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Value of Mineralogical Monitoring for the Mining and Minerals Industry

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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 (22 October 2021) | Viewed by 43841

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Special Issue Editors

Prof. Dr. Herbert Pöllmann

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Guest Editor

Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Saxony-Anhalt, Germany
Interests: applied mineralogy; process mineralogy; characterization techniques; raw materials; mineral synthesis; immobilization of hazardous wastes; cementitious materials; recycling

Dr. Uwe König

E-Mail Website
Guest Editor

PA Nalytical B.V., Almelo, The Netherlands
Interests: mining; process monitoring; exploration; analytical methods; geology; metals; building materials

Special Issue Information

Dear Colleagues,

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control:

Lithium ores—determination of lithium contents by XRD methods;
Copper ores and their different mineralogy;
Nickel lateritic ores;
Iron ores and sinter;
Bauxite and bauxite overburden;
Heavy mineral sands.

The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated.

Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data.

Prof. Dr. Herbert Pöllmann
Dr. Uwe König
Guest Editors

Manuscript Submission Information

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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

lithium ore
nickel ore
iron ore
iron sinter
bauxite
heavy mineral sands
copper ores
process monitoring
mineralogy

Published Papers (10 papers)

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Editorial

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3 pages, 170 KiB

Open AccessEditorial

Value of Mineralogical Monitoring for the Mining and Minerals Industry

by Uwe König and Herbert Pöllmann

Minerals 2022, 12(7), 902; https://0-doi-org.brum.beds.ac.uk/10.3390/min12070902 - 19 Jul 2022

Viewed by 1140

Abstract

The shift towards lower grade ore deposits, sustainable energy, CO₂ reduction, volatile market conditions and digitalization has pushed the mining and minerals industry towards predictive, sustainable and agile analytical solutions to improve safety and increase operational efficiency [...] Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

Research

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12 pages, 5593 KiB

Open AccessArticle

Heavy Mineral Sands Mining and Downstream Processing: Value of Mineralogical Monitoring Using XRD

by Uwe König and Sabine M. C. Verryn

Minerals 2021, 11(11), 1253; https://0-doi-org.brum.beds.ac.uk/10.3390/min11111253 - 11 Nov 2021

Cited by 5 | Viewed by 4304

Abstract

Heavy mineral sands are the source of various commodities such as white titanium dioxide pigment and titanium metal. The three case studies in this paper show the value of X-ray diffraction (XRD) and statistical methods such as data clustering for process optimization and quality control during heavy mineral processing. The potential of XRD as an automatable, reliable tool, useful in the characterization of heavy mineral concentrates, product streams and titania slag is demonstrated. The recent development of ultra-high-speed X-ray detectors and automated quantification allows for ‘on the fly’ quantitative X-ray diffraction analysis and truly interactive process control, especially in the sector of heavy mineral concentration and processing. Apart from the information about the composition of a raw ore, heavy mineral concentrate and the various product streams or titania slag, this paper provides useful information by the quantitative determination of the crystalline phases and the amorphous content. The analysis of the phases can help to optimize the concentration of ores and reduction of ilmenite concentrate. Traditionally, quality control of heavy mineral concentrates and titania slag relies mainly on elemental, chemical, gravimetrical, and magnetic analysis. Since the efficiency of concentration of minerals in the different product streams and reduction depends on the content of the different minerals, and for the latter on the titanium and iron phases such as ilmenite FeTiO₃, rutile TiO₂, anatase TiO_2, or the various titanium oxides with different oxidation stages, fast and direct analysis of the phases is required. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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12 pages, 2269 KiB

Open AccessArticle

Effects of Pyrite Texture on Flotation Performance of Copper Sulfide Ores

by İlkay B. Can, Seda Özçelik and Zafir Ekmekçi

Minerals 2021, 11(11), 1218; https://0-doi-org.brum.beds.ac.uk/10.3390/min11111218 - 01 Nov 2021

Cited by 6 | Viewed by 2554

Abstract

Pyrite particles, having framboidal/altered texture, are known to significantly affect pulp chemistry and adversely affect flotation performance. Therefore, the main objectives of this study were to demonstrate influence of pyrite mineralogy on the flotation of copper (sulphidic) ores and develop alternative conditions to improve the performance. Two copper ore samples (Ore A and Ore B) having different textural/modal mineralogy and flotation characteristics were taken from different zones of the same ore deposit. Ore B contained framboidal pyrite and altered pyrite/marcasite, which is considered the main reason for the low flotation performance in both copper and pyrite flotation sections of the process plant. Flotation tests were conducted under different conditions using the two ore samples and a 50:50 blend. The results showed that Ore A could be concentrated under the base conditions, as applied in the existing flotation plant. On the other hand, Ore B did not respond to the base conditions and a copper recovery of only 5% could be obtained. Besides, blending Ore B with Ore A negatively affected the flotation behavior of Ore A. An alternative flotation chemistry was applied on Ore B using Na₂S for surface cleaning and Na-Metabisulfite (MBS) for pyrite depression in the copper flotation stage. The surface cleaning reduced the rate of oxidation of the framboidal pyrite in Ore B. As a result, the copper recovery could be increased to 52% Cu for Ore B, and 65% for the mixed ore sample. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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16 pages, 5694 KiB

Open AccessArticle

Nickel Laterites—Mineralogical Monitoring for Grade Definition and Process Optimization

by Uwe König

Minerals 2021, 11(11), 1178; https://0-doi-org.brum.beds.ac.uk/10.3390/min11111178 - 24 Oct 2021

Cited by 6 | Viewed by 7210

Abstract

Nickel laterite ore is used to produce nickel metal, predominantly to manufacture stainless steel as well as nickel sulfate, a key ingredient in the batteries that drive electric vehicles. Nickel laterite production is on the rise and surpassing conventional sulfide deposits. The efficiency of mining and processing nickel laterites is defined by their mineralogical composition. Typical profiles of nickel laterites are divided into a saprolite and a laterite horizon. Nickel is mainly concentrated and hosted in a variety of secondary oxides, hydrous Mg silicates and clay minerals like smectite or lizardite in the saprolite horizon, whereas the laterite horizon can host cobalt that could be extracted as a side product. For this case study, 40 samples from both saprolite and laterite horizons were investigated using X-ray diffraction (XRD) in combination with statistical methods such as cluster analysis. Besides the identification of the different mineral phases, the quantitative composition of the samples was also determined with the Rietveld method. Data clustering of the samples was tested and allows a fast and easy separation of the different lithologies and ore grades. Mineralogy also plays a key role during further processing of nickel laterites to nickel metal. XRD was used to monitor the mineralogy of calcine, matte and slag. The value of mineralogical monitoring for grade definition, ore sorting, and processing is explained in the paper. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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13 pages, 2288 KiB

Open AccessArticle

Value of Rapid Mineralogical Monitoring of Copper Ores

by Matteo Pernechele, Ángel López, Diego Davoise, María Maestre, Uwe König and Nicholas Norberg

Minerals 2021, 11(10), 1142; https://0-doi-org.brum.beds.ac.uk/10.3390/min11101142 - 17 Oct 2021

Cited by 2 | Viewed by 2420

Abstract

An essential operation in the mineral processing of copper ores into concentrates is blending, as it guarantees a constant feed for the flotation cells, increases metal recovery rate and reduces tailings. In this study, copper ores from Huelva province (Spain) were investigated by quantitative XRD (X-ray diffraction) methods to optimize blending and detect penalty minerals, which can affect flotation and concentrate quality. The Rietveld method in combination with cluster analysis, PLSR and more traditional chemical analysis provide a more complete and in-depth characterization of the ore and the whole process. The mineralogical monitoring can be fully automated to enable real-time decision making. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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35 pages, 28921 KiB

Open AccessEditor’s ChoiceArticle

Monitoring of Lithium Contents in Lithium Ores and Concentrate-Assessment Using X-ray Diffraction (XRD)

by Herbert Pöllmann and Uwe König

Minerals 2021, 11(10), 1058; https://0-doi-org.brum.beds.ac.uk/10.3390/min11101058 - 28 Sep 2021

Cited by 8 | Viewed by 10456

Abstract

Lithium plays an increasing role in battery applications, but is also used in ceramics and other chemical applications. Therefore, a higher demand can be expected for the coming years. Lithium occurs in nature mainly in different mineralizations but also in large salt lakes in dry areas. As lithium cannot normally be analyzed using XRF-techniques (XRF = X-ray Fluorescence), the element must be analyzed by time consuming wet chemical treatment techniques. This paper concentrates on XRD techniques for the quantitative analysis of lithium minerals and the resulting recalculation using additional statistical methods of the lithium contents. Many lithium containing ores and concentrates are rather simple in mineralogical composition and are often based on binary mineral assemblages. Using these compositions in binary and ternary mixtures of lithium minerals, such as spodumene, amblygonite, lepidolite, zinnwaldite, petalite and triphylite, a quantification of mineral content can be made. The recalculation of lithium content from quantitative mineralogical analysis leads to a fast and reliable lithium determination in the ores and concentrates. The techniques used for the characterization were quantitative mineralogy by the Rietveld method for determining the quantitative mineral compositions and statistical calculations using additional methods such as partial least square regression (PLSR) and cluster analysis methods to predict additional parameters, like quality, of the samples. The statistical calculations and calibration techniques makes it especially possible to quantify reliable and fast. Samples and concentrates from different lithium deposits and occurrences around the world were used for these investigations. Using the proposed XRD method, detection limits of less than 1% of mineral and, therefore down to 0.1% lithium oxide, can be reached. Case studies from a hard rock lithium deposit will demonstrate the value of mineralogical monitoring during mining and the different processing steps. Additional, more complex considerations for the analysis of lithium samples from salt lake brines are included and will be discussed. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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12 pages, 30611 KiB

Open AccessArticle

A Method for Quality Control of Bauxites: Case Study of Brazilian Bauxites Using PLSR on Transmission XRD Data

by Caio C. A. Melo, Rômulo S. Angélica and Simone P. A. Paz

Minerals 2021, 11(10), 1054; https://0-doi-org.brum.beds.ac.uk/10.3390/min11101054 - 28 Sep 2021

Cited by 3 | Viewed by 1976

Abstract

Available Alumina (AvAl₂O₃) and Reactive Silica (RxSiO₂), the main parameters of bauxite controlled in the beneficiation process are traditionally measured by laborious, expensive, and time-consuming wet chemistry methods. Alternative methods based on XRD analysis, capable to provide a reliable estimation of these parameters and valuable mineralogical information of the ore, are being studied. In this work, X-ray diffraction data in transmission mode was used to estimate AvAl₂O₃ and RxSiO₂ from Brazilian bauxites using the Partial Least Square Regression (PLSR) statistical tool. The proposed method comprises a routine of sample classification according to their similarities by Principal Component Analysis (PCA) and K-means, calibration of the PLSR model for each group of samples, grouping new bauxite samples according to the generated clustering model, and subsequent estimation of the parameters AvAl₂O₃ and RxSiO₂ using the PLSR models for these samples. The results showed good accuracy and precision of the models generated for samples of the main ore lithology. The quality and pre-processing of the XRD data required for this method are discussed. The results demonstrated that this method has the potential to be industrially applied to quality control of bauxites as a rapid and automated procedure. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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14 pages, 3930 KiB

Open AccessArticle

Mineralogical Appraisal of Bauxite Overburdens from Brazil

by Leonardo Boiadeiro Ayres Negrão, Herbert Pöllmann and Tiago Kalil Cortinhas Alves

Minerals 2021, 11(7), 677; https://0-doi-org.brum.beds.ac.uk/10.3390/min11070677 - 24 Jun 2021

Cited by 9 | Viewed by 2571

Abstract

Mineralogical appraisal is an important tool for both mining and industrial processes. X-ray powder diffraction analysis (XRPD) can deliver fast and reliable mineralogical quantification results to aid industrial processes and improve ore recoveries. Furthermore, X-ray fluorescence (XRF) chemical data, thermal analysis (TA), and Fourier-transformed infrared spectroscopy (FTIR) can be used to validate and refine XRPD results. Mineralogical assessment of non-traditional ores, such as mining wastes, is also an important step to consider them for near-future industries. In the Brazilian Amazon, alumina-rich clays cover the largest and most important bauxitic deposits of the region and have been considered as a possible raw material for the local cement and ceramic industry. In this work, a mineralogical evaluation of these clays (Belterra Clays) is performed using XRPD, XRF, TA, and FTIR. XRPD-Rietveld quantification confirmed that kaolinite is the main phase of the clay overburden, followed by variable contents of gibbsite and goethite and minor quantities of hematite, anatase, and quartz. The chemistry derived from Rietveld, based on stoichiometric phase compositions, presents a good correlation with the XRF data and is also supported by the TA and FTIR data. The initially assumed homogeneous composition of Belterra Clay is revealed to be variable by the present mineralogical study. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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11 pages, 2207 KiB

Open AccessArticle

Characterization and Suitability of Nigerian Barites for Different Industrial Applications

by Itohan Otoijamun, Moses Kigozi, Adelana Rasak Adetunji and Peter Azikiwe Onwualu

Minerals 2021, 11(4), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/min11040360 - 30 Mar 2021

Cited by 4 | Viewed by 2503

Abstract

This work aimed to characterize barite samples from selected different locations in Nigeria and determine their suitability for various industrial applications. The properties determined include mineralogy, chemical composition, morphology, functional groups, and specific gravity. Samples were obtained from ten locations in Nasarawa and Taraba states as well as a standard working sample (WS) obtained from a drilling site. The samples were characterized using scanning electron microscope and energy dispersive X-ray (SEM-EDX), Fourier infrared analysis (FTIR), and X-ray diffraction (XRD). Specific gravity (SG) was determined using the pycnometer method. Results of SEM-EDX analysis show that the WS has a Ba-S-O empirical composition of 66.5% whereas these of the ten samples investigated vary between 59.36% and 98.86%. The FTIR analysis shows that the functional groups of S-O, SO₄²⁻, Ba-S-O, OH of the ten samples match that of the WS. Results of XRD show that the ten samples have the same mineralogical composition as the WS and all meet American Petroleum Institute (API) standards for industrial barite. Similar matching results are shown from EDXRF spectra intensity, position, and composition analysis of the ten samples compared to the WS. Specific gravity (SG) results show that six out of the ten samples have SG above 4.2 which is the recommended minimum for the American Petroleum Institute (API) standard. The other four samples will require beneficiation to meet the standard for drilling mud application. Using all the parameters of the assessment together, results show that while some (6) of the samples can be used for drilling fluid application, some (4) require beneficiation but all ten samples can be used for other industrial applications including healthcare, construction, plastic, cosmetics, paper, and rubber industries. The results of the study can be used for value addition in developing beneficiation procedures, processes, and technology for purification along with new materials for the industries. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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Review

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20 pages, 3168 KiB

Open AccessFeature PaperReview

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

by Mohammad Jooshaki, Alona Nad and Simon Michaux

Minerals 2021, 11(8), 816; https://0-doi-org.brum.beds.ac.uk/10.3390/min11080816 - 28 Jul 2021

Cited by 24 | Viewed by 6295

Abstract

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area. Full article

(This article belongs to the Special Issue Value of Mineralogical Monitoring for the Mining and Minerals Industry)

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