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 H
2
O
2
/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 H
2
O
2
/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.
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