Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology

Abstract

This paper utilizes a statistical approach, the response surface optimization methodology, to determine the optimum conditions for the Acid Black 172 dye removal efficiency from aqueous solution by electrocoagulation. The experimental parameters investigated were initial pH: 4–10; initial dye concentration: 0–600 mg/L; applied current: 0.5-3.5 A and reaction time: 3–15 min. These parameters were changed at five levels according to the central composite design to evaluate their effects on decolorization through analysis of variance. High R2 value of 94.48% shows a high correlation between the experimental and predicted values and expresses that the second-order regression model is acceptable for Acid Black 172 dye removal efficiency. It was also found that some interactions and squares influenced the electrocoagulation performance as well as the selected parameters. Optimum dye removal efficiency of 90.4% was observed experimentally at initial pH of 7, initial dye concentration of 300 mg/L, applied current of 2 A and reaction time of 9.16 min, which is close to model predicted (90%) result.

Improvement of the /Taguchi/ design optimization using artificial intelligence in three acid azo dyes removal by electrocoagulation

“…As illustrated in Figure , dye removal efficiency improved by increasing the current density and reaction time and decreasing the initial dye concentration and initial pH. These results are in agreement with our previous work .…”

“…In the recent years, various processes such as biological methods , adsorption , chemical coagulation , ion exchange , membrane filtration , photocatalysis , oxidation , and electrocoagulation (EC) have been used for the treatment of azo dye‐containing wastewaters. EC treatment process has been gaining popularity in the treatment of wastewaters containing azo dyes because of providing a simple, reliable, and cost‐effective procedure [, ].…”

Applying an Artificial Neural Network to Predict Coagulation Capacity of Reactive Dyeing Wastewater by Chitosan

“…This value is not exceptional, since the fi tness of models for dye processing in aquaeous solution is usually high in previous research, e.g. Arabi [29] reached 0.950 for the prediction of vat Green 1 dye decolourization by nano zero valent Iron, Sadri [10] reached 0.945 for prediction of acid Red 119 decolourization by PAC, and Taheri [30] reached 0.945 for the prediction of acid Black 172 decolourization by the electrocoagulation method using alum electrode, even though they used a complex mathematical method like response surface methodology, which required statistical and experimental knowledge. Therefore, the neural network modeling effectively simulates and predicts the infl uential factors of the gum coagulation process.…”

Adsorption of cadmium onto modified nanosized magnetite: kinetic modeling, isotherm studies, and process optimization

“…Two most common designs extensively used in RSM are the central composite design (CCD) and the Box-Behnken design. The CCD is immensely appropriate for sequential experimentation and allows a reasonable amount of information for testing the lack of fit while not involving an unusually large number of design points [42].…”