Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems

A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the compression-permeability (C-P) characteristics of a solid-liquid system. Extensive cake properties database containing experimental data spanning various material types, particle size distribution,...

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Main Authors:	Iwata, Masashi, Jami, Mohammed Saedi, Shiojiri, Susumu
Format:	Article
Language:	English
Published:	The Filtration Society and the American Filtration & Separations Society 2007
Subjects:	TP155 Chemical engineering
Online Access:	http://irep.iium.edu.my/40934/ http://irep.iium.edu.my/40934/ http://irep.iium.edu.my/40934/3/Published.pdf

id	iium-40934
recordtype	eprints
spelling	iium-409342015-03-24T03:30:56Z http://irep.iium.edu.my/40934/ Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems Iwata, Masashi Jami, Mohammed Saedi Shiojiri, Susumu TP155 Chemical engineering A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the compression-permeability (C-P) characteristics of a solid-liquid system. Extensive cake properties database containing experimental data spanning various material types, particle size distribution, flocculated and unflocculated particles has been developed and used to train two ANNs. The input parameters for the first ANN were the applied pressure and the particle size distribution whereas the output parameter was the porosity of the compressed cake. In the case of the second ANN, the input parameters were the porosity and the particle size distribution. The logarithm of the specific cake resistance multiplied by the particle true density of the cake (log (s)) was chosen as the output parameter of this network. The use of porosity obtained from gravitational and centrifugal sedimentation experiments as one of the input parameters to the ANNs gave excellent results in predicting both the cake porosity and the specific cake resistance. With the help of this method, after the ANN is thoroughly trained with various slurries, the C-P characteristics of another slurry can be predicted with much less amount of the target slurry compared with conventional C-P test. The Filtration Society and the American Filtration & Separations Society 2007 Article PeerReviewed application/pdf en http://irep.iium.edu.my/40934/3/Published.pdf Iwata, Masashi and Jami, Mohammed Saedi and Shiojiri, Susumu (2007) Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems. FILTRATION, 7 (4). 337 -344 . ISSN 1479-0602 http://www.filtsoc.org/journal/2007/volume-7-issue-4/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TP155 Chemical engineering
spellingShingle	TP155 Chemical engineering Iwata, Masashi Jami, Mohammed Saedi Shiojiri, Susumu Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
description	A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the compression-permeability (C-P) characteristics of a solid-liquid system. Extensive cake properties database containing experimental data spanning various material types, particle size distribution, flocculated and unflocculated particles has been developed and used to train two ANNs. The input parameters for the first ANN were the applied pressure and the particle size distribution whereas the output parameter was the porosity of the compressed cake. In the case of the second ANN, the input parameters were the porosity and the particle size distribution. The logarithm of the specific cake resistance multiplied by the particle true density of the cake (log (s)) was chosen as the output parameter of this network. The use of porosity obtained from gravitational and centrifugal sedimentation experiments as one of the input parameters to the ANNs gave excellent results in predicting both the cake porosity and the specific cake resistance. With the help of this method, after the ANN is thoroughly trained with various slurries, the C-P characteristics of another slurry can be predicted with much less amount of the target slurry compared with conventional C-P test.
format	Article
author	Iwata, Masashi Jami, Mohammed Saedi Shiojiri, Susumu
author_facet	Iwata, Masashi Jami, Mohammed Saedi Shiojiri, Susumu
author_sort	Iwata, Masashi
title	Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
title_short	Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
title_full	Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
title_fullStr	Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
title_full_unstemmed	Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
title_sort	artificial neural network model to predict compression-permeability characteristics of solid-liquid systems
publisher	The Filtration Society and the American Filtration & Separations Society
publishDate	2007
url	http://irep.iium.edu.my/40934/ http://irep.iium.edu.my/40934/ http://irep.iium.edu.my/40934/3/Published.pdf
first_indexed	2023-09-18T20:58:39Z
last_indexed	2023-09-18T20:58:39Z
_version_	1777410474772529152

Artificial neural network model to predict compression-permeability characteristics of solid-liquid systems

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