Artificial Neural Networks,
Optimization and Kinetic
Modeling of Amoxicillin Degradation in Photo-Fenton Process
Using Aluminum Pillared Montmorillonite-Supported Ferrioxalate Catalyst
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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 H<sub>2</sub>O<sub>2</sub> 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 H<sub>2</sub>O<sub>2</sub> 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 H<sub>2</sub>O<sub>2</sub> 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<sup>–1</sup> S<sup>–1</sup>. These results provided invaluable insights into the catalytic degradation
of AMX in photo-Fenton process
