Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants

Yaorong Shu; Linghao Chen; Tianming Wu; Mengjie Song; Wen Zhang; Juan Mao

doi:10.47297/taposatWSP2633-456904.20240504

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Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants

Volume 5, Issue 4

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Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants
Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants
1.Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China
2.School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
3.Yangtze Ecology and Environment Co., Ltd., Wuhan 430074, China
Vol. 5, Issue 4, Pages: 30-35(2024)
Published： 27 August 2024 ，
DOI：10.47297/taposatWSP2633-456904.20240504
Accepted：

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Shu Yaorong,Chen Linghao,Wu Tianming,et al.Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants[J].Theory and Practice of Science and Technology,2024,05(04):30-35.
DOI：

Shu Yaorong,Chen Linghao,Wu Tianming,et al.Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants[J].Theory and Practice of Science and Technology,2024,05(04):30-35. DOI： 10.47297/taposatWSP2633-456904.20240504.

摘要

Abstract

In response to issues such as high process load and potential risks of failing to meet discharge standards caused by sudden water pollution incidents in combined industrial and domestic wastewater treatment plants

this study focused on the wastewater treatment plant in an indus

trial park. The Autoregressive Integrated Moving Average (ARIMA) algorithm was utilized to construct an influent water quality prediction and anomaly warning model. Autocorrelation function (ACF)

partial autocorrelation function (PACF)

and augmented Dickey-Fuller (ADF) tests are employed to determine the optimal parameters for predicting chemical oxygen demand (COD)

ammonia nitrogen (NH

-N)

total nitrogen (TN)

and total phosphorus (TP)

optimizing the water quality prediction models. The impact of different monitoring frequencies and prediction steps on the model's performance were studied. Furthermore

an anomaly warning model for water quality was developed using an autoencoder (AE) and K-nearest neighbors (KNN) algorithm. This work provides a scientific basis for wastewater treatment plants to proactively respond to sudden water pollution events and lays a theoretical foundation for the intelligent transformation of traditional wastewater treatment plants.

关键词

Keywords

Time Series PredictionARIMAAbnormal Warning

references

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