RESUMEN
An artificial neural network (ANN) approach with response surface methodology (RSM) technique has been applied to model and optimize the removal process of Brilliant Green dye by batch electrocoagulation process. A multilayer perceptron (MLP) - ANN model has been trained by four input neurons which represent the reaction time, current density, pH, NaCl concentration, and two output neurons representing the dye removal efficiency (%) and electrical energy consumption (kWh/kg). The optimized hidden layer neurons were obtained based on a minimum mean squared error. The batch electrocoagulation process was optimized using central composite design with RSM once the ANN network was trained and primed to anticipate the output. At optimized condition (electrolysis time 10 min, current density 80 A/m2, initial pH 5 and electrolyte NaCl concentration 0.5 g/L), RSM projected decolorization of 98.83% and electrical energy consumption of 14.99 kWh/kg. This study shows that the removal of brilliant green dye can be successfully carried out by a batch electrocoagulation process. Therefore, the process is successfully trained by ANN and optimized by RSM for similar applications.
RESUMEN
In the study the electrochemical oxidation process for decolorization of Rhodamine-B dye was studied using an anode coated with mixed metal oxides: TiO2, RuO2, and IrO2. Batch experimental studies were conducted to assess the effect of four important performance variables, current density, electrolyte concentration, initial pH and electrolysis time, on the decolorization and energy consumption. The process was modeled using an artificial neural network. Response surface methodology using central composite design (CCD) was utilized for optimization of the decolorization process. Based on the experimental design given by CCD, the results obtained by the statistical analysis show that the electrolysis time was the most influential parameter for decolorization whereas the current density had the greatest influence on the energy consumption. According to the optimized results given by the CCD model, maximum color removal of 97% and minimum energy consumption of 1.01 kWh/m3 were predicted in 4.9 minute of electrolysis time, using 0.031 M NaCl concentration at current density 10 mA/cm2 and an initial pH of 3.7. A close conformity was observed between the optimized predicted results and experimental results. The process was found to be efficient and consisted of indirect chemical oxidation producing strong oxidizing agents such as Cl2, HClO and OCl-.