From advanced hydraulic modelling to performance indicator for the efficiency of investments in leakage management of pressurized water systems

INTRODUCTION

The digital transition is meant as the review of processes using digital technologies and strategies to increase efficiency based on a simpler collection of representative data. The process of interest here is the leakage management in systems distributing water.

OBJECTIVE

To develop a novel key performance indicator (KPI) for leakage management considering the needs of ongoing digital transition in the water sector, which is opening a new era in the management of drinking water infrastructures.

METHODOLOGY

The novel KPI, named Asset Management Support Indicator, is developed starting from advanced hydraulic modelling and the physical laws governing leakage outflows, in order to be physically based and rational for increasing the efficiency of leakage management activities using representative process data.

RESULTS

The Asset Management Support Indicator supports effective leakage management strategies by driving towards efficiency, as discussed and demonstrated using several real case studies.

CONCLUSION

The novel indicator is consistent with the digital transition perspective and current need of increasing the efficiency of water utilities, and it is also suitable to be adopted by water authorities to benchmark their performances, because it overcomes the weaknesses of traditional KPIs.

Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks

Chlorine remains the most widely used disinfectant in drinking water treatment and distribution systems worldwide. To maintain a minimum residual throughout the distribution network, chlorine dosage needs to be regulated by optimizing the locations of chlorine boosters and their scheduling (i.e., chlorine injection rates). Such optimization can be computationally expensive since it requires numerous evaluations of water quality (WQ) simulation models. In recent years, Bayesian optimization (BO) has garnered considerable attention due to its efficiency in optimizing black-box functions in a wide range of applications. This study presents the first attempt to implement BO for the optimization of WQ in water distribution networks. The developed python-based framework couples BO with EPANET-MSX to optimize the scheduling of chlorine sources, while ensuring the delivery of water that satisfies water quality standards. Using Gaussian process regression to build the BO surrogate model, a comprehensive analysis was conducted to evaluate the performance of different BO methods. To that end, systematic testing of different acquisition functions, including the probability of improvement, expected improvement, upper confidence bound, and entropy search, in conjunction with different covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic, was conducted. Additionally, a thorough sensitivity analysis was performed to understand the influence of different BO parameters, including the number of initial points, covariance kernel length scale, and the level of exploration vs exploitation. The results revealed substantial variability in the performance of different BO methods and showed that the choice of the acquisition function has a more profound influence on the performance of BO than the covariance kernel.

Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran)

One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation-optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58 min and the best detection time of 5.06 min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood.

Drinking water management strategies for distribution networks: An integrated performance assessment framework

Due to rapid population growth, urbanization, water contamination, and climate change, global water resources are under increasing pressure. Water utilities apply drinking water management strategies (DWMS) to ensure that water is safe for drinking. However, in recent years, due to increased inclination towards climate change, environmental emissions, and sustainable development goals; the environmental and economic performance of DWMSs is getting attention. An integrated framework combining life cycle thinking and water quality assessment techniques was developed in this study to evaluate the DWMSs' performance in terms of water quality, environment, and economics. Six DWMSs were assessed using the integrated framework as a case study. The environmental impacts in terms of human health, ecosystem, and resource use ranged from 1.46E-06 to 4.01E-06 DALY, 9.35E-10 to 3.80E-09 species.yr, and 0.0025-0.0071 USD-$, respectively. Pollution water index (PWI) and cost-benefit analysis (CBA) were used as decision-making techniques to assess the overall performance and suitability of DWMSs under given settings. The DWMSs with surface water as a source or ones providing relatively more degree of treatment have a relatively high PWI score (i.e., ≈0.31), reflective of high environmental impacts and water pollution compared to other alternatives. The CBA scores of selected alternatives ranged between 0.22 and 1.0. Furthermore, it was identified that DWMSs applied on relatively bigger water distribution systems can outweigh their costs (i.e., environmental and economic impacts). The proposed framework and approaches are flexible as they can incorporate different criteria in evaluating the performance and applicability of DWMSs.

Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks

The objective of the design and operation of any water distribution network (WDN) includes meeting the desired demand at sufficient pressure at all nodes. However, this requires situational awareness; in other words, the knowledge of system state variables such as pressure and flow throughout the network. In this work, a hybrid approach is developed for sensor placement (SP) and state estimation (SE) that exploits the underlying correlation structure in the data, along with the principles governing the flow through circular pipes. The problem of SP in WDN is addressed since measuring the state variables throughout the network is not practical. The problem of SE that maps to a matrix completion problem under certain physical and logical constraints is solved later. The completed matrix represents the state of WDN at any given time. Benchmark networks used in literature were used to evaluate the proposed approach. The mean absolute percentage error (MAPE) of less than 5% was obtained while estimating the head available at nodes. The knowledge of the states in the entire network could help operate the network adaptively.

Increasing the capacity of water distribution networks using fitness function transformation

Even the most carefully designed water distribution network (WDN) can suffer from local capacity deficiencies as a result of the quick and unpredictable growth of the urbanization of new industrial sites. To solve this problem, this paper focuses on the identification of the best possible location for a new pipeline within an existing WDN, which maximizes the node-wise capacity. To determine the optimal solution, a parameter, namely pressure sensitivity, is defined, which can localize nodes with local capacity problems computationally efficiently. During our research, a fitness function transformation technique was defined, which increases the effectivity of the optimization on a larger scale by the formulation of a feasible fitness function. Combining this technique with an extended version of the genetic algorithm, the topology of eleven real-life WDN was optimized. A scrutiny is performed on three networks, highlighting typical deficiencies.

Vulnerability analysis of water distribution networks to accidental pipe burst

Even the best-maintained water distribution network (WDN) might suffer pipe bursts occasionally, and the utility company must reconstruct the damaged sections of the system. The affected area must be segregated by closing the corresponding isolation valves; as a result, the required amount of drinking water might not be available. This paper explores the behaviour and topology of segments, especially their criticality from the viewpoint of the whole system. A novel, objective, dimensionless, segment-based quantity is proposed to evaluate the vulnerability of both the segments and the whole WDN against a single, incidental pipe break, computed as the product of the probability of failure within the segment and the amount of unserved consumption. 27 comprehensive real-life WDNs have been examined by means of the new metric and with the help of complex network theory, exploiting the concept of the degree distribution and topology-based structural properties (e.g. network diameter, clustering coefficient). It was found that metrics based purely on topology suggest different network behaviour as vulnerability analysis, which also includes the hydraulics. The investigation of the global network vulnerabilities has revealed several critically exposed systems, and the local distributions unveiled new properties of WDNs in the case of a random pipe break.

Predicting unregulated disinfection by-products in small water distribution networks: an empirical modelling framework

Disinfection is used to deactivate pathogens in drinking water. However, disinfectants react with natural organic matter present in water to form disinfection by-products (DBPs). While a few of these DBPs have been studied extensively and are regulated in many countries, new unregulated DBPs (UR-DBPs) have also recently been identified in drinking water. The UR-DBPs are considered to be more toxic than regulated DBPs (R-DBPs). To understand the occurrence of UR-DBPs in a water distribution network (WDN), this research presents an approach to predicting the behaviour of emerging UR-DBPs such as dichloroacetonitrile (DCAN), trichloropropanone (TCP), and trichloronitromethane (TCNM) in WDNs. Water quality data, generated by sampling and laboratory analysis of 12 small communities, was used to develop predictive models. A framework was also proposed alongside the predictive models to estimate the concentration of emerging UR-DBPs under limited water quality sampling information. Moreover, the relationship between emerging UR-DBP concentrations and their identified predictors was further observed and evaluated by developing contour profiles. DCAN and TCP predictive models have coefficient of determination (R²) > 85%, whereas for TCNM model, the R² was > 65%. Water quality parameters including water temperature, turbidity, conductivity, and dissolved organic carbon concentrations were identified as key predictors. Similarly, trichloroacetic acid and bromodichloromethane were identified as key predictors among DBP families, to predict the occurrence of emerging UR-DBPs. Developed models and relationships between the UR-DBPs and predictors can help water utilities and regulators to manage the occurrence of UR-DBPs in small WDNs.

A novel framework of multivariate modeling of water distribution network through 33 factorial design and artificial neural network

The water distribution network is largely affected by the change in the influencing factors, such as input pressure, demand and supply duration. The change in each parameter requires the extensive design of the network and the interactive effect of the influencing parameters are hardly explored. The main hurdles for the water providers lie in the absence of a prediction model, which can be used as a decision tool to assess the effect of the change in parameter and estimating the cost for the changed scenario. The present study developed a novel framework based on the artificial neural network for multivariate prediction modeling taking the response as the cost of the pipe network. The application of the 3³ factorial design was used for the selection of the influencing parameters and outcome was taken as the input to the neural network model. The adequacy of the model was tested through error functions and analysis of variance. The low values of the error functions (0.0004-0.228) and high F value (162,442) and R² (0.999) established the significance of the model. The model can be used for predicting the cost of the changed scenarios and assessment of the optimal solution for the system variables.

Many-objective optimization model for the flexible design of water distribution networks

This paper proposes a many-objective optimization model for the flexible design of water distribution networks (WDNs), including four objectives. Two objectives are related to the WDNs' hydraulic capacity, the minimization of the pressure deficit and the undelivered demand. The third objective is the traditional cost minimization while the fourth minimizes carbon emissions. These objectives concern network reliability, and financial and environmental concerns. They can give rise to solutions embedding new trade-off in design perspectives. There is a gap in the literature when it comes to dealing with many-objective problems for designing and constructing a WDN over a long-term planning horizon and using a staged design scheme that includes the consideration of uncertainty. A solution obtained through this process can be implemented in the first stage and the WDN is prepared for the possible occurrence of various future scenarios. These scenarios can consider expansions of WDNs to different development areas, in different time stages. Furthermore, defining a multi-staged design allows implementing the design of the first stage and reassessing the whole process in the end of each stage when more plausible future scenarios can be investigated. The solution of complex problems such as these needs improved algorithms to produce the Pareto front and so enable the trade-off between the objectives to be examined. An enhanced algorithm, based on the simulated annealing concept and capable of handling the critical scalability issues encountered in previous algorithms with respect to drawing the Pareto front for many-objective problems where a high-dimensional space is involves, is presented. The results obtained allow a thorough analysis of trade-offs between objectives and confirm the importance of considering the minimization of all those four objectives and the advantages of using a flexible approach to design WDNs to better inform decision makers.

Hydraulically informed graph theoretic measure of link criticality for the resilience analysis of water distribution networks

Water Distribution Networks (WDN) are complex and highly interconnected systems. To maintain operation under failure conditions, WDNs should have built-in resilience based on topological and energy redundancy. There are various methods for analysing the resilience of WDNs based on either hydraulic models or surrogate network measures; however, not a single universally accepted method exists. Hydraulic modeling of disruptive operational scenarios suffer from combinatorial restrictions and uncertainties. Methods that rely on surrogate network measures do not take into account the complex interactions between topological and energy redundancy. To bridge this gap, the presented work introduces a hydraulically informed surrogate measure of pipe criticality for the resilience analysis of WDNs, called Water Flow Edge Betweenness Centrality (WFEBC). The WFEBC combines the random walk betweenness centrality with hydraulic (energy) loss principles in pipes. The proposed network resilience estimation method is applied to a case study network and an operational network. Furthermore, a network decomposition approach is proposed to complement the network estimation method and facilitate its scalability to large operational networks. The described resilience analysis method is benchmarked against a hydraulic model-based analysis of WDN reserve capacity. WFEBC is also applied to assess the improvement in resilience allowed by the implementation of a dynamically adaptive topology in an operational network. The benefits and limitations of the proposed method are discussed.