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Intrusion of toxic heavy-metal cations into water-distribution systems (WDS) may cause severe adverse health-effects on large populations, along with an undesirable psychological impact. The corrosion (scale) layer, that invariably develops on the pipes' inner walls, is capable of adsorbing a significant mass of metal-cations and releasing them thereafter via diffusion to the water once operation is resumed, thereby causing a secondary contamination event. To overcome this, the contaminant should be completely removed, in a controlled fashion, from both the aqueous and scale phases, with minimum damage to the pipe's physical stature. This study determined the range of the Cd(II) adsorption capacity of corrosion-scales and quantified alternative treatments for desorbing it, using an assortment of metal water-pipes, extracted from the WDS. Batch, water-recirculation and flow-through experiments were conducted to determine the extent of Cd(II) adsorption and the best way to desorb it. Corrosion-scales showed substantial Cd(II)-absorption capacity (up to 0.75 mg Cd(II)/g scale) with an approximately linear relation between the aqueous Cd(II) concentration and the adsorbed mass. Desorption experiments included dosages of various acids. Sequential rinsing (eight pipe-volumes) by pH3 solution was found to be the best approach, releasing close to ~100% of the adsorbed Cd(II), with only a minor effect on the pipes' integrity. [ABSTRACT FROM AUTHOR]

The effect of downstream valve closure scheduling was analyzed to find optimal closure parameters that lead to the minimal maximum pressure head in the water distribution system. Several valve closure strategies were explored, combining the known valve performance curve (change in flow as a function of change in valve's opened area) with unknown valve closure curve (change in valve's opened area as a function of time). Second-order polynomial curve, power function curve, and piecewise linear curve were implemented and compared. Genetic algorithm and quasi-Newton (QN) optimization methods were applied. The methodology was tested for three networks, including looped gravitational and pressurized networks. The results demonstrate that flexible multiparametric valve closure curve and QN optimization method are more effective in minimizing the maximum pressure head in the system. [ABSTRACT FROM AUTHOR]

Linear water balance optimal operation models are common with relative short solution times but suffer from a lack of certainty whether the given solution is at all hydraulically feasible. Introducing hydraulic headloss, water leakage and changing pump energy consumption, effect the resulting system optimal operation but also create a non-linear problem due to the convex relation between flow, headloss, water leakage and total head. This study utilizes a methodology published by the authors for linearization of convex or concave equations. An iterative linear programming (LP) minimal cost optimal operation supply model is solved including the Hazen- Williams headloss equation, pressure related water leakage equation, changing pump energy consumption and source cost. The model is demonstrated using an example application. 'Greater than' or 'less than' water head constraints at nodes may force the system to maintain certain water levels in water tanks reducing the available operating volume forcing pumping stations to operate in peak tariff periods as less storage is available in low tariff periods. Operationally, reducing water leakage may be achieved by reducing water heads along the system by means of shifting pump operation periods and maintaining low water levels in water tanks. Source costs may serve as penalties or rewards discouraging or encouraging the use of certain water sources. [ABSTRACT FROM AUTHOR]

Previous studies on booster disinfection optimization were commonly based on 'blank networks', neglecting the impact of existing disinfection facilities, which could result in misleading solutions. To overcome this limitation, a method, which incorporates the existing disinfection facilities, is developed and demonstrated in this study. A particle backtracking algorithm, which traces the upstream pathways of the disinfection insufficiency nodes, is employed to narrow down the potential positions for booster stations. Deterministic optimization results are then efficiently yielded by the introduction of a 'coverage matrix'. The proposed method is applied to a real life water distribution system in Beijing, China. Results show the methodology effectiveness in optimizing booster disinfection placement and operation for real life water distribution systems. For the explored case study, results suggest that adding a booster disinfection station at 0.1% of the nodes of the system can satisfy chlorine residual at about 97.5% of all nodes. [ABSTRACT FROM AUTHOR]

In this study, a non-probabilistic robust counterpart (RC) approach is demonstrated and applied to the least-cost design/rehabilitation problem of water distribution systems (WDSs). The uncertainty of the information is described by a deterministic user-defined ellipsoidal uncertainty set that implies the level of risk. The advantages of the RC approach on previous modelling attempts to include uncertainty are in making no assumptions about the probability density functions of the uncertain parameters and their interdependencies, having no requirements on the construction of a representative sample of scenarios, and the deterministic equivalent problem preserves the same size (i.e. computational complexity) as the original problem. The RC is coupled with the cross-entropy heuristic optimization technique for seeking robust solutions. The methodology is demonstrated on an illustrative example and on the Hanoi network. The results show considerable promise of the proposed approach to incorporate uncertainty in the least-cost design problem of WDSs. Further research is warranted to extend the model for more complex WDSs, incorporate extended period simulations, and develop RC schemes for other WDSS related management problems. [ABSTRACT FROM AUTHOR]

Near real-time continuous monitoring systems have been proposed as a promising approach for enhancing drinking water utilities detect and respond efficiently to threats on water distribution systems. Water quality sensors are aimed at revealing contamination intrusions, while hydraulic pressure and flow sensors are utilized for estimating the hydraulic system state. To date optimization models for placing sensors in water distribution systems are targeting separately water quality and hydraulic sensor network goals. Deploying two independent sensor networks within one distribution system is expensive to install and maintain. It might thus be beneficial to consider mutual sensor locations having dual hydraulic and water quality monitoring capabilities (i.e. sensor nodes which collect both hydraulic and water quality data at the same locations). In this study a multi-objective sensor network placement model for conjunctive monitoring of hydraulic and water quality data is developed and demonstrated using the multi-objective non-dominated sorted genetic algorithm NSGA II methodology. Two water distribution systems of increasing complexity are explored showing tradeoffs between hydraulic and water quality sensor location objectives. The proposed method provides a new tool for sensor placements. [ABSTRACT FROM AUTHOR]

This manuscript describes the application of two recent methodologies developed by the authors for single and multi-objective optimal design of water distribution systems. The single-objective model is a hybrid algorithm incorporating decomposition, spanning tree search, and evolutionary computation, while the multi-objective algorithm integrates features form multi-objective genetic algorithms with the Cross Entropy combinatorial optimization scheme. The two models are implemented on the Hanoi water distribution system, one of the more explored systems in the research literature, through base runs and sensitivity analysis. The single-objective model produced the best known least cost solution for split pipe design, while the multi-objective model has shown robustness and well explanatory outcomes. Discussion of the accomplished results and suggestions for future research are provided. [ABSTRACT FROM AUTHOR]

Following the events of 9/11/2001 in the US, the world public awareness to possible terrorist attacks on water supply systems has increased significantly. The security of drinking water distribution systems has become a foremost concern around the globe. Water distribution systems are spatially diverse and thus are inherently vulnerable to intentional contamination intrusions. In this study, a multiobjective optimization evolutionary model for enhancing the response against deliberate contamination intrusions into water distribution systems is developed and demonstrated. Two conflicting objectives are explored: (1) minimization of the contaminant mass consumed following detection, versus (2) minimization of the number of operational activities required to contain and flush the contaminant out of the system (i.e. number of valves closure and hydrants opening). Such a model is aimed at directing quantitative response actions in opposition to the conservative approach of entire shutdown of the system until flushing and cleaning is completed. The developed model employs the multiobjective Non-Dominated Sorted Genetic Algorithm-II (NSGA-II) scheme, and is demonstrated using two example applications. [ABSTRACT FROM AUTHOR]

Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system's physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Dmitri P. Solomatine and Avi Ostfeld on the data-driven modeling (DDM) techniques and another by Yongdae Lee and colleagues on the stochastic multi-reservoir operation.