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.

在本文中，几种水生因素（催化剂的性质，初始pH和污染物的初始浓度）对双氯芬酸（DFC）的光催化降解的影响，双氯芬酸是处方最广泛的抗炎非甾体药物之一，进行了研究。此外，为了检查强化过程，分析了在过硫酸钠（PPS）存在下光催化DFC降解的变化。发现，与二氧化钛（TiO ₂），氧化锌（ZnO）光催化剂的性能非常好，在150分钟后DFC的降解效率为96.13％。ZnO催化剂对DFC的光降解很好地拟合了Langmuir-Hinshelwood动力学模型。模拟太阳-UVA / ZnO / PPS的最大效率为97.27％，模拟太阳-UVA / ZnO的最大效率为77％。为了确定导致DFC去除最大化的最佳条件，应用了一种与遗传算法（GA）相结合的人工神经网络（ANN）建模方法。确定的最佳ANN具有0.999的相关性，并将其进一步用于工艺优化中，在以下条件下，将99.7％的降解效率确定为最佳：DFC初始浓度37.9 mg L ^-1，pH 5,88和PPS初始浓度500 mg L ^-1。还通过使用小家鼠肝脏和肾脏进行的生物学测试，评估并确认了该方法的有效性以及药物污染物及其副产物的毒性。