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

Ibrar, I; Yadav, S; Braytee, A; Altaee, A; HosseinZadeh, A; Samal, AK; Zhou, JL; Khan, JA; Bartocci, P; Fantozzi, F

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

Ibrar, I

Yadav, S

Braytee, A Altaee, A

HosseinZadeh, A Samal, AK Zhou, JL Khan, JA Bartocci, P Fantozzi, F

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Membrane Science, 2022, 646, pp. 120257
Issue Date:: 2022-03-15

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Field	Value	Language
dc.contributor.author	Ibrar, I https://orcid.org/0000-0001-7460-944X
dc.contributor.author	Yadav, S https://orcid.org/0000-0002-2442-8664
dc.contributor.author	Braytee, A
dc.contributor.author	Altaee, A https://orcid.org/0000-0001-9764-3974
dc.contributor.author	HosseinZadeh, A
dc.contributor.author	Samal, AK
dc.contributor.author	Zhou, JL
dc.contributor.author	Khan, JA
dc.contributor.author	Bartocci, P
dc.contributor.author	Fantozzi, F
dc.date.accessioned	2022-04-26T06:19:27Z
dc.date.available	2022-04-26T06:19:27Z
dc.date.issued	2022-03-15
dc.identifier.citation	Journal of Membrane Science, 2022, 646, pp. 120257
dc.identifier.issn	0376-7388
dc.identifier.issn	1873-3123
dc.identifier.uri	http://hdl.handle.net/10453/156615
dc.description.abstract	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.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Membrane Science
dc.relation.isbasedon	10.1016/j.memsci.2022.120257
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.subject.classification	Chemical Engineering
dc.title	Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis
dc.type	Journal Article
utslib.citation.volume	646
utslib.for	03 Chemical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
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/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
dc.date.updated	2022-04-26T06:19:17Z
pubs.publication-status	Published
pubs.volume	646

Abstract:

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.

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http://hdl.handle.net/10453/156615