Prediction Modeling of External Heat Exchangers in a 660 MW Ultra-Supercritical Circulating Fluidized Bed Boiler Based on Model Reduction

To ensure the safe operation of the external heat exchanger (EHE) in a circulating fluidized bed (CFB) boiler, it is essential to obtain real-time information on the flow conditions within the bed. This paper establishes a predictive model for the external heat exchanger of the high-temperature reheater in an ultra-supercritical CFB boiler by combining computational fluid dynamics (CFD) with model order reduction and artificial neural networks. The model enables rapid prediction of the solid volume fraction, solid temperature, and gas temperature within the external heat exchanger. The results show that the three predictive models can accurately forecast flow field information under unknown operating conditions. For inlet velocities of 0.225 m/s and 0.325 m/s, the calculation errors are 2.89%, 1.04%, 1.03% and 2.99%, 1.08%, 1.09%, respectively. The predictive models significantly save computational resources, reducing the computation time from 6000 min for the full-order model to approximately 1 s. This lays the foundation for real-time monitoring of the external heat exchanger.