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A Soft Sensor Based on Orthogonal Nonlinear Principal Component Analysis
Author(s) -
Zhao Yuhong
Publication year - 2005
Publication title -
developments in chemical engineering and mineral processing
Language(s) - English
Resource type - Journals
eISSN - 1932-2143
pISSN - 0969-1855
DOI - 10.1002/apj.5500130304
Subject(s) - principal component analysis , nonlinear system , soft sensor , representation (politics) , artificial neural network , component (thermodynamics) , computer science , pattern recognition (psychology) , control theory (sociology) , artificial intelligence , biological system , process (computing) , control (management) , physics , quantum mechanics , politics , political science , law , thermodynamics , operating system , biology
An accurate on‐line measurement of quality variables is essential for the successful monitoring and control tasks in chemical process operations. A soft sensor is developed based on orthogonal nonlinear principal component analysis, due to its ability to capture the linear and nonlinear features of the data. An orthogonal nonlinear principal component analysis network is utilized to retain a compact representation of the data optimally. Then the linear relationship between the scores and estimated variable is gained by robust linear regression based on M‐estimation. The proposed method is applied on an industrial crude oil atmospheric distillation tower, and illustrated by comparison with other familiar methods. The results have shown that the proposed method gives a better performance over the conventional PCA method and neural networks method.