Heterogeneous Fenton-like degradation of Acid Red 17 using Fe-impregnated nanoporous clinoptilolite: artificial neural network modeling and phytotoxicological studies


Abstract: 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. A complete characterization including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), inductively coupled plasma (ICP), and Brunauer--Emmett--Teller (BET) analyses was done to describe the physical and chemical properties of NP-Clin and Fe-NP-Clin samples. The effects of five operational parameters, i.e. solution pH, H$_{2}$O$_{2}$ dosage, catalyst loading, AR17 concentration, and reaction time, on the removal efficiency of AR17 were studied. For the first time, an artificial neural network (ANN) model with five neurons at the input layer, 14 layers in the hidden layer, and one neuron at the output layer was designed to predict the removal efficiency of 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. The phytotoxicity of AR17 and its intermediate compounds formed in the Fenton process was evaluated using the aquatic species \textit{Lemna minor}.

Keywords: Heterogeneous Fenton, nanoporous clinoptilolite, decolorization, neural network, phytotoxicity

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