Treatment of Effluents from Vehicle Wash Stations with the Electrocoagulation Process: Proposition of a Predictive Statistical Model for the Estimation of the Efficiency of Chemical Oxygen Demand and Methylene Blue Active Substance Reduction

Abstract

The effluent generated in car and motorcycle wash processes contains several contaminants, including metals, oils, greases, surfactants (MBAS), microorganisms, high chemical oxygen demand (COD), and turbidity. When not properly treated, these compounds can cause severe environmental damage. In this study, the EC process was applied to treat this effluent. A predictive model was obtained using an experimental design methodology. The effects of the area and distance between aluminum electrodes and treatment time on the percentage reduction of COD and MBAS in a real vehicle wash station effluent (VWSE) were evaluated. The process was tested for 30 to 330 min at an electrical potential of 10.0 V. A 3³ full factorial design was used to develop the mathematical model, which was experimentally validated. Under the best-predicted model conditions (1.0 cm electrode spacing, 18.0 cm² area, and 330 min), COD reductions of 90% and MBAS reductions of 97% were achieved. The validated models and optimized parameters support the sizing and design of the electrolytic reactors for VWSE treatment. The 3³ full factorial design with statistical validation provided predictive capability and process optimization, evaluating the efficiency of the EC process and also allowed the prediction of system behavior under different operating conditions.

Electrocoagulation treats car wash effluent with up to 97% pollutant removal, validating a predictive model through experimental design.