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The optimal operation of water distribution systems (WDS) is a paramount objective for water utilities due to the substantial energy consumption associated with pumping. A major challenge in optimizing WDS operation is addressing uncertainties such as those related to consumer demands. Real‐time operation under uncertainty necessitates a dynamic approach that can utilize the newly observed information and adjust the operational policy accordingly. This study presents an adjustable robust optimization (ARO) approach to tackle this challenge. Unlike static optimization methods, ARO generates a decision rule policy that is dynamically adjusted as new data becomes available and the operational horizon evolves, thereby ensuring adaptability to changing conditions. Furthermore, the study includes a quantitative analysis of typical demand uncertainty that supports the formulation of the ARO model. The proposed method is evaluated through two case studies and compared with traditional folding horizon approaches. The results indicate that the ARO method is competitive with traditional methods in terms of objective value and surpasses them in terms of robustness. An additional advantage of the method is its offline operation capability which enables it to produce decision rules independent of real‐time programs. This feature facilitates various practical applications such as what‐if analyses, maintenance work planning, and preparation for other special events. Plain Language Summary: The study is focused on developing strategies to optimize the operation of water distribution systems under uncertainty. Pumping and distributing water consumes a lot of energy, therefore water utilities strive to optimize the system's efficiency. This optimization problem involves uncertainties, such as consumer water demands. To address this challenge, a method is developed based on adjustable robust optimization (ARO) theory. Unlike conventional optimization methods that produce a fixed policy for the entire operational horizon, ARO generates an adjustable policy as a decision rule. It creates a dynamic decision‐making policy that can adapt to new data and evolving conditions. The study also includes an analysis to quantify the typical uncertainty associated with water demand which supports the formulation of the ARO model and justifies parts of the theory behind it. The proposed method is tested on two case studies and compared with traditional approaches. Several sensitivity analyses were held to present the ARO advantages and compare it with other methods. The results showed that ARO performs competitively with other methods and outperforms some of them. Key Points: Optimal operation of water distribution systems under uncertainty is addressed with a dynamic, data‐driven approachAn adjustable robust optimization model is developed to provide a decision rule that adapts to the latest revealed informationComparative analysis of the method against traditional folding horizon methods demonstrates superior performance [ABSTRACT FROM AUTHOR]

The increasing risk of intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants in water distribution systems is becoming a major concern that has significant and adverse impacts on public health. As such, it is important to have an effective, robust, and flexible plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on the strategic planning of the decontamination process of the contaminated infrastructure. This paper proposes an analytical method for modeling the slug‐feed method of disinfection given the drainage and disinfectant dosage profiles. The efficacy of the proposed method has been demonstrated by using it as the evaluation function in a genetic algorithm optimization of two case studies. Results show the proposed method exhibits higher robustness compared to the procedures defined in the current standard and literature. The main contributions of this study are to (1) provide a robust and accurate model to describe the slug‐feed method of disinfection, (2) offer additional resource utilization flexibility for water authorities, and (3) providing additional levels of district metered areas prioritization. Key Points: A robust, accurate, and efficient method to model the slug‐feed method of disinfectionA different disinfectant injection time can be assigned to each of the injection points when there are multiple injection pointsProviding additional levels of district metered areas prioritization and resource utilization [ABSTRACT FROM AUTHOR]

This paper focuses on evaluating a scenario-based multiobjective evolutionary algorithm for real-world design problems in which the environment where a system will operate is dynamic, and uncertain. Subsequently, the performance of a stochastic scenario selection scheme, inspired by methods to reduce overfitting in genetic programming, is investigated for scenario-based optimization. Using a scenario-based scheme to address uncertainty in a real-world system's operational environment, system designs are developed via aggregating the performance of a solution evaluated across many scenarios. Within each generation of the evolutionary algorithm the evaluation suite is resampled and evaluated by the current generation's solutions. This scheme is evaluated on two historical noisy test problems and two real-world water resources design problem instances. For each case, the stochastic scenario selection scheme is compared to a static selection scheme at various evaluation suite sizes. Results show the proposed scenario selection scheme to outperform static sampling schemes and increase efficiency of a multiobjective evolutionary algorithm for robust optimization objectives. [ABSTRACT FROM AUTHOR]

This paper presents a short history of water resources systems analysis from its beginnings in the Harvard Water Program, through its continuing evolution toward a general field of water resources systems science. Current systems analysis practice is widespread and addresses the most challenging water issues of our times, including water scarcity and drought, climate change, providing water for food and energy production, decision making amid competing objectives, and bringing economic incentives to bear on water use. The emergence of public recognition and concern for the state of water resources provides an opportune moment for the field to reorient to meet the complex, interdependent, interdisciplinary, and global nature of today's water challenges. At present, water resources systems analysis is limited by low scientific and academic visibility relative to its influence in practice and bridled by localized findings that are difficult to generalize. The evident success of water resource systems analysis in practice (which is set out in this paper) needs in future to be strengthened by substantiating the field as the science of water resources that seeks to predict the water resources variables and outcomes that are important to governments, industries, and the public the world over. Doing so promotes the scientific credibility of the field, provides understanding of the state of water resources and furnishes the basis for predicting the impacts of our water choices. [ABSTRACT FROM AUTHOR]

Despite their potential catastrophic impact, transients are often ignored or presented ad hoc when designing water distribution systems. To address this problem, we introduce a new piece-wise function fitting model that is integrated with mixed integer programming to optimally place and size surge tanks for transient control. The key features of the algorithm are a model-driven discretization of the search space, a linear approximation nonsmooth system response surface to transients, and a mixed integer linear programming optimization. Results indicate that high quality solutions can be obtained within a reasonable number of function evaluations and demonstrate the computational effectiveness of the approach through two case studies. The work investigates one type of surge control devices (closed surge tank) for a specified set of transient events. The performance of the algorithm relies on the assumption that there exists a smooth relationship between the objective function and tank size. Results indicate the potential of the approach for the optimal surge control design in water systems. [ABSTRACT FROM AUTHOR]

The objective of the least cost design problem of a water distribution system is to find its minimum cost with discrete diameters as decision variables and hydraulic controls as constraints. The goal of a robust least cost design is to find solutions which guarantee its feasibility independent of the data (i.e., under model uncertainty). A robust counterpart approach for linear uncertain problems is adopted in this study, which represents the uncertain stochastic problem as its deterministic equivalent. Robustness is controlled by a single parameter providing a trade-off between the probability of constraint violation and the objective cost. Two principal models are developed: uncorrelated uncertainty model with implicit design reliability, and correlated uncertainty model with explicit design reliability. The models are tested on three example applications and compared for uncertainty in consumers' demands. The main contribution of this study is the inclusion of the ability to explicitly account for different correlations between water distribution system demand nodes. In particular, it is shown that including correlation information in the design phase has a substantial advantage in seeking more efficient robust solutions. [ABSTRACT FROM AUTHOR]

Reliability in general, and in water distribution systems in particular, is a measure of probabilistic performance. A system is said to be reliable if it functions properly for a given time interval and within boundary conditions. Although water distribution system reliability has attracted considerable research attention over the last three decades, there is still no consensus on what reliability measures or evaluation methodologies should be used for the design/operation of water distribution systems. No system is perfectly reliable. In every system undesirable events--failures--can cause a decline or interruption in system performance. Failures are of a stochastic nature and are the result of unpredictable events that occur in the system itself and/or in its environs. A least cost design problem with normal design loadings will result in the cheapest system, but this system will have minimum residual capacity. However, if an increased loading (i.e., higher than the normal design) is implemented, the system's capacity will be increased, thus improving its residual capacity. Finding this "virtual increased loading," which results in a minimum cost residual system capacity that sustains a required reliability level, is the essence of the proposed methodology, which follows decomposition. The methodology is demonstrated on two example applications of increasing complexity. The main limitation of the suggested method for further extensions to real sized water distribution systems is the computational effort associated with the computation of the "inner" problem. Exploring the required computational burden divided between the "outer" and "inner" problems is a major challenge for future elaborations of this approach. [ABSTRACT FROM AUTHOR]

In this paper, the robust counterpart (RC) approach (Ben-Tal et al., 2009) is applied to optimize management of a water supply system (WSS) fed from aquifers and desalination plants. The water is conveyed through a network to meet desired consumptions, where the aquifers recharges are uncertain. The objective is to minimize the net present value cost of multiyear operation, satisfying operational and physical constraints. The RC is a min-max guided approach, which converts the original problem into a deterministic equivalent problem, requiring only that the uncertain parameters resides within a user-defined uncertainty set. The robust policy obtained by the RC approach is compared with polices obtained by other decision-making approaches including stochastic approaches. [ABSTRACT FROM AUTHOR]

This manuscript describes the application of a genetic algorithm model for the optimal design of regional wastewater systems comprised of transmission gravitational and pumping sewer pipelines, decentralized treatment plants, and end users of reclaimed wastewater. The algorithm seeks the diameter size of the designed pipelines and their flow distribution simultaneously, the number of treatment plants and their size and location, the pump power, and the required excavation work. The model capabilities are demonstrated through a simplified example application using base runs and sensitivity analyses. Scaling of the proposed methodology to real life wastewater collection and treatment plants design problems needs further testing and developments. The model is coded in MATLAB using the GATOOL toolbox and is available from the authors. Water Environ. Res., 83, 53 (2011). [ABSTRACT FROM AUTHOR]

A methodology extending the Cross Entropy combinatorial optimization method originating from an adaptive algorithm for rare events simulation estimation, to multiobjective optimization of water distribution systems design is developed and demonstrated. The single objective optimal design problem of a water distribution system is commonly to find the water distribution system component characteristics that minimize the system capital and operational costs such that the system hydraulics is maintained and constraints on quantities and pressures at the consumer nodes are fulfilled. The multiobjective design goals considered herein are the minimization of the network capital and operational costs versus the minimization of the maximum pressure deficit of the network demand nodes. The proposed methodology is demonstrated using two sample applications from the research literature and is compared to the NSGA-II multiobjective scheme. The method was found to be robust in that it produced very similar Pareto fronts in almost all runs. The suggested methodology provided improved results in all trails compared to the NSGA-II algorithm. [ABSTRACT FROM AUTHOR]

With the emphasis in recent years on intentional and nonintentional contamination events and optimal sensor placement, water utility managers are interested in network skeletonization issues because some degree of network simplification or aggregation is required to obtain both hydraulic and water quality results and assessment estimates within reasonable time frames and restrictive budgets. A method has been developed that can simplify complex water distribution system network modeling so that the reduced or simplified network provides reliable results for both pressures and contaminant concentrations. The methodology for network skeletonization presented here is based on both hydraulic and water quality aggregation of an all-pipes network. In this research, the aggregation method was capable of reducing system size by almost half, while still preserving system characteristics in terms of reliably simulating pressures and concentrations. These results demonstrated that even when an aggregated representation of an all-pipes network is used, reliable hydraulic and water quality results can be obtained. Utility managers using a reduced network that is based on the methodology described in the article can be confident that the reliability and robustness of simulated results have not been compromised. [ABSTRACT FROM AUTHOR]