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Textural and Mineralogical Characterization of Li-pegmatite Deposit: Using Microanalytical and Image Analysis to Link Micro and Macro Properties of Spodumene in Drill Cores.: Keliber Lithium Project, Finland.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering. (MiMer)
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Lithium represents one of the strategic elements for the rest of the 21st century due to its increasing demand in technological applications. Therefore, new efforts should be focused on the optimization of mineral characterization processes, which link the ore properties with its behaviour during downstream processes. These efforts should result in reducing operational risks and increasing resources utilization. The methodology presented in this study is based on the application of several classification techniques, aiming the mineral and textural characterization of two spodumene pegmatite deposits within the Keliber Lithium Project.

Twelve textural classes have been proposed for the textual classification of the ore, which have been defined through the recognition of the main mineral features at macro- and micro-scale. The textural classification was performed through the application of drill core logging and scanning electron microscopy. Six classes are proposed to describe the characteristics of the spodumene ore. Six additional classes describe the main properties of the rocks surrounding the ore zone.

Image analysis was implemented for the generation of mineral maps and the subsequent quantification of spodumene and Li2O within the analysed drill core images. The image segmentation process was executed in Fiji-ImageJ and is based on eight mineral classes and a set of seven feature extraction procedures. Thus, quantification of spodumene and Li2O is estimated by textural class. Hyperspectral images were used as a reference for assessing the estimations made through images analysis.

A machine learning model in Weka allowed forecasting the behaviour of the twelve textural classes during spodumene flotation. This model is fed by metallurgical data from previous flotation tests and uses Random Forest classifier.

The proposed methodology serves as an inexpensive but powerful approach for the complete textural characterization of the ore at Keliber Lithium Project. It provides information about: (1) mineral features at different scales, (2) spatial distribution of textures within the pegmatite body, (3) quantification of spodumene and Li2O within the drill cores and (4) processing response of each textural class. However, its application requires wide knowledge and expertise in the mineralogy of the studied deposits.

Place, publisher, year, edition, pages
2018. , p. 68
Keywords [en]
Lithium, Image Analysis, Spodumene, Pegmatite, Hyperspectral, Geometallurgy, Machine learning, Mineralogy, characterization, Mineral mapping, Texture, Scanning electron microscopy, SEM, Mineral Processing, Kaustinen, Keliber, Lithium deposit
Keywords [es]
Geometalurgia, procesamiento mineral, litio, espodumena, pegmatita, machine learning, procesamiento mineral
National Category
Mineral and Mine Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-70422OAI: oai:DiVA.org:ltu-70422DiVA, id: diva2:1239258
External cooperation
Keliber Oy
Subject / course
Student thesis, at least 30 credits
Educational program
Geosciences, master's level (120 credits)
Presentation
2018-06-01, F-531, Luleå University of Technology, Luleå, 10:00 (English)
Supervisors
Examiners
Note

Thesis Presentation.

Textural and Mineralogical Characterization of Li-pegmatite Deposit: Using Microanalytical and Image Analysis to Link Micro and Macro Properties of Spodumene in Drill Cores.  Keliber Lithium Project, Finland.

Available from: 2018-08-22 Created: 2018-08-16 Last updated: 2018-08-22Bibliographically approved

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