基于机器学习的生物炭重金属和吸附的预测建模,基于机器学习
Chenxi Zhao1, Wenjing Yue1, Zihao Jiang1
1School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, China.
International journal of phytoremediation
|November 3, 2025
概括
机器学习模型可以预测生物碳.
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:
- 生物炭在吸附重金属离子方面表现有前途.
- 通过机器学习预测生物碳吸附性能是一个新兴领域.
研究的目的:
- 开发机器学习模型 (LightGBM和DNN) 来预测生物炭的Pb2+和Cd2+去除率.
- 用SHAP值分析功能组对重金属吸附的影响.
- 评估元素分析作为输入特征对模型准确性的影响.
主要方法:
- 使用了轻量级梯度增强机 (LightGBM) 和深度神经网络 (DNN) 算法.
- 使用Pb2+的419个样本和Cd2+的240个样本的数据集开发了预测模型.
- 用Shapley添加式解释 (SHAP) 来进行特征重要性分析.
主要成果:
- 元素分析提高了Pb2+去除率的预测准确性 (R2从0.920增加到0.923).
- 对于Pb2+的MAE和RMSE预测分别下降了0.761和0.641.
- 元素分析对Cd2+移除率预测准确度的影响最小.
结论:
- 机器学习模型有效地预测生物炭的重金属吸附能力.
- 元素分析是预测Pb2+吸附的一个有价值的特性.
- 功能组对各种重金属离子的吸附有不同的影响.
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