The treatment and reuse of industrial wastewater plays a foremost role in succeeding environmental protection and water security. The electrochemical treatment technology has attracted a great deal of attention because of its compact, high particulate removal, free from chemicals, automation, and minimum sludge generation. The objective of the study is to review the existing literature on COD (Chemical oxygen demand) removal from various industrial effluents using electrocoagulation technology, as well as the factors that influence the process. Electricity is passed through electro plates dipped in wastewater during the electrocoagulation process. Metal hydroxide formations occur, which removes pollutants from wastewater via the sedimentation and flotation mechanisms. After a thorough review of various literatures, a detailed discussion on the process influencing parameters such as pH, Current Density, Electrolysis Time, Conductivity, Stirring Speed, and Retention Time has been done which gives useful information on future scope of research in this area.

The dye wastewater discharge from the textile industries mainly affects the aquatic environment. Hence, the treatment of this wastewater is essential for a pollutant-free environment. The purpose of this research is to optimize the dye removal efficiency for process influencing independent variables such as pH, electrolysis time (ET), and current density (CD) by using Box-Behnken design (BBD) optimization and Genetic Algorithm (GA) modelling. The electrocoagulation treatment technique was used to treat the synthetically prepared Reactive Black dye solution under batch mode potentiometric operations. The percentage of error for the BBD optimization was significantly greater than for the GA modelling results. The optimum factors determined by GA modelling were CD-59.11 mA/cm 2 , ET-24.17 minutes, and pH-8.4. At this moment, the experimental and predicted dye removal efficiencies were found to be 96.25% and 98.26%, respectively. The most and least predominant factors found by the beta coefficient were ET and pH respectively. The outcome of this research shows GA modeling is a better tool for predicting dye removal efficiencies as well as process influencing factors.

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