Improving formaldehyde removal from water and wastewater by fenton, photo-fenton and ozonation/fenton processes through optimization and modeling

Hosseinzadeh, A; Najafpoor, AA; Navaei, AA; Zhou, JL; Altaee, A; Ramezanian, N; Dehghan, A; Bao, T; Yazdani, M

Improving formaldehyde removal from water and wastewater by fenton, photo-fenton and ozonation/fenton processes through optimization and modeling

Hosseinzadeh, A Najafpoor, AA Navaei, AA Zhou, JL Altaee, A

Ramezanian, N Dehghan, A Bao, T Yazdani, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Water (Switzerland), 2021, 13, (19)
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Hosseinzadeh, A
dc.contributor.author	Najafpoor, AA
dc.contributor.author	Navaei, AA
dc.contributor.author	Zhou, JL
dc.contributor.author	Altaee, A https://orcid.org/0000-0001-9764-3974
dc.contributor.author	Ramezanian, N
dc.contributor.author	Dehghan, A
dc.contributor.author	Bao, T
dc.contributor.author	Yazdani, M
dc.date.accessioned	2022-05-05T03:31:42Z
dc.date.available	2022-05-05T03:31:42Z
dc.date.issued	2021-10-01
dc.identifier.citation	Water (Switzerland), 2021, 13, (19)
dc.identifier.issn	2073-4441
dc.identifier.issn	2073-4441
dc.identifier.uri	http://hdl.handle.net/10453/157041
dc.description.abstract	This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Water (Switzerland)
dc.relation.isbasedon	10.3390/w13192754
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Improving formaldehyde removal from water and wastewater by fenton, photo-fenton and ozonation/fenton processes through optimization and modeling
dc.type	Journal Article
utslib.citation.volume	13
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	*
pubs.consider-herdc	false
dc.date.updated	2022-05-05T03:31:39Z
pubs.issue	19
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	19

Abstract:

This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.

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http://hdl.handle.net/10453/157041