Electrocoagulation process for greywater treatment: Statistical modeling, optimization, cost analysis and sludge management

The present study investigates the removal of greywater pollutants such as COD, BOD, nitrate, and phosphate using the electrocoagulation treatment process. The influence of operating parameters such as current density (CD) (1–5 A/m2), contact time (CT) (10–90 min), and initial pH (3–11) of the solution was investigated using aluminum electrode. The results demonstrate that 70% COD removal, 87.5% BOD removal, 82.7% nitrate removal, and 84.7% phosphate removal is achieved at optimum operating condition (CD = 3 A/m2, CT = 60 min, and pH = 7, energy consumption = 0.153 kWhm−3, and operating cost = 0.114 US$m−3). The kinetics study analysis confirms that the electrocoagulation process follows pseudo-first-order kinetics model. The combination of response surface methodology (RSM) and artificial neural network (ANN) based statistical models were employed to optimize the electrocoagulation process parameters as well as to accomplish the individual limitations. The correlation coefficient value closer to ∼ 1 and lower error governs the feasibility of the developed models. The results exhibited that, the ANN model had a higher R2 and a lower MSE value than the RSM model, indicating that ANN is better at predicting process output than RSM, although RSM appropriately predicts process parameter interaction and its relevance. The study found that using a combinational approach to represent the electrocoagulation process for greywater treatment is more effective.