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Digital Twin in Mining Industry : A Study on
Automation Commissioning Efficiency and Safety
Implementation of a Stacker Machine in an Open-
Pit Mine
Nabil Elbazi1,2
1 Laboratory of Industrial Engineering
Faculty of Science and Technology
University Sultan Moulay Slimane,
Beni Mellal, Morocco
2 Green Tech Institute (GTI)
Mohammed VI Polytechnic University
Benguerir, Morocco
nabil.elbazi@um6p.ma
Adila El Maghraoui2
2 Green Tech Institute (GTI)
Mohammed VI Polytechnic University
Benguerir, Morocco
adila.elmaghraoui@um6p.ma
Hicham El Hadraoui2
2 Green Tech Institute (GTI)
Mohammed VI Polytechnic University
Benguerir, Morocco
hicham.elhadraoui@um6p.ma
Ahmed chebak2
2 Green Tech Institute (GTI)
Mohammed VI Polytechnic University
Benguerir, Morocco
ahmed.chebak@um6p.ma
Oussama Laayati2
2 Green Tech Institute (GTI)
Mohammed VI Polytechnic University
Benguerir, Morocco
oussama.laayati@um6p.ma
Mustapha Mabrouki1
1 Laboratory of Industrial Engineering
Faculty of Science and Technology
University Sultan Moulay Slimane,
Beni Mellal, Morocco
m.mabrouki@usms.ma
Abstract— Digital transformation has emerged as a crucial
aspect in the mining industry to enhance productivity,
efficiency, and availability of various machines and equipment.
The integration of digital twin technology in the mining industry
has demonstrated significant potential in addressing challenges
pertaining to maintenance, production, and safety. This paper
presents a comparative study of developed digital twins in
various industrial applications, including building information
modeling, energy management, manufacturing, healthcare, and
optimization. The aim of this research is to design a digital twin
of a Stacker Machine (SM) in the experimental open pit mine of
OCP Benguerir, organized into four layers: physical and link
layer, application layer, and control layer. The implementation
study conducted on the SM used in mining demonstrates
practical results through the simulation of the system's
automatism. The resulting digital twin can be employed to
simulate critical scenarios and monitor the behavior of the SM
to enhance safety, reliability, and availability.
Keywords—Digital Twin, Cyber Physical System, Mining
Industry, Hardware In the Loop, Artificial Intelligence,
Automation, Conceptualization.
I. INTRODUCTION
The mining industry is well-known for its harsh and
arduous environment, which can cause significant strain on
the machines and equipment employed. Maintaining optimal
machine availability and reliability is paramount to ensure
safety and maximize productivity [1][2]. To achieve this,
several techniques, such as condition monitoring and
preventive maintenance, have been implemented. However,
these methods are often reactive and may result in unexpected
downtime and reduced efficiency[3].
In recent years, digital twin technology has emerged as a
promising solution for improving the maintenance and
operation of complex systems, including those in the mining
industry. A digital twin is a virtual replica of a physical system
that enables real-time monitoring and simulation of system
behavior[4]. By leveraging digital twin technology, mining
companies can optimize their equipment's performance and
reduce downtime [5].
This paper presents an implementation study of a digital
twin for a SM used for ore stacking in an experimental open-
pit mine located in OCP Benguerir, Morocco, one of the
largest phosphate mining companies globally. The objective
of this study is to design and develop a digital twin for the SM,
simulating its behavior and components to optimize usage,
improve availability and reliability. This study offers a
practical example of digital twin technology's application in
the mining industry and its potential benefits.
II. STATE OF ART
A. Digital Twin
A digital twin for industrial applications is a key concept
in the digitalization of industrial processes and systems. It
involves creating a virtual replica of a physical asset or
system, which can then be used for simulation, analysis, and
control. This allows for virtual testing and optimization of
industrial processes, equipment, and systems, resulting in
improved efficiency, reduced downtime, and increased
productivity [6]. The technology has advanced rapidly in
recent years and is now being used in a variety of industries,
including manufacturing, energy, and transportation. The use
of digital twins in Industry 4.0 and the Internet of Things (IoT)
is expected to continue to grow in the coming years as more
companies adopt the technology to improve their operations
[7]. The implementation of Digital twin requires a high level
of integration of data and information from different sources
and different systems.
The digital twin is typically created through the integration
of various technologies, including sensors, data analytics, and
simulation tools. In the context of Cyber-Physical Systems
(CPS), the digital twin is often used to model the behavior of
a physical system and its interactions with the surrounding
environment [8]. To design a digital twin, a framework is
required that considers the physical and cyber components of
the system, as well as the interactions between these
components. This framework can include co-simulation, in
which the digital twin and the physical system are simulated
together, as well as hardware-in-the-loop testing, in which the
digital twin is integrated with the physical hardware to
simulate real-world scenarios. Despite the potential benefits
of digital twin technology, there are still research gaps and
limitations in its application, particularly in the integration of
digital twin, CPS, and hardware-in-the-loop testing in
industrial applications [9]. Further research is needed to
address these gaps and limitations, and to develop more
effective and efficient approaches for designing and
implementing digital twin systems in industry.
B. Digital Twin Application
In Table 1, multiple digital twin (DT) implementations
across various industrial domains are outlined. These
applications are presently being developed by global
researchers and include a range of capabilities, such as
anticipatory maintenance, enhancement, tracking, prediction,
diagnosis, manufacturing, industrial training, and integration
with cyber-physical systems.
TABLE I. DIGITAL TWIN INDUSTRIAL APPLICATIONS
Team Year Applicati
on
Features Digital -
Twin
Ahmed [8] 2020 Cyber-
physical
system
Energy
management
Cyber-physical
Design and
implementa
tion
Nada [10] 2021 Shop
floor
monitorin
g
Cloud
collaboration
Edge cloud
Concept
Juuso [11] 2021 Overhead
crane
Accelerating
production pace
Case study
Damir [12] 2019 Energy
managem
ent
systems
Distributed EMS
Prediction
Simulation
Concept
and
simulation
Sangsu [13] 2022 Smart
manufact
uring
Digital Twin-
based integrated
monitoring system
Case study
Zhansheng
[14]
2020
Building
informati
on
modellin
g
Indoor safety
management
system based on
digital twin
Framework
and Case
study
Nabil [15]
2023 Mining
industry
Digital twin
framework
enabled Asset
lifecycle
management
Framework
and concept
Yinping
[16]
2022
Storage
yard
schedulin
g
Genetic
optimization
Neural network
prediction
Co-simulation
Integration
Ayoub [17]
2022
Grinding
mill case
study
Predictive,
maintenance-
based, da-ta-
driven
Case study
Zongmin
[18] 2021
Populatio
n Health
managem
ent
virtual-real
integration of
industrial IoT
Concept
III. MATERIALS AND METHODS
In this section, we provide a detailed account of the
experimental pilot used in our study, the SM, along with its
operating and process characteristics. Additionally, we
describe the software and devices utilized in the study that
allowed us to carry out the experiments and analyze the data
obtained. Together, these subsections provide a
comprehensive overview of the materials used in our study
and ensure the reproducibility of the results by other
researchers in the field.
A. The Experimental Pilot: Stacker Machine
For the purpose of our study, the SM was selected as the
experimental pilot due to its significant role in the value chain
of the Phosphate Experimental Open Pit Mine of Benguerir.
The SM is situated at the center of the production value chain
and is utilized in two of the three major stations in the primary
process of the mine as seen in Figure 1. Once the raw material
is extracted, the phosphate undergoes pre-processing, which
includes three stages: Destoning, Screening, and Train
loading. Phosphate extracted from the destoning station is
conveyed by the SM to Storage Parc 1 before being
transported to the subsequent processing station via conveyor
belts. At the screening station, the product undergoes several
processes aimed at enhancing its quality and commercial
viability. The resulting output is transported to the final
storage parc (Storage Parc 2) via conveyor belts, where the
product is stored according to its quality in dedicated storage
areas. The entire process is overseen by the SM, serving as the
Experimental Pilot for this study.
Fig. 1. SM in the production value chain
B. Operating and Process Characteristics
The SM used in our work possesses several process
characteristics that are vital for its efficient and effective
operation. The machine has an output capacity of 1200 tons
per hour and a boom length of 38.265 meters. The stacker's
swing angle is 190°, and it has a slewing speed of 10 meters
per minute. Its boom inclination is 17°, and it can discharge
materials at a height of 17,150 meters. The SM has a track
gauge of 6 meters and can travel at a speed of 30 meters per
minute.
TABLE II. STACKER OPERATING CHARACTERISTICS
Mechanism
Power Tra
in
system
Component
Characteristics Sensor
Translation 6 Motors with
gearboxes
Power: 7.5 kW
Voltage: 500 V
2Magnetic sensors
13 Capacitif sensors
Boom
lifting and
lowering
Hydraulic
unit
Power: 55/75
kW
Voltage: 500 V
4 Magnetic sensors
Arrow
orientation
motor and
reducer
Power: 5.5 kW
Voltage: 500 V 6 Magnetics sensors
power
winder
motor and
reducer
Power: 1.5 kW
Voltage: 500 V
boom
conveyor
motor and
reducer
Power: 5.5 kW
Voltage: 500 V
These process characteristics play an important role in the
overall performance of the SM and highlight the importance
of monitoring and optimizing its operation to ensure optimal
productivity and safety in the mining industry. In addition, the
SM exhibits several distinctive process characteristics as
illustrated in the Table 2.
C. Utilized Software and Devices
a) Schneider Modicon 340
Is a programmable logic controller (PLC) that has been
widely used in industrial automation applications. It was first
introduced by Schneider Electric, a global specialist in energy
management and automation, and has since been adopted in
various industries, including the mining industry. The
Modicon 340 PLC provides a flexible and scalable automation
solution that can be customized to meet the specific needs of
various industrial applications. Its advanced features and
capabilities, such as real-time monitoring, data acquisition,
and communication, have made it a popular choice among
industry professionals. As a result, it has played a significant
role in improving industrial processes, increasing
productivity, and enhancing overall efficiency.
b) Unity Pro XLS
Is a programming software developed by Schneider
Electric for Programmable Logic Controllers (PLCs) in
industrial automation environments. It provides a user-
friendly interface for efficient development, testing, and
commissioning of control applications. The software supports
multiple programming languages, including Ladder Diagram,
Sequential Function Chart, Structured Text and Functional
Block Diagram, giving programmers the flexibility to choose
the language that best suits their project requirements. Unity
Pro XLS also includes advanced debugging and testing
capabilities, simulation and monitoring tools, and detailed
analysis of PLC operations for quick issue identification,
reducing system downtime and maintenance costs. It is an
integrated solution that enhances the efficiency and reliability
of industrial automation systems.
c) SIMAC Prosyst
Is a digital simulation tool developed and marketed by
ProSyst, a company specialized in providing innovative and
original solutions to industrial problems. It is designed to
simulate automated systems using virtual models that can be
customized and connected to programmable logic controllers
for the purpose of commissioning control commands. SIMAC
offers an integrated development environment that combines
virtual models with physical controllers within connected
simulation.
d) Citect SCADA
Is a widely-used software suite in industrial automation,
specifically designed for supervisory control and data
acquisition (SCADA) applications. Developed by Schneider
Electric, Citect SCADA provides a robust and scalable
platform for real-time monitoring and control of industrial
processes and systems. It offers a comprehensive range of
features and tools for data acquisition, visualization, alarm
management, and reporting, making it a valuable tool for
operators, engineers, and managers in industrial automation.
IV. DESIGNED DIGITAL TWIN OF THE STACKER MACHINE
A. System Architecture Framework
The presented architecture framework seen in Figure.2,
structures the system architecture into four layers. The
Physical layer is a physical replica that represents the SM,
material handling systems including actuators and sensors.
The Link layer is a segment in which edge device (i.e. the
PLC) is located, and data is gathered from the physical layer,
namely the SM. Application layer is the area encompassing
development and configuration activities predicated on the
results of commissioning tests conducted within the operative
interface of the simulation software. The Control layer
contains all data connections between the industrial SCADA
system, the simulation, and the test reports that rely on the
performed what-if scenarios
B. System Associated Functions
a) Physical and Link layers
The physical layer, which serves as the foundation of the
system, is a physical replica that is designed to accurately
represent the SM and its associated material handling systems,
including various actuators and sensors. This layer serves as a
bridge between the physical world and the virtual
environment, and it provides a basis for the rest of the system's
components. The link layer, on the other hand, is a critical
segment of the system that is responsible for connecting the
edge device, specifically the PLC, with the physical layer. The
data obtained from the sensors and other components of the
SM is collected by the edge device and transmitted to the link
layer for processing and analysis. This layer is also
responsible for ensuring the accuracy and reliability of the
data transmitted to the next layer, which is the Application
layer. Thus, the link layer is a crucial component of the overall
system framework, as it forms the foundation for data
collection and transmission throughout the entire system.
b) Application layer
The application layer in the system framework is a crucial
domain that encompasses various development and
configuration activities. It is based on the results of
commissioning tests that are conducted within the operative
interface of the simulation software. This layer serves as a
bridge between the lower and upper layers of the system
framework, allowing for the smooth transmission and
manipulation of data. Within the application layer, developers
and engineers use simulation software to create virtual
rackable modules of the PLC, material handling systems, and
other related components. This process includes defining
input parameters, specifying the simulation scenario, and
running various simulations to validate the design and test
different operating conditions.
Moreover, the application layer also plays an essential role
in the development of control and monitoring systems. At the
Engineer Station, developers and specialist engineers use this
layer to design and test automation programming code,
setpoints, and other parameters that are relevant for the
operation of the SM. In addition, knowing that the application
layer is located at the Engineer Station, it allows for the
configuration of other software tools, such as data logging,
visualization, and reporting tools. By configuring these tools
within the application layer, engineers can effectively manage
and monitor the SM's performance, and gather critical data
that can be used for further analysis and optimization. Overall,
the application layer is a critical component of the proposed
system architecture framework, as it allows for the efficient
development, configuration, and controlling of the SM and its
associated systems.
Fig. 2. System architecture framework of the designed digital twin.
c) Control layer
The Control layer plays a crucial role in the system
architecture as the centerpiece of the Digital Twin. It functions
as the primary interface for the runtime simulation
environment, facilitating data exchange and communication
between the CPU hardware, specifically the Schneider
Modicon 340 similar to the implemented CPU on the SM, and
the process simulation performed in the user interface.
Through this connection, the Control layer establishes a link
between the Hardware in the Loop (HIL) and the simulation
function, enabling seamless integration of real-world
hardware and software simulation as seen in Figure.3.
Moreover, the Control layer plays a significant role in
enabling the creation and execution of what-if scenarios,
which are hypothetical situations that allow operators to test
various process configurations - including the most critical -
and evaluate their outcomes. These scenarios are typically
designed to examine how different process configurations and
system parameters impact the overall process performance,
efficiency, and safety. In addition to data exchange and what-
if scenario capabilities, the Control layer is responsible for
generating test reports based on the data collected during the
simulation process. These reports are essential for evaluating
the simulation's accuracy and reliability, as well as for
providing a decision-making support for the control room
operators. They contain data on the process's performance,
including efficiency metrics, production rates, and any
deviations from normal operating conditions, providing a
comprehensive picture of the system's behavior.
Fig. 3. HIL enabling CPU connection with the simulation.
C. Discussion
The digital twin system for the SM has proven to be highly
effective in enhancing the reliability of the automation
programming code commissioning and increasing safety. By
providing operators with the capability to run and test what-if
scenarios before implementing changes in the physical asset.
The four-layer system architecture, including the physical
layer, link layer, application layer, and control layer, allows
for precise testing and evaluation of the automation
programming code, ensuring optimal performance and safety
of the SM. Overall, the digital twin system has greatly
improved the efficiency and safety of the commissioning
process, reducing the potential for costly and hazardous errors.
During the development and implementation of the digital
twin system for the SM, several limitations and challenges
were encountered. A significant limitation of the designed
system was the requirement for human agents, (i.e. the
operators) to link the output of the simulation, notably the test
report, with the SM thorough the SCADA monitoring system.
This human dependency poses a risk of errors or delays in
communication between the two sides of the system, thereby
limiting the autonomy of the digital twin system. Furthermore,
the development and implementation of the digital twin
system necessitated substantial resources in terms of time and
expertise to ensure its proper functioning and seamless
integration with existing systems[19]. These limitations and
challenges must be considered when evaluating the
effectiveness and practicality of the digital twin system in
industrial applications [20]. Potential future directions for
improving the capabilities of the designed digital twin system
for the SM could be to reduce reliance on human agents and
increase the system's autonomy by utilizing advanced
algorithms and artificial intelligence. Additionally, the system
could be enhanced to incorporate real-time data from multiple
sensors and sources, which would improve the accuracy and
reliability of the simulation. Furthermore, the system could be
expanded to include more complex scenarios and test cases to
provide operators with a more comprehensive understanding
of the physical asset's behavior and performance [21]. Finally,
the system could be integrated with other digital twin systems
for different assets or systems, resulting in more
comprehensive and holistic simulations and testing.
V. CONCLUSION
In this paper, we presented an implementation study of a
digital twin for a SM in an experimental open-pit mine. The
study aimed to design and develop a digital twin for the SM,
the developed system consists of four layers, namely physical
layer, link layer, application layer, and control layer. The
digital twin simulates the behavior of the SM and its
components, enabling the optimization of its usage and
improving its availability and reliability. The developed
digital replica of the SM can be utilized to perform crucial
situations and observe the performance of the stacker parts,
allowing for optimization of the machine's usage. The digital
twin can also be used for predictive maintenance, enabling
mining companies to identify potential issues before they
result in unexpected downtime.
In conclusion, this study provides a practical example of
the application of digital twin technology in the mining
industry and its potential benefits. The implementation of
digital twin technology for mining equipment can lead to
improve productivity, safety, and reliability, making it a
valuable asset for the mining industry.In particular, further
research can explore the application of digital twin technology
in other mining equipment and processes within the utilization
of co-simulation technology in order to ensure the interaction
between the models representing the different equipment and
services of the entire mine.
ACKNOWLEDGMENT
This project has received funding from the Experimental
Mine program (MineEx), a research and innovation
programme led by Mohammed VI Polytechnic University
(UM6P) and OCP Benguerir.
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