Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network

Mojiri, A; Ozaki, N; Kazeroon, RA; Rezania, S; Baharlooeian, M; Vakili, M; Farraji, H; Ohashi, A; Kindaichi, T; Zhou, JL

Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network

Mojiri, A Ozaki, N Kazeroon, RA Rezania, S Baharlooeian, M Vakili, M Farraji, H Ohashi, A Kindaichi, T Zhou, JL

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: WATER, 2022, 14, (24)
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Mojiri, A
dc.contributor.author	Ozaki, N
dc.contributor.author	Kazeroon, RA
dc.contributor.author	Rezania, S
dc.contributor.author	Baharlooeian, M
dc.contributor.author	Vakili, M
dc.contributor.author	Farraji, H
dc.contributor.author	Ohashi, A
dc.contributor.author	Kindaichi, T
dc.contributor.author	Zhou, JL
dc.date.accessioned	2023-01-19T01:42:22Z
dc.date.available	2023-01-19T01:42:22Z
dc.date.issued	2022-12-01
dc.identifier.citation	WATER, 2022, 14, (24)
dc.identifier.issn	2073-4441
dc.identifier.issn	2073-4441
dc.identifier.uri	http://hdl.handle.net/10453/165172
dc.description.abstract	<jats:p>The potential of microalgal photobioreactors in removing total ammonia nitrogen (TAN), chemical oxygen demand (COD), caffeine (CAF), and N,N-diethyl-m-toluamide (DEET) from synthetic wastewater was studied. Chlorella vulgaris achieved maximum removal of 62.2% TAN, 52.8% COD, 62.7% CAF, and 51.8% DEET. By mixing C. vulgaris with activated sludge, the photobioreactor showed better performance, removing 82.3% TAN, 67.7% COD, 85.7% CAF, and 73.3% DEET. Proteobacteria, Bacteroidetes, and Chloroflexi were identified as the dominant phyla in the activated sludge. The processes were then optimized by the artificial neural network (ANN). High R2 values (>0.99) and low mean squared errors demonstrated that ANN could optimize the reactors’ performance. The toxicity testing showed that high concentrations of contaminants (>10 mg/L) and long contact time (>48 h) reduced the chlorophyll and protein contents in microalgae. Overall, a green technology for wastewater treatment using microalgae and bacteria consortium has demonstrated its high potentials in sustainable management of water resources.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	WATER
dc.relation.isbasedon	10.3390/w14244046
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network
dc.type	Journal Article
utslib.citation.volume	14
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	2023-01-19T01:42:11Z
pubs.issue	24
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	24

Abstract:

The potential of microalgal photobioreactors in removing total ammonia nitrogen (TAN), chemical oxygen demand (COD), caffeine (CAF), and N,N-diethyl-m-toluamide (DEET) from synthetic wastewater was studied. Chlorella vulgaris achieved maximum removal of 62.2% TAN, 52.8% COD, 62.7% CAF, and 51.8% DEET. By mixing C. vulgaris with activated sludge, the photobioreactor showed better performance, removing 82.3% TAN, 67.7% COD, 85.7% CAF, and 73.3% DEET. Proteobacteria, Bacteroidetes, and Chloroflexi were identified as the dominant phyla in the activated sludge. The processes were then optimized by the artificial neural network (ANN). High R2 values (>0.99) and low mean squared errors demonstrated that ANN could optimize the reactors’ performance. The toxicity testing showed that high concentrations of contaminants (>10 mg/L) and long contact time (>48 h) reduced the chlorophyll and protein contents in microalgae. Overall, a green technology for wastewater treatment using microalgae and bacteria consortium has demonstrated its high potentials in sustainable management of water resources.

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http://hdl.handle.net/10453/165172