In the heart of Saint Petersburg, researchers are pioneering a technology that could revolutionize how we monitor and maintain our energy infrastructure. V. A. Baronova, a leading expert from Saint Petersburg Electrotechnical University, has been at the forefront of this innovation, focusing on the application of digital twin technology in heat supply systems. Her work, recently published in *Известия высших учебных заведений России: Радиоэлектроника* (translated as *Proceedings of the Higher Educational Institutions of Russia: Radioelectronics*), promises to bring significant advancements to the energy sector.
The research addresses a critical challenge in the energy industry: creating an automated system for data collection from heat metering units and developing a digital twin of such a system. Digital twins, virtual replicas of physical systems, are already making waves in the energy sector, optimizing the operation of thermal power plants, predicting emergencies, and planning thermal energy production. Baronova’s work takes this a step further by introducing a distributed information and measurement system for heat supply monitoring, integrated with a digital twin.
The system simulates parameters like temperature, pressure, and flow of the heat-carrying agent, using this data to predict the state of technological equipment. One of the most compelling aspects of this research is its ability to forecast the depth of cavity defects in pipelines. “The strength condition was used as a criterion for the failure limit state,” Baronova explains. “To determine the ultimate strength of the pipe wall, we used OST 153-39.4-010–2002 and the Barlow formula.”
The practical implications of this research are vast. By predicting pipeline defects, energy companies can prevent costly failures and downtime. The digital twin can simulate optimal values of the heat-carrying agent based on environmental parameters, optimizing the thermal graph of the object. “The use of digital twin technology in heat supply monitoring makes it possible to optimize the thermal graph of the object by simulating the optimal values of the heat-carrying agent based on environmental parameters with an error in modeling the water temperature in the supply pipeline of Δt = ±5°C at ambient temperatures from –8 to +3 °C,” Baronova notes.
This innovation could lead to significant cost savings and improved efficiency in the energy sector. As digital twin technology continues to evolve, it is likely to become an integral part of energy infrastructure management, shaping the future of the industry. Baronova’s work is a testament to the power of digital twins and their potential to transform the way we manage and maintain our energy systems.