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预测Cd(II) 生物炭材料的吸附能力,使用典型的机器学习模型进行水生环境的有效修复

Long Chen¹, Jian Hu², Hong Wang³

¹School of Chemistry and Materials Science, Hunan Engineering Research Center for Biochar, Hunan Agricultural University, Changsha, Hunan 410128, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China.

The Science of the total environment

|June 15, 2024

概括

机器学习模型准确地预测了Cd (II) 吸附到生物炭上的情况. CatBoost模型表现出色,确定生物炭化学成分和吸附条件是有效废水处理的关键因素.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
数据科学数据科学数据科学

背景情况:

可持续处理含有 (Cd) 的废水至关重要.
开发具有高吸附能力的"绿色"生物炭材料是关键战略.
预测建模可以优化生物炭设计和废水整治应用.

研究的目的:

采用和比较六种机器学习 (ML) 模型来预测Cd的吸附能力.
使用ML.II识别影响Cd (II) 吸附的关键因素.
优化实验条件,以提高使用生物炭的Cd (II) 清除.

主要方法:

使用了来自批量吸附实验的1,051个数据点的数据集,其中有21个输入变量.
评估了线性回归,随机森林,梯度提升决策树,CatBoost,K-最近邻居和反向传播神经网络模型.
采用沙普利增量解释 (SHAP) 进行特征重要性和部分依赖图 (PDP) 进行优化.

主要成果:

CatBoost模型实现了最高的预测准确性 (R2 = 0.971) 和最低的误差 (RMSE = 20.54 mg/g).
生物炭化学成分 (42.2%) 和吸附条件 (37.57%) 是吸附能力的最重要的预测因素.
优化条件包括高Cd(II) 度,特定的生物炭特性 (例如C%,N%,O%,pH) 和吸附/热解时间.

结论:

机器学习,特别是CatBoost,为预测和优化Cd (II) 废水处理中的生物碳性能提供了强大的框架.
了解特征的重要性指导了高性能生物炭材料的合理设计.
这项研究为生物炭的可持续工程提供了宝贵的见解,用于重金属的去除.

关键词:

吸附方式吸附方式吸附方式生物炭是一种生物炭.是的组成部分.机器学习是机器学习.建模和预测的模型和预测.

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