工业流化床反应器结块监视的动态PCA方法  

DYNAMIC PRINCIPAL COMPONENT ANALYSIS METHOD FOR CHUNK MONITORING OF INDUSTRIAL FLUIDIZED-BED REACTOR

作　　者：刘育明[1] 梁军[1] 钱积新[1] 

机构地区：[1]浙江大学工业控制技术国家重点实验室,系统工程研究所,浙江杭州310027

出　　处：《化工学报》2004年第9期1546-1549,共4页CIESC Journal

基　　金：国家高技术研究发展计划资助 (No 863 5 11 92 0 0 11;No 2 0 0 1AA4112 3 0 )~~

摘　　要：Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis(PCA) for dealing with multivariate dynamic data serially correlated in time.Based on the fact that the measured variables in relation to chunk monitoring of the industrial fluidized-bed reactor are highly cross-correlated and auto-correlated, this paper presents a practical strategy for chunk monitoring by adopting DPCA in order to overcome the shortcomings of the conventional method.After introducing the basic principle of DPCA, both how to determine the time lagged length of data matrix and how to calculate the nonparametric control limits when the dynamic data are not subject to the assumption of independently identically distribution(IID) were discussed.An appropriate DPCA model based on the real data from a industrial fluidized-bed reactor was built, with parallel analysis and empirical reference distribution(ERD)method to select time lagged length and control limits, respectively.During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree.The simulation test results showed the effectiveness of the DPCA based method.Dynamic principal component analysis (DPCA) is an extension of conventional principal component analysis (PCA) for dealing with multivariate dynamic data serially correlated in time. Based on the fact that the measured variables in relation to chunk monitoring of the industrial fluidized-bed reactor are highly cross-correlated and auto-correlated, this paper presented a practical strategy for chunk monitoring by adopting DPCA in order to overcome the shortcomings of the conventional method. After introducing the basic principle of DPCA, both how to determine the time lagged length of data matrix and how to calculate the nonparametric control limits when the dynamic data are not subject to the assumption of independently identically distribution (IID) were discussed. An appropriate DPCA model based on the real data from a industrial fluidized-bed reactor was built, with parallel analysis and empirical reference distribution (ERD) method to select time lagged length and control limits, respectively. During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree. The simulation test results showed the effectiveness of the DPCA based method.

关 键 词：流化床反应器 结块监视 动态主元分析 控制限 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] O212.4[自动化与计算机技术—控制科学与工程]