A self-adaptive particle-tracking method for minerals processing

Pereira, Lucas; Frenzel, Max; Khodadadzadeh, Mahdi; Tolosana‐Delgado, Raimon; Gutzmer, Jens

doi:10.1016/j.jclepro.2020.123711

Cited by 24 publications

(18 citation statements)

References 26 publications

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“…As seen in Figure 6, the points are closer to the line 1:1 as particle size decreases, which could be linked to the increasing uniformity of particle shape and size in fine size fractions. Automated mineralogy studies are associated with an inherent stereological bias as a result of the method being limited to two dimensions, but providing data which are used to describe three dimensional sample characteristics (Leißner et al, 2016;Pereira et al, 2020;Ueda et al, 2016). This stereological effect can help to explain the higher deviation from the 1:1 line for increasing particle size.…”

Section: Data Validationmentioning

confidence: 99%

Automated mineralogy as a novel approach for the compositional and textural characterization of spent lithium-ion batteries

Vanderbruggen¹,

Gugala²,

Blannin³

et al. 2021

Preprint

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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 Xray 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 different separation unit operations in recycling of waste electronics and batteries. Element and LIB components Spectra examples and name for the reference list Cu from foilAl from foil C from graphite Metals from lithium metal oxides: Co, Ni, Mn and mix Si from filler particles Others from alloys particlesSupplementary material S2 -Compositional thresholds of the collected spectra.

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Section: Data Validationmentioning

confidence: 99%

Automated mineralogy as a novel approach for the compositional and textural characterization of spent lithium-ion batteries

Vanderbruggen¹,

Gugala²,

Blannin³

et al. 2021

Preprint

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“…After importing the particle datasets, the variables describing particle properties need to be pre-treated before further processing (Pereira et al, 2021), as follows:…”

Section: Data Collectionmentioning

confidence: 99%

“…Extending this previous work, Pereira et al (2021) recently proposed a novel particlebased separation model (PSM) that allows for the estimation of recovery probabilities for individual particles. The close link between such probabilities and bulk recoveries (Jowett, 1986;King et al, 2012;Tromp, 1937) provides the unique opportunity to understand particle flotation kinetics at the single-particle level.…”

Section: Introductionmentioning

confidence: 99%

“…In this contribution we present a PSM for the analysis of the kinetic behavior at the particle level. This builds upon an extension of Pereira et al (2021) in order to deal with more than two output streams. This allows us to compute the cumulative recovery probability of individual particles during flotation test-work as a function of time, which can then be fit by kinetic flotation models.…”

Section: Introductionmentioning

confidence: 99%

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Computing single-particle flotation kinetics using automated mineralogy data and machine learning

Pereira¹,

Frenzel²,

Hoang³

et al. 2020

Preprint

Self Cite

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Flotation kinetic studies are essential for predicting, understanding, and optimizing the selective recovery of an ore through flotation. Recently, much effort has been put into incorporating intrinsic ore properties in the understanding of their flotation behavior. Particle-based characterization systems (e.g. automated mineralogy) drove much of this development. However, the currently available methods for flotation kinetic studies cannot accommodate single-particle data, and most of the available data end up not being used. Here we demonstrate a method to fit flotation kinetic models to each single particle characterized in a sample. Our method, based on the lasso-regularized multinomial logistic regression, allows for an in-depth understanding of a particle’s flotation behavior according to every particle-descriptive variable available. We validate the efficiency of our new method in an apatite flotation case study that had been previously studied following traditional approaches. With the proposed method we can show the joint influence of particle size, shape, and modal and surface compositions in the recovery of single particles – such holistic understanding could not be captured before. We expect our method to help developing the field of flotation further and ultimately assist the implementation of more efficient mineral recovery plants – key for a more sustainable use of raw materials.

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“…2D imaging techniques typically used to characterize raw materials have an associated stereological bias [2][3][4] due to the loss of information (what is under the surface), whereas 3D imaging can provide a full description of a material. The stereological bias increases with the complexity and the texture variability within an ore. That suggests that 2D and bulk particle characterization could neglect properties of individual particles that are important to describe and to simulate a process [5][6][7]. For example, the separation of particles from a feed material into a concentrate or into tailings are typically the sum of discrete particle events that depend mostly on the particle properties and not necessarily on the overall properties of all particles [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

et al. 2021

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This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by x-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.

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A self-adaptive particle-tracking method for minerals processing

Cited by 24 publications

References 26 publications

Automated mineralogy as a novel approach for the compositional and textural characterization of spent lithium-ion batteries

Automated mineralogy as a novel approach for the compositional and textural characterization of spent lithium-ion batteries

Computing single-particle flotation kinetics using automated mineralogy data and machine learning

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

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