Optimization of module pressure retarded osmosis membrane for maximum energy extraction

Chen, Y; Alanezi, AA; Zhou, J; Altaee, A; Shaheed, MH

Optimization of module pressure retarded osmosis membrane for maximum energy extraction

Chen, Y Alanezi, AA Zhou, J

Altaee, A

Shaheed, MH

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Publication Type:: Journal Article
Citation:: Journal of Water Process Engineering, 2019, 32
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	Alanezi, AA	en_US
dc.contributor.author	Zhou, J https://orcid.org/0000-0003-1393-1608	en_US
dc.contributor.author	Altaee, A https://orcid.org/0000-0001-9764-3974	en_US
dc.contributor.author	Shaheed, MH	en_US
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	Journal of Water Process Engineering, 2019, 32	en_US
dc.identifier.issn	2214-7144	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135867
dc.description.abstract	© 2019 Elsevier Ltd A full-scale Pressure Retarded Osmosis process (PRO) is optimized in non-ideal operating conditions using Grey Wolf Optimization (GWO) algorithms. Optimization process included the classical parameters that previous studies recommended such as operating pressure, and feed and draw fractions in the mixture solution. The study has revealed that the recommended operating pressure ΔP=Δπ/2 and the ratio of feed or draw solution to the total mixture solution, ̴ 0.5, in a laboratory scale unit or in an ideal PRO process are not valid in a non-ideal full-scale PRO module. The optimization suggested that the optimum operating pressure is less than the previously recommended value of ΔP=Δπ/2. The optimization of hydraulic pressure resulted in 4.4% increase of the energy output in the PRO process. Conversely, optimization of feed fraction in the mixture has resulted in 28%–70% higher energy yield in a single-module PRO process and 9%–54% higher energy yield in a four-modules PRO process. The net energy generated in the optimized PRO process is higher than that in the unoptimized (normal) PRO process. The findings of this study reveal the significance of incorporating machine-learning algorithms in the optimization of PRO process and identifying the preferable operating conditions.	en_US
dc.relation.ispartof	Journal of Water Process Engineering	en_US
dc.relation.isbasedon	10.1016/j.jwpe.2019.100935	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Optimization of module pressure retarded osmosis membrane for maximum energy extraction	en_US
dc.type	Journal Article
utslib.citation.volume	32	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0907 Environmental Engineering	en_US
pubs.embargo.period	Not known	en_US
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	*
utslib.copyright.embargo	2021-12-02T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

© 2019 Elsevier Ltd A full-scale Pressure Retarded Osmosis process (PRO) is optimized in non-ideal operating conditions using Grey Wolf Optimization (GWO) algorithms. Optimization process included the classical parameters that previous studies recommended such as operating pressure, and feed and draw fractions in the mixture solution. The study has revealed that the recommended operating pressure ΔP=Δπ/2 and the ratio of feed or draw solution to the total mixture solution, ̴ 0.5, in a laboratory scale unit or in an ideal PRO process are not valid in a non-ideal full-scale PRO module. The optimization suggested that the optimum operating pressure is less than the previously recommended value of ΔP=Δπ/2. The optimization of hydraulic pressure resulted in 4.4% increase of the energy output in the PRO process. Conversely, optimization of feed fraction in the mixture has resulted in 28%–70% higher energy yield in a single-module PRO process and 9%–54% higher energy yield in a four-modules PRO process. The net energy generated in the optimized PRO process is higher than that in the unoptimized (normal) PRO process. The findings of this study reveal the significance of incorporating machine-learning algorithms in the optimization of PRO process and identifying the preferable operating conditions.

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http://hdl.handle.net/10453/135867