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多种机器学习模型的比较分析:确定生物炭重金属吸附机制的影响因素

Zixin Zeng¹, Yingdong Wu², Wenjie Li³

¹Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, People's Republic of China.

Environmental geochemistry and health

|August 30, 2025

概括

这项研究使用机器学习来识别生物炭从水中除去重金属的关键因素. 最初的金属度和生物炭特性对于有效的吸附和水处理至关重要.

科学领域:

环境科学
水处理技术
材料科学

背景情况:

生物炭是从水系统中去除重金属的有希望的材料.
目前分析生物炭吸附的方法通常依赖于单个模型,导致潜在的偏差和对影响因素的不完全理解.
需要进行全面的分析,以系统地阐明控制生物炭介导重金属吸附的机制和相互作用.

研究的目的:

通过综合机器学习方法,全面调查影响生物炭中介重金属吸附的决定因素和关键变量.
克服单个模型分析的局限性,并提供吸附机制的系统阐明.
量化各种因素的贡献,包括初始金属度,生物炭特性和溶液条件.

主要方法:

整合12个机器学习模型与SHAP (Shapley添加式解释) 进行贡献分析.
评估不同模型架构的预测准确性和可解释性,特别是基于树的模型.
应用SHAP重要性和依赖性分析来识别和量化影响因素的影响.

主要成果:

基于树的机器学习模型显示出卓越的预测准确性和可解释性.
最初的金属度 (C0) 是最重要的因素 (23.1-67.9%),其次是生物碳因素 (20.1-57.4%).
确定的主要决定因素包括C0,阴离子交换能力 (CEC),溶液pH (pHsol) 和生物炭表面pH (pHH2O).

结论:

整体机器学习框架为分析生物炭介导重金属吸附提供了强大的方法,克服了单一模型的局限性.
初始金属度和CEC等生物炭特性对于增强吸附能力至关重要.
溶液和表面pH具有重要的作用,具体效果取决于pH范围,指导水处理的优化.

关键词:

生物炭重金属影响因素机器学习 SHAP分析

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