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Experimental and ALO-optimized machine learning interpretable models for pharmaceutical adsorption onto raw

Amina Bouaichaoui1, Nabila Boucherit1,2, Mohamed Kouider Amar3

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Summary

Raw bentonite effectively removes pharmaceuticals like ketotifen fumarate, doxycycline hyclate, and nystatin from water. An interpretable machine learning model linked molecular structure to high adsorption capacities, offering a sustainable solution for pharmaceutical contamination.

Keywords:
Adsorption kineticsBentoniteInterpretable machine learningIsotherm modelingPharmaceuticals removal

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Area of Science:

  • Environmental Science
  • Materials Science
  • Computational Chemistry

Background:

  • Pharmaceuticals in aquatic systems are persistent environmental pollutants.
  • Conventional wastewater treatment struggles with complete removal of these bioactive compounds.

Purpose of the Study:

  • To investigate the adsorptive removal of ketotifen fumarate (KF), doxycycline hyclate (DXC), and nystatin (Nyst) using raw bentonite (RB).
  • To evaluate adsorption kinetics, equilibrium, and thermodynamics using batch experiments and machine learning.
  • To develop an interpretable ML model predicting adsorption capacity and linking molecular structure to removal efficiency.

Main Methods:

  • Batch adsorption experiments were conducted with RB for KF, DXC, and Nyst.
  • Adsorption data were analyzed using isotherm, kinetic, and thermodynamic models.
  • Four Ant Lion Optimizer (ALO)-optimized machine learning models (ANN, SVR, RF, XGBoost) were trained to predict adsorption.
  • SHAP (SHapley Additive exPlanations) analysis was used for model interpretability.

Main Results:

  • RB demonstrated high adsorption capacities: 178.86 mg/g (KF), 222.91 mg/g (DXC), and 190.25 mg/g (Nyst).
  • Freundlich isotherm best described equilibrium, indicating multilayer adsorption on a heterogeneous surface.
  • XGBoost model achieved high accuracy (R²=0.972), with SHAP analysis identifying adsorbent dosage, initial concentration, pH, and molecular size (nC) as key factors.
  • Adsorption mechanisms involved electrostatic attraction, hydrogen bonding, cation exchange, and van der Waals forces.

Conclusions:

  • Raw bentonite is a cost-effective and scalable adsorbent for pharmaceutical removal.
  • The interpretable ML framework successfully linked pharmaceutical molecular structure to adsorption behavior.
  • This approach offers a promising strategy for understanding and mitigating pharmaceutical contamination in aquatic environments.