Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling
Ahmad Hosseinzadeh,
Ali Asghar Najafpoor,
Ali Asghar Navaei,
John L. Zhou,
Ali Altaee,
Navid Ramezanian,
Aliakbar Dehghan,
Teng Bao,
Mohsen Yazdani
Affiliations
Ahmad Hosseinzadeh
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, Australia
Ali Asghar Najafpoor
Social Determinants of Health Research Center, Department of Environmental Health Engineering, Mashhad University of Medical Sciences, Mashhad 9138813944, Iran
Ali Asghar Navaei
Social Determinants of Health Research Center, Department of Environmental Health Engineering, Mashhad University of Medical Sciences, Mashhad 9138813944, Iran
John L. Zhou
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, Australia
Ali Altaee
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, Australia
Navid Ramezanian
Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Aliakbar Dehghan
Social Determinants of Health Research Center, Department of Environmental Health Engineering, Mashhad University of Medical Sciences, Mashhad 9138813944, Iran
Teng Bao
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, Australia
Mohsen Yazdani
Student Research Committee, Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135733184, Iran
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.
