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Related Experiment Video

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Explainable machine learning framework for predicting cobalt ion removal by natural hematite.

Arwa Saad¹, Abdelmoty M Ahmed², Mohamed Shaban³

¹Faculty of Computer Science, Nahda University, Beni Suef, Egypt.

Scientific Reports

|October 10, 2025

Summary

Natural hematite effectively removes cobalt ions (Co2+) from water, achieving 89.7% removal. A hybrid artificial intelligence model accurately predicts removal efficiency, highlighting temperature and contact time as key factors.

Keywords:

Adsorption experiments Explainable artificial intelligence Heavy metal removal Machine learning Water treatment

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Stable Aqueous Suspensions of Manganese Ferrite Clusters with Tunable Nanoscale Dimension and Composition

Stable Aqueous Suspensions of Manganese Ferrite Clusters with Tunable Nanoscale Dimension and Composition

Published on: February 5, 2022

Area of Science:

Environmental Science
Materials Science
Artificial Intelligence

Background:

Cobalt contamination in aquatic environments poses significant environmental and public health risks.
Developing efficient and sustainable methods for cobalt ion removal is crucial.

Purpose of the Study:

To investigate natural hematite (α-Fe2O3) as a cost-effective adsorbent for cobalt ion (Co2+) removal.
To develop and validate a hybrid artificial intelligence framework for predicting cobalt adsorption efficiency.

Main Methods:

Batch adsorption experiments were conducted to assess cobalt removal under varying conditions (contact time, adsorbent dosage, initial concentration, temperature).
A contrastive learning XGBoost model was employed for accurate prediction of cobalt removal efficiency.
Statistical analyses and Shapley Additive Explanations (SHAP) were used to identify influential parameters.

Main Results:

Natural hematite achieved a maximum cobalt removal efficiency of 89.7% at 100 ppm.
Temperature and contact time were identified as the most significant factors influencing cobalt removal (p < 0.01).
The XGBoost model demonstrated high predictive accuracy (R² = 0.987) and robustness.

Conclusions:

Natural hematite is a promising, economical, and sustainable adsorbent for cobalt ion removal from aquatic systems.
The developed hybrid AI framework provides accurate and reliable predictions for cobalt adsorption.
Understanding the influence of operational parameters like temperature and contact time is key for optimizing cobalt removal processes.