在现场用在线传感器的水再利用系统中预测微生物水质
Eva Reynaert1, Philipp Steiner2, Qixing Yu3
1Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland; ETH Zürich, Institute of Environmental Engineering, 8093 Zürich, Switzerland.
Water research
|June 1, 2023
概括
在现场的水再利用系统中监测微生物水质是至关重要的. 使用廉价在线传感器的简单物流回归模型有效地预测水安全,优先考虑用户健康,具有较低的错误阳性率.
科学领域:
- 环境工程 环境工程
- 水处理技术水处理技术
- 微生物水质监测 微生物水质监测
背景情况:
- 现场再利用水的实施受到微生物质量监测挑战的限制.
- 现有的方法缺乏具有成本效益的实时操作监控解决方案.
研究的目的:
- 开发和评估机器学习方法,用于在现场再利用水的微生物水质监测.
- 使用廉价,商用在线传感器进行实时评估.
- 确保低虚假阳性率 (FPR ≤2%),以保护公众健康.
主要方法:
- 在各种干扰场景下,从化膜生物反应器收集了广泛的传感器和微生物水质数据.
- 测试了三个机器学习算法:逻辑回归,支持向量机器和随机森林.
- 优化模型优先考虑低假阳性率 (FPR) 和最大化真阳性率 (TPR).
主要成果:
- 使用自由和氧化降解电位传感器的物流回归模型实现了最高的TPR.
- 将传感器斜率作为功能改进了TPR,使得使用更少的传感器能够有效预测.
- 错误的预测主要作为早期警报,有利于系统管理.
结论:
- 用一个或两个在线传感器的简单物流回归模型对于预测再利用系统中的微生物水质非常有效.
- 这种方法为水质监测提供了一种具有成本效益,可解释性和计算效率的解决方案.
- 结果为水质建模提供了宝贵的见解,特别是在有限的数据集下.
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