###### Abstract  
Due to the simplified assumptions or unascertained equipment parameters, traditional mechanism models of boiler system in coal-fired power plant usually have predictive errors that cannot be ignored. In order to further improve the predictive accuracy of the model, this paper proposes a novel recurrent neural network-based hybrid modeling method for digital twin of boiler system. First, the mechanism model of boiler system is described through recurrent neural network (RNN) to facilitate training and updating parameters, while the interpretability of the model does not degenerate. Second, for the time-varying parameters in the mechanism model, the functional relationship between them and the state variables is constructed by neurons to improve the predictive accuracy. Third, the long short-term memory (LSTM) neural network model is established to describe the unascertained dynamic characteristics to compensate the predictive residual of the mechanism model. Fourth, the update architecture and training algorithm applicable to the hybrid model are established to realize the iterative optimization of model parameters. Finally, experimental results show that the hybrid modeling method proposed in this paper can improve the predictive performance of traditional models effectively.  
digital twin; hybrid modeling; LSTM; coal-fired power plant; modeling of boiler system +
Footnote †: journal: Applied Sciences  
power plants are gradually being proposed, the further in-depth and enriched research is still needed on digital twin modeling for boiler system in coal-fired power plants.  
Guided by the aforementioned obstacles and gaps, in this paper, we propose a novel hybrid modeling method to improve the accuracy of digital twin model for boiler system. The main contributions of this paper lie in the following four aspects. (1) On the basis of ensuring the interpretability of the mechanism model, in order to facilitate training and updating parameters, this paper uses the recurrent neural network to describe the mechanism dynamic characteristics of the boiler system. (2) By adding new neurons to the mechanism model, the functional relationship between time-varying parameters and input variables is properly described to decrease the errors caused by parameters uncertainty. (3) For the sake of reducing the predictive residual of the mechanism model, the LSTM-based error-compensation model is formed. (4) To achieve the iterative optimization of hybrid model parameters, the update architecture and training algorithm are established. Finally, the experimental results show that the proposed method could ensure the high fidelity of the digital twin hybrid model.  
The rest of this paper is organized as follows. In section \"State of the art\", the existing modeling methods and technologies is discussed in detail. The hybrid modeling method is established and described in detail in section \"Recurrent neural network-based hybrid modeling method for digital twin of boiler system\". Section \"Simulation results\" provides the simulation results to illustrate the effectiveness of the proposed method. The conclusions and future work are discussed in section \"Conclusion\".