Removal of Cr(VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network

Abstract

In the present work, the removal of Cr(VI) from synthetic and real wastewater using electrocoagulation (EC) process was studied. The influence of anode material, initial Cr(VI) concentration, initial pH of solution, type of electrolyte, current density and time of electrolysis was investigated. During 30 min of electrocoagulation, maximum removal efficiencies achieved by Al and Fe anodes were 0.15 and 0.98, respectively. High removal efficiency was achieved over pH range of 5–8. NaCl, Na 2SO 4 and NaNO 3 were used as supporting electrolyte during the electrolysis. NaCl was more effective than Na 2SO 4 and NaNO 3 in removal of hexavalent chromium. Also in this work, a real electroplating wastewater containing 17.1 mg/l Cr(VI) was treated successfully using EC process. Artificial neural network (ANN) was utilized for modeling of experimental results. The model was developed using a 3-layer feed forward backpropagation network with 4, 10 and 1 neurons in first, second and third layers, respectively. A comparison between the model results and experimental data gave high correlation coefficient ( R 2 = 0.976) shows that the model is able to predict the concentration of residual Cr(VI) in the solution.