TY - JOUR
T1 - Photo-fenton-like treatment of municipal wastewater
AU - Kanafin, Yerkanat N.
AU - Makhatova, Ardak
AU - Zarikas, Vasilios
AU - Arkhangelsky, Elizabeth
AU - Poulopoulos, Stavros G.
N1 - Funding Information:
Acknowledgments: The authors acknowledge funding support from Nazarbayev University. The technical support of Core Facilities of Nazarbayev University is greatly acknowledged.
Funding Information:
Funding: This research was funded by the Nazarbayev University project “Cost-Effective Photo-catalysts for the Treatment of Wastewaters containing Emerging Pollutants”, Faculty development competitive research grants program for 2020-2022, Grant Number 240919FD3932 awarded to Dr. S.G. Poulopoulos.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/10
Y1 - 2021/10
N2 - In 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.
AB - In 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.
KW - Artificial neural network
KW - Box-Behnken design
KW - Municipal wastewater
KW - Persulfate oxidation
KW - Photo-Fenton-like process
KW - Response surface methodology
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U2 - 10.3390/catal11101206
DO - 10.3390/catal11101206
M3 - Article
AN - SCOPUS:85116545805
SN - 2073-4344
VL - 11
JO - Catalysts
JF - Catalysts
IS - 10
M1 - 1206
ER -