A synthetic wastewater based on Algiers refinery real effluent was prepared and treated using anodic oxidation. Full factorial plan design was used to conduct the statistical analysis of the results. The aim of the study was to assess the interaction between current density (CD) and stirring degree (SD), and quantify their effects on chemical oxygen demand (COD) removal and electric energy specific consumption (EESC). With an initial COD of 487 mg/l, pH of 5.5 and 0.05 M of Na2SO4 as supporting electrolyte, it was found that a 55 rpm steering degree variation lead to a substantial gain in COD removal and energy consumption, 6% and 8.5 KWh/kg, respectively. Current density was found to have different effect on removal efficiency within the applied stirring domain, and that mass transport coefficient (km) is inversely correlated to energy consumption. Theoretical model describing the process was reviewed and the relation between concentration, hydrodynamics and applied current was emphasized.

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The present study aims to investigate the feasibility of implementing the Electrocoagulation (EC) process to treat Algiers refinery effluent. The electrocoagulation was performed by using scrap aluminum plate electrodes in monopolar-parallel mode. Several parameters namely current density, reaction time, the electrolyte dose, and the initial Chemical Oxygen Demand (COD) concentration were studied. The maximum removal of COD achieved was found to be 78.55%. Operating conditions at which maximum COD removal efficiencies were achieved at current density 8 mA/cm2, electrolyte dose 1 g/l, with 360 mg/l of initial COD concentration at working time of 40 min. An artificial neural network (ANN) was also utilized to determine predicted responses using neural networks for the 4-10-1 arrangement. The responses predicted by ANN were in alignment with the experimental results. The values of the determination coefficient (R2 = 0.978) and the root mean square error (RMSE = 21.28) showed good prediction results between the model and experimental data. Hence, the ANN model as a predictive tool has a great capacity to estimate the effect of operational parameters on the electrocoagulation process.