Soft Analyzer Modeling for Dearomatization Unit Using KPCR with Online Eigenspace Decomposition

  • Haiqing Wang
  • Daoying Pi
  • Ning Jiang
  • Steven X. Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


The application of kernel method to petrochemical industry is explored in this paper. A nonlinear soft analyzer for the flashpoint measurement of Dearomatization process is developed by using kernel principal component regression (KPCR) method. To trace the time varying dynamics and reject disturbances, a novel online eigenspace decomposing algorithm is proposed to update that of the Kernel Matrix, which is much faster than direct decomposition and meanwhile has stable numerical performance. Simulation results indicate the developed soft analyzer has satisfying prediction precision under both nominal and faulty operating conditions.


Singular Value Decomposition Model Predictive Control Kernel Method Kernel Matrix Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Haiqing Wang
    • 1
    • 2
  • Daoying Pi
    • 1
  • Ning Jiang
    • 3
  • Steven X. Ding
    • 2
  1. 1.National Lab of Industrial Control TechnologyZhejiang UniversityHangzhouP. R. China
  2. 2.Inst. Auto. Cont. and Comp. Sys.University of Duisburg-EssenDuisburgGermany
  3. 3.Institute of Process Equipment and Control EngineeringZhejiang university of Technology, HangzhouZhejiangChina

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