通过图形方法监测工业废水处理厂的泥积聚:动态图嵌入和贝叶斯网络方法
Jorge Loy-Benitez1, Shahzeb Tariq2, Hai Tra Nguyen2
1Integrated Engineering, Dept. of Environmental Science and Engineering, College of Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea; Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
Journal of environmental management
|August 18, 2023
概括
一个新的基于图形的框架有效地检测和诊断废水处理厂 (WWTP) 中的污泥堆积. 这种先进的系统实现了99%的检测准确度,最大限度地减少了运营中断,提高了废水质量.
科学领域:
- 环境工程 环境工程
- 废水处理 废水处理
- 数据科学数据科学数据科学
背景情况:
- 污泥积聚是废水处理厂 (WWTP) 的重大运营挑战,导致处理效率降低和废水质量差.
- 现有的监测方法往往缺乏精度,以有效和及时地检测和诊断污泥积聚事件.
研究的目的:
- 引入一种基于图形的新型监测框架,用于早期检测和根源原因诊断WWTP中的污泥积聚事件.
- 利用历史运营数据来提取特征和识别过程变量之间的因果关系.
主要方法:
- 利用动态图嵌入 (DGE) 通过分析过程变量之间的时间和邻近依赖来检测污泥积聚.
- 采用动态贝叶斯网络 (DBN) 来计算信念概率,并根据概率变化确定积聚事件的根本原因.
主要成果:
- DGE方法实现了99%的污泥积聚检测率,零假警报和不到1%的错误检测.
- 基于DBN的诊断方法准确地确定了污泥积聚的根本原因,特别是与化学氧气需求 (COD) 度突然下降相关的原因,准确率为98%.
结论:
- 拟议的基于图形的框架显著提高了检测和诊断的污泥在WWTPs.
- 这种方法比现有方法有了显著的改进,有望提高废水处理的运行稳定性和废水质量.
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