Neural Networks in Web Break Diagnosis of a Newsprint Paper Machine, 1997 Papermakers Conference Proceedings

Artificial neural networks hold great promise for solving problems that have been extremely difficult for standard methods. We applied artificial neural networks to the diagnosis of paper web breaks in a commercial newsprint paper machine. On-line wet end sensors such as zeta potential, first pass retention, conductivity and pH were installed to the paper machine. Process data of pulping and papermaking were collected from the DCS. A three-stage multilayer neural network and back propagation method were proposed to extract essential causes of paper web breaks. Great savings of production cost were achieved: the number of web breaks was reduced, fiber loss in the effluent was decreased, and a stable paper machine operation alleviated burden on the paper machine crew.

Author: Miyanishi, T., Shimada, H.
Neural Networks in Web Break Diagnosis of a Newsprint Paper
Neural Networks in Web Break Diagnosis of a Newsprint Paper Machine, 1997 Papermakers Conference Proceedings
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