Combining Electro-Fenton and adsorption processes for reclamation of textile industry wastewater and modeling by Artificial neural Networks

In the present study, coupling electro-Fenton (EF) and adsorption processes for textile industry wastewater remediation was investigated, in both batch and continuous flow modes. Sepiolite was used as an adsorbent in the coupled EF/adsorption processes. Various parameters such as reaction time, current intensity, Fe2+ concentration, sepiolite dose, and flow rate were found to affect the efficiency of the coupled processes. In comparison to the single EF process, a synergistic effect occurred in the coupled EF/adsorption processes, leading to better performance for COD and TOC removal from textile wastewater. Thus, in the single EF technology, using graphite felt electrodes, COD and TOC removal efficiencies from real textile wastewater, were 58 % and 36 %, respectively. However, in the coupled EF/adsorption processes, COD and TOC removal efficiencies increased to 85 % and to 63 %, respectively. The higher COD and TOC removals may be attributed to the combined effect of adsorption and oxidation reactions in coupled EF/adsorption process. Moreover, Artificial Neural Networks (ANN) model was built up in order to estimate COD and TOC removal efficiencies of the coupled EF/adsorption processes. A good correlation was found between the ANN model theoretical prediction, and the experimental data, for COD and TOC removals, in both batch and continuous modes. The novelty of the current work lies in the synergistic effect occurring between the EF and adsorption processes in wastewater treatment and provides the ANN model as a valuable tool for describing COD and TOC removal efficiencies under different experimental conditions.