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Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents
https://orcid.org/0000-0002-3472-0829
https://orcid.org/0000-0002-9288-627X
Phosphate removal is a crucial objective in wastewater engineering to reduce harmful environmental impacts like eutrophication. Adsorption, a low-cost and efficient process for phosphate abatement, primarily relies on trapping phosphate on low-solubility solid surfaces. Metal-based materials, due to their abundance, low cost, environmental friendliness, and chemical stability, are considered the most promising phosphate adsorbents. However, the synthesis of appropriate adsorbents is complex and time-consuming. In addition, the diverse textural properties, the presence of various metals, and the selection of adsorption parameters make it challenging to the underlying mechanism of phosphate adsorption. In this study, we compiled a data set including 1800 data points mined from 128 peer-reviewed papers and adopted machine learning (ML) to systematically evaluate phosphate adsorption concerning textural properties, metal compositions, and adsorption parameters. We applied three different tree-based algorithms, including random forest (RF), decision trees (DTs), and extreme gradient boosting (XGBoost), to guide the design of adsorbents and predict the phosphate adsorption performances. Among the three algorithms, RF showed the best predictive performance with a high R 2 of 0.984 and a low root-mean-squared error (RMSE) of 0.650. Feature importance, based on the Shapley values, demonstrated the contributions of adsorbents’ textural properties (e.g., surface area), adsorption parameters, and metal types in the order of precedence of phosphate adsorption, providing critical insights into guiding adsorbents design and synthesis for phosphate adsorption applications.
Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents
https://orcid.org/0000-0002-3472-0829
https://orcid.org/0000-0002-9288-627X
Phosphate removal is a crucial objective in wastewater engineering to reduce harmful environmental impacts like eutrophication. Adsorption, a low-cost and efficient process for phosphate abatement, primarily relies on trapping phosphate on low-solubility solid surfaces. Metal-based materials, due to their abundance, low cost, environmental friendliness, and chemical stability, are considered the most promising phosphate adsorbents. However, the synthesis of appropriate adsorbents is complex and time-consuming. In addition, the diverse textural properties, the presence of various metals, and the selection of adsorption parameters make it challenging to the underlying mechanism of phosphate adsorption. In this study, we compiled a data set including 1800 data points mined from 128 peer-reviewed papers and adopted machine learning (ML) to systematically evaluate phosphate adsorption concerning textural properties, metal compositions, and adsorption parameters. We applied three different tree-based algorithms, including random forest (RF), decision trees (DTs), and extreme gradient boosting (XGBoost), to guide the design of adsorbents and predict the phosphate adsorption performances. Among the three algorithms, RF showed the best predictive performance with a high R 2 of 0.984 and a low root-mean-squared error (RMSE) of 0.650. Feature importance, based on the Shapley values, demonstrated the contributions of adsorbents’ textural properties (e.g., surface area), adsorption parameters, and metal types in the order of precedence of phosphate adsorption, providing critical insights into guiding adsorbents design and synthesis for phosphate adsorption applications.
Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents
https://orcid.org/0000-0002-3472-0829
https://orcid.org/0000-0002-9288-627X
Phosphate removal is a crucial objective in wastewater engineering to reduce harmful environmental impacts like eutrophication. Adsorption, a low-cost and efficient process for phosphate abatement, primarily relies on trapping phosphate on low-solubility solid surfaces. Metal-based materials, due to their abundance, low cost, environmental friendliness, and chemical stability, are considered the most promising phosphate adsorbents. However, the synthesis of appropriate adsorbents is complex and time-consuming. In addition, the diverse textural properties, the presence of various metals, and the selection of adsorption parameters make it challenging to the underlying mechanism of phosphate adsorption. In this study, we compiled a data set including 1800 data points mined from 128 peer-reviewed papers and adopted machine learning (ML) to systematically evaluate phosphate adsorption concerning textural properties, metal compositions, and adsorption parameters. We applied three different tree-based algorithms, including random forest (RF), decision trees (DTs), and extreme gradient boosting (XGBoost), to guide the design of adsorbents and predict the phosphate adsorption performances. Among the three algorithms, RF showed the best predictive performance with a high R 2 of 0.984 and a low root-mean-squared error (RMSE) of 0.650. Feature importance, based on the Shapley values, demonstrated the contributions of adsorbents’ textural properties (e.g., surface area), adsorption parameters, and metal types in the order of precedence of phosphate adsorption, providing critical insights into guiding adsorbents design and synthesis for phosphate adsorption applications.
Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents
Wu, Yangyang (author) / Li, Yingze (author) / Jiang, Ze (author) / Xu, Ziyang (author) / Yang, Mingbao (author) / Ding, Jiahui (author) / Zhang, Changyong (author)
ACS ES&T Engineering ; 3 ; 1135-1146
2023-08-11
Article (Journal)
Electronic Resource
English
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