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  • 1.
    Ghorbani, Yousef
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Nwaila, Glen T.
    School of Geosciences, University of the Witwatersrand, Private Bag 3, Wits, 2050, South Africa.
    Zhang, Steven E.
    PG Techno Wox, 43 Patrys Avenue, Helikon Park, Randfontein 1759, South Africa.
    Hay, Martyn P.
    Eurus Mineral Consultants (EMC), Plettenberg Bay, South Africa.
    Bam, Lunga C.
    Department Radiation Science, Necsa, PO Box 582, Pretoria, South Africa.
    Guntoro, Pratama Istiadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Repurposing legacy metallurgical data Part I: A move toward dry laboratories and data bank2020In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 159, article id 106646Article, review/survey (Refereed)
    Abstract [en]

    Advancements in modern mineral processing has been driven by technology and fuelled by market economics of supply and demand. Over the last three decades, the demand for various minerals has steadily increased, while the mineral processing industry has seen an unavoidable increase in the treatment of complex ores, continuous decline in plant feed grade and poor plant performance partly due to blending of ores with dissimilar properties. Despite these challenges, production plant data that are routinely generated are usually underutilised. In this contribution and aligned with the direction of the 4th industrial revolution, we highlight the value of legacy metallurgical plant data and the concept of a dry laboratory approach. This study is presented in two parts. In the current paper (Part I), a comprehensive review of the potential for the combination of modern analytical technology with data analytics to generate a new competence for process optimisation are provided. To demonstrate the value of data within the extractive metallurgy discipline, we employ data analytics and simulation to examine gold plant performance and the flotation process in two separate case studies in the second paper (Part II). This was done with the aim of showcasing relevant plant data insights, and extract parameters that should be targeted for plant design and performance optimisation. We identify several promising technologies that integrate well with existing mineral processing plants and testing laboratories to exploit the concept of a dry laboratory, in order to enhance pre-existing mineral processing chains. It also sets the passage in terms of the value of innovative analysis of existing and simulation data as part of the new world of data analytics. Using data- and technology-driven initiatives, we propose the establishment of dry laboratories and data banks to ultimately leverage integrated data, analytics and process simulation for effective plant design and improved performance.

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  • 2.
    Guntoro, Pratama Istiadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In recent years, automated mineralogy has become an essential tool in geometallurgy. Automated mineralogical tools allow the acquisition of mineralogical and liberation data of ore particles in a sample. These particle data can then be used further for particle-based mineral processing simulation in the context of geometallurgy. However, most automated mineralogical tools currently in application are based on two-dimensional (2D) microscopy analysis, which are subject to stereological error when analyzing three-dimensional(3D) object such as ore particles. Recent advancements in X-ray microcomputed tomography (µCT) have indicated great potential of such system to be the next automated mineralogical tool. µCT's main advantage lies on its ability in monitoring 3D internal structure of the ore at resolutions down to few microns, eliminating stereological error obtained from 2D analysis. Aided with the continuous developments of computing capability of 3D data, it is only the question of time that µCT system becomes an interesting alternative in automated mineralogy system.

    This study aims to evaluate the potential of implementing µCT as an automated mineralogical tool in the context of geometallurgy. First, a brief introduction about the role of automated mineralogy in geometallurgy is presented. Then, the development of µCT system to become an automated mineralogical tool in the context of geometallurgy andprocess mineralogy is discussed (Paper 1). The discussion also reviews the available data analysis methods in extracting ore properties (size, mineralogy, texture) from the 3D µCT image (Paper 2). Based on the review, it was found that the main challenge inperforming µCT analysis of ore samples is the difficulties associated to the segmentation of the mineral phases in the dataset. This challenge is adressed through the implementation of machine learning techniques using Scanning Electron Microscope (SEM) data as a reference to differentiate the mineral phases in the µCT dataset (Paper 3).

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  • 3.
    Guntoro, Pratama Istiadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    X-ray microcomputed tomography (µCT) as a potential tool in particle-based geometallurgy2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In recent years, automated mineralogy has become an essential tool in geometallurgy. Automated mineralogical tools allows the acquisition of mineralogical, textural, and liberation information of ore samples. Such information is essential in the context of geometallurgy where it is needed for estimating the process response of the ores. Most automated mineralogical tools currently in application are based on two-dimensional (2D) microscopy analysis, which are subject to stereological error when analyzing three-dimensional (3D) object such as ore particles. Recent advancements in X-ray microcomputed tomography (μCT) have indicated great potential of such system to be the next automated mineralogical tool. μCT’s main advantage lies in its ability in monitoring 3D internal structure of the ore at resolutions down to few microns, eliminating stereological error obtained from 2D analysis. Aided with the continuous developments of computing capability of 3D data, it is only the question of time that μCT system becomes an interesting alternative automated mineralogical tool for ore characterization.

    This study systematically evaluates the applicability of μCT as an alternative tool for ore characterization in the context of geometallurgy. The focus has been to assess the potential strengths of 3D data generated from μCT as well as how such data can offer a new perspective in characterizing the ore. Some of the limitations of 3D μCT data in describing the ore were also discussed, with alternative methods proposed to address these limitations. The main hypothesis of the study is that 3D data generated from μCT can be of a value in a geometallurgical program. This study has been conducted in three different parts in order to systematically test the hypothesis. The first part of the study evaluated the use of μCT to obtain mineralogical characteristics of the ore. Mineralogy of the ore is the cornerstone information needed to proceed with further characterization of the ore. It is therefore important to establish whether μCT are capable to obtain such information. The study demonstrated the well-known limitation of μCT, namely its difficulty when dealing with minerals of similar attenuation. The study demonstrated how machine-learning based methods complemented with 2D data from automated mineralogy could address the limitation.

    The capability of μCT for ore texture characterization was evaluated in the second part of the study. The main strength of μCT for core scanning is highlighted in the study, in which the possibility of using μCT for automated drill core recognition was demonstrated. Some of the popular texture analysis methods in 2D such as Local Binary Pattern (LBP) and Association Index Matrix (AIM) were extended to 3D in order to capture the textural pattern in drill core samples. Furthermore, a classification scheme based on these textural characteristics was devised for the automated recognition of the drill cores. An accuracy of 84-88% was achieved in the classification scheme, illustrating the potential of μCT for such task.

    The last part of the study concerns the use of μCT for mineral liberation modeling. Combining both mineralogical and textural information obtained from the previous parts of the study, a liberation model to forecast particle population from the 3D ore texture was created. The model was based on various breakage types such as preferential, phase boundary, and random breakage. The contribution of each breakage type to the final particle population could then be adjusted with actual particles produced from experimental comminution. Accurate forecasting of particle population is one of the key componentin the particle-based geometallurgy, in which the particle carries the ore characteristics to the beneficiation process. Utilizing these particles in a process modeling and simulation would give some idea about the process response of the ore. The integration of 3D data from μCT with the liberation model could potentially complete the link from ore characteristics to the process behavior in the framework of particle-based geometallurgy.

    Combination of these three parts of the study can open up a 3D path of particle-based geometallurgy. The study has demonstrated the efficient extraction of crucial ore characteristics such as texture, mineralogy, and mineral liberation using μCT. Key limitations and potential measures to address them have also been discussed in the study. Coupled with a framework for process simulation using such ore characteristics as an input, the 3D path of particle-based geometallurgy can be realized. Future research should be dedicated to develop such framework, as the establishment of μCT as an alternative ore characterization tool should also be motivated by from the downstream processes, i.e.whether the 3D μCT data can unlock a new perspective in process modeling and simulation compared to the conventional 2D data. This new perspective can help to build more accurate process prediction and production forecasting, which can ultimately guide the decision-making process for efficient resource management as the essential core of a geometallurgical program.

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  • 4.
    Guntoro, Pratama Istiadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Butcher, Alan R.
    Geological Survey of Finland GTK, PO Box 96, 02151, Espoo, Finland.
    Kuva, Jukka
    Geological Survey of Finland GTK, PO Box 96, 02151, Espoo, Finland.
    Rosenkranz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (µCT)2020In: Natural Resources Research, ISSN 1520-7439, E-ISSN 1573-8981, Vol. 29, no 6, p. 3547-3565Article in journal (Refereed)
    Abstract [en]

    Texture is one of the critical parameters that affect the process behavior of ore minerals. Traditionally, texture has been described qualitatively, but recent works have shown the possibility to quantify mineral textures with the help of computer vision and digital image analysis. Most of these studies utilized 2D computer vision to evaluate mineral textures, which is limited by stereological error. On the other hand, the rapid development of X-ray microcomputed tomography (µCT) has opened up new possibilities for 3D texture analysis of ore samples. This study extends some of the 2D texture analysis methods, such as association indicator matrix (AIM) and local binary pattern (LBP) into 3D to get quantitative textural descriptors of drill core samples. The sensitivity of the methods to textural differences between drill cores is evaluated by classifying the drill cores into three textural classes using methods of machine learning classification, such as support vector machines and random forest. The study suggested that both AIM and LBP textural descriptors could be used for drill core classification with overall classification accuracy of 84–88%.

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  • 5.
    Guntoro, Pratama Istiadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Koch, Pierre-Henri
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Rosenkranz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods2019In: Minerals, E-ISSN 2075-163X, Vol. 9, no 3, article id 183Article in journal (Refereed)
    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.

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  • 6.
    Guntoro, Pratama Istiadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Parian, Mehdi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Butcher, Alan R.
    Geological Survey of Finland GTK, PO Box 96, 02151 Espoo, Finland.
    Kuva, Jukka
    Geological Survey of Finland GTK, PO Box 96, 02151 Espoo, Finland.
    Rosenkranz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Development and experimental validation of a texture-based 3D liberation model2021In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 164, article id 106828Article in journal (Refereed)
    Abstract [en]

    Prediction of mineral liberation is one of the key steps in establishing a link between ore texture and its processing behavior. With the rapid development of X-ray Microcomputed Tomography (µCT), the extension of liberation modeling into 3D realms becomes possible. Liberation modeling allows for the generation of particle population from 3D texture data in a completely non-destructive manner. This study presents a novel texture-based 3D liberation model that is capable of predicting liberation from 3D drill core image acquired by µCT. The model takes preferential, phase-boundary, and random breakage into account with differing relative contributions to the liberation depending on the ore texture itself. The model was calibrated using experimental liberation data measured in 3D µCT. After calibration, the liberation model was found to be capable of explaining on average of around 84% of the variance in the experimental liberation data. The generated particle population can be used for particle-based process simulation to evaluate the process responses of various ore textures subjected to various modes of breakage.

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  • 7.
    Guntoro, Pratama Istiadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering. Kaunis Iron AB, Bert-Ove Johanssons väg 8, SE-984 91, Pajala, Sweden.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Rosenkranz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing2021In: Berg- und Huttenmännische Monatshefte (BHM), ISSN 0005-8912, E-ISSN 1613-7531, Vol. 166, no 8, p. 384-389Article in journal (Refereed)
    Abstract [en]

    Current advances and developments in automated mineralogy have made it a crucial key technology in the field of process mineralogy, allowing better understanding and connection between mineralogy and the beneficiation process. The latest developments in X‑ray micro-computed tomography (µCT) have shown a great potential to let it become the next-generation automated mineralogy technique. µCT’s main benefit lies in its capability to allow 3D monitoring of the internal structure of the ore sample at resolutions down to a few hundred nanometers, thus excluding the common stereological error in conventional 2D analysis. Driven by the technological and computational progress, µCT is constantly developing as an analysis tool and successively it will become an essential technique in the field of process mineralogy. This study aims to assess the potential application of µCT systems, for 3D ore characterization through relevant case studies. The opportunities and platforms that µCT 3D ore characterization provides for process design and simulation in mineral processing are presented.

  • 8.
    Guntoro, Pratama Istiadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Rosenkranz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Use of X-ray Micro-computed Tomography (µCT) for 3-D Ore Characterization: A Turning Point in Process Mineralogy2019In: IMCET 2019 - Proceedings of the 26th International Mining Congress and Exhibition of Turkey, Baski , 2019, p. 1044-1054Conference paper (Refereed)
    Abstract [en]

    In recent years, automated mineralogy has become an essential enabling technology in the field of process mineralogy, allowing better understanding between mineralogy and the beneficiation process. Recent developments in X-ray micro-computed tomography (μCT) as a non-destructive technique have indicated great potential to become the next automated mineralogy technique. μCT’s main advantage lies in its ability to allow 3-D monitoring of internal structure of the ore at resolutions down to a few hundred nanometers, thereby eliminating the stereological error encountered in conventional 2-D analysis. Driven by the technological and computational progress, the technique is continuously developing as an analysis tool in ore characterization and subsequently it foreseen thatμCT will become an indispensable technique in the field of process mineralogy. Although several software tools have been developed for processing μCT dataset, but the main challenge in μCT data analysis remains in the mineralogical analysis, where μCT data often lacks contrast between mineral phases, making segmentation difficult. In this paper, an overview of some current applications of μCT in ore characterization is reviewed, alongside with it potential implications to process mineralogy. It also describes the current limitations of its application and concludes with outlook on the future development of 3-D ore characterization.

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  • 9.
    Guntoro, Pratama Istiadi
    et al.
    Aalto University, School of Chemical Engineering.
    Jokilaakso, Ari
    Aalto University, School of Chemical Engineering.
    Hellstén, Niko
    Aalto University, School of Chemical Engineering.
    Taskinen, Pekka
    Aalto University, School of Chemical Engineering.
    Copper matte - slag reaction sequences and separation processes in matte smelting2018In: Journal of Mining and Metallurgy, Section B: Metallurgy, ISSN 1450-5339, Vol. 54, no 3, p. 301-311Article in journal (Refereed)
    Abstract [en]

    While particle combustion and chalcopyrite oxidation in suspension smelting is well understood, few studies are available regarding the melt-melt reactions and the separation between copper matte and slag in matte smelting. In the present work, experimental investigations in air and argon atmosphere were conducted using a mixture of synthetic slag and chalcopyrite concentrate. The sequential reaction and separation processes occurring in matte smelting are outlined. Possible limiting factors in the overall process are also proposed. The result of the present work forms an important foundation for future work in the kinetic rate formulation of molten phase reactions between copper matte and slag in matte smelting.

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  • 10.
    Guntoro, Pratama Istiadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Tiu, Glacialle
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Lund, Cecilia
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Rosenkranz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data2019In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 142, article id 105882Article in journal (Refereed)
    Abstract [en]

    X-ray microcomputed tomography (µCT) offers a non-destructive three-dimensional analysis of ores but its application in mineralogical analysis and mineral segmentation is relatively limited. In this study, the application of machine learning techniques for segmenting mineral phases in a µCT dataset is presented. Various techniques were implemented, including unsupervised classification as well as grayscale-based and feature-based supervised classification. A feature matching method was used to register the back-scattered electron (BSE) mineral map to its corresponding µCT slice, allowing automatic annotation of minerals in the µCT slice to create training data for the classifiers. Unsupervised classification produced satisfactory results in terms of segmenting between amphibole, plagioclase, and sulfide phases. However, the technique was not able to differentiate between sulfide phases in the case of chalcopyrite and pyrite. Using supervised classification, around 50–60% of the chalcopyrite and 97–99% of pyrite were correctly identified. Feature based classification was found to have a poorer sensitivity to chalcopyrite, but produced a better result in segmenting between the mineral grains, as it operates based on voxel regions instead of individual voxels. The mineralogical results from the 3D µCT data showed considerable difference compared to the BSE mineral map, indicating stereological error exhibited in the latter analysis. The main limitation of this approach lies in the dataset itself, in which there was a significant overlap in grayscale values between chalcopyrite and pyrite, therefore highly limiting the classifier accuracy.

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  • 11.
    Patel, Alok
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Chemical Engineering.
    Enman, Josefine
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Chemical Engineering.
    Gulkova, Anna
    Boliden Mineral AB, SE- 936 32 Boliden, Sweden.
    Guntoro, Pratama Istiadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Dutkiewicz, Agata
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Chemical Engineering.
    Ghorbani, Yousef
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Rova, Ulrika
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Chemical Engineering.
    Christakopoulos, Paul
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Chemical Engineering.
    Matsakas, Leonidas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Chemical Engineering.
    Integrating biometallurgical recovery of metals with biogenic synthesis of nanoparticles2021In: Chemosphere, ISSN 0045-6535, E-ISSN 1879-1298, Vol. 263, article id 128306Article in journal (Refereed)
    Abstract [en]

    Industrial activities, such as mining, electroplating, cement production, and metallurgical operations, as well as manufacturing of plastics, fertilizers, pesticides, batteries, dyes or anticorrosive agents, can cause metal contamination in the surrounding environment. This is an acute problem due to the non-biodegradable nature of metal pollutants, their transformation into toxic and carcinogenic compounds, and bioaccumulation through the food chain. At the same time, platinum group metals and rare earth elements are of strong economic interest and their recovery is incentivized. Microbial interaction with metals or metals-bearing minerals can facilitate metals recovery. Metal nanoparticles are gaining increasing attention due to their unique characteristics and application as antimicrobial and antibiofilm agents, biocatalysts, in targeted drug delivery, for wastewater treatment, and in water electrolysis. Ideally, metal nanoparticles should be homogenous in shape and size, and not toxic to humans or the environment. Microbial synthesis of nanoparticles represents a safe, and environmentally friendly, alternative to chemical and physical methods. In this review article, we mainly focus on metal and metal salts nanoparticles synthesized by various microorganisms, such as bacteria, fungi, microalgae, and yeasts, as well as their advantages in biomedical, health, and environmental applications.

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