基于结渣因子监测与机器学习的水冷壁精准吹灰算法研究

史建忠; 洪兵; 温小豪; 施子福; 李培; 周永刚

doi:10.19666/j.rlfd.202507080

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基于结渣因子监测与机器学习的水冷壁精准吹灰算法研究

发电技术论坛 | 更新时间：2026-06-12

- 基于结渣因子监测与机器学习的水冷壁精准吹灰算法研究
- 基于结渣因子监测与机器学习的水冷壁精准吹灰算法研究
- 热力发电 2026年55卷第4期页码：140-147
- 作者机构：
  
  1. 国能浙江宁海发电有限公司
  2. 浙江大学能源工程学院
  3. 浙江大学能源高效清洁利用全国重点实验室
- 作者简介：
- 基金信息：
  
  能源高效清洁利用全国重点实验室自主课题（ZJUCEU2025012）~~
- DOI：10.19666/j.rlfd.202507080
  中图分类号： TM621.2;TP181
- 纸质出版：2026
- 稿件说明：
移动端阅览
[1]史建忠,洪兵,温小豪,等.基于结渣因子监测与机器学习的水冷壁精准吹灰算法研究[J].热力发电,2026,55(04):140-147.
[1]史建忠,洪兵,温小豪,等.基于结渣因子监测与机器学习的水冷壁精准吹灰算法研究[J].热力发电,2026,55(04):140-147. DOI： 10.19666/j.rlfd.202507080.

DOI：10.19666/j.rlfd.202507080.

摘要

【目的】针对传统定时定量吹灰模式易导致水冷壁局部过吹、欠吹的问题，依托有效监测手段开展研究，旨在构建精准吹灰算法。【方法】利用新型水冷壁结渣监测传感器获取水冷壁炉内壁温，对比了3种机器学习方法构建水冷壁洁净状态下的理论炉内壁温模型，提出一种水冷壁结渣因子的计算方法，并据此构建精准吹灰算法，通过实际应用并与原定时定量吹灰模式进行对比，验证了该算法的优化效果。【结果】研究表明：随机森林回归方法建立的理论炉内壁温模型表现最佳，R

为0.92、δ

MSE

为73.77、δ

MAPE

为1.16%；精准吹灰算法实施后效果明显优于原定时定量吹灰模式，吹灰频次大幅度降低，且未造成水冷壁结渣状态恶化。【结论】该算法在保证锅炉运行安全性的前提下，有效降低了吹灰蒸汽消耗量，具有极高的工程应用价值。

Abstract

[Objective

]

Against the problem that the conventional timed and quantitative soot blowing mode is prone to cause local over-blowing and under-blowing of the waterwall

studies are carried out by relying on effective monitoring methods. As slag deposition on waterwall is a key factor affecting the safe and economic operation of thermal power boilers

long-term unresolved local over-blowing or under-blowing will not only accelerate the corrosion and wear of the waterwall

but also increase energy consumption and operational costs of power plants. Therefore

the core goal of this research is to establish a precise soot blowing algorithm to replace the conventional timed and quantitative soot blowing mode and realize adaptive and efficient soot blowing control. [Methods

]

A new type of waterwall slagging monitoring

sensor was used to monitor the in-furnace waterwall surface temperature

which can collect real-time

continuous and high-precision temperature data to lay a reliable foundation for subsequent model construction. Three machine learning methods

including eXtreme gradient boosting(XGBoost)

light gradient boosting machine(LightGBM) and random forest regression(RFR)

were compared to construct theoretical in-furnace waterwall surface temperature models under clean waterwall conditions

and a calculation method for waterwall slagging factor was proposed. On this basis

a precise soot blowing algorithm was established. To verify the optimization effect of this algorithm

a 3-month practical application test was carried out in a 1 030 MW thermal power unit

and the operation data was compared with the original timed and quantitative soot blowing mode. [Results

]

The research shows that the theoretical in-furnace waterwall surface temperature model established by the random forest regression method performed the best

with an R

of 0.92

MSE

of 73.77

and δ

MAPE

of 1.16%. The implementation of the precise soot blowing algorithm is significantly better than the original quantitative soot blowing mode

with a significant reduction in soot blowing frequency and no deterioration of the waterwall slagging state

and the local maximum temperature of the waterwall is controlled within the safe range

avoiding the risk of tube explosion caused by overheating. [Conclusion

]

This algorithm reduces the consumption of soot blowing steam while ensuring the safety of boiler operation

which directly reduces the daily operation cost of the power plant. Moreover

the reduction of soot blowing frequency also reduces the influence of high-temperature steam on the waterwall

effectively extending the service life of the waterwall and reducing the maintenance cost of the boiler. It can be popularized and applied in thermal power plants of different capacities

and has extremely high application value.

关键词

Keywords

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