Zero-valent iron based materials selection for permeable reactive barrier using machine learning | JoVE Visualize

Area of Science:

Environmental Engineering
Materials Science
Data Science

Background:

Zero-valent iron (ZVI) based reactive materials are crucial for permeable reactive barriers (PRBs) in groundwater remediation.
Selecting effective ZVI materials is challenging due to the vast number of emerging iron-based materials and the need for long-term stability.
Existing data limitations hinder the practical application of advanced screening methods for PRB reactive materials.

Purpose of the Study:

To develop and validate a machine learning (ML) approach for efficiently screening ZVI-based reactive materials for PRBs.
To integrate ML models with evaluation indices and experimental data to enhance material selection practicality.
To identify key material properties influencing the performance of ZVI in remediation applications.

Main Methods:

Utilized the XGBoost model to predict kinetic data for ZVI-based materials.
Employed SHAP (SHapley Additive exPlanations) to enhance model accuracy and interpret feature importance.
Conducted batch and column tests to evaluate geochemical characteristics and validate ML predictions.
Investigated mechanistic pathways and endpoint products of iron compound transformations.

Main Results:

Machine learning, combined with experimental data, significantly improved the prediction accuracy of ZVI material performance (RMSE reduced from 1.84 to 0.6).
SHAP analysis identified specific surface area as a fundamental factor correlating with kinetic constants of ZVI-based materials.
Experimental results demonstrated that ZVI exhibited superior performance compared to AC-ZVI, with 3.2 times higher anaerobic corrosion reaction kinetic constants and 3.8 times lower selectivity.

Conclusions:

This study presents a successful initial application of machine learning for the selection of reactive materials in PRBs.
The developed ML approach enhances the efficiency and practicality of identifying optimal ZVI-based materials for environmental remediation.
Specific surface area is a critical parameter for optimizing ZVI material selection, guiding future material design and application.