USING NEURAL NETWORKS TO DIAGNOSE WEB BREAKS ON A NEWSPRINT PAPER-MACHINE

Artificial neural networks hold great promise for solving problems tha t are extremely difficult to solve using conventional methods. We used an artificial neural network to diagnose paper web breaks on a commer cial newsprint paper machine. Process data for pulping and papermaking operations were collected from the paper machine's distributed contro l system. Additional data were obtained from on-line wet-end sensors ( zeta potential, first-pass retention, conductivity, and pH) that were installed for this study. The essential variables contributing to pape r web breaks were extracted using a three-stage multi-layer neural net work and back-propagation method. Great savings of production costs we re achieved: the number of web breaks was reduced, fiber loss in the e ffluent was decreased, and workers spent less time cleaning, rethreadi ng, and restarting the paper machine.