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Minerals
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Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining...
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Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 23265

Special Issue Editors

Prof. Dr. Daniel Sbarbaro

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Guest Editor
Department of Electrical Engineering, Universidad de Concepción, Concepción 4030000, Chile
Interests: sensor signal processing; control systems and applications in mining and mineral processing
Prof. Dr. Eduardo Balladares

E-Mail Website
Guest Editor
Department of Metallurgical Engineering, Universidad de Concepción, 4030000 Concepción, Chile
Interests: high-temperature processes; metallurgical waste treatment and minimization; development of sensors for high-temperature metallurgical processes
Prof. Dr. Jorge Yañez

E-Mail Website
Guest Editor
Department of Analytic and Inorganic Chemistry, Universidad de Concepción, 4030000 Concepción, Chile
Interests: analytic chemistry; environmental chemistry; forensic chemistry; spectroscopic methods for mineral and metal analysis

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to contribute to this Special Issue of Minerals titled “Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing", which aims to cover advances and trends in spectral sensing systems for real-time characterization of mineral samples in mining and metallurgical processes.

As you know, the mineralogical and elemental characterization of ores, slurries, concentrates, and molten phases are key tasks during the extraction and processing of mineral resources. Most of these analytical characterizations are performed in specialized laboratories using time-consuming and costly procedures, which include sampling, physical and chemical treatments, and spectroscopic measurements and calibrations. This analytical information is often delivered to decision-makers with delay. In contrast, advances in online sensors are based on improved optics, opto-electronics, computing, data analysis, and communication technologies, which provide contactless measurements in real time, seamlessly connected to information systems.

We encourage you to publish your latest developments with respect to spectral sensors and analytical methods, including data processing that contributes to overcoming the existing gap in real-time analytics for the mining and metallurgical industry.

Prof. Dr. Daniel Sbarbaro
Prof. Dr. Eduardo Balladares
Prof. Dr. Jorge Yañez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mineralogy
  • Characterization
  • Elemental analysis
  • Chemometrics
  • LIBS
  • Spectroscopy
  • XRF
  • Reflectance spectroscopy
  • On-line sensors

Published Papers (7 papers)

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Research

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27 pages, 16333 KiB  
Article
Analysis of Garnet by Laser-Induced Breakdown Spectroscopy—Two Practical Applications
by Peter A. Defnet, Michael A. Wise, Russell S. Harmon, Richard R. Hark and Keith Hilferding
Minerals 2021, 11(7), 705; https://doi.org/10.3390/min11070705 - 29 Jun 2021
Cited by 8 | Viewed by 3126
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a simple and straightforward technique of atomic emission spectroscopy that can provide multi-element detection and quantification in any material, in-situ and in real time because all elements emit in the 200–900 nm spectral range of the LIBS optical [...] Read more.
Laser-induced breakdown spectroscopy (LIBS) is a simple and straightforward technique of atomic emission spectroscopy that can provide multi-element detection and quantification in any material, in-situ and in real time because all elements emit in the 200–900 nm spectral range of the LIBS optical emission. This study evaluated two practical applications of LIBS—validation of labels assigned to garnets in museum collections and discrimination of LCT (lithium-cesium-tantalum) and NYF (niobium, yttrium and fluorine) pegmatites based on garnet geochemical fingerprinting, both of which could be implemented on site in a museum or field setting with a handheld LIBS analyzer. Major element compositions were determined using electron microprobe analysis for a suite of 208 garnets from 24 countries to determine garnet type. Both commercial laboratory and handheld analyzers were then used to acquire LIBS broadband spectra that were chemometrically processed by partial least squares discriminant analysis (PLSDA) and linear support vector machine classification (SVM). High attribution success rates (>98%) were obtained using PLSDA and SVM for the handheld data suggesting that LIBS could be used in a museum setting to assign garnet type quickly and accurately. LIBS also identifies changes in garnet composition associated with increasing mineral and chemical complexity of LCT and NYF pegmatites. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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15 pages, 6855 KiB  
Article
Spectral Tomography for 3D Element Detection and Mineral Analysis
by Jose R. A. Godinho, Gabriel Westaway-Heaven, Marijn A. Boone and Axel D. Renno
Minerals 2021, 11(6), 598; https://doi.org/10.3390/min11060598 - 01 Jun 2021
Cited by 11 | Viewed by 4020
Abstract
This paper demonstrates the potential of a new 3D imaging technique, Spectral Computed Tomography (sp-CT), to identify heavy elements inside materials, which can be used to classify mineral phases. The method combines the total X-ray transmission measured by a normal polychromatic X-ray detector, [...] Read more.
This paper demonstrates the potential of a new 3D imaging technique, Spectral Computed Tomography (sp-CT), to identify heavy elements inside materials, which can be used to classify mineral phases. The method combines the total X-ray transmission measured by a normal polychromatic X-ray detector, and the transmitted X-ray energy spectrum measured by a detector that discriminates between X-rays with energies of about 1.1 keV resolution. An analysis of the energy spectrum allows to identify sudden changes of transmission at K-edge energies that are specific of each element. The additional information about the elements in a phase improves the classification of mineral phases from grey-scale 3D images that would be otherwise difficult due to artefacts or the lack of contrast between phases. The ability to identify the elements inside the minerals that compose ore particles and rocks is crucial to broaden the application of 3D imaging in Earth sciences research and mineral process engineering, which will represent an important complement to traditional 2D imaging mineral characterization methods. In this paper, the first applications of sp-CT to classify mineral phases are showcased and the limitations and further developments are discussed. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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32 pages, 12230 KiB  
Article
A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy
by Álvaro F. Egaña, Felipe A. Santibáñez-Leal, Christian Vidal, Gonzalo Díaz, Sergio Liberman and Alejandro Ehrenfeld
Minerals 2020, 10(12), 1139; https://doi.org/10.3390/min10121139 - 18 Dec 2020
Cited by 3 | Viewed by 3360
Abstract
Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory [...] Read more.
Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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26 pages, 3683 KiB  
Article
Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining
by Feven Desta and Mike Buxton
Minerals 2020, 10(12), 1110; https://doi.org/10.3390/min10121110 - 10 Dec 2020
Cited by 4 | Viewed by 2662
Abstract
Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, [...] Read more.
Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data-driven strategies. In the present study, the fusion of image and point data is proposed, aiming for the enhanced classification of ore and waste materials in a polymetallic sulphide deposit at 3%, 5% and 7% cut-off grades. The image data were acquired in the visible-near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. The point data cover the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions. A multi-step methodological approach was developed for the fusion of the image and point data at multiple levels using the supervised and unsupervised classification techniques. Several possible combinations of the data blocks were evaluated to select the optimal combinations in an optimised way. The obtained results indicate that the individual image and point techniques resulted in a successful classification of ore and waste materials. However, the classification performance greatly improved with the fusion of image and point data, where the K-means and support vector classification (SVC) models provided acceptable results. The proposed approach enables a significant reduction in data volume while maintaining the relevant information in the spectra. This is principally beneficial for the integration of data from high-throughput and large data volume sources. Thus, the effectiveness and practicality of the approach can permit the enhanced separation of ore and waste materials in operational mines. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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11 pages, 1454 KiB  
Article
Analysis of Ilmenite Slag Using Laser-Induced Breakdown Spectroscopy
by Avishek Kumar Gupta, Matti Aula, Erwan Negre, Jan Viljanen, Henri Pauna, Pasi Mäkelä, Juha Toivonen, Marko Huttula and Timo Fabritius
Minerals 2020, 10(10), 855; https://doi.org/10.3390/min10100855 - 27 Sep 2020
Cited by 5 | Viewed by 2164
Abstract
The feasibility of using laser-induced breakdown spectroscopy (LIBS) for the compositional analysis of ilmenite slag was explored. The slag was obtained from a pilot-scale ilmenite smelting furnace. The composition of major oxides TiO2, FeO, and MgO are determined by the calibrated [...] Read more.
The feasibility of using laser-induced breakdown spectroscopy (LIBS) for the compositional analysis of ilmenite slag was explored. The slag was obtained from a pilot-scale ilmenite smelting furnace. The composition of major oxides TiO2, FeO, and MgO are determined by the calibrated LIBS method. LIBS measurements are done under normal atmosphere and temperature. A Q-switched Nd:YAG laser operating at 355 nm was used to create a plasma on an ilmenite slag sample. The characteristic lines based on the NIST database of Fe, Mg, and Ti can be identified on the normalized LIBS spectra for the slag samples. The spectral range chosen for the study is 370 to 390 nm. Calibration curves were plotted using the data collected from various industrial ilmenite samples of varying compositions of TiO2, FeO, and MgO. The univariate simple linear regression technique was used to do the analysis and the prediction accuracy was checked by the root mean square error (RMSE). To validate the application of LIBS, both qualitative and quantitative analysis is done and compared to the analytical ICP-OES results. The model predicts the magnesium content with the highest accuracy and gives good prediction for iron and titanium content. This study demonstrates the capability of using LIBS for the surface analysis of the ilmenite slag sample. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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Review

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22 pages, 2721 KiB  
Review
LIBS as a Spectral Sensor for Monitoring Metallic Molten Phase in Metallurgical Applications—A Review
by Ashwin Kumar Myakalwar, Claudio Sandoval, Marizú Velásquez, Daniel Sbarbaro, Benjamín Sepúlveda and Jorge Yáñez
Minerals 2021, 11(10), 1073; https://doi.org/10.3390/min11101073 - 30 Sep 2021
Cited by 13 | Viewed by 3907
Abstract
This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from [...] Read more.
This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from the plasma can be transferred through open-path optical configuration or via an optical fiber to a spectrometer to receive analytical information in the form of elemental composition. Thus, a relatively long-distance analysis can be performed using LIBS. Several modern experimental arrangements, patents and industrial notes are assessed, and the literature is reviewed. The review includes applications of LIBS to analyze steel, iron, aluminum, copper, slags, metal melts, and other materials. Temperature, pressure, and atmospheric composition are crucial parameters of any melting process. Hence, past studies on molten phases describing these parameters have been discussed. Finally, the review addresses the last technological advances for these types of applications. It also points out the need of development in some fields and some limitations to overcome. In addition, the review highlights the use of modern machine learning and data processing techniques to increase the effectiveness of calibration and quantification approaches. These developments are expected to improve the performance of LIBS systems already implemented at an industrial scale and ease the development of new applications in pyrometallurgical processes to address the stringent market and environmental regulations. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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17 pages, 5663 KiB  
Review
Sensors and Process Control in Copper Smelters: A Review of Current Systems and Some Opportunities
by Luis Arias, Eduardo Balladares, Roberto Parra, Daniel Sbarbaro and Sergio Torres
Minerals 2021, 11(1), 1; https://doi.org/10.3390/min11010001 - 22 Dec 2020
Cited by 10 | Viewed by 3172
Abstract
Despite the widespread and extended use of sensor systems in different industries, there is an important gap to reach equivalent conditions in pyrometallurgical processes for primary production. In the specific case of copper pyrometallurgy, the situation is particularly challenging to incorporate the Industry [...] Read more.
Despite the widespread and extended use of sensor systems in different industries, there is an important gap to reach equivalent conditions in pyrometallurgical processes for primary production. In the specific case of copper pyrometallurgy, the situation is particularly challenging to incorporate the Industry 4.0 concept for the optimization of their operations. Currently, only two instruments can be identified at the commercial level: the Noranda pyrometer and the Online Production Control (OPC) system. The iron-making and steelmaking industries, however, present an advanced level of control based on monitoring and sensing networks throughout the entire process. This reality has served as a basis for developing a series of solutions based on radiometric sensors for copper pyrometallurgy. We present two types of sensing concept. The first one is applied to smelting and converting reactors based on the measurements of the radiation of the oxidation of different copper and iron sulfides. The second one considers hyperspectral imaging of molten phases flow during operations. The idea of this proposal is to transfer some commercial sensing technologies already in use in the steelmaking industry. In this article, the fundamentals of the sensor design, proofs of concept, and the initial industrial validations are reviewed. Finally, a discussion on the contribution of this knowledge and development opportunities within the framework of Industry 4.0 are addressed. Full article
(This article belongs to the Special Issue Advanced Spectral Techniques for Mineralogical and Elemental Analysis in Mining and Mineral Processing)
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