Artificial Neural Networks and Genetic Algorithms: An Efficient Modeling and Optimization Methodology for Degradation of Atenolol Using Activated Persulfate with Ultrasound

Abstract

Atenolol (ATN) is a slowly biodegradable antagonist β-blocker drug and remains in the environment for a long period of time. This drug has a harmful effect on the environment and human and animal bodies. In this study, using activated persulfate with ultrasound for the degradation of ATN was investigated. The effect of independent variables including pH, ATN concentration, persulfate dose, contact time, and ultrasonic power has been studied at 5 levels. Central composite design (CCD) was used for designing the experiments in Design Expert 11.0 software. The ATN concentration was measured using high-performance liquid chromatography (HPLC). Genetic algorithms (GA) and artificial neural network (ANN) were used for optimization and prediction, respectively. The results indicated that at the optimal conditions for the experiment (pH of 6.79, reaction time of 19 min, initial ATN concentration of 15.92 mg/L, US power of 109.56 W, and PS dose of 1317.88 mg/L), the highest ATN degradation efficiency was 98.9%. The ATN degradation could be represented by the pseudo-zero-order kinetics. Also, the data of ATN were well fitted with the ANN model (R2 = 0.98). The results showed that the best pH range to eliminate ATN is the near neutral range and the GA was found to be an effective tool to optimize the experimental conditions for the removal of ATN. The ultrasonic/persulfate process as a useful technique has a high potential to remove ATN from aqueous solutions.