Treatment of Textile Effluents by Electrochemical Advanced Oxidation Processes

Abstract

Textile industries are associated with the generation of large volumes of wastewater containing recalcitrant dyes and other textile processing chemicals. The discharged effluents from these industries have been demonstrated bearing high pollution load (high dissolved solids, COD, color and chloride content) with poor biodegradability. Therefore, untreated textile wastewater causes sever damage to environment, if discharged without treatment. There are various traditional techniques have been used for the treatment of real textile wastewater, including biological processes, adsorption, membrane process and chemical coagulation. Treatment of real textile wastewater, which is of high strength and complex in nature, has become a real challenge with the traditional methods. Recently, Electro-oxidation (EO) and electro-Fenton (EF) is drawing attentions for the treatment of non-biodegradable wastewater such as textile wastewater. In the present study, the treatment performance of the EO and EF treatment processes for actual textile wastewater in batch and continuous mode of operation using RuO2 coated Ti electrode (Ti/RuO2) was studied. The effects of various selected process parameters of EO and EF processes (batch and continuous) parameters on percentage chemical oxygen demand removal (X1), percentage color removal (X2) and energy consumed (X3) were investigated. Parametric study and multiple response optimization for EO and EF (batch and continuous) was performed using RSM. Experimental data were then analyzed by multiple regression analysis of RSM. Simultaneous optimization of process parameters for the responses was performed with the desirability function approach. Box Behnken Design (BBD) under RSM was used for experimental design, data analysis, optimization, interaction analysis between the various batch EO parameters whereas Central Composite Design (CCD) under RSM was used for continuous EO process parameters and steady state time analysis.