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

Abstract

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 (Qm) of hydrochar are not adequately explored. Four artificial intelligence models were used in this study to predict the Qm of hydrochar and identify the key influencing factors. The gradient boosting decision tree (GBDT) showed excellent predictive capability for this study (R2 = 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 °C) and longer hydrothermal time (>10 h) lead to the optimal type and density of surface functional groups for heavy metal adsorption, which increased the Qm values. This study has great potential for instructing industrial applications of hydrochar in treating heavy metal pollution.