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凝固剂虚拟测试的方法,以改善表面水处理中溶解有机物去除的方法

Christian Ortiz-Lopez¹, Christian Bouchard², Manuel J Rodriguez³

¹École supérieure d'aménagement du territoireet de développement régional (ÉSAD), Université Laval, 2325 allée des Bibliothèques, Québec (QC) G1V 0A6, Canada.

The Science of the total environment

|July 18, 2025

概括

本研究引入了一种机器学习方法,使用支持向量回归 (SVR) 来优化饮用水处理厂 (DWTP) 自然有机物 (NOM) 清除的凝固剂剂剂量. 该方法准确预测紫外线吸收和凝固pH值,提高运营效率.

科学领域:

水处理技术水处理技术.
环境工程环境工程
应用机器学习应用机器学习

背景情况:

在饮用水处理厂 (DWTP) 中,凝固对于去除颗粒和自然有机物 (NOM) 非常重要.
传统的用于确定凝固剂剂量的试验耗时,并且难以应对原水质量的快速变化,例如降雨后的变化.
优化凝血对于有效的NOM去除和保持水质至关重要.

研究的目的:

开发和验证一种机器学习方法,用于在全尺度DWTP中估计NOM去除 (UV254) 的联合凝固剂剂剂量.
在没有独立的pH控制的情况下,与UV254去除一起预测凝血pH.
根据预测的凝血pH值,为评估剩余水平提供一个框架.

主要方法:

利用支持向量回归 (SVR),一种机器学习技术,以建模凝固剂剂量性能.
开发了一种虚拟测试凝固剂剂量对UV254去除和凝固pH值的方法.
实施了一项基于预测凝血pH值的剩余评估策略.

主要成果:

在SVR模型中,测试数据集中的UV254去除和凝固pH的准确预测能力很高.
拟议的方法有效地估计了NOM去除的联合凝固剂剂量.
绩效指标证实了预测模型的可靠性.

结论:

开发的机器学习框架为优化DWTP中的凝血提供了传统jar测试的有效替代方案.
这种方法有助于对凝固作业的决策,特别是在动态的原水条件下.
该方法支持通过预测NOM去除和管理剩余来维持水质.

关键词:

凝固剂的虚拟测试凝固剂的虚拟测试饮用水处理厂饮用水处理厂自然有机物质是天然的有机物质.原水质量原水质量原水质量支持向量回归的支持向量回归.

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