3222 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
of unit processes with different objectives. Sorting, deac-
tivation, disassembly, crushing, milling, magnetic separa-
tion, and sieving are among the available processes (Yu et
al. 2021).
The existing industrial-scale recycling processes pri-
oritise the recovery of Co, Ni, Li, and Cu (Shet et al.,
2023 Zanoletti et al., 2024) as these are the components
with highest economic valuable in NMC battery types
(Statista, 2020). To the best of the authors’ knowledge,
no commercially available process can recover all materi-
als in end-of-life (EoL) batteries. Indeed, current recycling
processes target only the recovery of metals found in the
casing or cathode active materials, while anode materials
(namely, graphite) and electrolytes are lost, and Li is often
neglected or downcycled into materials of low economic
value (Brückner, Frank, and Elwert, 2020). Consequently,
industrial-scale processes do not fully align with circular
economy principles, as they fail to reintegrate all materials
into the production loop. This places significant pressure
on recyclers, as it is expected that by 2030, Li should be
recovered by 80% and Co, Ni, and Cu by 95% while the
overall recycling efficiency of Li-ion batteries is expected to
be at least 65% (European Union, 2023). To achieve this
target, it will be necessary to recover materials of lower eco-
nomic value too, particularly graphitic anodes (Patry et al.,
2014).
Various authors (Atia et al. 2019 Santos et al. 2021
Velázquez-Martínez et al. 2019) have introduced innova-
tive recycling processes whose design is based on a circu-
lar economy perspective. While their proposals show high
recovery rates and efficiencies for various materials, these
processes are presently at laboratory or pilot scale. Some
have been modelled in commercially available simulation
software. The complexity of the flowsheets requires exten-
sive study before they can be scaled up to an industrial level.
Additionally, the intrinsic nature of waste streams requires a
deep understanding of how materials interact during recy-
cling. Worrell and Reuter (2014) developed the concept
of “Design for Recycling and Resource Efficiency,” which
emphasises creating processes that maximise material recov-
ery and support circular economy principles.
Process simulation stands out as a valuable tool in
addressing these obstacles. These computational tools aim
to represent real processes based on experimental data or
theoretical data into models of unit processes and opera-
tions (Perry and Green 2007 Worrell and Reuter 2014).
Process simulation has proven useful in the design and opti-
mization of recycling processes for complex EoL products,
such as vehicles, waste electric and electronic equipment
(WEEE), and rechargeable batteries (Reuter, van Schaik,
and Gediga 2015 Rinne et al. 2021 Elomaa et al. 2020).
Moreover, the results obtained from process simulation can
be used to calculate parameters of environmental impact
(Elomaa et al. 2020 Petrescu et al. 2021 Sajid, Khan, and
Zhang 2016) and circularity (Vierunketo et al. 2023).
The typical approach in the use of process simulation
for the analysis and design of process requires that the oper-
ator manually screens a series of possible operating condi-
tions trying to identify an optimal outcome. This remains
a reasonable scenario when a limited number of variables
or unit operations require testing. However, as products
become more complex, the recycling processes also grow
in sophistication, requiring various unit processes and
operations that resulting in several flow streams carrying
potentially valuable materials (Velazquez-Martinez et al.,
2019). Even with the use of a powerful simulation tool, the
manual programing of several scenarios and the interpreta-
tion of results can be time-consuming, and in the case of
entirely new processes, there is high uncertainty on whether
the limited number of process variables chosen will result in
the desired outcome.
This work presents a new computational approach for
designing and optimizing complex recycling processes with
the aid of process simulation software (i.e., HSC-Sim©)
and using an automation routine to screen different oper-
ating conditions in all of the unit operations in the pro-
cess. This allowed us to sample tens-of-thousands scenarios
and consider the range of possible outcomes for materials
flow. To produce sufficient data, it was necessary to develop
parallel computational workflows on cloud computing ser-
vices. We focused on material flow, recovery and grade of
critical materials. We explored the statistical distributions
to identify a combination of desired outcomes. The aim
of this study is to demonstrate a data driven approach to
identifying the operating conditions in complex processes
under which the target objectives can be achieved.
METHODS
In this research, a computational framework for mass-
scale analysis of the LIB recycling process was created. This
allowed to analyze distributions of many possible process
outcomes in terms of the quality of materials recovery, and
to focus on the optimal outcomes. The framework encom-
passes the following steps: constructing a model process,
defining parameter choices and variables, generating simu-
lation files, performing simulations to produce a large data-
set, and analyzing results.
The information on composition and grain size dis-
tribution of LIB black mass was compiled from vari-
ous sources available in the published scientific literature
of unit processes with different objectives. Sorting, deac-
tivation, disassembly, crushing, milling, magnetic separa-
tion, and sieving are among the available processes (Yu et
al. 2021).
The existing industrial-scale recycling processes pri-
oritise the recovery of Co, Ni, Li, and Cu (Shet et al.,
2023 Zanoletti et al., 2024) as these are the components
with highest economic valuable in NMC battery types
(Statista, 2020). To the best of the authors’ knowledge,
no commercially available process can recover all materi-
als in end-of-life (EoL) batteries. Indeed, current recycling
processes target only the recovery of metals found in the
casing or cathode active materials, while anode materials
(namely, graphite) and electrolytes are lost, and Li is often
neglected or downcycled into materials of low economic
value (Brückner, Frank, and Elwert, 2020). Consequently,
industrial-scale processes do not fully align with circular
economy principles, as they fail to reintegrate all materials
into the production loop. This places significant pressure
on recyclers, as it is expected that by 2030, Li should be
recovered by 80% and Co, Ni, and Cu by 95% while the
overall recycling efficiency of Li-ion batteries is expected to
be at least 65% (European Union, 2023). To achieve this
target, it will be necessary to recover materials of lower eco-
nomic value too, particularly graphitic anodes (Patry et al.,
2014).
Various authors (Atia et al. 2019 Santos et al. 2021
Velázquez-Martínez et al. 2019) have introduced innova-
tive recycling processes whose design is based on a circu-
lar economy perspective. While their proposals show high
recovery rates and efficiencies for various materials, these
processes are presently at laboratory or pilot scale. Some
have been modelled in commercially available simulation
software. The complexity of the flowsheets requires exten-
sive study before they can be scaled up to an industrial level.
Additionally, the intrinsic nature of waste streams requires a
deep understanding of how materials interact during recy-
cling. Worrell and Reuter (2014) developed the concept
of “Design for Recycling and Resource Efficiency,” which
emphasises creating processes that maximise material recov-
ery and support circular economy principles.
Process simulation stands out as a valuable tool in
addressing these obstacles. These computational tools aim
to represent real processes based on experimental data or
theoretical data into models of unit processes and opera-
tions (Perry and Green 2007 Worrell and Reuter 2014).
Process simulation has proven useful in the design and opti-
mization of recycling processes for complex EoL products,
such as vehicles, waste electric and electronic equipment
(WEEE), and rechargeable batteries (Reuter, van Schaik,
and Gediga 2015 Rinne et al. 2021 Elomaa et al. 2020).
Moreover, the results obtained from process simulation can
be used to calculate parameters of environmental impact
(Elomaa et al. 2020 Petrescu et al. 2021 Sajid, Khan, and
Zhang 2016) and circularity (Vierunketo et al. 2023).
The typical approach in the use of process simulation
for the analysis and design of process requires that the oper-
ator manually screens a series of possible operating condi-
tions trying to identify an optimal outcome. This remains
a reasonable scenario when a limited number of variables
or unit operations require testing. However, as products
become more complex, the recycling processes also grow
in sophistication, requiring various unit processes and
operations that resulting in several flow streams carrying
potentially valuable materials (Velazquez-Martinez et al.,
2019). Even with the use of a powerful simulation tool, the
manual programing of several scenarios and the interpreta-
tion of results can be time-consuming, and in the case of
entirely new processes, there is high uncertainty on whether
the limited number of process variables chosen will result in
the desired outcome.
This work presents a new computational approach for
designing and optimizing complex recycling processes with
the aid of process simulation software (i.e., HSC-Sim©)
and using an automation routine to screen different oper-
ating conditions in all of the unit operations in the pro-
cess. This allowed us to sample tens-of-thousands scenarios
and consider the range of possible outcomes for materials
flow. To produce sufficient data, it was necessary to develop
parallel computational workflows on cloud computing ser-
vices. We focused on material flow, recovery and grade of
critical materials. We explored the statistical distributions
to identify a combination of desired outcomes. The aim
of this study is to demonstrate a data driven approach to
identifying the operating conditions in complex processes
under which the target objectives can be achieved.
METHODS
In this research, a computational framework for mass-
scale analysis of the LIB recycling process was created. This
allowed to analyze distributions of many possible process
outcomes in terms of the quality of materials recovery, and
to focus on the optimal outcomes. The framework encom-
passes the following steps: constructing a model process,
defining parameter choices and variables, generating simu-
lation files, performing simulations to produce a large data-
set, and analyzing results.
The information on composition and grain size dis-
tribution of LIB black mass was compiled from vari-
ous sources available in the published scientific literature