4
• Lack of staff with skills to manage a complex system
like a DT (e.g., knowledge in data science and pre-
dictive modelling with ML) these skills are part of
a challenge for several industries that are currently
going through a digital transformation process.
• Complexity in integrating all internal areas of a min-
ing company as disaggregation could affect the devel-
opment of a comprehensive DT.
• Perception of the cost-effectiveness of advanced sim-
ulation models in TSF.
• Lack of TSF data governance and security.
Modern simulation tools themselves mitigate the effects of
information gaps and improve understanding of TSF pro-
cesses by integrating all variables and their behavior in one
place. Resistance to change and cost-effectiveness can be
addressed through a progressive implementation of a DT,
by defining critical parameters to improve safety and reduce
costs.
CONCLUSION
Digital transformation is the vehicle that drives mining
towards Industry 4.0, being the DT tools that allow us
advantage of all the latest technological advances for the
benefit of safety and business profitability. Along with this,
the development of a DT requires having abundant infor-
mation obtained from the field, systematized and online, if
possible, a condition that most TSFs still do not have. This
is partly because companies do not yet visualize the added
value of the intensive use of technology for the operation
of a waste storage.
However, with the implementation of GISTM in
various mining operations, the demands regarding infor-
mation management, decision-making processes, and the
use of standardized operational methods have increased. In
line with this, the International Council on Mining and
Metals (ICMM) foresees that in the future the operational
and monitoring aspects of a TSF will be managed using
DTs [4].
Applicability of a DT mainly depends on the potential
benefits in terms of safety and cost-effectiveness for a TSF
in particular, depending on the implementation and main-
tain costs, which should be progressive and modular with a
long-term development vision.
Once the implementation decision has been made, the
development must allow the integration of available infor-
mation (instantaneous and historical), existing simulation
and monitoring tools, and advanced simulation software,
but also incorporate the experience of the system operators,
the governance roles of the dam (e.g., Responsible Tailings
Facility Engineer, Dam Owner) and the Engineer of Record
during its calibration.
It is necessary for mining company users to under-
stand their TSF needs and problems, in order to define the
level of DT to be developed and implemented (Level 0 –
Digital Twin Ready, Level 1 – Informational Twin, Level
2 – Operational Twin and Level 3 – Connected Twins) and
the added value that this type of technology can generate
for them.
However, for a proper implementation, there is still a
need to generate corporate standards for this type of tech-
nology, to define clear governance and security in data
management, and to designate internal roles within mining
companies that have advanced data management skills.
From this applicability proposal, a DT should not
be understood as an automatically operating system, but
rather as an advanced simulation tool for decision making
that requires interpretation by professionals with the appro-
priate skills. Furthermore, DTs could be an excellent tool to
complement a comprehensive change management system
of a TSF.
REFERENCES
[1] Arcadis (2020) Digital Twin – Technical Whitepaper,
written by Andrew Victory, Global Digital
Transformation Lead for D&E.
[2] McKinsey (2023) Technology Trends Outlook 2023,
McKinsey &Company, July 2023. https://www
.mckinsey.com/Technology-trends-outlook‑2023.
[3] Vohra, M. (2022) Digital Twin Technology -
Fundamentals and Applications, 1rst Edition, Wiley &
Sons, USA.
[4] Finke, K. (2022) Tailings Management Handbook -A
Lifecycle Approach, 1rst Edition, Society for Mining,
Metallurgy &Exploration Inc (SME), Canada.
[5] Hariri-Ardebili, M. A., Mahdavi, G., Nuss, L.
K., &Lall, U. (2023) The role of artificial intel-
ligence and digital technologies in dam engineering:
Narrative review and outlook. https://doi.org/10.1016
/J.ENGAPPAI.2023.106813.
[6] Jia, W., Wang, W., &Zhang, Z. (2023). From
simple digital twin to complex digital twin part II:
Multi-scenario applications of digital twin shop floor.
Advanced Engineering Informatics, 56, 101915.
https://doi.org/10.1016/J.AEI.2023.101915.
[7] Tao, F., Xiao, B., Qi, Q., Cheng, J., &Ji, P. (2022).
Digital twin modeling. Journal of Manufacturing
Systems, 64, 372–389. https://doi.org/10.1016
/J.JMSY.2022.06.015.
• Lack of staff with skills to manage a complex system
like a DT (e.g., knowledge in data science and pre-
dictive modelling with ML) these skills are part of
a challenge for several industries that are currently
going through a digital transformation process.
• Complexity in integrating all internal areas of a min-
ing company as disaggregation could affect the devel-
opment of a comprehensive DT.
• Perception of the cost-effectiveness of advanced sim-
ulation models in TSF.
• Lack of TSF data governance and security.
Modern simulation tools themselves mitigate the effects of
information gaps and improve understanding of TSF pro-
cesses by integrating all variables and their behavior in one
place. Resistance to change and cost-effectiveness can be
addressed through a progressive implementation of a DT,
by defining critical parameters to improve safety and reduce
costs.
CONCLUSION
Digital transformation is the vehicle that drives mining
towards Industry 4.0, being the DT tools that allow us
advantage of all the latest technological advances for the
benefit of safety and business profitability. Along with this,
the development of a DT requires having abundant infor-
mation obtained from the field, systematized and online, if
possible, a condition that most TSFs still do not have. This
is partly because companies do not yet visualize the added
value of the intensive use of technology for the operation
of a waste storage.
However, with the implementation of GISTM in
various mining operations, the demands regarding infor-
mation management, decision-making processes, and the
use of standardized operational methods have increased. In
line with this, the International Council on Mining and
Metals (ICMM) foresees that in the future the operational
and monitoring aspects of a TSF will be managed using
DTs [4].
Applicability of a DT mainly depends on the potential
benefits in terms of safety and cost-effectiveness for a TSF
in particular, depending on the implementation and main-
tain costs, which should be progressive and modular with a
long-term development vision.
Once the implementation decision has been made, the
development must allow the integration of available infor-
mation (instantaneous and historical), existing simulation
and monitoring tools, and advanced simulation software,
but also incorporate the experience of the system operators,
the governance roles of the dam (e.g., Responsible Tailings
Facility Engineer, Dam Owner) and the Engineer of Record
during its calibration.
It is necessary for mining company users to under-
stand their TSF needs and problems, in order to define the
level of DT to be developed and implemented (Level 0 –
Digital Twin Ready, Level 1 – Informational Twin, Level
2 – Operational Twin and Level 3 – Connected Twins) and
the added value that this type of technology can generate
for them.
However, for a proper implementation, there is still a
need to generate corporate standards for this type of tech-
nology, to define clear governance and security in data
management, and to designate internal roles within mining
companies that have advanced data management skills.
From this applicability proposal, a DT should not
be understood as an automatically operating system, but
rather as an advanced simulation tool for decision making
that requires interpretation by professionals with the appro-
priate skills. Furthermore, DTs could be an excellent tool to
complement a comprehensive change management system
of a TSF.
REFERENCES
[1] Arcadis (2020) Digital Twin – Technical Whitepaper,
written by Andrew Victory, Global Digital
Transformation Lead for D&E.
[2] McKinsey (2023) Technology Trends Outlook 2023,
McKinsey &Company, July 2023. https://www
.mckinsey.com/Technology-trends-outlook‑2023.
[3] Vohra, M. (2022) Digital Twin Technology -
Fundamentals and Applications, 1rst Edition, Wiley &
Sons, USA.
[4] Finke, K. (2022) Tailings Management Handbook -A
Lifecycle Approach, 1rst Edition, Society for Mining,
Metallurgy &Exploration Inc (SME), Canada.
[5] Hariri-Ardebili, M. A., Mahdavi, G., Nuss, L.
K., &Lall, U. (2023) The role of artificial intel-
ligence and digital technologies in dam engineering:
Narrative review and outlook. https://doi.org/10.1016
/J.ENGAPPAI.2023.106813.
[6] Jia, W., Wang, W., &Zhang, Z. (2023). From
simple digital twin to complex digital twin part II:
Multi-scenario applications of digital twin shop floor.
Advanced Engineering Informatics, 56, 101915.
https://doi.org/10.1016/J.AEI.2023.101915.
[7] Tao, F., Xiao, B., Qi, Q., Cheng, J., &Ji, P. (2022).
Digital twin modeling. Journal of Manufacturing
Systems, 64, 372–389. https://doi.org/10.1016
/J.JMSY.2022.06.015.