Optimizing the efficient removal of ciprofloxacin from aqueous solutions by polyethylene terephthalate microplastics using multivariate statistical approach

Abstract

Emerging pollutants such as ciprofloxacin (CIP) and microplastics (MPs) has been found in environment, as a result of their widespread usage. In this study, the use of different polyethylene terephthalate microplastics (PET MPs) such as pristine (Pr-PET), acid modified (Mod-PET) and thermal-oxidatively aged (Ag-PET) were optimized for their removal efficiencies for CIP (5 to 50 mg/L) from aqueous solution using Response Surface Methodology with Central Composite Design (4 factors − 2 levels) and Machine Learning (ML) algorithm such as Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Through the use of the desirability function, the optimal conditionswere determined and amaximum removal efficiency of93.95%, 94.36%, and 96.29% for Pr-PET, Mod-PET, and Ag-PET MPs, respectively. The Dumwald-Wagner intraparticle model and the Dual-Exponential Model (DEM) were used to describe the adsorption process. The Dumwald-Wagner modelwas able to describe the adsorption process before equilibrium was reached at 12 hrs. The DEM model divided the adsorption process into two stages, a rapid, initial stage and a slower, secondary stage.These results suggest that the use of PET MPs for removing CIP through adsorption is a viable option and can be implemented in treatment systems.