Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds.

Sulfamethoxazole and metronidazole are emerging pollutants commonly found in surface water and wastewater. These compounds have a significant environmental impact, being necessary in the design of technologies for their removal. Recently, the advanced oxidation process has been proven successful in the elimination of this kind of compounds. In this sense, the present work discusses the application of UV/HO and ozonation for the degradation of both molecules in single and binary systems. Experimental kinetic data from O and UV/HO process were adequately described by a first and second kinetic model, respectively. From the ANOVA analysis, it was determined that the most statistically significant variables were the initial concentration of the drugs (0.03 mmol L) and the pH = 8 for UV/HO system, and only the pH (optimal value of 6) was significant for degradation with O. Results showed that both molecules were eliminated with high degradation efficiencies (88-94% for UV/HO and 79-98% for O) in short reaction times (around 30-90 min). The modeling was performed using a quadratic regression model through response surface methodology representing adequately 90 % of the experimental data. On the other hand, an artificial neural network was used to evaluate a non-linear multi-variable system, a 98% of fit between the model and experimental data was obtained. The identification of degradation byproducts was performed by high-performance liquid chromatography coupled to a time mass detector. After each process, at least four to five stable byproducts were found in the treated water, reducing the mineralization percentage to 20% for both molecules.