1
25-025
Digital Twins: The Future of Tailings Management
Alvaro Veizaga
Arcadis, Santiago, Chile
Samuel Cuellar
Arcadis, Santiago, Chile
INTRODUCTION
In recent years, Industry 4.0 has generated a change in the
way companies in various industrial sectors design, produce
and deliver products and services, and is focused on the
interconnection of systems, the advanced access and man-
agement of large amounts of data in real time, and the imple-
mentation of emerging technologies such as the Internet of
Things (IoT). Artificial intelligence (AI), machine learning
(ML), digital twins (DT), robotics, virtual and augmented
reality (VR/AR), among others [1].
DTs are accurate and dynamic virtual models that
replicate physical systems, where it is possible to simulate
and optimize operations before implementing real-world
changes [3]. These features make it possible to reduce
investment costs, minimize risks and improve strategic
decision-making. These benefits are of utmost importance
for tailings storage facilities (TSF), since they are facilities
aimed at the safe storage of tailings and with the constant
need for growth [4].
This paper evaluates the applicability of a DT in a TSF
from a conceptual perspective, providing the advantages of
its implementation, but also the barriers identified in the
industry based on the authors’ experience.
GENERAL CONCEPTS
What is a DT?
A DT is way of working using interoperative services in
an enabling environment to understand, monitor, inform,
optimize, or simulate an asset, system, process, or organiza-
tion from planning to inception and throughout its full life-
cycle with the purpose of creating better insights and better
decisions [1]. It is usually created using a combination of
sensor data, engineering simulation models, expert experi-
ence, and other types of information [3]. To create a DT,
it is necessary to have up-to-date and orderly information
about the physical asset, and its design, construction, and
operational history [5].
Main objectives of a DT are the following [3], [6]:
• Visualization: Allows to observe the physical entities
in the virtual space.
• Prediction and Simulation: Enables forecasting the
future behavior and performance of the product, sys-
tem or process from simulations.
• Interrogation and control: Allows to obtain infor-
mation about the current and past histories of the
product, system or process, to control the process.
• Optimization: Allows to optimize the performance
of the physical entities and to make decisions on the
development of the process.
DT Levels
DT maturity can be described as a series of layers of twins
that should be considered as an addition to the level before.
Arcadis define 4 levels [1]:
• Level 0 – Digital Twin Ready: A virtual representa-
tion of an asset developed to allow for seamless inter-
action with other digital services. Examples of tools
used are Building Information Modelling (BIM) and
Geographic Information System (GIS).
• Level 1 – Informational Twin: Considers Level
0 plus the Asset Performance Integration, Asset
Management System and Operational Predictive
Modelling.
• Level 2 – Operational Twin: Considers Level 1 plus
live data streams and real-time or near real-time pre-
diction and analytics. Examples are Smart Buildings,
Web Services and IoT integration.
• Level 3 – Connected Twins: Considers Level 2
plus data interaction across different DTs for entire
25-025
Digital Twins: The Future of Tailings Management
Alvaro Veizaga
Arcadis, Santiago, Chile
Samuel Cuellar
Arcadis, Santiago, Chile
INTRODUCTION
In recent years, Industry 4.0 has generated a change in the
way companies in various industrial sectors design, produce
and deliver products and services, and is focused on the
interconnection of systems, the advanced access and man-
agement of large amounts of data in real time, and the imple-
mentation of emerging technologies such as the Internet of
Things (IoT). Artificial intelligence (AI), machine learning
(ML), digital twins (DT), robotics, virtual and augmented
reality (VR/AR), among others [1].
DTs are accurate and dynamic virtual models that
replicate physical systems, where it is possible to simulate
and optimize operations before implementing real-world
changes [3]. These features make it possible to reduce
investment costs, minimize risks and improve strategic
decision-making. These benefits are of utmost importance
for tailings storage facilities (TSF), since they are facilities
aimed at the safe storage of tailings and with the constant
need for growth [4].
This paper evaluates the applicability of a DT in a TSF
from a conceptual perspective, providing the advantages of
its implementation, but also the barriers identified in the
industry based on the authors’ experience.
GENERAL CONCEPTS
What is a DT?
A DT is way of working using interoperative services in
an enabling environment to understand, monitor, inform,
optimize, or simulate an asset, system, process, or organiza-
tion from planning to inception and throughout its full life-
cycle with the purpose of creating better insights and better
decisions [1]. It is usually created using a combination of
sensor data, engineering simulation models, expert experi-
ence, and other types of information [3]. To create a DT,
it is necessary to have up-to-date and orderly information
about the physical asset, and its design, construction, and
operational history [5].
Main objectives of a DT are the following [3], [6]:
• Visualization: Allows to observe the physical entities
in the virtual space.
• Prediction and Simulation: Enables forecasting the
future behavior and performance of the product, sys-
tem or process from simulations.
• Interrogation and control: Allows to obtain infor-
mation about the current and past histories of the
product, system or process, to control the process.
• Optimization: Allows to optimize the performance
of the physical entities and to make decisions on the
development of the process.
DT Levels
DT maturity can be described as a series of layers of twins
that should be considered as an addition to the level before.
Arcadis define 4 levels [1]:
• Level 0 – Digital Twin Ready: A virtual representa-
tion of an asset developed to allow for seamless inter-
action with other digital services. Examples of tools
used are Building Information Modelling (BIM) and
Geographic Information System (GIS).
• Level 1 – Informational Twin: Considers Level
0 plus the Asset Performance Integration, Asset
Management System and Operational Predictive
Modelling.
• Level 2 – Operational Twin: Considers Level 1 plus
live data streams and real-time or near real-time pre-
diction and analytics. Examples are Smart Buildings,
Web Services and IoT integration.
• Level 3 – Connected Twins: Considers Level 2
plus data interaction across different DTs for entire