基于mRMR-BO优化Stacking集成模型的NO_(x)浓度动态软测量  

Dynamic soft measurement of NO_(x)concentration based on mRMR-BO Stacking ensemble model

作　　者：金秀章[1] 乔鹏 史德金 JIN Xiuzhang;QIAO Peng;SHI Dejin(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003

出　　处：《热力发电》2023年第10期122-128,共7页Thermal Power Generation

摘　　要：针对火电厂选择性催化还原(selective catalytic reduction,SCR)烟气脱硝系统中,由于影响入口NO_(x)质量浓度因素过多及系统大迟延大惯性,导致入口NO_(x)质量浓度难以准确及时测量的问题,提出了利用最大相关-最小冗余算法(max-relevance and min-redundancy,mRMR)结合贝叶斯优化算法(Bayesian optimization,BO)优化Stacking集成模型的SCR烟气脱硝系统入口NO_(x)质量浓度动态软测量模型。针对动态NO_(x)生成过程中静态单一模型预测精度降低及辅助变量与入口NO_(x)质量浓度时间异步的问题,利用mRMR-BO结合模型进行辅助变量筛选,Copula熵(copula entropy,CE)确定辅助变量迟延,BO结合模型确定辅助变量阶次,将TCN及LASSO利用Stacking法集成,使用含有迟延时间及阶次信息的辅助变量构建动态Stacking集成软测量模型。仿真结果显示:集成模型较TCN及LASSO单一网络的均方根误差、平均绝对误差、平均绝对百分比误差最小;动态集成模型对比静态集成模型,预测精度更高,能够实现对入口NO_(x)质量浓度的准确软测量。Aiming at the problem that it is difficult to accurately and timely measure the inlet NO_(x)concentration in the denitrification system of selective catalytic reduction(SCR)in thermal power plants,due to the excessive factors affecting the inlet NO_(x)concentration and the large delay and inertia of the system,the Max-Relevance and Min�Redundancy(mRMR)combined with Bayesian optimization(BO)algorithm is proposed,optimize the dynamic soft measurement model of NO_(x)concentration at the inlet of the SCR denitration system of the stacking ensemble model.Aiming at the problem of reduced prediction accuracy of static single model and asynchronous timing of auxiliary variables and inlet NO_(x)concentration in the process of dynamic NO_(x)generation,the mRMR-BO combined with model was used to screen the auxiliary variables,Copula Entropy(CE)determined the delay of auxiliary variables,the BO combined with model determined the order of auxiliary variables,and TCN and LASSO were integrated by Stacking method.The auxiliary variables containing delay time and order information were used to construct a dynamic stacking ensemble soft measurement model,and the simulation results showed that the root mean square error,average absolute error,and average absolute percentage error of the integrated model compared with TCN and LASSO single networks were the smallest.Compared with the static ensemble model,the dynamic ensemble model has higher prediction accuracy and can achieve accurate soft measurement of the inlet NO_(x)concentration.

关 键 词：NO_(x)动态建模 最大相关-最小冗余 贝叶斯优化 Stacking集成模型 

分 类 号：X773[环境科学与工程—环境工程]