Application of Digital Twin Technology in Information and Measurement Systems

Introduction. The article addresses the problem of creating an automated system for data collection from heat metering units, as well as a digital twin of such a system. Digital twins are widely used in the energy sector to optimize the operation of thermal power plants, including their timely maintenance, forecasting various emergency scenarios, or planning thermal energy production. Practical examples of such systems are presented. The relevance of this work lies in the possibility of predicting the size of pipeline defects based on both measurement and digital twin data.
Aim. Development of a distributed information and measurement system for heat supply monitoring with the introduction of a digital twin.
Materials and methods. The parameters of the heat-carrying agent, such as temperature, pressure, and flow, were simulated according to the normal distribution law and the thermal schedule of power plants. This information was further used to develop a mathematical and algorithmic support for predicting the state of technological equipment. The depth of a cavity defect which may occur in the pipeline was predicted. The strength condition was used as a criterion for the failure limit state. To determine the ultimate strength of the pipe wall, OST 153-39.4-010–2002 and the Barlow formula were used.
Results. The obtained results include a digital twin of the heat supply control system, the structure of a distributed information and measurement system for the unit of heat supply monitoring, algorithmic and software systems for the operation of the distributed information and measurement system and for predicting the failure state of the pipeline. The software operability was verified in normal operation and in the absence of access to the server.
Conclusion. 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.

1. Alekseev V. V., Konovalova V. S., Romantsova N. V, Tsareva A. V. Information and Measuring System of the Control Node of the Main Heat Pipeline. Soft Measurements and Computing. 2022, vol. 54, no. 5, pp. 16–26. (In Russ.) doi: 10.36871/2618-9976.2022.05.002

2. Kulikov A. N., Ugrevatov A. Yu., Uglov P. V. Mini-CHP Plants – Small Energy Facilities, and How to Automate Them. Industrial and heating Boilers and Mini-TPP. 2023, vol. 80, no. 5, pp. 26–29. (In Russ.)

3. Sosfenov D. A. Digital Twin: History of Origin and Development Prospects. Intellect. Innovations. Investments. 2023, no. 4, pp. 35–43. (In Russ.) doi: 10.25198/2077-7175-2023-4-35

4. Cai Y., Starly B., Cohen P., Lee Y.-S. Sensor Data and Information Fusion to Construct Digital-Twins Virtual Machine Tools for Cyberphysical Manufacturing. Procedia Manufacturing. 2017, vol. 10, pp. 1031–1042. doi: 10.1016/j.promfg.2017.07.094

5. Sun W., Lei S., Wang L., Liu Z., Zhang Y. Adaptive Federated Learning and Digital Twin for Indus-Trial Internet of Things. IEEE Transactions on Industrial Informatics. 2021, vol. 17, no. 8, pp. 5605–5614. doi: 10.1109/TII.2020.3034674

6. Jeon S. M., Schuesslbauer S. Digital Twin Application for Production Optimization. IEEE Intern. Conf. on Industrial Engineering and Engineering Management (IEEM), Singapore, 14–17 Dec. 2020. IEEE, 2020, pp. 542–545. doi: 10.1109/IEEM45057.2020.9309874

7. St Petersburg Heat Supply Scheme 2020. Vol. 1 (Pt. 1, 2.1). Available at: https://www.gov.spb.ru/static/writable/ckeditor/uploads/2019/08/26/27/%D1%82%D0%BE%D0%BC_1_%D1%87%D0%B0%D1%81%D1%82%D0%B8_1_2..pdf (accessed 04.04.2024). (In Russ.)

8. Regulation Diagram of Heat Supply from Nevsky branch TPP of TGC-1 in 2018–2019 Heating Period. Available at: https://energomonitoring.com/wp-content/uploads/2019/02/2018_grafik_regulirovanija__Teploset.pdf (accessed 04.04.2024). (In Russ.)

9. Baronova V. A., Romantsova N. V., Tyarkin Ya. A. Providing the Adequacy of the Heat Metering System Model. Conf. of Young Researchers in Electrical and Electronic Engineering (ElCon), St Petersburg, Russia, 29–31 Jan. 2024. IEEE, 2024, pp. 324–326. doi: 10.1109/ElCon61730.2024.10468386

10. Pritula V. V. Podzemnaya korroziya trubo-provodov i rezervuarov [Underground Corrosion of Pipelines and Reservoirs]. Moscow, Akela, 2003, 225 p. (In Russ.)

11. Gevlich S. O., Gevlich D. S., Vasiliev K. A. Diagnostics of Thermal Networks and Urban Water Pipes. Technical Sciences - from Theory to Practice. 2015, vol. 45, no. 9, pp. 114–123. (In Russ.)

12. Otstavnov A. A., Kharkov V. A. About Standardized Tubular Products Made of Fiberglass Reinforced Thermoplastics. Plumbing. 2014, no. 2, pp. 48–52. (In Russ.)

13. OST 153-39.4-010–2002 Methodology for Determining the Residual Resource of Oil and Gas Field Pipe-Lines and Pipelines of Head Structures. Approved and put into effect by the order of the Ministry of Energy of the Russian Federation: 5 Aug. 2002: introduction 01.10.2002.

14. Chapaev D. B., Olennikov A. A. Calculation of Internal Corrosion Rate of Pipelines of Wather Thermal Networks from Carbon Steel. Izvestiya. Ferrous Metallurgy. 2012, vol. 55, no. 4, pp. 33–36. (In Russ.) doi: 10.17073/0368-0797-2012-4-33-36

15. The results of production control of the quality and safety of hot water at the Nevsky branch of PJSC TGC-1 for 2020. Available at: https://www.tgc1.ru/fileadmin/clients/spb/disclosure/2020/rezultaty_proizvodstvennogo_kontrolja_kachestva_i_bezopasnosti_gorjachei_vody_tehc_filiala_nevskii_v_sankt-peterburge.pdf (accessed 04.04.2024). (In Russ.)