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Bringing predictability into a geometallurgical program: An iron ore case study
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-9227-2470
2019 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Skapande av predikterbarhet i ett geometallurgiskt program : en fallstudie med järbnmalm (Swedish)
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 [en]
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: urn:nbn:se:ltu:diva-71580ISBN: 978-91-7790-266-9 (print)ISBN: 978-91-7790-267-6 (electronic)OAI: oai:DiVA.org:ltu-71580DiVA, id: diva2:1263199
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
List of papers
1. The geometallurgical framework: Malmberget and Mikheevskoye case studies
Open this publication in new window or tab >>The geometallurgical framework: Malmberget and Mikheevskoye case studies
2015 (English)In: Mining Science, ISSN 2300-9586, Vol. 22, no Special Issue 2, p. 57-66Article in journal (Refereed) Published
Abstract [en]

Geometallurgy is a growing area within a mineral processing industry. It brings together tasks of geologists and mineral processing engineers to do short and medium term production planning. However, it is also striving to deal with long term tasks such as changes in either production flow sheet or considering different scenarios. This paper demonstrates capabilities of geometallurgy through two case studies from perspective of Minerals and Metallurgical Engineering division Lulea University of Technology. A classification system of geometallurgical usages and approaches was developed in order to describe a working framework. A practical meaning of classification system was proved in two case studies: Mikheevskoye (Russia) and Malmberget (Sweden) projects. These case studies, where geometallurgy was applied in a rather systematic way, have shown the amount of work required for moving the project within the geometallurgical framework, which corresponds to shift of the projects location within the geometallurgical classification system.

National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-31817 (URN)10.5277/ms150206 (DOI)000376381700007 ()61ccc4ff-d06f-480f-8831-8077d0c50aa2 (Local ID)61ccc4ff-d06f-480f-8831-8077d0c50aa2 (Archive number)61ccc4ff-d06f-480f-8831-8077d0c50aa2 (OAI)
Conference
Conference of Doctoral students and Young Scientists : 20/05/2015 - 22/05/2015
Note

Godkänd; 2015; 20150629 (viklis); Konferensartikel i tidskrift

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-11-14Bibliographically approved
2. Geometallurgical characterisation of Leveäniemi iron ore: Unlocking the patterns
Open this publication in new window or tab >>Geometallurgical characterisation of Leveäniemi iron ore: Unlocking the patterns
Show others...
2019 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 131, p. 325-335Article in journal (Refereed) Published
Abstract [en]

As part of a geometallurgical program for the Leveänimei iron ore mine, the Davis tube was used as proxy to classify ore types, predict iron recoveries in wet low-intensity magnetic separation (WLIMS), and to estimate liberation of mixed particles. The study was conducted by testing 13 iron ore samples with a Davis tube and a laboratory WLIMS. Ore feed was studied for modal mineralogy and liberation distribution with Automated Scanning Electron Microscopy. Data analyses to detect the patterns and data dependencies were done with multivariate statistics: principal component analysis, and projection to latent structures regression. Results show that a simple index (XLTU) based on mass pull (yield) in the Davis tube is capable of easy classification of magnetite ores. Using Davis tube mass pull and iron recovery, together with iron and Satmagan head grades may predict iron recovery in WLIMS. Also, the variability in Fe-oxides liberation pattern for magnetite semi-massive ores can be explained with the chemical composition of the Davis tube concentrate. It is concluded that the Davis tube test is better used only for marginal ores, since iron oxide minerals tend to be fully liberated in high-grade magnetite massive ores after grinding. The developed models may be used in populating a production block model.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Davis tube; Magnetic separation; Liberation; Apatite iron ore; Leveäniemi
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-71578 (URN)10.1016/j.mineng.2018.11.034 (DOI)000460495600036 ()2-s2.0-85057250019 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-12-05 (inah)

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2019-04-24Bibliographically approved
3. Evaluation of sampling in geometallurgical programs through synthetic deposit model
Open this publication in new window or tab >>Evaluation of sampling in geometallurgical programs through synthetic deposit model
2016 (English)In: (IMPC 2016), Canadian Institute of Mining, Metallurgy and Petroleum, 2016Conference paper, Published paper (Refereed)
Abstract [en]

The main purpose of geometallurgy is to develop a model to predict the variability in the mineralprocessing performance within the ore body. Geometallurgical tests used for developing such a model need to be fast, practical and inexpensive and include as an input data relevant and measureable geological parameters like elemental grades, mineral grades and grain size. Important in each geometallurgical program is to define the number of samples needed to be sent for geometallurgical testing to enable reliable metallurgical forecast. This is, however, a complicated question that does not have a generic answer.

To study the question on sampling a simulation environment was built including a synthetic orebody and sampling & assaying module. A synthetic Kiruna type iron oxide - apatite deposit was established based on case studies of Malmberget ore. The synthetic ore body includes alike variability in rock types, modal mineralogy, chemical composition, density and mineral textures as its real life counterpart. The synthetic ore body was virtually sampled with different sampling densities for a Davis tube testing, a geometallurgical test characterising response in magnetic separation. Based on the test results a forecast for the processing of the whole ore body was created. The forecasted parameters included concentrate tonnages, iron recovery and concentrate quality in terms of iron, phosphorous and silica contents.

The study shows that the number of samples required for forecasting different geometallurgicalparameters varies. Reliable estimates on iron recovery and concentrate mass pull can be made with about 5-10 representative samples by geometallurgical ore type. However, when the concentrate quality in terms of impurities needs to be forecasted, the sample number is more than 20 times higher. This is due to variation in mineral liberation and shows the importance of developing techniques to collect qualitative information on mineral and ore textures in geometallurgy.

Place, publisher, year, edition, pages
Canadian Institute of Mining, Metallurgy and Petroleum, 2016
Keywords
Sampling, synthetic ore body, simulation, geometallurgical testing framework.
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-59640 (URN)2-s2.0-85048351936 (Scopus ID)978-1-926872-29-2 (ISBN)
Conference
XXVIII International Mineral Processing Congress (IMPC 2016), Quebec City, Canada, 11–15 September 2016
Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2018-11-14Bibliographically approved
4. Development of a Synthetic Ore Deposit Model for Geometallurgy
Open this publication in new window or tab >>Development of a Synthetic Ore Deposit Model for Geometallurgy
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
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-32309 (URN)6c49c243-20d1-49dd-9cd3-bef089a91a23 (Local ID)9781925100457 (ISBN)9781925100464 (ISBN)6c49c243-20d1-49dd-9cd3-bef089a91a23 (Archive number)6c49c243-20d1-49dd-9cd3-bef089a91a23 (OAI)
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
5. Simulation of a Mining Value Chain with a Synthetic Ore Body Model: Iron Ore Example
Open this publication in new window or tab >>Simulation of a Mining Value Chain with a Synthetic Ore Body Model: Iron Ore Example
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
Keywords
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:nbn:se:ltu:diva-71577 (URN)10.3390/min8110536 (DOI)000451530500063 ()2-s2.0-85057331919 (Scopus ID)
Note

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

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2019-02-27Bibliographically approved
6. Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy
Open this publication in new window or tab >>Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy
2019 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 134, p. 156-165Article in journal (Refereed) Published
Abstract [en]

A spatial model for process properties allows for improvedproduction planning in mining by considering the process variability ofthe deposit. Hitherto, machine-learning modelling methods have beenunderutilised for spatial modelling in geometallurgy. The goal of thisproject is to find an efficient way to integrate process properties (ironrecovery and mass pull of the Davis tube, iron recovery and mass pull ofthe wet low intensity magnetic separation, liberation of iron oxides, andP_80) for an iron ore case study into a spatial model using machinelearningmethods. The modelling was done in two steps. First, the processproperties were deployed into a geological database by building nonspatialprocess models. Second, the process properties estimated in thegeological database were extracted together with only their coordinates(x, y, z) and iron grades and spatial process models were built.Modelling methods were evaluated and compared in terms of relativestandard deviation (RSD). The lower RSD for decision tree methodssuggests that those methods may be preferential when modelling non-linearprocess properties.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Data Integration, Spatial Model, WLIMS, Davis Tube, Iron Ore, Machine-learning, Geometallurgy.
National Category
Mineral and Mine Engineering Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-71579 (URN)10.1016/j.mineng.2019.01.032 (DOI)000462107200015 ()2-s2.0-85060907032 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-02-11 (svasva)

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2019-04-11Bibliographically approved

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