Details

Autor(en) / Beteiligte

• Zhao, Fangzhou
• Tang, Lingyi
• Jiang, Hanfeng
• Mao, Yajun
• Song, Wenjing
• Chen, Haoming

Titel

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

Ist Teil von

• Bioresource technology, 2023-09, Vol.383, p.129223-129223, Article 129223

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• [Display omitted] •Four machine learning models are used to predict the adsorption of hydrochar.•Gradient boosting decision tree predicts the best performance with R2 of 0.93–0.98.•The process of heavy metal adsorption by hydrochar is mainly chemisorption.•The range of the C/H/O/N content of the optimal hydrochar is specified. 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.