Machine learning-aided biochar design for the adsorptive removal of emerging inorganic pollutants in water
The escalating presence of emerging inorganic pollutants (EIPs) including vanadium (V), antimony (Sb), thallium (Tl), mercury (Hg), fluoride (F−), and rare earth elements (REEs) in aquatic environments poses a significant threat to water quality and human health. Therefore, remediation of EIPs conta...
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| Vydáno v: | Separation and purification technology Ročník 362; s. 131421 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
30.07.2025
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| Témata: | |
| ISSN: | 1383-5866 |
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| Abstract | The escalating presence of emerging inorganic pollutants (EIPs) including vanadium (V), antimony (Sb), thallium (Tl), mercury (Hg), fluoride (F−), and rare earth elements (REEs) in aquatic environments poses a significant threat to water quality and human health. Therefore, remediation of EIPs contaminated water is of pressing concern. Biochar adsorption offers a promising, environmentally benign, and cost-effective approach for EIP removal. However, inconsistent experimental methodologies and varying research objectives in previous studies hinder the selection of optimal biochar for specific EIP. Developing biochar materials with high adsorption capacity is crucial for effectively removing EIPs from water. However, the optimization of biochar designing using advanced artificial intelligence (AI) methodologies has not been thoroughly reviewed. This study employed a dataset of 528 data points from 61 biochar samples, collected from adsorption experiments conducted between 2014 and 2024, encompassing 24 variables related to various EIPs. To predict adsorption capacity and elucidate adsorption mechanisms, Random Forest (RF), Support Vector Regression (SVR), XGBoost, and CatBoost machine learning algorithms were applied. The XGBoost model outperformed the others, achieving a coefficient of determination (R2) of 0.96 and a lower root mean squared error (RMSE) of 0.4. Feature importance and SHAP value analysis identified reaction pH, initial concentration and pyrolysis temperature as key predictors of adsorption efficiency. Future predictions from the XGBoost model indicate that reaction pH, initial concentration pyrolysis temperature and biochar pH, are critical factors influencing EIP adsorption. This research offers novel insights into EIPs adsorption and establishes a framework for designing sustainable biochar-based adsorbents for wastewater treatment. |
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| AbstractList | The escalating presence of emerging inorganic pollutants (EIPs) including vanadium (V), antimony (Sb), thallium (Tl), mercury (Hg), fluoride (F−), and rare earth elements (REEs) in aquatic environments poses a significant threat to water quality and human health. Therefore, remediation of EIPs contaminated water is of pressing concern. Biochar adsorption offers a promising, environmentally benign, and cost-effective approach for EIP removal. However, inconsistent experimental methodologies and varying research objectives in previous studies hinder the selection of optimal biochar for specific EIP. Developing biochar materials with high adsorption capacity is crucial for effectively removing EIPs from water. However, the optimization of biochar designing using advanced artificial intelligence (AI) methodologies has not been thoroughly reviewed. This study employed a dataset of 528 data points from 61 biochar samples, collected from adsorption experiments conducted between 2014 and 2024, encompassing 24 variables related to various EIPs. To predict adsorption capacity and elucidate adsorption mechanisms, Random Forest (RF), Support Vector Regression (SVR), XGBoost, and CatBoost machine learning algorithms were applied. The XGBoost model outperformed the others, achieving a coefficient of determination (R2) of 0.96 and a lower root mean squared error (RMSE) of 0.4. Feature importance and SHAP value analysis identified reaction pH, initial concentration and pyrolysis temperature as key predictors of adsorption efficiency. Future predictions from the XGBoost model indicate that reaction pH, initial concentration pyrolysis temperature and biochar pH, are critical factors influencing EIP adsorption. This research offers novel insights into EIPs adsorption and establishes a framework for designing sustainable biochar-based adsorbents for wastewater treatment. |
| ArticleNumber | 131421 |
| Author | Khan, Sangar Rao, Zepeng Chen, Baoliang Idris, Abubakr M Zhu, Xiaoying Ullah, Habib Wu, Naicheng |
| Author_xml | – sequence: 1 givenname: Habib surname: Ullah fullname: Ullah, Habib organization: Innovation Center of Yangtze River Delta, Zhejiang University, Zhejiang 311400, China – sequence: 2 givenname: Sangar surname: Khan fullname: Khan, Sangar organization: Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, People's Republic of China – sequence: 3 givenname: Xiaoying surname: Zhu fullname: Zhu, Xiaoying organization: Innovation Center of Yangtze River Delta, Zhejiang University, Zhejiang 311400, China – sequence: 4 givenname: Baoliang orcidid: 0000-0001-8196-081X surname: Chen fullname: Chen, Baoliang email: blchen@zju.edu.cn organization: Innovation Center of Yangtze River Delta, Zhejiang University, Zhejiang 311400, China – sequence: 5 givenname: Zepeng surname: Rao fullname: Rao, Zepeng organization: Innovation Center of Yangtze River Delta, Zhejiang University, Zhejiang 311400, China – sequence: 6 givenname: Naicheng surname: Wu fullname: Wu, Naicheng organization: Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, People's Republic of China – sequence: 7 givenname: Abubakr M surname: Idris fullname: Idris, Abubakr M organization: Department of Chemistry, College of Science, King Khalid University, Abha 62529, Saudi Arabia |
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