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Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods.

Amir Sheikhmohammadi1, Hassan Alamgholiloo1, Mohammad Golaki2

  • 1Department of Environmental Health Engineering, School of Health, Khoy University of Medical Sciences, Khoy, Iran.

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|June 15, 2024
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Summary

This study developed an eco-friendly WO3/Co-ZIF nanocomposite process for removing Cefixime from water. Intelligent models optimized conditions, with the Support Vector Regression (SVR) model showing superior predictive accuracy for pollutant removal.

Keywords:
ANNCefiximeGASVRWO3/Co-ZIF

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Area of Science:

  • Environmental Science
  • Materials Science
  • Chemical Engineering

Background:

  • Antibiotic residues, such as Cefixime, pose environmental risks.
  • Developing efficient and sustainable methods for removing pharmaceutical pollutants from aqueous solutions is crucial.
  • Nanocomposite materials offer promising avenues for advanced water treatment technologies.

Purpose of the Study:

  • To develop and optimize an environmentally friendly process for Cefixime removal from aqueous solutions using a WO3/Co-ZIF nanocomposite.
  • To employ intelligent decision-making models for predicting and optimizing Cefixime degradation.
  • To compare the performance of various artificial intelligence and optimization models in achieving efficient pollutant removal.

Main Methods:

  • Utilized a WO3/Co-ZIF nanocomposite for Cefixime degradation.
  • Applied Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM) for modeling and optimization.
  • Evaluated model performance using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score).

Main Results:

  • The quadratic factorial model in RSM identified pH, reaction time, and catalyst amount as significant factors.
  • The SVR model demonstrated superior predictive performance with R2 Score of 0.98, MAE of 1.54, and RMSE of 3.91.
  • Both ANN and SVR models highlighted pH as the most influential parameter.
  • GA identified interactions between initial Cefixime concentration, reaction time, and catalyst amount as critical for optimization.
  • Optimal conditions determined by GA and SOLVER included specific concentrations of Cefixime, pH, time, and catalyst amount.

Conclusions:

  • The WO3/Co-ZIF nanocomposite provides an effective and eco-friendly method for Cefixime removal.
  • Intelligent decision-making models, particularly SVR, significantly enhance the prediction and optimization of pollutant removal processes.
  • This research contributes to advancing sustainable environmental remediation strategies through intelligent optimization techniques.