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Simulation of a Mining Value Chain with a Synthetic Ore Body Model: Iron Ore Example
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.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
2018 (English)In: Minerals, ISSN 2075-163X, E-ISSN 2075-163X, Vol. 8, no 11, article id 536Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2018. Vol. 8, no 11, article id 536
Keywords [en]
synthetic ore body, simulation, iron ore, prediction
National Category
Mineral and Mine Engineering Metallurgy and Metallic Materials Mathematical Analysis
Research subject
Mineral Processing; Mathematics
Identifiers
URN: urn:nbn:se:ltu:diva-71577DOI: 10.3390/min8110536ISI: 000451530500063Scopus ID: 2-s2.0-85057331919OAI: oai:DiVA.org:ltu-71577DiVA, id: diva2:1263176
Note

Validerad;2018;Nivå 2;2018-12-07 (marisr)

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2019-02-27Bibliographically 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, ViktorLund, CeciliaLamberg, PerttiMiroshnikova, Elena

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