Multi-object optimization of Navy-blue anodic oxidation via response surface models assisted with statistical and...

Area of Science:

• Environmental Chemistry
• Electrochemistry
• Process Engineering

Background:

• Industrial wastewater often contains persistent dyes like Navy-blue dye (NB).
• Electrochemical methods offer a promising route for dye degradation.
• Optimizing electrochemical processes requires balancing dye removal efficiency with energy consumption.

Purpose of the Study:

• To model and optimize the electrochemical removal of Navy-blue dye (NB).
• To minimize energy consumption (EC) during the dye degradation process.
• To compare the effectiveness of different computational models for process optimization.

Main Methods:

• Utilized Box-Behnken experimental design with current density, electrolyte concentration, pH, and time as inputs.
• Applied multiple linear regression, support vector regression, and artificial neural network models.
• Employed genetic algorithm for dual-response optimization and desirability function for ranking solutions.

Main Results:

• Identified optimal parameters: pH 6.4, current density 11.89 mA cm⁻², 0.055 M Na₂SO₄, and 21.5 min electrolysis time.
• Achieved 83.23% NB removal with 3.64 Wh energy consumption under optimal conditions.
• Sensitivity analysis indicated current density as the most influential variable, followed by time and electrolyte concentration.

Conclusions:

• Statistical and computational modeling effectively optimizes electrochemical dye degradation processes.
• Artificial neural network models demonstrated the highest accuracy in predicting process outcomes.
• The study provides a framework for efficient and energy-conscious wastewater treatment.