Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes

Fetanat, M; Keshtiara, M; Low, ZX; Keyikoglu, R; Khataee, A; Orooji, Y; Chen, V; Leslie, G; Razmjou, A

Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes

Fetanat, M Keshtiara, M Low, ZX Keyikoglu, R Khataee, A Orooji, Y Chen, V Leslie, G Razmjou, A

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Publisher:: AMER CHEMICAL SOC
Publication Type:: Journal Article
Citation:: Industrial and Engineering Chemistry Research, 2021, 60, (14), pp. 5236-5250
Issue Date:: 2021-04-14

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Field	Value	Language
dc.contributor.author	Fetanat, M
dc.contributor.author	Keshtiara, M
dc.contributor.author	Low, ZX
dc.contributor.author	Keyikoglu, R
dc.contributor.author	Khataee, A
dc.contributor.author	Orooji, Y
dc.contributor.author	Chen, V
dc.contributor.author	Leslie, G
dc.contributor.author	Razmjou, A
dc.date.accessioned	2021-10-20T05:32:54Z
dc.date.available	2021-10-20T05:32:54Z
dc.date.issued	2021-04-14
dc.identifier.citation	Industrial and Engineering Chemistry Research, 2021, 60, (14), pp. 5236-5250
dc.identifier.issn	0888-5885
dc.identifier.issn	1520-5045
dc.identifier.uri	http://hdl.handle.net/10453/151174
dc.description.abstract	Although the incorporation of nanoparticles into ultrafiltration polymeric membranes has shown promising outcomes, their commercial implementation has yet to be fulfilled due to inconsistency in data, lack of a reliable recipe for the optimum filler content, and reluctance in disrupting the production line which requires significant time and resources. There is a growing demand among membrane communities for a design platform that can accelerate the discovery of new nanocomposite membranes. In this work, a feed-forward ANN (artificial neural network) model that has one hidden layer and the Bayesian regularization training algorithm were chosen for designing a graphical user interface platform to predict the ultrafiltration nanocomposite membrane performance, that is, solute rejection, flux recovery, and pure water flux, thereby saving time and resources used in membrane design. Experimental data (735 samples from 200 reports published between 2006 and 2020) were derived from the literature for training, validation, and testing of the ANN models. The results indicated that the best 30 ANN models produce the most accurate estimation of membrane performance using the seven input variables of polymer concentration, polymer type, filler concentration, average filler size, solvent concentration (in the dope solution), solvent type, and contact angle on the unseen data set. Furthermore, a sensitivity analysis was performed on the achieved models to identify the most effective input variables for each nanocomposite membrane performance. This work has the potential to be extended to other mixed matrix membrane types that are going to be used for microfiltration, nanofiltration, reverse osmosis, and so forth.
dc.language	English
dc.publisher	AMER CHEMICAL SOC
dc.relation.ispartof	Industrial and Engineering Chemistry Research
dc.relation.isbasedon	10.1021/acs.iecr.0c05446
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.subject.classification	Chemical Engineering
dc.title	Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes
dc.type	Journal Article
utslib.citation.volume	60
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-10-20T05:32:53Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	60
utslib.citation.issue	14

Abstract:

Although the incorporation of nanoparticles into ultrafiltration polymeric membranes has shown promising outcomes, their commercial implementation has yet to be fulfilled due to inconsistency in data, lack of a reliable recipe for the optimum filler content, and reluctance in disrupting the production line which requires significant time and resources. There is a growing demand among membrane communities for a design platform that can accelerate the discovery of new nanocomposite membranes. In this work, a feed-forward ANN (artificial neural network) model that has one hidden layer and the Bayesian regularization training algorithm were chosen for designing a graphical user interface platform to predict the ultrafiltration nanocomposite membrane performance, that is, solute rejection, flux recovery, and pure water flux, thereby saving time and resources used in membrane design. Experimental data (735 samples from 200 reports published between 2006 and 2020) were derived from the literature for training, validation, and testing of the ANN models. The results indicated that the best 30 ANN models produce the most accurate estimation of membrane performance using the seven input variables of polymer concentration, polymer type, filler concentration, average filler size, solvent concentration (in the dope solution), solvent type, and contact angle on the unseen data set. Furthermore, a sensitivity analysis was performed on the achieved models to identify the most effective input variables for each nanocomposite membrane performance. This work has the potential to be extended to other mixed matrix membrane types that are going to be used for microfiltration, nanofiltration, reverse osmosis, and so forth.

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http://hdl.handle.net/10453/151174