机器学习在循环流化床锅炉技术中的应用进展

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

doi:10.19666/j.rlfd.202411246

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机器学习在循环流化床锅炉技术中的应用进展

热力发电 2025年54卷第7期页码：1-13

“超超临界循环流化床发电技术”专题 | 更新时间：2026-08-11

- 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
- 作者机构：
  
  1. 北京怀柔实验室
  2. 怀柔实验室山西研究院
  3. 中国石油大学(北京)机械与储运工程学院
  4. 哈尔滨锅炉厂有限责任公司
  5. 清华大学能源与动力工程系
- 作者简介：
- 基金信息：
  
  怀柔实验室项目(ZD2023008A)~~
- DOI：10.19666/j.rlfd.202411246
  中图分类号： TM621.2;TP181
- 纸质出版：2025
- 稿件说明：
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[1]肖红亮,柯希玮,潘帅,等.机器学习在循环流化床锅炉技术中的应用进展[J],2025,54(07):1-13.
[1]肖红亮,柯希玮,潘帅,等.机器学习在循环流化床锅炉技术中的应用进展[J],2025,54(07):1-13. DOI： 10.19666/j.rlfd.202411246.

DOI：10.19666/j.rlfd.202411246.

摘要

循环流化床（CFB）锅炉在我国发电行业具有重要地位，然而炉膛燃烧系统具有多参数、多变量、非线性、时变性等强耦合特征，使系统的精确建模和预测困难。机器学习凭借强大的非线性处理能力和预测性能，在CFB领域展现出广阔的应用前景。探讨了机器学习技术在该领域的应用，包括最小流化速度预测、污染物排放预测、床层压力预测、床层温度/热效率预测、颗粒循环流率预测、计算流体力学（computational fluid dynamics

CFD）场的降阶模型、锅炉安全控制系统模型等方面。分析了这些技术在不同场景中的优势和局限，展望了CFB锅炉在大数据时代面临的机遇和挑战，关注模型解释性、泛化能力提升、数据质量多样性、模型结合传统方法、实验验证等是未来研究的重点方向。

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

Keywords

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