Bringing predictability into a geometallurgical program : An iron ore case study
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 wit...
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Luleå tekniska universitet, Mineralteknik och metallurgi
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ftluleatu:oai:DiVA.org:ltu-71580 2024-05-12T08:06:49+00:00 Bringing predictability into a geometallurgical program : An iron ore case study Skapande av predikterbarhet i ett geometallurgiskt program : en fallstudie med järbnmalm Lishchuk, Viktor 2019 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71580 eng eng LuleÃ¥ tekniska universitet, Mineralteknik och metallurgi LuleÃ¥ Doctoral thesis / LuleÃ¥ University of Technology 1 jan 1997 → …, 1402-1544 orcid:0000-0002-9227-2470 http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71580 urn:isbn:978-91-7790-266-9 urn:isbn:978-91-7790-267-6 info:eu-repo/semantics/openAccess 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 Mineral and Mine Engineering Mineral- och gruvteknik Metallurgy and Metallic Materials Metallurgi och metalliska material Doctoral thesis, comprehensive summary info:eu-repo/semantics/doctoralThesis text 2019 ftluleatu 2024-04-17T14:01:42Z 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 ... Doctoral or Postdoctoral Thesis Malmberget Luleå University of Technology Publications (DiVA) Niemi ENVELOPE(24.516,24.516,66.701,66.701) |
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Open Polar |
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Luleå University of Technology Publications (DiVA) |
op_collection_id |
ftluleatu |
language |
English |
topic |
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 Mineral and Mine Engineering Mineral- och gruvteknik Metallurgy and Metallic Materials Metallurgi och metalliska material |
spellingShingle |
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 Mineral and Mine Engineering Mineral- och gruvteknik Metallurgy and Metallic Materials Metallurgi och metalliska material Lishchuk, Viktor Bringing predictability into a geometallurgical program : An iron ore case study |
topic_facet |
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 Mineral and Mine Engineering Mineral- och gruvteknik Metallurgy and Metallic Materials Metallurgi och metalliska material |
description |
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 ... |
format |
Doctoral or Postdoctoral Thesis |
author |
Lishchuk, Viktor |
author_facet |
Lishchuk, Viktor |
author_sort |
Lishchuk, Viktor |
title |
Bringing predictability into a geometallurgical program : An iron ore case study |
title_short |
Bringing predictability into a geometallurgical program : An iron ore case study |
title_full |
Bringing predictability into a geometallurgical program : An iron ore case study |
title_fullStr |
Bringing predictability into a geometallurgical program : An iron ore case study |
title_full_unstemmed |
Bringing predictability into a geometallurgical program : An iron ore case study |
title_sort |
bringing predictability into a geometallurgical program : an iron ore case study |
publisher |
Luleå tekniska universitet, Mineralteknik och metallurgi |
publishDate |
2019 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71580 |
long_lat |
ENVELOPE(24.516,24.516,66.701,66.701) |
geographic |
Niemi |
geographic_facet |
Niemi |
genre |
Malmberget |
genre_facet |
Malmberget |
op_relation |
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, 1402-1544 orcid:0000-0002-9227-2470 http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71580 urn:isbn:978-91-7790-266-9 urn:isbn:978-91-7790-267-6 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1798849351410253824 |