Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems

Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks

Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming. Researchers have resorted to cheaper-to-run models, i.e. metamodels, as alternatives of computationally expensive models. With the recent surge in artificial intelligence applications, machine learning has become a key approach for metamodelling urban water networks. Specifically, deep learning methods, such as feed-forward neural networks, have gained importance in this context. However, these methods require generating a sufficiently large database of examples and training their internal parameters. Both processes defeat the purpose of using a metamodel, i.e., saving time. To overcome this issue, this research focuses on the application of inductive biases and transfer learning for creating SWS metamodels which require less data and retain high performance when used elsewhere. In particular, this study proposes an auto-regressive graph neural network metamodel of the Storm Water Management Model (SWMM) from the Environmental Protection Agency (EPA) for estimating hydraulic heads. The results indicate that the proposed metamodel requires a smaller number of examples to reach high accuracy and speed-up, in comparison to fully connected neural networks. Furthermore, the metamodel shows transferability as it can be used to predict hydraulic heads with high accuracy on unseen parts of the network. This work presents a novel approach that benefits both urban drainage practitioners and water network modeling researchers. The proposed metamodel can help practitioners on the planning, operation, and maintenance of their systems by offering an efficient metamodel of SWMM for computationally intensive tasks like optimization and Monte Carlo analyses. Researchers can leverage the current metamodel's structure for developing new surrogate model architectures tailored to their specific needs or start paving the way for more general foundation metamodels of urban drainage systems.

Deep fuzzy mapping nonparametric model for real-time demand estimation in water distribution systems: A new perspective

Hydraulic modeling has been recognized as a valuable tool for improving the design, operation, and management of water distribution systems (WDSs) as it allows engineers to simulate and analyze behaviors of WDSs in real time and help them make scientific decisions. The informatization of urban infrastructure has motivated the real-time fine-grained control of WDSs, making it one of the hotspots in recent years, thereby putting higher requirements on WDS online calibration in terms of efficiency and accuracy, especially when dealing with large-complex WDSs. To achieve this purpose, this paper proposes a novel approach (i.e., deep fuzzy mapping nonparametric model (DFM)) from a new perspective for developing a real-time WDS model. To our knowledge, this is the first work that considers uncertainties in modeling problems using fuzzy membership functions and establishes the precise inverse mapping from pressure/flow sensors to nodal water consumption for a given WDS based on the proposed DFM framework. Unlike most traditional calibration methods that require time to optimize model parameters, the DFM approach has a unique analytical solution derived through rigorous mathematical theory, thus the DFM is computationally fast as a result of sensibly handling the problems whose solutions typically require iterative numerical algorithms and large computational time. The proposed method is applied to two case studies and the results obtained show that it can produce a real-time estimation of nodal water consumption with higher accuracy, computational efficiency, and robustness relative to traditional calibration methods.

Psyllium-Husk-Assisted Synthesis of ZnO Microstructures with Improved Photocatalytic Properties for the Degradation of Methylene Blue (MB)

Wastewater from the textile industry is chronic and hazardous for the human body due to the presence of a variety of organic dyes; therefore, its complete treatment requires efficient, simple, and low cost technology. For this purpose, we grew ZnO microstructures in the presence of psyllium husk, and the role of psyllium husk was to modify the surface of the ZnO microstructures, create defects in the semiconducting crystal structures, and to alter the morphology of the nanostructured material. The growth process involved a hydrothermal method followed by calcination in air. Additionally, the psyllium husk, after thermal combustion, added a certain value of carbon into the ZnO nanomaterial, consequently enhancing the photocatalytic activity towards the degradation of methylene blue. We also investigated the effect of varying doses of photocatalyst on the photocatalytic properties towards the photodegradation of methylene blue in aqueous solution under the illumination of ultraviolet light. The structure and morphology of the prepared ZnO microstructures were explored by scanning electron microscopy (SEM) and powder X-ray diffraction (XRD) techniques. The degradation of methylene blue was monitored under the irradiation of ultraviolet light and in the dark. Also, the degradation of methylene blue was measured with and without photocatalyst. The photodegradation of methylene blue is highly increased using the ZnO sample prepared with psyllium husk. The photodegradation efficiency is found to be approximately 99.35% for this sample. The outperforming functionality of psyllium-husk-assisted ZnO sample is attributed to large surface area of carbon material from the psyllium husk and the synergetic effect between the incorporated carbon and ZnO itself. Based on the performance of the hybrid material, it is safe to say that psyllium husk has high potential for use where surface roughness, morphology alteration, and defects in the crystal structure are vital for the enhancing the functionality of a nanostructured material. The observed performance of ZnO in the presence of psyllium husk provides evidence for the fabrication of a low cost and efficient photocatalyst for the wastewater treatment problems.

A learning-based approach towards the data-driven predictive control of combined wastewater networks - An experimental study

Smart control in water systems aims to reduce the cost of infrastructure expansion by better utilizing the available capacity through real-time control. The recent availability of sensors and advanced data processing is expected to transform the view of water system operators, increasing the need for deploying a new generation of data-driven control solutions. To that end, this paper proposes a data-driven control framework for combined wastewater and stormwater networks. We propose to learn the effect of wet- and dry-weather flows through the variation of water levels by deploying a number of level sensors in the network. To tackle the challenges associated with combining hydraulic and hydrologic modelling, we adopt a Gaussian process-based predictive control tool to capture the dynamic effect of rain and wastewater inflows, while applying domain knowledge to preserve the balance of water volumes. To show the practical feasibility of the approach, we test the control performance on a laboratory setup, inspired by the topology of a real-world wastewater network. We compare our method to a rule-based controller currently used by the water utility operating the proposed network. Overall, the controller learns the wastewater load and the temporal dynamics of the network, and therefore significantly outperforms the baseline controller, especially during high-intensity rain periods. Finally, we discuss the benefits and drawbacks of the approach for practical real-time control implementations.

Modeling of Water Distribution System Based on Ten-Minute Accuracy Remote Smart Demand Meters

In the process of water distribution system modeling, nodal demands have been deemed to be the most significant input parameters causing uncertainties. Conventionally, users of the same type are assigned the same demand pattern, which does not reflect the variability in demand patterns. In Hefei City, a lot of remote smart demand meters were installed in recent years, providing real-time water consumption data for different water user sites. In this study, the water consumption data based on 10 min accuracy were collected and used in the established EPANET model of the study area. A 48 h simulation, including workdays and weekends, was carried out, and each node’s base demand and demand pattern were accurately calculated according to the remote data. The results show that the accuracy of the model has met the modeling criteria (less than 2 m for all pressure monitoring points), and there is no need to calibrate the nodal demand. The established offline hydraulic model can better reflect the water consumption characteristics of each type of user and has revealed the significant influence of secondary water supply systems.

Towards the long term implementation of real time control of combined sewer systems: a review of performance and influencing factors

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future. Despite decades of research, inconsistent definitions of the performance of RTC are used, hindering an objective and quantitative examination of the benefits and drawbacks of different control strategies with regard to their performance and robustness. Furthermore, a discussion on the changes occurring and projected to occur to UDS reveals that the potential impact of these changes on the functioning of RTC systems can be significant and should be considered in the design stage of the RTC strategy. Understanding this 'best-before' characteristic of an RTC strategy is the key step to ensure long term optimal functioning of the UDS. Additionally, unexplored potential for RTC systems might exist in the transitions, rehabilitation and construction of drainage systems. The research gaps highlighted here could guide the way for further development of RTC strategies, and enabling more optimal, long term implementation of RTC for urban drainage systems.

Identification of sampling points for the detection of SARS-CoV-2 in the sewage system

A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.

Foul sewer model development using geotagged information and smart water meter data

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.