Paper curl prediction - neural networks applied to the papermaking industry

摘要

This paper describes the application of neural network techniques to the papermaking industry, particularly for the prediction of paper "curl". Paper curl is a common problem and can only be measured reliably off-line, after manufacture.Conventional approaches to control this aspect of paper quality have thus proven difficult. Here neural network model development is carried out using imperfect data, typical of that collected in many manufacturing environments, and addresses issuespertinent to real-world use. Predictions then are presented in terms that are relevant to the machine operator, as a measure of paper acceptability, a direct prediction of the quality measure, and always with a measure of prediction confidence. Therefore, the techniques described in this paper are widely applicable to industry.