数字农科院2.0

Applied machine learning for predicting the properties and carbon and phosphorus fate of pristine and engineered hydrochar

文献类型： 外文期刊

作者： Xie, Shiyu;Zhang, Tao;You, Siming;Mukherjee, Santanu;Pu, Mingjun;Chen, Qing;Wang, Yaosheng;Ali, Esmat F.;Abdelrahman, Hamada;Rinklebe, Jorg;Lee, Sang Soo;Shaheen, Sabry M.

作者机构：

关键词： Engineered hydrochar;Hydrothermal carbonization;Ferric chloride impregnation;Nutrients stability and availability;Machine learning

期刊名称： BIOCHAR

ISSN： 2524-7972

年卷期： 2025 年 7 卷 1 期

页码：

收录情况： SCIE（2025版） ; ; CSCD（2025-2026年度） ; ; 农林核心（2024版）

摘要： Application of advanced techniques and machine learning (ML) for designing and predicting the properties of engineered hydrochar/biochar is of great agro-environmental concern. Carbon (C) stability and phosphorus (P) availability in hydrochar (HC) are among the key limitations as they cannot be accurately predicted by traditional one-factor tests and might be overcome by engineering the pristine HC. Therefore, the aims of this study were (1) to determine the optimal production conditions of engineered swine manure HC with high C stability and P availability, and (2) to develop the best ML models to predict the properties of HC derived from different feedstocks. Pristine- (HC) and FeCl3 impregnated swine manure-derived HC (HC-Fe) were produced by hydrothermal carbonization under different pH (4, 7, and 10), reaction temperature (180, 220, and 260 degrees C), and residence time (60, 120, and 180 min) and characterized using thermo-gravimetric, microscopic, and spectroscopic analyses. Also, different ML algorithms were used to model and predict the hydrochar solid yield, properties, and nutrients content. FeCl3 impregnation increased Fe-phosphate content, while it reduced H/C and O/C ratios and hydroxyapatite P content, and therefore improved C stability and P availability in the HC-Fe as compared to HC, particularly under lower pH (4), temperature of 220 degrees C, and at 120 min. The generalized additive ML model outperformed the other models for predicting the HC properties with a correlation coefficient of 0.86. The ML analysis showed that the most influential features on the hydrochar C stability were the H and O contents in the biomass, while P availability in HC was more dependent on the C, N and O contents in biomass. These results provided optimal production conditions for Fe-engineered manure hydrochar and identified the best performing ML model for predicting hydrochar properties. The main implication of this study is that it offers a high potential to improve the utilization of biowastes and produce biowaste-derived engineered hydrochar with high C stability and P availability on a large scale.

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