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Evaluation of sampling in geometallurgical programs through synthetic deposit model
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-9227-2470
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
Number of Authors: 32016 (English)In: (IMPC 2016), Canadian Institute of Mining, Metallurgy and Petroleum, 2016Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Canadian Institute of Mining, Metallurgy and Petroleum, 2016.
Keywords [en]
Sampling, synthetic ore body, simulation, geometallurgical testing framework.
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-59640Scopus ID: 2-s2.0-85048351936ISBN: 978-1-926872-29-2 (print)OAI: oai:DiVA.org:ltu-59640DiVA, id: diva2:1034005
Conference
XXVIII International Mineral Processing Congress (IMPC 2016), Quebec City, Canada, 11–15 September 2016
Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2018-11-14Bibliographically approved
In thesis
1. Bringing predictability into a geometallurgical program: An iron ore case study
Open this publication in new window or tab >>Bringing predictability into a geometallurgical program: An iron ore case study
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Skapande av predikterbarhet i ett geometallurgiskt program : en fallstudie med järbnmalm
Abstract [en]

The risks of starting, operating and closing mining projects have become higher than ever. In order to stay ahead of the competition, mining companies have to manage various risks: technical, environmental, legal, regulatory, political, cyber, financial and social. Some of these can be mitigated with the help of geometallurgy. Geometallurgy aims to link geological variability with responses in the beneficiation process by a wide usage of automated mineralogy, proxy metallurgical tests, and process simulation. However, traditional geometallurgy has neglected the non-technical aspects of mining. This has caused wide-spread discussion among researchers on the benefits of geometallurgy and its place in industry.

In order to improve predictability in geometallurgy, such programs should cover planning, and the testing of hypotheses, and only then should there be an attempt to develop suitable technical tools. Such approach would ensure that those tools would be useful and are needed, not only from the technical point of view, but also from the users’ perspective. Therefore, this thesis introduces methodology on how to decrease uncertainty in the production planning and thus determine how much effort to put into decreasing uncertainty in geometallurgical programs.

The predictability improvement of a geometallurgical program starts at the planning stage. The classification system developed here, through the survey (interviews) and literature review, indicates different ways to link geological information with metallurgical responses, and suggests areas where technical development is called for. The proposed developments can be tested before the start of the geometallurgical program with synthetic data. For the iron ore reference study (Malmberget), it was shown that implementation of geometallurgy is beneficial in terms of net present value (NPV) and internal rate of return (IRR), and building geometallurgical spatial model for the process properties (recovery and total concentrate tonnages), and that it requires fewer samples for making a reliable process prediction than concentrate quality. The new process and proxy for mineralogical characterisation models were developed as part of the geometallurgical program for the iron ore case study (Leveäniemi): an estimator of ore quality (ܺ௅்௎), a model for iron recovery in WLIMS, a model for iron-oxides liberation prediction. Additionally, it was found that DT may be applied only for studying marginal ores. The evaluation of different spatial process modelling methods showed that tree methods can be successfully employed in predicting non-additive variables (recoveries).

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Additivity, Apatite iron ore, AIO, Block model, Change of support, Classification, Data integration, DT, Feed quality, Geometallurgical program, Geometallurgy, Iron ore, Iron recovery, Leveäniemi, Liberation, Machine learning, Magnetic separation, Malmberget, Mineralogical approach, Mineralogy, Prediction, Proxies, Proxies approach, Sampling, Simulation, Synthetic ore body, Traditional approach, WLIMS
National Category
Mineral and Mine Engineering Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-71580 (URN)978-91-7790-266-9 (ISBN)978-91-7790-267-6 (ISBN)
Public defence
2019-02-04, D770, Lulea, 10:00 (English)
Opponent
Supervisors
Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2019-02-07Bibliographically approved

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Lishchuk, ViktorLamberg, PerttiLund, Cecilia

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