Neural Prediction of Product Quality Based on Pilot Paper Machine Process Measurements

  • Paavo Nieminen
  • Tommi Kärkkäinen
  • Kari Luostarinen
  • Jukka Muhonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


We describe a multilayer perceptron model to predict the laboratory measurements of paper quality using the instantaneous state of the papermaking production process. Actual industrial data from a pilot paper machine was used. The final model met its goal accuracy 95.7% of the time at best (tensile index quality) and 66.7% at worst (beta formation). We anticipate usage possibilities in lowering machine prototyping expenses, and possibly in quality control at production sites.


MLP Prediction Paper quality Pilot paper machine 


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  1. 1.
    Leiviskä, K. (ed.): Papermaking Science and Technology, 2nd edn. Process and Maintenance Management, vol. 14. Paperi ja Puu Oy (2009)Google Scholar
  2. 2.
    Ribeiro, B., Dourado, A., Costa, E.: Industrial kiln multivariable control: MNN and RBFNN approaches. In: ICANNGA 1995, pp. 408–411. Springer, Heidelberg (1995)Google Scholar
  3. 3.
    Ribeiro, B.: Prediction of the lime availability on an industrial kiln by neural networks. In: IJCNN 1998, vol. 3, pp. 1987–1991. IEEE, Los Alamitos (1998)Google Scholar
  4. 4.
    Rajesh, K., Ray, A.K.: Artificial neural network for solving paper industry problems: A review. Journal of Scientific & Industrial Research 65, 565–573 (2006)Google Scholar
  5. 5.
    Edwards, P., Murray, A., Papadopoulos, G., Wallace, A., Barnard, J., Smith, G.: The application of neural networks to the papermaking industry. IEEE Transactions on Neural Networks 10(6), 1456–1464 (1999)CrossRefGoogle Scholar
  6. 6.
    Wang, F., Sanguansintukul, S., Lursinsap, C.: Curl forecasting for paper quality in papermaking industry. In: Asia Simulation Conference – 7th International Conference on System Simulation and Scientific Computing, pp. 1079–1084 (2008)Google Scholar
  7. 7.
    Lampinen, J., Taipale, O.: Optimization and simulation of quality properties in paper machine with neural networks. In: ICNN 1994, pp. 3812–3815. IEEE, Los Alamitos (1994)Google Scholar
  8. 8.
    Kärkkäinen, T.: MLP in layer-wise form with applications to weight decay. Neural Computation 14(6), 1451–1480 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kärkkäinen, T., Heikkola, E.: Robust formulations for training multilayer perceptrons. Neural Computation 16(4), 837–862 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Nieminen, P., Kärkkäinen, T.: Ideas about a regularized MLP classifier by means of weight decay stepping. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 32–41. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Nieminen, P., Kärkkäinen, T.: Comparison of MLP cost functions to dodge mislabeled training data. In: IJCNN 2010. IEEE, Los Alamitos (2010)Google Scholar
  12. 12.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)zbMATHGoogle Scholar
  13. 13.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, New Jersey (1999)zbMATHGoogle Scholar
  14. 14.
    Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)CrossRefGoogle Scholar
  15. 15.
    Kobyashi, M., Zamani, A., Ozawa, S., Abe, S.: Reducing computations in incremental learning for feedforward neural network with long-term memory. In: IJCNN 2001, pp. 1989–1994. IEEE, Los Alamitos (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paavo Nieminen
    • 1
  • Tommi Kärkkäinen
    • 1
  • Kari Luostarinen
    • 2
  • Jukka Muhonen
    • 2
  1. 1.Department of Mathematical Information TechnologyUniversity of JyväskyläFinland
  2. 2.Metso PaperJyväskyläFinland

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