基于IGRA和CNN-LSTM的垃圾焚烧炉主蒸汽温度预测

王印松; 张炬

doi:10.19805/j.cnki.jcspe.2025.240374

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基于IGRA和CNN-LSTM的垃圾焚烧炉主蒸汽温度预测

数字化与智能化 | 更新时间：2026-10-10

- 基于IGRA和CNN-LSTM的垃圾焚烧炉主蒸汽温度预测
- Main Steam Temperature Prediction for Waste Incinerator Based on IGRA and CNN-LSTM
- 动力工程学报 2025年45卷第8期页码：1308-1318
- 作者机构：
  
  1. 华北电力大学自动化系,河北,保定,071003
  2. 保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071003
  3. 河北省发电过程仿真与优化控制技术创新中心,河北,保定,071003
- 作者简介：
  
  王印松(1967—),男,河北河间人,教授,博士,研究方向为先进控制策略及在电力系统中的应用、控制系统性能诊断
  张炬(通信作者),男,硕士研究生,E-mail:zhangju@ncepu.edu.cn
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240374
  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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王印松,张炬. 基于IGRA和CNN-LSTM的垃圾焚烧炉主蒸汽温度预测动力工程学报, 2025, 45(8): 1308-1318 https://doi.

org/10.19805/j.cnki.jcspe.2025.240374
王印松,张炬. 基于IGRA和CNN-LSTM的垃圾焚烧炉主蒸汽温度预测动力工程学报, 2025, 45(8): 1308-1318 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240374.

org/10.19805/j.cnki.jcspe.2025.240374 DOI：

摘要

为了解决传统建模方法在建立焚烧炉主蒸汽温度预测模型时预测精度不高的问题

提出了一种基于改进的灰色关联分析(IGRA)和卷积-长短期记忆(CNN-LSTM)神经网络的垃圾焚烧炉主蒸汽温度预测方法。首先

使用IGRA筛选出与主蒸汽温度关联程度高的分布式控制系统(DCS)变量作为输入;其次

采用主成分分析(PCA)方法提取包含焚烧炉燃烧图像绝大部分信息的主成分特征并将其作为输入;然后

基于IGRA和粒子群优化(PSO)算法

估计出输入变量与主蒸汽温度之间的迟延向量并进行了时延补偿;最后

构建了由DCS变量和图像特征组成的时序矩阵作为输入变量的CNN-LSTM模型

实现了对主蒸汽温度未来6 min内变化趋势的预测。结果表明:相较于已有的主蒸汽温度预测模型

本文所提出模型的平均绝对误差

降低了13.07%

均方根误差

MSE

降低了13.89%

决定系数

提升了13.08%。

Abstract

In order to solve the problem of low prediction accuracy of traditional modeling methods in establishing a prediction model for the main steam temperature (MST) of incinerators

a waste incinerator MST prediction method based on improved gray relevance analysis (IGRA) and convolutional-long short-term memory (CNN-LSTM) neural network was proposed. Firstly

DCS variables with high correlation degree with MST were selected by IGRA. Secondly

principal component analysis (PCA) was applied to extract principal component features containing most of the information of the incinerator combustion image. Then

based on IGRA and particle swarm optimization

(PSO) algorithms

the delay between DCS parameters and the MST was estimated and compensated. Finally

a CNN-LSTM model with the input of time series matrix composed of DCS variables and image features was constructed to predict the change trend of MST in the next 6 minutes. Results show that compared with the existing MST prediction model

the proposed model reduces the mean absolute error

by 13.07%

reduces the root mean square error

MSE

by 13.89%

and improves the determination coefficient

by 13.08%.

关键词

Keywords

references

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