School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, 518055, China.
School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, 518055, China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China.
Water Res. 2021 Dec 1;207:117797. doi: 10.1016/j.watres.2021.117797. Epub 2021 Oct 25.
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 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.
下水道网络沉积物中脂肪、油和油脂（FOG）的含量是诊断下水道堵塞和溢出的关键指标。然而，传统的 FOG 检测既耗时又昂贵，并且基于统计方法建立的数学模型预测 FOG 含量的方法也无法提供令人满意的准确性。在此，提出了一种使用数据驱动的 FOG 含量预测模型的深度学习算法，以实现更准确的 FOG 含量预测。同时，利用全局敏感性分析（GSA）评估模型中输入指标对输出指标（FOG）的贡献，从而筛选出对预测性能影响较小的一些输入指标，确定输入指标的最佳组合，并降低模型的运行成本。为了评估所提出模型的有效性，在中国南方的一个城市进行了案例研究。实验结果表明，该预测模型能够对单个站点和多个站点的 FOG 进行很好的估计，具有很好的泛化性能，其平均 R 值为 0.922。通过 GSA，确定了模型中的关键输入指标为 pH 值、水温度（T）、相对湿度（RH）、污水流量（Flow）、饮用水供应（DWS）、速度（V）和电导率（σ），而空气压力（AP）、人口（Pop.）和液位（LV）等输入指标可以减少，而不会影响模型的预测精度。
