文献类型： 外文期刊

作者： Xie, Shiyu ¹ ; Zhang, Tao ¹ ; You, Siming ³ ; Mukherjee, Santanu ⁴ ; Pu, Mingjun ¹ ; Chen, Qing ¹ ; Wang, Yaosheng ² ; Ali, Esmat F. ⁵ ; Abdelrahman, Hamada ⁶ ; Rinklebe, Jorg ⁷ ; Lee, Sang Soo ⁸ ; Shaheen, Sabry M. ⁷ ;

作者机构： 1.China Agr Univ, Coll Resources & Environm Sci, State Key Lab Nutrient Use & Management, Beijing Key Lab Farmland Soil Pollut Prevent & Rem, Beijing 100193, Peoples R China

2.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing 100081, Peoples R China

3.Univ Glasgow, James Watt Sch Engn, Gilmorehill, Glasgow G12 8QQ, Scotland

4.Shoolini Univ Biotechnol & Management Sci, Sch Pharmaceut Sci, Solan 173229, Himachal Prades, India

5.Taif Univ, Coll Sci, Dept Biol, Taif 21944, Saudi Arabia

6.Cairo Univ, Fac Agr, Soil Sci Dept, Giza 12613, Egypt

7.Univ Wuppertal, Inst Fdn Engn Water & Waste Management, Sch Architecture & Civil Engn, Lab Soil & Groundwater Management, Pauluskirchstr 7, D-42285 Wuppertal, Germany

8.Yonsei Univ, Dept Environm & Energy Engn, Wonju 26493, South Korea

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

期刊名称：BIOCHAR （ 影响因子：13.5； 五年影响因子：14.1 ）

ISSN： 2524-7972

年卷期： 2025 年 7 卷 1 期

页码：

收录情况： SCI

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.