Resumen
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.
| Título traducido de la contribución | Aprendizaje automático para la eliminación por adsorción de ciprofloxacino utilizando bagazo de caña de azúcar como biosorbente de bajo costo: comparación de modelos analíticos, mecanísticos y de redes neuronales. |
|---|---|
| Idioma original | Inglés |
| Páginas (desde-hasta) | 48674-48686 |
| Número de páginas | 13 |
| Publicación | Environmental Science and Pollution Research |
| Volumen | 31 |
| N.º | 35 |
| DOI | |
| Estado | Publicada - 22 jul. 2024 |
Palabras clave
- Adsorción Columna de lecho fijo Bagosta de caña de azúcar Biomasa Contaminantes emergentes Inteligencia artificial Red neuronal