Sensors In Water Distribution System Research Articles

Risk-Based Design Optimization of Contamination Detection Sensors in Water Distribution Systems: Application of an Improved Whale Optimization Algorithm

In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m3 and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs.

Sensor placement for robust burst identification in water systems: Balancing modeling accuracy, parsimony, and uncertainties

Urban water systems are seeing an uptake in using advanced sensing technology. Incorporating sensors for monitoring water distribution systems (WDSs) provides promising benefits to water utilities by enabling a shift from reactive to proactive pipe failure detection and from delayed water loss management to automatic sense-and-respond capabilities. Despite the opportunities that new sensing technologies create, a budget-constrained utility is challenged with identifying sensing locations in the WDS that will maximize information gain. To address this gap, this paper studies the problem of optimal placement of high-frequency pressure sensors in WDSs for pipe burst identification. This paper proposes a sensor placement strategy to address the challenges of data and modeling uncertainty by incorporating robust representation and tolerance analysis into an optimization framework with the objective of achieving the best detection and identification of burst events. Transient simulations are first used to predict system’s response to burst events, demonstrating the importance of modeling accuracy over approximation methods. A robust event representation approach is then presented to summarize system response to pipe bursts using signature matrices. Subsequently, the identification problem is cast as a minimum test cover problem when the number of available sensors is unlimited, and as the maximum covering test problem when the number of available sensors is limited. The optimization problems are then formulated and solved using mixed integer linear programming. Four complementary metrics are suggested to evaluate the performance of the sensor placement designs. Multiple criteria decision analysis is then applied to select the placement design while balancing information gain and cost. The results show that incorporating more information can improve event identification, but sufficient accuracy of the extracted information is required to accrue the benefits.

A K-Sensor correlation-based evolutionary optimization algorithm to cluster contamination events and place sensors in water distribution systems

Contaminants that are introduced to drinking water systems can threaten large populations, and the potential for catastrophic consequences accentuates the need for efficient post-disaster strategies, including optimal hydrant flushing. Efficient hydrant flushing can significantly reduce impacts on public health, but performance relies on information about the propagation of a contaminant and the affected regions in a water network. While observations from water quality sensors are useful in timely detections of contaminants, little information on its source, propagation, and affected regions can be inferred. In the absence of such information, opening or closing hydrants might not help discharge contaminants but could accelerate propagation of a plume through the water network due to drops in pressure. To address this limitation of sensor layout optimization models, this research has developed a new model to identify the optimal location of sensors to effectively support hydrant flushing mechanisms. The model has been developed in three steps: (1) contamination events were simulated in a water network; (2) spatially similar propagating contamination events were identified; and (3) the layout of water quality sensors was optimized. In the first step, a representative number of potential contamination events were simulated using a hydraulic model. The second step clustered contamination events based on spatial similarity in their propagation regimes. Finally, the last step identified locations for placing water quality sensors within clusters (identified in the previous step) while minimizing detection time and maximizing probability of detection. This model ensures that when a sensor alarm is activated, contaminated region where hydrants should be opened or closed are spatially restricted. The approach developed in this research was applied to design a sensor network for a benchmark case study, Mesopolis. The layout of 10 water quality sensors was optimized over a set of 9161 contamination events, leading to 76% probability of detection with an average detection time of 8.2 h. The solution was compared with sensor layouts based on existing approaches, and it was found that the new approach could improve the mass of contaminant that was removed from the pipe network through hydrant flushing strategies. The new approach model improves the effectiveness of hydrant flushing strategies by restricting the area where hydrants are flushed to predefined zones based on the activation of sensors.

A Hybrid Fuzzy-Probabilistic Bargaining Approach for Multi-objective Optimization of Contamination Warning Sensors in Water Distribution Systems

Water Distribution System (WDS) are strategic infrastructures in all countries. In recent decades, several optimization-based frameworks have been developed to detect potential contaminant events through risk mitigation strategies using the optimal placement of water quality sensors. However, outcomes of the optimization models may not sufficiently represent the priorities of involved stakeholders in real-world case studies where conflicts of interest arise. Therefore, conflict resolution frameworks that consider stakeholder engagement are needed to enhance the security of a WDS through an agreed-upon layout of an optimal number of sensors. In this study, according to the uncertain nature of input contamination into the network, a Fuzzy Transformation Method (FTM) and a Monte-Carlo Simulation (MCS) were employed to address uncertainties in the EPANET simulation model. A fuzzy-based NSGA-II optimization model was developed to determine trade-offs among targets of stakeholders. Social Choice Theories (SCTs) was used to specify the compromise solutions on each trade-off curve. Using the possibility degree method, the obtained fuzzy intervals were ranked based on each stakeholder’s point of view. Finally, the most appropriate SCTs were introduced through a negotiation method (unanimity fallback bargaining) at each confidence level. The application of the proposed methodology was evaluated in the WDS of the city of Lamerd in Fars, Iran. The capability of the proposed methodology in selecting socio-optimal sensor placement was compared with the results of previous studies. The obtained results demonstrated that the proposed framework yielded a reliable outcome and enhance the decision-making condition for stakeholders to improve the security of a WDS.

Water contaminants detection using sensor placement approach in smart water networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an evolutionary algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the water sensor network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensors.

A novel hybrid entropy-clustering approach for optimal placement of pressure sensors for leakage detection in water distribution systems under uncertainty

ABSTRACT This study presents a novel hybrid entropy-clustering framework for placing pressure sensors in water distribution systems (WDS) to detect leakage. Leakages are simulated at all potential nodes of WDS, and then potential pressure sensors (PPS) in WDS are classified using a K-means clustering algorithm. Transinformation entropy for each potential pair of PPS was also computed, which in turn helped to reduce redundant information. PPS locations were subsequently optimized using a multi-objective optimization model. Furthermore, to capture the sensitivity of sensors' layout in WDS to sensor error, a fuzzy-based analysis is integrated with a multi-objective optimization model. Finally, the best compromise solution of PPS placement in each category was selected using an ELECTRE multi-criteria decision making model. Reducing redundant information of pressure sensors based on information theory and choosing the best possible solution based on the ELECTRE model are the main novelties of this study. Results of C-Town WDS attest to the proposed framework' efficiency.

Active Contamination Detection in Water-Distribution Systems

AbstractIn this paper, we propose a novel methodology for altering the area monitored by water quality sensors in water distribution systems (WDS) when there is suspicion of a contamination event. ...

Optimal selection of number and location of pressure sensors in water distribution systems using geostatistical tools coupled with genetic algorithm

Abstract This paper presents a novel methodology for designing an optimal pressure sensor to make average pressure field in water distribution systems (WDS) more accurate via geostatistical tools coupled with genetic algorithm (GA) under normal operating condition. In light of this, the objective function is introduced based on geostatistical technique as variance of residual of block ordinary kriging (BOK). In order to solve the problem of sensor placement, three different approaches, so-called, simplified, exhaustive, and random search optimization are considered. To the best of the authors' knowledge, this is the first time whereby geostatistical tools are used to design a pressure monitoring network in the WDS. The proposed methodology is first tested and verified on a literature case study of Anytown WDS and then is applied to a real-world case study referred to as C-Town consisting of five district metered areas (DMAs). The proposed methodology has several advantages over existing more conventional approaches which will be demonstrated in this paper. The results indicate that this method outperforms the conventional paradigms in current use in terms of mathematical labor and the results are quite promising.

Optimal and objective placement of sensors in water distribution systems using information theory

Optimization-based deployment of contamination warning system in water distribution systems has been widely used in the literature, due to their superior performance compared to rule- and opinion-based approaches. However, optimization techniques impose an excessive computational burden, which in turn is compensated for by shrinking the problem's decision space and/or using faster optimization algorithms with less accuracy. This imposes subjectivity in interpretation of the system and associated risks, and undermines model's accuracy by not exploring the entire feasible space. We propose a framework that uses information theoretic techniques, including value of information and transinformation entropy, for optimal sensor placement. This can be used either as pre-selection, i.e. pinpointing best potential locations of sensors to be in turn used in optimization framework, or ultimate selection, i.e. single-handedly selecting sensor locations from the feasible space. The proposed framework is then applied to Lamerd water distribution system, in Fars province, Iran, and the results are compared to the suggested potential locations of sensors in previous studies and results of TEVA-SPOT model. The proposed information theoretic scheme enhances the decision space, provides more accurate results, significantly reduces the computational burden, and warrants objective selection of sensor placement.

A risk-based multi-objective model for optimal placement of sensors in water distribution system

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation–optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value of losses in WDS.

Optimal Placement of Pressure Sensors in Water Distribution System Based on Clustering Analysis of Pressure Sensitive Matrix

Optimizing the placement of pressure sensors is of great significance for understanding the performance of water distribution systems (WDSs). The pressure sensitive matrix of WDSs contains the gradient information regarding nodal pressure to nodal outflow, which can be regarded as a series of observation vectors reflecting the correlations of nodal pressure change and used for the optimal placement of pressure sensor. This paper presents a method for optimizing the placement of pressure sensor in WDSs based on clustering analysis of pressure sensitive matrix, which aims to use the limited number of pressure sensors to understand the pressure changes of other nodes unmonitored as accurate as possible. To enhance the computing efficiency especially for larger systems with thousands of nodes, the matrix analysis method is developed to calculate the analytical solution of the pressure sensitive matrix. Based on it, the K-means algorithm is applied to solve the optimization problem for cluster analysis and determining the pressure sensors placement, considering it has the advantage of being computational efficient. A real network is applied to illustrate the application of the proposed method. The obtained results indicate that it is reasonable to arrange the pressure senor on the node with the minimum Euclidean distance to the clustering center.

Sampling Design for Leak Detection in Water Distribution Networks

The leak detection, as well as other tasks related to control and management of water distribution systems (WDS), depends on representative data on the actual state of the system, ie, reliable values for state variables acquired by sensors. Given that the number of such sensors, as well as its location in the WDS, interferes with the usability of the acquired data, the careful design of the sampling points is necessary. Although many researchers have already addressed this issue, only some of them have focused on the specific purpose of leak detection, so that there is still no known efficient solution to this problem and improvements can be achieved by exploring different criteria and solving methods.The aim of the study here described is develop a sampling design (SD) method for localization and quantification of pressure sensors in WDS, aiming leak detection. According to the proposed method, the search for the proper SD is driven by four criteria: maximization of total leak sensitivity and sensitivity consistence, and minimization of information redundancy and sensors number. The sensitivity analysis is developed using a hydraulic simulation model of the network and incorporating artificial node leaks as pressure-driven demands. Entropy is used to estimate the consistence of sensitivity to all the considered leak events. Redundancy is evaluated by the correlation between simulated responses, in terms of pressure at potential nodes for sensors installation. Finally, reducing the number of sensors is targeted, admitting that generally their availability is limited. The optimization procedure uses the multiobjective genetic algorithm NSGAII approach to search for the complete set of nodes for sensors placement.In addition to the method explanation, its application for an existing water supply district is presented, producing a consistent near-optimal set of nine well distributed places for sensor installation. The SD method proposed here could be applied to any WDS and assist advances for data-driven detection of leaks, and even for intelligent systems development for WDS.

A survey on sensor placement for contamination detection in water distribution systems

Frequent water pollution incidents have occurred recently, leading to severe damages, economic loss, and long-lasting society impact. Therefore, installation of water quality monitoring sensors in water distribution system (WDS) has been advocated as a viable solution to enable real-time pollution detection and thus the mitigation of the risks associated with catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy the sensors within the distribution system while the network still gets covered. Although there exist a significant number of articles on sensor placement, WDS for contamination detection is unique comparing to other networks such as power grids, road networks, structural networks and microwave radio networks. In this paper, we conduct a comprehensive literature survey on the sensor placement problem for contamination detection in WDS, with a special focus on optimization strategies and framework. Current challenges, issues, and research directions are also identified and discussed.

Inclusion of Mobile Sensors in Water Distribution System Monitoring Operations

AbstractThis study describes a binary integer programming model for mutually-operating fixed and mobile sensors in water distribution systems. The proposed method applies a deterministic optimization scheme for maximizing the monitored volume within network clusters. For a given budget, the model determines the ratio of mobile sensors to fixed sensors along with their placement and release strategies. Through assessing the benefit of placing each fixed sensor and the time and location of mobile sensors release, the combination of fixed and mobile sensors is determined. Utilizing mobile sensors for water quality monitoring is still in its infancy. Such sensors are equipped with self-powered sensing, sampling, data acquisition, and wireless transmission units. The model initiates with the combined operation of mobile and fixed sensors. It then explores the benefits of mobile sensors compared to fixed. The two battle of the water sensor networks (BWSN) are utilized for demonstrating the model’s capabilities....

Sensor Placement in Water Distribution Systems Using the S-PLACE Toolkit

This work presents a new software, the Sensor Placement (S-PLACE) Toolkit, for computing at which locations to install contam- inant sensors in water distribution systems to reduce the impact risks. The S-PLACE Toolkit has been designed to be user-friendly, suitable for both the professional and the research community, programmed in Matlab utilizing the EPANET software library, with a modular software architecture to make it extensible. The use of the software is illustrated using benchmark networks which capture different types of real network topologies.

Operation of remote mobile sensors for security of drinking water distribution systems

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.

Multi-objective optimization for conjunctive placement of hydraulic and water quality sensors in water distribution systems

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.

엔트로피 이론을 이용한 상수관망의 최적 압력 계측 위치 결정

Determination of optimal pressure monitoring location is essential to manage water distribution system efficiently and safely. In this study, entropy theory is applied to overcome defects of previous researches about determining the optimal sensor location. The previous studies required the calibration using historical data, therefore, it was difficult to apply the proposed method in the place where the enough data were not available. Also, most researches have focused on the locations to minimize cost and maximize accuracy of the model, which is not appropriate for the purpose of maintenance of the water distribution system. The proposed method in this study quantify the entropy which is defined as the amount of information calculated from the pressure change due to the variation of discharge. When abnormal condition is occurred in a node, the effect on the entire network is presented by the entropy, and the emitter is used to reproduce actual pressure change pattern in EPANET. The optimal location to install pressure sensors in water distribution system is the nodes having the maximum information from other nodes. The looped and branched networks are evaluated using the proposed model. As a result, entropy theory provides general guideline to select the locations to install pressure sensors and the results can be used to help decision makers.