3
estimated that the economic cost of not integrating deci-
sions exceeds 6% of operating costs in large-scale mining
[11].
DEVELOPMENT AND IMPLEMENTATION
OF A DT IN A TSF
The concept of a DT implies an instantaneous data con-
nection between the physical element and the mathemati-
cal simulation model. The flow of data and the method of
information exchange within a DT is vital to its success,
and offers the opportunity to scale that asset. It should be
understood that the data structure is likely to be a mix of
different data sets, which should be aligned with Corporate
Technical Specifications, Private Standards, Open Industry
Standards, National and International Standards [1].
However, the operation of TSF does not require instan-
taneous interaction, as they are operations with numerous
manual components and gradual operational changes.
Nevertheless, it is necessary to have a database that inte-
grates the operation and monitoring data of the different
processes of the deposit, which is not yet the case in most
of the TSFs.
It is also necessary to consider that not all reservoir pro-
cesses can be modeled by deterministic formulas (e.g., the
geometry of deposition as a function of production param-
eters due to sedimentation phenomena). Therefore, the use
of data science is recommended to establish patterns that
such algorithms can detect to generate more accurate simu-
lations and with less effort to calibrate parameters. AI and
digital technologies are currently used in water dam engi-
neering, specifically predictive modeling (ML), real-time
monitoring, planning and design [5].
The technology landscape to support the emerging
needs of DT customers is complex, with a wide competitive
variety of solutions and use cases emerging. It is necessary
to be aligned with all the global technology partners and
suppliers that Arcadis works with on its DT propositions
and projects, for various industry sectors and with varying
levels of complexity.
On the other hand, tailings dams have different pro-
cesses and monitoring systems allowing modules to be
generated to simulate these processes separately and then
integrate them into a single model. Table 1 identifies these
modules according to the tailings processing method.
Of note, the monitoring module considers physical and
chemical stability parameters of the TSF which are related
to the phenomena present in the different processes. It can
work and interact with existing stability modeling software
(static, seudostatic and dynamic analysis).
Along with the identification of potential advantages
mentioned, it is necessary to evaluate the real contributions
in safety and cost reduction that a DT can bring to a spe-
cific TSF, both in terms of investment and the personnel
required to keep it updated. Main aspects to be evaluated
are electrical energy consumption, water recovery, physical-
chemical stability, and occupation of available space in the
TSF.
DTs are working in different mining operations, but
mostly applied to the process of extracting metals from ore,
where any optimization has high profitability. For this rea-
son, it is important to transfer technology to the tailings
area to apply the available experience.
POTENTIAL LIMITATIONS FOR DTS
IMPLEMENTATION
Along with the great potential of implementing DTs, it is
also important to recognize existing potential limitations:
• Uncertainties in hydrological, geotechnical and
hydraulic phenomena that affect the TSF’s operation
and performance.
• Gaps and lack of information on the characteristics
of the existing infrastructure and its historical opera-
tional data.
• The necessity to plan the medium- and long-term
incorporation of new sensors and instrumentation
systems that measure variables in real time as cur-
rently, between 80–90% of the variables measured
in a DT are manual by field operational personnel .
• Misinterpretation of monitoring data and engineer-
ing studies.
• Organizational culture resistant to change, mainly by
key TSF personnel (e.g., dam operators)
Table 1. Proposed Modules of a DT According to Tailings
Treatment Type
Tailings Type
Proposed for a DT Modules
Based on Processes Involved
Conventional Transport, Distribution, Water
recovery, Monitoring
Cycloned Transport, Cycloning, Dam
Construction, Distribution,
Monitoring
Thickened Transport, Distribution, Water
Recovery, Monitoring
Filtered Filtering, Transport, Disposal,
Monitoring
estimated that the economic cost of not integrating deci-
sions exceeds 6% of operating costs in large-scale mining
[11].
DEVELOPMENT AND IMPLEMENTATION
OF A DT IN A TSF
The concept of a DT implies an instantaneous data con-
nection between the physical element and the mathemati-
cal simulation model. The flow of data and the method of
information exchange within a DT is vital to its success,
and offers the opportunity to scale that asset. It should be
understood that the data structure is likely to be a mix of
different data sets, which should be aligned with Corporate
Technical Specifications, Private Standards, Open Industry
Standards, National and International Standards [1].
However, the operation of TSF does not require instan-
taneous interaction, as they are operations with numerous
manual components and gradual operational changes.
Nevertheless, it is necessary to have a database that inte-
grates the operation and monitoring data of the different
processes of the deposit, which is not yet the case in most
of the TSFs.
It is also necessary to consider that not all reservoir pro-
cesses can be modeled by deterministic formulas (e.g., the
geometry of deposition as a function of production param-
eters due to sedimentation phenomena). Therefore, the use
of data science is recommended to establish patterns that
such algorithms can detect to generate more accurate simu-
lations and with less effort to calibrate parameters. AI and
digital technologies are currently used in water dam engi-
neering, specifically predictive modeling (ML), real-time
monitoring, planning and design [5].
The technology landscape to support the emerging
needs of DT customers is complex, with a wide competitive
variety of solutions and use cases emerging. It is necessary
to be aligned with all the global technology partners and
suppliers that Arcadis works with on its DT propositions
and projects, for various industry sectors and with varying
levels of complexity.
On the other hand, tailings dams have different pro-
cesses and monitoring systems allowing modules to be
generated to simulate these processes separately and then
integrate them into a single model. Table 1 identifies these
modules according to the tailings processing method.
Of note, the monitoring module considers physical and
chemical stability parameters of the TSF which are related
to the phenomena present in the different processes. It can
work and interact with existing stability modeling software
(static, seudostatic and dynamic analysis).
Along with the identification of potential advantages
mentioned, it is necessary to evaluate the real contributions
in safety and cost reduction that a DT can bring to a spe-
cific TSF, both in terms of investment and the personnel
required to keep it updated. Main aspects to be evaluated
are electrical energy consumption, water recovery, physical-
chemical stability, and occupation of available space in the
TSF.
DTs are working in different mining operations, but
mostly applied to the process of extracting metals from ore,
where any optimization has high profitability. For this rea-
son, it is important to transfer technology to the tailings
area to apply the available experience.
POTENTIAL LIMITATIONS FOR DTS
IMPLEMENTATION
Along with the great potential of implementing DTs, it is
also important to recognize existing potential limitations:
• Uncertainties in hydrological, geotechnical and
hydraulic phenomena that affect the TSF’s operation
and performance.
• Gaps and lack of information on the characteristics
of the existing infrastructure and its historical opera-
tional data.
• The necessity to plan the medium- and long-term
incorporation of new sensors and instrumentation
systems that measure variables in real time as cur-
rently, between 80–90% of the variables measured
in a DT are manual by field operational personnel .
• Misinterpretation of monitoring data and engineer-
ing studies.
• Organizational culture resistant to change, mainly by
key TSF personnel (e.g., dam operators)
Table 1. Proposed Modules of a DT According to Tailings
Treatment Type
Tailings Type
Proposed for a DT Modules
Based on Processes Involved
Conventional Transport, Distribution, Water
recovery, Monitoring
Cycloned Transport, Cycloning, Dam
Construction, Distribution,
Monitoring
Thickened Transport, Distribution, Water
Recovery, Monitoring
Filtered Filtering, Transport, Disposal,
Monitoring