Machine learning-driven optimization of metal-modified biochar for phosphorus adsorption and wastewater remediation

文献类型： 外文期刊

第一作者： Chen, Yudong

作者： Chen, Yudong;Lu, Xinyue;Han, Yuheng;Feng, Yanfang;Xue, Lihong;Lu, Xinyue;Han, Yuheng;Feng, Yanfang;Xue, Lihong;Han, Yuheng;Wang, Lisha;Chen, Haoming;Feng, Yuanyuan

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关键词： Phosphorus; Metal-modified biochar; Machine Learning; Random Forest model

期刊名称：JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING （ 影响因子：7.2； 五年影响因子：7.6 ）

ISSN： 2213-2929

年卷期： 2025 年 13 卷 5 期

页码：

收录情况： SCI

摘要： This study employed machine learning approaches to systematically analyze pyrolysis parameters, structural characteristics, modification condition and adsorption conditions for identifying critical determinants of phosphorus adsorption capacity in biochar. Four predictive models were evaluated, with Random Forest (RF) demonstrating optimal performance (Test R-2=0.89, RMSE=5.29) through effective overfitting resistance and generalization capability. Key findings revealed that: (1) Structural properties exhibited non-linear interactions affecting adsorption efficiency, with specific surface area showing limited correlation (contrary to conventional assumptions). (2) Adsorption conditions collectively contributed 35.1 % feature importance, dominated by solution pH (3.0-5.7), phosphorus concentration (905-1000 mg/L), and adsorbent dosage (<1 g/L). (3) Metal composition emerged as the most significant factor (31.7 % importance), particularly Mg content demonstrating dose-dependent enhancement effects. Notably, the model revealed synergistic interactions between modification parameters and adsorption variables, suggesting that optimal performance requires coordinated optimization of both material properties and operational conditions. These findings challenge traditional surface-area-centric evaluation methods, emphasizing the necessity for multifactorial analysis in biochar design, while highlighting the need for comprehensive considering environmental risks and economic viability in practical applications.

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