Neural prediction of product quality based on pilot paper machine process measurements

Authors:
Paavo Nieminen;Tommi Kärkkäinen;Kari Luostarinen;Jukka Muhonen
Affiliations:
Department of Mathematical Information Technology, University of Jyväskylä, Finland;Department of Mathematical Information Technology, University of Jyväskylä, Finland;Metso Paper, Jyväskylä, Finland;Metso Paper, Jyväskylä, Finland
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

Citing 5
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Abstract

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.