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Lund, Cecilia
Publications (10 of 26) Show all publications
Koch, P.-H., Lund, C. & Rosenkranz, J. (2019). Automated drill core mineralogical characterization method for texture classification and modal mineralogy estimation for geometallurgy. Minerals Engineering, 136, 99-109
Open this publication in new window or tab >>Automated drill core mineralogical characterization method for texture classification and modal mineralogy estimation for geometallurgy
2019 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 136, p. 99-109Article in journal (Refereed) Published
Abstract [en]

In geometallurgy, a process model operating at the mineral liberation level needs quantitative textural information about the ore. The utilization of this information within process modeling and simulation will increase the quality of the predictions.

In this study, descriptors derived from color images and machine learning algorithms are used to group drill core intervals into textural classes and estimate mineral maps by automatic pixel classification. Different descriptors and classifiers are compared, based on their accuracy and capacity to be automated. Integration of the classifier approach with mineral processing simulation is also demonstrated. The quantification of textural information for mineral processing simulation introduced new tools towards an integrated information flow from the drill cores to a geometallurgical model.

The approach has been verified by comparing traditional geological texture classification against the one obtained from automatic methods. The tested drill cores are sampled from a porphyry copper deposit located in Northern Sweden.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2019
Keywords
Geometallurgy Drill core scanning Classification Texture Process mineralogy
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-73323 (URN)10.1016/j.mineng.2019.03.008 (DOI)000470338700012 ()2-s2.0-85063084058 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-03-26 (inah)

Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-06-20Bibliographically approved
Lishchuk, V., Lund, C. & Ghorbani, Y. (2019). Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy. Minerals Engineering, 134, 156-165
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
Lishchuk, V., Lund, C., Koch, P.-H., Mattias, G. & Pålsson, B. (2019). Geometallurgical characterisation of Leveäniemi iron ore: Unlocking the patterns. Minerals Engineering, 131, 325-335
Open this publication in new window or tab >>Geometallurgical characterisation of Leveäniemi iron ore: Unlocking the patterns
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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
Lishchuk, V., Lund, C., Lamberg, P. & Miroshnikova, E. (2018). Simulation of a Mining Value Chain with a Synthetic Ore Body Model: Iron Ore Example. Minerals, 8(11), Article ID 536.
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
Bauer, T., Andersson, J., Sarlus, Z., Lund, C. & Kearney, T. (2018). Structural controls on the setting, shape and hydrothermal alteration of the Malmberget IOA deposit, northern Sweden. Economic geology and the bulletin of the Society of Economic Geologists, 113(2), 377-395
Open this publication in new window or tab >>Structural controls on the setting, shape and hydrothermal alteration of the Malmberget IOA deposit, northern Sweden
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2018 (English)In: Economic geology and the bulletin of the Society of Economic Geologists, ISSN 0361-0128, E-ISSN 1554-0774, Vol. 113, no 2, p. 377-395Article in journal (Refereed) Published
Abstract [en]

The Malmberget iron oxide-apatite (IOA) deposit in northern Sweden is one of the largest underground iron ore mine operations in the world with estimated ore reserves in 2015 of 346 million metric tons (Mt) at 42.5% Fe. The underground operation is concentrated in 10 orebodies of 5 to 245 Mt each, which currently produce 17.4 Mt of apatite iron ore per year. Structural investigations were combined with data on hydrothermal mineral assemblages in order to reconstruct the relative timing of ore-forming, deformation, and overprinting hydrothermal events. The results improve the understanding of structural geometries, relationships, and control on orebody transposition in the deposit. A first compressional event (D1) around 1.88 Ga represents the main metamorphic event (M1) in the area and was responsible for a strong transposition of potential primary layering and the orebodies and led to the formation of a composite S0/1 fabric. A subsequent F2 folding event around 1.80 Ga resulted in the formation of an open, slightly asymmetric synform with a steeper southeast limb and a roughly SW-plunging fold axis. The result of structural modeling implies that the ore formed at two separate horizons. The folding was accompanied by stretching, resulting in boudinage of the iron orebodies. D2-related high-strain zones and syntectonic granites triggered the remobilization of amphibole, biotite, magnetite, and hematite and controlled the formation of iron oxide-copper-gold (IOCG)-type hydrothermal alteration, including an extensive K-feldspar alteration accompanied with sulfides, scapolite, and epidote. This shows a distinct time gap of at least 80 m.y. between the formation of iron oxides and sulfides. Brittle structures and the lack of an axial planar parallel fabric in conjunction with previous results suggest upper crustal, low-pressure, and high-temperature conditions during this D2 deformation phase, indicating a hydrothermal event rather than a purely regional metamorphic compression. It is proposed in the present study that the Malmberget IOA deposit was deformed and metamorphosed during a 1.88 Ga crustal shortening event. Moreover, the Malmberget IOA deposit was affected by a 1.8 Ga folding and hydrothermal event that is related to a regional IOCG overprint.

Place, publisher, year, edition, pages
Society of Economic Geologists, 2018
Keywords
Palaeoproterozoic, IOA, IOCG, deformation, 3D-modelling
National Category
Geology Metallurgy and Metallic Materials
Research subject
Ore Geology; Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-66455 (URN)10.5382/econgeo.2018.4554 (DOI)000429317200003 ()2-s2.0-85043379790 (Scopus ID)
Projects
Multi-scale 4-dimensional geological modeling of the Gällivare area
Note

Validerad;2018;Nivå 2;2018-03-19 (andbra)

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2018-11-19Bibliographically approved
Lishchuk, V., Lund, C. & Lamberg, P. (2016). Development of a Synthetic Ore Deposit Model for Geometallurgy (ed.). In: (Ed.), Geomet16: Third AusIMM International Geometallurgy Conference 2016 : Conference Proceedings. Paper presented at The Third AusIMM International Geometallurgy Conference : Geometallurgy - Beyond Conception 15/06/2016 - 16/06/2016 (pp. 275-286). Parkville, Victoria: The Australian Institute of Mining and Metallurgy
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
Lishchuk, V., Lamberg, P. & Lund, C. (2016). Evaluation of sampling in geometallurgical programs through synthetic deposit model. In: IMPC 2016: XXVIII International Mineral Processing Congress Proceedings. Paper presented at XXVIII International Mineral Processing Congress, Québec City, September 11-15 2016.
Open this publication in new window or tab >>Evaluation of sampling in geometallurgical programs through synthetic deposit model
2016 (English)In: IMPC 2016: XXVIII International Mineral Processing Congress Proceedings, 2016Conference paper, Oral presentation with published abstract (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.

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-59500 (URN)978-1-926872-29-2 (ISBN)
Conference
XXVIII International Mineral Processing Congress, Québec City, September 11-15 2016
Available from: 2016-10-05 Created: 2016-10-05 Last updated: 2018-03-01Bibliographically approved
Lishchuk, V., Lamberg, P. & Lund, C. (2016). Evaluation of sampling in geometallurgical programs through synthetic deposit model. In: (IMPC 2016): . Paper presented at XXVIII International Mineral Processing Congress (IMPC 2016), Quebec City, Canada, 11–15 September 2016. Canadian Institute of Mining, Metallurgy and Petroleum
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
Lishchuk, V., Lamberg, P. & Lund, C. (2015). Classification of Geometallurgical Programs Based on Approach and Purpose (ed.). In: (Ed.), A.S. Andre-Mayer; M. Cathelineau; P. Muchez; E. Pirard; S. Sindern (Ed.), Mineral Resources in a Sustainable World: . Paper presented at SGA Biennial Meeting on Mineral Resources in a Sustainable World : 24/08/2015 - 27/08/2015 (pp. 1431-1434).
Open this publication in new window or tab >>Classification of Geometallurgical Programs Based on Approach and Purpose
2015 (English)In: Mineral Resources in a Sustainable World / [ed] A.S. Andre-Mayer; M. Cathelineau; P. Muchez; E. Pirard; S. Sindern, 2015, p. 1431-1434Conference paper, Published paper (Refereed)
Abstract [en]

Geometallurgy is a rapidly developing holistic approach for combining geological and metallurgical information for production management purposes in mining operations. The industrial application of geometallurgy is called a geometallurgical program and one of the largest challenges within geometallurgical programs is to select appropriate methods for resource characterization. Aim of such characterization is the prediction of metallurgical performance of different ore types and geometallurgical domains with the required accuracy. More than 25 geometallurgical programs from mining operations around the world were reviewed and a classification system developed with aim to clarify how geometallurgy is used and what methods are applied. The result is summarized as a two-dimensional classification which illustrates what geometallurgical approaches are used and how collected data is applied. In addition the proposed classification system gives a perspective of what are the minimum requirements for a geometallurgical program at different levels of application and who are the main participants that should be engaged in a geometallurgical program. The classification system can also be used as a reference system for benchmarking of different geometallurgical endeavours.

National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-35009 (URN)95c0fff5-30db-43f2-9d44-190ba9f3729d (Local ID)978-2-85555-066-4 (ISBN)95c0fff5-30db-43f2-9d44-190ba9f3729d (Archive number)95c0fff5-30db-43f2-9d44-190ba9f3729d (OAI)
Conference
SGA Biennial Meeting on Mineral Resources in a Sustainable World : 24/08/2015 - 27/08/2015
Note
Validerad; 2016; Nivå 1; 20160621 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-03-01Bibliographically approved
Lishchuk, V., Lamberg, P. & Lund, C. (2015). Classification of geometallurgical programs based on approach and purpose (ed.). Paper presented at SGA 2015 : 24/08/2015 - 27/08/2015. Paper presented at SGA 2015 : 24/08/2015 - 27/08/2015.
Open this publication in new window or tab >>Classification of geometallurgical programs based on approach and purpose
2015 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Geometallurgy is a rapidly developing holistic approach for combining geological and metallurgical information for production management purposes in mining operations. The industrial application of geometallurgy is called a geometallurgical program and one of the largest challenges within geometallurgical programs is to select appropriate methods for resourcecharacterization. Aim of such characterization is the prediction of metallurgical performance of different ore types and geometallurgical domains with the required accuracy.More than 25 geometallurgical programs from mining operations around the world were reviewed and a classification system developed with aim to clarify how geometallurgy is used and what methods are applied. The result is summarized as a two-dimensional classification which illustrates what geometallurgical approaches are used and how collected data is applied.In addition the proposed classification system gives a perspective of what are the minimum requirements for a geometallurgical program at different levels ofapplication and who are the main participants that should be engaged in a geometallurgical program. The classification system can also be used as a reference system for benchmarking of different geometallurgicalendeavours.

National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-33109 (URN)7e3c3df0-e160-46aa-964f-8e11d2988b6f (Local ID)7e3c3df0-e160-46aa-964f-8e11d2988b6f (Archive number)7e3c3df0-e160-46aa-964f-8e11d2988b6f (OAI)
Conference
SGA 2015 : 24/08/2015 - 27/08/2015
Note
Godkänd; 2015; 20150827 (viklis)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-03-01Bibliographically approved
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