Machine learning-based performance evaluation and sludge characterization studies of oxidized starch-aluminum electrode assisted by direct current treatment of dye laden wastewater

Abstract

The performance evaluation, sludge characterization and bi-optimization of treating dye-laden wastewater using oxidized starch-aluminum electrode assisted by direct current was investigated. Variables considered are current density (CD), wastewater pH, oxidized starch (OS) dosage and electrode inter-distance. Electrocoagulation batch reactor incorporated with jar test module was used for the experiment. FTIR, XRD and SEM were conducted to investigate structure, composition and morphology of starch and generated sludge. Sludge settling characteristics and filterability were studied. Response surface methodology (RSM) and artificial neural network (ANN) approach were used to optimize the process. The FTIR peaks revealed alcohol and carboxylic OH groups, while atomic structure indicated partly crystalline pattern. The results showed 96.22 % color removal using 6.6 mA/cm2 CD, 1.0 g/L OS, 4 cm inter-distance, and wastewater pH 4; 100 % COD removal using 4.4 mA/cm2 CD, 1.0 g/L OS, and 3 cm inter-distance at pH 7; and 99.99 % phosphate removal applying 2.2 mA/cm2 CD, 1.0 g/L OS, and 4 cm between electrode at pH 7. The sludge settling indicated lag, hindered, transition and compression zones, while sludge volume indices were less than 80 mg/g. The ANOVA revealed significant models with Prob > F 0.99 for all the response variables, indicated better optimization approach. From the forgoing, the use of combined technology; electro and chemical coagulation is beneficial toward achieving better result in the treatment of dye laden wastewater.

Keywords

• Electro-chemical coagulation
• Oxidized starch-aluminum electrode
• Performance evaluation
• Dye-laden wastewater
• Machine learning