Fixed-bed studies and artificial neural network modeling for removal of fluoroquinolone antibiotics using a green MWCNT@E adsorbent.

Abstract

Ciprofloxacin (CIP) and ofloxacin (OFL), commonly used fluoroquinolone antibiotics, have been frequently detected in water sources which can cause environmental toxicity. This study explored the application of carbon nanotubes (CNTs) functionalized using a green method in a continuous adsorption system for the removal of CIP and OFL by comparing traditional mass-transfer models and artificial neural networks (ANN). Results demonstrated that the system was more effective at low concentrations (0.2 mmol/L) and flow rates (0.2 mL/min), for both antibiotics, with extended breakthrough times, indicating that OFL (1256.39 min) and CIP (1314.60 min) were completely removed for a longer period, and the lowest mass transfer zone for OFL (2.75 cm) and CIP (3.44 cm). The mathematical models showed good fits to the acquired data, although the model developed by Yan et al. described the systems accurately (R² > 0.99) under all tested conditions. ANN modeling showed accurate prediction of the fixed-bed dataset. The phytotoxicity study indicated a significant reduction in the toxicity of the effluent after treatment. Therefore, the green-functionalized CNTs exhibited prominent performance in a continuous system, offering a promising approach for scaling up wastewater treatment processes.