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Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. [ABSTRACT FROM AUTHOR]

• Analogy between systems biology and water supply engineering for the first time. • Novel strategy for analyzing the changes in delivered drinking water quality. • System-level understanding of the reaction mechanisms within water pipes. • Multiscale-based modeling inspired by the flux balance analysis concepts. • Achieving higher water quality and resource efficiency in water supply management. The complexity of modeling water quality variations in water distribution systems (WDS), studied for decades, stems from multiple constraints and variables involved and the complexity of the system behavior. The conventional macroscale-based WDS water quality models are founded on continuum mechanics. In attempts to provide a broad picture of the multi-species interactions, these models overlook the stochasticity corresponding to the reaction mechanisms within the WDS domain. Furthermore, owing to the black-box type modeling adopted in simulating the multi-species interactions, the existing state-of-the-art models have limitations in representing intermediates and/or by-products formation. Accordingly, they remain ineffective in describing the water chemistry-stoichiometric interactions within the WDS domain. Only a radically new modeling approach could overcome the limitations of the macroscale-based approaches and enables analyzing the stochastic WDS mechanisms by keeping the true nature of the system behavior. Stimulated by the metabolic network modeling principles in systems biology, this article outlines the prospect of developing an innovative 'water'bolic network modeling approach to provide a new outlook to the existing WDS water quality modeling research. [Display omitted] [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]

Abstract: The presented study features an event detection model alerting for contamination events in water distribution systems. The developed model comprises a minimum volume ellipsoid (MVE) classifier, detecting outlier measurements, and a following sequence analysis utilizing the MVE binary output, for the classification of events. The model is updated continuously and exploits a constantly growing data base. The MVE enables simultaneous analysis of the water quality parameters. The multivariate analysis explores the relations between water quality parameters and detects changes in their common patterns. The suggested model applied an un-supervised classification method, eliminates the need for simulated events examples in the classifier construction. In the absent of satisfying information regarding the influence of contamination event on the parameter measurements, eliminating the use of any assumption contributes to the model reliability and generality. The model was trained on a real water utility data, and tested on randomly simulated events that were superimposed on the original data base. The model showed high accuracy and detection ability compared to previous studies. [Copyright &y& Elsevier]

Abstract: This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. [Copyright &y& Elsevier]

Abstract: The deployment of fixed online water quality sensors in water distribution systems has been recognized as one of the key components of contamination warning systems for securing public health. This study proposes to explore how the inclusion of mobile sensors for inline monitoring of various water quality parameters (e.g., residual chlorine, pH) can enhance water distribution system security. Mobile sensors equipped with sampling, sensing, data acquisition, wireless transmission and power generation systems are being designed, fabricated, and tested, and prototypes are expected to be released in the very near future. This study initiates the development of a theoretical framework for modeling mobile sensor movement in water distribution systems and integrating the sensory data collected from stationary and non-stationary sensor nodes to increase system security. The methodology is applied and demonstrated on two benchmark networks. Performance of different sensor network designs are compared for fixed and combined fixed and mobile sensor networks. Results indicate that complementing online sensor networks with inline monitoring can increase detection likelihood and decrease mean time to detection. [Copyright &y& Elsevier]

Abstract: Decision-making processes often involve uncertainty. A common approach for modeling uncertain scenario-based decision-making progressions is through multi-stage stochastic programming. The size of optimization problems derived from multi-stage stochastic programs is frequently too large to be addressed by a direct solution technique. This is due to the size of the optimization problems, which grows exponentially as the number of scenarios and stages increases. To cope up with this computational difficulty, solution schemes turn to decomposition methods for defining smaller and easier to solve equivalent sub-problems, or through using scenario-reduction techniques. In our study a new methodology is proposed, titled Limited Multi-stage Stochastic Programming (LMSP), in which the number of decision variables at each stage remains constant and thus the total number of decision variables increases only linearly as the number of scenarios and stages grows. The LMSP employs a decision-clustering framework, which utilizes the optimal decisions obtained by solving a set of deterministic optimization problems to identify decision nodes, which have similar decisions. These nodes are clustered into a preselected number of clusters, where decisions are made for each cluster instead of for each individual decision node. The methodology is demonstrated on a multi-stage water supply system operation problem, which is optimized for flow and salinity decisions. LMSP performance is compared to that of classical multi-stage stochastic programming (MSP) method. [Copyright &y& Elsevier]

Abstract: A seasonal multi-year model for management of water quantities and salinities in regional water supply systems (WSS) was developed and implemented. Water is taken from sources which include aquifers, reservoirs, and desalination plants, and conveyed through a distribution system to consumers who require quantities of water under salinity constraints. The year is partitioned into seasons, and the operation is subject to technological, administrative, and environmental constraints such as water levels and salinities in the aquifers, capacities of the pumping, distribution system, and the desalination plants, and the desalination plants maximum removal ratios. The objective is to operate the system at minimum total cost. The objective function and some of the constraints are nonlinear, leading to a nonlinear optimization problem. The nonlinear optimization problem is solved efficiently by adapting (1) a set of manipulations that reduce the problem size and (2) a novel finite difference scheme for calculating the derivatives required by the optimization solver, entitled the Time-Chained-Method (TCM). The model is demonstrated on a small illustrative example and on a real sized regional water supply system in Israel. [Copyright &y& Elsevier]

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]

Abstract: Municipal water distribution systems may consist of thousands to tens of thousands of hydraulic components such as pipelines, valves, tanks, hydrants, and pumping units. With the capabilities of today’s computers and database management software, “all pipe” hydraulic simulation models can be easily constructed. However, the uncertainty and complexity of water distribution systems interrelationships makes it difficult to predict its performances under various conditions such as failure scenarios, detection of sources of contamination intrusions, sensor placement locations, etc. A possible way to cope with these difficulties is to gain insight in to the system behavior by simplifying its operation through topological/connectivity analysis. In this study a tool of this kind based on graph theory is developed and demonstrated. The algorithm divides the system into clusters according to the flow directions in pipes. The resulted clustering is generic and can be utilized for different purposes such as water security enhancements by sensor placements at clusters, or efficient isolation of a contaminant intrusion. The methodology is demonstrated on a benchmark water distribution system from the research literature. [Copyright &y& Elsevier]

• Distribution network as an indirect PFASs contamination and exposure risks source. • Mechanistic model for predicting PFOA fate and transport in the distribution system. • PFASs exposure risks by comparing the simulated PFOA levels with guideline values. • Child population group was found susceptible to PFOA exposure beyond 3 ng/kg/day. • Reducing chlorine dose decreased the PFOA exposure risks in the distribution network. Recent studies identified fluoroalkyl amides (FAs) transformation to perfluorooctanoic acid (PFOA) during disinfection as an indirect source of PFASs contamination of drinking water. This paper discerns the position of water disinfection systems (WDSs) as a PFOA exposure pathway. A new mechanistic model incorporating the derived knowledge about the zwitterionic/cationic FAs transformation to PFOA with the unsteady-state hydraulic characteristics of WDSs was developed. The simulation outputs from model application to a WDS from the USA established the significant role of delivery via distribution network in the PFOA formation in drinking water. PFOA exposure risk assessment studies predicted >95% of the system nodes to be at high risk when the existing stringent health-based guideline values are adopted. The 1 to 3 years and 4 to 8 years old age groups were found susceptible to PFOA exposure through drinking water beyond the tolerable limit of 3 ng/kg/day. The model predicted that reducing the chlorine dose from 2±0.2 to 1±0.1 mg/L at the treatment units drops the share of 1 to 3 years old and 4 to 8 years old consumers falling to PFOA exposure from 4.32 to 0.45% and 0.32 to <0.01%, respectively. Besides, 24.9% more, including ∼x223C10% of the consumers of 1 to 3 years old age group, were found exposed to PFOA risks when the organic loading of water was reduced by 60%. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary: The rapid advance in information processing systems along with the increasing data availability have directed research towards the development of intelligent systems that evolve models of natural phenomena automatically. This is the discipline of data driven modeling which is the study of algorithms that improve automatically through experience. Applications of data driven modeling range from data mining schemes that discover general rules in large data sets, to information filtering systems that automatically learn users’ interests. This study presents a data driven modeling algorithm for flow and water quality load predictions in watersheds. The methodology is comprised of a coupled model tree–genetic algorithm scheme. The model tree predicts flow and water quality constituents while the genetic algorithm is employed for calibrating the model tree parameters. The methodology is demonstrated through base runs and sensitivity analysis for daily flow and water quality load predictions on a watershed in northern Israel. The method produced close fits in most cases, but was limited in estimating the peak flows and water quality loads. [Copyright &y& Elsevier]

Abstract: This paper presents a calibration model for CE-QUAL-W2. CE-QUAL-W2 is a two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model for surface water bodies, modeling eutrophication processes such as temperature–nutrient–algae–dissolved oxygen–organic matter and sediment relationships. The proposed methodology is a combination of a ‘hurdle-race’ and a hybrid Genetic-k-Nearest Neighbor algorithm (GA-kNN). The ‘hurdle race’ is formulated for accepting–rejecting a proposed set of parameters during a CE-QUAL-W2 simulation; the k-Nearest Neighbor algorithm (kNN)—for approximating the objective function response surface; and the Genetic Algorithm (GA)—for linking both. The proposed methodology overcomes the high, non-applicable, computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. Base runs and sensitivity analysis are demonstrated on two example applications: a synthetic hypothetical example calibrated for temperature, serving for tuning the GA-kNN parameters; and the Lower Columbia Slough case study in Oregon US calibrated for temperature and dissolved oxygen. The GA-kNN algorithm was found to be robust and reliable, producing similar results to those of a pure GA, while reducing running times and computational efforts significantly, and adding additional insights and flexibilities to the calibration process. [Copyright &y& Elsevier]

Ipclass — an interactive program for calibrating activated sludge systems is formulated and demonstrated. The model involves a heuristic screening algorithm for exploring the system equations structure, analytical computations of the sensitivities of the variables to the model coefficients, analytical computations of the gradients of the objective functions selected for the calibration process, and a gradient interactive steepest descent minimization scheme. The methodology was implemented in an end-user PC program: Ipclass, that uses the TK Solver® and Matlab® as computational engines, and Visual Basic as the shell. Applications to the activated sludge system models of and are presented. [Copyright &y& Elsevier]

• A pragmatic solution to regulating water pressures and leakage by introducing PRVs. • Optimal selection of positions and setpoints of PRVs in a distribution network. • Graph theory-based algorithm employing the depth-first search method. • Pipes leading to the most extended downstream networks are ideal locations for PRVs. • The algorithm maintains the minimum pressure at the minimum required service level. Optimal pressure management is a standard strategy for water loss minimization in water distribution systems (WDS). A pragmatic solution to regulating water pressures and leakage is introducing pressure-reducing valves (PRVs). This paper presents a valve positioning algorithm for optimally deciding the positions and setpoints of PRVs in a WDS. The algorithm derives the hydraulic solution of a WDS as a directed graph, established on the flow directions, using EPANET 2.2 and develops the downstream network supplied by water flowing out of every pipe in the network by applying the depth-first search method. The algorithm later recognizes the pipes leading to the most extended downstream networks, with pressures above the minimum required service pressure, and prioritizes them as the ideal locations for PRV placement. In this way, the proposed algorithm overcomes the limitations of the state-of-the-art in realistically conceptualizing the leakage reduction for optimally positioning the PRVs in WDS. Four studies with varying complexities were selected to demonstrate the algorithm's applicability for deriving pressure management solutions. The solution time for PRV positioning was in seconds for the first three networks and several minutes for the extensive fourth case study. The results corroborate the algorithm's ability to pinpoint the critical nodes with the most increased potential for downstream pressure control and for maintaining the pressure at the least required service pressure level through optimally allocating the PRVs, with acceptable setpoint values, within the pipe network. [Display omitted]. [ABSTRACT FROM AUTHOR]

This study deals with the integration of contamination simulations and a spatial event detection model. The simulation of contaminant intrusion includes detailed chemical-specific reactions within a multi-species water quality model. This set-up generates a scenario of contaminant distribution and produces a continuous multiple sensor stations database. Three organophosphates pesticides, Chlorpyrifos, Malathion, and Parathion, are modeled as possible contaminants. The event detection model comprises both local and spatial data analysis. The local model applies a previously developed single-sensor event detection model with a higher alert threshold that reduces false alarm rates. The spatial model considers upstream sensor datasets which are examined for their uniqueness and mutual resemblance in a sliding time window. The model utilizes outlier detection, data analyses, and network hydraulics for the detection of suspicious spatial trends. The proposed algorithm is capable of detecting events with low contamination signatures and spatial influence. Two case studies are explored and compared to the single sensor model. The proposed methodology resulted in a lower number of false alarms compared to the previous single sensor event detection modeling approach. [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]

As a complementary step towards solving the general event detection problem of water distribution systems, injection of the organophosphate pesticides, chlorpyrifos (CP) and parathion (PA), were simulated at various locations within example networks and hydraulic parameters were calculated over 24-h duration. The uniqueness of this study is that the chemical reactions and byproducts of the contaminants' oxidation were also simulated, as well as other indicative water quality parameters such as alkalinity, acidity, pH and the total concentration of free chlorine species. The information on the change in water quality parameters induced by the contaminant injection may facilitate on-line detection of an actual event involving this specific substance and pave the way to development of a generic methodology for detecting events involving introduction of pesticides into water distribution systems. Simulation of the contaminant injection was performed at several nodes within two different networks. For each injection, concentrations of the relevant contaminants' mother and daughter species, free chlorine species and water quality parameters, were simulated at nodes downstream of the injection location. The results indicate that injection of these substances can be detected at certain conditions by a very rapid drop in Cl2, functioning as the indicative parameter, as well as a drop in alkalinity concentration and a small decrease in pH, both functioning as supporting parameters, whose usage may reduce false positive alarms. [ABSTRACT FROM AUTHOR]

Abstract: Sustainable design and implementation of greywater reuse (GWR) has to achieve an optimum compromise between costs and potable water demand reduction. Studies show that GWR is an efficient tool for reducing potable water demand. This study presents a multi-objective optimization model for estimating the optimal distribution of different types of GWR homes in an existing municipal sewer system. Six types of GWR homes were examined. The model constrains the momentary wastewater (WW) velocity in the sewer pipes (which is responsible for solids movement). The objective functions in the optimization model are the total WW flow at the outlet of the neighborhoods sewer system and the cost of the on-site GWR treatment system. The optimization routing was achieved by an evolutionary multi-objective optimization coupled with hydrodynamic simulations of a representative sewer system of a neighborhood located at the coast of Israel. The two non-dominated best solutions selected were the ones having either the smallest WW flow discharged at the outlet of the neighborhood sewer system or the lowest daily cost. In both solutions most of the GWR types chosen were the types resulting with the smallest water usage. This lead to only a small difference between the two best solutions, regarding the diurnal patterns of the WW flows at the outlet of the neighborhood sewer system. However, in the upstream link a substantial difference was depicted between the diurnal patterns. This difference occurred since to the upstream links only few homes, implementing the same type of GWR, discharge their WW, and in each solution a different type of GWR was implemented in these upstream homes. To the best of our knowledge this is the first multi-objective optimization model aimed at quantitatively trading off the cost of local/onsite GW spatially distributed reuse treatments, and the total amount of WW flow discharged into the municipal sewer system under unsteady flow conditions. [Copyright &y& Elsevier]

Abstract: Blending desalinated water with surface and/or ground water may result in water that has a negative precipitation potential with respect to CaCO3(s), rendering it chemically unstable. In this paper a simulation tool for calculating the pH and calcium carbonate precipitation potential (CCPP) values at the nodes of a water distribution system is introduced. This computerized tool is then used to simulate the CCPP values that would develop in a schematic distribution system fed by three water sources (desalinated, surface and ground waters) under a simulative water consumption pattern. The simulation demonstrates, for a case study that is based on typical Israeli conditions, that an increase in the alkalinity value of the desalinated water from 50 to 100 mg/L as CaCO3 results in a positive CCPP value at all times whereas at the low alkalinity value (which is the concentration which is currently supplied by the 100 million-m3/y and 30 million-m3/y Ashkelon and Palmachim plants in Israel) the CCPP values at the nodes are often negative as a result of blending the desalinated water with groundwater. The conclusion is that there is a need to increase the alkalinity value in desalinated waters. This request is augmented by additional arguments in support of this approach. The negative effect of high alkalinity values on copper-tubing corrosion rates is also noted. [Copyright &y& Elsevier]

Water distribution system contamination events caused by intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants have significant impacts on the health of the populations that it services. Therefore, it is important to have an effective plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on strategic planning of the decontamination process of the contaminated infrastructure. This paper proposed a framework for assembling a disinfection plan in real-time by (1) partitioning a WDS into a number of district metered areas (DMAs), (2) generating a solution region for each of the DMAs, and (3) assemble an effective decontamination plan using solution region generated. This framework has been applied to three contamination events. The results show that, when planning for the decontamination stage of a contamination event, the use of the proposed framework can (1) significantly reduce the response time, (2) improve the quality of the decontamination plan, and (3) provide a model for optimizing the resource allocation. Image 1 • A framework for planning water distribution system decontamination is introduced. • A decontamination plan can be readily assembled in real-time. • Resources can be allocated dynamically subject to objectives and constraints. • Decontamination can be planned to be performed in parallel, serially, or hybridly. [ABSTRACT FROM AUTHOR]

Leak and backflow detections are essential aspects of Water Distribution Systems (WDSs) monitoring and are commonly fulfilled using approaches that are based on static sensor networks and point measurements. Alternatively, we propose a mobile, wireless sensor network solution composed of mobile sensor nodes that travel freely inside the pipes with the water flow, collect and transmit measurements in near-realtime (called sensors) and static access points (called beacons). This study complements the tremendous progress in mobile sensor technology. We formulate the sensor and beacon optimal placement task as a Mixed Integer Nonlinear Programming (MINLP) problem to maximize localization accuracy with budget constraint. Given the high time complexity of MINLP formulation, we propose a disjoint scheme that follows the strategy of splitting the sensor and beacon placement problems and determining the respective number of sensors and beacons by exhaustive search in linear time. [ABSTRACT FROM AUTHOR]

Optimal sensor placement for detecting contamination events in water distribution systems is a well explored problem in water distribution systems security. We study herein the problem of sensor placement in water networks to minimize the consumption of contaminated water prior to contamination detection. For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric. A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation. Compared to the state of the art results of the battle of the water sensor networks (BWSN) study, the proposed methodology demonstrated a significant performance enhancement, in particular by applying greedy heuristics to repeated sampling of random subsets of events. [ABSTRACT FROM AUTHOR]

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]

Abstract: Biofouling development on nanofiltration membranes treating tertiary effluents was studied at low (5bar) and high (25bar) pressures at different feedwater concentrations, temperatures and lengths of operation. The bacterial community profile composing the biofouling layer was characterized. Most of the bacterial species identified were Gram-negative, with Proteobacteria (approximately equally divided between β, α and γ subdivisions) and Bacteroidetes being the prevalent groups. At high-pressure, scaling was the primary source of fouling whereas at low-pressure, biofouling was dominant. For these conditions, an empirical approach to forecasting the contribution of biofouling resistance to total resistance was derived based on the resistance in series theory. This approach showed that biofouling becomes a dominating factor after approximately 20L of permeate volume has been produced. A data-driven modeling algorithm for forecasting the reduction in permeate flux due to biofouling was also established. The reduction in permeate flux rates was related to the development of a fouling layer on the membrane. Pressure, total organic carbon, pH and conductivity of the feedwater were the most influential parameters. These results are novel in the area of model tree algorithms as they apply to forecasting the development of biofouling on membranes. [Copyright &y& Elsevier]

• Empirical and kinetic models dominate data-driven models in SN water quality modeling. • Models are mainly developed for prediction and process understanding. • Models are developed using limited field data, while experimental data are often used. • Models exhibit overall low to moderate prediction performance for real SNs. • Future work should focus on appropriate data resolution, hybrid models and model transferability. Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability. [Display omitted] [ABSTRACT FROM AUTHOR]