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

机译：使用在线特征空间分解的KPCR对脱芳香化装置进行软分析器建模

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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.

机译：本文探讨了核方法在石油化工中的应用。利用核主成分回归（KPCR）方法，开发了一种用于脱香过程闪点测量的非线性软分析仪。为了追踪时变动力学和拒绝干扰，提出了一种新颖的在线特征空间分解算法来更新内核矩阵算法，该算法比直接分解要快得多，同时具有稳定的数值性能。仿真结果表明，所开发的软分析仪在标称和故障操作条件下均具有令人满意的预测精度。

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来源
《Neural Information Processing pt.1; Lecture Notes in Computer Science; 4232》|2006年|495-504|共10页
会议地点 Hong Kong(CN)
作者
Haiqing Wang; Daoying Pi; Ning Jiang; Steven X. Ding;
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作者单位

National Lab of Industrial Control Technology, Zhejiang University Hangzhou, 310027, P. R. China;

University of Duisburg-Essen, Inst. Auto. Cont. and Comp. Sys. Bismarckstr. 81, BB-510, 47057 Duisburg, Germany;

Institute of Process Equipment and Control E;

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正文语种 eng
中图分类信息处理（信息加工）;
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Soft Analyzer Modeling for Dearomatization Unit Using KPCR with Online Eigenspace Decomposition

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