基于图像卷积神经网络的燃高碱煤锅炉积灰识别与分析

史凯迪; 胡丽娜; 程泽宁; 王鹏

doi:10.19805/j.cnki.jcspe.2025.240411

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基于图像卷积神经网络的燃高碱煤锅炉积灰识别与分析

动力设备与系统 | 更新时间：2026-10-10

- 基于图像卷积神经网络的燃高碱煤锅炉积灰识别与分析
- Ash Deposit Identification and Analysis in High-alkali Coal-fired Boilers Using Image-based Convolutional Neural Networks
- 动力工程学报 2025年45卷第8期页码：1179-1185
- 作者机构：
  
  1. 新疆大学电气工程学院,新疆,乌鲁木齐,830000
  2. 新疆天池能源有限责任公司,新疆,昌吉,831100
- 作者简介：
  
  史凯迪(2000—),男,河北石家庄人,硕士研究生,研究方向为锅炉积灰结渣的可视化
  胡丽娜(通信作者),女,副教授,硕士生导师,E-mail:hulina@xju.edu.cn
- 基金信息：
  
  国家重点研发计划资助项目(2023YFB4102704);新疆碳中和能源科学与技术研究"天山英才"培养计划资助项目(2022TSYCLJ0001)
- DOI：10.19805/j.cnki.jcspe.2025.240411
  中图分类号：
- 网络首发：2025-08-12，
  
  纸质出版：2025
- 稿件说明：
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史凯迪,胡丽娜,程泽宁,王鹏. 基于图像卷积神经网络的燃高碱煤锅炉积灰识别与分析动力工程学报, 2025, 45(8): 1179-1185 https://doi.

org/10.19805/j.cnki.jcspe.2025.240411
史凯迪,胡丽娜,程泽宁,王鹏. 基于图像卷积神经网络的燃高碱煤锅炉积灰识别与分析动力工程学报, 2025, 45(8): 1179-1185 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240411.

org/10.19805/j.cnki.jcspe.2025.240411 DOI：

摘要

以660 MW燃新疆高碱煤的锅炉屏式过热器积灰为研究对象

搭建了高温可视化成像系统

在线获取积灰图像。通过对积灰图像进行灰度转化、灰度线性化、高斯滤波等预处理

并进行峰值降噪比(PSNR)与结构相似度(SSIM)计算

用图像增强的方法构建积灰图像数据集。鉴于积灰图像的厚度与热阻的关系

提出一种基于图像卷积神经网络(CNN)的锅炉积灰识别监测模型。结果表明:基于CNN方法可实现对运行工况下屏式过热器积灰状态的监测与识别

模型的准确率、召回率、

score

分别为98.8%、98.2%、98.8%。

Abstract

Targeting ash deposits on the platen superheater of a 660 MW boiler firing Xinjiang high-alkali coal

a high-temperature visual imaging system was established to acquire real-time ash deposition images. Ash deposition images were preprocessed through grayscale conversion

grayscale linearization

and Gaussian filtering

and quantitatively evaluated via peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). An enhanced image dataset was constructed through image augmentation techniques. Leveraging the correlation between deposit thickness and thermal resistance

a convolutional neural network (CNN)-based monitoring model was developed for ash deposit identification. Results show that the CNN method achieves effective real-time monitoring of ash deposit status on operat

ing platen superheaters

with accuracy

recall

and

score

reaching 98.8%

98.2%

and 98.8% respectively.

关键词

Keywords

references

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