基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型

徐世明; 何至谦; 彭献永; 商忠宝; 范景玮; 王俊略; 曲舒杨; 刘洋; 周怀春

doi:10.19805/j.cnki.jcspe.2026.240658

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基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型

数字化与智能化 | 更新时间：2026-10-10

- 基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型
- A Boiler Reheater Wall Temperature Prediction Model Based on CNN-BiLSTM-SSA
- 动力工程学报 2026年46卷第1期页码：121-130
- 作者机构：
  
  1. 中国矿业大学低碳能源与动力工程学院,江苏,徐州,221116
  2. 华能营口热电有限责任公司,辽宁,营口,115003
  3. 江苏省智慧能源技术及装备工程研究中心,江苏,徐州,221116
- 作者简介：
  
  徐世明(1983—),男,博士研究生,高级工程师,主要研究方向为汽轮机优化控制及智能发电
  彭献永(通信作者),男,博士,副教授,E-mail:xianyong.peng@cumt.edu.cn
- 基金信息：
  
  中国华能集团公司科技项目(HNKJ23-HF31);国家自然科学基金资助项目(52574291, 51706081)
- DOI：10.19805/j.cnki.jcspe.2026.240658
  中图分类号：
- 网络首发：2026-01-15，
  
  纸质出版：2026-01-15
- 稿件说明：
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徐世明,何至谦,彭献永,商忠宝,范景玮,王俊略,曲舒杨,刘洋,周怀春. 基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型动力工程学报, 2026, 46(1): 121-130 https://doi.

org/10.19805/j.cnki.jcspe.2026.240658
徐世明,何至谦,彭献永,商忠宝,范景玮,王俊略,曲舒杨,刘洋,周怀春. 基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型动力工程学报, 2026, 46(1): 121-130 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240658.

org/10.19805/j.cnki.jcspe.2026.240658 DOI：

摘要

针对锅炉高温再热器壁温动态特点

提出了一种基于稀疏自注意力(SSA)、卷积神经网络(CNN)及双向长短期记忆神经网络(BiLSTM)相融合的再热器壁温软测量模型。首先

采用核主成分分析(KPCA)算法对原始候选变量进行筛选降维

选择前26个主成分变量作为模型的最终输入。其次

考虑利用CNN捕捉局部相关性

BiLSTM学习数据的长期序列依赖性的优势

使用卷积神经网络-双向长短期记忆神经网络(CNN-BiLSTM)捕捉时序数据中的短期和长期依赖关系

引入稀疏自注意力SSA机制

通过为不同特征部分分配自适应权重

从而增强CNN-BiLSTM模型的特征提取与建模能力

最后利用在役1 000 MW超超临界锅炉的历史数据进行仿真实验。结果表明:CNN-BiLSTM-SSA模型在高温再热器壁温预测中的均方根误差(RMSE)、平均绝对误差(MAE)及平均绝对百分比误差(MAPE)分别为4.92℃、3.81℃和0.624 1%

相应的指标均优于CNN、LSTM、BiLSTM、CNN-LSTM和CNN-BiLSTM模型。

Abstract

In response to the dynamic characteristics of the boiler's high-temperature reheater wall temperature

a soft-sensor model for reheater wall temperature was proposed

which integrates a sparse self-attention mechanism (SSA)

a convolutional neural network (CNN)

and a bidirectional long short-term memory network (BiLSTM). First

the kernel principal component analysis (KPCA) algorithm was employed to screen and reduce the dimensionality of the original candidate variables

and the top twenty-six principal components were selected as the final inputs for the model. Secondly

leveraging the advantages of CNN in capturing local correlations and BiLSTM in learning long-term sequential dependencies

the CNN-BiLSTM framework was used to capture both short-term and long-term dependencies in the time-series data. The SSA mechanism was introduced to enhance the feature extraction and modeling capabilities of the CNN-BiLSTM model by assigning adaptive weights to different feature components. Finally

simulation experiments were conducted using historical data from an in-service 1 000 MW ultra-supercritical boiler. Results show that for high-temperature reheater wall temperature prediction

the proposed CNN-BiLSTM-SSA model achieves a root mean square error (RMSE) of 4.92 ℃

a mean absolute error (MAE) of 3.81 ℃

and a mean absolute percentage error (MAPE) of 0.624 1%. These corresponding metrics are all superior to those of the CNN

LSTM

BiLSTM

CNN-LSTM

and CNN-BiLSTM models.

关键词

Keywords

references

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