1577
Mineralogical Insights in the Recovery of Lepidolite from the
Beauvoir (Li, Be, F) Rare Metals Granite: LIBS Imaging as a Tool
to Improve Beneficiation Routes
C. Korbel, B. Demeusy, Z.S. Kahou, J. Cauzid, C. Fabre, I.V. Filippova, L.O. Filippov
1CNRS, Université de Lorraine, GeoRessources
V. Motto-Ros
CNRS, Université Lyon 1, Institut Lumière Matière
ABSTRACT: The ongoing global energy transformation heavily relies on a steady supply of essential metals
and minerals, with lithium occupying a crucial role in facilitating this transition, particularly in energy storage
through Li-ion batteries. Consequently, lepidolite, a lithium-bearing phyllosilicate, has emerged as a promising
source of lithium for European countries. This valuable mineral is concentrated from other silicates using froth
flotation with amine under acidic conditions. Notably, lepidolite is enriched in the Beauvoir granite (Allier,
France), which also contains other critical elements, including fluorine, tin, niobium, tantalum, and beryllium.
In this study, we explore the liberation of lepidolite and the impact of size fraction on the beneficiation of lithium,
encompassing crushing, grinding, desliming, flotation, and centrifugal separation. By employing mineralogical
studies and automated mineralogy methods, the limitations of the flowsheet are identified. Particularly, the
integration of Laser-Induced Breakdown Spectroscopy (LIBS) imaging enables precise elemental distribution
analysis of Li, F, and Be, which conventional methods like scanning electron microscopy (SEM) or micro
X-ray fluorescence (µXRF) cannot provide. This powerful tool allows to effectively differentiate lepidolite from
muscovite or other gangue minerals through the computation of lithium and fluorine distributions. Utilizing the
micro LIBS (µLIBS) imaging technique, we assess the successful liberation of lepidolite, while also uncovering
scientific and technical constraints. Furthermore, by applying µLIBS imaging to non-floated products, we can
precise the contribution of amblygonite, a lithium-bearing phosphate present in the Beauvoir granite, during
the flotation stage. The findings from this study shed light on the strengths and limitations of the developed
flowsheet, leading to a comprehensive understanding and potential enhancement of the recovery of lepidolite
and by-products from the Beauvoir granite in different streams. Ultimately, this research contributes to the
sustainable supply of lithium, vital for advancing Europe’s clean energy ambitions.
INTRODUCTION
Lithium, classified as a critical raw material in Europe and
the United States, is a key element in the energy transition
due to its unique properties. It is the lightest metal known
to date and possesses high electrochemical and redox
potentials, making it electrochemically active (Christmann
et al., 2015 Garrett, 2004 Swain, 2017).It is this latter
point that makes the metal attractive and its use ubiquitous
for energy storage, such as batteries. Additionally, lithium
is used as a lubricant or in the ceramics and metallurgy

Previous Page Next Page

Extracted Text (may have errors)

1577
Mineralogical Insights in the Recovery of Lepidolite from the
Beauvoir (Li, Be, F) Rare Metals Granite: LIBS Imaging as a Tool
to Improve Beneficiation Routes
C. Korbel, B. Demeusy, Z.S. Kahou, J. Cauzid, C. Fabre, I.V. Filippova, L.O. Filippov
1CNRS, Université de Lorraine, GeoRessources
V. Motto-Ros
CNRS, Université Lyon 1, Institut Lumière Matière
ABSTRACT: The ongoing global energy transformation heavily relies on a steady supply of essential metals
and minerals, with lithium occupying a crucial role in facilitating this transition, particularly in energy storage
through Li-ion batteries. Consequently, lepidolite, a lithium-bearing phyllosilicate, has emerged as a promising
source of lithium for European countries. This valuable mineral is concentrated from other silicates using froth
flotation with amine under acidic conditions. Notably, lepidolite is enriched in the Beauvoir granite (Allier,
France), which also contains other critical elements, including fluorine, tin, niobium, tantalum, and beryllium.
In this study, we explore the liberation of lepidolite and the impact of size fraction on the beneficiation of lithium,
encompassing crushing, grinding, desliming, flotation, and centrifugal separation. By employing mineralogical
studies and automated mineralogy methods, the limitations of the flowsheet are identified. Particularly, the
integration of Laser-Induced Breakdown Spectroscopy (LIBS) imaging enables precise elemental distribution
analysis of Li, F, and Be, which conventional methods like scanning electron microscopy (SEM) or micro
X-ray fluorescence (µXRF) cannot provide. This powerful tool allows to effectively differentiate lepidolite from
muscovite or other gangue minerals through the computation of lithium and fluorine distributions. Utilizing the
micro LIBS (µLIBS) imaging technique, we assess the successful liberation of lepidolite, while also uncovering
scientific and technical constraints. Furthermore, by applying µLIBS imaging to non-floated products, we can
precise the contribution of amblygonite, a lithium-bearing phosphate present in the Beauvoir granite, during
the flotation stage. The findings from this study shed light on the strengths and limitations of the developed
flowsheet, leading to a comprehensive understanding and potential enhancement of the recovery of lepidolite
and by-products from the Beauvoir granite in different streams. Ultimately, this research contributes to the
sustainable supply of lithium, vital for advancing Europe’s clean energy ambitions.
INTRODUCTION
Lithium, classified as a critical raw material in Europe and
the United States, is a key element in the energy transition
due to its unique properties. It is the lightest metal known
to date and possesses high electrochemical and redox
potentials, making it electrochemically active (Christmann
et al., 2015 Garrett, 2004 Swain, 2017).It is this latter
point that makes the metal attractive and its use ubiquitous
for energy storage, such as batteries. Additionally, lithium
is used as a lubricant or in the ceramics and metallurgy

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XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1571
Campos, P.H., Costa, J.F., Koppe, V., and Bassani, M.A.
2022. Geometallurgy-oriented mine scheduling con-
sidering volume support and non-additivity. Mining
Technology 131(1):1–11.
Coward, S., and Dowd, P. 2015. Geometallurgical models
for the quantification of uncertainty in mining project
value chains. In Application of Computers and Operations
Research in the Mineral Industry—Proceedings of the
35th International Symposium. Fairbanks: Society for
Mining, Metallurgy &Exploration.
Deutsch, J., Palmer, K., Deutsch, C., Szymanski, J., and
Etsell, T. 2016. Spatial Modeling of Geometallurgical
Properties: Techniques and a Case Study. Natural
Resources Research 25(2):161–181.
Dominy, S.C., O’Connor, L., Parbhakar-Fox, A., Glass, H.J.,
and Purevgerel, S. 2018. Geometallurgy—A Route to
More Resilient Mine Operations. Minerals 8(12):560.
Donda, J., and Rosa, A. 2014. A lei da moagem: comprova-
ção para minério de ferro. Ouro Preto: L&E Graphar.
Donda, J. 2003. Um método para prever o consumo específico
de energia na (re)moagem de concentrados de minérios de
ferro em moinhos de bolas. Doctoral Thesis in Mineral
Processing. Belo Horizonte: Universidade Federal de
Minas Gerais.
Kittler, P., Liebezeit, V., Ehrig, K., Macmillan, E., and
Lower, C., 2011. “It Seemed like a Good Idea at the
Time …” Common Mistakes in Geometallurgy.
In Proceedings of the First AusIMM International
Geometallurgy Conference. Brisbane: AusIMM.
Lishchuk, V., 2016. Geometallurgical Programs—Critical
Evaluation of Applied Methods and.
Techniques. Licentiate Thesis. Luleå: Luleå University of
Technology.
Lishchuk, V., and Pettersson, M. 2021. The mechanisms
of decision-making when applying geometallurgical
approach to the mining industry. Mineral Economics
34:71–80.
Lund, C., and Lamberg, P. 2014. Geometallurgy: A tool for
better resource efficiency. European Geologist Magazine,
(37):39–43.
McKay, N., Vann, J., Ware, W., Morley, W., and
Hodkiewicz, P. 2016. Strategic and tactical geomet-
allurgy—a systematic process to add and sustain
resource value. In The Third AUSIMM International
Geometallurgy Conference. Perth: AusIMM.
Michaux, S., and O’Connor, L. 2020. How to Set Up and
Develop a Geometallurgical Program. GTK Open File
Work Report. Espoo: Geological Survey of Finland.
Parian, M. 2017. Development of a geometallurgical frame-
work for iron ores—A mineralogical approach to particle-
based modeling. Doctoral Thesis in Mineral Processing.
Luleå: Luleå University of Technology.
Rendu, J.-M. 2017. Risk Management in Evaluating Mineral
Deposits. Englewood: Society of Mining, Metallurgy
and Exploration. p. 310.
Van den Boogaart, K., Tolosana-Delgado, R. 2018.
Predictive geometallurgy: an interdisciplinary key chal-
lenge for mathematical geosciences. In: Handbook of
Mathematical Geosciences. Berlin: Springer.
Vann, J., Jackson, S., Bye, A., Coward, S., Moayer, S.,
Nicholas, G., and Wolff, R. 2012. Scenario thinking—
A powerful tool for strategic planning and evaluation
of mining projects and operations. In Proceedings of the
Project Evaluation Conference. Melbourne: Australasian
Institute of Mining and Metallurgy.

Previous Page Next Page

Extracted Text (may have errors)

XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1571
Campos, P.H., Costa, J.F., Koppe, V., and Bassani, M.A.
2022. Geometallurgy-oriented mine scheduling con-
sidering volume support and non-additivity. Mining
Technology 131(1):1–11.
Coward, S., and Dowd, P. 2015. Geometallurgical models
for the quantification of uncertainty in mining project
value chains. In Application of Computers and Operations
Research in the Mineral Industry—Proceedings of the
35th International Symposium. Fairbanks: Society for
Mining, Metallurgy &Exploration.
Deutsch, J., Palmer, K., Deutsch, C., Szymanski, J., and
Etsell, T. 2016. Spatial Modeling of Geometallurgical
Properties: Techniques and a Case Study. Natural
Resources Research 25(2):161–181.
Dominy, S.C., O’Connor, L., Parbhakar-Fox, A., Glass, H.J.,
and Purevgerel, S. 2018. Geometallurgy—A Route to
More Resilient Mine Operations. Minerals 8(12):560.
Donda, J., and Rosa, A. 2014. A lei da moagem: comprova-
ção para minério de ferro. Ouro Preto: L&E Graphar.
Donda, J. 2003. Um método para prever o consumo específico
de energia na (re)moagem de concentrados de minérios de
ferro em moinhos de bolas. Doctoral Thesis in Mineral
Processing. Belo Horizonte: Universidade Federal de
Minas Gerais.
Kittler, P., Liebezeit, V., Ehrig, K., Macmillan, E., and
Lower, C., 2011. “It Seemed like a Good Idea at the
Time …” Common Mistakes in Geometallurgy.
In Proceedings of the First AusIMM International
Geometallurgy Conference. Brisbane: AusIMM.
Lishchuk, V., 2016. Geometallurgical Programs—Critical
Evaluation of Applied Methods and.
Techniques. Licentiate Thesis. Luleå: Luleå University of
Technology.
Lishchuk, V., and Pettersson, M. 2021. The mechanisms
of decision-making when applying geometallurgical
approach to the mining industry. Mineral Economics
34:71–80.
Lund, C., and Lamberg, P. 2014. Geometallurgy: A tool for
better resource efficiency. European Geologist Magazine,
(37):39–43.
McKay, N., Vann, J., Ware, W., Morley, W., and
Hodkiewicz, P. 2016. Strategic and tactical geomet-
allurgy—a systematic process to add and sustain
resource value. In The Third AUSIMM International
Geometallurgy Conference. Perth: AusIMM.
Michaux, S., and O’Connor, L. 2020. How to Set Up and
Develop a Geometallurgical Program. GTK Open File
Work Report. Espoo: Geological Survey of Finland.
Parian, M. 2017. Development of a geometallurgical frame-
work for iron ores—A mineralogical approach to particle-
based modeling. Doctoral Thesis in Mineral Processing.
Luleå: Luleå University of Technology.
Rendu, J.-M. 2017. Risk Management in Evaluating Mineral
Deposits. Englewood: Society of Mining, Metallurgy
and Exploration. p. 310.
Van den Boogaart, K., Tolosana-Delgado, R. 2018.
Predictive geometallurgy: an interdisciplinary key chal-
lenge for mathematical geosciences. In: Handbook of
Mathematical Geosciences. Berlin: Springer.
Vann, J., Jackson, S., Bye, A., Coward, S., Moayer, S.,
Nicholas, G., and Wolff, R. 2012. Scenario thinking—
A powerful tool for strategic planning and evaluation
of mining projects and operations. In Proceedings of the
Project Evaluation Conference. Melbourne: Australasian
Institute of Mining and Metallurgy.

1578 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
industries (Penner, 1978). In 2015, global lithium usage
was primarily distributed between energy storage and/or
batteries (37%) and ceramics (30%). Lubricants and met-
allurgy accounted for 8% and 6% of global usage, respec-
tively (Gloaguen et al., 2018). The main lithium reserves
are located in Chile, Australia, and China (Tkachev et al.,
2018). Australia was the top lithium producer in 2017, with
11,000 tonnes of spodumene produced, followed by Chile
and China (Jaskula, 2017). The distribution and type of
lithium deposits, i.e., rock or brine, are well defined, which
may explain the high supply risk associated with this metal.
Lithium is present in very low concentrations in the
Earth’s crust, as evidenced by its Clarke value of approxi-
mately 20 ppm (Robb, 2005). Therefore, it is essential to
identify lithium occurrences. The main lithium deposits are
brines (also known as salars) and rock deposits (Kesler et
al., 2012). In the case of rock deposits, several types exist,
such as pegmatites, rare metal granites, or sedimentary
deposits (Alekseev et al., 2019). These deposits hold 32%
of the world’s Li resources. However, only LCT pegma-
tites (Lithium Cesium Tantalum) and rare metal granites
can contain economically viable concentrations of lithium
(Garrett, 2004). Rare metal granites typically contain lepid-
olite, zinnwaldite, and amblygonite, with lepidolite being
the primary lithium-bearing mineral. These deposits are
the main sources of lithium in Europe and are increasingly
under study, as evidenced by emerging research projects on
Keliber pegmatites and St-Austell, Cinovec/Zinnwald, and
Beauvoir granites.
A thorough understanding of rock lithium deposits
relies on a detailed characterization of the mineral phases
bearing this metal, their size and texture, as well as their
spatial distribution within the deposit. This knowledge
is essential for understanding the efficiency or challenges
related to mineral separations, especially in the case of
flotation, a separation method based on mineral surface
properties (Fandrich et al., 2007 Sutherland and Gottlieb,
1991). Moreover, the use of such methods has often led to
adaptation and improvement of deposit processing schemes
(Buchmann et al., 2018 Vanderbruggen et al., 2021). Since
lithium is both a lightweight element and concentrated in
complex mineral phases, this characterization is only partial
and costly in terms of time, energy, and capital. Therefore,
the present study focused, based on the Beauvoir granite
case study, on the development of an analytical method
overcoming these hurdles. This method combines innova-
tive analytical methods such as µXRF and µLIBS (Menzies
et al., 2022 Nikonow and Rammlmair, 2017). Such meth-
ods are suitable for detecting and quantifying light elements
present in the Beauvoir granite and improve the flexibility
of the developed automated mineralogy method.
MATERIALS AND METHODS
SEM and µXRF analysis were carried out at the Service
Commun de Miscroscopie Electronique et de Microanalyse
X (SCMEM) of the GeoRessources research center
(Université de Lorraine, Nancy, France).
Sample
Samples of cores were provided by the company Imerys.
The sampling campaign conducted aimed to collect repre-
sentative samples of the Beauvoir granite. In addition, rep-
resentative samples of the different degrees of alteration of
this granite were provided to identify the issues associated
with these facies for the valorization of the deposit. These
samples were used, on one hand, to prepare thin sections
and, on the other hand, for ore processing tests. Following
the ore processing tests, the separation concentrates were
sampled and cast in resin to produce polished sections.
These sections are used for characterization studies. The
resin used has a high viscosity, which limits the sedimenta-
tion of grains during resin curing.
Characterization
Optical Microscopy
The thin sections were observed under a transmitted light
optical microscope. Scans of the thin sections were per-
formed using a VHX A525 macroscope (Keyence, Japan),
in plane and cross polarized light.
SEM Analysis
The scanning electron microscope used is a Tescan VEGA3
(Bruker, Germany). Using Esprit 2.3 software (Bruker,
Germany), backscattered electron (BSE) imaging was con-
ducted using mapping routines. The acquired images were
5% overlaid using the software. The experimental condi-
tions for acquisition were approximately 15 kV voltage, 12
kV beam intensity, and approximately 30 seconds acquisi-
tion time per image. Prior to mapping, EDS measurements
were performed to assign the observed shades in BSE to the
various minerals in the sample.
X-Ray Fluorescence (XRF)
Micro X-ray fluorescence (µXRF). The samples were
mapped using micro-X-ray fluorescence (µXRF) with an
M4 Tornado spectrometer (Bruker, Germany), equipped
with a Rh tube and two 30 mm2 EDS detectors. The anal-
yses were conducted at a pressure of 20 mbar. Analytical
conditions were set with a 20 μm diameter X-ray beam

Previous Page Next Page

Extracted Text (may have errors)

1578 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
industries (Penner, 1978). In 2015, global lithium usage
was primarily distributed between energy storage and/or
batteries (37%) and ceramics (30%). Lubricants and met-
allurgy accounted for 8% and 6% of global usage, respec-
tively (Gloaguen et al., 2018). The main lithium reserves
are located in Chile, Australia, and China (Tkachev et al.,
2018). Australia was the top lithium producer in 2017, with
11,000 tonnes of spodumene produced, followed by Chile
and China (Jaskula, 2017). The distribution and type of
lithium deposits, i.e., rock or brine, are well defined, which
may explain the high supply risk associated with this metal.
Lithium is present in very low concentrations in the
Earth’s crust, as evidenced by its Clarke value of approxi-
mately 20 ppm (Robb, 2005). Therefore, it is essential to
identify lithium occurrences. The main lithium deposits are
brines (also known as salars) and rock deposits (Kesler et
al., 2012). In the case of rock deposits, several types exist,
such as pegmatites, rare metal granites, or sedimentary
deposits (Alekseev et al., 2019). These deposits hold 32%
of the world’s Li resources. However, only LCT pegma-
tites (Lithium Cesium Tantalum) and rare metal granites
can contain economically viable concentrations of lithium
(Garrett, 2004). Rare metal granites typically contain lepid-
olite, zinnwaldite, and amblygonite, with lepidolite being
the primary lithium-bearing mineral. These deposits are
the main sources of lithium in Europe and are increasingly
under study, as evidenced by emerging research projects on
Keliber pegmatites and St-Austell, Cinovec/Zinnwald, and
Beauvoir granites.
A thorough understanding of rock lithium deposits
relies on a detailed characterization of the mineral phases
bearing this metal, their size and texture, as well as their
spatial distribution within the deposit. This knowledge
is essential for understanding the efficiency or challenges
related to mineral separations, especially in the case of
flotation, a separation method based on mineral surface
properties (Fandrich et al., 2007 Sutherland and Gottlieb,
1991). Moreover, the use of such methods has often led to
adaptation and improvement of deposit processing schemes
(Buchmann et al., 2018 Vanderbruggen et al., 2021). Since
lithium is both a lightweight element and concentrated in
complex mineral phases, this characterization is only partial
and costly in terms of time, energy, and capital. Therefore,
the present study focused, based on the Beauvoir granite
case study, on the development of an analytical method
overcoming these hurdles. This method combines innova-
tive analytical methods such as µXRF and µLIBS (Menzies
et al., 2022 Nikonow and Rammlmair, 2017). Such meth-
ods are suitable for detecting and quantifying light elements
present in the Beauvoir granite and improve the flexibility
of the developed automated mineralogy method.
MATERIALS AND METHODS
SEM and µXRF analysis were carried out at the Service
Commun de Miscroscopie Electronique et de Microanalyse
X (SCMEM) of the GeoRessources research center
(Université de Lorraine, Nancy, France).
Sample
Samples of cores were provided by the company Imerys.
The sampling campaign conducted aimed to collect repre-
sentative samples of the Beauvoir granite. In addition, rep-
resentative samples of the different degrees of alteration of
this granite were provided to identify the issues associated
with these facies for the valorization of the deposit. These
samples were used, on one hand, to prepare thin sections
and, on the other hand, for ore processing tests. Following
the ore processing tests, the separation concentrates were
sampled and cast in resin to produce polished sections.
These sections are used for characterization studies. The
resin used has a high viscosity, which limits the sedimenta-
tion of grains during resin curing.
Characterization
Optical Microscopy
The thin sections were observed under a transmitted light
optical microscope. Scans of the thin sections were per-
formed using a VHX A525 macroscope (Keyence, Japan),
in plane and cross polarized light.
SEM Analysis
The scanning electron microscope used is a Tescan VEGA3
(Bruker, Germany). Using Esprit 2.3 software (Bruker,
Germany), backscattered electron (BSE) imaging was con-
ducted using mapping routines. The acquired images were
5% overlaid using the software. The experimental condi-
tions for acquisition were approximately 15 kV voltage, 12
kV beam intensity, and approximately 30 seconds acquisi-
tion time per image. Prior to mapping, EDS measurements
were performed to assign the observed shades in BSE to the
various minerals in the sample.
X-Ray Fluorescence (XRF)
Micro X-ray fluorescence (µXRF). The samples were
mapped using micro-X-ray fluorescence (µXRF) with an
M4 Tornado spectrometer (Bruker, Germany), equipped
with a Rh tube and two 30 mm2 EDS detectors. The anal-
yses were conducted at a pressure of 20 mbar. Analytical
conditions were set with a 20 μm diameter X-ray beam