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Related Experiment Videos

Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine

Zaher Mundher Yaseen1, Farah Loui Alhalimi2

  • 1Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia. z.yaseen@kfupm.edu.sa.

Scientific Reports
|April 18, 2025
PubMed
Summary

Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

390
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
390

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Machine learning models accurately predict heavy metal removal efficiency using biochar. XGBoost model showed superior performance, identifying initial metal concentration and pH as key factors for effective environmental remediation.

Area of Science:

  • Environmental Science
  • Material Science
  • Data Science

Background:

  • Heavy metal contamination in water and soil presents a critical environmental challenge.
  • Effective heavy metal removal strategies are essential for environmental monitoring and protection.

Purpose of the Study:

  • To apply ensemble machine learning (ML) models for predicting heavy metal adsorption efficiency onto biochar.
  • To identify key factors influencing heavy metal removal by biochar.

Main Methods:

  • Utilized ensemble ML models including Random Forest Regressor, Adaboost, Gradient Boosting, HistGradientBoosting, XGBoost, and LightGBM.
  • Processed 353 data samples from literature, including data cleaning and scaling.
  • Conducted feature importance analysis to determine influential parameters.
Keywords:
Biochar characteristicsEnsemble learningEnvironmental applicationsHeavy metals adsorptionPredictive modeling

Related Experiment Videos

Main Results:

  • The XGBoost model achieved the highest prediction accuracy with a determination coefficient (R²) of 0.92.
  • Initial metal concentration to biochar ratio and pH were identified as the most significant factors affecting adsorption efficiency.
  • Pyrolysis temperature had a moderate influence, while surface area and pore structure showed minimal impact.

Conclusions:

  • Ensemble ML models are effective tools for predicting heavy metal adsorption efficiency.
  • Findings guide the selection of optimal biochar characteristics and conditions for heavy metal removal applications.
  • This research aids in developing efficient environmental remediation strategies for heavy metal pollution.