基于深度学习的锅炉热效率动态预测模型研究

王志; 尹泳博; 徐世明; 彭献永; 周怀春

doi:10.19805/j.cnki.jcspe.2025.240549

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基于深度学习的锅炉热效率动态预测模型研究

数字化与智能化 | 更新时间：2026-10-10

- 基于深度学习的锅炉热效率动态预测模型研究
- Study on Dynamic Prediction Model of Boiler Thermal Efficiency Based on Deep Learning
- 动力工程学报 2025年45卷第11期页码：1892-1904
- 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
- 作者简介：
- 基金信息：
  
  江苏省卓越博士后计划资助项目(2024ZB049);国家重点研发计划资助项目(2023YFB4102800);国家自然科学基金重大科研仪器研制资助项目(51827808)
- DOI：10.19805/j.cnki.jcspe.2025.240549
  中图分类号：
- 网络首发：2025-11-17，
  
  纸质出版：2025-11-17
- 稿件说明：
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王志,尹泳博,徐世明,彭献永,周怀春. 基于深度学习的锅炉热效率动态预测模型研究动力工程学报, 2025, 45(11): 1892-1904 https://doi.

org/10.19805/j.cnki.jcspe.2025.240549
王志,尹泳博,徐世明,彭献永,周怀春. 基于深度学习的锅炉热效率动态预测模型研究动力工程学报, 2025, 45(11): 1892-1904 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240549.

org/10.19805/j.cnki.jcspe.2025.240549 DOI：

摘要

热效率是锅炉设备性能和机组运行经济性评价的重要指标。针对热效率与辅助变量之间的高维非线性

利用随机森林算法进行有监督降维

针对锅炉典型时序特性

根据适用于变负荷工况的卷积神经网络(convolutional neural network

CNN)构建热效率动态模型。为保证模型轻量化

在三层卷积层的常规CNN中引入通道均衡块以解决因特征值被抑制而存在的通道崩塌问题

构建了基于通道均衡卷积神经网络(channel equalization CNN

CE-CNN)的热效率预测模型

并在600 MW真实锅炉历史数据上开展了仿真实验。结果表明:CE-CNN的均方根误差为(0.1010.008)%

与6-layer CNN相比

节省了29.97%的训练时间

验证了模型的有效性。

Abstract

Thermal efficiency is an important indicator for the evaluation of boiler equipment performance and unit operation economy. For the high-dimensional nonlinearity between thermal efficiency and auxiliary variables

Random forest algorithm was used to carry out supervised dimensionality reduction

and the convolutional neural network (CNN) for variable load conditions was proposed to construct a dynamic model of thermal efficiency in view of the typical time-series characteristics of boiler. In order to ensure that the model was lightweight

a channel equalization block was introduced into the conventional CNN with three convolutional layers to solve the problem of channel collapse due to the suppression of feature maps

and a thermal efficiency prediction model based on channel equalization CNN (CE-CNN) was constructed. Simulation experiments were conducted using historical data from a 600 MW real boiler. Results show that the CE-CNN achieves a root mean square error of (0.1010.008)%

reducing training time by 29.97% compared to a 6-layer CNN

which validates the effectiveness of the model.

关键词

Keywords

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