Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling
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...
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MDPI AG
2021-10-01
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| author | Ahmad Hosseinzadeh Ali Asghar Najafpoor Ali Asghar Navaei John L. Zhou Ali Altaee Navid Ramezanian Aliakbar Dehghan Teng Bao Mohsen Yazdani |
| author_facet | Ahmad Hosseinzadeh Ali Asghar Najafpoor Ali Asghar Navaei John L. Zhou Ali Altaee Navid Ramezanian Aliakbar Dehghan Teng Bao Mohsen Yazdani |
| author_sort | Ahmad Hosseinzadeh |
| collection | DOAJ |
| description | 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 H<sub>2</sub>O<sub>2</sub> 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 <i>R</i><sup>2</sup> of 0.9454 than the RSM model with a <i>R</i><sup>2</sup> of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H<sub>2</sub>O<sub>2</sub> 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. |
| first_indexed | 2024-03-10T06:48:31Z |
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| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T06:48:31Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
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| series | Water |
| spelling | doaj.art-b8b32861d8a24a8ebf04a2aca77fc9372023-11-22T17:02:08ZengMDPI AGWater2073-44412021-10-011319275410.3390/w13192754Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and ModelingAhmad Hosseinzadeh0Ali Asghar Najafpoor1Ali Asghar Navaei2John L. Zhou3Ali Altaee4Navid Ramezanian5Aliakbar Dehghan6Teng Bao7Mohsen Yazdani8Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, AustraliaSocial Determinants of Health Research Center, Department of Environmental Health Engineering, Mashhad University of Medical Sciences, Mashhad 9138813944, IranSocial Determinants of Health Research Center, Department of Environmental Health Engineering, Mashhad University of Medical Sciences, Mashhad 9138813944, IranCentre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, AustraliaCentre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, AustraliaDepartment of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad 9177948974, IranSocial Determinants of Health Research Center, Department of Environmental Health Engineering, Mashhad University of Medical Sciences, Mashhad 9138813944, IranCentre for Green Technology, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, AustraliaStudent Research Committee, Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135733184, IranThis 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 H<sub>2</sub>O<sub>2</sub> 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 <i>R</i><sup>2</sup> of 0.9454 than the RSM model with a <i>R</i><sup>2</sup> of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H<sub>2</sub>O<sub>2</sub> 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.https://www.mdpi.com/2073-4441/13/19/2754formaldehyde removalwastewaterphoto-Fentonozonationartificial neural network |
| spellingShingle | Ahmad Hosseinzadeh Ali Asghar Najafpoor Ali Asghar Navaei John L. Zhou Ali Altaee Navid Ramezanian Aliakbar Dehghan Teng Bao Mohsen Yazdani Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling Water formaldehyde removal wastewater photo-Fenton ozonation artificial neural network |
| title | Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling |
| title_full | Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling |
| title_fullStr | Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling |
| title_full_unstemmed | Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling |
| title_short | Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling |
| title_sort | improving formaldehyde removal from water and wastewater by fenton photo fenton and ozonation fenton processes through optimization and modeling |
| topic | formaldehyde removal wastewater photo-Fenton ozonation artificial neural network |
| url | https://www.mdpi.com/2073-4441/13/19/2754 |
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