热力发电2025，Vol.54Issue(7)：1-13,13.DOI:10.19666/j.rlfd.202411246
机器学习在循环流化床锅炉技术中的应用进展
Advancements in the application of machine learning in circulating fluidized bed boiler technology
摘要
Abstract
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.关键词
燃煤锅炉/CFB锅炉/机器学习/非线性/预测Key words
coal-fired boiler/circulating fluidized bed boiler/machine learning/nonlinearity/prediction引用本文复制引用
肖红亮,柯希玮,潘帅,郎丽萍,王君峰,祁海鹰,张守玉,吕俊复,黄中..机器学习在循环流化床锅炉技术中的应用进展[J].热力发电,2025,54(7):1-13,13.基金项目
怀柔实验室项目(ZD2023008A) Program of Beijing Huairou Laboratory(ZD2023008A) （ZD2023008A）
