4.5 Article

Optimization and effect of pH on treatability of metal plating wastewater by electrocoagulation process: a pilot study

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SPRINGER
DOI: 10.1007/s13762-023-04972-z

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Metal plating; Electrocoagulation; Optimization; Response surface methodology

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Metal plating industry wastewater is highly toxic due to the presence of heavy metals and cyanide. Conventional chemical treatment methods are commonly used, but they result in the generation of hazardous sludge. In this study, a pilot scale electrocoagulation (EC) process was developed as an alternative. The effect of pH adjustment on the removal efficiency of the EC process was investigated, and significant removals of problematic heavy metals such as copper and nickel were achieved.
Metal plating industry wastewater is a highly toxic wastewater due to its heavy metal and cyanide. This characteristic of wastewater is due to different types of processes used in the metal plating industry. In order to meet discharge limits for the receiving environment, classical chemical treatment methods are widely applied in this type of industry. Consequently, high treatment chemicals are required, resulting in excessive amounts of hazardous sludge. For these reasons, in this study, a pilot scale electrocoagulation (EC) process was developed as an alternative to the conventional chemical treatment currently applied in a metal plating plant. In this study, the effect of pH adjustment on the removal efficiency of the EC process was investigated before and after EC processing in a pilot scale reactor. Particularly, two heavy metals such as copper (Cu) and nickel (Ni), which are problematic to be eliminated in the current treatment, removals were invesigated. With the optimization studies, it was observed that Cu and Ni removals were over 93.75%. Similarly, Cu and Ni removal efficiencies were determined over 95% in the optimization of the EC process after pH adjustment. Indeed, these efficiencies were also achieved in the control study. As a result of the optimization of the study, model analyses were made with response surface methodology and it was observed that the regression coefficients were > 94.00% which were within the 95% confidence interval. This indicated that both the real operating conditions and the results obtained from the model are consistent with each other.

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