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Lithium-ion batteries (LIBs) are currently the most important electrochemical energy storage sys-tems for electronic mobile devices and electric vehicles. The growing global demand for LIBs is accompanied by an increase in the need for Co, Mn, Ni, Li and graphite. In order to narrow the gap between supply and demand and to achieve the European sustainability goals, the recycling of LiBs has attracted a lot of attention in recent years. Here, the focus is on valuable metals such as cobalt, nickel and lithium as well as graphite. In addition, due to its chemical and phase compositions, the battery material remains to be a huge challenge for a proper materials characterization. Hence, there is not only a need to find innovative and comprehensive LiB recycling process solutions but also to develop new analytical workflows to enhance the understanding of the battery material. In this study, LIBs are fed to a mechanical and thermo-mechanical recycling process route to release active materials from electrodes foils. In addition to the valuable metals, the focus is particularly on the recovery of graphite. The mechanical route works with an impact shear crusher, while for the thermo-mechanical tests the batteries were vacuum pyrolyzed at 500-650 °C before crushing. The so-called black mass fraction smaller than 1 mm was separated and further classified into four size fractions. Accurate characterization of both the main chemistry and detailed characterization of the phases contained in the recycled material remains a major challenge. Therefore, each fraction has been characterized by different analytical methods, including X-ray fluorescence (XRF), inductively coupled plasma optical emission spectroscopy (ICP-OES). For a good visualization and quantifica-tion of the results of the processing success and process efficiency, a more detailed analytical char-acterization is required. This study proposes an innovative and novel characterization method based on automated mineralo

Presentation about Li-ion battery characterization ia automated mineralogy with a particular focus on the flotation of graphite in black mass.

To close the loop of the respective material demands, the role of battery recycling is steadily increasing, especially in the light of changing mobility. The analytical requirements for the battery material are challenging, since they are complex and heterogeneous secondary materials. They contain Li in different compounds, carbon in the form of graphite (“black mass”), but also metals like Mn, Fe, Cu, Al, Co, Ni etc. partly in several oxidation states (pure metal foils vs. different compounds). All these components are also highly affected by the recycling stages (e.g. pyrolysis, leaching, ect.). On the other hand, with the main aim of an effective recycling the demand for as detailed as possible analytical data is likewise high. Generally, there is no single method available for tackling all the analytical issues and a combination of methods is inevitable. In different recycling projects, we developed methods for scanning electron microscope (SEM) techniques (in our case MLA mineral liberation analyser and TIMA Tescan integrated mineral analyzer), quantitative XRD (X-ray diffraction) and XRF (X-ray fluorescence) with support of ICP-OES. While SEM techniques are essential to provide information on a particle base (incl. chemical composition, grain size, liberation etc.) the other techniques provide pure bulk chemical analytics. An approach – with slight amendments – known from the so called automated mineralogy applied to the materials revealed very useful results for understanding processing parameters, i.e. for the beneficiation of black mass in high quality (Vanderbruggen et al. 2021). On the other hand by simply adding an internal standard (we chose ZrO2) it is possible to develop a quick and easy method for XRF analysis where the sum of “invisible” elements (e.g. C, Li, O, F) can be determined easily with accurate quantification of the other elements, mainly focusing on the metal contents. Lithium containing compounds can be detected by XRD, but the methods ha

Lithium-Ionen-Batterien (LIBs) gehören zu den derzeit wichtigsten elektrochemischen Energiespeichersystemen für elektronische Mobilgeräte und Elektrofahrzeuge. Die wachsende weltweite Nachfrage nach LIBs geht mit einer Erhöhung des Bedarfs an Co, Mn, Ni, Li und Graphit einher. Diese Erhöhung der Nachfrage dieser Rohstoffe stellt eine besondere Herausforderung für den schon jetzt angespannten weltweiten Rohstoffmarkt dar, verbunden mit Versorgungsrisiken, Preisschwankungen und Marktmonopolen. Tatsächlich sind Co und natürlicher Graphit in Europa seit 2010 als kritische Rohstoffe (CRM) geführt, Li sowie Mn befinden sich an der Grenze der Kritikalität. Um potenziell die Kluft zwischen Angebot und Nachfrage zu verringern sowie die europäischen Nachhaltigkeitsziele zu erreichen, hat das Recycling von Lithium-Ionen-Batterien (LIB) in den letzten Jahren viel Aufmerksamkeit auf sich gezogen. Hierbei wird sich hauptsächlich auf die wertvollen Metalle wie Kobalt, Nickel und Lithium konzentriert. Allerdings gehen während des Recyclingprozesses erhebliche Mengen anderer Komponenten wie Elektrolyt, Separator oder Graphit verloren. So kann Graphit zum Beispiel während der pyrometallurgischen Behandlung entweder abgeschlackt oder als Reduktionsmittel verbraucht werden. Darüber hinaus gehen einige andere wertvolle Metalle wie Co in den Grobfraktionen durch einen zu geringen Aufschlussgrad an die Berge verloren. Aus diesem Grund müssen neue und umfassende LIB-Recyclingverfahren gefunden werden. In dieser Studie werden wir auf die aktuellen Ergebnisse der Schwarzmasseaufbereitung und der Charakterisierung der Lithium-Ionen Batterie Recyclatströmen.

Lithium-ion batteries (LIBs) are currently one of the most important electrochemical energy storage devices, powering electronic mobile devices and electric vehicles. Growing global demands for Co, Mn, Ni, Li, and graphite which are all used and present in LIBs as energy storage added difficulties to the already deficit and imbalance supply of sources of raw materials worldwide, which resulted to supply risks, price fluctuations and monopoly of market. In fact, Co and natural graphite are listed as critical raw materials (CRMs) in Europe since 2010 and Mn, and Li are on the boundary of criticality. While recycling is identified as a solution to potentially reduce the gap between the demand and supply. Recycling of lithium ion battery (LIB) has attracted a lot of attention in the recent years and focuses primarily on valuable metals such as cobalt, nickel and lithium. During the recycling processes, considerable amount of the components are lost like electrolyte, separator or graphite. For instance, graphite can either be slagged-off or consumed as a reductant agent during pyrometallurgical treatment. Some other loses are due to a lack of liberation of the targeted particles, elements such as Co are lost in the coarse fractions and a not recovered in the right product. Hence, there is a need to find innovative and comprehensive LIB recycling operations. For understanding and be able to quantify the loses and recycling process efficiency a deeper characterization is required. This research proposes a new characterization method based on automated mineralogy. In this study, the particles morphologies (size, composition and phases associations) are analysed. The liberation of active materials from electrodes is quantified by comparing two recycling processes: mechanical and thermochemical-mechanical. The mechanical route operates with an impact shear crusher while for the thermomechanical operation the batteries were vacuum pyrolyzed at 500-650oC before to be crushed. T

Mechanical recycling processes aim to separate particles based on their physical properties, such as size, shape and density, and physico-chemical surface properties, such as wettability. Secondary materials, including electronic waste, are highly complex and heterogeneous, which complicates recycling processes. In order to improve recycling efficiency, characterization of both recycling process feed materials and intermediate products is crucial. Textural characteristics of particles in waste mixtures cannot be determined by conventional characterization techniques, such as X-ray fluorescence and X-ray diffraction spectroscopy. This paper presents the application of automated mineralogy as an analytical tool, capable of describing discrete particle characteristics for monitoring and diagnosis of lithium ion battery (LIB) recycling approaches. Automated mineralogy, which is well established for the analysis of primary raw materials but has not yet been tested on battery waste, enables the acquisition of textural and chemical information, such as elemental and phase composition, morphology, association and degree of liberation. For this study, a thermo-mechanically processed black mass (<1 mm fraction) from spent LIBs was characterized with automated mineralogy. Each particle was categorized based on which LIB component it comprised: Al foil, Cu foil, graphite, lithium metal oxides and alloys from casing. A more selective liberation of the anode components was achieved by thermo-mechanical treatment, in comparison to the cathode components. Therefore, automated mineralogy can provide vital information for understanding the properties of black mass particles, which determine the success of mechanical recycling processes. The introduced methodology is not limited to the presented case study and is applicable for the optimization of differentseparation unit operations in recycling of waste electronics and batteries.

The second law of thermodynamics, through exergy analysis, is commonly applied to quantify process inefficiencies in metallurgical reactors, however, it is not yet being used to understand physical processes and changes in particle-based systems. Correlating the state of mixing of particle texture and homogeneous liquid mixtures is of importance. This paper applies the exergy analysis and excess entropy method to two sets of experiments highlighting the differential breakage as microwave pre-treatment is applied to a gold-copper ore. Grinding kinetic properties were measured following the top-size fraction method and calculated using the population balance model. The approach combines the mixing entropy on the system level (streams) and the entropy for multicomponent particle systems, using automated mineralogy data to quantify the effects of intergrowth and improvements in grinding performance. This is a first step towards understanding mineral processing not only in terms of energy conservation (first law of thermodynamics) but also in terms of the quality of energy available at multicomponent systems (second law of thermodynamics). When applied to comminution processes, this methodology enables us to understand the change in particle composition (its degree of liberation) as well as changes in particle size, being an important measure of process efficiency and selectivity.

Mechanical recycling processes aim at separating individual particles based on their properties, such as size, shape, density and composition. However, secondary material such as spent lithium ion battery are highly heterogeneous and complex in element and also material form. In order to improve recycling efficiency, characterization of the recycled products, on a particle level, is crucial. Nevertheless, conventional characterization techniques, such ICP-OES, XRF or XRD, provide bulked information for the products only. Tis research presents the development of a new analytical procedure based on individual particle characterization in order to monitor and diagnose lithium ion battery (LiB) recycling. In this study, cylindrical lithium-ion batteries (INR18650-29E) are fed to a mechanical and thermo-mechanical recycling process route to release coated particles from the electrodes foils. In addition to the valuable metals, the focus is particularly on the recovery of graphite. The mechanical route works with a shear crusher, while for the thermo-mechanical tests the batteries were vacuum pyrolyzed at 500-650 °C before crushing. The fraction smaller than 1 mm, called black mass, was separated and classified into 4 size fractions based on the particle size distribution. The samples were analysed by automated mineralogy, this analytical tool enables the acquisition of particle-based information such as elemental and phase composition, morphology, association and degree of liberation. The Mineral Liberation Analyzer (MLA) system used for the measurements uses a combination of scanning electron microscopy (SEM) image analysis and energy-dispersive X-ray spectroscopy (EDS) and is established as a powerful method in the primary raw materials sector. However, there are no dedicated databases for use in the secondary raw materials sector in order to analyze black mass material in a fast and precise manner. An analytical challenge of this study is therefore to create a databas

Through a series of in-house routines of R, an open source programming language for statistical computing, statistical analysis is applied to automated process mineralogy data in order to describe the performance of an industrial scheelite rougher flotation bank. These routines allow: 1) exploring all particles properties over residence time, not only particle size or surface liberation but also mineral association and a wealth of other particle properties, 2) to free the user from the limitations of the menu-driven built-in mineralogy software or spreadsheets, for calculation, data plotting or predictive model fitting, in particular for the parallel analysis of several streams; and 3) a more flexible manipulation of the data, both class and particle wise, for instance allowing for data mining across streams. In an illustration case study, these functions are used to show the separation efficiency shift over residence time and over particle size; to indicate which associated minerals have a greater influence on the flotation of scheelite; to determine which gangue minerals are more impacted by entrainment; and finally to link said entrainment to particle shape. In general, the Helmholtz Institute Freiberg for Resource Technology intends to use such a programming platform on automated mineralogy data as a routine to understand processes better, as a potential diagnostic tool for process troubleshooting, and also for predictive model building within the frame of geometallurgy.

Dieser Beitrag stellt Erzlabor, ein Spin-Off des HZDR, den Transferassistenten aus Sachsen vor.

Geometallurgy (the science of predicting the behaviour of ores along the value chain on the basis of their geoscientific characteristics) has barely moved beyond process mineralogy studies. These studies typically describe the mineralogical and microstructural characteristics of pristine or processed ores, for qualitative or semi-quantitative assessment of the processing performance and, eventually, a trial-and-error optimisation of the operation. But to deliver on its promise, geometallurgy should progress beyond this to provide fully quantitative models of such primary ore characteristics and the performance of their processing, both in average value and, most importantly, keeping track of their uncertainty. Only then it will be possible to optimise the operation in a predictive way. For this goal, we have developed a data mining platform consisting on a SQL database, an R front-end in-house package, and back-ends interpreting data from several analytical instruments into the database. A structured relational SQL database is useful to organise all available information (geometry, phase, chemistry, physical properties) of mineral grains, particles and samples. The R front-end allows to use the statistical and data mining power of this open-source environment in a transparent way for any geomet task, from ore characterisation to process fitting and forecasting, from geostatistical prediction to operational optimisation. With regard to the back-ends, currently we import XRD, XRF and mineral liberation analysis measurements, and we will incorporate other data sources. The scripting abilities of R allow to process many streams in loop freeing the user of repeating tedious click-and-drop tasks; compute virtually any quantity; distribute the work among several clusters; or keep a log file of the calculations done, for accountability. This contribution presents the building blocks of this package, and their use with several examples in geometallurgical tasks.

Lithium-Ionen-Batterien (LIBs) gehören zu den derzeit wichtigsten elektrochemischen Energiespeichersystemen für elektronische Mobilgeräte und Elektrofahrzeuge. Die wachsende weltweite Nachfrage nach LIBs, geht mit einer Erhöhung des Bedarfs an Co, Mn, Ni, Li und Graphit einher. Diese Erhöhung der Nachfrage dieser Rohstoffe stellt eine besondere Herausforderung für den schon jetzt angespannten weltweiten Rohstoffmarkt dar, verbunden mit Versorgungsrisiken, Preisschwankungen und Marktmonopolen. Tatsächlich sind Co und natürlicher Graphit in Europa seit 2010 als kritische Rohstoffe (CRM) geführt, Li sowie Mn befinden sich an der Grenze der Kritikalität. Um potenziell die Kluft zwischen Angebot und Nachfrage zu verringern sowie die europäischen Nachhaltigkeitsziele zu erreichen, hat das Recycling von Lithium-Ionen-Batterien (LIB) hat in den letzten Jahren viel Aufmerksamkeit auf sich gezogen. Hierbei wird sich hauptsächlich auf die wertvollen Metalle wie Kobalt, Nickel und Lithium konzentriert. Allerdings gehen während des Recyclingprozesses erhebliche Mengen anderer Komponenten wie Elektrolyt, Separator oder Graphit verloren. So kann Graphit zum Beispiel während der pyrometallurgischen Behandlung entweder abgeschlackt oder als Reduktionsmittel verbraucht werden. Darüber hinaus gehen einige andere wertvolle Metalle wie Co in den Grobfraktionen durch einen zu geringen Aufschlussgrad an die Berge verloren. Aus diesem Grund müssen neue und umfassende LIB-Recyclingverfahren gefunden werden. In dieser Studie werden zur Freisetzung von aktiven Materialien aus Elektroden sowohl eine mechanische als auch thermo-mechanische Recyclingprozessroute angewendet. Dabei wird neben den werthaltigen Metallen insbesondere die Rückgewinnung von Graphit in den Fokus gestellt. Die mechanische Route arbeitet mit einem Schlagscherbrecher, während für die thermo-mechanischen Versuche die Batterien vor dem Zerkleinern bei 500-650 °C vakuumpyrolysiert wurden. Die sogenannte Schwarzmasse-Fraktion

There is a general lack of reliable quantitative data on the mineralogy and spatial distribution of indium and other by-product metals in ore deposits. This contribution showcases a new approach to integrate by-product metals into geometallurgical assessments. As an example, it uses the distribution and deportment of indium at Neves-Corvo, a major European base-metal mine (Cu + Zn). This study is the first to develop a quantitative model of indium deportment in volcanic hosted massive sulfide ores, demonstrating how regularities in indium partitioning between different minerals can be used to predict its mineralogical deportment in individual drill-core samples. Bulk-ore assays of As, Cu, Fe, Pb, S, Sb, Sn, Zn, and In are found to be sufficient for reasonably accurate predictions. The movement of indium through the ore processing plants is well explained by its mineralogical deportment, allowing for specific mine and process planning. The novel methodologies implemented in this contribution should be of general applicability to the geometallurgical assessment of many other byproduct metals in polymetallic sulfide ores, including Ga, Ge, Mo, Re, Se, Te, as well as the noble metals.

This presentations shows novel analytical methods and geometallurgical modelling techniques for re-mining of tailing storage facilities.

Dieser Beitrag stellt Erzlabor, ein Spin-Off des HZDR, den Partnern des EIT RawMaterials in Europa vor.

‚Automatisierte Mineralogie‘ bezeichnet eine analytische Untersuchungsmethode auf Basis einer Kombination von Rasterelektronenmikroskopie (REM) und energiedispersiver Röntgenspektroskopie (EDS). Automatisierte mineralogische Untersuchungen bieten die einzigartige Möglichkeit quantitative Daten einer Vielzahl von Parametern aufzustellen, die für die Entwicklung und kritische Bewertung von Aufbereitungstests greifbar sind. Diese Parameter umfassen den modalen Mineralbestand, den kalkulierten Elementgehalt, Elementverteilungen, Mineralassoziationen, Größenverteilungen von Partikeln und Mineralkörnern, Partikeldichteverteilungen sowie die Mineralfreisetzung. Aus den Analysedaten können auch Proben-Übersichtsbilder (‚Mineralkarten‘) und Bilder bestimmter Mineralgruppen extrahiert werden. Die Möglichkeiten werden anhand von zwei Beispielen verdeutlicht. Zum Einen wurde sogenannte Schwarzmasse charakterisiert, ein sekundärer Rohstoff, welche beim Recycling von Li-Ionenbatterien entsteht. Zum Anderen, geht es um primäre Lagerstätten, hier die Gewinnung von Platingruppenelementen in einer Chromitlagerstätte in Südafrika.

Der moderne Bergbausektor steht vor gewaltigen technischen Herausforderungen. Dazu zählen der Rückgang von Erzgehalten, der zunehmende Umgang mit komplexen Mineralparagenesen und sehr feinen Korngrößen aber auch einer steigenden Erzvariabilität. Mit Hilfe geometallurgischer Modelle versucht die Bergbauindustrie diese Herausforderungen zu bewältigen und das Verhalten des Erzes während des Abbaus, der Aufbereitung und der Verhüttung quantitativ vorherzusagen. Um hier einen Beitrag zu leisten, wird am Standort Freiberg seit 2008 geometallurgische Kernkompetenz aufgebaut. Diese Entwicklung wurde mit der Gründung des Helmholtz-Instituts Freiberg für Ressourcentechnologie (HIF), einer gemeinsamen Gründung des Helmholtz-Zentrums Dresden-Rossendorf (HZDR) und der TU Bergakademie Freiberg (TU Bergakademie Freiberg) im Jahr 2011, stark beschleunigt. Aus diesem Grund zählt Freiberg heute weltweit zu den wichtigsten Forschungs- und Entwicklungsstandorten der Geometallurgie. In diesem Artikel werfen wir einen Blick auf den aktuellen Stand der Entwicklungen, mit Fokus auf zwei sehr erfolgreiche Projekte im Bereich der geometallurgischen Modellierung, sowohl primärer als auch sekundärer Rohstoffe.

The modern mining industry faces a number of important technical challenges, such as declining ore grades, complex mineral associations, fine-grain size and increasing geological variability. To meet these challenges geometallurgical models are constructed to quantita-tively predict how ores will behave during extraction and beneficiation. Current geometal-lurgical programs carried out by industry, focus on the definition of larger spatial domains that have similar characteristics. However, a consequent application and further develop-ment of geometallurgical programs should lead to an implementation of spatially more highly resolved geometallurgical resource models that are truly predictive for each mined block (including uncertainty), in order to improve raw material quality control and the process efficiency of mining operations. Such an approach would also enable the targeted recovery of by-products, which may generate significant additional revenue and improve overall efficiency. In order to construct relevant resource models, detailed quantitative information on the spatial distribution and geometallurgical behaviour of the by-products within the deposit is crucial. The aim of this thesis is the development and creation of a predictive geometallurgical model by means of a case study and the presentation of the resulting advantages for the extraction of ores. Based on this, a general structure for the development of predictive geo-metallurgical models is developed, which can be applied to different types of commodities, as well as by-products, is cost-efficient, able to adapt to future data, and predicts metallurgi-cal parameters. As the case study serves the Thaba Chromium Mine in the western Bushveld Complex (South Africa), which is operated by Cronimet Chrome Mining SA (Pty) Ltd. In par-ticular, this thesis focusses on four distinct chromitite seams of the Lower and Middle Groups (LG and MG) at Thaba Mine, namely the LG-6, LG-6A, MG-1 and MG-2, which are con

Lithium-ion batteries (LIBs) are currently one of the most important electrochemical energy storage devices, powering electronic mobile devices and electric vehicles. Growing global demands for Co, Mn, Ni, Li, and graphite which are all used and present in LIBs as energy storage added difficulties to the already deficit and imbalance supply of sources of raw materials worldwide, which resulted to supply risks, price fluctuations and monopoly of market. In fact, Co and natural graphite are listed as critical raw materials (CRMs) in Europe since 2010 and Mn, and Li are on the boundary of criticality. While recycling is identified as a solution to potentially reduce the gap between the demand and supply. Recycling of lithium ion battery (LIB) has attracted a lot of attention in the recent years and focuses primarily on valuable metals such as cobalt, nickel and lithium. During the recycling processes, considerable amount of the components are lost like electrolyte, separator or graphite. For instance, graphite can either be slagged-off or consumed as a reductant agent during pyrometallurgical treatment. Some other loses are due to a lack of liberation of the targeted particles, elements such as Co are lost in the coarse fractions and a not recovered in the right product. Hence, there is a need to find innovative and comprehensive LIB recycling operations. For understanding and be able to quantify the loses and recycling process efficiency a deeper characterization is required. This research proposes a new characterization method based on automated mineralogy. In this study, the particles morphologies (size, composition and phases associations) are analysed. The liberation of active materials from electrodes is quantified by comparing two recycling processes: mechanical and thermochemical-mechanical. The mechanical route operates with an impact shear crusher while for the thermomechanical operation the batteries were vacuum pyrolyzed at 500-650oC before to be crushed. T