DocumentCode :
1277862
Title :
The application of neural networks to the papermaking industry
Author :
Edwards, Peter J. ; Murray, Alan F. ; Papadopoulos, Georgios ; Wallace, A. Robin ; Barnard, John ; Smith, Gordon
Author_Institution :
Dept. of Electron. & Electr. Eng., Edinburgh Univ., UK
Volume :
10
Issue :
6
fYear :
1999
fDate :
11/1/1999 12:00:00 AM
Firstpage :
1456
Lastpage :
1464
Abstract :
This paper describes the application of neural network techniques to the papermaking industry, particularly for the prediction of paper “curl”. Paper curl is an important quality measure that can only be measured reliably off-line after manufacture, making it difficult to control. Here, we predict, before paper manufacture from characteristics of the current reel, whether the paper curl will be acceptable and the level of curl. For both issues the case of predicting the probability that paper will be “out-of-specification” and that of predicting the level of curl, we include confidence intervals indicating to the machine operator whether the predictions should be trusted. The results and the associated discussion describe a successful application of neural networks to a difficult, but important, real-world task taken from the papermaking industry. In addition the techniques described are widely applicable to industry where direct prediction of a quality measure and its acceptability are desirable
Keywords :
Bayes methods; estimation theory; multilayer perceptrons; paper industry; probability; process control; quality control; Bayesian inference; collinearity reduction; confidence measures; learning; multilayer perceptron; neural networks; paper curl; paper industry; probability; quality control; symbolic data; Bayesian methods; Humidity; Industrial control; Manufacturing industries; Multilayer perceptrons; Neural networks; Particle measurements; Printers; Pulp and paper industry; Pulp manufacturing;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.809090
Filename :
809090
Link To Document :
بازگشت