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Simulation of a Mining Value Chain with a Synthetic Ore Body Model: Iron Ore Example
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-9227-2470
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Matematiska vetenskaper.
2018 (Engelska)Ingår i: Minerals, ISSN 2075-163X, E-ISSN 2075-163X, Vol. 8, nr 11, artikel-id 536Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2018. Vol. 8, nr 11, artikel-id 536
Nyckelord [en]
synthetic ore body, simulation, iron ore, prediction
Nationell ämneskategori
Mineral- och gruvteknik Metallurgi och metalliska material Matematisk analys
Forskningsämne
Mineralteknik; Matematik
Identifikatorer
URN: urn:nbn:se:ltu:diva-71577DOI: 10.3390/min8110536ISI: 000451530500063Scopus ID: 2-s2.0-85057331919OAI: oai:DiVA.org:ltu-71577DiVA, id: diva2:1263176
Anmärkning

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

Tillgänglig från: 2018-11-14 Skapad: 2018-11-14 Senast uppdaterad: 2019-02-27Bibliografiskt granskad
Ingår i avhandling
1. Bringing predictability into a geometallurgical program: An iron ore case study
Öppna denna publikation i ny flik eller fönster >>Bringing predictability into a geometallurgical program: An iron ore case study
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[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).

Ort, förlag, år, upplaga, sidor
Luleå: Luleå University of Technology, 2019
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Nyckelord
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
Nationell ämneskategori
Mineral- och gruvteknik Metallurgi och metalliska material
Forskningsämne
Mineralteknik
Identifikatorer
urn:nbn:se:ltu:diva-71580 (URN)978-91-7790-266-9 (ISBN)978-91-7790-267-6 (ISBN)
Disputation
2019-02-04, D770, Lulea, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-11-14 Skapad: 2018-11-14 Senast uppdaterad: 2019-02-07Bibliografiskt granskad

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Lishchuk, ViktorLund, CeciliaLamberg, PerttiMiroshnikova, Elena

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