Добірка наукової літератури з теми "Geometallurgey"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Geometallurgey".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Geometallurgey"
1
Bowell, R. J., J. Grogan, M. Hutton-Ashkenny, C. Brough, K. Penman, and D. J. Sapsford. "Geometallurgy of uranium deposits." Minerals Engineering 24, no. 12 (October 2011): 1305–13. http://dx.doi.org/10.1016/j.mineng.2011.05.005.
Повний текст джерела
2
Dominy, Simon, Louisa O’Connor, Anita Parbhakar-Fox, Hylke Glass, and Saranchimeg Purevgerel. "Geometallurgy—A Route to More Resilient Mine Operations." Minerals 8, no. 12 (December 1, 2018): 560. http://dx.doi.org/10.3390/min8120560.
Повний текст джерелаАнотація:
Geometallurgy is an important addition to any evaluation project or mining operation. As an integrated approach, it establishes 3D models which enable the optimisation of net present value and effective orebody management, while minimising technical and operational risk to ultimately provide more resilient operations. Critically, through spatial identification of variability, it allows the development of strategies to mitigate the risks related to variability (e.g., collect additional data, revise the mine plan, adapt or change the process strategy, or engineer flexibility into the system). Geometallurgy promotes sustainable development when all stages of extraction are performed in an optimal manner from a technical, environmental, and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire mine value chain are being established. Geometallurgy has been shown to intensify collaboration among operational stakeholders, creating an environment for sharing orebody knowledge and improving data acquisition and interpretation, leading to the integration of such data and knowledge into mine planning and scheduling. These aspects create better business optimisation and utilisation of staff, and lead to operations that are more resilient to both technical and non-technical variability. Geometallurgy encompasses activities that utilise improved understanding of the properties of ore and waste, which impact positively or negatively on the value of the product, concentrate, or metal. Properties not only include those that impact on processing efficiency, but also those of materials which will impact on other actions such as blasting and waste management. Companies that embrace the geometallurgical approach will benefit from increased net present value and shareholder value.
3
Pownceby, M. I., and C. Johnson. "Geometallurgy of Australian uranium deposits." Ore Geology Reviews 56 (January 2014): 25–44. http://dx.doi.org/10.1016/j.oregeorev.2013.07.001.
Повний текст джерела
4
Ellefmo, Steinar L., Kurt Aasly, Aleksandra Lang, Veena S. Vezhapparambu, and Camilo A. M. Silva. "Geometallurgical Concepts Used in Industrial Mineral Production." Economic Geology 114, no. 8 (December 1, 2019): 1543–54. http://dx.doi.org/10.5382/econgeo.4685.
Повний текст джерелаАнотація:
Abstract Geometallurgy has developed since the 1970s, primarily on metallic ore operations. In parallel, industrial mineral operations have been optimized through detailed deposit knowledge and market development, without making specific reference to geometallurgical concepts. The Norwegian mining industry is dominated by industrial mineral and construction material operations, and, in this paper, key differences between the industrial mineral and the metallic ore sectors are investigated, along with their influence on the development and the use of economic block models and optimization methodologies. Further, the key levers and factors (mining method selection, processing route, scale, sequence, and cutoff policy) for value creation in industrial mineral operations are discussed, along with how and to what extent geometallurgy has been used. It is concluded that the five key levers cannot be used in industrial minerals operations as effectively as they are used in metallic ore operations. In industrial minerals, in situ strength variations are an important parameter in estimating key performance indicators such as recovery and product quality. When modeling the spatial variation in rock strength potential, additivity issues must be resolved by investigating the process the raw material is exposed to. The Norwegian industrial mineral sector has been using elements of geometallurgy but is facing unresolved issues related to strength variations and the use of measurement while drilling data.
5
Hoal, Karin E. Olson, and Max Frenzel. "Ores Drive Operations—Economic Geology Is the Foundation of Geometallurgy." SEG Discovery, no. 129 (April 1, 2022): 30–43. http://dx.doi.org/10.5382/geo-and-mining-15.
Повний текст джерелаАнотація:
Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract Economic geology and geometallurgy are intimately linked. Geologists understand the value in knowing the details of ore variability, the formation of mineral deposits, the continuity and the spatial distribution of ore types, and the mineral and textural characteristics that control grades. Beyond exploration and discovery, however, explorers may not recognize that the geologic knowledge developed around a mineral prospect is also essential to miners and metallurgists, reclamation and environmental specialists, and economists and investors who are interested in developing the discovery. Geometallurgy is the interdisciplinary method that links geologic, mineralogical, and geochemical characteristics of mineral deposits to the mining, processing, and metallurgical activities that are involved in the development of mines. Geometallurgy is not a new field, but recent developments in analytical capabilities and the ability to conduct statistical analysis and predictive modeling of large data sets have resulted in geometallurgy becoming a widely used method for optimizing mining operations. While there are many approaches, depending upon the nature of the ore deposit and the mine operating conditions and goals, the most important step explorers can take is to establish partnerships with the other areas of specialization in the project (mining, metallurgy, environmental, economics) and work together to understand the critical factors in order to best develop the deposit. Representative sampling to determine geologic variability and uncertainty and understanding the controls of throughput and recovery in the mining operation are fundamental to optimizing projects. For exploration and prefeasibility timelines, information on ore characteristics and spatial variability can provide a preliminary assessment of how material in a potential ore deposit can be processed.
6
Dehaine, Q., and K. Esbensen. "Multivariate methods for improved geometallurgy sampling." TOS Forum 2022, no. 11 (May 27, 2022): 411. http://dx.doi.org/10.1255/tosf.167.
Повний текст джерелаАнотація:
Geometallurgy is at the core of life-of-mine value chain optimisation, with the aim of integrating geoscientific disciplines along with mining engineering and minerals processing. The objective is to link comprehensive geological, geochemical, mineralogical and geotechnical information with metallurgical and mining variability - based on spatially distributed samples. The spatial coverage is a crucial element in this process. Geometallurgy samples are used for metallurgical testing in the service of plant and process design and optimisation. To avoid discrepancies between the expected and actual process performance, geometallurgical test work must be based on representative samples collected and processed in compliance with the Theory of Sampling (TOS). However, even if samples are initially collected to populate a multivariate block model, most of TOS’ recommendations for estimating sampling protocols and sample representativeness is univariate. While the univariate approach is sufficient when a sample must be representative for one property only e.g., for grade estimation, it fails to properly qualify representativeness of a sample which must be representative for multiple properties such as for geometallurgical purposes. Indeed, a geometallurgy sample is considered representative sensu stricto only if its metallurgical behaviour is representative of that of the full zone of the orebody it represents. This can only be achieved if-and-when geo-metallurgical samples are representative for the full set of ore properties that influence process performance. The critical success factor of multivariate representativeness can be assessed using multivariate approaches, such as the multi-variogram, which allow us to summarise the global variability of multiple properties into a single characteristic function. This approach could be optimised by using downstream results from geo-metallurgical process modelling, to select or weight, the individual property contributions to the multi-variogram according to their importance, thereby allowing to optimise a specific geometallurgical sampling procedure in terms of sampling mode, sampling frequency and the number of increments involved according to the overall process performance.
7
ENDERS, M. Stephen. "Applied Geometallurgy: A Common Sense Approach." Acta Geologica Sinica - English Edition 88, s2 (December 2014): 1272. http://dx.doi.org/10.1111/1755-6724.12380_24.
Повний текст джерела
8
Hunt, Julie A., and Ron F. Berry. "Geological Contributions to Geometallurgy: A Review." Geoscience Canada 44, no. 3 (October 6, 2017): 103–18. http://dx.doi.org/10.12789/geocanj.2017.44.121.
Повний текст джерелаАнотація:
Geometallurgy is a cross-disciplinary science that addresses the problem of teasing out the features of the rock mass that significantly influence mining and processing. Rocks are complex composite mixtures for which the basic building blocks are grains of minerals. The properties of the minerals, how they are bound together, and many other aspects of rock texture affect the entire mining value chain from exploration, through mining and processing, waste and tailings disposal, to refining and sales. This review presents rock properties (e.g. strength, composition, mineralogy, texture) significant in geometallurgy and examples of test methods available to measure or predict these properties. Geometallurgical data need to be quantitative and spatially constrained so they can be used in 3D modelling and mine planning. They also need to be obtainable relatively cheaply in order to be abundant enough to provide a statistically valid sample distribution for spatial modelling. Strong communication between different departments along the mining value chain is imperative so that data are produced and transferred in a useable form and duplication is avoided. The ultimate aim is to have 3D models that not only show the grade of valuable elements (or minerals), but also include rock properties that may influence mining and processing, so that decisions concerning mining and processing can be made holistically, i.e. the impacts of rock properties on all the cost centres in the mining process are taken into account. There are significant costs to improving ore deposit knowledge and it is very important to consider the cost-benefit curve when planning the level of geometallurgical effort that is appropriate in individual deposits.RÉSUMÉLa géométallurgie est une science interdisciplinaire qui s’intéresse aux caractéristiques de la masse rocheuse qui influent de manière significative sur l'exploitation minière et le traitement du minerai. Les roches sont des mélanges composites complexes dont les éléments structurant de base sont des grains de minéraux. Les propriétés des minéraux, la façon dont ils sont liés entre eux, et de nombreux autres aspects de la texture des roches déterminent l'ensemble de la chaîne de valeur minière, de l'exploration à l'extraction à la transformation, à l'élimination des déchets et des résidus, jusqu'au raffinage et à la vente. La présente étude passe en revue les propriétés significatives de la roche (par ex. sa cohésion, sa composition, sa minéralogie, sa texture) en géométallurgie ainsi que des exemples de méthodes d'essai disponibles pour mesurer ou prédire ces propriétés. Les données géométallurgiques doivent être quantitatives et localisées spatialement afin qu'elles puissent être utilisées dans la modélisation 3D et la planification de la mine. Elles doivent également être peu couteuses afin d'être suffisamment nombreuses pour fournir une distribution d'échantillon statistiquement valide pour la modélisation spatiale. Une communication efficace entre les différents segments de la chaîne de valeur minière est impérative pour que les données soient produites et transférées sous une forme utilisable et que les duplications soient évitées. Le but ultime est d'avoir des modèles 3D qui montrent non seulement la qualité des éléments précieux (ou minéraux), mais aussi les propriétés de roche qui déterminent l'exploitation minière et le traitement du minerai, de sorte que les décisions concernant l'exploitation minière et le traitement du minerai peuvent être réalisées de façon holistique, c.-à-d. que l’impact des propriétés de roche sur tous les maillons de la chaîne des coûts du processus minier sont prises en compte. Les coûts d’amélioration des connaissances sur le gisement de minerai étant importants, il faut tenir compte de la courbe coûts-bénéfices lors de la planification du niveau d'investissement géométallurgique approprié pour le gisement considéré.
9
Dehaine, Quentin, Laurens T. Tijsseling, Hylke J. Glass, Tuomo Törmänen, and Alan R. Butcher. "Geometallurgy of cobalt ores: A review." Minerals Engineering 160 (January 2021): 106656. http://dx.doi.org/10.1016/j.mineng.2020.106656.
Повний текст джерела
10
Hunt, Julie, Ron Berry, Megan Becker, and Regina Baumgartner. "A Special Issue Dedicated to Geometallurgy: Preface." Economic Geology 114, no. 8 (December 1, 2019): 1473–79. http://dx.doi.org/10.5382/econgeo.4688.
Повний текст джерелаАнотація:
AbstractGeometallurgy is an interdisciplinary field aimed at describing potential ore deposits in terms that mine planners and economists can use to design and run profitable mining operations. The major geologic contribution to the field is defining the spatial variability of potential and active mining resources so that planning and scheduling can accurately predict the economic performance and environmental impact of mining in time to respond efficiently to variations in ore type. This information is needed at the feasibility stage and throughout the mine life. We review the available literature on how geologists have contributed to these predictions in the past. There have been substantial advances in predicting comminution behavior. Prediction of recovery and environmental impacts are less advanced. This introductory paper provides a brief review of geometallurgy and a synopsis of the papers in the Special Issue, along with suggestions on future directions.
Дисертації з теми "Geometallurgey"
1
Mwanga, Abdul. "Test Methods for Characterising Ore Comminution Behavior in Geometallurgy." Licentiate thesis, Luleå tekniska universitet, Mineralteknik och metallurgi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-18689.
Повний текст джерелаАнотація:
Comminution test methods used within mineral processing have mainly been developed for selecting the most appropriate comminution technology for a given ore, designing a grinding circuit as well as sizing the equipment needed. Existing test methods usually require comparatively large sample amounts and are time-consuming to conduct. This makes comprehensive testing of ore comminution behavior – as required in the geometallurgical context – quite expensive. Currently the main interest in the conduct of comminution test lies in the determination of particle size reduction and related energy consumption by grindability test methods, which provide the necessary information about mill throughput. In this procedure mineral liberation is regarded as a fixed parameter due to missing this information in ore characterization as well as a lack of suitable comminution models. However, ignoring the connection between particle size and mineral liberation prevents the scheduling and controlling of the production process from being optimal.For these reasons new comminution tests need to be developed or alternatively the existing test methods need to be suited to geometallurgical testing where the aim is to map the variation of processing properties of an entire ore body. The objective of this research work is on the one hand to develop small-scale comminution test methods that allow linking comminution behavior and liberation characteristics to mineralogical parameters, and on the other hand establish a modeling framework including mineral liberation information.Within the first stage of the study the comminution of drill cores from Malmberget’s magnetite ore, classified by modal mineralogy and texture information, have been investigated. It was found that there is a direct correlation between the mechanical strength of the rock, as received from unconfined compressive or point load tests, and the crusher reduction ratio as a measure for crushability. However, a negative correlation was found between crushability and grindability for the same samples. The grindability showed inverse correlation with both magnetite grade and the magnetite’s mineral grain size. The preliminary conclusion is that modal mineralogy and micro-texture (grain size) can be used to quantitatively describe the ore comminution behavior although the applied fracture mechanism of the mill cannot be excluded.With crushed ore samples from Malmberget also grindability tests and mineral liberation analyses were conducted using laboratory tumbling mills of different size. Starting from the dimensions of the Bond ball mill a modified test method was developed where small size samples of approximately 220 g were pre-crushed and ground in a down-scaled one-stage grindability test. Down-scaling was done by keeping similar impact effects between the mills. Mill speed and grinding time were used for adjusting the number of fracture events in order to receive similar particle size distributions and specific grinding energy when decreasing mill size by the factor 1.63. A detailed description of the novel geometallurgical comminution test (GCT) is given.With respect to ore crushability and autogenous and semiautogenous grinding (AG/SAG) also drop weight tests were conducted. For a more accurate and precise measurement of the energy transferred to the sample a novel instrumented drop weight was used. Initial tests with fractions of drill cores and pre-crushed ore particles showed that the simple energy calculation based on potential energy needs to be corrected. For the future work these tests will be extended to other ore types in order to investigate the effects of mineralogy and to include mineral liberation in comminution models suitable for geometallurgy.Godkänd; 2014; 20140402 (abdmwa); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Abdul Mwanga Ämne: Mineralteknik/Mineral Processing Uppsats: Test Methods for Characterising Ore Comminution Behavior in Geometallurgy Examinator: Professor Jan Rosenkranz, Institutionen för samhällsbyggnad och naturresurser, Luleå tekniska universitet Diskutant: Professor Hakan Benzer, Hacettepe University, Department of Mining Engineering Beytepe, Ankara, Turkey Tid: Torsdag den 12 juni 2014 kl 10.00 Plats: F531, Luleå tekniska universitet
2
Guntoro, Pratama Istiadi. "X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy." Licentiate thesis, Luleå tekniska universitet, Mineralteknik och metallurgi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76576.
Повний текст джерелаАнотація:
In recent years, automated mineralogy has become an essential tool in geometallurgy. Automated mineralogical tools allow the acquisition of mineralogical and liberation data of ore particles in a sample. These particle data can then be used further for particle-based mineral processing simulation in the context of geometallurgy. However, most automated mineralogical tools currently in application are based on two-dimensional (2D) microscopy analysis, which are subject to stereological error when analyzing three-dimensional(3D) object such as ore particles. Recent advancements in X-ray microcomputed tomography (µCT) have indicated great potential of such system to be the next automated mineralogical tool. µCT's main advantage lies on its ability in monitoring 3D internal structure of the ore at resolutions down to few microns, eliminating stereological error obtained from 2D analysis. Aided with the continuous developments of computing capability of 3D data, it is only the question of time that µCT system becomes an interesting alternative in automated mineralogy system. This study aims to evaluate the potential of implementing µCT as an automated mineralogical tool in the context of geometallurgy. First, a brief introduction about the role of automated mineralogy in geometallurgy is presented. Then, the development of µCT system to become an automated mineralogical tool in the context of geometallurgy andprocess mineralogy is discussed (Paper 1). The discussion also reviews the available data analysis methods in extracting ore properties (size, mineralogy, texture) from the 3D µCT image (Paper 2). Based on the review, it was found that the main challenge inperforming µCT analysis of ore samples is the difficulties associated to the segmentation of the mineral phases in the dataset. This challenge is adressed through the implementation of machine learning techniques using Scanning Electron Microscope (SEM) data as a reference to differentiate the mineral phases in the µCT dataset (Paper 3).
3
Tiu, Glacialle. "Classification of Drill Core Textures for Process Simulation in Geometallurgy : Aitik Mine, Sweden." Thesis, Luleå tekniska universitet, Mineralteknik och metallurgi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65207.
Повний текст джерелаАнотація:
This thesis study employs textural classification techniques applied to four different data groups: (1) visible light photography, (2) high-resolution drill core line scan imaging (3) scanning electron microscopy backscattered electron (SEM-BSE) images, and (4) 3D data from X-ray microtomography (μXCT). Eleven textural classes from Aitik ores were identified and characterized. The distinguishing characteristics of each class were determined such as modal mineralogy, sulphide occurrence and Bond work indices (BWI). The textural classes served as a basis for machine learning classification using Random Forest classifier and different feature extraction schemes. Trainable Weka Segmentation was utilized to produce mineral maps for the different image datasets. Quantified textural information for each mineral phase such as modal mineralogy, mineral association index and grain size was extracted from each mineral map. Efficient line local binary patterns provide the best discriminating features for textural classification of mineral texture images in terms of classification accuracy. Gray Level Co-occurrence Matrix (GLCM) statistics from discrete approximation of Meyer wavelets decomposition with basic image statistical features[PK1] (e.g. mean, standard deviation, entropy and histogram derived values) give the best classification result in terms of accuracy and feature extraction time. Differences in the extracted modal mineralogy were observed between the drill core photographs and SEM images which can be attributed to different sample size[PK2] . Comparison of SEM images and 2D μXCT image slice shows minimal difference giving confidence to the segmentation process. However, chalcopyrite is highly underestimated in 2D μXCT image slice, with the volume percentage amounting to only half of the calculated value for the whole 3D sample. This is accounted as stereological error. Textural classification and mineral map production from basic drill core photographs has a huge potential to be used as an inexpensive ore characterization tool. However, it should be noted that this technique requires experienced operators to generate an accurate training data especially for mineral identification and thus, detailed mineralogical studies beforehand is required.Primary Resource Efficiency by Enhanced Prediction (PREP)
Center for Advanced Mining and Metallurgy (CAMM)
4
Barton, Isabel Fay, and Isabel Fay Barton. "Mineralogical and Metallurgical Study of Supergene Ores of the Mike Cu-Au(-Zn) Deposit, Carlin Trend, Nevada." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625323.
Повний текст джерелаАнотація:
This paper presents the results of a mineralogical and metallurgical study of supergene ores at the Mike Cu-Au(-Zn) deposit on the Carlin trend of Nevada, USA, currently held by Newmont Gold Corporation. With a metal endowment totaling >8.5 M oz. Au, 1027 M lbs. Cu, and 809 M lbs. Zn, Mike is one of the largest deposits on the Carlin trend, but it is currently uneconomic to develop. It contains an unusual and complicated suite of metals and ore minerals. This study was undertaken as a first step to investigate process options for recovering both its Cu and Au by 1) comparing the metal recoveries achieved from the supergene ores by six different lixiviants, and 2) identifying which minerals failed to dissolve in each lixiviant. The reagents selected were sulfuric, sulfurous, and methanesulfonic acids, to recover Cu, and cyanide, thiourea, and glycine, to recover Cu and Au.
QEMSCAN and SEM study of six samples of different ore types and grades indicate that the Au occurs as varieties of native gold, including auricupride and electrum. Major Cu minerals are native Cu, cuprite, malachite, chrysocolla, and conichalcite (Ca-Cu arsenate), with locally significant Cu in jarosite and goethite. Gangue mineralogy is dominated by quartz, sericite, chlorite, alunite, smectite and kaolinite, K-feldspar, jarosite, and iron oxides.
Bottle roll testing indicates that no single-step leaching process is likely to provide economic recovery of both Cu and Au. Sulfuric and methanesulfonic acid both recovered > 70% of the Cu except from the samples dominated by conichalcite, which was not leached effectively by any of the reagents tested. Only cyanide and thiourea recovered significant Au. Reagent consumption for cyanide, sulfuric acid, and methanesulfonic acid was generally within acceptable levels. Glycine and sulfurous acid are both uneconomic based on low recovery. Further work will focus on developing an economic process in two steps.
Mineralogical study of QEMSCAN residue indicates that the non-leaching ore minerals are conichalcite and Cu-bearing Fe oxides. In addition, native Cu and cuprite do not leach well in glycine and chrysocolla does not leach well in thiourea or cyanide. Other observed mineralogical changes include the total loss of dolomite and partial loss of alunite and iron oxide from all samples, with apparent gains in jarosite.
5
Anderson, Kelvin Frederick Esebewa. "Geometallurgical evaluation of the Nkout (Cameroon) and Putu (Liberia) iron ore deposits." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15019.
Повний текст джерелаАнотація:
The Nkout (Cameroon) and Putu (Liberia) oxide facies iron ore deposits comprise fresh magnetite banded iron formation (BIF) at depth, which weathers towards the surface, forming high grade martite–goethite ores. This study aimed to improve the mineralogical understanding of these deposits in order to predict their metallurgical responses. It concentrated on developing the QEMSCAN® technique and testing its application to these ore types, but also used a variety of other analysis methods. The QEMSCAN® species identification protocol was developed to include three goethite entries: goethite/limonite, phosphorus-bearing and aluminium-bearing goethite. QEMSCAN® was also used to distinguish between the iron oxides using their backscattered electron signals. To test the correlation between the mineralogy and metallurgical characteristics, magnetic separations were carried out. The samples were divided into 4 main groups based on their whole rock Fe content, determined by XRF analysis, and their degree of weathering: enriched material, weathered magnetite itabirite, transitional magnetite itabirite and magnetite itabirite. Quartz and Al oxide and hydroxide minerals such as gibbsite are the major gangue minerals in the magnetite BIF and martite–goethite ores respectively. From the QEMSCAN® analysis it was concluded that the iron oxides are closely associated and liberation of them individually is poor. Liberation increases when they are grouped together as iron oxide. Chamosite concentrations > 6 wt. % significantly lower liberation of the iron oxides. From the metallurgical testing, it was concluded that iron oxide modal mineralogy gives an indication of iron recovery but other QEMSCAN® data such as mineral association and liberation could be important especially if the iron oxide minerals are not liberated. Grain size and instrument characteristics also affect recovery of iron minerals. There is no evidence to show that there is any structural control on the BIF mineralisation at Nkout because metamorphism has significantly affected the lithological characteristics. The BIF mineralised zones occur as stacks with no particular stratigraphic relationship. Alteration and stratigraphy are the main controls on the martite–goethite ores. These results are applicable to most other BIFs so that as direct shipping ores are exhausted, the approach used here can help to develop the lower grade portions of the deposits.
6
Schmitt, Raoul. "A Geometallurgical Approach Towards the Correlation Between Rock Type Mineralogy and Grindability: A case study in Aitik mine, Sweden." Thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-87012.
Повний текст джерелаАнотація:
Aitik is a large copper porphyry type deposit located in northern Sweden, currently exploited at an annual rate of approximately 45Mt. The ore's exceptionally low head grade of 0.22 % Cu and varying degrees of hardness across the entire deposit pose challenges to the two fully autogenous grinding lines, each of which comprises a 22.5 MW primary autogenous mill in series with a pebble mill. The variability in ore grindability frequently leads to fluctuations in mill throughput. Within the framework of a geometallurgical approach, the present study investigated the relationships between ore grindability and modal mineralogy. For this purpose, drill core samples from different lithologies were subjected to Boliden AB's in-house grindability tests. This laboratory-scale autogenous grinding test generates a grindability index Ks mainly related to abrasion breakage, which is a significant breakage mechanism within autogenous mills. The test results suggested divergent degrees of grindability within and across the selected rock types. Furthermore, subsequent sieve analyses identified a relationship between the grindability index, PSD, and the proportions of fines generated by abrasive grinding. A combination of scanning electron microscopy, X-ray powder diffraction, and X-ray fluorescence analyses was performed for the grinding products and bulk mineral samples. The resulting mineralogical and elemental properties were correlated to the parameters from the grindability tests. It was shown that the main mineral phases, such as plagioclase, quartz, and micas, correlate well with the grindability indices. Similar correlations were found regarding the sample's chemical composition, attributable to the main mineral phases. Derived from the previous findings, two exemplary linear empirical models for the calculation of grindability based on either mineral contents or chemical composition were presented. Careful examination of the mineralogical data revealed that the prevalent abrasion breakage mechanism leads to constant and continuous removal of mineral particles from the sample's surface. No indications for a preferential abrasion of any mineral phases were found. A further inverse correlation between the sample's calculated average weighted Mohs hardness based on modal mineralogy and the grindability index Ks was established. Hence, it was proposed that a higher Mohs hardness results in a finer grinding product, oppositional to the Ks-values. Since Ks can be interpreted as a measure of abrasiveness, it can be stated that abrasiveness decreases with an increasing average sample hardness and vice versa. Moreover, mineral liberation information provided by scanning electron microscopy was associated with the parameters mentioned earlier. It was determined that different degrees of mineral liberation were reached within specific particle size classes. The identified relationships between grindability, modal mineralogy, and element grades may help Boliden develop a predictive throughput model for Aitik to be integrated into the mine's block model. Based on this information, a strategy for smart blending could be developed, where run of mine material from ore blocks of varying grindabilities could be blended to attain the target plant throughput.
7
Patton, William. "Modelling of unequally sampled rock properties using geostatistical simulation and machine learning methods." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2530.
Повний текст джерелаАнотація:
Important orebody characteristics that determine viability of the mineral resource and ore reserve potential such as physical properties, mineralogical and geochemical compositions often vary substantially across an ore deposit. Geometallurgical models aim to capture the spatial relationships between mineral compositions, physical properties of rock and their interactions with mechanical and chemical processes during mining extraction and processing. This characterisation of physical and chemical properties of ores can in turn be used to inform mining and processing decisions that enable the extraction of the maximum value from the ore deposit most efficiently. During the construction of such spatial geometallurgical models, practitioners are presented with many challenges. These include modelling high-dimensional data of various types including categorical, continuous and compositional attributes and their uncertainties. Decisions on how to segregate samples data into spatially and statistically homogeneous groups to satisfy modelling assumptions such as stationarity are often a requirement. Secondary properties such as metallurgical test results are often few in number, acquired on larger scales than that of primary rock property data and non-additive in nature. In this thesis a data driven workflow that aims to address these challenges when constructing geometallurgical models of ore deposits is devised. Spatial machine learning techniques are used to derive geometallurgical categories, or classes, from multiscale, multiresolution, high dimensional rock properties. In supervised mode these methods are also used to predict geometallurgical classes at samples where rock property information is incomplete. Realisations of the layout of geometallurgical classes and the variabilities of associated rock properties are then mapped using geostatistical simulations and machine learning. The workflow is demonstrated using a case study at Orebody H; a complex stratabound Bedded Iron Ore deposit in Western Australia’s Pilbara. A detailed stochastic model of five compositions representing primary rock properties and geometallurgical responses in the form of lump and fine product iron ore quality specifications was constructed. The predicted product grade recoveries are realistic values that honour constraints of the predicted head grade compositions informed by more abundant and regularly spaced sampling than metallurgical tests. Finally, uncertainties are quantified to assess risk following a confidence interval based framework. This could be used to identify zones of high uncertainty where collection of additional data might help mitigate or minimise risks and in turn improve forecast production performances.
8
Westberg, Fredrik. "Textural characterization of gold in the Björkdal gold deposit, northern Sweden." Thesis, Luleå tekniska universitet, Geovetenskap och miljöteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-82496.
Повний текст джерелаАнотація:
The Björkdal gold deposit is located in the eastern part of the Skellefte district, northern Sweden. Twenty thin sections from four production areas in the open pit and four drifts from the underground mine were analysed for mineral association and grain size distribution of gold. In addition, the texture of gold was investigated in order to find out how that affects the recovery of gold. The overall gold grain size distribution shows an interval from very fine-grained (2 μm) to coarse grained(856 μm) while the overall median size is 7 μm. Gold from the Quartz Mountain production area displays the smallest median size of 4 μm, whereas gold from the sampled drifts at 340m- and 385m- level has the largest median size of 14 μm. Gold at grain boundary is the dominant textural mode of gold from all sampled locations and varies from 62% to 92%. This is followed by intergrown which ranges between 8% and 29%. Of the sulfides, pyrite, chalcopyrite and pyrrhotite are the most common. Galena and was also present in the samples. Gold is significantly and positively correlated with tellurium (Appendix 10.1.1), and weakly positive correlated to silver and mercury. Gold show a close association to bismuth-tellurides in the samples. Apart from native gold, which is the dominant mineral phase of gold, two additional gold-bearing tellurium minerals were detected with SEM-EDS, a Au-Te-mineral and a Ag-Au-Te-mineral. One additional bismuth-telluride mineral aside from the most commonly occurring tsumoite (BiTe) was also detected with SEM, with a elemental composition of Bi-Te-S. Liberated gold in the tailings was optically identified in two thick sections, TB1-02feb-1 and TB1-07feb-1 (Fig. 32A and B), where the flotation circuit failed to float the free gold. One grain of gold was also identified intergrown with bismuth-telluride as an inclusion in silicate (Fig. 33), where the flotation properties of the larger silicate grain likely dominated in the flotation process. This thesis highlights the importance of further quantitative analysis utilizing SEM/QEMSCAN/MLA to retrieve representative mineralogical data to benefit the mineral processing of the ore from the active mine. Keywords: Björkdal gold deposit, gold, gold-telluride, SEM, mineral association, grain size,geometallurgy.
9
Koch, Pierre-Henri. "Particle generation for geometallurgical process modeling." Licentiate thesis, Luleå tekniska universitet, Mineralteknik och metallurgi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-63270.
Повний текст джерелаАнотація:
A geometallurgical model is the combination of a spatial model representing an ore deposit and a process model representing the comminution and concentration steps in beneficiation. The process model itself usually consists of several unit models. Each of these unit models operates at a given level of detail in material characterization - from bulk chemical elements, elements by size, bulk minerals and minerals by size to the liberation level that introduces particles as the basic entity for simulation (Paper 1). In current state-of-the-art process simulation, few unit models are defined at the particle level because these models are complex to design at a more fundamental level of detail, liberation data is hard to measure accurately and large computational power is required to process the many particles in a flow sheet. Computational cost is a consequence of the intrinsic complexity of the unit models. Mineral liberation data depends on the quality of the sampling and the polishing, the settings and stability of the instrument and the processing of the data. This study introduces new tools to simulate a population of mineral particles based on intrinsic characteristics of the feed ore. Features are extracted at the meso-textural level (drill cores) (Paper 2), put in relation to their micro-textures before breakage and after breakage (Paper 3). The result is a population of mineral particles stored in a file format compatible to import into process simulation software. The results show that the approach is relevant and can be generalized towards new characterization methods. The theory of image representation, analysis and ore texture simulation is briefly introduced and linked to 1-point, 2-point, and multiple-point methods from spatial statistics. A breakage mechanism is presented as a cellular automaton. Experimental data and examples are taken from a copper-gold deposit with a chalcopyrite flotation circuit, an iron ore deposit with a magnetic separation process. This study is covering a part of a larger research program, PREP (Primary resource efficiency by enhanced prediction).PREP
10
Parian, Mehdi. "Development of a geometallurgical framework for iron ores - A mineralogical approach to particle-based modeling." Doctoral thesis, Luleå tekniska universitet, Mineralteknik och metallurgi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-62515.
Повний текст джерелаАнотація:
The demands for efficient utilization of ore bodies and proper risk management in the mining industry have resulted in a new cross-disciplinary subject called geometallurgy. Geometallurgy connects geological, mineral processing and subsequent downstream processing information together to provide a comprehensive model to be used in production planning and management. A geometallurgical program is an industrial application of geometallurgy. Various approaches that are employed in geometallurgical programs include the traditional way, which uses chemical elements, the proxy method, which applies small-scale tests, and the mineralogical approach using mineralogy or the combination of those. The mineralogical approach provides the most comprehensive and versatile way to treat geometallurgical data. Therefore it was selected as a basis for this study. For the mineralogical approach, quantitative mineralogical information is needed both for the deposit and the process. The geological model must describe the minerals present, give their chemical composition, report their mass proportions (modal composition) in the ore body and describe the ore texture. The process model must be capable of using mineralogical information provided by the geological model to forecast the metallurgical performance of different geological volumes and periods. A literature survey showed that areas, where more development is needed for using the mineralogical approach, are: 1) quick and inexpensive techniques for reliable modal analysis of the ore samples; 2) ore textural characterization of the ore to forecast the liberation distribution of the ore when crushed and ground; 3) unit operation models based on particle properties (at mineral liberation level) and 4) a system capable of handling all this information and transferring it to production model. This study focuses on developing tools in these areas. A number of methods for obtaining mineral grades were evaluated with a focus on geometallurgical applicability, precision, and trueness. A new technique developed called combined method uses both quantitative X-ray powder diffraction with Rietveld refinement and the Element-to-Mineral Conversion method. The method not only delivers the required turnover for geometallurgy but also overcomes the shortcomings if X-ray powder diffraction or Element-to-Mineral Conversion were used alone. Characterization of ore texture before and after breakage provides valuable insights about the fracture pattern in comminution, the population of particles for specific ore texture and their relation to parent ore texture. In the context of the mineralogical approach to geometallurgy, predicting the particle population from ore texture is a critical step to establish an interface between geology and mineral processing. A new method called Association Indicator Matrix developed to assess breakage pattern of ore texture and analyze mineral association. The results of ore texture and particle analysis were used to generate particle population from ore texture by applying particle size distribution and breakage frequencies. The outcome matches well with experimental data specifically for magnetite ore texture. In geometallurgy, process models can be classified based on in which level the ore, i.e. the feed stream to the processing plant and each unit operation, is defined and what information subsequent streams carry. The most comprehensive level of mineral processing models is the particle-based one which includes practically all necessary information on streams for modeling unit operations. Within this study, a particle-based unit operation model was built for wet low-intensity magnetic separation, and existing size classification and grinding models were evaluated to be used in particle level. A property-based model of magnetic beneficiation plant was created based on one of the LKAB operating plants in mineral and particle level and the results were compared. Two different feeds to the plant were used. The results revealed that in the particle level, the process model is more sensitive to changes in feed property than any other levels. Particle level is more capable for process optimization for different geometallurgical domains.
Частини книг з теми "Geometallurgey"
1
Emery, Xavier, and Serge Antoine Séguret. "Recovery and geometallurgy." In Geostatistics for the Mining Industry, 145–69. Boca Raton : CRC Press, 2020. | Translation of: Géostatistique de gisements de cuivre chiliens (Séguret and Emery), 2019.: CRC Press, 2020. http://dx.doi.org/10.1201/9781003050469-7.
Повний текст джерела
2
van den Boogaart, K. G., and R. Tolosana-Delgado. "Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences." In Handbook of Mathematical Geosciences, 673–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78999-6_33.
Повний текст джерела
3
Parbhakar-Fox, Anita. "Predicting Waste Properties Using the Geochemistry-Mineralogy-Texture-Geometallurgy Approach." In Environmental Indicators in Metal Mining, 73–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42731-7_5.
Повний текст джерела
4
Parbhakar-Fox, Anita, and Bernd Lottermoser. "Predictive Waste Classification Using Field-Based and Environmental Geometallurgy Indicators, Mount Lyell, Tasmania." In Environmental Indicators in Metal Mining, 157–77. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42731-7_9.
Повний текст джерела
5
Parbhakar-Fox, Anita, and Bernd Lottermoser. "Predictive Waste Classification Using the Geochemistry-Mineralogy-Texture-Geometallurgy (GMTG) Approach at a Polymetallic Mine." In Environmental Indicators in Metal Mining, 179–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42731-7_10.
Повний текст джерелаТези доповідей конференцій з теми "Geometallurgey"
1
Vaisberg, L. A., and I. D. Ustinov. "Practical geometallurgy titanium-tantalum-niobates." In Modern Problems of Theoretical, Experimental and Applied Mineralogy (Yushkin Readings — 2020). Institute of Geology FRC Komi SC UB RAS, 2020. http://dx.doi.org/10.19110/98491-014-332.
Повний текст джерела
2
Bye, Alan. "Mine planning case studies demonstrating value from geometallurgy initiatives." In Fourth International Seminar on Strategic versus Tactical Approaches in Mining. Australian Centre for Geomechanics, Perth, 2011. http://dx.doi.org/10.36487/acg_rep/1108_25_bye.
Повний текст джерела
3
Spieth, Volker. "KUPFERSCHIEFER UPPER PERMIAN BLACK SHALE - HOT HYDROTHERMAL HIGH ENERGY GENESIS AND GEOMETALLURGY AT THE SOUTHERN RIM OF THE KUPFERSCHIEFER SEA." In GSA Annual Meeting in Seattle, Washington, USA - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017am-304768.
Повний текст джерела