中国电机工程学报Issue(23)：6083-6088,6.DOI:10.13334/j.0258-8013.pcsee.2015.23.015
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
摘要
Abstract
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Key words
selective catalytic reduction (SCR) denitration/partial least squares (PLS)/multi-scale kernel/self-adaptive/data modeling分类
能源科技引用本文复制引用
刘吉臻,秦天牧,杨婷婷,吕游..基于自适应多尺度核偏最小二乘的 SCR烟气脱硝系统建模[J].中国电机工程学报,2015,(23):6083-6088,6.基金项目
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)。 The National Basic Research Program (973 Program)(2012CB215203) （973计划）
Beijing Higher Education Young Elite Teacher Project(YEPT0705) （YEPT0705）
