Dosage Control of the Fenton Process for Color Removal of Textile Wastewater Applying ORP Monitoring and Artificial Neural Networks

Abstract

Textile wastewater containing a high level of color and refractory chemical oxidation demand (COD) is difficult to treat using traditional wastewater treatment processes. Typically, a chemical process was suggested as a pretreatment to remove color and increase biodegradability of refractory organic materials. A biological process was then used to remove organic materials and reduce chemical costs for textile wastewater treatment. Fenton oxidation is one of the most effective chemical processes for removing color and COD for textile wastewater. In Fenton processes, oxidations by generated hydroxyl radical are the key factor for color removal in textile wastewaters; thus, monitoring oxidation reduction potential (ORP) should have high potential in Fenton dosage control for color removal in textile wastewater treatment. The main object of this study is to build a Fenton dosage control strategy that uses ORP monitoring and artificial neural network (ANN) models for removing color from textile wastewaters. Two wastewaters, synthetic and real textile, were used in this study. Experimental results have shown that the ANN models precisely represent the correlation between monitoring ORP, Fenton doses, color removal efficiency, and effluent color value, and therefore can be used to control Fenton doses for removing color from textile wastewater. Finally, another series of Fenton dose-control experiments for different color removal control targets were conducted to evaluate this proposed Fenton dose control strategy. Experimental results indicate that the proposed control strategy precisely controls the required Fenton doses for different control targets for both synthetic and real textile wastewaters, and result in reduced chemical costs.