In this paper, the influence of several aquatic factors (the nature of catalyst, the initial pH and the initial concentration of the pollutant) on the photocatalytic degradation of diclofenac (DFC), one of the most widely prescribed anti-inflammatory non-steroidal drug, was studied. Also, in order to examine the intensification process, the variation of the photocatalytic DFC degradation in the presence of sodium persulfate (PPS) was analyzed. It was found that, compared to titanium dioxide (TiO2), the zinc oxide (ZnO) photocatalyst performed exceptionally well, with a 96.13% DFC degradation efficiency after 150 min. The photodegradation of DFC by ZnO catalyst fitted well the Langmuir-Hinshelwood kinetic model. The maximum efficiency is 97.27% for simulated solar-UVA/ZnO/PPS and 77% for simulated solar-UVA/ZnO. In order to determine the optimal conditions leading to the maximization of DFC removal, an artificial neural network (ANN) modeling approach combined with genetic algorithm (GA) was applied. The best ANN determined had a correlation of 0.999 and it was further used in the process optimization where a 99.7% degradation efficiency was identified as the optimum under the following conditions: DFC initial concentration 37,9 mg L-1, pH 5,88 and PPS initial concentration 500 mg L-1. The effectiveness of the process and the toxicity of the pharmaceutical pollutants and their by-products were also evaluated and confirmed by the biological tests using liver and kidney of Mus musculus mice.
Keywords: Artificial neural network; Biotoxicity; Diclofenac removal; Genetic algorithm; Photocatalysis.
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