Review of Modeling Methodologies for Managing Water Distribution Security

Identification of illicit discharges in sewer networks by an SWMM-Bayesian coupled approach

Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy.

Investigation of innovative designs of high-velocity channels for damping kinetic energy of flows

In our contemporary world, demanding sustainable resource management, the study focuses on innovative fast flow channel designs. It investigates their efficacy in reducing flow kinetic energy, aiming to optimize water and energy management and diminish flood risks. Employing diverse methodologies, it analyzes and develops these designs, proving their substantial impact on stream energy management. These innovations not only enhance energy efficiency but also mitigate risks associated with excess kinetic energy, promoting safer stream management. This research significantly contributes to fluid dynamics and engineering, deepening the understanding of kinetic energy control in flows and offering potential solutions for water supply, environmental sustainability, and infrastructure safety challenges.

Gated graph neural networks for identifying contamination sources in water distribution systems

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications.

Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review

Drinking water may undergo spatiotemporal changes in quality as it leaves the treatment plant and enters the distribution system. This variability means that not all consumers receive water of the same quality. Monitoring water quality in distribution networks makes it possible to verify the compliance of current regulations and reduce consumption risks associated with water quality degradation. An inaccurate interpretation of the spatiotemporal variability of water quality affects the selection of monitoring locations and the sampling frequency, which may conceal problems with the water quality and increase consumers' risk. This paper presents a chronological and critical review of the literature on the evolution, benefits and limitations of methodologies for the optimization of water quality degradation monitoring in water distribution systems supplied by surface sources. This review compares the different methodologies and examines the types of approaches, optimization objectives, variables, and types of spatial and temporal analysis, as well as the main advantages and limitations. A cost-benefit analysis was conducted to assess applicability in different-sized municipalities (small, medium and large). Future research recommendations for optimal water quality monitoring in distribution networks are also provided.

Optical monitoring of detergent pollutants in greywater

Large amount of wastewater is produced by washing machines and dishwashers, which are used in a daily basis. This domestic wastewater generated in households or office buildings (also called greywater) is drained directly to the drainpipes without differentiation from that with fecal contamination from toilets. Detergents are arguably the pollutants most frequently found in greywater from home appliances. Their concentrations vary in the successive stages in a wash cycle, which could be taken into account in a rational design of home appliances wastewater management. Analytical chemistry procedures are commonly used to determine the pollutant content in wastewater. They require collecting samples and their transport to properly equipped laboratories, which hampers real time wastewater management. In this paper, optofluidic devices based on planar Fabry-Perot microresonators operating in transmission mode in the visible and near infrared spectral ranges have been studied to determine the concentration of five brands of soap dissolved in water. It is found that the spectral positions of the optical resonances redshift when the soap concentration increases in the corresponding solutions. Experimental calibration curves of the optofluidic device were used to determine the soap concentration of wastewater from the successive stages of a washing machine wash cycle either loaded with garments or unloaded. Interestingly, the analysis of the optical sensor indicated that the greywater from the last water discharge of the wash cycle could be reused for gardening or agriculture. The integration of this kind of microfluidic devices into the home appliances design could lead to reduce our hydric environmental impact.

Can Water Price Improve Water Productivity? A Water-Economic-Model-Based Study in Heihe River Basin, China

Water demand management through price and market mechanisms is crucial for agricultural water management. However, how to set an appropriate agricultural water price remains unclear due to the uncertainty regarding the response of water demand to price changes and the complexity of the hydro-economic system. Thus, this study developed a water-economic model to examine both issues in the Heihe River Basin. The empirical results revealed that the basin’s agricultural water is currently price-inelastic, with a value of −0.26, but that at 0.27 yuan/m3, elasticity is gained. At this tipping point, water demand and economic output decline by up to 10.2% and 1.6%, respectively, while water productivity increases by 7.2%. It is noteworthy that the reallocation of water and land resources from agricultural sectors to non-agricultural sectors facilitated by a water price change is the main contributor towards water productivity improvement. This signifies the importance of managing water and land resources in an integrated framework to improve water productivity in the future. Our study contributes to the literature by suggesting that future policies for water-demand management should consider pricing that encourages water saving and the reallocation of water resources to high-value uses in order to increase water productivity.

A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment

A key purpose of a Supervisory Control and Data Acquisition (SCADA) system is to enable either an on-site or remote supervisory control and monitoring of physical processes of various natures. In order for a SCADA system to operate safely and securely, a wide range of experts with diverse backgrounds must work in close rapport. It is critical to have an overall view of an entire system at a high level of abstraction which is accessible to all experts involved, and which assists with gauging and assessing risks to the system. Furthermore, a SCADA system is composed of a large number of interconnected technical and non-technical sub-elements, and it is crucial to capture the dependencies between these sub-elements for a comprehensive and rigorous risk assessment. In this paper, we present a generic configurable dependency model of a SCADA system which captures complex dependencies within a system and facilitates goal-oriented risk assessment. The model was developed by collecting and analysing the understanding of the dependencies within a SCADA system from 36 domain experts. We describe a methodology followed for developing the dependency model, present an illustrative example where the generic dependency model is configured for a SCADA system controlling water distribution, and outline an exemplary risk assessment process based on it.

Management and health risk assessment of chemical contamination events in water distribution systems using PSO

An accidental or intentional contamination event can raise health and sociopolitical concerns, erode public trust, and affect the operation of water distribution systems. In this regard, emergency management plans are required to describe the necessary measures in order to deal with a threat. This study was carried out to investigate the best ways to manage intrusion in a water distribution network. In this research, the optimal management approach to deal with chemical contamination in a water distribution network was examined under three scenarios using the particle swarm optimization method. In each scenario, three management solutions were used to manage the contamination, including closing the pipe, opening the fire hydrant, and using a combination of pipe closure and fire hydrant opening. Contamination risk impact on consumers' health was assessed in the network's emergency status and after implementation of the best pollution management scenarios. The results showed that in the benchmark network, pipe closure was slightly more successful than opening of the fire hydrant valve. In pollution management of a real network, pipe closure was less effective than the hydrant opening in all scenarios. Generally, all applied scenarios were successful in reducing the contamination risk among the exposed people, so that carcinogenic and non-carcinogenic risks reduced by 100% in all scenarios compared to the non-management state.

Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic

During the coronavirus disease 2019 (COVID-19) pandemic, the daily pattern of activities changed dramatically for people across the globe, as they socially distanced and worked remotely. Changes in daily routines created changes in water consumption patterns. Significant changes in water demands can affect the operation of water distribution systems, resulting in new patterns of flow, with implications for water age, pressure, and energy consumption. This research develops a digital twin to couple Advanced Metering Infrastructure (AMI) data with a hydraulic model to assess impacts on infrastructure due to changes in water demands associated with the COVID-19 pandemic for a case study. Using 2019 and COVID-19 modeling scenarios, the hydraulic model was executed to evaluate changes to water quality based on water age, pressure across nodes in the network, and the energy required by the system to distribute potable water. A water supply interruption event was modeled as a water main break to assess network resiliency for 2019 and COVID-19 demands. A digital twin provides the capabilities to explore and visualize emerging consumption patterns and their effects on the functioning of water systems, providing valuable analyses for water utility managers and insight for optimizing infrastructure operations and planning for long-term impacts.

A Bayesian-SWMM coupled stochastic model developed to reconstruct the complete profile of an unknown discharging incidence in sewer networks

Unknown illicit discharges from manufactories often contain toxic chemical matters that are detrimental to the receiving waterbody by deteriorating the performance of wastewater treatment plants. Numerical models that identify these sources and reconstruct the discharging profiles are highly desired for environment management purpose. In this study, a stochastic source identification model that couples Bayesian inference with SWMM is developed to reconstruct the profile of an instantaneous dumpling incidence in sewer networks. The unknown source parameters include location, dumping rate and time of the dumping incidence. Key factors that impact the convergence and performance of the model including walking step size, numbers of unknown source parameters and numbers of monitoring sites are investigated. Results show that the Bayesian-SWMM coupled model is effective and accurate in identifying the unknown sources parameters in an instantaneous dumping event. It is also found that walking step size is crucial for the results to converge to true solutions. Furthermore, it shows that the identified results are highly dependent on the numbers of unknown source parameters. More unknowns result to unsatisfying results. However, the study shows that this limitation could be significantly reduced by using more monitoring site data. One contribution of the study is that errors from measurements and numerical simulation are considered in the identification while results are presented in probabilities with all possible values revealed. This feature is highly practical and efficient when it comes to assist further field screening efforts to pinpoint the true sources.

Water Environmental Capacity Calculation Based on Control of Contamination Zone for Water Environment Functional Zones in Jiangsu Section of Yangtze River, China

In recent years, due to unsustainable production methods and the demands of daily life, the water quality of the Yangtze River has deteriorated. In response to Yangtze River protection policy, and to protect and restore the ecological environment of the river, a two-dimensional model of the Jiangsu section was established to study the water environmental capacity (WEC) of 90 water environment functional zones. The WEC of the river in each city was calculated based on the results of the water environment functional zones. The results indicated that the total WECs of the study area for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP) were 251,198 t/year, 24,751 t/year, and 3251 t/year, respectively. Among the eight cities studied, Nanjing accounted for the largest proportion (25%) of pollutants discharged into the Yangtze River; Suzhou (11%) and Zhenjiang (12%) followed, and Wuxi contributed the least (0.4%). The results may help the government to control the discharge of pollutants by enterprises and sewage treatment plants, which would improve the water environment and effectively maintain the water ecological function. This research on the WEC of the Yangtze River may serve as a basis for pollution control and water quality management, and exemplifies WEC calculations of the world’s largest rivers.

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.