# Addressing Data Confidentiality and Interconnectivity in Digital Twins: A Case Study on the Hot Water Cycle of a Heat Pump Test Bench

Harimanga-Yannick Rakotonirina, Bassel Chokr, Abderafi Charki, David Espes, Antonio Costanzo

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Harimanga-Yannick Rakotonirina, Bassel Chokr, Abderafi Charki, David Espes, Antonio Costanzo.

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# Addressing Data Confidentiality and Interconnectivity in Digital Twins: A Case Study on the Hot Water Cycle of a Heat Pump Test Bench

Harimanga-Yannick Rakotonirina<sup>a,*</sup>, Bassel Chokr<sup>a</sup>, Abderafi Charki<sup>a</sup>, David Espes<sup>b</sup>, Antonio Costanzo<sup>c</sup>

<sup>a</sup>LARIS, Université d'Angers, 62 Avenue de Notre Dame du Lac, 49000, Angers, France

<sup>b</sup>Lab-STICC, Université de Bretagne Occidentale, Technopole Brest-Iroise, 29238, Brest, France

<sup>c</sup>ESAIP, 18 Rue du 8 Mai 1945, 49124, Saint-Barthélemy-d'Anjou, France

## Abstract

**Context:** The concept of Industry 4.0 is increasingly significant in today's industrial landscape. There are numerous emerging technologies associated with Industry 4.0, including Digital Twin (DT). However, the adoption of DT faces several research challenges that hinder its seamless integration into industrial environments. These challenges include ensuring the security of data exchanged and improving the interconnectivity among industrial assets within DT systems. Tackling these issues is crucial to enhance the benefits of DT in the context of Industry 4.0.

**Objective:** This paper proposes and evaluates a DT prototype that ensures encryption of data at rest and in transit during transmission over the Internet. Additionally, the design of this prototype facilitates signal exchange between heterogeneous physical equipment and software.

**Method:** Established on the Six-Step DT Design Framework outlined in the literature and aligned with the International Organization for Standardization (ISO) on DT, this paper presents, a Digital Twin prototype of the heat pump test bench's hot water cycle (DT-HP-HWPrototype) using the Node-RED platform as a case study.

**Results:** This study introduces a novel deployment diagram of a data-driven DT hosted on a remote cloud server accessible regardless of the DT Users'

*Correspondance to: 62 Avenue de Notre Dame du Lac 49000 Angers, France
E-mail: harimanga.rakotonirina@etud.univ-angers.fr

Preprint submitted to Information and Software Technology

August 2, 2025
location. The average round-trip time measured is 851 ms for sending DT control commands over a 4G Internet network.

**Conclusion:** The case study highlights that a soft real-time DT can be effectively implemented, particularly for industrial asset monitoring, while also considering data confidentiality. Nevertheless, further research is required to define the information technology network needs of the DT component and to enhance the granularity of industrial process representation within the DT-HP-HW prototype.

*Keywords:* Industry 4.0, Digital Twins, Data Confidentiality, Interconnectivity, Node-RED, Prototype

# 1. Introduction

Industry 4.0 integrates traditional manufacturing with modern digital technologies, such as the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI), in production facilities and operational processes [1]. Among these digital technologies, Digital Twin (DT) is one of the primary drivers of Industry 4.0 [2]. DT technologies enable the digital representation of machinery [3] in the factory setting. DT also enhances the accuracy of preventive maintenance and empowers analytics to optimize test times and verification costs for industrial organizations, enabling significant returns on investment [4][5]. To illustrate this, in 2021, approximately 70% of manufacturing firms and 60% of healthcare organizations began utilizing DT [3].

Additionally, DT can perform analyses and conduct security assessments without impacting the physical system [6]. Thus, DT aligns with the goals of industrial firms aiming to strengthen the efficiency of their production processes, elevate the quality of their products, and minimize waste and environmental impacts [7]. Indeed, the use of DT has gained popularity in various sectors, including healthcare, supply chain management, and engineering [8].

A DT is a virtual counterpart of a physical asset, continuously updated with hard or soft real-time data. It goes beyond traditional monitoring by integrating semantic model, enabling remote analysis and supporting informed, data-driven decision-making related to the asset's operational behaviour. Sensors detect and transmit operational data to the DT, which maintains
a contextualized, continuously evolving representation of the physical sys-
tem.

This paper proposes a data confidentiality-preserving digital twin proto-
type of the heat pump test bench hot water cycle (DT-HP-HWPrototype) as
an industrial DT case study. To summarize, this paper makes the following
key contributions:

* proposes a novel deployment diagram for a data-driven industrial DT hosted on a remote cloud.

* proposes a reusable DT prototype in both academic and industrial environments as an experimental DT based on saving sensor data in realistic conditions.

* proposes a soft real-time Digital Twin prototype for monitoring, achieving an average round-trip time (RTT) of less than 1 second. This performance enables the responsive transmission of control commands over a 4G network without requiring any Quality of Service configurations.

The paper is structured as follows: Section 2 describes the research con-
text, with Section 2.1 providing a background on DT, Section 2.2 presenting
similar work to our prototype, and Section 2.3 outlining the motivation and
goals for this work. Section 3 details the methodology adopted to implement
the DT. Section 4 focuses on the DT prototype as a result of the methodology.
Specifically, Section 4.1 lists the frameworks and libraries used to develop the
DT, and Section 4.2 describes the capabilities of the DT-HP-HW prototype.
Section 5 discusses potential advantages of the prototype proposal. Section
6 concludes the paper and also suggests a future research direction.

2. Research Context

2.1. *Background*

Professor Michael Grieves introduced the DT concept in his 2002 PLM
presentation [10]. In 2012, NASA first applied DT to aircraft structural
maintenance [11]. Grieves provided the first characterization of the DT con-
cept by stating that it consisted of three elements: a physical product in real
space, a virtual representation of that product in virtual space, and the data
and information connections that link the virtual and real products together
[12]. Interest in DT has subsequently grown across various sectors, leading
to multiple definitions and characterizations that have diluted the original description [13].

This prototype of Heat Pump Test Bench's DT is a case study of DT in Industry 4.0, founded on the consolidated and generalized definition of a DT according to VanDerHorn et al. [13], through reviewing the scope of definitions in the literature. VanDerHorn et al. [13] define the DT as “a virtual representation of a physical system (and its associated environment and processes) that is updated through the exchange of information between the physical and virtual systems”.

However, some critics have argued that certain DT proposals are not true DTs but rather Digital Shadows, due to a lack of bidirectional communication [14]. Bidirectional communication in DT refers to the interaction between acquiring data from the physical counterpart and sending control commands to the physical equipment. In this regard, Gehrmann & Gunnarsson [15] introduce a finite state machine model to represent the state of the Digital Twin at a specific time "t", using data inputs synchronously captured from the physical system.

## 2.2. Related work

The DT development stage began in 2019, and various implementation techniques were proposed to address distinct problems [16]. Between 2021 and 2024, a literature search conducted on the Web of Science database identified three DT proposals that exhibit key similarities with our prototype. Each of these papers presents a case study illustrating the industrial application of the prototype developed. For example, Lv et al. [17] propose a model-driven DT for continuous monitoring of Air-Source Heat Pumps (ASHP), predicting the coefficient of performance (CoP) using advanced algorithms such as FCPM-SBLS. The proposed approach contributes to the measurement of prediction accuracy and computational speed of the DT model. However, these authors mention the need to collect a large amount of data to increase the computational efficiency and scalability of the model. Ugarte Querejeta et al. [18] present the development of DT for virtual design prototyping and commissioning resulting from the interconnection of heterogeneous proprietary tools. The article highlights the feasibility of collaborations between tools for implementing DT through a use case on solar assembly. Nevertheless, the study identifies a significant shortage of operational data, which hinders dynamic system adjustments during runtime. In the field of smart agriculture, Barbie et al. [19] deploy SunFounder's DT
prototype, called PiCar-X, on an industrial site. Their prototype is based
on a DT deployed as microservices in Docker containers, providing a virtual
environment for developing and testing an embedded control system aimed
at enhancing silage production. Additionally, these authors consider a per-
spective for improving the quality of the model representing the ICS on the
virtual replica.

Nonetheless, between 2020 and 2025, searches carried out with the query AB=("digital twin") AND AB=(prototype) AND AB=(privacy OR "data confidentiality") on the academic database Web of Science reveal three articles mentioning these keywords in their abstracts. Therefore, a gap persists in the literature regarding the consideration of data confidentiality in DT data exchanges.

Our DT proposal is entirely based on soft real-time data from sensors on industrial equipment to overcome limitations within operational data. Additionally, this proposal enhances data confidentiality in DTs by adopting a protocol that facilitate secure communication between the Operational Technology (OT) and Information Technology (IT) domains. This protocol should incorporate encryption and integrity mechanisms to protect data exchanges between programmable logic controllers (PLCs).

## 2.3. Motivation and goals

Although academic research on DTs has increased significantly, industrial DT implementations with a focus on security tend to simulate industrial equipment as the physical counterpart of the Digital Replica. In these simulations, the network layer responsible for data transmission is often abstracted. Consequently, the communication between sensors/actuators and the server hosting the DT remains largely unexplored, especially within the context of cyber-physical systems. As a result, few works observe and measure the control command sending delays as well as the frequency of data acquisition time on DTs. However, these measurements are necessary for the quantitative validation of DT proposals. Indeed, to consolidate a framework based on industrial virtual equipment, temporal quality of service measurements in real conditions are relevant.

Therefore, this work is motivated by the need to propose a prototype of DT that incorporates concrete physical equipment to facilitate validation through temporal measurements in real-world conditions. Moreover, large volumes of data can be collected and stored from sensors and actuators operating under real-world conditions on the heat pump test bench. Researchers
will be able to use this data for future industrial experiments, addressing a
significant barrier caused by the current lack of relevant datasets.

Furthermore, the objective of this paper is to develop a prototype for con-
ducting experiments on the security of DTs. Based on this proposed proto-
type, two possible approaches for experimental testing are identified: i) either
on the DT prototype in real conditions, or ii) on the DT-HP-HWPrototype
operating based on a pre-programmed script data feed from backups previ-
ously made in real conditions. Thus, the second possibility "ii" promotes the
reproducibility of future works resulting from this prototype.

3. Methodology

DT generates a complex interconnected system formed by interdisci-
plinary and collaborative technologies to ensure digital representation of
physical equipment. Prior reflection is necessary to implement these DT
through a proven systemic approach or a DT design framework mentioned
in the literature.

The methodology for implementing the DT prototype consists of instan-
tiating the DT Design Framework of Human et al. [21] with an addition at
each stage of the specifications relating to the ISO 23247 [22] on DT. ISO
23247 provides a standardized specification for the implementation and in-
tegration of DT-related industrial domains [22]. It serves as a reference for
initiating, modifying, and integrating DT systems across various stages of
industrial processes. This methodology results in the proposal of the DT-
HP-HWPrototype deployment diagram, strengthening stakeholder intercon-
nection and data confidentiality.

The DT Design Framework consists of six steps (marked by numbers)
and four design phases (denoted by letters) that facilitate a systematic and
traceable approach to designing a DT system. These steps are: A1) needs
and constraints analysis, A2) physical system decomposition, B3) services
allocation, C4) performance and quality considerations, C5) implementation
considerations and D6) verification and validation [21].

3.1. *Physical Equipment*

The Heat Pump Test Bench is an industrial device equipped with seven
temperature probes (sensors), three flow meters (sensors), an electric current
sensor, and two 3-way valves (actuators). The objective of the testing bench
is to simulate heat loss in a building, allowing the heat pump to continue operating and enabling the calculation of its Coefficient of Performance (CoP). The CoP is calculated by dividing the calorific energy produced by the heat pump by the electric energy it consumes. The heat pump being tested in Figure 1 (on the right) achieved a CoP equal to 3.

<figure><img src="image_2.png" /><figcaption>(a) Photo of the heat pump test bench</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(b) Hot water cycle of heat pump test bench
synoptic schema</figcaption></figure>

<caption>Figure 1: Heat Pump Test Bench</caption>

The building is being simulated in terms of heat loss, as the hot cycle water from the testing bench passes through an aerotherm. There are 2 Proportional-Integral-Derivative (PID) functions to command this test bench process:

i) a PID function no.1 (PID-1) controlling a 3-way valve that is installed at the inlet of the aerotherm allowing the water to either pass through it or bypass it.

ii) a PID function no.2 (PID-2) controlling a 3-way valve that is installed at the cold-water tank (shown in Figure 1-a at the left), allowing the heat exchanger to mix the hot water with cold water to drop the hot water cycle temperature further if the aerotherm cannot handle the load alone.

A LabVIEW Community Edition program implements an Industrial Control System (ICS) that manages data acquisition and control commands for a hot water cycle. Every second ($t = 1$ s), the ICS communicates with a National Instruments cDAQ-9178 device to send commands and retrieve sensor data. Control commands involve setting a nominal temperature setpoint,
<table><caption>Table 1: Summary of DT sensor and actuator specifications</caption><thead><tr><th>Industrial Equipment</th><th>Main features</th></tr></thead><tbody><tr><td>4 x Pt100 Temperature Sensor Probes</td><td>- 4-Wire Pt100 Class 110D Probes<br>- 316L Stainless Steel Sheath, Ø3, 50 mm Length<br>- Cable Output's internal and external insulation in Perfluoroalkoxy<br>- Temperature Range: -50°C +250°C</td></tr><tr><td>1 x Electromagnetic Sensor Flowmeters</td><td>- Electrode type: 2 measuring electrodes + 1 reference electrode<br>- Electrode material: 316L stainless steel, standard finish<br>- Nominal diameter: 15 mm</td></tr><tr><td>1 x Proportional Valve Actuator</td><td>- Feedback Signal 0 to 10 VDC<br>- Rotation Range 0 to 90° in 5-degree increments (mechanically limited to 93°)<br>- Output Position Feedback</td></tr></tbody></table>

which is used to compute the optimal output of PID-1 that adjusts the proportional valve regulating the hot water flow to the aerothermal unit.

This paper proposes the DT prototype of the Heat Pump Test Bench's hot water cycle. Additionally, to construct the digital replica of the hot water cycle of the heat pump test bench, the digital twin integrates data from sensors and actuators, as illustrated in Figure 1-b. A more detailed description of these components is provided in Table 1. As a planned extension of the project, the integration of the cold water cycle into the digital twin prototype is currently under consideration.

## 3.2. *Needs and constraints analysis*

The first step of the DT Design Framework is to identify the Functional Requirements (FR) and Non-Functional Requirements (NFR). There are two key FR concerning the DT prototype:

i) Soft real-time DT of industrial hardware with a predictive maintenance perspective. In this study, soft real-time is defined as the ability to
perform data acquisition and transmit control commands with a round-
trip time (RTT) of less than 1 second, specifically in the context of
monitoring industrial equipment.

ii) Remote control command that changes the current state of the heat pump test bench's hot water cycle.

NFR listed in Table 2 outlines the quality or manner in which the system
should execute its tasks [21].

Based on the NFR’s implications for the DT prototype, the reliability of the digital model of the physical equipment and the verification before sending the control command emerge as essential points during DT’s development.

3.3. *Physical System Decomposition*

The goal of physical system decomposition is to equip the user with suffi-
cient knowledge to segment the system into appropriate components that DT
can accurately represent [20]. Based on the description of the Heat Pump
Test Bench presented in Section 3.1, four hierarchical levels are distinguished
in the decomposition of this physical system.

Table 3 displays the features associated with each of the DT Prototype
decomposition levels.

3.4. *Services Allocation*

This phase involves identifying the services and digital technologies that need to be created and assigning each service to a specific DT or service network [21]. The Services allocation phase focuses on identifying the DT to be instantiated, specifying its expected interactions, and the services it is intended to provide [21]. For the DT prototype, a single service is necessary according to the list of FRs in Section 4.2: the “Twining Service” detailed in Table 4. The “Twining Service” can result in a ”Simulation Service”.

3.5. *Performance and Quality Considerations*

These steps include choices at a more granular architectural level con-
cerning the DT’s internal structure and aggregation methods [21]. The DT-
HP-HWPrototype deployment diagram results from this step and previous
design steps.
<table><caption>Table 2: Non-Functional Requirements (NFR) for the DT-HP-HWPrototype.</caption><thead><tr><th>NFR Group</th><th>NFR</th><th>NFR Implication</th></tr></thead><tbody><tr><td rowspan="7">Implementation constraints</td><td>Soft real-time control command and data acquisition</td><td>ISO 23247 standard emphasizes the accuracy and efficiency of data collection [21]</td></tr><tr><td>Semantic modeling of ICS in the Digital Sphere</td><td>ISO 23247 evokes the credibility of simulation models and its applicability in real-life scenarios [21]</td></tr><tr><td>Implementation of an interactive dashboard</td><td>DT should present a synthetic dashboard of sensor or actuator data representing the physical system</td></tr><tr><td>Notification of success or failure of a control command</td><td>Feedback to the end user is essential in industrial engineers' decision-making</td></tr><tr><td>Execution of the control command</td><td>A necessary feature to comply with the definition of DT adopted in this paper</td></tr><tr><td>Guaranteeing Twinning Consistency: The DT is continuously in phase with the ICS</td><td>Avoid a gap between the physical equipment and its DT, which can alter the operation of the DT</td></tr><tr><td rowspan="2">Operational safety</td><td>Verification before sending a control command</td><td>Avoid operational disruption due to sending an aberrant control command (e.g. value above the tolerated threshold)</td></tr><tr><td>Failure notification via Human-Machine Interface (HMI)</td><td>Notify relevant stakeholders about failures that could cause damage to the ICS</td></tr><tr><td>Quality attribute</td><td>Low/high-fidelity representation of the Heat Pump Test Bench</td><td>Improve the precision and reliability of digital models representing physical systems</td></tr></tbody></table>
<table><caption>Table 3: Physical System Characterization of DT-HP-HWPrototype</caption><thead><tr><th>Physical Component's Characteristics</th><th>Hot water cycle of Heat Pump test bench with local ICS</th></tr></thead><tbody><tr><td>Scope of the physical system</td><td>Heat Pump test bench managed by several industrial engineers</td></tr><tr><td>Scope of data</td><td>Data in Reading: Temperature entering/leaving the Heat Pump, water's temperature coming out of the aerotherm, water's temperature coming out of the Heat Exchanger, and hot water flow rate.<br>Written data: Industrial Engineer modifies the aerotherm set point from the ICS.</td></tr><tr><td>Data format</td><td>Double values converted from analog proportional signals)</td></tr><tr><td>Communication</td><td>Serial Communication : Serial Communication : USB-to-Serial</td></tr><tr><td>Considerations and constraints</td><td>DT-HP-HWPrototype with local ICS requiring communication between the server and the data acquisition hardware</td></tr></tbody></table>
<table><caption>Table 4: Twining Service Characterization</caption><thead><tr><th colspan="2">Twining service characterization</th></tr></thead><tbody><tr><td>Description:</td><td>The current status of DT-HP-HWPrototype can be displayed to a research team</td></tr><tr><td>Related functional requirements:</td><td>Soft real-time monitoring and dispatching control commands.</td></tr><tr><td>Required physical and data scope</td><td>When depicting DT, it is assumed that the DT only mirrors a relevant portion of the states of the physical counterpart without including any states not found in the physical twin [15]. In other words, DT queries a limited list of sensors and actuators listed in section 3.1.</td></tr><tr><td>Service characteristics:</td><td><ul><li><em>Required data update frequency:</em> Data is uploaded to the service in soft real-time</li><li><em>Persistence:</em> Periodic data collection every 900 milliseconds with persistent data storage.</li></ul></td></tr><tr><td>Considerations and constraints:</td><td>Scientist or industrial engineers can access the DT remotely regardless of their location. Data confidentiality becomes an important factor for DT security.</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Figure 2: DT-HP-HWPrototype Deployment Diagram</figcaption></figure>
In the deployment diagram shown in Figure 2, the data acquisition hardware is responsible for receiving analog signals from the physical equipment via I/O modules, converting these signals into digital form, and performing the reverse conversion when needed. The LabVIEW server hosts the OPC UA server, while the Supervisory Control and Data Acquisition (SCADA) application monitors the industrial equipment. Local data backup is performed.

The OPC UA server communicates with the SCADA system by copying sensor/actuator data stored in memory to the server. A Labview flow-based program handles the SCADA system. Then, the OPC UA client reads data from industrial sensors or actuators via secure TLS communication. A Port Forwarding mechanism on the OPC UA Server allows remote access from the OPC UA client.

This OPC UA client also sends control commands to the OPC UA server, which returns these commands to the SCADA system. Finally, from a single URL with the HTTPS protocol, one or more remote PCs access the low-fidelity representation of the physical equipment.

### 3.6. *Implementation Considerations*

This phase aims to develop a comprehensive system design that will guide the construction of specific components. The integration of the various components depicted in the deployment diagram is a key factor in successfully implementing the Digital Twin prototype. In this sense, the ISO 23247 standard emphasises improving interoperability in DTs for efficient data exchange and seamless communication between systems [22]. Consequently, issues related to the cybersecurity of DTs become a priority. Alcaraz et al. [23] highlight the lack of exploration of DT cybersecurity issues, which creates a problem because DTs contain intellectual property elements derived from the digital copy of the physical world.

To address this issue, the OPC UA protocol is well-suited for communication between industrial devices and remote machines hosting DTs. The OPC UA service specifies several key functions for data exchange between distributed devices [24]. OPC UA is a communication protocol based on the client-server model that allows access to data and functionalities organized within an object-oriented information model, enabling clients to engage through a standardized set of services [25]. OPC UA Protocol enables the OPC UA Client to access and modify one or more attributes of nodes housed within
the address space of the OPC UA Server [26]. Additionally, communication
occurs over a TLS-encrypted channel, which leverages SHA-256 for crypto-
graphic integrity, ensuring authenticated access to the DT and protecting
against network sniffing.

The system architecture should incorporate a mix of local and cloud-
based databases [21]. This distributed data backup will allow simulations to
be rerun on the previous state of the physical equipment. All this data could
also be the subject of in-depth research on the industrial environment.

3.7. *Verification and Validation*

The functional version of the DT prototype meets the resolution require-
ments set during the design. Integrating the OPC UA protocol into the DT
implementation ensures the interconnectivity requirements of the DT system.
Indeed, the management of connections and routing within an industrial set-
ting is facilitated through protocols that enable data transmission between
sensor nodes, data storage, and the digital model [20]. The design decisions
so far also address the gaps in data confidentiality considerations through
communication encryption and strong authentication on the DT prototype.

The DT prototype refreshes the data from the paired sensors and actu-
ators every p=900 ms, which is the DT prototype's polling interval. Addi-
tionally, for 20 temperature setpoints ranging from 16°C to 35°C successively
injected from the DT, the average RTT of the control commands (RTT_CC)
is 851 ms with a standard deviation of 392 ms. Figure 3 presents a lollipop
diagram summarizing the RTT measurements conducted. This RTT_CC
measures the time it takes for a control command signal to transit from the
DT to the ICS, plus the time it takes to resynchronize the DT. Thus, our
DT-HP-HWPrototype allows sending control commands in soft real-time for
industrial process monitoring.

4. Result: a Digital Twin Prototype

The choice of a methodology, as well as the selection of appropriate tech-
nological tools, constitutes an essential step to have an operational DT pro-
totype. In this sense, the design methodology adopted is based on an itera-
tive, sequential approach, accompanied by technological choices for the DT’s
implementation that depend on the robustness and maturity of the avail-
able tools. In addition, to facilitate future extensibility and compatibility,
only open-source tools released under widely adopted licenses, such as the
<figure><img src="image_5.png" /><figcaption>Figure 3: RTT measurement on sending 20 successive control commands</figcaption></figure>

GNU Public License, Apache License 2.0, and occasionally Community Editions, were used in the development of the DT prototype. Thus, the adopted methodology and technological choices result in a functional Digital Twin prototype of the hot water cycle of the heat pump test bench.

## 4.1. DT Prototype's Components Description

The core component of the DT-HP-HW prototype is the server hosting the Node-RED framework. While several comparable tools for implementing DT are available under commercial licenses, Node-RED was chosen for its open-source license and comprehensive technical documentation. Node-RED, developed by IBM, is a graphical development tool that allows visual programming to create web-based remote monitoring and control systems on multiple platforms [27]. Additionally, Node-RED has several powerful and ready-to-use libraries, such as:

i) **node-red-contrib-iiot-opcua**<fnref n="1" />: to provide an OPC UA client responsible for reading data from and writing data to the OPC UA server.

ii) **node-red-contrib-digital-twin**<fnref n="2" />: to digitally represent the virtual

<footnote n="1">¹https://flows.nodered.org/node/node-red-contrib-iiot-opcua</footnote>

<footnote n="2">²https://github.com/steinmetz/node-red-contrib-digital-twin.git</footnote>
replica of the physical device from the data describing the hot water cycle of the heat pump test bench. The DT prototype is implemented using a modified fork of the authors' library. This modified version is available on a public GitHub repository<fnref n="3" />.

iii) **node-red-contrib-easy-pid-controller**<fnref n="4" />: To develop a digital twin of the PID-1 function within the digital infrastructure.

Regarding the semantic modelling of the virtual replica, as well as the virtual replica of the PID control for the hot water cycle of the heat pump, they rely entirely on data from sensors and actuators that are periodically uploaded. Actually, in this prototype, sensor and actuator data change dynamically over time, whereas the semantic model remains static unless it is explicitly modified. In this sense, the DT prototype is data-driven and therefore, an interruption in data acquisition results in the unavailability of the pairing. This digital representation is articulated in the form of Node-Red flow, the extract of which is shown in Figure 4, which is available to researchers via a GitHub repository<fnref n="5" />.

As for the OPC UA server and the server-side program managing the reading, writing, and updating of sensor and actuator data, they are developed using the open-source Python OPC-UA<fnref n="6" /> library. Then, the same Labview program managing the heat pump test bench’s industrial control system calls and uses this OPC UA server-side program as shown in Figure 5.

## 4.2. Functional Capabilities of the DT Prototype

After starting the OPC UA server program connected to the Internet, any PC connected to the Internet can load the main access web page of the DT prototype. However, usernames associated with valid passwords are required before accessing the functionality of the DT prototype. Indeed, the OPC UA protocol natively supports user authentication before each remote connection. Besides that, the OPC UA protocol related to the DT-HP-HWPrototype allows for multiple connections to industrial devices via a

<footnote n="3">³https://github.com/kambana4200/node-red-contrib-digital-twin.git</footnote>

<footnote n="4">⁴https://flows.nodered.org/node/node-red-contrib-easy-pid-controller</footnote>

<footnote n="5">⁵https://github.com/kambana4200/HeatPump_DigitalTwin</footnote>

<footnote n="6">⁶https://python-opcua.readthedocs.io/en/latest/</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 4: Segment of the Node-RED Flow Representing the Digital Twin of the Heat Pump Test Bench</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 5: Labview CE flow integrating an OPC UA server-side program</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 6: Human-Machine Interface for sending control command on the DT-HP-HWPrototype</figcaption></figure>

remote PC over the Internet.

Additionally, the DT dashboard provides a visualization of sensor data from the heat pump test bench, updated every 900 milliseconds, which enables monitoring of the heat pump’s hot water cycle. Through the PID control curve in Figure 6, the DT users can diagnose the test bench and modify the aerotherm temperature setpoint from the user interface. The DT synchronization status is also observable from the HMI, as shown in Figure 6, via the actuator data display on the virtual replica, which is equivalent to that of the ICS.

Furthermore, the semantic structure of the industrial equipment in the virtual replica is based on the approach proposed by Steinmetz et al. [28], which supports the generation of Cypher queries for graph databases modelled as property graphs. Cypher queries are updated upon recorded flow modification on the Node-RED server. Indeed, this modelling is implemented through the node-red-contrib-digital-twin library, specifically via the dt-graph node, as illustrated in Figure 4. The semantic model of the heat pump test bench is encoded using Cypher queries generated by the dt-graph node, as shown in Figure 7. To visualize the resulting knowledge graph of the DT-HP-HW prototype, this study utilizes the Neo4j Browser<fnref n="7" />.

<footnote n="7">⁷https://neo4j.com/docs browser-manual/current/</footnote>
```sql
MERGE (a2:Asset {nodered_id: '6e41fa44d13cb01b'})
SET a2.name = '3_vann', a2.nodered_type = 'dt-asset';

MERGE (p0:Property {nodered_id: '8bcc11aa07a406a8'})
SET p0.name = 'Vann Adjustment(in Volt)', p0.a_context = 'vann property', p0.nodered_type = 'dt-property';

MATCH (a2:Asset {nodered_id: '6e41fa44d13cb01b'}) MATCH (p0:Property {nodered_id: '8bcc11aa07a406a8'})
MERGE (a2)-[:hasProperty]->(p0);

MERGE (a3:Asset {nodered_id: '59e437bdea6be76c'})
SET a3.name = 'Aerotherme', a3.nodered_type = 'dt-asset';

MERGE (p1:Property {nodered_id: 'e3bebd02cf4665aa'})
SET p1.name = 'Temp_outcoming_aerotherme', p1.a_context = 'aerotherme property', p1.nodered_type = 'dt-property';

MATCH (a3:Asset {nodered_id: '59e437bdea6be76c'}) MATCH (p1:Property {nodered_id: 'e3bebd02cf4665aa'})
MERGE (a3)-[:hasProperty]->(p1);

MERGE (a4:Asset {nodered_id: '9664640dcf847ad0'})
SET a4.name = 'Heat Exchanger', a4.nodered_type = 'dt-asset';

MERGE (p2:Property {nodered_id: 'dbc41da0b506fb07'})
SET p2.name = 'Temp_Outcoming_HE', p2.a_context = 'he property', p2.nodered_type = 'dt-property';

MATCH (a4:Asset {nodered_id: '9664640dcf847ad0'}) MATCH (p2:Property {nodered_id: 'dbc41da0b506fb07'})
MERGE (a4)-[:hasProperty]->(p2);

MATCH (ao0:Asset {nodered_id: '6e41fa44d13cb01b'}) MATCH (at1:Asset {nodered_id: '59e437bdea6be76c'})
MERGE (ao0)-[:Feed_hot_water_to]->(at1);

MATCH (ao0:Asset {nodered_id: '6e41fa44d13cb01b'}) MATCH (at1:Asset {nodered_id: '9664640dcf847ad0'})
MERGE (ao0)-[:Relay_hot_water_to]->(at1);

MATCH (ao0:Asset {nodered_id: '59e437bdea6be76c'}) MATCH (at1:Asset {nodered_id: '9664640dcf847ad0'})
MERGE (ao0)-[:Redirect_hot_water_to]->(at1);
...
```

<caption>Figure 7: Illustrative Fragment of Cypher Queries Generated by the dt-graph of Node-RED</caption>

Finally, DT end users can simulate the addition of a new component to the test bench. In this case, the nodes representing the new hardware, along with the associated data, are concatenated onto the initial Node-RED flow on figure 4. Thus, evaluations of the modified test bench's behaviour under simulated conditions can be carried out without interrupting the on-site industrial process.

## 5. Discussion on the DT Prototype's Potential

This DT prototype implementation covers the necessary functions for the soft real-time digital representation of a Heat Pump Test Bench device hosted on a remote server. Furthermore, this prototype enables the integration of industrial hardware from various manufacturers and the hosting of virtual replicas on any cloud platform. Indeed, the DT prototype is free from the restrictions imposed by commercial products. For example, only one cloud platform is compatible with Azure Digital Twins. Other solutions, such as Siemens MindSphere, limit the choice of cloud platform based on the geographical region of the DT end user and only allow connections with Siemens
PLCs. Thus, this paper proposes using this prototype to conduct experimen-
tal research on industrial DT, as it is vendor-independent and offers similar
functionalities to the commercial version.

However, our prototype proposal faces some limitations, particularly re-
garding the time taken to send control commands over a 4G network. Consid-
ering a primarily dedicated network is a way to improve the average RTT_CC
of the DT-HP-HW prototype, which is one of the performance indicators of
web-based industrial solutions. Another limitation of this DT prototype is
that the test bench integration is currently only partial. Expanding the
prototype to cover the entire test bench remains essential to ensure more
comprehensive data collection on the heat pump. The completeness of data
is required to enable the integration of machine learning programs dedicated
to predictive maintenance on the virtual replica.

**6. Conclusion and future work**

The implementation of the basic functionalities of the DT-HP-HW proto-
type illustrates that interconnectivity and data confidentiality are compatible
with a functional DT. This paper presents an approach to developing a soft
real-time secure DT that can establish communication with industrial equip-
ment, regardless of the type of communication protocol in the industrial
environment. The average round-trip time for sending control commands
of our DT prototype is 851 ms. This implementation approach results from
a methodology based on the DT Design Framework [21] and the ISO 23247
[22]. Furthermore, this DT prototype contributes to the current process of
widespread deployment of DT in the industry. In this sense, this paper also
makes a significant contribution to the management of industrial projects
and equipment.

Nonetheless, the use of a dedicated communication network, such as pri-
vate 5G or time-sensitive networks in the IT part of the DT-HP-HWPrototype,
could be an approach to improve the temporal quality of service, especially
RTT_CC. Indeed, a quantitative study on the IT networks solicited in cloud-
based DTs will dissolve the doubts about its future adoption in factories.
Moreover, mapping the communication flows of industrial protocols (such
as DNP3, Modbus and BACnet) into the virtual replica represents a valu-
able opportunity to improve the fidelity of data-driven DT. Thus, the DT
will be able to provide a more detailed representation of ongoing industrial
processes.
**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgement**

We would like to sincerely thank our colleagues. This work has been co-founded by the Pays de la Loire Region and the Angers Loire Metropolis.

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