Department of Civil and Environmental Engineering, Technion Israel Institute of Technology, Haifa 3200, Israel.
ETH Zürich, Ecovision Lab, Photogrammetry and Remote Sensing, Zürich, Switzerland.
Water Res. 2022 Jun 1;216:118247. doi: 10.1016/j.watres.2022.118247. Epub 2022 Mar 3.
Understanding the negative effects of widespread implementation of optimal water efficient solutions may have on existing centralised sewer systems is still limited - one of these effects is the accumulation of solids in sewer pipes. Predicting these effects requires setting up and simulating complex detailed hydraulic sewer network models. Often, precise details of the sewer network layout and diurnal patterns of the wastewater flows are not available, limiting the applicability of using model predictions for such phenomena. In this study, the applicability of supervised machine learning (ML) algorithms for the development of a simplified surrogate model to predict solid accumulation in sewer pipes was investigated. A large number of highly variable sewer networks were synthetically generated and used to produce results that can be generalizable within the limitations of the current study. A hydrodynamic sewer model was set up and simulated for each synthetic sewer network and various scenarios in which different water-efficient solutions were considered. Simulation results indicated that the most impacts are expected to occur in the upstream part of the sewer networks, and that with 50% reduction in (waste-)water flows, 3-20% more pipes are expected to accumulate solids. It was further found that ML algorithms can be used to successfully predict locations of solids accumulation in sewer pipes without using hydrodynamic models. A simple tool based on the findings of this study, sparing the need to conduct complex hydraulic simulations, was developed. It allows the user to enter a set of pipe characteristics and the proportion of flow that is reduced due to the implementation of water efficient solutions, and it predicts whether the pipe will accumulate solids or not. The study results and the proposed ML algorithms can support the implementation of optimal water-efficient solutions that will promote designing and managing the water sensitive cities of the future.
了解广泛实施最佳节水解决方案对现有集中式污水系统可能产生的负面影响仍然有限——其中一个影响是污水管道中固体的积累。预测这些影响需要建立和模拟复杂的详细水力污水管网模型。通常，污水管网的精确布局和污水流量的昼夜模式细节并不可用，限制了使用模型预测来研究这些现象的适用性。在这项研究中，研究了监督机器学习 (ML) 算法在开发用于预测污水管道中固体积累的简化替代模型中的适用性。大量高度可变的污水管网被综合生成，并用于产生可在当前研究的限制范围内具有通用性的结果。为每个合成污水管网和考虑不同节水解决方案的各种情况建立并模拟了一个水动力污水管网模型。模拟结果表明，预计最主要的影响将出现在污水管网的上游部分，并且如果污水（废）流量减少 50%，预计将有 3-20%的更多污水管道会积累固体。进一步发现，ML 算法可用于成功预测污水管道中固体积累的位置，而无需使用水动力模型。基于本研究的结果，开发了一种简单的工具，无需进行复杂的水力模拟。它允许用户输入一组管道特性和由于实施节水解决方案而减少的流量比例，并预测管道是否会积累固体。该研究结果和提出的 ML 算法可以支持实施最佳节水解决方案，以促进未来设计和管理水敏感型城市。
