Textile dye effluent treatment using advanced sono-electrocoagulation techniques: A Taguchi and particle swarm optimization modeling approach

ABSTRACT

The removal of reactive black-WNN dye from aqueous solutions was optimized using Taguchi optimization (TA) and particle swarm optimization (PSO) modeling in a batch mode potentiometric sono-electro-coagulation (SOEC) process with the aid of aluminum plates. The optimization process was based on four influencing factors: pH, current density (CD), electrolysis time (ET), and ultrasonic power (UP). A Taguchi orthogonal array L25 design matrix was used to optimize these factors to maximize the dye removal efficiency. The analysis of variance showed that the percentage of contribution of ET, pH, CD, and UP was 67.64%, 12.83%, 5.03%, and 6.80%, respectively. PSO modeling was found to be a better tool for predicting dye removal efficiency than TA optimization, with a prediction error of 1.17% compared to -4.63%. Under the optimized conditions of pH-6.6, CD-66.66 mA/cm², ET-25 minutes, and UP-100 W, the dye removal efficiency was found to be 98.30%, with a total energy consumption of 0.1188 kW h/L. The kinetic degradation study between ET and CD was found to follow a second-order kinetic model. Additionally, the synergistic effect was evaluated by combining ultrasonication and electrocoagulation. The results showed that the combined process (sono-electrocoagulation) was more effective than either ultrasonication or electrocoagulation alone, with a synergistic effect of 15%. Overall, the results suggest that TA optimization and PSO modeling can be effective tools for optimizing process parameters in the removal of Reactive black-WNN dye from aqueous solutions. The optimized conditions identified in this study, as well as the demonstration of the synergistic effect, can be used to design more efficient treatment systems for the removal of dyes from wastewater.

KEYWORDS:

• Taguchi optimization
• particle swarm optimization
• reactive black-WNN
• sono-electrocoagulation
• electrolysis time

Abbreviations

PSO	=	Particle swarm optimization
CD	=	Current Density
ET	=	Electrolysis Time
UP	=	Ultrasonic Power
DOE	=	Design of Experiments
RSM	=	Response Surface Methodology
GA	=	Genetic Algorithm
FA	=	Firefly Algorithm
ACO	=	Ant Colony Optimization
DE	=	Differential Evolution
ANN	=	Artificial Neural Network
GA	=	Genetic Algorithm
DRE	=	Dye Removal Efficiency
SNR	=	Signal-to-Noise ratio
OA	=	Orthogonal Array
ANOVA	=	Analysis of Variance
SS	=	Sum of Squares
MS	=	Mean Square
K1	=	First-Order rate constant
K2	=	Second-Order rate Constant
t	=	Time
ENRSOEC	=	Energy Consumption Sono Electrocoagulation Process
ENREC	=	Energy Consumption Electrocoagulation Process
ENRUS	=	Energy Consumption Sonication Process
V	=	Voltage
A	=	Ampere
P	=	Power

Acknowledgements

This study work has been carried at Environmental Engineering Laboratory at the Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India. The authors convey their thanks to the Management and Principal of Coimbatore Institute of Technology for providing a Seed Money Grant which was useful for carrying out research from the equipment ultrasonic water bath (GT SONIC-P3).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Manikandan S

Manikandan S is a full time research scholar in department of civil engineering at Coimbatore institute of technology, Coimbatore, Tamil nadu, India. His research interest includes electrochemical wastewater treatment, modelling by machine learning algorithm and environmental impact assessment.

Saraswathi R

Dr. Saraswathi R is working professor as department of civil engineering at Coimbatore institute of technology, Coimbatore, Tamil nadu, India. His research interest includes water and wastewater treatment, ground water quality modeling and environmental impact assessment.