PHOTO-FENTON-LIKE TREATMENT OF MUNICIPAL WASTEWATER

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dc.contributor.authorKanafin, Yerkanat N.
dc.contributor.authorMakhatova, Ardak
dc.contributor.authorZarikas, Vasilios
dc.contributor.authorArkhangelsky, Elizabeth
dc.contributor.authorPoulopoulos, Stavros G.
dc.date.accessioned2022-07-14T10:35:52Z
dc.date.available2022-07-14T10:35:52Z
dc.date.issued2021
dc.description.abstractIn this work, the photochemical treatment of a real municipal wastewater using a persulfate driven photo-Fenton-like process was studied. The wastewater treatment efficiency was evaluated in terms of total carbon (TC), total organic carbon (TOC) and total nitrogen (TN) removal. Response surface methodology (RSM) in conjunction Box-Behnken design (BBD) and multilayer artificial neural network (ANN) have been utilized for the optimization of the treatment process. The effects of four independent factors such as reaction time, pH, K2S2O8 concentration and K2S2O8/Fe2+ molar ratio on the TC, TOC and TN removal have been investigated. The process significant factors have been determined implementing Analysis of Variance (ANOVA). Both RSM and ANN accurately found the optimum conditions for the maximum removal of TOC (100% and 98.7%, theoretically), which resulted in complete mineralization of TOC at the reaction time of 106.06 min, pH of 7.7, persulfate concentration of 30 mM and K2S2O8/Fe2+ molar ratio of 7.5 for RSM and at the reaction time of 104.93 min, pH of 7.7, persulfate concentration of 30 mM and K2S2O8/Fe2+ molar ratio of 9.57 for ANN. On the contrary, the attempts to find the optimal conditions for the maximum TC and TN removal using statistical, and neural network models were not successful.en_US
dc.identifier.citationKanafin, Y. N., Makhatova, A., Zarikas, V., Arkhangelsky, E., & Poulopoulos, S. G. (2021). Photo-Fenton-Like Treatment of Municipal Wastewater. Catalysts, 11(10), 1206. https://doi.org/10.3390/catal11101206en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6437
dc.language.isoenen_US
dc.publisherCatalystsen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Open Accessen_US
dc.subjectphoto-Fenton-like processen_US
dc.subjectmunicipal wastewateren_US
dc.subjectpersulfate oxidationen_US
dc.subjectresponse surface methodologyen_US
dc.subjectBox-Behnken designen_US
dc.subjectartificial neural networken_US
dc.titlePHOTO-FENTON-LIKE TREATMENT OF MUNICIPAL WASTEWATERen_US
dc.typeArticleen_US
workflow.import.sourcescience

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