机器学习框架准确估计有机材料对树脂和生物炭的吸附
Raouf Hassan1, Mohammad Reza Kazemi2
1Civil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
Scientific reports
|April 30, 2025
概括
机器学习准确地预测生物炭和树脂上的有机成分吸附. XGBoost,LightGBM和CatBoost模型表现出色,确定平衡度和表面积是工业应用的关键因素.
科学领域:
- 环境化学环境化学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 有机化合物对生物炭和聚合物树脂的吸附对于环境修复和工业过程至关重要.
- 准确预测吸附对于优化材料选择和工艺设计至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测生物炭和聚合物树脂上的有机成分的吸附.
- 确定影响吸附行为的关键物理化学参数.
主要方法:
- 编制了1750个吸附异温的数据集,包括50个生物炭和30个树脂样本上的73种有机材料的数据.
- 八个输入参数 (包括亚伯拉罕溶解描述符,毛孔体积,表面积和平衡度) 用于训练机器学习模型.
- 评估了各种算法 (线性回归,SVR,KNN,随机森林,ANN,XGBoost,LightGBM,CatBoost) 的预测准确度.
主要成果:
- 集成模型,特别是XGBoost (R2=0.974),LightGBM (R2=0.964) 和CatBoost (R2=0.984),在预测吸附度 (logKd) 中表现出卓越的性能.
- 像线性回归和弹性网等更简单的模型显示预测能力明显较低 (R2 < 0.875).
- 灵敏度和SHAP分析强调平衡度和特定表面积是最有影响力的因素.
结论:
- 机器学习,特别是渐变增强算法,提供了一个非常准确的方法来预测生物炭和树脂上的有机吸附.
- 这项研究提供了对影响吸附的主要因素的宝贵见解,有助于设计先进的材料和工艺.
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