| dc.description.abstract |
Electrocoagulation is an effective wastewater treatment process for the removal of heavy metals. This study focuses on deriving optimal conditions for removing heavy metals, viz. Lead (Pb), Cobalt (Co), and Manganese (Mn) from simulated wastewater by investigating removal efficiency and energy consumption of electrocoagulation process. Five operational parameters namely 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 (5–25 cm) have been analyzed. To improve the treatment of heavy metals, a novel coupled approach, namely Artificial neural network - non-dominated sorting Biogeography based optimization (ANN-NSBBO), has been proposed. Using the experimental data, a feed-forward backpropagation ANN model is used with removal efficiency and energy consumption as the outputs. Optimal values of operational parameters for maximum removal efficiency and minimum energy consumption were obtained using multi-objective NSBBO over the trained ANN model. True pareto fronts for Cobalt, Lead and Manganese were obtained after 100 iterations of the optimization algorithm. The maximum removal efficiency of 98.66% was obtained for Cobalt at the electrical energy consumption of 0.204 kWh. Minimum energy consumption for electrocoagulation of Lead (5.34 x 10−6 kWh) gave 82.48% removal efficiency. The maximum removal efficiency of Manganese (101.238%) was achieved at 7.64 pH, 0.084 A/cm2 current density, 3.188 cm inter-electrode distance, 47.49 °C solution temperature, 19.758 min charging time, and 0.145 kWh energy consumption. The non-dominated optimum tradeoff between removal efficiency and energy consumption provides clarity on operating conditions for the electrocoagulation process. The proposed approach of enhancing heavy metal treatment could assist municipalities, industries, and the scientific communities in achieving the United Nation's sustainable development goal of heavy metal remediation. |
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