Development of an IoT Cloud Control System for Managing
Pressurized Gas Containers
FRANCESCO ZITO, NICOLA IVAN GIANNOCCARO, ROBERTO SERIO,
SERGIO STRAZZELLA
Department of Engineering Innovation,
University of Salento,
Via per Monteroni 73100 Lecce,
ITALY
Abstract: - Automatic remote management of gas cylinders is an important task for gas companies, especially if
it saves time and money on replacement and maintenance. The system presented here can be used in various
industries that require monitoring of cylinders containing gas, such as industrial, medical, and food. The main
advantage offered is the ability to arrange for cylinder replacement in advance to speed up, minimize
downtime, and facilitate logistics. Monitoring uses a microcontroller from the Arduino family, which can
continuously detect the state inside the cylinder via a pressure transducer. The customer can monitor the system
by viewing the cylinder's status locally or through an online dashboard. In addition, if the level inside the
cylinder becomes critical, the system turns on a warning LED light and sends an Alert message to the Cloud
containing location information. The alert message was generated in the AWS IoT Cloud environment using
the MQTT protocol and will be received on a mobile device owned by the cylinder replacement operator.
Finally, an energy analysis has been carried out to evaluate the autonomy characteristics of the device.
Key-Words: - Technical gas cylinder; Mkr Wi-Fi 1010 microcontroller; pressure detection; IoT, energy design,
AWS.
Received: April 26, 2022. Revised: August 23, 2023. Accepted: November 19, 2023. Published: December 31, 2023.
1 Introduction
One of the main problems encountered in a
production cycle is waiting times, which cause stops
to the detriment of the production itself. Avoiding
some situations that could cause interruptions is the
common thread, [1] that led to the development of
this research. There are many real industrial
situations in which the gas cylinder becomes empty
and operators must stop operating while waiting to
replace the cylinder. Just think, for example, of an
operator using an argon cylinder in a TIG (Tungsten
Inert Gas) welding operation, [2] or the use of food
gases (carbon dioxide, nitrogen, argon, and oxygen)
in the food and beverage industry, [3] and many
others.
This study investigated possibilities for reducing
process interruptions through automatic and remote
sensing of gas cylinder pressure and its precise
location. The primary objective is to develop a
digital pressure manometer capable of detecting the
pressure inside cylinders. Unlike traditional pressure
manometers, which merely display pressure
readings, this digital solution eliminates the need for
the constant presence of the operator near the
cylinder to ensure an uninterrupted gas supply.
The scope of application of the device
developed in this study is broad, as it can be
integrated into various situations requiring gas
pressure monitoring, whether used in a production
cycle or stored in depots. The system ensures
continuous cylinder monitoring and provides real-
time alerts when the cylinder is nearly empty,
facilitating preventive replacement.
The model created is a point of reference to be
replicated for any control system that requires
monitoring an industrial parameter; with low
investment, it is possible to make hardware
components not born for Industry 4.0 suitable for
the IoT environment.
The proposed control system was developed
using a low-cost microcontroller of the Arduino
family, [4], which can continuously detect internal
pressure by communicating with a suitable pressure
sensor. Similar recent IoT applications for
monitoring gas cylinders are described in, [5], [6],
[7], [8], [9], [10], [11], [12], with different types of
sensors and purposes but the same MCU.
In our case, the realized IoT application adds to
the measure of gas pressure inside the cylinder and
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DOI: 10.37394/232017.2023.14.10
Francesco Zito, Nicola Ivan Giannoccaro,
Roberto Serio, Sergio Strazzella
E-ISSN: 2415-1513
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the localization. Recent research using Arduino for
GPS using a GSM module is shown in, [13].
In the proposed application, all the data
collected are displayed locally through a simple
LCD screen and collected remotely in a database.
The system has been integrated with a GPS module,
[14], that geolocates the cylinder and a GMS
module, [15] equipped with a SIM to send the alert
message. The pressure manometer is located near
the cylinder outlet pipe. It is specially designed to
make it possible to display the pressure value and to
visualize an alert consisting of the lighting of a red
LED once the minimum threshold has been reached.
Remotely, the person in charge of replacing the
cylinder must have a device that allows the
reception and display of the alert message, whether
a simple smartphone or a mobile device.
The system can be customized in several
aspects, such as feedback times and the pressure or
battery level at which the alarm will be sent. The
operator can remotely control the cylinders via a
graphical interface by integrating the proposed
control system. Geolocation plays an important role,
being able to easily identify the position of the
empty cylinder in the set of several hundred
cylinders, speeding up their replacement. Finally, an
energy analysis has been carried out to evaluate the
autonomy characteristics of the proposed device,
demonstrating its applicability for industrial
purposes.
2 Hardware and Firmware
The prototype device consists of two parts:
•The first part is a box with the microcontroller,
GPS module, and batteries to power the system. The
box has an LCD screen that shows the data revealed
by the pressure sensor and the status of the batteries
in real time. The box is attached to the cylinder via a
Velcro strap. This docking system was chosen
because it allows the box to be assembled and
disassembled very quickly, thus facilitating
logistical operations for cylinder refilling or
maintenance.
•The second part is a hydraulic pipe used for
pressure measurement. The pipe has two fittings for
the analog pressure sensor and a differential
pressure gauge. The pipe nozzle is installed
upstream of the rolling valve and downstream of the
cylinder shut-off valve. The system layout is shown
in Figure 1.
The main hardware components (shown in Figure 2)
are:
▪MKR Wi-Fi 1010 microcontroller, which, by
integrating the ESP 32 module, allows connection to
a Wi-Fi network (detailed in 2.1),
▪NEO 6m V2 GPS module (detailed in 2.2), which
allows the cylinder to be located,
▪Analog Pressure transducer (detailed in 2.3) for
pressure measurement,
▪LCD and a red LED for direct visualization of the
pressure value.
2.1 MKR Wi-Fi 1010
The MKR Wi-Fi 1010 (Figure 3) is a miniature-
sized module containing a SAMD21G18A
Processor, the Nina W102 Module, and a crypto
chip (the ATECC508). It has been designed to speed
up and simplify the prototyping of IoT applications
based on Wi-Fi connectivity, thanks to the U-BLOX
NINA-W10 ESP32 module's flexibility and low
power consumption.
The Arduino MKR WIFI 1010 can be powered
using external 3.7 Volt or 5 Volt Li-Po batteries,
recharging the Li-Po battery while running on
external power, [16].
Fig. 1: System Layout
The microcontroller's logic locally monitors the
cylinder level and sends the appropriate feedback, as
shown in Figure 4.
The system's main task is to send an alert
message that the operator responsible for replacing
the cylinder will receive. In this way, the criticality
linked to the operational stop required for the
replacement is overcome. This will allow it to
integrate seamlessly into the production cycle,
improving it.
The firmware is structured as follows:
At boot, the GPS and pressure sensors are
initialized; a microcontroller pin is also initialized as
an analog input. This pin is physically connected to
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Francesco Zito, Nicola Ivan Giannoccaro,
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a voltage divider that allows the state of charge of
the batteries to be derived; this information will be
added to the message sent.
Fig. 2: Hardware components in the box
Fig. 3: MKR Wi-Fi 1010
The control cycle occurs every two minutes,
mainly based on checking the pressure level. If the
pressure is low, the microcontroller connects to the
MQTT, [17], broker via Wi-Fi and sends an alert
message. On the other hand, if the state of charge of
the cylinders is above the threshold, the
microcontroller sends feedback every hour.
The message sent contains the following
information:
• state of charge of the batteries,
• state of charge of the cylinders,
• GPS coordinates.
• System ID
Fig. 4: Firmware logic flow chart
2.2 GPS Module
The integration of the GPS module allows the
localization of cylinders. It is useful to speed up
replacement operations, and it can be used to
optimize logistics operations.
Specifically, the GY-NEO 6M V2 GPS module
was selected (Figure 5). The main part of the
module is a NEO-6M GPS chip from U-Blox 6. It
can track up to 22 satellites across 50 channels and
achieves a sensitivity level of -161dB tracking while
consuming only 45mA of supply current, [18].
Power-saving mode (PSM) reduces system power
consumption by selectively turning on and off
certain parts of the receiver. This significantly
reduces the electric current of the module to only 11
mA, making it compatible for use in stand-alone
applications.
Fig. 5: GPS module
2.2.1 GPS Module Operation
The LED flashing on the NEO-6M GPS module
indicates the Position Fix status. It will flash at
various speeds depending on the state it is in:
• No flash: it means it is searching for satellites.
Flashes every second: means that the position fix
has been found.
The operating voltage of the NEO-6M chip is
2.7 to 3.6 V. However, the module comes with
MICREL's MIC5205 ultra-low dropout 3V3
regulator. The logic pins are also 5 volt tolerant, so
you can easily connect to an Arduino or any 5V
logic microcontroller without any logic-level
converters.
It is possible to read data formatted according to
the NMEA 0183 standard by connecting the pins of
the GPS module to Arduino using UASRT NMEA
0183, a combined electrical and data specification
for communication between marine electronics.
NMEA 0183 is a proprietary protocol issued by the
National Marine Electronics Association for use in
boat navigation and control systems, e.g., the
standard is used for the operation of many
instruments such as depth sounders, sonar,
anemometers, gyrocompasses, and GPS receivers,
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[19], [20]. All NMEA's sentences have a structure
like:
The prefix is the first part of the string, which is
used to specify what type of talker is; in this case,
the device is a GPS module, so the prefix is GP
followed by the type of the sentence. All sentences
are identified with three letters, e.g., RMC, RMB,
etc. Specifically, the information provided by the
GPGGA sentence is used for the extrapolation of the
Global Positioning System fixed data as reported
below:
2.3 Pressure Transducer
The selected commercial pressure transducer
(Sensata, BTE7100 series) has three main
characteristics:
It's powered by a voltage of 5 volts corresponding
to the output voltage supplied by the used
microcontroller.
Its measurement range covers pressure detection
from up to 200 bars.
The electrical output signal is in [0-5] V range.
The purpose is to have a general indication of the
charged state of the cylinder; after experimental
analysis, it was derived that commercial sensors
(sensitivity in the order of 1% of full scale) are
suitable for the purpose. The pressure transducer
must be calibrated to work properly. The sensor was
calibrated to a compressed air cylinder via a
connecting pipe and a manual manometer for the
calibration values (Figure 6).
One hundred fifty tests were performed by
gradually increasing the pressure from 0 bar to 5 bar
for the pressure transducer calibration. The sensor
was cabled to Arduino MKR Wi-Fi 1010 for data
analysis, and the calibration curve was obtained
with test results. The equation that best interpolates
the data trend was derived from experimental data in
Table 1.
Fig. 6: Setup for the pressure sensor calibration.
Table 1. Pressure data
Bar
Analog
Readings
Average
Number
Of
Measures
0,0
562,5
54
1,4
1140,7
13
1,5
1178,0
13
1,8
1427,4
5
2,0
1563,6
5
2,4
1653,0
5
2,5
1789,4
5
2,8
1940,6
5
3,0
2060,0
5
3,4
2194,5
6
3,4
2215,9
7
3,5
2309,5
13
4,4
2751,0
7
4,5
2805,0
7
2.3.1 Pressure Linear Regression
The chart shows (Figure 7) that the coefficient of
determination R2 value tends toward unity, so
linearity can be assumed between the value read by
the microcontroller and the cylinder pressure. The
coefficients of the straight line were obtained by
generating a linear regression model from the
experimental data obtained.
$PREFIX, data1, data2.. dataN-
1,dataN*CHECKSUM
$GPGGA: hhmmss.ss, llll.ll, a,
yyyyy.yy, a, x, xx, xx, xx, M, xx,
M, xx, xxxx
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Fig. 7: Interpolation curve obtained by the
calibration
The analysis results in Table 2 show the
standard error and R squared value on the linear
regression carried out by 150 measurements and
coefficients of the linear regression line. Figure 8,
Figure 9 and Figure 10 show the distribution of
residuals from the statistical analysis, the graph of
approximations, and the normal probability graph.
Table 2. Linear regression is outgoing
Regression statistics
R squared
0,9951
Standard error
54,83
Angular coefficient
497,35
Intercept
537,33
Measurements
150
Fig. 8: Tracing of residuals
Fig. 9: Linear approximation
Fig. 10: Tracing of normal probability
3 AWS IoT
The proposed system is based on the AWS IoT core
for allowing a smart use of the data. This service
easily allows the connection between a physical
device and the Cloud. AWS IoT Core can support
billions of devices and trillions of messages; it can
reliably process and route those messages securely
from AWS endpoints to other devices. The logical
part connection is schematized in Figure 11.
Devices can connect to AWS IoT Core using the
following protocols: HTTP, Web-Socket, and
MqTT.Message Queue Telemetry Transport
(MQTT), [17], is an ISO standard lightweight
messaging protocol that sits on top of TCP/IP. It
was designed for situations where low impact is
required and where bandwidth is limited. The
communication scheme is published-subscribe and
requires a message broker to work.
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Fig. 11: Design system layout
Figure 12 was extracted from the MQTT tester
made available in the AWS IoT core service. The
image shows the structure of the JSON message
underlying the communication between the system
and the Cloud.
Fig. 12: JSON payload format
3.1 Security on AWS IoT Core
AWS IoT Core requires devices that connect using
the MQTT protocol to use X.509 certificates for
authentication, [21]. A CSR (Certificate Signing
Request) must be generated to do that. This will
allow the microcontroller to be associated with an
AWS' thing,' allowing a unique connection to its
physical counterpart. Another relevant aspect is the
definition of the policy of 'things.' It allows
establishing and limiting the actions that individual
'things' can undergo and perform.
3.2 Database Creation
Therefore, it was decided to support the developed
control system with a non-relational database as it is
the most flexible and simplest solution to
implement. A database was then created with a
partition key (cylinder ID) and a sort of key (time),
[22]. Thanks to the IoT rule, it is possible to check
the JSON content of MQTT messages arriving at the
relevant topic. If the partition key is present (and
satisfies the necessary parameters), it is possible to
transfer the message content or part of it to the
database. The micro-service will organize the table
with the necessary column division and add the
timestamps.
4 Power and Energy Analysis
Preliminary experimental tests show that the
average power consumption is 0.5 W.
Assuming constant power, the hourly consumption
equals 0.5 [Wh/h]. The application must be
guaranteed an energy autonomy of at least two days.
Therefore, the energy is expressed in (1 and 2):
Assuming a 12V battery, its capacity is calculated
for conversion from Wh to Ah. (3):
Such a battery would make the device
autonomous for a period sufficient for transport and
replacement operations. There are some improving
solutions for increasing the prototype autonomy,
such as:
- The system has a photovoltaic power generator
and a smaller storage system.
- Change the microcontroller using one custom that
allows it to enter low power mode by lowering its
power consumption.
- Provide an external power supply.
4.1 Battery Charge Measurement
To calculate the battery's charge state, the voltage
divider rule was used by taking an analog voltage
reading between nodes A and B of the circuit shown
in Figure 13, [9], [23], [24].
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Fig. 13: Voltage divider electrical circuit.
The choice of resistors is related to the
maximum voltage readable by the microcontroller
of 3.3 volts and the electric current flowing through
them, as it affects the system's power consumption,
which must be minimized. The choice of resistors is
related to the maximum voltage readable by the
microcontroller of 3.3 volts and the electric current
flowing through them, as it affects the system's
power consumption, which must be minimized. Eq.
(4 and 5) allow to derive the voltage between A and
B (VAB) based on the input voltage (Vin) and the
resistors (R1 and R2):
With
4.1.1 Electric Test Circuit
Fig. 14: Electric test circuit
The test circuit described above (Figure 13) was
made with the experimental set-up shown in Figure
14, which includes a multimeter (1) connected to the
AB node, two resistors (2), an Arduino MKR Wi-Fi
1010 (3), a variable voltage generator (4) and the
LCD (5).
4.1.2 Initial Calibration
The code shown in Figure 15 was used to
extrapolate the voltage data.
The variable val is used as the AB voltage. It is
scaled to compensate for the resampling of the
microcontroller, as reported in Eq. (6).
The values valscaled were recorded for different
Vin voltage levels; during the measurements, the
actual voltage of node AB was also continuously
checked through the multimeter to verify that the
circuit was working properly. Table 3 and Figure 12
show a grouped view of the collected data.
Fig. 15: Arduino code for battery charge
measurement
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Table 3. Data table
Vin
Number of
measures
VAB real
Average
val_scaled
14,50
46
3,37
3,30
13,50
30
3,14
3,15
12,50
13
2,90
2,92
11,50
13
2,67
2,69
10,50
12
2,44
2,46
9,50
14
2,21
2,22
8,50
10
1,98
1,98
7,50
9
1,74
1,76
6,50
11
1,51
1,52
4.1.3 Linear Regression
The coefficients of the straight line were obtained
by generating a linear regression model from the
experimental data. The analysis results are shown in
Table 4 and Figure 16. Table 4 shows the standard
error and R squared value on the linear regression
carried out by 274 measurements and the coefficient
of the linear regression line. Figure 17 shows the
graph of the residuals from the statistical analysis.
Fig. 16: Data graph
Table 4. Summary output
Regression statistics
R squared
0,9997
Standard error
0,0403
Measurements
274
Coefficient
0,231
Fig. 17: Tracing of residuals.
Approximating to the second decimal place, the
theoretical equation derived from the voltage
partition rule is found, so it was possible to confirm
Eq. (7) that binds the voltages:
4.2 Battery Life Evaluation
AWS, [25], services were used to develop a cloud
architecture that allows data to be historicized in a
database and visualized on a dashboard
implemented on Grafana, [26]. This made it
possible to visualize battery voltage trends over time
to conduct a system energy design review. The
graph in Figure 18 has the time reported in the day-
hour format in the abscissa and is representative of a
system battery discharge cycle. The value is 13 volts
when the battery is powered and fully charged,
while it decreases by about 0.5 volts when
disconnected from the power supply; the tests show
that the resulting battery discharge time is about 48
hours, thus validating the energy design.
Fig. 18: Timing trend from 29/08/2023 to
01/09/2023 of battery voltage from Grafana
Dashboard.
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5 Discussion and Potential Future
Implementations
The prototype control system can now be
considered complete: all the major components
required for its implementation have been
opportunely connected, and several operational
procedures have been introduced for using the AWS
IoT cloud-centric environment by taking advantage
of the capabilities offered.
For the proposed prototype, two aspects would
need to be improved: connectivity and power
supply.
The main limitation of the connection is due to
the use of Wi-Fi that does not allow communication
over long distances (and the need to associate the
smart box with the client's Wi-Fi).
There are several possibilities to overcome this
problem, such as using radio communications with a
gateway (Zigbee, Bluetooth, LoRa) or an LTE 4G
SIM card Wi-Fi gateway to be added to the box.
The choice of how to proceed will depend on the
client's specific case. Regarding the power supply
issue, one improvement would be to include a
power supply. Still, in this case, the applications of
the system should be limited to cases where a power
supply is available.
The research found that the system is an
excellent tool for optimizing resource management
processes in the cylinder rental industry over a wide
territory. In addition, a key step for further
improvement is integrating artificial intelligence
(AI) systems to address emerging challenges and
improve the efficiency of management, logistics,
and maintenance operations.
5.1 Possibilities of Artificial Intelligence
Implementation
5.1.1 Application of Expert Systems
Artificial intelligence-based systems can be used to
improve delivery planning and optimize inventory
management. These systems can predict peak
demand and plan deliveries by analyzing historical
data on cylinder consumption, weather patterns, and
other factors. In this way, operating costs can be
reduced, and customers can be assured of more
timely and efficient service: a neural network model
could learn from real-time data, adapting
dynamically to unexpected changes in demand or
traffic conditions. This would enable faster response
to sudden changes in customer needs and
environmental conditions, [27], [28].
5.1.2 Example of Implementation in a Cylinder
Rental Company
Consider, for example, a cylinder rental company
that operates over a large territory. A centralized
system that constantly monitors demand, cylinder
locations, and traffic conditions could be
implemented for this company. If a sudden increase
in demand occurs in a specific area, the system
could automatically reallocate resources, adjust
delivery routes, and maintain optimal inventory in
each area. In addition, a user interface could be
developed through the collected data to allow
customers to anticipate their needs and book
deliveries in advance, further contributing to
resource planning.
5.2 Realized Benefits and Practical Impact
The Artificial Intelligence (AI) system for cylinder
rental management could be highly versatile and
beneficial for various entities beyond rental
companies. Some potential beneficiaries include
User Industries (companies in industries like
welding, food production, or manufacturing that use
cylinders for industrial purposes. An advanced
cylinder management system could optimize the
supply chain and ensure a continuous and efficient
supply), Gas Suppliers (businesses specializing in
supplying industrial or medical gases could benefit
from AI to optimize distribution, manage stocks,
and ensure operational safety), Logistics, and
Transport Services (companies in logistics and
transportation could use the system to optimize
delivery routes, enhance transport efficiency, and
reduce operating costs), Environmental Companies
(organizations involved in environmental
monitoring and management could benefit from AI
to ensure sustainable cylinder distribution practices
and minimize environmental impact), Regulatory
and Safety Entities (regulatory bodies and safety
authorities could use AI to monitor and ensure
compliance with safety regulations in the cylinder
rental sector), Maintenance and Technical Service
Companies (Businesses specializing in cylinder
maintenance and repair could integrate AI for
predictive and timely maintenance services),
Healthcare Sector (medical facilities using gas
cylinders could benefit from AI to ensure a reliable
supply and effectively manage stocks, especially in
critical situations), Technology and Software
Companies (technology and software solution
providers could develop and implement the AI-
based cylinder rental system, offering an advanced
and customizable platform).
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Adopting an AI-based system could extend
across various industries and sectors, providing
tailored solutions to meet the specific needs of each
entity involved in cylinder management.
6 Conclusions
This work has described and introduced an
innovative system that can guarantee a breakthrough
at a relatively low cost for all those industrial
activities that need the monitoring and localization
of a pressurized gas cylinder.
The entire system was developed using
Arduino, a low-cost microcontroller, allowing the
prototype phase of the control system to be further
explored using a more consolidated technology in
an industrial environment.
The proposed system can realize simultaneously
two different important tasks: from one side, it can
display the pressure level and a warning for an
empty cylinder, and simultaneously, this
information is automatically sent to the company
that manages the charge and the substitution of the
cylinders, together with the position of the empty
one.
For the eventual marketing of a product, it is
necessary to assess the return on investment time of
the asset. In this case, evaluating the return on
investment time is not easily identifiable as the
variables involved are numerous and mostly tied to
the management of the cylinder fleet carried out by
the specific owning company. However, the
system's research and development have been
conducted to minimize material costs and optimize
component choices to make the system
commercially more appealing. The initial prototype
was developed to provide an excellent solution in
the cylinder rental industry. The current
implementation is a preliminary step, but it is
designed to allow for further improvements and
adaptations based on the needs of the operating
environment, including those related to artificial
intelligence.
The system's modular architecture facilitates the
implementation of future upgrades and the
possibility of new features in response to evolving
industrial needs. Of course, the focus will remain on
data security and system stability to maintain
industrial product standards. In conclusion, this
initial prototype represents a significant step toward
the realization of a complete cylinder rental system
that optimizes the management and efficiency of the
process for the customer's benefit while also
leveraging neural networks or artificial intelligence.
The research prospects will mainly focus on refining
the artificial intelligence model for parts
management based on the collected data. The goal is
to improve the understanding of the operating
environment by improving the system's ability to
dynamically adapt to changes in the demands of the
cylinder model. In addition, it plans to explore
opportunities to implement other emerging
technologies inherent in the Internet of Things (IoT)
to improve cylinder monitoring and management
further. This evolution aims to create a more
interconnected and responsive system, further
improving a cylinder facility's overall efficiency and
sustainability.
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[2] A.B. Basygut, Mechanical and corrosion
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[3] N.M. Shaalan, F. Ahmed, O. Saber, S. Kumar,
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Contribution of Individual Authors to the
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that are relevant to the content of this article.
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