By Topic

Neural network model for paper-forming process

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Scharcanski, J. ; Dept. of Chem. Eng. & Appl. Chem., Toronto Univ., Ont., Canada ; Dodson, C.T.J.

Paper is made by a continuous high-speed filtration drainage of an aqueous suspension of fibers. This paper presents a new approach to the controllable simulation of paper forming, using artificial neural network methods. The model incorporates dynamics of the forming process, like turbulence, drainage speed, and preferential drainage through earlier less-dense regions and fiber properties, like propensity to clump, or “flocculate,” fiber flexibility, and concentration of fibers in the suspension. Results for monofiber layer structures are described, showing effects of turbulence and its decay during drainage in causing clumping, or “flocculation.” The commercial process has, as one of its main goals, the reduction to tolerable levels of the nonuniformity in mass distribution resulting from flocculation. The new model yields data corresponding to that obtainable along arbitrary scanning lines in planar stochastic fibrous structures, providing profiles, variances, histograms of local areal density, and histograms of local free-fiber lengths. These results closely resemble experimental data from commercial paper samples obtained from radiographic or optical transmission images subjected to image analysis

Published in:

Industry Applications, IEEE Transactions on  (Volume:33 ,  Issue: 3 )