ABSTRACT
Azithromycin (AZM), an antibacterial considered one of the most consumed drugs, especially during the period against the Covid 19 pandemic, and it is one of the persistent contaminants that can be released into aquatic ecosystems. The purpose of this study is to determine the efficacy of a Fenton-like process (chlorine/iron) for the degradation of AZM in an aqueous medium by determining the impact of several factors (the initial concentration of (FeSO4, NaClO, pollutant), and the initial pH) on the degradation rate. The Response Surface Methodology (RSM) based on the Box-Wilson design as well as the Artificial Neural Network (ANN) modeling combined with a genetic algorithm (GA) approaches were used to determine the optimal levels of the selected variables and the optimal rate of degradation. The quadratic model of multi-linear regression developed indicated that the optimal conditions were a concentration of chlorine of 600 µM, the concentration of AZM is 32.8 mg/L, the mass of the catalyst FeSO4 is 3.5 mg and a pH of 2.5, these optimal values gave a predicted and experimental yield of 64.05% and 70% respectively, the lack of fit test in RSM modeling (F0 = 3.31 which is inferior to Fcritic (0.05, 10.4) = 5.96) indicates that the true regression function is not linear therefore, the ANN-GA modeling as non-linear regression indicated that the optimal conditions were a concentration of chlorine of 256 µM, the concentration of AZM is 5 mg/L, the mass of the catalyst FeSO4 is 9.5 mg and a pH of 2.8, these optimal values gave a predicted and experimental yield of 79.69% and close to 80% respectively, Furthermore, biotoxicity tests were conducted to confirm the performance of our process using bio-indicators called daphnia (Daphnia magna), which demonstrated the efficacy of the like-Fenton process after 4 h of degradation.