Wavelet neural networks applied to pulping of oil palm fronds

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was...

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Main Authors: Zainuddin, Zarita, Wan Daud, Wan Rosli, Pauline, Ong, Shafie, Amran
Format: Article
Language:English
Published: Elsevier 2011
Subjects:
Online Access:http://eprints.uthm.edu.my/4224/1/AJ%202017%20%28583%29.pdf
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author Zainuddin, Zarita
Wan Daud, Wan Rosli
Pauline, Ong
Shafie, Amran
author_facet Zainuddin, Zarita
Wan Daud, Wan Rosli
Pauline, Ong
Shafie, Amran
author_sort Zainuddin, Zarita
collection UTHM
description In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.
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spelling uthm.eprints-42242021-12-01T06:31:24Z http://eprints.uthm.edu.my/4224/ Wavelet neural networks applied to pulping of oil palm fronds Zainuddin, Zarita Wan Daud, Wan Rosli Pauline, Ong Shafie, Amran TS1080-1268 Paper manufacture and trade In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained. Elsevier 2011 Article PeerReviewed text en http://eprints.uthm.edu.my/4224/1/AJ%202017%20%28583%29.pdf Zainuddin, Zarita and Wan Daud, Wan Rosli and Pauline, Ong and Shafie, Amran (2011) Wavelet neural networks applied to pulping of oil palm fronds. Bioresource Technology, 102 (23). pp. 10978-10986. ISSN 0960-8524 https://doi.org/10.1016/j.biortech.2011.09.080
spellingShingle TS1080-1268 Paper manufacture and trade
Zainuddin, Zarita
Wan Daud, Wan Rosli
Pauline, Ong
Shafie, Amran
Wavelet neural networks applied to pulping of oil palm fronds
title Wavelet neural networks applied to pulping of oil palm fronds
title_full Wavelet neural networks applied to pulping of oil palm fronds
title_fullStr Wavelet neural networks applied to pulping of oil palm fronds
title_full_unstemmed Wavelet neural networks applied to pulping of oil palm fronds
title_short Wavelet neural networks applied to pulping of oil palm fronds
title_sort wavelet neural networks applied to pulping of oil palm fronds
topic TS1080-1268 Paper manufacture and trade
url http://eprints.uthm.edu.my/4224/1/AJ%202017%20%28583%29.pdf
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AT paulineong waveletneuralnetworksappliedtopulpingofoilpalmfronds
AT shafieamran waveletneuralnetworksappliedtopulpingofoilpalmfronds