Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas. (1st December 2021)

• Record Type: Journal Article
• Authors: Jiang, Yiqi; Li, Chaolin; Zhang, Yituo; Zhao, Ruobin; Yan, Kefen; Wang, Wenhui
• Is Part Of: Water research. Volume 207(2021)
• Publication Date: 2021-12-01
• Subjects: Fat, oil, and grease (FOG) -- Sewer networks -- Deep learning algorithm -- Gated recurrent unit (GRU) -- Multi-source data -- global sensitivity analysis (GSA); Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2021.117797
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: Data-driven method based on deep learning can predict FOG content in sewers. The FOG prediction model based on GRU has good generalization ability. GRU network structure adjustment achieves the optimal model performance. The key indicators are identified based on GSA for rapid FOG content monitoring. Abstract: The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R 2 of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model. Graphical abstract: Image, graphical abstract