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<p>Cobalt contamination in aquatic systems presents considerable environmental and public health concerns. A hybrid artificial intelligence framework was developed for accurate modeling, and this study investigated natural hematite (α-Fe<sub>2</sub>O<sub>3</sub>) as an economical and sustainable adsorbent for the extraction of cobalt ions (Co<sup>2+</sup>). A maximum removal effectiveness of 89.7% was achieved at 100 ppm during batch adsorption tests conducted under various operational conditions, including contact time (10–120&#xa0;min), adsorbent dosage (0.01–0.10&#xa0;g), initial cobalt concentration (100–1000 ppm), and temperature (34–90&#xa0;°C). Statistical analyses indicated that temperature and contact time were the most influential parameters (<i>p</i> &lt; 0.01). Among the evaluated models, the contrastive learning XGBoost approach demonstrated superior predictive accuracy, achieving R<sup>2</sup> = 0.987 ± 0.0137 (95% CI: 0.9748–0.9845), RMSE = 1.8676 ± 0.8509 (95% CI: 1.2590–3.4763), MAE = 0.0500 ± 0.0200 (95% CI: 0.0100–0.0300), and MAPE = 1.6753 ± 0.5802 (95% CI: 1.2602–2.0904). The proposed framework’s stability and robustness were confirmed by confidence interval analysis, and Shapley Additive Explanations (SHAP) indicated that temperature and contact time were the primary factors influencing removal efficiency.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Arwa Saad,
  • Abdelmoty M. Ahmed,
  • Mohamed Shaban,
  • Ahmed A. Elngar

摘要

Cobalt contamination in aquatic systems presents considerable environmental and public health concerns. A hybrid artificial intelligence framework was developed for accurate modeling, and this study investigated natural hematite (α-Fe2O3) as an economical and sustainable adsorbent for the extraction of cobalt ions (Co2+). A maximum removal effectiveness of 89.7% was achieved at 100 ppm during batch adsorption tests conducted under various operational conditions, including contact time (10–120 min), adsorbent dosage (0.01–0.10 g), initial cobalt concentration (100–1000 ppm), and temperature (34–90 °C). Statistical analyses indicated that temperature and contact time were the most influential parameters (p < 0.01). Among the evaluated models, the contrastive learning XGBoost approach demonstrated superior predictive accuracy, achieving R2 = 0.987 ± 0.0137 (95% CI: 0.9748–0.9845), RMSE = 1.8676 ± 0.8509 (95% CI: 1.2590–3.4763), MAE = 0.0500 ± 0.0200 (95% CI: 0.0100–0.0300), and MAPE = 1.6753 ± 0.5802 (95% CI: 1.2602–2.0904). The proposed framework’s stability and robustness were confirmed by confidence interval analysis, and Shapley Additive Explanations (SHAP) indicated that temperature and contact time were the primary factors influencing removal efficiency.