%0 Journal Article
%T Machine learning for the adsorptive removal of ciprofloxacin using sugarcane bagasse as a low-cost biosorbent: comparison of analytic, mechanistic, and neural network modeling.
%A Vera M
%A Aguilar J
%A Coronel S
%A Juela D
%A Vanegas E
%A Cruzat C
%J Environ Sci Pollut Res Int
%V 31
%N 35
%D 2024 Jul 22
%M 39037629
%F 5.19
%R 10.1007/s11356-024-34345-z
%X Contamination with traces of pharmaceutical compounds, such as ciprofloxacin, has prompted interest in their removal via low-cost, efficient biomass-based adsorption. In this study, classical models, a mechanistic model, and a neural network model were evaluated for predicting ciprofloxacin breakthrough curves in both laboratory- and pilot scales. For the laboratory-scale (d = 2.2 cm, Co = 5 mg/L, Q = 7 mL/min, T = 18 °C) and pilot-scale (D = 4.4 cm, Co = 5 mg/L, Q = 28 mL/min, T = 18 °C) setups, the experimental adsorption capacities were 2.19 and 2.53 mg/g, respectively. The mechanistic model reproduced the breakthrough data with high accuracy on both scales (R2 > 0.4 and X2 < 0.15), and its fit was higher than conventional analytical models, namely the Clark, Modified Dose-Response, and Bohart-Adams models. The neural network model showed the highest level of agreement between predicted and experimental data with values of R2 = 0.993, X2 = 0.0032 (pilot-scale) and R2 = 0.986, X2 = 0.0022 (laboratory-scale). This study demonstrates that machine learning algorithms exhibit great potential for predicting the liquid adsorption of emerging pollutants in fixed bed.