A Soft Measurement Method of Fly Ash Carbon Content Based on Improved Adaptive Least Squares Support Vector Machine

GUO Wenkang; MO Zhengyang; LI Yiguo

doi:10.19805/j.cnki.jcspe.2025.240298

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A Soft Measurement Method of Fly Ash Carbon Content Based on Improved Adaptive Least Squares Support Vector Machine

更新时间：2026-10-10

- A Soft Measurement Method of Fly Ash Carbon Content Based on Improved Adaptive Least Squares Support Vector Machine
- Chinese Journal of Power Engineering Vol. 45, Issue 7, Pages: 1082-1090(2025)
- 作者机构：
  
  东南大学大型发电装备安全运行与智能测控国家工程研究中心,江苏,南京,211189
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240298
  CLC：
- Online First：14 July 2025，
  
  Published：2025
- 稿件说明：
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郭文康,莫正阳,李益国. 基于改进自适应最小二乘支持向量机的飞灰含碳量软测量方法动力工程学报, 2025, 45(7): 1082-1090 https://doi.

org/10.19805/j.cnki.jcspe.2025.240298
郭文康,莫正阳,李益国. 基于改进自适应最小二乘支持向量机的飞灰含碳量软测量方法动力工程学报, 2025, 45(7): 1082-1090 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240298.

org/10.19805/j.cnki.jcspe.2025.240298 DOI：

摘要

飞灰含碳量是实现锅炉效率在线测量的重要参数之一

然而目前的飞灰含碳量测量装置存在测量周期长和故障率高等缺点。为此

通过改进模型更新方法提出一种新的改进自适应最小二乘支持向量机(IALSSVM)算法

并且将其用于建立某660 MW燃煤锅炉飞灰含碳量的动态软测量模型

其中采用皮尔逊相关性分析筛选出重要变量

利用核主成分分析(KPCA)法融合重要变量信息。仿真测试结果表明:该软测量模型在测试集上的平均绝对预测误差(MAE)、平均绝对百分比误差(MAPE)分别为0.171%和19.814%

拟合优度(

)为0.843

具有较高的精度和稳定性。另外

新的模型更新方法在计算速度上相比于传统方法提升30%左右

对促进该模型的在线应用和实现锅炉闭环燃烧优化具有重要作用。

Abstract

The carbon content of fly ash is one of the important parameters to realize the online measurement of boiler efficiency

but the current measurement device for fly ash carbon content has the disadvantages of a long measurement period and a high failure rate. To solve this problem

a novel improved adaptive least squares support vector machine (IALSSVM) algorithm with improved model update method was proposed

and this algorithm was used to establish a dynamic soft measurement model of fly ash carbon content for a 660 MW coal-fired boiler

in which important variables were selected by Pearson correlation analysis

while the information of important variables was fused by kernel principal component analysis (K

PCA) method. Simulation results show that

the average absolute prediction error (MAE) and mean absolute percentage error (MAPE) of the soft measurement model on the test set are 0.171% and 19.814%

respectively

and the goodness of fit (

) is 0.843

which has a high level of accuracy and stability. In addition

compared with the traditional method

the calculation speed of the novel model update method can be improved by about 30%

which plays an important role in promoting the online application of the model and realizing boiler closed-loop combustion optimization.

关键词

Keywords

references

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