Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

One of the most serious effects micropollutants in environment is biological magnification, which causes adverse on humans and ecosystem. Among all micro-pollutants, antibiotics are commonly present aquatic due to their wide use treating or preventing various diseases infections for humans, plants, animals. Therefore, an aluminum-based electrocoagulation unit has been used this study remove cephalexin antibiotics, as a model from water. Computational statistical models were optimize key parameters electrochemical removal cephalexin, including initial concentration (15–55 mg/L), pH (3–11), electrolysis time (20–40 min), electrode type (insulated non-insulated). The response surface methodology-central composite design (RSM-CCD) was investigate dependency studied variables, while artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) methods applied predicting experimental training data. results showed that best predicted removals (CEX) 88.21% 93.87%, respectively, obtained at 6.14 34.26 min. also ANFIS predicts interprets better than ANN RSM-CCD models. Sensitivity analysis using Garson method comparative significance variables follows: (30%) > (27%) CEX (24%) (19%).