Electrochemical treatment of wastewater containing reactive Blue 4 (RB 4) dye: RSM and ANN optimization, technoeconomic analysis and sludge characterization

• Optimization of the electrochemical, electro-coagulation (EC) decolorization of RB4 dye. • Five input process variables (pH, initial dye concentration, current density, operating time, and electrodes gap) are used. • Response surface methodology (RSM) and artifial neural network (ANN) with central composite design (CCD) design are employed to predict and optimize the removal efficiencies of colour (90 %) and COD (84.32 %). • Techno-economic analysis is carried out to determine efficacy of EC process at the optimum variables. • FTIR and FESEM/EDX investigations on EC derived sludge are carried out. In the present study, electrochemistry based electro-coagulation (EC) process, known as green process is used for the decolorization of Reactive Blue 4 (RB4) from simulated textile wastewater. A multivariate approach, response surface methodology (RSM) and central composite design (CCD) is employed to model and optimize the EC process with five input variables (pH, initial concentration of dye, current density, operating time, and electrodes gap) to treat the wastewater containing RB 4 dye. The efficiency of EC process is calculated in terms of % decolorization and % chemical oxygen demand (COD) removal. A back-propagation Artificial Neural Network (BP - ANN) is also engaged to predict the % color and % COD removal. The experimental values of % decolorization (89.3 %) and % COD removal (84.3 %) are found very close to predicted % decolorizations (88.6 % and 89.4 %) and % COD removal (83.4 % and 84.4 %) at optimized conditions [pH ( X 1 ) = 7.0; initial dye concentration ( X 2 ) = 1297.6 mg l -1 ; current density ( X 3 ) = 13.42 mA cm -2 ; contact time ( X 4 ) = 70 min and initial electrodes gap ( X 5 ) = 1.0 cm] using RSM and ANN, respectively. Techno-economic efficacy is determined in terms of an operating cost as ₹114.82 m -3 . The physico-chemical properties of the EC process generated sludge are analyzed using FTIR and FESEM/EDX. The comparative analysis with previous studies and future perspectives of the EC process for the removal of RB 4 from wastewater is also carried out.

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Publication Details

Journal
Cleaner Chemical Engineering
Published
2024-12-16
DOI
https://doi.org/10.1016/j.clce.2024.100138
Citations
4
Primary Topic
Advanced oxidation water treatment
Type
article
Field-Weighted Citation Impact
0.81
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article

Electrochemical treatment of wastewater containing reactive Blue 4 (RB 4) dye: RSM and ANN optimization, technoeconomic analysis and sludge characterization

Shriram S. Sonawane, Sushil Kumar, Yatindra Kumar, Kajal Gautam
4 citations
Cleaner Chemical Engineering
Advanced oxidation water treatment
0.81
article

Electrochemical treatment of wastewater containing reactive Blue 4 (RB 4) dye: RSM and ANN optimization, technoeconomic analysis and sludge characterization

Shriram S. Sonawane, Sushil Kumar, Yatindra Kumar, Kajal Gautam
article en
4 citations

Abstract

• Optimization of the electrochemical, electro-coagulation (EC) decolorization of RB4 dye. • Five input process variables (pH, initial dye concentration, current density, operating time, and electrodes gap) are used. • Response surface methodology (RSM) and artifial neural network (ANN) with central composite design (CCD) design are employed to predict and optimize the removal efficiencies of colour (90 %) and COD (84.32 %). • Techno-economic analysis is carried out to determine efficacy of EC process at the optimum variables. • FTIR and FESEM/EDX investigations on EC derived sludge are carried out. In the present study, electrochemistry based electro-coagulation (EC) process, known as green process is used for the decolorization of Reactive Blue 4 (RB4) from simulated textile wastewater. A multivariate approach, response surface methodology (RSM) and central composite design (CCD) is employed to model and optimize the EC process with five input variables (pH, initial concentration of dye, current density, operating time, and electrodes gap) to treat the wastewater containing RB 4 dye. The efficiency of EC process is calculated in terms of % decolorization and % chemical oxygen demand (COD) removal. A back-propagation Artificial Neural Network (BP - ANN) is also engaged to predict the % color and % COD removal. The experimental values of % decolorization (89.3 %) and % COD removal (84.3 %) are found very close to predicted % decolorizations (88.6 % and 89.4 %) and % COD removal (83.4 % and 84.4 %) at optimized conditions [pH ( X 1 ) = 7.0; initial dye concentration ( X 2 ) = 1297.6 mg l -1 ; current density ( X 3 ) = 13.42 mA cm -2 ; contact time ( X 4 ) = 70 min and initial electrodes gap ( X 5 ) = 1.0 cm] using RSM and ANN, respectively. Techno-economic efficacy is determined in terms of an operating cost as ₹114.82 m -3 . The physico-chemical properties of the EC process generated sludge are analyzed using FTIR and FESEM/EDX. The comparative analysis with previous studies and future perspectives of the EC process for the removal of RB 4 from wastewater is also carried out.

Cleaner Chemical EngineeringVol. 11
Motilal Nehru National Institute of Technology (IN), Visvesvaraya National Institute of Technology (IN), GIET University (IN)
Clean water and sanitation
Openalex Percentile: Top 28%
Advanced oxidation water treatment
0.81
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