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  

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

作　　者：赵征[1,2] 梁磊 刘赛恒 ZHAO Zheng;LIANG Lei;LIU Saiheng(Department of Automation,North China Electric Power University,Baoding 071003,Hebei Province,China;Baoding Key Laboratory of Integrated Energy System State Detection and Optimized Regulation,Baoding 071003,Hebei Province,China)

机构地区：[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

出　　处：《动力工程学报》2025年第4期592-601,共10页Journal of Chinese Society of Power Engineering

基　　金：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)。

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。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 optimization 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：垃圾焚烧炉 SNCR 快速相关过滤算法 NARX神经网络 鹈鹕优化算法 软测量 

分 类 号：X773[环境科学与工程—环境工程]