Modelling and Multi-Objective Optimization of Continuous Indirect Electro-Oxidation Process for RTB21 Dye Wastewater...

Area of Science:

• Environmental Chemistry
• Water Treatment Technologies
• Applied Electrochemistry

Background:

• Textile industry wastewater poses significant environmental challenges due to persistent synthetic dyes.
• Reactive Turquoise Blue RTB21 dye is a common recalcitrant pollutant in industrial effluents.
• Conventional treatment methods often struggle with efficient removal of such dyes.

Purpose of the Study:

• To develop and optimize a continuous indirect electro-oxidation (EO) process for treating reactive turquoise blue RTB21 dye wastewater.
• To investigate the influence of operating parameters such as initial pH, current density, hydraulic retention time (HRT), and electrolyte concentration.
• To model and optimize the process using advanced computational techniques.

Main Methods:

• Indirect electro-oxidation (EO) using graphite electrodes in a continuous flow system.
• Central composite design (CCD) for experimental planning and data collection.
• Artificial neural networks (ANNs) for process modeling and prediction.
• Multi-objective optimization using genetic algorithm (MOGA) to determine optimal operating conditions.

Main Results:

• ANN model accurately predicted color and COD removal efficiencies (R2 values > 0.9998).
• MSE values for color and COD removal were 0.748 and 0.870, respectively.
• MOGA identified optimal operating conditions yielding high color and COD removal percentages, presented via a Pareto front.

Conclusions:

• Continuous indirect electro-oxidation is a viable and effective method for treating RTB21 dye wastewater.
• The combination of ANNs and MOGA provides a powerful tool for optimizing complex wastewater treatment processes.
• The study offers valuable insights into achieving efficient dye removal and pollutant reduction in industrial effluents.