Dynamic Soft Measurement Model of NO<sub><i>x</i></sub> Concentration at the Outlet of SNCR Denitrification System Based on Variable Selection and POA-NARX

ZHAO Zheng; LIANG Lei; LIU Saiheng

doi:10.19805/j.cnki.jcspe.2025.240018

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Dynamic Soft Measurement Model of NO_x Concentration at the Outlet of SNCR Denitrification System Based on Variable Selection and POA-NARX

更新时间：2026-01-27

- Dynamic Soft Measurement Model of NO_x Concentration at the Outlet of SNCR Denitrification System Based on Variable Selection and POA-NARX
- Chinese Journal of Power Engineering Vol. 45, Issue 4, Pages: 592-601(2025)
- 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
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240018
  CLC： X773
- Published Online：28 April 2025，
  
  Published：2025
- 稿件说明：
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赵征,梁磊,刘赛恒. 基于变量选择和POA-NARX的SNCR脱硝系统出口NO_x浓度动态软测量模型动力工程学报, 2025, 45(4): 592-601 https://doi.

org/10.19805/j.cnki.jcspe.2025.240018
赵征,梁磊,刘赛恒. 基于变量选择和POA-NARX的SNCR脱硝系统出口NO_x浓度动态软测量模型动力工程学报, 2025, 45(4): 592-601 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240018.

org/10.19805/j.cnki.jcspe.2025.240018 DOI：

摘要

针对垃圾焚烧炉选择性非催化还原(SNCR)脱硝系统内部工况不稳定、影响出口NO

浓度因素多以及无法及时准确测量出口NO

浓度等问题

提出了一种基于变量选择和鹈鹕优化算法-非线性自回归(POA-NARX)的SNCR脱硝系统出口NO

浓度动态软测量模型。通过机理分析SNCR脱硝系统出口NO

浓度的影响因素

初筛特征变量;利用改进的快速相关过滤(FCBF)算法选择高相关变量

去除强冗余的变量;再利用数据趋势分析法和互信息算法进行迟延估计;最后利用鹈鹕优化算法确定最佳系统变量阶次

建立SNCR脱硝系统出口NO

浓度动态软测量模型。实验结果表明:经过变量筛选和时滞分析的NARX动态模型准确性显著提升; POA-NARX模型的预测效果明显优于其他他软测量模型。

Abstract

To address issues of unstable working conditions within waste incinerator selective non-catalytic reduction (SNCR) denitrification system

including many factors affecting outlet NO

concentration and the inability to timely and accurately measure outlet NO

concentration

a dynamic soft measurement model for NO

concentration at the outlet of SNCR denitrification system was proposed based on variable selection and the pelican op

timization algorithm-nonlinear autoregressive(POA-NARX). The factors affecting NO

concentration at the outlet of SNCR denitrification system were firstly analyzed by mechanism and the characteristic variables were selected. An improved fast correlation-based filter (FCBF) algorithm was then used to select highly correlated variables and remove redundant ones. Time delay estimation was carried out by using data trend analysis method and mutual information algorithm. Finally

pelican optimization algorithm was used to determine the optimal order of the system variables and establish a dynamic soft measurement model of NO

concentration at the outlet of SNCR denitrification system. The experimental results show that the accuracy of the NARX dynamic model after variable filtering and time lag analysis is significantly improved

and the prediction effect of the POA-NARX model is significantly better than other soft measurement models.

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references

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Dynamic Soft Measurement Model of NOx Concentration at the Outlet of SNCR Denitrification System Based on Variable Selection and POA-NARX

DOI：10.19805/j.cnki.jcspe.2025.240018

摘要

Abstract

关键词

Keywords

references

Dynamic Soft Measurement Model of NO_x Concentration at the Outlet of SNCR Denitrification System Based on Variable Selection and POA-NARX