Artificial Neural Networks, Optimization and Kinetic Modeling of Amoxicillin Degradation in Photo-Fenton Process Using Aluminum Pillared Montmorillonite-Supported Ferrioxalate Catalyst

Abstract

An artificial neural network (ANN) was applied to study the hierarchy of significance of process variables affecting the degradation of amoxicillin (AMX) in a heterogeneous photo-Fenton process. Catalyst and H2O2 dosages were found to be the most significant variables followed by degradation time and concentration of AMX. The significant variables were optimized and the optimum condition to achieve degradation of 97.87% of 40 ppm AMX was 21.54% excess H2O2 dosage, 2.24 g of catalyst in 10 min. A mathematical model (MM) for the degradation of AMX was developed on the basis of the combined results of the ANN and the optimization studies. The MM result showed that increases in both catalyst and H2O2 dosage enhanced the rate of AMX degradation as shown by the rate constants evaluated from the model. The highest rate constant at the optimum conditions was 122 M−1 S−1 . These results provided invaluable insights into the catalytic degradation of AMX in photo-Fenton process.