Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.

Hosseinzadeh, A; Zhou, JL; Altaee, A; Baziar, M; Li, X

Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.

Hosseinzadeh, A Zhou, JL Altaee, A

Baziar, M Li, X

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Bioresource technology, 2020, 310, pp. 123391
Issue Date:: 2020-04-18

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Field	Value	Language
dc.contributor.author	Hosseinzadeh, A
dc.contributor.author	Zhou, JL
dc.contributor.author	Altaee, A https://orcid.org/0000-0001-9764-3974
dc.contributor.author	Baziar, M
dc.contributor.author	Li, X
dc.date.accessioned	2020-05-08T05:14:25Z
dc.date.available	2020-04-13
dc.date.available	2020-05-08T05:14:25Z
dc.date.issued	2020-04-18
dc.identifier.citation	Bioresource technology, 2020, 310, pp. 123391
dc.identifier.issn	0960-8524
dc.identifier.issn	1873-2976
dc.identifier.uri	http://hdl.handle.net/10453/140585
dc.description.abstract	Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	Bioresource technology
dc.relation.isbasedon	10.1016/j.biortech.2020.123391
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Biotechnology
dc.title	Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.
dc.type	Journal Article
utslib.citation.volume	310
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-04-18T00:00:00+1000Z
dc.date.updated	2020-05-08T05:14:24Z
pubs.publication-status	Published
pubs.volume	310
utslib.start-page	123391

Abstract:

Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.

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http://hdl.handle.net/10453/140585