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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
Koch, P.-H. (2019). Computational methods and strategies for geometallurgy. (Doctoral dissertation). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Computational methods and strategies for geometallurgy
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Numeriska metoder och strategier för geometallurgi
Abstract [en]

At the interface of geology and mineral processing, geometallurgy is a powerful tool for enhancingresource efficiency. A spatial model that represents the ore body in terms ofmineralogyand physical properties is combined with a process model that describes the concentrationprocess. The performance of a given ore in the process is computed in terms of gradeand recovery of the mineral of interest in the concentrate, but also the presence of potentialpenalty elements and energy costs. The inclusion of ore performance indicators in a blockmodel yields a geometallurgical model that considers the variations in an ore body.Progress has been made in recent years to list and study different processing options interms of data requirements and implementation costs. While providing useful data, littleadvance was made to guide decision-making and to handle uncertainty. The objective has,therefore, been to develop, choose and validate computational methods that suggest optimaldecisions in the scope of geometallurgical strategies for an iron ore and a porphyry copperdeposit.The selected approach is based on an analysis of structure and regularity fromthe ore blockdown to the mineral grains. By selecting the appropriate mathematical tool for each scale,the dimension of the data is reduced and the different scales are then taken into account inmaking decisions. Methods introduced for dimension reduction include machine learningmodels, statistical models and spectral descriptors. The decision models rely on stochasticmulti-armed bandits which are a form of reinforcement learning. The presentation of thedifferent models proceeds by zooming in from coarse scale to fine scale then taking a stepback and analyze the implications. Data that was collected during sampling campaigns andindustrial plant surveys is used to design and verify the proposedmodels.iWith regard to the dimension reduction problem, results showed the method’s ability toclassify mineral textures and identify mineral phases with more than 90 percent accuracy onthe selected data sets of optical images and incorporate different physical properties into ageometallurgical ore type classification. Decision results showed that strategies in the case ofa feed grade control or when different ore types were identified, resulted in a twofold increaseof a reward function which is either Boolean (the product fulfills quality requirements ornot), or continuous (an economic objective). The cumulative value of the reward functionmeasured the optimality of a processing strategy. Quantitative methods were introduced toevaluate ore classification as well as geometallurgical strategies.The achieved results suggest the introduction of these computationalmethods in the practiceof geometallurgy. The increased knowledge of different ore type performances and appropriatemodels lead to optimal decisions for improved resource efficiency along the ore valuechain. This is achieved by bothmaximizing profit and decreasing environmental impact, forexample by choosing processing routes that minimize energy consumption.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2019
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Geometallurgy, process simulation, minerals, machine learning, textures, decision-making
National Category
Mineral and Mine Engineering Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-73363 (URN)978-91-7790-346-8 (ISBN)978-91-7790-347-5 (ISBN)
Public defence
2019-06-13, F531, F-huset, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2019-04-03 Created: 2019-03-29 Last updated: 2019-05-24Bibliographically 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
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
Guntoro, P. I., Ghorbani, Y., Koch, P.-H. & Rosenkranz, J. (2019). X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods. Minerals, 9(3), Article ID 183.
Open this publication in new window or tab >>X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods
2019 (English)In: Minerals, ISSN 2075-163X, E-ISSN 2075-163X, Vol. 9, no 3, article id 183Article in journal (Refereed) Published
Abstract [en]

The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
X-ray microcomputed tomography, data analysis, mineral characterization, texture, mineralogy
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-73224 (URN)10.3390/min9030183 (DOI)000464421700002 ()2-s2.0-85064225739 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-03-18 (svasva)

Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-04-30Bibliographically approved
Koch, P.-H. (2018). A numerical study of the effects of microwave pre-treatment on value liberation from a zinc ore. In: : . Paper presented at 29th International Mineral Processing Congress, IMPC 2018; Moscow; Russian Federation; 17-21 September 2018.
Open this publication in new window or tab >>A numerical study of the effects of microwave pre-treatment on value liberation from a zinc ore
2018 (English)Conference paper (Refereed)
Identifiers
urn:nbn:se:ltu:diva-72852 (URN)2-s2.0-85059374627 (Scopus ID)
Conference
29th International Mineral Processing Congress, IMPC 2018; Moscow; Russian Federation; 17-21 September 2018
Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-02-12
Koch, P.-H. (2018). A numerical study of the effects of microwavepre-treatment on value liberation from a zinc ore. In: Proceedings of the IMPC 2018: . Paper presented at IMPC 2018.
Open this publication in new window or tab >>A numerical study of the effects of microwavepre-treatment on value liberation from a zinc ore
2018 (English)In: Proceedings of the IMPC 2018, 2018Conference paper, Published paper (Refereed)
National Category
Mineral and Mine Engineering
Identifiers
urn:nbn:se:ltu:diva-73508 (URN)
Conference
IMPC 2018
Available from: 2019-04-08 Created: 2019-04-08 Last updated: 2019-04-08
Koch, P.-H. (2017). Particle generation for geometallurgical process modeling. (Licentiate dissertation). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Particle generation for geometallurgical process modeling
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A geometallurgical model is the combination of a spatial model representing an ore deposit and a process model representing the comminution and concentration steps in beneficiation. The process model itself usually consists of several unit models. Each of these unit models operates at a given level of detail in material characterization - from bulk chemical elements, elements by size, bulk minerals and minerals by size to the liberation level that introduces particles as the basic entity for simulation (Paper 1).

In current state-of-the-art process simulation, few unit models are defined at the particle level because these models are complex to design at a more fundamental level of detail, liberation data is hard to measure accurately and large computational power is required to process the many particles in a flow sheet. Computational cost is a consequence of the intrinsic complexity of the unit models. Mineral liberation data depends on the quality of the sampling and the polishing, the settings and stability of the instrument and the processing of the data.

This study introduces new tools to simulate a population of mineral particles based on intrinsic characteristics of the feed ore. Features are extracted at the meso-textural level (drill cores) (Paper 2), put in relation to their micro-textures before breakage and after breakage (Paper 3). The result is a population of mineral particles stored in a file format compatible to import into process simulation software. The results show that the approach is relevant and can be generalized towards new characterization methods.

The theory of image representation, analysis and ore texture simulation is briefly introduced and linked to 1-point, 2-point, and multiple-point methods from spatial statistics. A breakage mechanism is presented as a cellular automaton. Experimental data and examples are taken from a copper-gold deposit with a chalcopyrite flotation circuit, an iron ore deposit with a magnetic separation process.

This study is covering a part of a larger research program, PREP (Primary resource efficiency by enhanced prediction).

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2017. p. 76
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Geometallurgy, ore texture, texture classification, breakage simulation, process modeling
National Category
Mineral and Mine Engineering
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-63270 (URN)978-91-7583-904-2 (ISBN)978-91-7583-905-9 (ISBN)
Presentation
2017-06-02, C305, Luleå, 10:00 (English)
Supervisors
Projects
PREP
Funder
VINNOVA, 155152
Available from: 2017-05-08 Created: 2017-05-08 Last updated: 2017-11-24Bibliographically approved
Koch, P.-H. & Rosenkranz, J. (2017). Texture-based liberation models for comminution. In: Konferens i Mineralteknik 2017: Luleå 7-8 februari 2017. Paper presented at Konferens i Mineralteknik 2017, Luleå 7-8 februari 2017 (pp. 83-96). Luleå
Open this publication in new window or tab >>Texture-based liberation models for comminution
2017 (English)In: Konferens i Mineralteknik 2017: Luleå 7-8 februari 2017, Luleå, 2017, p. 83-96Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

The relation between breakage mechanisms and liberation is critical in mineral processing. Recent studies underline the importance of texture in liberation. This study reviews relevant liberation models and proposes a new method for generating particles using image processing algorithms. One new texture simulation method and its relevance for liberation simulation is also introduced.

Place, publisher, year, edition, pages
Luleå: , 2017
Keywords
geometallurgy, texture, mineral processing, simulation
National Category
Mineral and Mine Engineering
Identifiers
urn:nbn:se:ltu:diva-63205 (URN)
Conference
Konferens i Mineralteknik 2017, Luleå 7-8 februari 2017
Projects
PREP
Funder
VINNOVA, 155152
Available from: 2017-05-01 Created: 2017-05-01 Last updated: 2017-11-24Bibliographically approved
Koch, P.-H., Lamberg, P. & Rosenkranz, J. (2015). How to Build a Process Model in a Geometallurgical Program? (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. 1419-1422).
Open this publication in new window or tab >>How to Build a Process Model in a Geometallurgical Program?
2015 (English)In: Mineral Resources in a Sustainable World / [ed] A.S. Andre-Mayer; M. Cathelineau; P. Muchez; E. Pirard; S. Sindern, 2015, p. 1419-1422Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a literature review on ways to acquire relevant experimental data for the process model of a geometallurgical program. It identifies the needs in several unit models and proposes ideas for future developments

National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-37309 (URN)b49f7271-0a4d-4c65-b6bd-9ef4a02f9764 (Local ID)978-2-85555-066-4 (ISBN)b49f7271-0a4d-4c65-b6bd-9ef4a02f9764 (Archive number)b49f7271-0a4d-4c65-b6bd-9ef4a02f9764 (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: 2016-10-03 Created: 2016-10-03 Last updated: 2017-11-25Bibliographically approved
Lishchuk, V., Koch, P.-H., Lund, C. & Lamberg, P. (2015). The geometallurgical framework: Malmberget and Mikheevskoye case studies (ed.). Paper presented at Conference of Doctoral students and Young Scientists : 20/05/2015 - 22/05/2015. Mining Science, 22(Special Issue 2), 57-66
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4800-9533

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