Abstract
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.
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Wang, H., Pi, D., Jiang, N., Ding, S.X. (2006). Soft Analyzer Modeling for Dearomatization Unit Using KPCR with Online Eigenspace Decomposition. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_55
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DOI: https://doi.org/10.1007/11893028_55
Publisher Name: Springer, Berlin, Heidelberg
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