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Development of a Synthetic Ore Deposit Model for Geometallurgy
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.
2016 (English)In: Geomet16: Third AusIMM International Geometallurgy Conference 2016 : Conference Proceedings, Parkville, Victoria: The Australian Institute of Mining and Metallurgy , 2016, p. 275-286Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Parkville, Victoria: The Australian Institute of Mining and Metallurgy , 2016. p. 275-286
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-32309Local ID: 6c49c243-20d1-49dd-9cd3-bef089a91a23ISBN: 9781925100457 (print)ISBN: 9781925100464 (electronic)OAI: oai:DiVA.org:ltu-32309DiVA, id: diva2:1005543
Conference
The Third AusIMM International Geometallurgy Conference : Geometallurgy - Beyond Conception 15/06/2016 - 16/06/2016
Note

Godkänd; 2016; 20160617 (viklis)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-11-29Bibliographically 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)
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, 09:30 (English)
Opponent
Supervisors
Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-11-14Bibliographically approved

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Lishchuk, ViktorLund, CeciliaLamberg, Pertti

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