Mixture semisupervised principal component regression model and soft sensor application

Zhiqiang Ge; Biao Huang

doi:10.1002/aic.14270

journal article Nov 09, 2013

Mixture semisupervised principal component regression model and soft sensor application

Zhiqiang Ge

Biao Huang

AIChE Journal Vol. 60 No. 2 pp. 533-545 · Wiley

View at Publisher Save 10.1002/aic.14270

Abstract

Traditionally, data‐based soft sensors are constructed upon the labeled historical dataset which contains equal numbers of input and output data samples. While it is easy to obtain input variables such as temperature, pressure, and flow rate in the chemical process, the output variables, which correspond to quality/key property variables, are much more difficult to obtain. Therefore, we may only have a small number of output data samples, and have much more input data samples. In this article, a mixture form of the semisupervised probabilistic principal component regression model is proposed for soft sensor application, which can efficiently incorporate the unlabeled data information from different operation modes. Compared to the total supervised method, both modeling efficiency and soft sensing performance are improved with the inclusion of additional unlabeled data samples. Two case studies are provided to evaluate the feasibility and efficiency of the new method. © 2013 American Institute of Chemical Engineers AIChE J 60: 533–545, 2014

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Metrics

96

Citations

33

References

Details

Published: Nov 09, 2013
Vol/Issue: 60(2)
Pages: 533-545
License: View

Authors

Z

Zhiqiang Ge

State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control, Dept. of Control Science and Engineering, Zhejiang University Hangzhou 310027 China; Dept. of Chemical and Materials Engineering University of Alberta Edmonton AB T6G2G6 Canada

B

Biao Huang

Dept. of Chemical and Materials Engineering University of Alberta Edmonton AB T6G2G6 Canada

Funding

Fundamental Research Funds for the Central Universities Award: 2013QNA5016

National Natural Science Foundation of China (NSFC) Award: 61370029

National Science Engineering Research Council of Canada (NSERC)

National Project 973 Award: 2012CB720500

Cite This Article

Zhiqiang Ge, Biao Huang (2013). Mixture semisupervised principal component regression model and soft sensor application. AIChE Journal, 60(2), 533-545. https://doi.org/10.1002/aic.14270

Mixture semisupervised principal component regression model and soft sensor application

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