Application of PIML Methods for Steam Turbine Modeling in Digital Twin Development

This article discusses the creation of a prototype for a turbine digital twin, specifically focusing on developing a solution for constructing and continuously updating a mathematical model of the stationary operating modes of a heating turbine. A significant challenge in creating turbine digital twins is that the equipment often operates for extended periods within a limited range of permissible parameters. However, the model must be updated to cover the entire range of operating modes. To address this issue, a hybrid method combining machine learning with equations of physical laws was employed for extrapolating the model. The dependencies obtained through this hybrid method exhibited an error margin of about 2%. The proposed method can be used to tackle several important problems in the digital transformation process of the Siberian Generating Company. By applying this method, the company can enhance its operational efficiency, particularly by compensating for measurement errors and ensuring compliance with material and energy balances. © Springer Science+Business Media, LLC, part of Springer Nature 2024.