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A least-cost optimization of a water distribution system (WDS) results in a branched network if some system reliability measures are not considered. Using an explicit level of system redundancy is one way to enhance reliability in the WDS network. This approach consists of first dividing the network into subsystems. Then, during the optimization stage, the subsystems are optimized simultaneously by demanding each one to maintain some level of service. Which pair of subsystems is ultimately selected determines the outcome of the optimization problem. However, there is little if no literature on the optimization of the selection of a pair of subsystems. This study addresses both subsystem and component sizing optimization in designing a level-1 redundant network. Candidate subsystems are enumerated using graph theory where st-numbering for the network is assigned first, and then the backups are generated following the decrement and increment orders of the node's st-numbering. [ABSTRACT FROM AUTHOR]

This study presents an approximated model for pressure transient simulations as well as a local wall shear stress analysis to control and dislodge biofilm growth in water distribution systems. It demonstrates the potential of taming hydraulic transients to manage and disrupt the growth of biofilm colonies attached to the inner walls of pipeline systems. The systems are subjected to consecutive controlled transient pressure waves by manipulating valves positioned strategically along the water distribution system. Because the controlled transient waves are generated at different locations in the system, the interference properties of the waves can be capitalized on at points where different pressure waves can come together and merge in the system, thus creating high pressures and powerful shear stresses. Nevertheless, it is vital to keep the head pressure confined within the allowed pressure range to avert extreme devastating pressures and ensure the integrity of the system. Three case study applications of increasing complexity are presented to demonstrate the potential of this approach. These transient wave simulations implemented the Lagrangian-based wave characteristic transient model, and the optimization of valve operation was governed by an evolutionary genetic algorithm. The results indicate that it is possible to trigger transient events for biofilm management purposes without exceeding the allowed critical pressure in various network layouts. [ABSTRACT FROM AUTHOR]

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. [ABSTRACT FROM AUTHOR]

Abstract: This study describes a new methodology for the disinfection booster design, placement, and operation problem in water distribution systems. Disinfectant residuals, which are in most cases chlorine residuals, are assumed to be sufficient to prevent growth of pathogenic bacteria, yet low enough to avoid taste and odor problems. Commonly, large quantities of disinfectants are released at the sources outlets for preserving minimum residual disinfectant concentrations throughout the network. Such an approach can cause taste and odor problems near the disinfectant injection locations, but more important hazardous excessive disinfectant by-product formations (DBPs) at the far network ends, of which some may be carcinogenic. To cope with these deficiencies booster chlorination stations were suggested to be placed at the distribution system itself and not just at the sources, motivating considerable research in recent years on placement, design, and operation of booster chlorination stations in water distribution systems. The model formulated and solved herein is aimed at setting the required chlorination dose of the boosters for delivering water at acceptable residual chlorine and TTHM concentrations for minimizing the overall cost of booster placement, construction, and operation under extended period hydraulic simulation conditions through utilizing a multi-species approach. The developed methodology links a genetic algorithm with EPANET-MSX, and is demonstrated through base runs and sensitivity analyses on a network example application. Two approaches are suggested for dealing with water quality initial conditions and species periodicity: (1) repetitive cyclical simulation (RCS), and (2) cyclical constrained species (CCS). RCS was found to be more robust but with longer computational time. [Copyright &y& Elsevier]

This study introduces a new search method for box-constrained optimization problems called the search method for box optimization (SMBO). SMBO is a population heuristic-based search methodology that solves global optimization problems. SMBO represents the population as a probability density function (PDF) inside the problem bounds. The PDF shape is dynamically adapted during the process to guide to a 'good' search domain. The applicability and the efficiency of the method are demonstrated using two benchmark sets, which include unimodal, multimodal, expanded, and hybrid composition functions. The performance of SMBO is compared with several genetic algorithms (GAs); the first benchmark compares it with nine codes of traditional/classic GAs, and the second compares SMBO with two recent variants of genetic algorithms. The results show that SMBO performs as well as or better than the GAs in both comparisons. The method is demonstrated on a nonlinear model for management of a water supply system (WSS), and the results are compared with the commercial GA toolbox of matrix laboratory (MATLAB). [ABSTRACT FROM AUTHOR]

Abstract: A computerized learning algorithm was developed for assessing the extent of biofouling formations on the inner surfaces of water supply pipelines. Four identical pipeline experimental systems with four different types of inlet waters were set up as part of a large cooperative project between academia and industry in Israel on biofouling modeling, prediction, and prevention in pipeline systems. Samples were taken periodically for hydraulic, chemical, and biological analyses. Biofilm sampling was done using Robbins devices, carrying stainless steel coupons. An MT–GA, a hybrid model combining model trees (MTs) and genetic algorithms (GAs) in which the sampled input data are selected by the proposed methodology, was developed. The method outcome is a set of empirical linear rules which form a model tree, iteratively optimized by a GA and verified using the dataset resulting from the empirical field studies. Good correlations were achieved between modeled and observed cell coverage area within the biofilm. Sensitivity analysis was conducted by testing the model’s response to changes in: (1) the biofilm measure used as output (target) variable; (2) variability of GA parameters; and (3) input attributes. The proposed methodology provides a new tool for biofouling assessment in pipelines. [Copyright &y& Elsevier]

This work presents a model for the inclusion of chemical water stability in optimizing the operation of water distribution systems. When desalinated water is mixed with surface water and/or groundwater, the blend can become chemically unstable. Such a state can cause the phenomena of 'red water,' an increase in corrosion rates, and a reduction in disinfection efficiency. In this study, a methodology is developed that links a genetic algorithm, a hydraulic and water quality extended period simulator, a numerical scheme for computing the calcium carbonate precipitation potential (CCPP) [the quantitative measure of the precise thermodynamic potential of a solution to precipitate (or dissolve) CaCO3(s)], and the pH of the water. The model minimizes the cost of pumping and treatment subject to quantities, pressures, CCPP, and pH constraints. Two example applications are utilized for demonstrating the methodology capabilities. Although the model provides a new tool for the explicit inclusion of chemical water stability in optimal operation of water distribution systems, it overlooks variations in pump efficiency at operational points, does not constrain the number of pump switches, the minimum pump operation and off times, the durations between pump start and shutoff, and the plant or source capacity. Those limitations should be considered in possible extensions of this study. [ABSTRACT FROM AUTHOR]

Water-distribution systems least-cost pipe sizing/design is probably the most explored problem in water-distribution systems optimization. Attracted numerous studies over the last 4 decades, two main approaches were employed: decomposition in which an “inner” linear programming problem is solved for a fixed set of flows/heads, while the flows/heads are altered at an “outer” problem using a gradient or a subgradient type technique; and the employment of a general evolutionary optimization algorithm. In 1995 Loganathan and his colleagues proposed to couple these two approaches into one framework, thus overcoming the limitations of each. This study employs this framework with two modifications: (1) application of a genetic algorithm for the “outer” optimization search instead of simulated annealing; and (2) constraining the sought solution to the lowest cost spanning tree layout with the spanning tree chords kept at their minimum permissible pipe diameters. A comparison of the methodology to a genetic algorithm application without the refinement of using a spanning tree with minimal chord diameters was explored, showing the proposed methodology dominance. The suggested method is limited to one loading gravitational systems, and is demonstrated using a simple example application. [ABSTRACT FROM AUTHOR]

A simple, straightforward, modified genetic algorithm scheme for contaminant source characterization using imperfect sensors is presented and demonstrated in this study. Previous work on this subject concentrated on developing source-inversion models using sensors that provide accurate, unbiased, contamination concentration measurements. The developed contamination source-detection model is implemented using three sensor types: (1) perfect sensors providing accurate, unbiased, contamination concentration measurements; (2) sensors transmitting fuzzy measured information (i.e., high, medium, and low contamination); and (3) '0-1' (Boolean) sensors indicating only a contamination presence. A comparison between the three sensor types is explored taking into consideration thesystem's response time (i.e., the time elapsed between a contaminant detection and a decision-maker's response action). The methodology capabilities are demonstrated using two example applications of increasing complexity, showing the trade-offs between the sensor types and the model abilities to receive a unique solution to the source-detection problem. [ABSTRACT FROM AUTHOR]

A method incorporating a genetic algorithm tailored to EPANET for the conjunctive optimal design and operation of multiquality water distribution systems under unsteady hydraulics is presented and demonstrated. The objective is to minimize the total cost of designing and operating the system for a selected operational time horizon while delivering to consumers the required quantities at acceptable qualities and pressures. The decision variables for the design are the pipe diameters, tank maximum storage, maximum pumping unit power, and maximum removal ratios at the treatment facilities. For the operation phase, the decision variables are set for each time step of the total operational time horizon. These decisions include the scheduling of the pumping units and the treatment removal ratios at the treatment facilities. The constraints are domain heads and concentrations at consumer nodes, maximum removal ratios at the treatment facilities, maximum allowable amounts of water withdrawals at the sources, and return at the end of the operational time horizon to a prescribed total storage in the water distribution system tanks. The model is explored through two example applications. [ABSTRACT FROM AUTHOR]

A water distribution system is a collection of hydraulic control elements jointly connected to convey quantities of water from sources to consumers. Such a system can be described as a graph with the nodes representing the sources and consumers, and the arcs—the connecting elements (e.g., pipes, pumps, and valves). Theoretically, the flow in each arc can reach either direction, resulting in 2n possible digraphs, where n equals the number of arcs. However, this number is substantially reduced as Kirchoff’s Laws 1 and 2 (continuity of mass and energy, respectively) hold, and as in certain arcs the flow is constrained to only one direction (e.g., the pipe leading out of a well). This study describes a methodology for establishing the most flexible pair: Operational and backup digraphs of a water distribution system that maintains Kirchoff’s Laws 1 and 2, and yields (if possible) a one-level system redundancy (i.e., if one arc fails, at least one path from at least one source to all consumers is retained by the operational or backup digraphs). The proposed methodology is cast in a genetic algorithm framework and demonstrated through two example applications. [ABSTRACT FROM AUTHOR]

This paper describes the methodology and application of a genetic algorithm scheme tailor-made to EPANET, for optimizing the operation of a water distribution system under unsteady water quality conditions. The water distribution system consists of sources of different qualities, treatment facilities, tanks, pipes, control valves, and pumping stations. The objective is to minimize the total cost of pumping and treating the water for a selected operational time horizon, while delivering the consumers the required quantities at acceptable qualities and pressures. The decision variables for each of the time steps that encompass the total operational time horizon include: the scheduling of the pumping units, settings of the control valves, and treatment removal ratios at the treatment facilities. The constraints are: head and concentrations at the consumer nodes, maximum removal ratios at the treatment facilities, maximum allowable amounts of water withdrawals at the sources, and returning at the end of the operational time horizon to a prescribed total volume in the tanks. The model is explored through two example applications. [ABSTRACT FROM AUTHOR]

This study presents a heuristic multiobjective approach for segmenting and operating water distribution systems (WDS). The methodology employs a two-pronged strategy: the first is a heuristic method for dividing the network into clusters (i.e., district metering areas) based on connectivity analysis. The second is the application of the evolutionary multiobjective optimization method non-dominated sorting genetic algorithm (NSGA)-II for trading off the operational cost, excess pressure (serving as a proxy to leakage reduction), and water age (acting as a surrogate to water quality) in the WDS. Three example applications of increasing complexities with various cluster partitioning are explored, showing a clear trade-off among the objectives. This study introduces an unprecedented heuristic approach for jointly solving the multiobjective problem under a given system partitioning. However, by enforcing a priori clustering formation (rather than including it in the optimization), optimality, completeness, and precision are compromised in favor of computational speed and effort. Thus, additional sensitivities need to be conducted outside of the optimization for the clusters' impact. Challenges of extending this study are in embedding the clusters' formations in the optimization considering other objectives such as residual capacity, developments of other optimization frameworks outside of the generic link of simulation-optimization, and uncertainty inclusion (e.g., in demands). All data and codes are included for allowing full replications and comparisons. [ABSTRACT FROM AUTHOR]

Cyanobacteria blooming in surface waters have become a major concern worldwide, as they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches of mathematical process-based (deterministic), statistically based, rule-based (heuristic), and artificial neural networks have been the subject of extensive research for cyanobacteria forecasting. This study suggests a new framework of linking an evolutionary computational method (a genetic algorithm) with a data driven modeling engine (model trees) for external loading, physical, chemical, and biological parameters selection, all coupled with their associated time lags as decision variables for cyanobacteria prediction in surface waters. The methodology is demonstrated through trial runs and sensitivity analyses on Lake Kinneret (the Sea of Galilee), Israel. Model trials produced good matching as depicted through the results correlation coefficient on verification data sets. Temperature was reconfirmed as a predominant parameter for cyanobacteria prediction. Model optimal input variables and forecast horizons differed in various solutions. Those in turn raised the problem of best variables selection, pointing towards the need of a multiobjective optimization model in future extensions of the proposed methodology. [ABSTRACT FROM AUTHOR]

The multiobjective optimization model described in this study is aimed at exploring the tradeoff between cost and resiliency for water distribution systems optimal design. Many have dealt previously with minimizing cost where reliability was quantified as a constraint. Fewer considered both cost and reliability as objectives. This work suggests a methodology for least cost versus reliability (quantified as resiliency) optimal design, introducing the following contributions: (1) a genetic algorithm multiobjective formulation integrating a previous theoretical result of a possible maximum of two adjacent discrete pipe diameters for a single pipe; (2) comparable results to previous best least-cost design solutions for the two-looped and Hanoi networks; (3) a real life-sized example application analysis for pipes reinforcement; and (4) an interpretation of resiliency through its comparison to two explicit reliability measures involving demands increase and pipes failure, reconfirming that resiliency improvement does not necessarily imply a reliability increase. Three example applications are explored through base runs and sensitivity analyses for demonstrating the study findings. [ABSTRACT FROM AUTHOR]

Effective decision support and model predictive control of real-time environmental systems require that evolutionary algorithms operate more efficiently. A suite of model predictive control (MPC) genetic algorithms are developed and tested offline to explore their value for reducing combined sewer overflow (CSO) volumes during real-time use in a deep-tunnel sewer system. MPC approaches include the micro-GA, the probability-based compact GA, and domain-specific GA methods that reduce the number of decision variable values analyzed within the sewer hydraulic model, thus reducing algorithm search space. Minimum fitness and constraint values achieved by all GA approaches, as well as computational times required to reach the minimum values, are compared to large population sizes with long convergence times. Optimization results for a subset of the Chicago combined sewer system indicate that genetic algorithm variations with a coarse decision variable representation, eventually transitioning to the entire range of decision variable values, are best suited to address the CSO control problem. Although diversity-enhancing micro-GAs evaluate a larger search space and exhibit shorter convergence times, these representations do not reach minimum fitness and constraint values. The domain-specific GAs prove to be the most efficient for this case study. Further MPC algorithm developments are suggested to continue advancing computational performance of this important class of problems with dynamic strategies that evolve as the external constraint conditions change. [ABSTRACT FROM AUTHOR]

Placement of water quality sensors in a water distribution system is a common approach for minimizing contamination intrusion risks. This study incorporates detailed chemistry of organophosphate contaminations into the problem of sensor placement and links quantitative measures of the affected population as a result of such intrusions. The suggested methodology utilizes the stoichiometry and kinetics of the reactions between organophosphate contaminants and free chlorine for predicting the number of affected consumers. This is accomplished through linking a multi-species water quality model and a statistical dose–response model. Three organophosphates (chlorpyrifos, malathion, and parathion) are tested as possible contaminants. Their corresponding by-products were modeled and accounted for in the affected consumers impact calculations. The methodology incorporates a series of randomly generated intrusion events linked to a genetic algorithm for minimizing the contaminants impact through a sensors system. Three example applications are explored for demonstrating the model capabilities through base runs and sensitivity analyses. [ABSTRACT FROM AUTHOR]

This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system-aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system. [ABSTRACT FROM AUTHOR]

Operational strategies to mitigate combined sewer overflows (CSOs) in older urban areas may be enhanced through real-time decision support provided to sewer operators. During severe rainfall events, real-time hydraulic simulations, coupled with control algorithms, can explore a large number of potential changes to control procedures at short time intervals to provide dynamic feedback and optimization. A model predictive control (MPC) genetic algorithm was developed in previous work and tested offline to explore the efficiency and effectiveness of alternative MPC approaches. This paper extends the MPC methodology to evaluate potential impacts of long-term capital investments on CSO frequency. An alternative strategy to mitigating CSOs in real time with sluice gates may involve replacing small-diameter pipes that cause high hydraulic grade lines throughout the system. CSO reductions may also be significantly enhanced through consideration of larger spatial scales. Replacing conduits is effective but expensive, and optimization over a larger spatial extent (without conduit replacement) has been shown to reduce CSOs by 14%. Optimization over the entire large-scale system is recommended for future work. [ABSTRACT FROM AUTHOR]

China's fast pace industrialization and growing population has led to several accidental surface water pollution events in the last decades. The government of China, after the 2005 Songhua River incident, has pushed for the development of early warning systems (EWS) for drinking water source protection. However, there are still many weaknesses in EWS in China such as the lack of pollution monitoring and advanced water quality prediction models. The application of Data Driven Models (DDM) such as Artificial Neural Networks (ANN) has acquired recent attention as an alternative to physical models. For a case study in a south industrial city in China, a DDM based on genetic algorithm (GA) and ANN was tested to increase the response time of the city's EWS. The GA-ANN model was used to predict NH3–N, CODmn and TOC variables at station B 2 h ahead of time while showing the most sensitive input variables available at station A, 12 km upstream. For NH3–N, the most sensitive input variables were TOC, CODmn, TP, NH3–N and Turbidity with model performance giving a mean square error (MSE) of 0.0033, mean percent error (MPE) of 6% and regression (R) of 92%. For COD, the most sensitive input variables were Turbidity and CODmn with model performance giving a MSE of 0.201, MPE of 5% and R of 0.87. For TOC, the most sensitive input variables were Turbidity and CODmn with model performance giving a MSE of 0.101, MPE of 2% and R of 0.94. In addition, the GA-ANN model performed better for 8 h ahead of time. For future studies, the use of a GA-ANN modelling technique can be very useful for water quality prediction in Chinese monitoring stations which already measure and have immediately available water quality data. [Copyright &y& Elsevier]

Abstract: In this study, a dynamic thresholds scheme is developed and demonstrated for contamination event detection in water distribution systems. The developed methodology is based on a recently published article of the authors (Perelman et al., 2012). Event detection in water supply systems is aimed at disclosing abnormal hydraulic or water quality events by exploring the time series behavior of routine hydraulic (e.g., flow, pressure) and water quality measurements (e.g., residual chlorine, pH, turbidity). While event detection raises alerts to the possibility of an event occurrence, it does not relate to origins, thus an event may be hydraulically-driven, as a consequence of problems like sudden leakages or pump/pipe malfunctions. Most events, however, are related to deliberate, accidental, or natural contamination intrusions. The developed methodology herein is based on off-line and on-line stages. During the off-line stage, a genetic algorithm (GA) is utilized for tuning five decision variables: positive and negative filters, positive and negative dynamic thresholds, and window size. During the on-line stage, a recursively Bayes'' rule is invoked, employing the five decision variables, for real time on-line event detection. Using the same database, the proposed methodology is compared to Perelman et al. (2012), showing considerably improved detection ability. Metadata and the computer code are provided as Supplementary material. [Copyright &y& Elsevier]

During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field’s subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises. [ABSTRACT FROM AUTHOR]