Model Prediction and Optimization of Cefixime Trihydrate Removal from Simulated Wastewater through Advanced Plasma Technology

Abstract

Plasma technology has received significant attention as an advanced oxidation technique for the efficient removal of pharmaceuticals from various wastewaters. In this study, cefixime trihydrate (CFX) was used as a typical pharmaceutical, and a stepwise optimization strategy was adopted to determine the experimental parameters that benefiting the generation of free radical. The removal rate of 20 mg/L CFX reached 94.8% after 60 min plasma treatment when the voltage was 12 kV, the discharge gap was 15 mm, and the number of needles was 5. Furthermore, through the prediction and optimization of the response surfaces mythology (RSM) and artificial neural networks (ANN) model, the removal rate of CFX increased to 95.8% when the voltage was 11.9 kV, discharge gap was 15 mm, the number of needles was 5, and treatment time was 60 min. Finally, based upon the change of solution absorbance before and after the UV-Vis scan for plasma treatment and free radical quenching experiments, ·OH, 1O2, and ·O2− mainly attacked the C=C of CFX and played an important role during the plasma treatment. Moreover, the toxicity of CFX solution after ·OH attack was reduced evidently compared with that of the initial CFX solution via further toxicity assessment.