Mehrangiz FATHINIA, Soghra BOZORG, Alireza KHATAEE

Heterogeneous Fenton-like degradation of Acid Red 17 using Fe-impregnatednanoporous clinoptilolite: arti cial neural network modeling andphytotoxicological studies

Heterogeneous Fenton-like removal of Acid Red 17 (AR17) from aqueous solution was investigated. Fe-impregnated nanoporous clinoptilolite (Fe-NP-Clin) was prepared by an impregnation method and used as a catalyst. Acomplete characterization including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanningelectron microscopy (SEM), inductively coupled plasma (ICP), and Brunauer{Emmett{Teller (BET) analyses was doneto describe the physical and chemical properties of NP-Clin and Fe-NP-Clin samples. The effects of ve operationalparameters, i.e. solution pH, H2O2dosage, catalyst loading, AR17 concentration, and reaction time, on the removalefficiency of AR17 were studied. For the rst time, an arti cial neural network (ANN) model with ve neurons at theinput layer, 14 layers in the hidden layer, and one neuron at the output layer was designed to predict the removal efficiencyof AR17. The correlation coefficient between the predicted results by the ANN model and experimental data was 0.993,demonstrating that the ANN could efficiently predict AR17 removal efficiency under different operating conditions. Thephytotoxicity of AR17 and its intermediate compounds formed in the Fenton process was evaluated using the aquaticspeciesLemna minor.

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