Modeling of Textile Dye Removal from Wastewater Using Innovative Oxidation Technologies (Fe(II)/Chlorine and...

Area of Science:

• Environmental Chemistry
• Water Treatment Technologies
• Computational Chemistry

Background:

• Textile dyes like reactive green 12 (RG12) and toluidine blue (TB) pose environmental challenges in wastewater.
• Oxidation processes, including Fe(II)/chlorine and H2O2/periodate, are effective for dye removal but sensitive to operating conditions.

Purpose of the Study:

• To develop and validate an efficient hybrid model for optimizing textile dye removal from wastewater.
• To accurately forecast the removal efficiency of RG12 and TB using advanced computational techniques.

Main Methods:

• A hybrid model combining artificial neural network (ANN) and particle swarm optimization (PSO) was developed.
• PSO was employed to determine optimal ANN parameter values for dye removal prediction.
• Experimental data from Fe(II)/chlorine and H2O2/periodate oxidation processes were utilized.

Main Results:

• The ANN-PSO hybrid model demonstrated high success in optimizing ANN parameters.
• The model achieved excellent forecasting accuracy for RG12 and TB removal yields.
• A coefficient of determination (R2) exceeding 0.99 was obtained across three distinct data sets.

Conclusions:

• The hybrid ANN-PSO model is a powerful tool for optimizing wastewater treatment processes.
• This approach offers a reliable method for predicting and enhancing the removal of textile dyes.
• The study highlights the potential of metaheuristic and ANN algorithms in environmental remediation.