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.
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Nieminen, P., Kärkkäinen, T., Luostarinen, K., Muhonen, J. (2011). Neural Prediction of Product Quality Based on Pilot Paper Machine Process Measurements. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_25
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DOI: https://doi.org/10.1007/978-3-642-20282-7_25
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