Using Sono-Electro-Persulfate Process for Atenolol Removal from Aqueous Solutions: Prediction and Optimization with the ANFIS Model and Genetic Algorithm

Abstract

Atenolol (ATN) is a drug that is widely used to treat some heart diseases, and since it cannot be completely decomposed in the human body, some amounts of it are found in surface water. These amounts may bring risks to the environment and humans, and for this reason, its removal is a must. In the present study, the combined sono-electro-persulfate method was used for ATN removal. Based on the design of the experiment conducted by response surface methodology (RSM), the effects of 5 main factors (pH, time, PS concentration, current intensity, and initial ATN concentration) have been investigated at 5 levels. After passing the test steps in different conditions, the remaining amount of ATN has been measured by high-performance liquid chromatography (HPLC). Finally, an adaptive neuro-fuzzy inference system (ANFIS) with 99.63% accuracy and a genetic algorithm (GA) were used to analyze and interpret data and predict optimal conditions. The obtained results indicate the possibility of a maximum efficiency of 99.8% in the mentioned conditions (Ph of 7.4, time of 18 min, PS concentration of 2000 mg/L, current intensity of 3.35 A, and initial ATN concentration of 11.2 mg/L). According to the obtained results, the initial concentration of ATN can be considered as the most effective factor in this process, and the best Ph range for this experiment was the neutral range. The sono-electro persulfate process can be mentioned as a new and effective method for removing ATN from water sources.