RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: Multi objective optimization using genetic algorithm approach Original Research Article

Performance of electrocoagulation system for the removal of copper from synthetic wastewater was investigated using aluminum electrode pair at four operational parameters: copper concentration (2.5-32.5 mg L− 1), pH (5–9), voltage (6–18 V) and treatment time (5–25 min). Metal removal efficiency and energy consumption were monitored as responses. Experiments were conducted as per center composite design, and the data was used for model building employing response surface methodology (RSM) and artificial neural network approach (ANN). Multi objective optimization for maximizing the copper removal efficiency and minimizing the energy consumption was carried out using genetic algorithm (GA) over the ANN model. The optimization procedure resulted in the creation of nondominated optimal points which gave an insight regarding the optimal operating conditions of the process.