Hybrid modeling of a circulating fluidized bed boiler for development of a prediction and prescription system for power plant operation

Abstract

A novel hybrid modeling system of a circulating fluidized bed boiler was developed in this study. The proposed approach combines a computational fluid dynamics model, practice-oriented macro-scale models, and a data-driven reduced order model to capture both the physical and empirical behaviors of the boiler. The application of various modeling strategies allowed for capturing features of the simulated process at various scales and for developing a fast and accurate prediction system. The use of the computational fluid dynamics model improved the predictions of the practice-oriented macro-scale models providing more detailed flow information and the use of the reduced order model allowed for reducing the computation time by a factor of 1350. The hybrid model and its submodels were validated against real measurement data. The validation confirmed good accuracy in predicting key process variables. The developed model is used as a prediction and prescription system to enable operators to optimize the boiler operation and prevent abnormal events. The hybrid system is used for a supercritical, once-through boiler in Łagisza power plant in Poland, however, the presented modeling strategy can be applied in other power plants, which can improve efficiency, reduce emissions, enhance boiler availability, and ultimately contribute to more sustainable energy systems.