Electrochemical degradation of Reactive Black 5 with surface response and artificial neural networks optimization models

Artificial neural network modeling and statistical analysis are used to optimize electrochemical removal of Reactive Black 5 using a Ti/(RuO2)(0.8)-(Sb2O3)(0.2) electrode. Experimental design was used to analyze the influence of dye concentration (26.4-93.6 ppm), electrolyte concentration (NaCl (0.0062-0.1238 M), and current density (0.62-12.38 mA cm(-2)) in a batch treatment system. The response surface methodology and artificial neural network were appropriate methods to optimize the operating conditions in electrochemical degradation process. Optimization models was developed to assess the performance of the electrochemical degradation, where color removal percentage, COD removal percentage, and the energy consumption (KW/m(3)) were considered. A total removal of color and decrease in up to 73.77% of chemical oxygen demand within 180 min of treatment was obtained using the optimized conditions. The final neural network model, characterized by a 4-8-3 architecture, presenting performance index determination coefficient (R-2) of 0.982 and mean square error of 0.0146. The neural model developed demonstrated an efficiently predictive performance and to optimize the parameters of the electrochemical oxidation process.