Product Quality Prediction with Support Vector Machines

  • Xinggao Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Reliable prediction of melt index (MI) is crucial in practical propylene polymerization processes. In this paper, a least squares support vector machines (LS-SVM) soft-sensor model is developed first to infer the MI of polypropylene from other process variables. A weighted least squares support vector machines (weighted LS-SVM) approach is further proposed to obtain rather robust estimate. Detailed comparative researches are carried out among standard SVM, LS-SVM, and weighted LS-SVM. The research results confirm the effectiveness of the presented methods.


Support Vector Machine Root Mean Square Error Little Square Support Vector Machine Mean Relative Error Melt Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural Networks for Control Systems — a Survey. Automatica 28(6), 1083–1112 (1992)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Rallo, R., Ferre-Giné, J., Arenas, A., Giralt, F.: Neural Virtual Sensor for the Inferential Prediction of Product Quality from Process Variables. Comp. Chem. Eng. 26(12), 1735–1754 (2002)CrossRefGoogle Scholar
  3. 3.
    Han, I.-S., Han, C., Chung, C.-B.: Melt Index Modeling with Support Vector Machines, Partial Least Squares, and Artificial Neural Networks. J. Appl. Polym. Sci. 95(4), 967–974 (2005)CrossRefGoogle Scholar
  4. 4.
    Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Proc. Letters 9(3), 293–300 (1999)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Suykens, J.A.K., Vandewalle, J.: Weighted Least Squares Support Vector Machines: Robustness and Sparse Approximation. Neurocomputing 48(1), 85–105 (2002)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Smith, D.J.M.: Methods for External Validation of Continuous System Simulation Models: a Review. Math. Comput. Model Dyn. Syst. 4(1), 5–31 (1998)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xinggao Liu
    • 1
  1. 1.National Laboratory of Industrial Control Technology, Department of Control Science and TechnologyZhejiang UniversityHangzhouP.R. China

Personalised recommendations