Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp

The purpose of this study was to develop artificial neural network (ANN) models for predicting the effects of wood species, sodium perborate tetrahydrate (SPBTH) ratio, time, and beating degree on tensile index and brightness in bleaching pulp. Unbleached kraft-AQ bamboo and poplar pulps were expose...

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Detalles Bibliográficos
Autores principales:	Okan,Onur Tolga, Deniz,Ilhan, Tiryaki,Sebahattin
Lenguaje:	English
Publicado:	Universidad del Bío-Bío 2015
Materias:	Bleaching process neural network modeling Phyllostachys bambusoides Populus euramericana pulping properties
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2015000300011
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spelling	oai:scielo:S0718-221X20150003000112015-12-04Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulpOkan,Onur TolgaDeniz,IlhanTiryaki,Sebahattin Bleaching process neural network modeling Phyllostachys bambusoides Populus euramericana pulping properties The purpose of this study was to develop artificial neural network (ANN) models for predicting the effects of wood species, sodium perborate tetrahydrate (SPBTH) ratio, time, and beating degree on tensile index and brightness in bleaching pulp. Unbleached kraft-AQ bamboo and poplar pulps were exposed to first stage oxygen delignification for bleaching under 0,5 MPa, 3% NaOH and 12% consistency conditions. SPBTH bleaching was then carried out as the final stage. SPBTH bleached pulp was next beaten using two different degrees (55 SR° and 65 SR°). Tensile index and brightness data for training, validation and testing of the models were elicited from these experimental investigations. The models were established using the resulting data. The lowest R² value was 0,98 among training, testing and validation data sets in the prediction of both tensile index and brightness. The networks therefore explain at least 98% of the experimental data for all data sets. The results indicate that ANN is a useful and effective tool for predicting tensile index and brightness. This study thus describes a novel and alternative approach to predicting tensile index and brightness in bleaching pulp compared to the literature.info:eu-repo/semantics/openAccessUniversidad del Bío-BíoMaderas. Ciencia y tecnología v.17 n.3 20152015-07-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2015000300011en10.4067/S0718-221X2015005000051
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Bleaching process neural network modeling Phyllostachys bambusoides Populus euramericana pulping properties
spellingShingle	Bleaching process neural network modeling Phyllostachys bambusoides Populus euramericana pulping properties Okan,Onur Tolga Deniz,Ilhan Tiryaki,Sebahattin Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp
description	The purpose of this study was to develop artificial neural network (ANN) models for predicting the effects of wood species, sodium perborate tetrahydrate (SPBTH) ratio, time, and beating degree on tensile index and brightness in bleaching pulp. Unbleached kraft-AQ bamboo and poplar pulps were exposed to first stage oxygen delignification for bleaching under 0,5 MPa, 3% NaOH and 12% consistency conditions. SPBTH bleaching was then carried out as the final stage. SPBTH bleached pulp was next beaten using two different degrees (55 SR° and 65 SR°). Tensile index and brightness data for training, validation and testing of the models were elicited from these experimental investigations. The models were established using the resulting data. The lowest R² value was 0,98 among training, testing and validation data sets in the prediction of both tensile index and brightness. The networks therefore explain at least 98% of the experimental data for all data sets. The results indicate that ANN is a useful and effective tool for predicting tensile index and brightness. This study thus describes a novel and alternative approach to predicting tensile index and brightness in bleaching pulp compared to the literature.
author	Okan,Onur Tolga Deniz,Ilhan Tiryaki,Sebahattin
author_facet	Okan,Onur Tolga Deniz,Ilhan Tiryaki,Sebahattin
author_sort	Okan,Onur Tolga
title	Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp
title_short	Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp
title_full	Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp
title_fullStr	Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp
title_full_unstemmed	Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp
title_sort	application of artificial neural networks for predicting tensile index and brightness in bleaching pulp
publisher	Universidad del Bío-Bío
publishDate	2015
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2015000300011
work_keys_str_mv	AT okanonurtolga applicationofartificialneuralnetworksforpredictingtensileindexandbrightnessinbleachingpulp AT denizilhan applicationofartificialneuralnetworksforpredictingtensileindexandbrightnessinbleachingpulp AT tiryakisebahattin applicationofartificialneuralnetworksforpredictingtensileindexandbrightnessinbleachingpulp
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Application of Artificial Neural Networks for predicting tensile index and brightness in bleaching pulp

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