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

PDF
导出
分享
收藏
专辑

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

5卷第4期

浏览量: 0 Downloads: 1

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
2024年5卷第4期页码：30-35
纸质出版日期： 2024-08-27 ，
DOI：10.47297/taposatWSP2633-456904.20240504
Accepted：

Scan QR Code

(2024). Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants. 科技理论与实践（英文版）, 5(4), 30-35.

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.
(2024). Research on the Early Warning for Abnormal Inflow in Wastewater Treatment Plants. 科技理论与实践（英文版）, 5(4), 30-35. DOI： 10.47297/taposatWSP2633-456904.20240504.

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

NEWHART K B, HOLLOWAY R W, HERING A S, et al..Data-driven performance analyses of wastewater treatment plants: A review. Water Res,2019,157:498.

MARTíNEZ R, VELA N, EL AATIK A, et al..On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants. Water,2020,12(4):1096.

LEE M W, HONG S H, CHOI H, et al..Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors. Process Biochem,2008,43(10):1107-1113.

MELIDIS P, VAIOPOULOU E, AIVASIDIS A.Development and implementation of microbial sensors for efficient process control in wastewater treatment plants. Bioprocess Biosyst Eng,2008,31(3):277-282.

SONG C, ZHANG H.Study on turbidity prediction method of reservoirs based on long short term memory neural network. Ecol Model,2020,432.

CHING P M L, SO R H Y, MORCK T.Advances in soft sensors for wastewater treatment plants: A systematic review. J Water Process Eng,2021,44:102367.

GUST M, BURONFOSSE T, GEFFARD O, et al..In situ biomonitoring of freshwater quality using the New Zealand mudsnail Potamopyrgus antipodarum (Gray) exposed to waste water treatment plant (WWTP) effluent discharges. Water Res,2010,44(15):4517-4528.

HYBSKá H, LOBOTKOVá M, VANEK M, et al..Biomonitoring and its in the assessment of the quality of wastewater treatment process. Environ Nanotechnol, Monit Manag,2020,13:100292.

WANG Q, LI Z, CAI J, et al..Spatially adaptive machine learning models for predicting water quality in Hong Kong. J Hydrol,2023,622:129649.

YANG Z, ZOU L, XIA J, et al..Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models. Remote Sens-Basel,2022,14(7):1714.

TAHRAOUI H, AMRANE A, BELHADJ A-E, et al..Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting. Environ Techno Inno,2022,27:102419.

PESANTEZ J E, BERGLUND E Z, KAZA N.Smart meters data for modeling and forecasting water demand at the user-level. Environ Modell Softw,2020,125:104633.

CHEN K, CHEN H, ZHOU C, et al..Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Res,2020,171:115454.

EGBUERI J C, AGBASI J C.Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios. Geocarto Int,2022,37(26):14399-14431.

ZHOU P, LI Z, SNOWLING S, et al..A random forest model for inflow prediction at wastewater treatment plants. Stoch Env Res Risk A,2019,33(10):1781-1792.

KIM M, KIM Y, KIM H, et al..Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant. Front Env Sci Eng,2016,10(2):299-310.

MAN Y, HU Y, REN J.Forecasting COD load in municipal sewage based on ARMA and VAR algorithms. Resour Conserv Recy,2019,144:56-64.

LU H, MA X.Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere,2020,249:126169.

SINGHA S, PASUPULETI S, SINGHA S S, et al..Prediction of groundwater quality using efficient machine learning technique. Chemosphere,2021,276:130265.

NASIR N, KANSAL A, ALSHALTONE O, et al..Water quality classification using machine learning algorithms. Journal of Water Process Engineering,2022,48

MA M, ZHAO G, HE B, et al..XGBoost-based method for flash flood risk assessment. J Hydrol,2021,598:126382.

WANG G, SU H, MO L, et al..Forecasting of soil respiration time series via clustered ARIMA. Comput Electron Agr,2024,225.

BILGILI M, PINAR E.Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye. Energy,2023,284:128575.

文章被引用时，请邮件提醒。

Submit

Research and Implementation of Intelligent Data Analysis System for Online Education

Related Author

Mingwan LUO Yangjiang Vocational and Technical College;.Paul University Philippines, Tuguegarao, 3500, Cagayan

Marifel Capili Kummer .Paul University Philippines, Tuguegarao, 3500, Cagayan

Related Institution

Yangjiang Vocational and Technical College

.Paul University Philippines, Tuguegarao, 3500, Cagayan

⁰