Skip to main content
SpringerLink
Log in

Product quality prediction by a neural soft-sensor based on MSA and PCA

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

A novel soft-sensor model which incorporates PCA (principal component analysis), RBF (Radial Basis Function) networks, and MSA (Multi-scale analysis), is proposed to infer the properties of manufactured products from real process variables. PCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the melt index (MI), or quality of polypropylene produced in an actual industrial process, is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. G. Barto, R. S. Sutton, C. H. Anderson. Neuron-like adaptive elements that can solve difficult learning control problem. IEEE Transactions on Systems, Man, and Cybernetics, vol. 13, no. 5, 834–847, 1983.

    Google Scholar 

  2. J. G. Taylor. Towards the networks of the brain: from imaging to consciousness. Neural Networks, vol. 12, no. 7–8, pp. 943–959, 1999.

    Article  Google Scholar 

  3. M. Basseville, A. Benveniste, K. Chou, S. Golden, R. Nikoukhah, A. Willsky. Modeling and estimation of multiresolution stochastic process. IEEE Transactions on Information Theory, vol. 38, no. 2, 766–784, 1992.

    Article  Google Scholar 

  4. J. H. Li, J. Y. Zhang, W. Ge, X. H. Liu. Multi-scale methodology for complex systems. Chemical Engineering Science, vol. 59, no. 8–9, 1687–1700, 2004.

    Article  Google Scholar 

  5. M. K. Gobbert, T. P. Merchant, L. J. Borucki, T. S. Cale. A multiscale simulator for low pressure chemical vapor deposition. Journal of Electrochemical Society, vol. 144, no. 11, 3945–3951, 1997.

    Article  Google Scholar 

  6. K. J. D. Sbarbaro. Hunt, R. Zbikowski, P. J. Gawthrop. Neural networks for control systems-a survey. Automatica, vol. 28, no. 6, 1083–1112, 1992.

    Article  MathSciNet  Google Scholar 

  7. U. Levin, K. S. Narendra. Control of nonlinear dynamical systems using neural networks-part II: observability, identification and control. IEEE Transactions on Neural Networks, vol. 7, no. 1, 30–42, 1996.

    Article  Google Scholar 

  8. J. Li, M. Kwauk. Exploring complex systems in chemical engineering — the multi-scale methodology. Chemical Engineering Science, vol. 58, no. 3–6, 521–535, 2003.

    Article  Google Scholar 

  9. R. Rallo, J. Ferre-Gine, A. Arenas, F. Giralt. Neural virtual sensor for the inferential prediction of product quality from process variables. Computers and Chemical Engineering, vol. 26, no. 12, pp. 1735–1754, 2002.

    Article  Google Scholar 

  10. J. Park, I. W. Sandberg. Universal approximation using radial basis function networks. Neural Computation, vol. 3, no. 2, pp. 246–257, 1990.

    Google Scholar 

  11. S. Mallat. A wavelet tour of signal processing. Academic Press, San Diego, 1997.

    Google Scholar 

  12. W. M. Chan, P. E. Gloor, A. E. Hamielec. Kinetic model for olefin polymerization in high-pressure autoclave reactors. AIChE Journal, vol. 39, no. 1, 111–126, 1993.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Laboratory of Industrial Control Technology, Institute of Systems Engineering, Zhejiang University, Hangzhou, 310027, PRC

    Jian Shi & Xing-Gao Liu

Authors
  1. Jian Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing-Gao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xing-Gao Liu.

Additional information

This work is supported by the National Natural Science Foundation of China (No.20106008), the National Development and Reform Commission of China (No. Fagai Gaoji-2004-2080), and the science fund for distinguished young scholars of Zhejiang University (No.111000-581645).

Jian Shi received his BSc degree in Mechanical Engineering from Zhejiang University, China, in 2003. He is currently pursuing his PhD degree in the Institute of Systems Engineering of Zhejiang University. His research interests include olefin polymerization modeling, multivariate statistical process control, and neural networks.

Xing-Gao Liu received his PhD degree in Control Science and Engineering, from Zhejiang University in 2000. He was a Post Doctoral Fellow in the Automation Department, at Tsinghua University from 2000 to 2002. Currently, he is an Associate Professor in Process Systems Engineering, at the Department of Control Science and Engineering, Zhejiang University. His research interests include process modeling, control and optimization, fault detection and diagnosis, neural networks in olefin polymerization, and air separation and distillation processes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, J., Liu, XG. Product quality prediction by a neural soft-sensor based on MSA and PCA. Int J Automat Comput 3, 17–22 (2006). https://doi.org/10.1007/s11633-006-0017-9

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-006-0017-9

Keywords

Search

Navigation