EN基于变量选择和POA-NARX的SNCR脱硝系统出口NOx浓度动态软测量模型
Dynamic Soft Measurement Model of NOx Concentration at the Outlet of SNCR Denitrification System Based on Variable Selection and POA-NARX
- 动力工程学报 2025年45卷第4期 页码：592-601
- 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：
[ "赵征(1978—),女,河北保定人,副教授,博士,研究方向为热工过程建模与控制,E-mail:zheng_zhao@ncepu.edu.cn" ]
- 基金信息：国家自然科学基金资助项目(52276007);深圳市科技计划资助项目(2021N041);生活垃圾焚烧智能优化控制及污染物超低排放技术研发资助项目(KCXFZ20201221173402007)
DOI：10.19805/j.cnki.jcspe.2025.240018
中图分类号： X773-
网络出版：2025-04-28，
纸质出版：2025
- 稿件说明：
移动端阅览
赵征,梁磊,刘赛恒. 基于变量选择和POA-NARX的SNCR脱硝系统出口NOx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NOx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
x
浓度因素多以及无法及时准确测量出口NO
x
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
x
浓度动态软测量模型。通过机理分析SNCR脱硝系统出口NO
x
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
x
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
x
concentration and the inability to timely and accurately measure outlet NO
x
concentration
a dynamic soft measurement model for NO
x
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
x
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
x
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.
关键词
Keywords
references
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