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Comparison between linear and nonlinear prediction models for monitoring of a paperboard machine
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Biotechnology .
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Biotechnology .
2002 (English)In: Chemical Engineering & Technology, ISSN 0930-7516, Vol. 25, no 2, 197-202 p.Article in journal (Refereed) Published
Abstract [en]

Data from a paperboard machine were used to compare the performance of linear partial least squares (PLS) and nonlinear feed-forward neural network (FFNN) modeling of a continuous process. Fifteen selected variables were used as input parameters to the models, while the quality class of the manufactured product was the output response. The models were validated with external data different to those used in the design of the models. Evaluation with root mean square error of prediction (RMSEP) showed that the FFNN models were better for prediction than the PLS models. For monitoring, however, the PLS models detected deviations from normal settings in the paperboard machine more sensitively than the FFNN models. It is suggested that these findings have general relevance to other continuous processes in manufacturing industries too.

Place, publisher, year, edition, pages
2002. Vol. 25, no 2, 197-202 p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-44241DOI: 10.1002/1521-4125(200202)25:2<197::AID-CEAT197>3.0.CO;2-PLocal ID: 76104OAI: oai:DiVA.org:liu-44241DiVA: diva2:265103
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2011-01-13

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Brundin, AndersMandenius, Carl-Fredrik

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