Circulating fluidized bed(CFB) boilers play a pivotal role in China's power generation landscape. However

the intricate combustion system within the CFB boiler furnace exhibits strong coupling characteristics

characterized by multiple parameters

variables

nonlinearity

and time-varying dynamics

posing a significant challenge for precise system modeling and prediction. Machine learning(ML)

with its robust nonlinear processing capabilities and predictive performance

holds immense promise in the domain of CFB technology. This paper delves into the application of ML techniques in this field

encompassing the prediction of minimum fluidization velocity

emissions forecasting

bed pressure forecasting

bed temperature/thermal efficiency prediction

particle circulation rate prediction

reduced-order models of computational fluid dynamics(CFD) flow fields

and boiler safety control system models. The paper critically evaluates the strengths and limitations of these technologies in various scenarios

providing an insightful perspective on the opportunities and challenges faced by CFB boilers in the era of big data. Emphasizing aspects like model interpretability

enhancing generalization capabilities

improving data quality and diversity

integrating models with conventional methods

and experimental validation are crucial areas worth attention for future advancements.