# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 3 Digital Twin Architecture for Heat Upgrade Systems
### Tech Stack and Implementation Considerations  
To instantiate the digital twin architecture, a comprehensive set of technologies is required, each playing a critical role in the process of data acquisition, simulation, 3D rendering, and deployment across cloud and edge infrastructure. At the Hardware Control layer, low-level data acquisition and transmission to the Gateway layer are achieved through technologies such as OPC-UA, or MQTT, for example. OPC-UA represents a standardized means of facilitating secure and reliable data exchange between industrial machines, IoT devices, and sensors, making it particularly suited for manufacturing or energy management systems. Compared to OPC-UA, MQTT is a lightweight messaging protocol that enables IoT devices operating in low-bandwidth environments to efficiently transmit data to servers and receive commands and configuration parameters. The Gateway layer requires SCADA capabilities, coupled with a temporary storage to be able to do all the data preprocessing described above. Since the data is received in real-time, a time series database such as InfluxDB (v.2.7.10) or Prometheus (v3.0.1) can be used. As this data will not be available for long periods, an in-memory database such  
Figure 2: Digital twin—the twinning process (adapted from [38]). as Redis (v.7.4.2) is also viable. For protocol translation, solutions such as Eclipse Hono (v.2.4.1) can be used, where multiple protocol adapters are implemented, simplifying the process of connecting multiple devices that use different communication protocols. Again, in the Processed Data layer, InfluxDB or Prometheus can be used for persistent storage. The Data Analysis, AI Optimization, and AI Prediction will contain custom build or off-the-shelf AI and ML models. Considering the AI industry boom, there are many potential frameworks and services through which these architectural modules can be instantiated. While for prototyping, testing, and developing AI and ML models, Tensorflow or Pytorch are some of the most popular frameworks, deploying and training these models at large with real-time data requires complex operation, usually achieved using services offered by large cloud providers such as Amazon Web Services (AWS), Google Cloud, or Microsoft Azure. The Simulation module plays an important role in linking the physical system's real-time data with virtual models. As each use case has different simulation requirements, an FMI (Functional Mock-up Interface) standard compatible simulator is required. The FMI standard allows for the integration of different simulation tools, allowing them to exchange data and work together, providing a cohesive virtual model of a physical system. Data Visualization, Reports, and user interfaces can be easily configured using tools like Grafana, where the Playground can be developed using 3D game engines such as Unity or Unreal Engine. Deploying these components in the cloud is done using technologies such as Docker for modules or layer containerization, while Kubernetes is used for running and orchestrating these containers in the cloud. To conclude this section, to create a digital twin that operates efficiently, reliably, and at scale, multiple technologies are required to be interfaced, increasing the development complexity of such systems. Table 2 summarizes the main digital twin implementation requirements and provide examples of potential solutions that satisfy each of them.