# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 1 Introduction  
Complex industrial processes often rely on high-temperature heat, which is traditionally generated through fossil fuels or electricity. However, a significant shift towards renewable and sustainable heat sources is underway, supported by environmental policies and actions such as the European Green Deal [1]. Renewable heat upgrade systems offer a sustainable alternative by capturing waste heat and integrating it with renewable sources, requiring precise control to manage variable conditions. These systems are characterized by a high degree of interdependence between various components, requiring careful consideration of numerous parameters and their associated implications. This necessitates a focus on process optimization and orchestration to ensure efficient and reliable operation. Compared to traditional systems that focus only on cost minimization, newer energy systems transitioned to multi-objective optimization functions that take into account climate mitigation and renewable energy targets [2]. As objective functions grow in complexity, the optimization methods and algorithms also need to adapt, because classical techniques such as numerical, iterative, graphical, or probabilistic methods are limited given theparameter space, rigid, and have difficulty dealing with dynamic changes [3]. Artificial intelligence and deep learning overcome the drawbacks of classical methods achieving improved performance with the cost of requiring higher compute power, data requirements, explainability, and transparency [3; 4]. On a system level, we see that Industry 4.0 concepts such as IoT, Cloud Computing, or Real-Time Monitoring that showed their benefits in the manufacturing domain are integrated with renewable energy systems [5]. The intermittent nature of renewable energy--driven by external factors such as weather, seasonality, and geographical location--poses challenges for maintaining consistent heat supply [6]. A common solution to this is to use energy storage devices or mix multiple renewable energy sources to reduce possible downtime [7]. The resulting infrastructure can be considered a large system of systems that requires fine-tuned control frameworks that are flexible enough to manage different energy sources, energy storage devices, and prevent malfunctions while maintaining a robust energy output for the consumers [8]. This research is part of the SUSHEAT European funded project [9; 10]. SUSHEAT's main objective is to design and build an industrial high-temperature heat upgrade system that can harness low-temperature industrial waste heat and upgrade it for high-temperature demanding processes. The concept of the SUSHEAT heat upgrade system integrates technologies such as Stirling cycle-based heat pumps and thermal energy storage (TES), managed by a digital twin for real-time control and integration. Our previous analysis of SUSHEAT impact shows that by targeting industrial processes with heat requirements between 150 and 250 \({}^{\circ}\)C, there is a total theoretical potential of 134.92 TWh of heat demand that could be met, translating into a significant annual reduction of 19.40 million tons of CO\({}_{2}\) emissions [11]. The current paper focuses on designing the digital twin, providing the architectural details and process modeling of the control and integration system. As stated above, a complex system such as a high-temperature heat upgrade system requires a capable control system. To alleviate some of these issues, we propose the usage of a modular digital twin platform with focus on simulation and artificial intelligence capabilities that will assist the decision-making processes required to operate and control complex high-temperature heat upgrade systems. The concept of digital twins is relatively new and it was born in the product life cycle domain [12] and further developed in the area of Industry 4.0 [13]. Essentially, it means creating a virtual representation of a physical object or system.