基于自适应多尺度核偏最小二乘的SCR烟气脱硝系统建模  

SCR Denitration System Modeling Based on Self-adaptive Multi-scale Kernel Partial Least Squares

作　　者：刘吉臻 秦天牧 杨婷婷 吕游 

机构地区：[1]新能源电力系统国家重点实验室(华北电力大学),北京市昌平区102206

出　　处：《中国电机工程学报》2015年第23期6083-6088,共6页Proceedings of the CSEE

基　　金：国家重点基础研究发展计划项目(973计划)(2012CB215203);北京高等学校青年英才计划项目(YEPT0705)~~

摘　　要：针对选择性催化还原(selective catalytic reduction,SCR)脱硝系统中输入变量的多尺度特性及时变特性问题,将核偏最小二乘与多核学习相结合,同时引入自适应模型更新策略,提出了自适应多尺度核偏最小二乘(self-adaptive multi-scale KPLS,SMKPLS)方法。通过优化算法确定每个自变量对应的核函数宽度,然后利用多尺度核偏最小二乘方法建立非线性模型,采用自适应模型更新方法对模型进行更新。将该方法应用于SCR脱硝系统建模,并与其他建模方法进行对比,结果表明,SMKPLS预测精度明显高于其他模型,计算时间远小于其他模型,具有更好的泛化能力及鲁棒性。Based on the multi-scale characteristics and time-varying characteristics of selective catalytic reduction system, combining kernel partial least squares and multiple kernel learning, introducing the self-adaptive model updating method at the same time, self-adaptive multi-scale kernel partial least squares regression（SMKPLS） was proposed. Optimization algorithm was used to determine the kernel function width of each variable. Then multi-scale kernel partial least squares method was used to establish the nonlinear model. The model was updated with adaptive model updating method. By applying the method to SCR system modeling and comparing with other modeling methods, results show that the prediction accuracy of SMKPLS is significantly higher, calculation time of SMKPLS is far less than that of others, generalization ability and robustness of SMKPLS are both better.

关 键 词：选择性催化还原(SCR)脱硝 偏最小二乘 多尺度核 自适应 数据建模 

分 类 号：TK39[动力工程及工程热物理—热能工程]