Minimizing membrane bioreactor environmental footprint by multiple objective optimization.

Mannina, G; Ni, B-J; Ferreira Rebouças, T; Cosenza, A; Olsson, G

Minimizing membrane bioreactor environmental footprint by multiple objective optimization.

Mannina, G Ni, B-J Ferreira Rebouças, T Cosenza, A Olsson, G

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Bioresource technology, 2020, 302, pp. 122824
Issue Date:: 2020-04

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Field	Value	Language
dc.contributor.author	Mannina, G
dc.contributor.author	Ni, B-J
dc.contributor.author	Ferreira Rebouças, T
dc.contributor.author	Cosenza, A
dc.contributor.author	Olsson, G
dc.date.accessioned	2021-03-05T06:16:09Z
dc.date.available	2020-01-13
dc.date.available	2021-03-05T06:16:09Z
dc.date.issued	2020-04
dc.identifier.citation	Bioresource technology, 2020, 302, pp. 122824
dc.identifier.issn	0960-8524
dc.identifier.issn	1873-2976
dc.identifier.uri	http://hdl.handle.net/10453/146828
dc.description.abstract	This paper presents a modelling study aimed at minimizing the environmental foot print of a membrane bioreactor (MBR) for wastewater treatment. Specifically, an integrated model for MBR was employed in view of the management optimization of an MBR biological nutrient removal (BNR) pilot plant in terms of operational costs and direct greenhouse gases emissions. The influence of the operational parameters (OPs) on performance indicators (PIs) was investigated by adopting the Extended-FAST sensitivity analysis method. Further, a multi-objective analysis was performed by applying the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results show-up that the sludge retention time is the OP mostly affecting all the investigated PIs. By applying the set of optimal OPs, there was a reduction of 48% and 10% of the operational costs and direct emissions, respectively.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Bioresource technology
dc.relation.isbasedon	10.1016/j.biortech.2020.122824
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Biotechnology
dc.subject.mesh	Membranes, Artificial
dc.subject.mesh	Bioreactors
dc.subject.mesh	Sewage
dc.subject.mesh	Waste Disposal, Fluid
dc.subject.mesh	Waste Water
dc.subject.mesh	Greenhouse Gases
dc.subject.mesh	Bioreactors
dc.subject.mesh	Greenhouse Gases
dc.subject.mesh	Membranes, Artificial
dc.subject.mesh	Sewage
dc.subject.mesh	Waste Disposal, Fluid
dc.subject.mesh	Waste Water
dc.title	Minimizing membrane bioreactor environmental footprint by multiple objective optimization.
dc.type	Journal Article
utslib.citation.volume	302
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-05T06:16:07Z
pubs.publication-status	Published
pubs.volume	302

Abstract:

This paper presents a modelling study aimed at minimizing the environmental foot print of a membrane bioreactor (MBR) for wastewater treatment. Specifically, an integrated model for MBR was employed in view of the management optimization of an MBR biological nutrient removal (BNR) pilot plant in terms of operational costs and direct greenhouse gases emissions. The influence of the operational parameters (OPs) on performance indicators (PIs) was investigated by adopting the Extended-FAST sensitivity analysis method. Further, a multi-objective analysis was performed by applying the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results show-up that the sludge retention time is the OP mostly affecting all the investigated PIs. By applying the set of optimal OPs, there was a reduction of 48% and 10% of the operational costs and direct emissions, respectively.

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http://hdl.handle.net/10453/146828