Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration

Wang, C; Wang, L; Soo, A; Bansidhar Pathak, N; Kyong Shon, H

Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration

Wang, C Wang, L Soo, A Bansidhar Pathak, N Kyong Shon, H

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Separation and Purification Technology, 2023, 304, pp. 122328
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Wang, C
dc.contributor.author	Wang, L
dc.contributor.author	Soo, A
dc.contributor.author	Bansidhar Pathak, N
dc.contributor.author	Kyong Shon, H
dc.date.accessioned	2022-11-15T09:25:17Z
dc.date.available	2022-11-15T09:25:17Z
dc.date.issued	2023-01-01
dc.identifier.citation	Separation and Purification Technology, 2023, 304, pp. 122328
dc.identifier.issn	1383-5866
dc.identifier.issn	1873-3794
dc.identifier.uri	http://hdl.handle.net/10453/163481
dc.description.abstract	In this study, machine learning was used to form prediction models for thin film nanocomposite (TFN) organic solvent nanofiltration (OSN) membrane performance evaluation in terms of relative permeability (RP) and relative selectivity (RS). Twenty references including 9252 data points were collected to form four different models: linear, support vector machine (SVM), boosted tree (BT), and artificial neural network (ANN). Among the four models, BT exhibited optimal prediction accuracy in terms of root mean square error (RMSE) and coefficient of determination (R2) values for membrane RP (RMSE: 0.295, R2: 0.918) and RS (RMSE: 0.053, R2: 0.849) performance prediction. Parameter contribution analysis indicated that nanoparticle loading, amine concentration, chloride concentration, water contact angle, solvent viscosity, and molar volume are the main parameters influencing RP performance. For RS performance, nanoparticle loading, amine concentration, chloride concentration, and solute molecular weight play important roles. Partial dependence analysis indicated that the optimal conditions for TFN-OSN membrane fabrication are nanoparticle loading less than 5 wt%, the amine concentration around 2 wt%, and the chloride concentration around 0.15 wt%. In addition, membrane with super-hydrophilic or super-hydrophobic surface property exhibited higher RP performance based on different feed solvent types. Overall, this work introduces new ways both for TFN-OSN membrane performance prediction and for higher performance membrane design and development.
dc.language	en
dc.publisher	Elsevier BV
dc.relation	http://purl.org/au-research/grants/arc/DP210101361
dc.relation.ispartof	Separation and Purification Technology
dc.relation.isbasedon	10.1016/j.seppur.2022.122328
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0301 Analytical Chemistry, 0904 Chemical Engineering
dc.subject.classification	Chemical Engineering
dc.title	Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration
dc.type	Journal Article
utslib.citation.volume	304
utslib.for	0301 Analytical Chemistry
utslib.for	0904 Chemical 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	2022-11-15T09:25:15Z
pubs.publication-status	Accepted
pubs.volume	304

Abstract:

In this study, machine learning was used to form prediction models for thin film nanocomposite (TFN) organic solvent nanofiltration (OSN) membrane performance evaluation in terms of relative permeability (RP) and relative selectivity (RS). Twenty references including 9252 data points were collected to form four different models: linear, support vector machine (SVM), boosted tree (BT), and artificial neural network (ANN). Among the four models, BT exhibited optimal prediction accuracy in terms of root mean square error (RMSE) and coefficient of determination (R2) values for membrane RP (RMSE: 0.295, R2: 0.918) and RS (RMSE: 0.053, R2: 0.849) performance prediction. Parameter contribution analysis indicated that nanoparticle loading, amine concentration, chloride concentration, water contact angle, solvent viscosity, and molar volume are the main parameters influencing RP performance. For RS performance, nanoparticle loading, amine concentration, chloride concentration, and solute molecular weight play important roles. Partial dependence analysis indicated that the optimal conditions for TFN-OSN membrane fabrication are nanoparticle loading less than 5 wt%, the amine concentration around 2 wt%, and the chloride concentration around 0.15 wt%. In addition, membrane with super-hydrophilic or super-hydrophobic surface property exhibited higher RP performance based on different feed solvent types. Overall, this work introduces new ways both for TFN-OSN membrane performance prediction and for higher performance membrane design and development.

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http://hdl.handle.net/10453/163481