Electrocoagulation of Pb2+, Co2+, and Mn2+ from simulated wastewater: An algorithmic optimization using hybrid RSM–GA–PSO

Abstract

AbstractEfficiency of electrocoagulation process for the removal of heavy metals (Pb2+, Mn2+, and Co2+) from simulated wastewater was investigated using iron electrode pair at five operational variables: pH (2–10), current density (0.076–0.189 A/cm2), inter‐electrode distance (3–7 cm), solution temperature (30–70°C), and charging time (5–20 min). Experiments were conducted as per central composite design (CCD), and the data was used for model building. Algorithmic optimization via hybrid response surface methodology (RSM)–genetic algorithm (GA)–particle swarm optimization (PSO) was studied where Pb2+, Mn2+, and Co2+ removal efficiency were monitored as responses from the finding equations of the CCD. Transition to the optimum removal (≥90%) of Pb2+, Mn2+, and Co2+ ions was achieved with current density of 0.19 A/cm2, solution temperature of 70°C, mean electrode distance of 3 cm and pH of 6.6 and charging time of 25 min for PSO relative to GA and RSM. Corresponding experiments conducted with the global optimal conditions show that the actual results (98.5, 90, 99%) were in reasonable agreement with the theoretical results of RSM (94.09, 90.82, 85.08%), GA (102.08, 87.91, 101.55%), and PSO (100.58, 90.91, 101.05%) for Pb2+, Mn2+, and Co2+, respectively. The mean error indices of 6.43, 2.83, and 1.73% for RSM, GA, and PSO, respectively, indicate that PSO guarantees best convergences to true optimal.