An Integrated (Electrocoagulation and Adsorption) Approach for the Treatment of Textile Industrial Wastewater: RSM and ANN Based Optimization

Textile industries are one of the rapidly growing but on the contrary the most polluting industries in terms of the volume and complexity of their effluent discharge. Treatment of textile industry wastewater with turquoise blue dye using sequential electrocoagulation (EC) and adsorption (AD) process and its optimization was attempted in this study. Algal activated carbon (AAC) was used as adsorbent in the treatment process. These two-process parameters were optimized using the response surface methodology and artificial neural network (ANN) for the effective removal of COD (%COD). The experimental results were found to be in good agreement with the predicted results from both RSM and ANN model (R-2 values > 0.9). After EC treatment the maximum removal efficiency obtained for COD was 76.97% at an optimal condition of 1.5 A/dm(2) current density and 36-min time. Similarly, for AD treatment maximum removal efficiency was acquired under an optimal AAC dosage of 12.9 g/L and time of 44 min. Therefore, the overall removal efficiency obtained for COD was 91.28%. in sequential treatment process. Additionally, color, turbidity and TDS was also removed up to 68.82%, 90.96%, and 16.04%, respectively. This study concludes that the RSM and ANN were adequate statistical tools to predict the effective optimal conditions to attain the maximum COD removal for textile industry effluent using a sequential EC-AD treatment process.