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 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 R-2 of 0.9454 than the RSM model with a R-2 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.

Automated summary

This study evaluated, optimized, and modeled the efficiencies of Fenton, photo-Fenton, and ozonation/Fenton processes for formaldehyde elimination in water and wastewater using response surface methodology and artificial neural network. The combined ozonation/Fenton process demonstrated the best performance with 80% formaldehyde removal from wastewater. The sensitivity analysis identified pH as the most important factor influencing the Fenton process, highlighting the significance of these treatment processes and models for formaldehyde elimination from wastewater.