# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 2 Related Work
### Industrial Relevance  
While we are not aware of large-scale digital twins used in waste heat recovery from industrial systems, there are several digital twin applications and proof of concept that involve single components of such waste heat recovery systems. In this section, we describe the identified applications from the literature together with their use case and technological approach. As the authors of [25] also identified, there is a lack of research on digital twins applied to thermal energy storage devices. This gap may arise from the complex nature of integrating real-time data with the dynamic thermal behavior of storage systems, necessitating \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Target Domain** & **Abstraction Level** & **Structure** \\ \hline
3D-DT [12]—2017 & general & Conceptual & 3 components \\
5D-DT [13; 14]—2018 & manufacturing & Conceptual & 5 components \\ GDTA [16]—2020 & energy system & Logical & 6 layers \\ DT-IES [18]—2022 & energy system & Concrete & 5 layers \\ MVES-DT [22]—2022 & energy system & Concrete & 2 components \\ OpenTwins [19; 21]—2023 & general & Concrete & 12 components \\ RA-DT-FM [23]—2023 & general & Logical & 5 layers \\ D-Arc [24]—2023 & energy system & Concrete & 7 layers \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of digital twin architectures. advanced modeling techniques. One such application is detailed in [26], where the authors instantiate the DT-IES [18] architecture for a packed bed thermal energy storage (PBTES) used to regulate the input for a steam-generating process from the steel production industry. In their implementation, PLC data acquired using a SCADA system is processed into an ontology form and reasoned on using Ontop [27], a virtual knowledge graph framework. The stored structured data can be queried using SPARQL. Their experiment considers a fixed simulated virtual energy system with only the PBTES being physically operated. Focusing on the optimization of geothermal heating systems for large buildings, authors of [28] propose upgrading such systems by using a digital twin system coupled with thermal energy storage. Their digital twin features include machine learning used for heat demand prediction and cost reduction taking into account the electricity pricing fluctuations. Developing digital twins for industrial heat pumps is another research area found in the literature. One such case is presented in [29], where a digital twin model is used to calibrate an online simulation of fouling in large-scale industrial heat pumps. In [30], a digital twin incorporating a mixed-integer linear programming (MILP) model is used to reduce operational costs and increase the performance of industrial heat pumps used for district heating. A concept of a cloud-based digital twin using a numerical model of a heat pump is described in [31], consisting of multiple gray-box models for each heat pump component that can be combined in any way. Making use of digital twin technologies, the authors of [32] investigate the effects of transitioning from gas-based systems to air-based heat pumps for domestic heating in the UK in terms of electricity demand, required investments, and social inequalities. By extending The World Avatar [33] ecosystem to also include the heat pump coefficient of performance modeling based on the UK's climate model, they were able to measure the potential impact of this transition, concerning gas and electricity consumption changes, reduction of carbon equivalent emission, but also the social impact reflected by the changes of energy cost. Using a combination of fuzzy means clustering and stack broad learning system algorithm, the authors of [34] propose a high-fidelity digital twin of air-based heat pumps used in HVAC systems. To train and educate users on performing maintenance operations of air-to-water heat pumps, the authors of [35] develop a virtual reality based digital twin. They experiment using students by introducing two maintenance operations.