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Article
Integrated soft sensor using just-in-time support vector regressionand probabilistic analysis for quality prediction of multi-grade processes
Journal of Process Control (2013)
  • Yi Liu, Ph.D.
Abstract

Multi-grade processes have played an important role in the fine chemical and polymer industries. Anintegrated nonlinear soft sensor modeling method is proposed for online quality prediction of multi-gradeprocesses. Several single least squares support vector regression (LSSVR) models are first built for eachproduct grade. For online prediction of a new sample, a probabilistic analysis approach using the statisticalproperty of steady-state grades is presented. The prediction can then be obtained using the correspondingLSSVR model if its probability of the special steady-state grade is large enough. Otherwise, the querysample is considered located in the transitional mode because it is not similar to any steady-state grade.In this situation, a just-in-time LSSVR (JLSSVR) model is constructed using the most similar samplesaround it. To improve the efficiency of searching for similar samples of JLSSVR, a strategy combinedwith the characteristics of multi-grade processes is proposed. Additionally, the similarity factor andsimilar samples of JLSSVR can be determined adaptively using a fast cross-validation strategy with lowcomputational load. The superiority of the proposed soft sensor is first demonstrated through a simulationexample. It is also compared with other soft sensors in terms of online prediction of melt index in anindustrial plant in Taiwan.

Publication Date
Summer July 1, 2013
Citation Information
Yi Liu. "Integrated soft sensor using just-in-time support vector regressionand probabilistic analysis for quality prediction of multi-grade processes" Journal of Process Control Vol. 23 Iss. 6 (2013)
Available at: http://works.bepress.com/inter_liu/10/