Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis

15 figures, 4 tables.-- © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

Detalhes bibliográficos
Autores: Ibrar, Ibrar, Yadav, Sudesh, Braytee, Ali, Altaee, Ali, Hosseinzadeh, Ahmad, Samal, Akshaya K., Zhou, John, Khan, Jamshed Ali, Bartocci, Pietro, Fantozzi, Francesco
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2022
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::a991628eec0a58f66061d7953c71eb8b
Acesso em linha:http://hdl.handle.net/10261/257973
Access Level:Acceso aberto
Palavra-chave:Forward osmosis (FO)
Internal concentration polarization (ICP)
Machine learning modelling
Artificial neural networks
Wastewater treatment
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spelling Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosisIbrar, IbrarYadav, SudeshBraytee, AliAltaee, AliHosseinzadeh, AhmadSamal, Akshaya K.Zhou, JohnKhan, Jamshed AliBartocci, PietroFantozzi, FrancescoForward osmosis (FO)Internal concentration polarization (ICP)Machine learning modellingArtificial neural networksWastewater treatment15 figures, 4 tables.-- © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Internal concentration polarization (ICP) is currently a major bottleneck in the forward osmosis process. Proper modelling of the internal concentration polarization is therefore vital for improving the process performance and efficiency. This study assessed the feasibility of several machine learning methods for internal concentration polarization prediction, including artificial neural networks, extreme gradient boosting (XGBoost), Categorical boosting (CatBoost), Random forest, and linear regression. Among the many algorithms evaluated, the CatBoost regression outperformed other methods in terms of coefficient of determination (R2) and the mean square error. The CatBoost algorithm's prediction power was then evaluated using non-training (user-provided) data and compared to solution diffusion models. The results indicated that the machine learning algorithms could predict ICP in the process with high accuracy for the provided dataset and excellent generalizability for future testing data. Furthermore, machine learning algorithms may offer insights into the input features that majorly affect ICP modelling in the forward osmosis process.Thanks to the Australian government for providing a research training scholarship to Ibrar. © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Peer reviewedElsevier BVAustralian GovernmentIbrar, Ibrar [0000-0001-7460-944X]Braytee, Ali [0000-0003-2561-6496]Altaee, Ali [0000-0001-9764-3974]Hosseinzadeh, Ahmad [0000-0001-8441-2069]Samal, Akshaya K. [0000-0002-7623-3711]Bartocci, Pietro [0000-0002-9888-6852]Fantozzi, Francesco [0000-0002-8674-8364]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/257973reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1016/j.memsci.2022.120257Síinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::a991628eec0a58f66061d7953c71eb8b2026-05-22T06:33:51Z
dc.title.none.fl_str_mv Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
title Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
spellingShingle Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
Ibrar, Ibrar
Forward osmosis (FO)
Internal concentration polarization (ICP)
Machine learning modelling
Artificial neural networks
Wastewater treatment
title_short Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
title_full Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
title_fullStr Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
title_full_unstemmed Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
title_sort Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
dc.creator.none.fl_str_mv Ibrar, Ibrar
Yadav, Sudesh
Braytee, Ali
Altaee, Ali
Hosseinzadeh, Ahmad
Samal, Akshaya K.
Zhou, John
Khan, Jamshed Ali
Bartocci, Pietro
Fantozzi, Francesco
author Ibrar, Ibrar
author_facet Ibrar, Ibrar
Yadav, Sudesh
Braytee, Ali
Altaee, Ali
Hosseinzadeh, Ahmad
Samal, Akshaya K.
Zhou, John
Khan, Jamshed Ali
Bartocci, Pietro
Fantozzi, Francesco
author_role author
author2 Yadav, Sudesh
Braytee, Ali
Altaee, Ali
Hosseinzadeh, Ahmad
Samal, Akshaya K.
Zhou, John
Khan, Jamshed Ali
Bartocci, Pietro
Fantozzi, Francesco
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Australian Government
Ibrar, Ibrar [0000-0001-7460-944X]
Braytee, Ali [0000-0003-2561-6496]
Altaee, Ali [0000-0001-9764-3974]
Hosseinzadeh, Ahmad [0000-0001-8441-2069]
Samal, Akshaya K. [0000-0002-7623-3711]
Bartocci, Pietro [0000-0002-9888-6852]
Fantozzi, Francesco [0000-0002-8674-8364]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Forward osmosis (FO)
Internal concentration polarization (ICP)
Machine learning modelling
Artificial neural networks
Wastewater treatment
topic Forward osmosis (FO)
Internal concentration polarization (ICP)
Machine learning modelling
Artificial neural networks
Wastewater treatment
description 15 figures, 4 tables.-- © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/257973
url http://hdl.handle.net/10261/257973
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.memsci.2022.120257
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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