Prediction of heavy metals adsorption by hydrochars and identification of critical factors using machine learning algorithms

文献类型： 外文期刊

第一作者： Zhao, Fangzhou

作者： Zhao, Fangzhou;Jiang, Hanfeng;Mao, Yajun;Chen, Haoming;Tang, Lingyi;Song, Wenjing

作者机构：

关键词： Hydrothermal treatment; Adsorption; Pollution remediation; Gradient boosting decision tree; Simulation

期刊名称：BIORESOURCE TECHNOLOGY （ 影响因子：11.4； 五年影响因子：10.6 ）

ISSN： 0960-8524

年卷期： 2023 年 383 卷

页码：

收录情况： SCI

摘要： Hydrochar has become a popular product for immobilizing heavy metals in water bodies. However, the relationships between the preparation conditions, hydrochar properties, adsorption conditions, heavy metal types, and the maximum adsorption capacity (Q(m)) of hydrochar are not adequately explored. Four artificial intelligence models were used in this study to predict the Q(m) of hydrochar and identify the key influencing factors. The gradient boosting decision tree (GBDT) showed excellent predictive capability for this study (R-2 = 0.93, RMSE = 25.65). Hydrochar properties (37%) controlled heavy metal adsorption. Meanwhile, the optimal hydrochar properties were revealed, including the C, H, N, and O contents of 57.28-78.31%, 3.56-5.61%, 2.01-6.42%, and 20.78-25.37%. Higher hydrothermal temperatures (>220 ?) and longer hydrothermal time (>10 h) lead to the optimal type and density of surface functional groups for heavy metal adsorption, which increased the Q(m )values. This study has great potential for instructing industrial applications of hydrochar in treating heavy metal pollution.

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