基于机器学习模型的新出现污染物的化降解率的预测
Yufan Du1, Ting Tang2, Dehao Song1
1School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China.
Environmental pollution (Barking, Essex : 1987)
|March 6, 2025
概括
这项研究引入了一种机器学习模型,用于预测水化过程中新出现的污染物的降解率. 该模型准确地预测了污染物分解,有助于水处理策略.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 水处理技术水处理技术
背景情况:
- 水中新出现的污染物给监管带来了挑战.
- 化是一种关键的水处理工艺.
- 预测污染物降解对于有效监测至关重要.
研究的目的:
- 开发一种机器学习模型,用于预测化过程中有机污染物的二次反应速率常数.
- 提高新出现的污染物降解的预测模型的准确性和适用性.
主要方法:
- 用587个有机污染物反应速率常数训练了一种机器学习模型.
- 评估了十个算法,通过贝叶斯优化优化超参数.
- 使用了Modred分子描述符和MACCS分子指纹.
主要成果:
- 优化的高斯过程回归 (GPR) 模型实现了R2train = 0.866和R2test = 0.801.
- 预测的降解值显示,四种污染物与实验数据的偏差最小 (≤14.8%).
- SHAP分析确定了影响降解的关键分子特征,确保了模型的可解释性.
结论:
- 开发的模型准确地预测了化过程中新出现的污染物降解.
- 它为缺乏特定处理标准的水处理厂提供了指导.
- 促进在现实环境中改善新兴污染物的预防和控制策略.
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