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探索使用k-means和高斯混合物集群的废水处理厂运行和性能

Michael De Santi¹, Ahmed Al-Sayed², Stephanie Gora³

¹Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Canada.

Water science and technology : a journal of the International Association on Water Pollution Research

|July 31, 2025

概括

无监督的集群识别了废水处理厂 (WWTP) 的操作指纹. 这种方法优化了能源使用和废水合规性,同时预测了关键事件.

科学领域:

环境工程环境工程
数据科学数据科学数据科学
水资源管理水资源管理

背景情况:

高效的废水处理对于环境保护和最小化人类影响至关重要.
优化废水处理厂 (WWTP) 的能源效率和合规性是具有挑战性的,特别是手动操作.
从大型数据集中提取可操作的见解来理解WWTP行为可能很困难.

研究的目的:

应用无监督的集群,以更好地了解WWTP运营.
开发"操作指纹"以简化复杂的植物数据.
在二次处理中确定节能和废水合规性的最佳策略.

主要方法:

使用无监督集群算法,特别是k-means和高斯混合.
将许多操作信号减少到可解释的操作状态 (指纹).
分析了与WWTP性能的上游/下游关联,并确定了关键事件的风险因素.

主要成果:

证明操作指纹可以提高对WWTP流程的理解.
确定了减少二次处理中的能源消耗的策略,同时满足合规性.
展示了指纹能够预测潜在的植物超标或绕道的能力.
对比了k-means和高斯混合物聚类对WWTP分析的有效性.

结论:

无监督的集群为WWTP运营商和工程师提供了有价值的工具.
操作指纹有助于更好的决策,提高效率,合规性和风险管理.
集群算法为分析WWTP性能提供了明显的优点和缺点.

关键词:

高斯混合模型的高斯混合模型.集群集成是指集群集成.这意味着k-means.过程优化优化过程优化废水处理是废水处理的方法之一.

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