基于增强灰狼优化LSTM神经网络的火电机组SCR脱硝反应器建模

吴磊; 顾华; 姚一鸣; 张军; 苏军; 陈依

doi:10.19666/j.rlfd.202502032

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基于增强灰狼优化LSTM神经网络的火电机组SCR脱硝反应器建模

热力发电 2025年54卷第11期页码：136-141

热能科学研究 | 更新时间：2026-08-11

- 基于增强灰狼优化LSTM神经网络的火电机组SCR脱硝反应器建模
- Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network
- 作者机构：
  
  1. 国网上海青浦供电公司
  2. 上海电力大学自动化工程学院
  3. 上海市电站自动化技术重点实验室
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目（61273190）;上海市电站自动化技术重点实验室资助项目（13DZ2273800）~~
- DOI：10.19666/j.rlfd.202502032
  中图分类号： X773;TP18
- 纸质出版：2025
- 稿件说明：
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[1]吴磊,顾华,姚一鸣,等.基于增强灰狼优化LSTM神经网络的火电机组SCR脱硝反应器建模[J],2025,54(11):136-141.
[1]吴磊,顾华,姚一鸣,等.基于增强灰狼优化LSTM神经网络的火电机组SCR脱硝反应器建模[J],2025,54(11):136-141. DOI： 10.19666/j.rlfd.202502032.

DOI：10.19666/j.rlfd.202502032.

摘要

针对传统机理建模方法预测选择性催化还原技术(SCR)脱硝反应器出口NO

质量浓度精度不高的问题，提出了一种结合增强型灰狼优化算法(EGWO)与长短时记忆(LSTM)神经网络的混合预测模型。首先，基于主成分分析(PCA)对原始数据进行处理与筛选，实现输入变量的降维。然后，利用EGWO优化LSTM神经网络的超参数。最终，将输入变量作为EGWO-LSTM模型的输入，预测出口NO

质量浓度。以国内某超超临界1 000 MW火电机组为例，仿真结果表明，该模型在误差控制方面表现最优，均方根误差较传统LSTM模型下降50.36%，较BP模型下降76.14%，模型平均绝对百分比误差仅为1.01%。EGWO相对于GWO收敛至最优解时的迭代次数更少，且具有更高的收敛精度。

Abstract

A hybrid prediction model combining enhanced grey wolf optimization algorithm(EGWO) and long short-term memory(LSTM) neural network is proposed to address the problem of low accuracy in predicting the mass concentration of NO

at the outlet of selective catalytic reduction(SCR) denitrification reactors using conventional mechanism modeling methods. Firstly

based on principal component analysis(PCA)

the raw data is processed and filtered to achieve dimensionality reduction of input variables. Then

the EGWO is used to optimize the hyperparameters of LSTM. Finally

the input variables are used as in

puts for the EGWO-LSTM model to predict the mass concentration of NO

at the outlet. Taking a 1 000 MW ultra supercritical thermal power unit in China as an example

simulation results show that the proposed model performs the best in error control

with root mean square error reduces by 50.36% compared to the conventional LSTM model

and by 76.14% compared to the BP model

and the mean absolute percentage error of the model is only 1.01%. The EGWO has fewer iterations and higher convergence accuracy compared to the GWO when converging to the optimal solution.

关键词

Keywords

references

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