Chemical oxygen demand and color removal from textile wastewater by UV/H2O2 using artificial neural networks

Abstract

The photooxidation of pollutants, especially chemical oxygen demand (COD) and color, in textile industrial wastewater was performed in the presence of hydrogen peroxide (H2O2), using 256 nm UV light (15 W), to model the discoloration and COD elimination processes and characterize the influence of process variables. Within this study, data were obtained through a NeuroSolutions 5.06 model and successfully tested. Each sample was characterized by three independent variables (i.e., pH, H2O2 concentration, and time of operation) and two dependent variables (i.e., color and COD). The results indicated that pH was the predominant variable, and the reaction mean time and H2O2 volume were the less influential variables. The neural model obtained presented coefficients of correlation of 99% for COD and 97% for color, indicating the prediction power of the model and its character of generalization.