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  3. Chitosan-based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models.
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  3. Chitosan-based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models.

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Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models.

Zaher Mundher Yaseen1,2, Ziaul Haq Doost1, Rauf Khan1

  • 1Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

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|October 20, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models accurately predict heavy metal removal from wastewater using chitosan-based flocculants. Hist gradient boosting regressor (HGBR) shows strong performance for combined metal removal, aiding environmental monitoring.

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Area of Science:

  • Environmental Science
  • Water Treatment Technologies
  • Computational Chemistry

Background:

  • Heavy metal contamination poses significant environmental and public health risks.
  • Effective wastewater treatment requires precise monitoring and remediation strategies.
  • Chitosan-based flocculants (CBFs) show promise for heavy metal removal.

Purpose of the Study:

  • To evaluate novel machine learning (ML) models for predicting heavy metal (HM) removal efficiency using CBFs.
  • To assess the performance of Gradient Boosting Regressor (GBR), Hist Gradient Boosting Regressor (HGBR), Random Forest Regressor (RFR), and Extreme Gradient Boosting Regressor (XGBR).
  • To enhance ML model accuracy by incorporating K-means clustering labels.

Main Methods:

  • Developed four ML models (GBR, HGBR, RFR, XGBR) using a dataset of 484 flocculation experiments.
  • Included K-means clustering labels as an additional feature for improved model learning.
  • Tested models on predicting the removal of cadmium (Cd²⁺), copper (Cu²⁺), nickel (Ni²⁺), lead (Pb²⁺), and zinc (Zn²⁺).

    • Main Results:

    • The HGBR model demonstrated superior performance in combined HM removal (R² = 0.94/0.97 for testing/training).
    • All models achieved high accuracy for individual metal removal, particularly for nickel (Ni²⁺).
    • The GBR model exhibited the lowest error rate for individual metal testing.

    Conclusions:

    • The HGBR model is a reliable tool for environmental monitoring due to its robust generalization capabilities.
    • ML models show significant potential for optimizing HM removal processes in wastewater treatment.
    • Future work should focus on integrating these models into real-time monitoring systems and exploring wider environmental applications.