Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor

Abstract

This study evaluates the effectiveness of an artificial neural network-genetic algorithm (ANN-GA) artificial intelligence (AI) model in the prediction of behavior and optimization of an electro-oxidation pilot press-type reactor, which treats a synthetic wastewater prepared with a dye. The ANN was built from real experimental data using as input the following variables: time, flow, j, dye concentration, and as output discoloration. The performance of the ANN was measured with MAPE (8.3868%), calculated from real and predicted values. The coupled AI model was used to find the best operational conditions: discoloration efficiency (above 90%) at j = 27 mA/cm2 and dye concentration of 230 mg/L.