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Proceedings Paper

Using OSL-SVM for the concentration prediction of 4-CBA
Author(s): Yugang Fan; Hua Wang; Jiande Wu
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Paper Abstract

To track the dynamics of nonlinear time-varying systems, a new adaptive model based nonlinear function estimation is proposed for online monitoring of nonlinear processes. After Least Squares Support Vector Machine (LS-SVM) was trained offline, the model is regulated online by the Kalman filter. The online regulated LS-SVM(OLS-SVM) is suitable for real time system recognition and time series prediction. Time series prediction can be a very useful tool in the field of process chemo metrics to forecast and to study the behavior of key process parameters in time. This creates the possibility to give early warnings of possible process malfunctioning. In this paper, OLS-SVM is applied to predict the concentration of 4-Carboxybenzaldchydc (4-CBA) in purified terephthalic acid (PTA) oxidation process. Results indicate that the proposed method is effective and its accuracy is very high.

Paper Details

Date Published: 1 October 2011
PDF: 6 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828551 (1 October 2011); doi: 10.1117/12.913370
Show Author Affiliations
Yugang Fan, Kunming Univ. of Science and Technology (China)
Hua Wang, Kunming Univ. of Science and Technology (China)
Ministry of Education (China)
Jiande Wu, Kunming Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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