Implementation of Fuzzy Logic Model on Textile Wastewater Treatment by Electrocoagulation Process

Abstract

A high concentration of colour, chemical oxidation demand (COD) and low biodegradability make textile wastewater a major source of pollution. While physicochemical processes, adsorption and chemical coagulation were the most commonly applied treatment methods for textile wastewater, the electrocoagulation process as compared to these conventional methods, produces less sludge with high colour removal efficiency. In this study, textile wastewater obtained from industry was treated by electrocoagulation process and the results were evaluated with fuzzy logic, an artificial intelligence-based modelling, which is used for advanced control and is employed in many environmental engineering applications, from wastewater treatment to air pollution estimation. Current density, initial pH and electrolysis time were selected as variables and utilized to predict COD, total organic carbon (TOC) and colour removal efficiencies. The experimental studies were applied to the developed fuzzy logic model to demonstrate the model accuracy. Using fuzzy logic model, surface maps were prepared for COD, TOC and colour removal. According to the obtained results, while Colour can be removed by 90%, COD and TOC can be removed by approximately 55 and 80%, respectively. While low pH was more efficient in COD and TOC removal, high pH values were shown to be efficient in colour removal. Time was the most efficient parameter for the electrocoagulation treatment and 15 minutes of reaction time was obtained as optimum value. Moreover, the results were evaluated with the multiple regression model. The obtained equations were maximized to determine the optimum conditions. These optimum conditions are 60 mA/cm(2) of current density, pH 5 and 20 min of reaction time. When these parameters were examined, colour, TOC and COD removal efficiencies were attained as 89, 86 and 61%, respectively.