Water Utility Managers Research Articles

Influence of physical and water quality parameters on residual chlorine decay in water distribution network

Chlorine is the most common disinfectant in drinking water distribution practice. World Health Organization recommends 0.2–5.0 mg/l of residual chlorine in drinking water. This paper analyzed influence of physical and water quality parameters on chlorine decay in drinking water distribution. Principal component analysis, directed tree and regression were used to investigate influence of these parameters on chlorine from water treatment plant to water consumption points. Results show that initial chlorine, electrical conductivity and distance explain 62 % of chlorine decay with estimated error of 0.045 mg/l. The decision-tree feature importance scores of initial chlorine and electrical conductivity were 0.47 and 0.23 respectively. The combined feature importance scores of physical parameters of distance (0.09), pipe diameter (0.06), flow velocity (0.03), pressure (0.02) and travel time (0.046) were less than that for initial chlorine concentration (0.47) alone. These results show that conventional chlorination at water treatment plants removes largely fast inorganic reactants leaving traces of slow organic reactants as the dominant secondary contaminants in water distribution system. The key policy recommendation is to use water quality parameters more than physical parameters in order to enable water utility managers maintain residual chlorine within safe public health standards.

Anomalies identification in Smart Water Metering Networks: Fostering improved water efficiency

Smart Water Metering Networks (SWMNs) stand as pivotal infrastructure, crucial for communities and industries. The escalating value of water resources due to climate change and overexploitation underscores the urgency of optimizing these networks for efficiency and resilience. This study focuses on identifying anomalies within SWMNs to address challenges impeding efficient water resource management. Leveraging a comprehensive 72-month dataset from Windhoek, Namibia, this research employs a meticulous analytical approach to unveil diverse anomaly types prevalent within SWMNs. Anomalies, including irregular consumption patterns, leakages, and inaccurate meters, contribute significantly to both apparent and real losses. By scrutinizing this dataset, the study reveals nuanced anomaly patterns like persistent zero consumption and unexpected fluctuations, highlighting the pervasive nature of these issues within the network. The findings not only shed light on these multifaceted anomalies but also lay the groundwork for future advancements in machine learning-based anomaly detection techniques. This research holds promise beyond academia, offering practical implications for water utility management. Identifying and understanding these anomalies serves as a stepping stone toward developing robust detection systems, ultimately fostering heightened efficiency and resilience in water networks. This study serves as a catalyst for strategic improvements, enabling more sustainable and efficient utilization of water resources amidst evolving environmental challenges.

Integrated intelligent models for predicting water pipe failure probability

Sustainable management of water distribution networks (WDNs) is essential to ensure the continuous supply of water. However, the water pipes in WDNs often experience unprecedented failure, which causes disruption in services, flooding, increased maintenance costs, and reduced water quality. Although researchers have developed models to predict the failure of water pipes, the literature lacks fully optimized and robust models. Therefore, this study proposes a new methodology to develop optimized models for predicting the failure probability of water pipes by fusing logistic regression with genetic algorithms. The methodology was applied to the data of the Hong Kong WDN, and experiments were conducted to optimize the hyperparameters and features of logistic regression models. The performance of the proposed methodology is evaluated using five key metrics: accuracy, precision, recall, F1 score, and Area Under the Curve (AUC). The results show significant improvement over conventional approaches, with the best model achieving an F1 score of 0.868 and an AUC of 0.944. These results show that the model can effectively predict the failure probability of water pipes. The relative contribution of each feature to the model's outcome was investigated using the SHapley Additive exPlanations. Additionally, a web application based on the proposed methodology in this study was developed for Hong Kong that other water utility management can benefit from, which can facilitate reliable decision-making for the management of WDNs.

The Safe Drinking Water Act at 50: A Policy Model for Grand Challenges

AbstractSince its passage in 1974, the U.S. Safe Drinking Water Act (SDWA) has become a pillar of water resources engineering, utility management, and public health policy. Complementing other environmental legislation from the same period, SDWA set standards for drinking water suppliers, service, and quality and has made an unmistakable positive impact on U.S. communities for the past 50 years. While drinking water faces different specific challenges today, the general principles established by SDWA are the same. Its success may be attributed to its broad political commitment at the federal level, firm enforcement at the state level, and dutiful execution at the local (public water supplier) level—all while customers know exactly what to expect—thus creating clear accountability for safe water. Just as SDWA helped resolve last century's drinking water problems, it can serve as a policy model for addressing this century's grand challenges.

Blockchain applicability in the management of urban water supply and sanitation systems in Spain

Blockchain constitutes a disruptive technology that is currently changing business models and the way organizations operate. This paper explores the applicability of blockchain technology, and of distributed ledger technologies in general, to urban water supply and sanitation services in Spain. The potential of this technology for improving processes in this sector is assessed through a specific methodology of strategic analysis developed for this purpose. First, the technical, legal and managerial factors that condition the potential use of this technology to address the global, operational and governance challenges faced by urban water management are explored. Second, strategic analysis tools (e.g. value chain, factors of competition, and benchmarking) are used to model a water utility organization as a set of processes and characterize blockchain as an enabling technology with both benefits (traceability, immutability, and disintermediation) and limitations. Based on cost-benefit analysis, use cases in which the implementation of a blockchain could improve the organization's performance can be discerned. The results identify the processes and sub-processes of urban water utility management for which the use of a blockchain could improve performance: comprehensive maintenance, the management of serious incidents, and supplier management. Essentially, the methodology developed identifies management processes whose requirements are efficiently met through automation derived from blockchain solutions. Regardless of the management models currently in place in Spain, the traceability and disintermediation benefits of blockchain solutions can help to overcome governance and efficiency challenges associated with the management of urban water supply and sanitation services.

Pragmatism as an approach for decision-making: Why two Kenyan water utilities opted for pre-paid water dispensers

Since the late 1990's a growing number of urban water utility providers in Africa have adopted pre-paid water dispensers as a service delivery option for low-income areas within their service areas. Meanwhile, the use of such pre-paid technology for water delivery especially for the low-income consumer has been subject to debate in the water services sector. While one stream of literature mainly explores the potentials of the technology, a second stream focuses on the limits such technology presents, particularly for the low-income consumer. While acknowledging the contributions of both streams of literature on the debate on pre-paid dispensers in water services, this article explores why managers of water utilities adopt pre-paid dispensers for their operations in the low-income areas and how the technology is implemented in practice. Through a case study of two Kenyan cities (Nakuru and Kisumu), this article argues that in opting for pre-paid dispenser, the water utilities adopt a pragmatic approach, accepting solutions they may consider sub-optimal but which, at a particular moment and within a given context, may be the best they can do.

Drinking Water Utility-Level Understanding of Climate Change Effects to System Reliability.

Climate change hazards, including increased temperatures, drought, sea level rise, extreme precipitation, wildfires, and changes in freeze-thaw cycles, are expected to degrade drinking water utility system infrastructure and decrease the reliability of water provision. To assess how drinking water utility manager perceptions of these risks affect utility planning, 60 semistructured interviews were conducted with utilities of various sizes, source water supplies, and United States geographical regions. This study analyzes these interviews (1) to evaluate which climate hazards are of primary concern to drinking water managers, (2) to develop a mental model framework for assessing utility-level understanding of climate change risks to system reliability, and (3) to examine the status of current water utility adaptation planning. The results show that concern and awareness of climate hazard risks vary geographically and are grounded in historical exposure; some participants do not believe climate change will influence their system's overall reliability. When considering climate change risks, utility managers tend to focus on effects to water supply and infrastructure, as opposed to changes in operations and maintenance, water quality, or business functions. Most surveyed utilities do not have comprehensive climate adaptation plans despite federal and professional recommendations. The range of beliefs and actions concerning climate adaptation planning indicates that utilities need directed guidance, and policymakers should consider including climate hazards and projections as part of required utility risk and resilience assessments.

Deliberate stakeholder engagement: a framework of considerations for integrated asset management of water utilities

ABSTRACT The importance of stakeholder engagement in asset management is widely accepted and emphasised. Nonetheless, in asset management practice, stakeholder engagement is still often applied in an unsystematised and ad hoc manner. By proposing a framework of considerations for the deliberate design of stakeholder engagement, this study aims to contribute to the realisation of integrated asset management by offering a means to explicate the often implicit considerations regarding stakeholder engagement. By providing a conceptual exploration, as well as an analysis of three asset management cases in the Netherlands, this article provides building blocks for deliberate stakeholder engagement, guided by consideration of the mission, local context, and available time. Taking the daily reality of asset managers as a reference, this study develops a synthesis between literature on stakeholder engagement and asset management, hereby showing that both material and relational considerations should be made deliberately in all phases of an asset management project.

Water Utility Conservation and Management Approach Using IoT

Abstract: The demand for water supply has increased unpredictably worldwide over the past few decades, posing a major challenge. To overcome this challenge, an IoT-based water monitoring and control approach is presented, which supports realtime internet-based data collection. The device comprises a computer system (central hub), an IoT module, a switching unit, and a Zigbee module. The Internet of Things is a network of devices embedded with electronics, software, and sensors that enable them to exchange and analyze data. The centralized hub distributes water individually based on the needs of each household, assisted by IoT integration of communications, control, and information processing across the systems used. This allows water quantity and quality to be sensed at each structure in a particular sector, providing remote access control of the water supply. The centralized hub monitors and shares water among localized tanks or resupplies water from its reservoir according to a rank’s deficit or excess water level. Personal area networks are created in this scenario where a wireless connection is required across the system. UV treatment is deployed to improve the quality of water distributed to every household, and fluoride content level along with hardness checking is done using respective test meters. Overall, the IoT-based water monitoring and control approach design presents an innovative solution to the increasing demand for water supply, enabling efficient distribution and ensuring water quality.

Improvement of water utility management in Tajikistan: reduction in water wastage using a metered tariff system

AbstractTajikistan is a country in Central Asia with rich water resources. However, drinking water is used for agriculture and other non-drinking purposes owing to the fixed tariff system. Consequently, the overall level of water services has deteriorated and generated revenue is insufficient for operation and maintenance costs. Therefore, a project aimed to improve the quality of water supply services by providing hardware support, such as construction of wells and renewal of water distribution pipes, was conducted. However, because hardware improvement alone cannot sufficiently enhance water supply services, software support to improve the capacity of Vodokanal (utility) staff, including preparation of a metered tariff system, was implemented through a technical cooperation project. The project successfully raised customer awareness of water conservation, and the water consumption rate was reduced to two-thirds of that before implementation of the project. Additionally, the water quantity, quality, pressure, and supply hours in the target area were significantly improved. Despite the resulting increase in financial burden, customer satisfaction regarding the water supply was enhanced from 51% before the project to 100% in the second half of the project. Simultaneously, the water supply revenue increased by 24% and this increase contributed to sustainable water supply management.

AWWA Water Science Author Spotlight

<i>AWWA Water Science</i> Author Spotlight

THE ROLES THAT PROJECT SPONSORS PLAY AT STAGE GATES OF A PROJECT LIFE CYCLE MODEL: A CASE STUDY OF A WATER UTILITY IN SOUTH AFRICA

Project sponsors play significant roles at the stage gates of project life cycle models (PLCMs). However, research on the roles that sponsors play at stage gates, and the attributes required of the sponsor, are sparse. This study investigated the roles played by government and municipal sponsors at the PLCM stage gates of a water utility, and identified the attributes that the sponsor should have as well as the challenges they face. A case study approach was employed, and semi-structured interviews were conducted with relevant stakeholders. The findings could assist the senior management of water utilities, national government departments, and municipalities when appointing suitable project sponsors.

Consumption Change‐Induced Transients in a Water Distribution Network: Laboratory Tests in a Looped System

AbstractAt the Water Engineering Laboratory of the University of Perugia, Italy, a wide experimental program has been carried out in a polymeric pipe network with two 100 × 100 m square loops. The aim of this program is to analyze the dynamic response of the system to transients caused by a change in water consumption. To emphasize such a response, transients are generated by the complete and fast closure of an end‐user, located at the downstream end of a service line. During tests, the combined effect of simultaneous consumers, whose consumption is varied both deterministically and stochastically, has been analyzed. The tests allow examining the propagation of the generated pressure waves within the network for different water consumption variations and end‐user locations. The lessons learnt from the experimental results may help the water utility managers to identify the reasons for the higher frequency of occurrence and severity of faults in some specific portions of water distribution networks apparently “similar” to others where damages are less frequent and severe.

Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Energy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems.

Household behavioral intention, environmental habit and attitude related to efficient water management: an empirical analysis on pro-environmental behavior among urban residents

Abstract The management of water utilities in urban contexts, which are particularly prone to water shortages and where future water security is of the utmost importance, has become a challenging task for the majority of municipal and regional planners. The present study attempts to evaluate how environmental knowledge and values influence possible pro-environmental behavior with respect to household water consumption behavior, attitudes, and habits among 500 (n = 313) urban dwellers in an urban setting in Jiangsu Province, China. We adapted the Theory of Planned Behavior (TPB) to elucidate pro-environmental behaviors while exploratory factor and Spearman correlation analysis were applied to datasets elicited from respondents. The study establishes important positive correlations using Spearman's rank (r = 0.50) based on the findings of our empirical analysis, which suggest that individuals with strong environmental values and rich environmental knowledge are more likely to exhibit behaviors, attitudes and habits that result in effective household water use and management. Additionally, based on the magnitude of the factor loadings, the key results from principal factor analysis were as follows: efficient household water consumption is contingent on fixing household leaks (0.73) and replacing old shower fixtures and water-using appliances (0.66). Nevertheless, China's water resources are insufficient and require efficient management (0.66), the quality of tap water supplies is compromised and cannot be trusted (0.66) and there has been little to no improvement in water quality and availability in the past 10 years in China (0.69). This research reinforces the role of knowledge in shaping behavior, and has policy effects and could serve as a benchmark for measures to reduce household water use.

Flowrate Time Series Processing in Engineering Tools for Water Distribution Networks

AbstractThe current paper presents a comprehensive methodology for processing unevenly (and evenly) spaced flowrate time series for subsequent use in engineering tools, such as the calibration of hydraulic models or the detection and location of leaks and bursts. The methodology is a four‐step procedure: (a) anomaly identification and removal, (b) short‐duration gap reconstruction, (c) time step normalization, and (d) long‐duration gap reconstruction. The time step normalization is carried out by a numerical procedure prior to the reconstruction process. This reconstruction process uses a pattern model coupled with regression techniques (i.e., autoregressive integrated moving average and exponential smoothing). The methodology is calibrated using Monte Carlo simulations applied to a water utility flowrate time series and validated with two additional time series from different water utilities. Obtained results demonstrate that the proposed methodology can process flowrate time series from water supply systems with different characteristics (e.g., consumption pattern, data acquisition system, transmission settings) both for normal operating conditions and during the occurrence of abnormal events (e.g., pipe bursts). This methodology is a very useful tool for the daily management of water utilities, preparing the time series to be used in different engineering tools, namely, hydraulic simulation, model calibration or online burst detection.

A Hybrid Data-Driven-Agent-Based Modelling Framework for Water Distribution Systems Contamination Response during COVID-19

Contamination events in water distribution systems (WDSs) are highly dangerous events in very vulnerable infrastructure where a quick response by water utility managers is indispensable. Various studies have explored methods to respond to water events and a variety of models have been developed to simulate the consequences and the reactions of all stakeholders involved. This study proposes a novel contamination response and recovery methodology using machine learning and knowledge of the topology and hydraulics of a water network inside of an agent-based model (ABM). An artificial neural network (ANN) is trained to predict the possible source of the contamination in the network, and the knowledge of the WDS and the possible flow directions throughout a demand pattern is utilized to verify that prediction. The utility manager agent can place mobile sensor equipment to trace the contamination spread after identifying the source to identify endangered and safe places in the water network and communicate that information to the consumer agents through water advisories. The contamination status of the network is continuously updated, and the consumers reaction and decision making are determined by a fuzzy logic system considering their social background, recent stress factors based on findings throughout the COVID-19 pandemic and their location in the network. The results indicate that the ANN-based support tool, paired with knowledge of the network, provides a promising support tool for utility managers to identify the source of a possible water event. The optimization of the ANN and the methodology led to accuracies up to 80%, depending on the number of sensors and the prediction types. Furthermore, the specified water advisories according to the mobile sensor placement provide the consumer agents with information on the contamination spread and urges them to seek for help or support less.

Dynamic segment criticality analysis: A precursor to scheduling of maintenance routines in water distribution networks

This study proposes a framework for dynamic segment criticality analysis and its implications on scheduling of some reactive maintenance routines in water distribution networks (WDNs). The framework harmonises multilayer networks which are capable of ascertaining the dynamics of WDNs in discrete time and a novel hybrid segment criticality measure (SCM) to achieve dynamic segment criticality analysis. The novel hybrid SCM utilises reachability theory from complex networks, which accounts for multiple sources, cyclic paths in WDNs, and demand shortfall attributed to segment isolation is computed using Pressure Driven Analysis (PDA) courtesy EPANET. Using a benchmark WDN, North Marin Water District Network, as a case study we have demonstrated the efficacy of the proposed framework and its ability to capture the dynamics of WDNs and evaluate the criticality of any segment given any temporal window. The results indicate the framework accurately evaluates the criticality of all segments within any temporal window and establishes the fact that segment criticality varies with time. Based on the results of this study reactive maintenance routines could be scheduled during temporal periods where the segment criticality measure is minimal to avoid excessive service disruption and customer displeasure. This study supports managers of water utilities with a decision support system to accurately schedule maintenance routines and evaluate segment criticality.

Risk management during the Covid-19 crisis: business responses in the Polish water utilities

Abstract Water utilities are an essential service that helps protect public health during crises. The Covid-19 pandemic revealed that crisis preparedness is a crucial capability that water utilities must possess. The purpose of this paper is to identify managerial actions and responses that were undertaken by water utility managers in order to reduce the risk related to the first economic lockdown caused by the unexpected Covid-19 crisis. As water utilities should learn from Covid-19 so as to strengthen their future risk preparedness, the paper offers some theoretical underpinnings on risk management. As a result of literature analysis, we focus on the risk management framework that distinguishes five types of risk. The survey was carried out among 116 waterworks in Poland in April 2020. The results indicate the importance of minimising liquidity risk and supply chain risk, which is relevant to the adopted theoretical framework. The findings also highlight the importance of a category that was not originally included in the research model – that is human resource risk, an area that requires managerial attention in the water utility sector. The results could also provide useful pointers for other water utilities, especially those operating in the same or similar legislative regime.

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.