Removal of amoxicillin from hospital waste using Fe2O3–Ag adsorbent and optimization by response surface methodology and machine learning prediction

Abstract

Purpose

Antibiotic pollution in hospital effluent is a significant environmental concern that contributes to the evolution of antibiotic-resistant bacteria. This study aims to provide effective removal strategies for antibiotics from wastewater.

Materials and methods

In this study, Fe₂O₃–Ag nanocomposites were prepared and characterized to enhance the sorption of Amoxicillin from water. We employed Response Surface Methodology (RSM) and machine learning models (XGBoost and Random Forest) to optimize the adsorption process, maximizing removal efficiency. The optimal conditions for Amoxicillin removal were determined to be a pH of 6.5, a contact time of 26 min, a temperature of 45 °C, and an adsorbent dosage of 0.185 g. Adsorption isotherm and kinetic studies indicated that the process followed the Langmuir model and pseudo-second-order kinetics, respectively.

Results and Discussion

Machine learning models demonstrated robust predictive performance, with an R² value of 0.97 for XGBoost. These findings highlight the potential of Fe₂O₃–Ag nanocomposites as effective adsorbents for antibiotic removal, paving the way for sustainable wastewater treatment solutions.

Conclusions

In conclusion, Fe₂O₃–Ag nanocomposites can be recognized as effective adsorbents in the removal of antibiotics from wastewater, contributing to the improvement of sustainable wastewater management solutions.