Modeling of multicomponent adsorption equilibria of phenol and ciprofloxacin on pristine, acid-modified and thermo-oxidatively-aged polyethylene terephthalate microplastics

Abstract

Multicomponent systems are representative of the most common real situations as many industrial discharges contain a mixture of several pollutants. This study examines the concurrent adsorption of phenol (PHE) and ciprofloxacin (CIP) onto three types of polyethylene terephthalate microplastics (PET MPs): pristine, acid-modified, and thermal-oxidatively aged. Using extended Langmuir (EL), extended Freundlich (EF) isotherms, and a new artificial neural network (ANN) model, equilibrium adsorption capacities were predicted. The EL isotherm fit for pristine and aged PET MPs, while EF fit for modified PET MPs. Monolayer adsorption capacities ranged from 342.10–3715.73 mg/g for PHE and 2518.23–14498.79 mg/g for CIP, exceeding single-component adsorption. The ANN model used one hidden layer with 3 neurons for pristine and aged PET MPs, and 2 hidden layers with five neurons for modified PET MPs, with a hyperbolic tangent activation function. Models showed excellent performance metrics, including R2 values of 0.989–0.999, RMSE of 0.001–0.413, and AAE of 0.009–0.327. Synergistic interactions were observed in the binary system, with PET MPs showing higher selectivity toward CIP. The study demonstrates the effectiveness of PET MPs for binary adsorption of PHE and CIP in aqueous solutions, highlighting their potential for multicomponent pollutant removal.

Keywords

• artificial neural network
• PPCPs
• selective adsorption
• synergistic interaction
• water treatment