Machine learning for the adsorptive removal of ciprofloxacin using sugarcane bagasse as a low-cost biosorbent: comparison of analytic, mechanistic, and neural network modeling

Abstract:

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, C<inf>o</inf> = 5 mg/L, Q = 7 mL/min, T = 18 °C) and pilot-scale (D = 4.4 cm, C<inf>o</inf> = 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.

Año de publicación:

2024

Keywords:

• Sugarcane bagasse
• Emerging pollutants
• fixed-bed column
• Adsorption
• Artificial intelligence
• Biomass
• Neural network

Fuente:

scopus

google