Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization

The temperature field is a critical factor for ensuring the safe combustion and energy conservation in boilers. However, an effective method for reconstructing the temperature field near the water wall is still under exploration. In this paper, a method for online reconstruction of the temperature field distribution near the water wall in a 330 MW tangentially fired coal boiler is proposed, which progresses from a well-established model for the entire furnace. A two-branch fusion network for transfer learning (TBFN-TL) method is proposed, incorporating additional key parameters, heat flux, during the transfer process to enhance the effectiveness. A Bayesian hierarchical neural architecture search (BHNAS) method is proposed to optimize the configuration of the hidden layers in building neural networks. Compared with computational fluid dynamics (CFD) results, the mean absolute percentage error (MAPE) of the reconstruction results for the entire furnace model, traditional transfer learning methods, and the proposed TBFN-TL are 11.57%, 5.738%, and 2.052%, respectively, demonstrating a significant enhancement. The proposed BHNAS method extends the search optimization space, obtaining more excellent configurations for the hidden layer nodes. The proposed methods have significant implications for temperature field reconstruction, the field of transfer learning, and the optimization of hidden layer configurations.