Modeling and optimization of antidepressant drug Fluoxetine removal in aqueous media by ozone/H2O2 process: Comparison of central composite design and artificial neural network approaches

Abstract

Modeling and optimization of Fluoxetine degradation in aqueous solution by ozone/H2O2 process was investigated using central composite design (CCD) and the results were compared with the artificial neural network (ANN) predicted values. We studied the influence of basic operational parameters such as ozone concentration, initial concentration of H2O2 and Fluoxetine and reaction time. The ANN model was developed by feed-forward back propagation network with trainscg algorithm and topology (4: 8: 1). A good agreement between predicted values of Fluoxetine removal using CCD and ANN with experimental results was observed (R2 values were 0.989 and 0.975 for the ANN and CCD models, respectively). The results showed that ANNs were superior in capturing the nonlinear behavior of the system and could estimate the values of Fluoxetine removal efficiency accurately. Pareto analysis indicated that all selected factors and some interactions were effective on removal efficiency. It was found that the reaction time with a percentage effect of 45.04% was the most effective parameter in the ozone/H2O2 process. The maximum removal efficiency (86.14%) was achieved at ozone concentration of 30mgL−1, initial H2O2 concentration of 0.02mM, reaction time of 20min and initial Fluoxetine concentration of 50mgL−1 as the optimal conditions.