PCA-DRBFN模型在精馏塔精苯干点估计中的应用

针对PCA(PrincipalComponentsAnalysis)技术中,由于重叠信息会严重影响主成分的正确提取这一问题,提出了一种改进的数据降维处理方法·首先,利用标准化变量间的相关系数大小找到重叠信息·然后,将重叠信息进行加权综合·最后,利用改进的数据降维处理方法以及分布式网络技术,建立了基于PCA DRBFN(PrincipalComponentsAnalysis DistributedRadialBasisFunctionNetwork)的软测量模型,并将其应用到某钢厂的精苯精馏过程,对精苯干点进行估计·通过仿真证明,所建立的模型具有较好的泛化效果·

PCA-DRBFN Model in Application to Estimating Dry Point of Pure Benzene in Rectifying Tower

An improved data dimension decreasing method is proposed to reduce the overlapped information in multivariable system,since a number of overlapped information will affect greatly the correct pick-up of PCs(Principal Components) in PCA(Principal Components Analysis). The overlapped information can be found using the correlation coefficients between standardized variables, then they are weighted and integrated altogether. An PCA-DRBFN(Principal Components Analysis-Distributed Radial Basis Function Network) based soft-sensing model is thus developed using the improved method. The model has been applied to the pure benzene rectifying process in a steel plant to estimate the dry point of pure benzene. Simulation results showed that the proposed method and developed model are both favorably versatile.