Modeling of Textile Dye Removal from Wastewater Using Innovative Oxidation Technologies (Fe(II)/Chlorine and H2O2/Periodate Processes): Artificial Neural Network-Particle Swarm Optimization Hybrid Model

An efficient optimization technique based on a metaheuristic and an artificial neural network (ANN) algorithm has been devised. Particle swarm optimization (PSO) and ANN were used to estimate the removal of two textile dyes from wastewater (reactive green 12, RG12, and toluidine blue, TB) using two unique oxidation processes: Fe(II)/chlorine and H2O2/periodate. A previous study has revealed that operating conditions substantially influence removal efficiency. Data points were gathered for the experimental studies that developed our ANN-PSO model. The PSO was used to determine the optimum ANN parameter values. Based on the two processes tested (Fe(II)/chlorine and H2O2/periodate), the proposed hybrid model (ANN-PSO) has been demonstrated to be the most successful in terms of establishing the optimal ANN parameters and brilliantly forecasting data for RG12 and TP elimination yield with the coefficient of determination (R2) topped 0.99 for three distinct ratio data sets.

Automated summary

An efficient optimization technique based on a metaheuristic and artificial neural network algorithm was developed to estimate the removal efficiency of two textile dyes using two oxidation processes. The proposed hybrid model (ANN-PSO) demonstrated excellent performance in establishing optimal ANN parameters and accurately predicting elimination yield.