scholarly journals Sociotechnical risk assessment for water distribution system contamination threats

2013 ◽  
Vol 16 (3) ◽  
pp. 531-549 ◽  
Author(s):  
Amin Rasekh ◽  
M. Ehsan Shafiee ◽  
Emily Zechman ◽  
Kelly Brumbelow
Keyword(s):  
Public Health ◽  
Risk Assessment ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Probabilistic Models ◽  
Health Consequences ◽  
Multi Objective ◽  
Contamination Event ◽  
Contamination Events

Water distribution systems (WDS) are vulnerable to contaminants, and systematic risk assessment can provide valuable information for assisting threat management. Contamination events are sociotechnical systems, in which the interactions among consumers and water infrastructure may generate unpredicted public health consequences. This research develops a sociotechnical risk assessment framework that simulates the dynamics of a contamination event by coupling an agent-based modeling (ABM) framework with Monte Carlo simulation (MCS), genetic algorithm (GA) optimization, and a multi-objective GA. The ABM framework couples WDS simulation with agents to represent consumers in a virtual city. MCS is applied to estimate the uncertainty in human exposure, based on probabilistic models of event attributes. A GA approach is used to identify critical contamination events by maximizing risk, and a multi-objective approach explores the trade-off between consequence and occurrence probabilities. Results that are obtained using the sociotechnical approach are compared with results obtained using a conventional engineering model. The sociotechnical approach removes assumptions that have been used in engineering analysis about the static, homogeneous, and stationary behaviors of consumers, and results demonstrate new insight about the impacts of these actions and interactions on the public health consequences of contamination events.

scholarly journals Development of Multi-Objective Optimal Redundant Design Approach for Multiple Pipe Failure in Water Distribution System

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 553 ◽  
Author(s):  
Young Choi ◽  
Joong Kim
Keyword(s):  
Optimal Design ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Search Algorithm ◽  
Harmony Search ◽  
Water Distribution Systems ◽  
Design Approach ◽  
Pipe Failure ◽  
Multi Objective

This study proposes a multi-objective optimal design approach for water distribution systems, considering mechanical system redundancy under multiple pipe failure. Mechanical redundancy is applied to the system’s hydraulic ability, based on the pressure deficit between the pressure requirements under abnormal conditions. The developed design approach shows the relationships between multiple pipe failure states and system redundancy, for different numbers of pipe-failure conditions (e.g., first, second, third, …, tenth). Furthermore, to consider extreme demand modeling, the threshold of the demand quantity is investigated simultaneously with multiple pipe failure modeling. The design performance is evaluated using the mechanical redundancy deficit under extreme demand conditions. To verify the proposed design approach, an expanded version of the well-known benchmark network is used, configured as an ideal grid-shape, and the multi-objective harmony search algorithm is used as the optimal design approach, considering construction cost and system mechanical redundancy. This optimal design technique could be used to propose a standard for pipe failure, based on factors such as the number of broken pipes, during failure condition analysis for redundancy-based designs of water distribution systems.

Risk Assessment Modeling for Water Distribution Systems in Mega Cities

2013 ◽  
Vol 353-356 ◽  
pp. 2957-2960
Author(s):  
Jia Sun ◽  
Guo Ping Yu
Keyword(s):  
Risk Assessment ◽  
Land Subsidence ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Water Distribution Systems ◽  
Assessment Model ◽  
Strategic Decision ◽  
Mega Cities

In study of a series of damages to water distribution systems caused by urban land subsidence, risk assessment modeling is necessary for risk management especially in Mega-cities. First of all, the Catastrophe Theory was employed to analyze the Catastrophe mechanism, and a function catastrophe simulation model was established accordingly to get the vulnerability index of water distribution system. Secondly, risk entropy model was used to analyze the risk of pipe network suffering the land subsidence with the disorder and uncertainty features according to risk theory. Finally, to get the risk index the water distribution system of Guangzhou city was taken to the risk assessment model utilizing the level of land subsidence identified by the dimensional analytical method. The results showed that the risk of land subsidence under the city water distribution system security upgrade is feasible to provide a risk assessment of the strategic decision-making model.

scholarly journals Self-Adaptive Models for Water Distribution System Design Using Single-/Multi-Objective Optimization Approaches

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1293 ◽  
Author(s):  
Choi ◽  
Kim
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Mathematical Problem ◽  
Benchmark Problems ◽  
Future Research ◽  
Multi Objective Optimization ◽  
Adaptive Approach ◽  
Multi Objective ◽  
Self Adaptive

This study compares the performance of self-adaptive optimization approaches in efficient water distribution systems (WDS) design and presents a guide for the selection of the appropriate method employing optimization utilizing the characteristic of each technique formulation. To this end, this study performs three types of analyses. First, the sensitivity analysis of each self-adaptive approach is conducted on single/multi-objective mathematical benchmark problems with various problem types (e.g., using solution shape or many local optimal solutions). Second, based on the applications and results of the mathematical problem, the performance of the algorithm is verified in the WDS design problem considering the minimum cost and the maximum system resilience under the single/multi-objective optimization framework. Third, the characteristics of search operators in the self-adaptive approach are compared according to the presence or absence of additional parameters and operators. Moreover, various performance indices are employed to compare the quantitative evaluation of each algorithm. Each algorithm is found to exhibit different characteristics depending on the problem scale and solution type. These results are expected to benefit future research in the formulation of new approaches and developments. Hence, this study provides rigorous testing of the performance of newly proposed algorithms in a highly simplified manner.

A New Energy Optimization Strategy for Pumping Operation in Water Distribution Systems

2012 ◽  
Author(s):  
Dhafar Al-Ani ◽  
Saeid Habibi
Keyword(s):  
Distribution System ◽  
Energy Cost ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Water Distribution Systems ◽  
Energy Optimization ◽  
Operating Conditions ◽  
Optimization Strategy ◽  
Multi Objective

As time goes on, more and more operating-modes based on changing demand profiles will be compiled to enrich the range of feasible solutions for a water distribution system. This implies the conservation of energy consumed by a water pumping station and improves the ability for energy optimization. Another important goal was improving safety, reliability, and maintenance cost. In this paper, three important goals were addressed: cost-effectives, safety, and self-sustainability operations of water distribution systems. In this work, the objective functions to optimize were total electrical energy cost, maintenance costs, and reservoir water level variation while preserving the service provided to water clients. To accomplish these goals, an effective Energy Optimization Strategy (EOS) that manages trade-off among operational cost, system safety, and reliability was proposed. Moreover, the EOS aims at improving the operating conditions (i.e., pumping schedule) of an existing network system (i.e., with given capacities of tanks) and without physical changes in the infrastructure of the distribution systems. The new strategy consisted of a new Parallel Multi-objective Particle Swarm optimization with Adaptive Search-space Boundaries (P-MOPSO-ASB) and a modified EPANET. This has several advantages: obtaining a Pareto-front with solutions that are quantitatively equally good and providing the decision maker with the opportunity to qualitatively compare the solutions before their implementation into practice. The multi-objective optimization approach developed in this paper follows modern applications that combine an optimization algorithm with a network simulation model by using full hydraulic simulations and distributed demand models. The proposed EOS was successfully applied to a rural water distribution system, namely Saskatoon West. The results showed that a potential for considerable cost reductions in total energy cost was achieved (approximately % 7.5). Furthermore, the safety and the reliability of the system are preserved by using the new optimal pump schedules.

scholarly journals Sensitivity of algorithm parameters and objective function scaling in multi-objective optimisation of water distribution systems

2015 ◽  
Vol 17 (6) ◽  
pp. 891-916 ◽  
Author(s):  
Helena Mala-Jetmarova ◽  
Andrew Barton ◽  
Adil Bagirov
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Parameter Setting ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimisation Model

This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional non-drinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked with the network analysis software EPANet. The sensitivity analysis employs a set of performance metrics, which were designed to capture the overall quality of the computed Pareto fronts. The performance and sensitivity of NSGA-II parameters using those metrics is evaluated. The results demonstrate that NSGA-II is sensitive to different parameter settings, and unlike in the single-objective problems, a range of parameter setting combinations appears to be required to reach a Pareto front of optimal solutions. Additionally, inadequately scaled objective functions cause the NSGA-II bias towards the second objective. Lastly, the methodology for performance and sensitivity analysis may be used for calibration of algorithm parameters.

scholarly journals Lost in Optimization of Water Distribution Systems: Better Call Bayes

2022 ◽  
Author(s):  
Antonio Candelieri ◽  
Andrea Ponti ◽  
Ilaria Giordani ◽  
Francesco Archetti
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Probabilistic Models ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Water Distribution Systems ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Hydraulic Simulation ◽  
Multi Objective

The main goal of this paper is to show that Bayesian optimization could be regarded as a general framework for the data driven modelling and solution of problems arising in water distribution systems. Hydraulic simulation, both scenario based, and Monte Carlo is a key tool in modelling in water distribution systems. The related optimization problems fall in a simulation/optimization framework in which objectives and constraints are often black-box. Bayesian Optimization (BO) is characterized by a surrogate model, usually a Gaussian process, but also a random forest and increasingly neural networks and an acquisition function which drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible and sample efficient particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given for instance by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by the two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection on contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes. Showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms have been proposed for multi-objective detection problem using two different acquisition functions.

scholarly journals Biofilm and Related Amoebas in an UK Chlorinated Drinking Water System

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3069
Author(s):  
Gonzalo Del Olmo ◽  
Natalia Malinowski ◽  
Geoffrey J. Puzon ◽  
Matthew J. Morgan ◽  
Carolina Calero ◽  
...  
Keyword(s):  
Public Health ◽  
Drinking Water ◽  
Distribution System ◽  
Bacterial Communities ◽  
Distribution Systems ◽  
Water Distribution ◽  
Bulk Water ◽  
Resistant Bacteria ◽  
Rrna Gene ◽  
The Uk

Drinking water distribution systems (DWDS) can host pathogenic amoebae, but the role of biofilms in supporting the occurrence of these organisms needs to be fully explored in the UK systems. The presence of amoebae and associated bacteria in biofilms attached to inner pipe surfaces was studied in an experimental full-scale chlorinated distribution system in the UK. Quantitative polymerase change reaction (qPCR) was used to identify and quantify amoebae, whilst the bacterial communities in the biofilms were characterised by sequencing the 16S rRNA gene. Despite the maintenance of a chlorine residual in the network (free chlorine ≥ 0.24 mg/L), several species of amoebae belonging to the genera Acanthamoeba, Vermamoeba, and Naegleria were identified in 30-day-old biofilm samples; however, no amoebae were detected in the water samples analysed. The dominant bacterial communities present in the biofilm samples were Variovorax, Pseudomonas, and Aquabacterium. These results indicate that the biofilm samples contained potential pathogenic amoebae and bacteria, such as Acanthamoeba and Pseudomonas, respectively, which implies a potential public health risk if the biofilms are mobilised into the bulk water. Several of the amoebae identified in this study are able to support the presence of resistant bacteria that can remain viable within these prokaryotic organisms until they reach people’s taps. The identification of the microorganisms associated with the pathogenic amoeba species in biofilms could be used to improve the surveillance of DWDS in order to protect public health.

scholarly journals Integrating evolution strategies and genetic algorithms with agent-based modeling for flushing a contaminated water distribution system

2013 ◽  
Vol 15 (3) ◽  
pp. 798-812 ◽  
Author(s):  
Emily M. Zechman
Keyword(s):  
Public Health ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Complex Adaptive System ◽  
Management Strategies ◽  
Evolution Strategies ◽  
Illustrative Case ◽  
Health Consequences ◽  
Agent Based

Water utilities can prepare for water distribution hazards, such as the presence of contaminants in the pipe network and failure of physical components. In contamination events, the complex interactions among managers' operational decisions, consumers' water consumption choices, and the hydraulics and contaminant transport in the water distribution system may influence the contaminant plume so that a typical engineering model may not properly predict public health consequences. A complex adaptive system (CAS) approach couples engineering models of a water distribution system with agent-based models of consumers and public officials. Development of threat management strategies, which prescribe a set of actions to mitigate public health consequences, is enabled through a simulation–optimization framework that couples evolutionary algorithms with the CAS model. Evolution strategies and genetic algorithm-based approaches are developed and compared for an illustrative case study to identify a flushing strategy for opening hydrants to minimize the number of exposed consumers and maintain acceptable levels of service in the network.

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