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Insights into levofloxacin adsorption with machine learning models using nano-composite hydrochars
Alaa El Din Mahmoud1, Radwa Ali1, Manal Fawzy2
1Environmental Sciences Department, Faculty of Science, Alexandria University, Alexandria, 21511, Egypt; Green Technology Group, Faculty of Science, Alexandria University, Alexandria, 21511, Egypt.
Chemosphere
|March 24, 2024
Summary
Taro peel waste was converted into silver@hydrochar composites for efficient levofloxacin removal from water. Machine learning models accurately predicted adsorption performance, offering a sustainable waste valorization solution.
Area of Science:
- Environmental Science
- Materials Science
- Chemical Engineering
Background:
- Food industry wastes, like taro peel, pose environmental challenges.
- Valorization of waste into functional materials is crucial for sustainability.
- Hydrothermal carbonization (HTC) offers a green route for waste conversion.
Purpose of the Study:
- To produce and characterize hydrochars from taro peel waste.
- To investigate the adsorption of levofloxacin onto hydrochar composites.
- To develop and validate machine learning models for predicting adsorption efficiency.
Main Methods:
- Hydrothermal carbonization of taro peel waste.
- Preparation of hydrochar (TPh), phosphoric-activated hydrochar (P-TPh), and silver@hydrochars (Ag@TPh, Ag@P-TPh).
- Characterization using EDX, adsorption experiments, and machine learning (CCD, ANN).
Main Results:
- Silver@hydrochar composites exhibited enhanced adsorption of levofloxacin.
- Optimal adsorption conditions: 45 min contact time, 0.15 g/L dose, pH 7.
- ANN and CCD models accurately predicted levofloxacin removal (R² > 0.989).
Conclusions:
- Taro peel-derived silver@hydrochars are effective adsorbents for levofloxacin.
- Machine learning models provide reliable predictions for adsorption processes.
- This study demonstrates a sustainable approach for food waste management and water treatment.
