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Development of a Synthetic Ore Deposit Model for Geometallurgy
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.
2016 (engelsk)Inngår i: Geomet16: Third AusIMM International Geometallurgy Conference 2016 : Conference Proceedings, Parkville, Victoria: The Australian Institute of Mining and Metallurgy , 2016, s. 275-286Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
Parkville, Victoria: The Australian Institute of Mining and Metallurgy , 2016. s. 275-286
HSV kategori
Forskningsprogram
Mineralteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-32309Lokal ID: 6c49c243-20d1-49dd-9cd3-bef089a91a23ISBN: 9781925100457 (tryckt)ISBN: 9781925100464 (digital)OAI: oai:DiVA.org:ltu-32309DiVA, id: diva2:1005543
Konferanse
The Third AusIMM International Geometallurgy Conference : Geometallurgy - Beyond Conception 15/06/2016 - 16/06/2016
Merknad

Godkänd; 2016; 20160617 (viklis)

Tilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2018-11-29bibliografisk kontrollert
Inngår i avhandling
1. Bringing predictability into a geometallurgical program: An iron ore case study
Åpne denne publikasjonen i ny fane eller vindu >>Bringing predictability into a geometallurgical program: An iron ore case study
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[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).

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2019
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Emneord
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
HSV kategori
Forskningsprogram
Mineralteknik
Identifikatorer
urn:nbn:se:ltu:diva-71580 (URN)978-91-7790-266-9 (ISBN)978-91-7790-267-6 (ISBN)
Disputas
2019-02-04, D770, Lulea, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-11-14 Laget: 2018-11-14 Sist oppdatert: 2019-02-07bibliografisk kontrollert

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Lishchuk, ViktorLund, CeciliaLamberg, Pertti

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