every now and then situations where the processing performance of the plant is poorer than expected. Usually this leads to a continual and useless debate whether the problems are related to the ore properties or to the process. This paper presents a systematic approach to problem diagnosis using an analysis method based on process mineralogical tools. The diagnosis first requires a base-case analysis where the key process streams of the circuit have been sampled and studied by applying mineral process simulation in combination with the particle tracking technique, i.e. by balancing mixed particles of different mineral composition. This creates the base model of the flotation process against which the deviations are compared. Common performance problems are divided in three groups, which refer to recovery, grade and impurity. The mineralogical reason can in each case be a change in (i) mineral assemblage, (ii) head grade, (iii) liberation degree, or (iv) mineral associations. The diagnosis progresses by classifying the indications and by ruling out causes by means of process mineralogical methods. The procedure is presented as a diagnosis chart with suggestions for how to cure the problem. To illustrate the application of the method several practical examples are presented.
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.
The area of geometallurgy is in its context not entirely new and LKAB has for ages worked according to the idea “minerals to products”, but the development especially on the instrumental analysis side the last ten years has made a geometallurgy a feasible area today. LKAB’s idea is to work quite broad, with internal educations, an active working process in ongoing projects, together with external parties (such as Luleå University of Technology), with PhD-projects and an upgrading on the instrumental side at LKAB R&D. This paper is a summary about the concept geometallurgy at LKAB and some of the actions within the framework of Geometallurgy@LKAB.
Geometallurgy is a growing area within a mineral processing industry. It brings together tasks of geologists and mineral processing engineers to do short and medium term production planning. However, it is also striving to deal with long term tasks such as changes in either production flow sheet or considering different scenarios. This paper demonstrates capabilities of geometallurgy through two case studies from perspective of Minerals and Metallurgical Engineering division Lulea University of Technology. A classification system of geometallurgical usages and approaches was developed in order to describe a working framework. A practical meaning of classification system was proved in two case studies: Mikheevskoye (Russia) and Malmberget (Sweden) projects. These case studies, where geometallurgy was applied in a rather systematic way, have shown the amount of work required for moving the project within the geometallurgical framework, which corresponds to shift of the projects location within the geometallurgical classification system.
Geometallurgy is a rapidly developing holistic approach for combining geological and metallurgical information for production management purposes in mining operations. The industrial application of geometallurgy is called a geometallurgical program and one of the largest challenges within geometallurgical programs is to select appropriate methods for resource characterization. Aim of such characterization is the prediction of metallurgical performance of different ore types and geometallurgical domains with the required accuracy. More than 25 geometallurgical programs from mining operations around the world were reviewed and a classification system developed with aim to clarify how geometallurgy is used and what methods are applied. The result is summarized as a two-dimensional classification which illustrates what geometallurgical approaches are used and how collected data is applied. In addition the proposed classification system gives a perspective of what are the minimum requirements for a geometallurgical program at different levels of application and who are the main participants that should be engaged in a geometallurgical program. The classification system can also be used as a reference system for benchmarking of different geometallurgical endeavours.
Geometallurgy is a rapidly developing holistic approach for combining geological and metallurgical information for production management purposes in mining operations. The industrial application of geometallurgy is called a geometallurgical program and one of the largest challenges within geometallurgical programs is to select appropriate methods for resourcecharacterization. Aim of such characterization is the prediction of metallurgical performance of different ore types and geometallurgical domains with the required accuracy.More than 25 geometallurgical programs from mining operations around the world were reviewed and a classification system developed with aim to clarify how geometallurgy is used and what methods are applied. The result is summarized as a two-dimensional classification which illustrates what geometallurgical approaches are used and how collected data is applied.In addition the proposed classification system gives a perspective of what are the minimum requirements for a geometallurgical program at different levels ofapplication and who are the main participants that should be engaged in a geometallurgical program. The classification system can also be used as a reference system for benchmarking of different geometallurgicalendeavours.
The main purpose of geometallurgy is to develop a model to predict the variability in the mineralprocessing performance within the ore body. Geometallurgical tests used for developing such a model need to be fast, practical and inexpensive and include as an input data relevant and measureable geological parameters like elemental grades, mineral grades and grain size. Important in each geometallurgical program is to define the number of samples needed to be sent for geometallurgical testing to enable reliable metallurgical forecast. This is, however, a complicated question that does not have a generic answer.
To study the question on sampling a simulation environment was built including a synthetic orebody and sampling & assaying module. A synthetic Kiruna type iron oxide - apatite deposit was established based on case studies of Malmberget ore. The synthetic ore body includes alike variability in rock types, modal mineralogy, chemical composition, density and mineral textures as its real life counterpart. The synthetic ore body was virtually sampled with different sampling densities for a Davis tube testing, a geometallurgical test characterising response in magnetic separation. Based on the test results a forecast for the processing of the whole ore body was created. The forecasted parameters included concentrate tonnages, iron recovery and concentrate quality in terms of iron, phosphorous and silica contents.
The study shows that the number of samples required for forecasting different geometallurgicalparameters varies. Reliable estimates on iron recovery and concentrate mass pull can be made with about 5-10 representative samples by geometallurgical ore type. However, when the concentrate quality in terms of impurities needs to be forecasted, the sample number is more than 20 times higher. This is due to variation in mineral liberation and shows the importance of developing techniques to collect qualitative information on mineral and ore textures in geometallurgy.
Reconciliation of geological, mining and mineral processing information is a costly and time demanding procedure with high uncertainty due to incomplete information, especially during the early stages of a project, i.e., pre-feasibility, feasibility studies. Lack of information at those project stages can be overcome by applying synthetic data for investigating different scenarios. Generation of the synthetic data requires some minimum sparse knowledge already available from other parts of the mining value chain, i.e., geology, mining, mineral processing. This paper describes how to establish and construct a synthetic testing environment, or “synthetic ore body model” by integrating a synthetic deposit, mine production, constrained by a mine plan, and a simulated beneficiation process. The approach uses quantitative mineralogical data and liberation information for process simulation. The results of geological and process data integration are compared with the real case data of an apatite iron ore. The discussed approach allows for studying the implications in downstream processes caused by changes in upstream parts of the mining value chain. It also opens the possibility of optimising sampling campaigns by investigating different synthetic drilling scenarios including changes to the spacing between synthetic drill holes, composite length, drill hole orientation and assayed parameters.
Higher environmental and socio-economic demands in the exploitation of the future mineral resources require comprehensive knowledge on ore bodies even in the early stages of the mining process. Geometallurgy combines geological and mineral processing information to create a spatial model for production planning and management. Applying a geometallurgical concept improves resource efficiency, reduces operational risks and helps in optimising production in such a way that sustainability and socio-economic factors also are considered. With a geometallurgical model it is possible to study different production scenario starting from exploration to the feasibility and production stages. There are some alternative ways for building a geometallurgical model but the mineralogical approach is generic and can be adopted to any kind of mineral resources. This paper describes how a concept like this has been used in the mining industry and demonstrates the benefits in terms of improved resource efficiency in different ore deposits.
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).
This is the first step in establishing a geometallurgical program for the Malmberget iron ore deposit, northern Sweden. Geometallurgy captures geological and metallurgical (processing) information into a spatially-based predictive model of mineral processing characteristics. This paper describes the development of a practical, fast and inexpensive technique to quantify minerals from routine chemical assays. Ore samples and process samples from two different orebodies were used in the process of developing this element to mineral conversion technique that involved electron microprobe (EPMA), X-ray fluorescence (XRF) and SATMAGAN analyses. The method was validated against QEMSCAN analyses. From the calculated modal mineralogy an ore classification system was established based on the iron mineralogy, iron mineral grades and gangue mineralogy to create a preliminary geological/geometallurgical model of the ore. However, in a geometallurgical context the modal composition is not sufficient and the geological model requires information on mineral textures, too.
The antimony (Sb) content of the Rockliden complex Zn–Cu massive sulphide ore lowers the quality of the Cu–Pb concentrate. The purpose of this study is to characterise the Sb mineralogy of the deposit. The Sb-bearing minerals include tetrahedrite (Cu,Fe,Ag,Zn)12Sb4S13, bournonite PbCuSbS3, gudmundite FeSbS and other sulphosalts. On a microscopic scale these minerals are complexly intergrown with base-metal sulphides in the ore. Based on these observations mineralogical controls on the distribution of Sb-bearing minerals in a standard flotation test are illustrated. Deposit-scale and rock-related variation in the Sb-content and distribution of Sb-bearing minerals were found. This underlines the importance in understanding the geological background as a basis of a 3D geometallurgical model for Rockliden. Such a model is expected to predict the Sb content of the Cu–Pb concentrate, among other process-relevant factors, and helps to forecast when the Cu–Pb concentrate has to be treated by alternative processes, such as alkaline sulphide leaching, before it is sold to the smelter.
The Rockliden massive sulphide Zn–Cu deposit contains minor amounts of Sb minerals. The Sb mineralogy is complex in terms of composition, micro textures and mineral associations. The main Sb minerals comprise tetrahedrite, bournonite, gudmundite and Sb–Pb sulphides such as meneghinite. The presence of these minerals is especially critical to the quality of the Cu–Pb concentrate. To study how they are distributed in a simplified flotation circuit and what controls their process behaviour Sb-rich drill core samples were selected from the Rockliden deposit and a standard laboratory flotation test was run on the composite samples. Scanning electron microscope-based automated mineralogy was used to measure the Sb mineralogy of the test products, and the particle tracking technique was applied to mass balance the different liberation classes to finally trace the distribution of liberated and locked Sb minerals. The mineralogical factors controlling the distribution of Sb minerals are mineral grain size, the degree of liberation, and associated minerals. Similarities in the distribution of specific particle types from the tested composites point towards systematics in the behaviour of particles and predictability of their distribution which is suggested to be used in a geometallurgical model of the deposit.
The polymetallic Cu–Zn ore of the Rockliden massive sulphide deposit in the Skellefte District in north-central Sweden contains a number of deleterious elements in relevant concentrations. Of particular concern is the amount of antimony (Sb) reporting to the Cu–Pb concentrate. The aim of this study was to compare different model options to simulate the distribution of Sb minerals in a laboratory flotation test based on different degrees of details in the mineralogical information of the flotation feed. Experimental data obtained from four composites were used for the modelling and simulation. The following different simulation levels were run (sorted from least to highest level of detail of their mineralogical information): chemical assays, unsized bulk mineralogy, sized bulk mineralogy and particle information. It was shown that recoveries simulated based on bulk mineralogy are mostly within the error margin acceptable in the exploration stage of the Rockliden deposit. Unexpected high deviation in the simulation using particle information from the original recovery has been partly attributed to the fact that recovery of non-liberated particles cannot be modelled appropriately in the present version of the modelling and simulation software. It is expected that the implementation of full particle information in simulation will improve the Sb distribution model for the mineralogically complex Rockliden deposit.
The Rockliden Zn-Cu massive sulphide mineralisation shows elevated concentrations of critical elements. In particularly the presence of Sb in the Cu–Pb concentrate causes metallurgical challenges in the treatment of this flotation product. The Sb mineralogy at Rockliden is complex, comprising of four main Sb minerals. For this study one mafic dyke and three Sb-rich massive sulphide samples with different base-metal and Sb mineralogy were collected and subjected to a simplified flotation test. The Sb mineralogy of the flotation products was analysed using scanning electron microscope-based image analysis. The distribution of liberated and locked Sb minerals between the flotation products was studied using a particle tracking technique. A comparison of results from the different mineralisation types indicates systematic behaviour of specific particle types, pointing towards the predictability of distribution of the Sb minerals during base-metal flotation.
The Rockliden Zn–Cu volcanic-hosted massive sulphide deposit is located approximately 150 km south of the Skellefte ore district, north-central Sweden. Most of the mineralisation is found at the altered stratigraphic top of the felsic volcanic rocks, which are intercalated in the metamorphosed siliciclastic sedimentary rocks of the Bothnian Basin. Mafic dykes cross-cut all lithological units, including the massive sulphides, at the Rockliden deposit. The relatively high Sb grade of some parts of the mineralisation results in challenges in handling of the Cu–Pb concentrate in the smelting process. The aim of this study is to characterise different host rock units and ore types by their main mineralogy, as well as by their trace mineralogy with focus on the Sb-bearing minerals. Ore types are distinguished largely on the basis of their main base-metal bearing sulphide minerals, which are chalcopyrite and sphalerite. Several Sb-bearing minerals are documented and differences in the trace mineralogy between rock and ore types are highlighted. Based on the qualitative ore characterisation, rock- and ore-intrinsic parameters, such as the pyrite, pyrrhotite and magnetite content of the massive sulphides, the trace mineralogy and its association with base-metal sulphide minerals, are outlined and discussed in terms of relevance to the ore processing.
The Rockliden Zn-Cu massive sulphide mineralisation is located at the stratigraphic top of altered rhyolitic-dacitic volcanic rocks, which in turn are intercalated by meta-sedimentary rocks of the Bothnian Basin, north-central Sweden. After the discovery, in the 1980’s, the project was put on hold due to metallurgical and geometallurgical challenges. Exploration drilling restarted in 2007 and resources have increased since then. However, little is known about the mineralogical variability of the ore and how that will affect the processing of the mineralised material. Examples of rock-intrinsic process-relevant parameters are the mineral grain size, the texture of the minerals and the mineral associations, i.e. the mode of occurrence of minerals in the different types of mineralisation, and also the presence and distribution of penalty and bonus elements. Rock-intrinsic parameters and their spatial variability are considered in this study and will form the basis of a 3D-geometallurgical model for the Rockliden mineralisation.
Comminution tests aim to measure the comminution properties of ore samples to be used in designing and sizing the grinding circuit and to study the variation within an ore body. In the geometallurgy context this information is essential for creating a proper resource model for production planning and management and process control of the resource’s exploitation before and during production.Standard grindability tests require at least 10 kg of ore sample, which is quite a lot at early project stages. This paper deals with the development of a method for mapping the variability of comminution properties with very small sample amounts. The method uses a lab-scale jaw crusher, standard laboratory sieves and a small laboratory tumbling mill equipped with a gross energy measurement device. The method was evaluated against rock mechanics tests and standard Bond grindability test. Within this approach textural information from drill cores is used as a sample classification criterion.Experimental results show that a sample of approximate 220 g already provides relevant information about the grindability behavior of iron ores at 19% mill fillings and 91% fraction of the critical mill speed. The gross energy measured is then used to calculate an equivalent grinding energy. This equivalent energy is further used for predicting the variations in throughput for a given deposit and process.Liberation properties of the ore connected to grindability elaborates energy required for grinding and significances of it when deciding to move to higher grinding energy considering the improvement of liberation of the desired mineral. However, high energy significantly enhanced the degree of liberation of magnetite and is expected to improve the concentrate grade after downstream treatment. The higher the magnetite content the better is the liberability of magnetite and the lower the energy required to liberate the desired mineral. Liberability of magnetite is also affected by texture classes containing low magnetite content. A methodology that combines this information has been developed as a practical framework of geometallurgical modeling and simulation in order to manage technical and economic exploitation of resource at early, project stages and during mining operations.
Crushability and grindability are traditionally used to describe the material properties in comminution. These parameters neglect the main objective of ore comminution, the mineral liberation and therefore information is incomplete. A new concept called liberability: the easiness of mineral liberation in comminution is introduced to fill the gap. Establishing a liberability map of a deposit requires grindability tests and liberation measurements for the grinding product. The liberability curve shows the degree of liberation of the mineral against grinding energy and offers better baseline for resource optimization than the grindability curve. A case study with a magnetite iron ore from Malmberget, northern Sweden, shows that certain shortcuts can be applied to keep the experimental effort reasonable which is important, particularly when applying the liberability in a geometallurgical program. In Malmberget the liberability is depending on the grade and grain size of magnetite. A significant difference between grindability and liberability can be observed.
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.
Traditionally comminution models are used to predict size reduction and the energy required for that. For better control of energy and quality of concentrate grade in subsequent concentration processes also the liberation of the minerals has to be considered. Mechanical forces are used to break the bonds of the mineral matrix of composite particle into mineral phases.Mineral composition and texture (i.e. the grain size) have been used to predict the breakage properties of an ore but are not fully used to predict the liberation of mineral particles during comminution. For this purpose an integrated breakage model is needed that links the energy used to reduce the size of the mineral particles with the mineral liberation achieved during comminution. Such a liberation model for forecasting the degree of mineral liberation has to be based on mineral texture information.As a novel approach, a liberability curve for various mineral textures has been developed and used to optimize the particle size and energy required for ore comminution. Tests with iron ore samples from the Malmberget mine in Northern Sweden show that, liberation and breakage of the mineral particles are controlled by the grade distribution and grain size of magnetite.
Comminution tests are necessary in order to investigate mineral liberation achieved during particle size reduction and the mechanical energy needed for that. Depending on the type of the comminution process and equipment respectively, various test methods have been established in the past. Most of these tests require comparatively large sample amounts and are also costly in terms of time.Within the geometallurgical approach comprehensive information about the ore’s processing properties is required for building a spatial geometallurgical model of a deposit. Comminution behavior has to be quantified when modeling mill capacity and liberation. Providing comminution test methods that are efficient in terms of sample utilization and effort is therefore an essential task.This paper summarizes several different approaches to quantifying ore comminution behavior in the geometallurgical context. The methods examined at Luleå University of Technology comprise linking crushing and grinding characteristics to mineralogical parameters as well as correlating them with geotechnical tests, the down-scaling of standard grindability tests, and the further development of drop weight test methods.
Based on the requirements and available sample amounts in geometallurgical studies of ore variability, a small scale batch grindability test has been developed, the Geometallurgical Comminution Test (GCT). The test requires 220 g of sample material and can be conducted within 2.5–3 h. Test results are evaluated using a modified Bond equation together with a linear correlation factor. The test and evaluation method have been validated against several ore types.
Comminution tests are an important element in the proper design of orebeneficiation plants. In the past, test work has been conducted for particular representative reference samples. Within geometallurgy the entire ore body is explored in order to further identify the variation within the resource and to establish spatial geometallurgical domains that show the differential response to mineral processing. Setting up a geometallurgical program for an ore deposit requires extensive test work. Methods for testing the comminution behavior must therefore be more efficient in terms of time and cost but also with respect to sample requirements. The integration of the test method into the geometallurgical modeling framework is also important. This paper provides an overview of standard comminution test methods used for the investigation of ore comminution behavior and evaluates their applicability and potential in the geometallurgical context.
Comminution tests aim at measuring the grindability of ore samples to be used in describing the variability within an orebody and designing the grinding circuit. Within the geometallurgical context this information is very important for creating a proper model for production planning,management and control of the resource’s exploitation before and during the production.Standard grindability tests require at least 2 kg of ore sample, which is quite a lot at early project stages and therefore often problematic. This presentation will deal with the development of a method for studying comminution behaviour of materials with very small sample amounts.The method uses a small laboratory tumbling mill equipped with a gross energy measurement device. The method evaluation comprises the correlation between rock mechanical testing and grindability test methods. Within this approach textural information from drill cores is used as a classification criterium.Experimental results show that a sample of approximately 350 g already provides relevant information about the grindability behaviour of iron ores in the range of 45 to 55 per cent solids, based on mill charge. The gross energy measured is then used to calculate an equivalent grinding energy. This equivalent energy is further used for predicting the variations in throughput for a given deposit and process.
Mineralogical information forms an essential basis in geometallurgy. Minimum information required in a mineralogical approach of a geometallurgical program is: modal mineralogy (mineral quantities) and mineral textures. Based on this information it is possible to link geological model with production model. Modal analysis is currently mostly done with Scanning Electron Microscopy (SEM) based image analysis, often called as automated mineralogy. As this method is tedious, slow, and costly, and has some limitation, an alternative technique was developed by combining quantitative X-ray diffraction (XRD) and chemical assays by X-ray fluorescence (XRF). In iron ores in Northern Sweden combined method gives a quantity of about ten minerals with adequate accuracy.
Knowledge of the grade of valuable elements and its variation is not sufficient for geometallurgy. Minerals define not only the value of the deposit, but also the method of extraction and concentration. However, mineralogy is quite rarely used as the key information in geometallurgy and it is even more exceptional in mineral resource estimation.One of the reasons is the lack of fast, low-cost but still reliable modal analysis. The other is that the results from various methods of modal mineralogy such as automated mineralogy and quantitative XRD are not consistent with chemical assay. In other words, the chemical composition back calculated from modal analysis does not match with the true chemical assay. Element-to-mineral conversion is the known method to get modal mineralogy that matches with the chemical composition of samples. However, in complicated mineralogy or the lack of enough chemical components assayed, it fails to provide accurate results. Reconciling the results of a modal analysis with chemical assays can improve the agreement between chemical assays and back-calculated chemical composition. This is achievable by doing minor adjustments to modal mineralogy. The method used here is called combined method and it principally uses Levenberg-Marquardt algorithm to minimize differences (residuals) between chemical assays and back-calculated chemical composition of a sample. The advantage of the method over other combined methods is that it does not use weighting factors. Additionally, the adjustments are minor unlike other methods that can cause mineral grades to drift away significantly. These features make it possible to apply the method for a large number of samples unsupervised.
Knowledge of the grade of valuable elements and its variation is not sufficient for geometallurgy. Minerals define not only the value of the deposit, but also the method of extraction and concentration. A number of methods for obtaining mineral grades were evaluated with a focus on geometallurgical applicability, precision and trueness. For a geometallurgical program, the number of samples to be analyzed is large, therefore a method for obtaining mineral grades needs to be cost-efficient, relatively fast, and reliable. Automated mineralogy based on scanning electron microscopy is generally regarded as the most reliable method for analyzing mineral grades. However, the method is time demanding and expensive. Quantitative X-ray diffraction has a relatively high detection limit, 0.5%, while the method is not suitable for some base and precious metal ores, it still provides significant details on gangue mineral grades. The application of the element-to-mineral conversion has been limited to the simple mineralogy because the number of elements analyzed limits the number of calculable mineral grades. This study investigates a new method for the estimation of mineral grades applicable for geometallurgy by combining both the element-to-mineral conversion method and quantitative X-ray diffraction with Rietveld refinement. The proposed method not only delivers the required turnover for geometallurgy, but also overcomes the shortcomings if quantitative X-ray diffraction or element-to-mineral is used alone
Process models in mineral processing can be classified based on the level of information required from the ore, i.e. the feed stream to the processing plant. Mineral processing models usually require information on total solid flow rate, mineralogical composition and particle size information. The most comprehensive level of mineral processing models is the particle-based one (liberation level), which gives particle-by-particle information on their mineralogical composition, size, density, shape i.e. all necessary information on the processed material for simulating unit operations. In flowsheet simulation, the major benefit of a particle-based model over other models is that it can be directly linked to any other particle-based unit models in the process simulation. This study aims to develop a unit operation model for a wet low intensity magnetic separator on particle property level. The experimental data was gathered in a plant survey of the KA3 iron ore concentrator of Luossavaara-Kiirunavaara AB in Kiruna. Corresponding feed, concentrate and tailings streams of the primary magnetic separator were sampled, assayed and mass balanced on mineral liberation level. The mass-balanced data showed that the behavior of individual particles in the magnetic separation is depending on their size and composition. The developed model involves a size and composition dependent entrapment parameter and a separation function that depends on the magnetic volume of the particle and the nature of gangue mineral. The model is capable of forecasting the behavior of particles in magnetic separation with the necessary accuracy. This study highlights the benefits that particle-based models in simulation offer whereas lower level process models fail to provide.