Soft Analyzer Modeling for Dearomatization Unit Using KPCR with Online Eigenspace Decomposition
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.
KeywordsSingular Value Decomposition Model Predictive Control Kernel Method Kernel Matrix Kernel Principal Component Analysis
Unable to display preview. Download preview PDF.
- 3.Wang, H.Q., Song, Z.H., Li, P., Ding, S.X.: AKL Networks for Industrial Analyzer Modeling and Fault Detection. In: The 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (safeprocess) (2006) (to appear)Google Scholar
- 4.Liikala, T.: The Use of Fault Detection in Model Predictive Control of a Refinery Process Unit. Master of Science (Tech.) Thesis, Helsinki University of Technology (2005)Google Scholar
- 6.Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2002)Google Scholar
- 7.Taylor, J.S., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, UK (2004)Google Scholar
- 8.Hoegaerts, L., De Lathauwer, L., Goethals, I., Suykens, J.A.K., Vandewalle, J., De Moor, B.: Efficiently Updating and Tracking the Dominant Kernel Principal Components, Internal Report 05-01, ESAT-SISTA, K.U. Leuven, Belgium (2005)Google Scholar
- 13.Rosipal, R., Trejo, L.J., Cichocki, A.: Kernel Principal Component Regression with EM Approach to Nonlinear Principal Components Extraction. Technical report, CIS, University of Paisley (2000)Google Scholar