在饮用水处理中,以机器学习为指导,预测化/胺消毒副产品的形成
Youheng Liang1, Ruixing Huang2, Jingrui Wang1
1Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, PR China.
Water research
|May 30, 2025
概括
机器学习模型准确地预测水处理中的危险消毒副产品 (DBPs). 一个新的网络平台,ACAI,使这些DBP预测可用于改善公共卫生保护.
科学领域:
- 环境化学环境化学
- 水处理技术水处理技术
- 计算化学的计算化学
背景情况:
- 化和胺化是标准的水消毒方法.
- 这些方法可以产生危险的消毒副产品 (DBPs).
- 预测DBP的形成对于水安全和公共卫生至关重要.
研究的目的:
- 开发机器学习 (ML) 模型来预测五个总DBP (TDBP) 和十个个别DBP (IDBP) 的水平.
- 解决现有的DBP预测模型中的局限性,特别是对于不受监管的DBP.
- 整合数据以创建统一的ML模型,以提高预测准确度.
主要方法:
- 利用数据整合策略构建统一的ML模型.
- 开发模型来预测由化和胺化产生的受监管和不受监管的DBPs.
- 用Shapley的附加性和部分依赖图表进行因子分析.
主要成果:
- 统一的ML模型在DBP预测中通常优于单个模型.
- 单个模型在预测总DBPs (TDBPs) 方面表现出更高的有效性.
- 确定了影响DBP形成的关键因素,与实验数据一致.
结论:
- 开发的ML模型为预测DBP形成提供了强大的方法.
- ACAI网络平台为DBP预测提供了一个可访问的工具.
- 这些工具支持在水消毒过程中的数据驱动决策,以加强公共卫生保护.
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