Relevant bibliographies by topics / Genetic Algorithms for Water Distribution Systems Operations

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Genetic Algorithms for Water Distribution Systems Operations'

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Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Genetic Algorithms for Water Distribution Systems Operations"

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Nono, Denis, and Innocent Basupi. "Robust booster chlorination in water distribution systems: design and operational perspectives under uncertainty." Journal of Water Supply: Research and Technology-Aqua 68, no. 6 (June 11, 2019): 399–410. http://dx.doi.org/10.2166/aqua.2019.007.

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Abstract Booster chlorination designs have been widely based on predefined (deterministic) network conditions and they perform poorly under uncertainty in water distribution systems (WDSs). This paper presents a scenario-based robust optimisation approach which was developed to obtain booster chlorination designs that withstand uncertain network operations and water demand conditions in the WDSs. An optimisation problem was formulated to minimise mass injection rates and the risk of chlorine disinfection. This problem was solved by a non-dominated sorting genetic algorithm (NSGA-II). The proposed approach was demonstrated using the Phakalane network in Botswana. The results present robust booster chlorination (RBC) designs, which indicate the number of boosters, locations and injection rates in the network. The performance of RBC designs evaluated under uncertainty reveals lower risks of chlorine disinfection compared to deterministic-based designs. The proposed approach obtains booster chlorination designs that respond better to uncertainty in the operations of WDSs.
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Janga Reddy, M., and D. Nagesh Kumar. "Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review." H2Open Journal 3, no. 1 (January 1, 2020): 135–88. http://dx.doi.org/10.2166/h2oj.2020.128.

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Abstract During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.
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Telci, Ilker, and Mustafa Aral. "Optimal Energy Recovery from Water Distribution Systems Using Smart Operation Scheduling." Water 10, no. 10 (October 17, 2018): 1464. http://dx.doi.org/10.3390/w10101464.

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Micro hydropower generators (micro turbines), are used to recover excess energy from hydraulic systems and these applications have important potential in renewable energy production. One of the most viable environments for the use of micro turbines is the water distribution network where, by design, there is always excess energy since minimum pressures are to be maintained throughout the system, and the system is designed to meet future water supply needs of a planning period. Under these circumstances, maintaining the target pressures is not an easy task due to the increasing complexity of the water distribution network to supply future demands. As a result, pressures at several locations of the network tend to be higher than the required minimum pressures. In this paper, we outline a methodology to recover this excess energy using smart operation management and the best placement of micro turbines in the system. In this approach, the best micro turbine locations and their operation schedule is determined to recover as much available excess energy as possible from the water distribution network while satisfying the current demand for water supply and pressure. Genetic algorithms (GAs) are used to obtain optimal solutions and a “smart seeding” approach is developed to improve the performance of the GA. The Dover Township pump-driven water distribution system in New Jersey, United States of America (USA) was selected as the study area to test the proposed methodology. This pump-driven network was also converted into a hypothetical gravity-driven network to observe the differences between the energy recovery potential of the pump-driven and gravity-driven systems. The performance of the energy recovery system was evaluated by calculating the equivalent number of average American homes that can be fed by the energy produced and the resulting carbon-dioxide emission reductions that may be achieved. The results show that this approach is an effective tool for applications in renewable energy production in water distribution systems for small towns such as Dover Township. It is expected that, for larger water distribution systems with high energy usage, the energy recovery potential will be much higher.
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Milašinović, M., D. Prodanović, and M. Stanić. "Pressure drop test as a hydroinformatic tool for preliminary network topology validation." Water Supply 19, no. 2 (May 11, 2018): 502–10. http://dx.doi.org/10.2166/ws.2018.095.

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Abstract Usage of the appropriate model of water distribution systems (WDS) enables easier everyday operations and management decisions. Creating a reliable model of WDS requires a large amount of system response data for different case scenarios. Commonly used software for creating models of WDS is EpaNet. Ongoing processes in WDS, such as pipe bursts, permanently closed valves which are not registered in the data base and other inconsistencies will change WDS network topology, so WDS validation tests are to be applied from time to time. This paper presents the WDS network topology validation test conducted on one district metered area of Belgrade with two inflows. The pressure drop test combined with genetic algorithm and ant colony optimization are simple hydroinformatic tools available for network topology validation. The system's reaction under a pressure change during the isolation test was measured at two observation points. Obtained results are then compared with assumed WDS topology using 55 potential locations of inconsistencies in the EpaNet model. This step is repeated until a good enough match between results from the real system and the created model's version is obtained. Heuristic optimization algorithms are used for speeding up the process of finding a satisfactory match (unknown locations of inconsistencies) by minimizing or maximizing the defined criteria function.
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Bi, Weiwei, Yihui Xu, and Hongyu Wang. "Comparison of Searching Behaviour of Three Evolutionary Algorithms Applied to Water Distribution System Design Optimization." Water 12, no. 3 (March 3, 2020): 695. http://dx.doi.org/10.3390/w12030695.

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Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.
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Bakanos, Panagiotis I., and Konstantinos L. Katsifarakis. "Optimizing Current and Future Hydroelectric Energy Production and Water Uses of the Complex Multi-Reservoir System in the Aliakmon River, Greece." Energies 13, no. 24 (December 9, 2020): 6499. http://dx.doi.org/10.3390/en13246499.

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In this work we study long-term maximization of hydroelectric energy generation from complex multi-purpose reservoir systems, using the reservoir system of the Aliakmon River, Greece, as an application example. This system serves various purposes, like urban water supply, irrigation, hydroelectric energy production, cooling thermoelectric power plants and flood control, while preserving environmental flow. The system operator uses institutional rules for the annual scheduling of the outflows of the 2 largest reservoirs (Ilarion and Polyfyto) for additional safety and smooth distribution of energy production through the year. In this work, we focus on maximization of energy production. We have considered three different hydrological scenarios (dry, average and wet), both for the current and for anticipated future water demand. The multi-reservoir system’s operation was simulated and then optimized using a rather simple form of genetic algorithms, in order to maximize hydro energy production. All other water uses were taken into account as constraints. Our conceptual and computational approach succeeded to identify and quantify hydro energy production increase and to indicate necessary changes to the operating rule curves of the reservoirs. The methodology can be easily adapted to other large-scale multi reservoir systems.
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Phạm Đức, Đại, and Tỉnh Phạm Văn. "Efficient optimization of pump scheduling for reduction of energy costs and Greenhouse gas emissions." Journal of Science and Technology Issue on Information and Communications Technology 17, no. 12.1 (December 31, 2019): 5. http://dx.doi.org/10.31130/jst-ud2019-128e.

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Optimal pump scheduling has been applying to decrease operating costs of water distribution systems (WDSs). However, the operations of pumping stations will result in an increase of Greenhouse gas emission (GHG). To reduce GHG, pumping stations should be operated with high efficiency. For this reason, optimal pump cheduling should take into account both energy cost savings and pumping station efficiency. The aim of this article is to suggest an efficient multi-objective optimization solution or minimizing pumping energy cost and maximizing pumping station efficiency. As a result, a trade-off solution compromising pumping energy cost and pumping system efficiency will be achieved. The Genetic Algorithm (GA) combined with a WDS simulator, EPANET will be applied to solve the pump scheduling problem in one benchmark WDS and the results from our solution will be compared to the ones in the literature in terms of pumping energy cost and efficiency.
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Costa, L. H. M., H. M. Ramos, and M. A. H. de Castro. "Hybrid genetic algorithm in the optimization of energy costs in water supply networks." Water Supply 10, no. 3 (July 1, 2010): 315–26. http://dx.doi.org/10.2166/ws.2010.194.

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Efficient operation of water distribution systems is linked with the assurance of water availability to the population. In general, the operational rules applied to water distribution networks have as a sole objective the continuity of the water supply and disregards saving energy costs related to the operation of the pumps. The task of determining optimal operational procedures involves several elements such as the daily variation of water consumption, energy cost rates and the level of the tanks and reservoirs. This work presents a hybrid genetic algorithm which is connected to the widely known software EPANET, in order to determine operational strategies in water supply with reduced energy costs. The model is applied to a hypothetical example and to a real water distribution network located in the city of Ourém, in Portugal.
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van Zyl, Jakobus E., Dragan A. Savic, and Godfrey A. Walters. "Operational Optimization of Water Distribution Systems Using a Hybrid Genetic Algorithm." Journal of Water Resources Planning and Management 130, no. 2 (March 2004): 160–70. http://dx.doi.org/10.1061/(asce)0733-9496(2004)130:2(160).

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Gencoglu, Gencer, and Nuri Merzi. "Trading-off Constraints in the Pump Scheduling Optimization of Water Distribution Networks." Journal of Urban and Environmental Engineering 10, no. 1 (August 23, 2016): 135–43. http://dx.doi.org/10.4090/juee.2016.v10n1.135-143.

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Pumps are one of the essential components of water supply systems. Depending of the topography, a water supply system may completely rely on pumping. They may consume non-negligible amount of water authorities' budgets during operation. Besides their energy costs, maintaining the healthiness of pumping systems is another concern for authorities. This study represents a multi-objective optimization method for pump scheduling problem. The optimization objective contains hydraulic and operational constraints. Switching of pumps and usage of electricity tariff are assumed to be key factors for operational reliability and energy consumption and costs of pumping systems. The local optimals for systems operational reliability, energy consumptions and energy costs are investigated resulting from trading-off pump switch and electricity tariff constraints within given set of boundary conditions. In the study, a custom made program is employed that combines genetic algorithm based optimization module with hydraulic network simulation software -EPANET. Developed method is applied on the case study network; N8-3 pressure zone of the Northern Supply of Ankara (Turkey) Water Distribution Network. This work offers an efficient method for water authorities aiming to optimize pumping schedules considering expenditures and operational reliability mutually.
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Dissertations / Theses on the topic "Genetic Algorithms for Water Distribution Systems Operations"

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Van, Zyl Jakobus Ernst. "A methodology for improved operational optimization of water distribution systems." Thesis, University of Exeter, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366606.

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Khan, Kashif. "A distributed computing architecture to enable advances in field operations and management of distributed infrastructure." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/a-distributed-computing-architecture-to-enable-advances-in-field-operations-and-management-of-distributed-infrastructure(a9181e99-adf3-47cb-93e1-89d267219e50).html.

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Distributed infrastructures (e.g., water networks and electric Grids) are difficult to manage due to their scale, lack of accessibility, complexity, ageing and uncertainties in knowledge of their structure. In addition they are subject to loads that can be highly variable and unpredictable and to accidental events such as component failure, leakage and malicious tampering. To support in-field operations and central management of these infrastructures, the availability of consistent and up-to-date knowledge about the current state of the network and how it would respond to planned interventions is argued to be highly desirable. However, at present, large-scale infrastructures are “data rich but knowledge poor”. Data, algorithms and tools for network analysis are improving but there is a need to integrate them to support more directly engineering operations. Current ICT solutions are mainly based on specialized, monolithic and heavyweight software packages that restrict the dissemination of dynamic information and its appropriate and timely presentation particularly to field engineers who operate in a resource constrained and less reliable environments. This thesis proposes a solution to these problems by recognizing that current monolithic ICT solutions for infrastructure management seek to meet the requirements of different human roles and operating environments (defined in this work as field and central sides). It proposes an architectural approach to providing dynamic, predictive, user-centric, device and platform independent access to consistent and up-to-date knowledge. This architecture integrates the components required to implement the functionalities of data gathering, data storage, simulation modelling, and information visualization and analysis. These components are tightly coupled in current implementations of software for analysing the behaviour of networks. The architectural approach, by contrast, requires they be kept as separate as possible and interact only when required using common and standard protocols. The thesis particularly concentrates on engineering practices in clean water distribution networks but the methods are applicable to other structural networks, for example, the electricity Grid. A prototype implementation is provided that establishes a dynamic hydraulic simulation model and enables the model to be queried via remote access in a device and platform independent manner.This thesis provides an extensive evaluation comparing the architecture driven approach with current approaches, to substantiate the above claims. This evaluation is conducted by the use of benchmarks that are currently published and accepted in the water engineering community. To facilitate this evaluation, a working prototype of the whole architecture has been developed and is made available under an open source licence.
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Sendil, Halil. "Operation Of Water Distribution Networks." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615484/index.pdf.

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With continuously increasing urbanization, consumer demands and expansion of water supply systems, determination of efficient pump schedules became a more difficult task. Pumping energy costs constitute a significant part of the operational cost of the water distribution networks. This study aims to provide an effective daily pump schedule by minimizing the energy costs for constant and also for multi tariff of electricity (3 Kademeli Elektrik Tarifesi) in water distribution network. A case study has been performed in an area covering N8.3 and N7 pressure zones which are parts of Ankara water distribution network. Both pressure zones consists of 3 multiple pumps in pump station and one tank having 5000 m3 storage volume each. By using genetic algorithm based software (WaterCAD Darwin Scheduler) least-cost pump scheduling and operation policy for each pump station has been determined while satisfying target hydraulic performance requirements such as minimum and maximum service pressures, final water level of storage tank and maximum velocity in pipeline. 32 different alternative scenarios have been created which include multi tariff energy prices, constant tariff energy price, insulated system condition, uninsulated system condition and different pump combinations. The existing base scenario and alternative scenarios which were prepared by using optimal pump schedules have been compared and the achievements of optimizing pump operation have been analyzed. At the end of the study, a satisfying result has been observed that by using determined optimal pump schedule, minimum % 14 of total energy cost can be saved in existing water supply system.
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Ali, Mohammed Elgorani A. "The optimal design and control of water distribution systems using genetic algorithms." Thesis, London South Bank University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367904.

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Morley, Mark S. "A framework for evolutionary optimization applications in water distribution systems." Thesis, University of Exeter, 2008. http://hdl.handle.net/10036/42400.

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The application of optimization to Water Distribution Systems encompasses the use of computer-based techniques to problems of many different areas of system design, maintenance and operational management. As well as laying out the configuration of new WDS networks, optimization is commonly needed to assist in the rehabilitation or reinforcement of existing network infrastructure in which alternative scenarios driven by investment constraints and hydraulic performance are used to demonstrate a cost-benefit relationship between different network intervention strategies. Moreover, the ongoing operation of a WDS is also subject to optimization, particularly with respect to the minimization of energy costs associated with pumping and storage and the calibration of hydraulic network models to match observed field data. Increasingly, Evolutionary Optimization techniques, of which Genetic Algorithms are the best-known examples, are applied to aid practitioners in these facets of design, management and operation of water distribution networks as part of Decision Support Systems (DSS). Evolutionary Optimization employs processes akin to those of natural selection and “survival of the fittest” to manipulate a population of individual solutions, which, over time, “evolve” towards optimal solutions. Such algorithms are characterized, however, by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of water networks incurs significant computational overheads, can limit the applicability and scalability of this technology in this domain. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to Water Distribution Systems. A number of new procedures are presented for improving the performance of such algorithms when applied to complex engineering problems. These techniques approach the problem of minimising the impact of the inherent computational complexity of these problems from a number of angles. A novel genetic representation is presented which combines the algorithmic simplicity of the classical binary string of the Genetic Algorithm with the performance advantages inherent in an integer-based representation. Further algorithmic improvements are demonstrated with an intelligent mutation operator that “learns” which genes have the greatest impact on the quality of a solution and concentrates the mutation operations on those genes. A technique for implementing caching of solutions – recalling the results for solutions that have already been calculated - is demonstrated to reduce runtimes for Genetic Algorithms where applied to problems with significant computation complexity in their evaluation functions. A novel reformulation of the Genetic Algorithm for implementing robust stochastic optimizations is presented which employs the caching technology developed to produce an multiple-objective optimization methodology that demonstrates dramatically improved quality of solutions for given runtime of the algorithm. These extensions to the Genetic Algorithm techniques are coupled with a supporting software library that represents a standardized modelling architecture for the representation of connected networks. This library gives rise to a system for distributing the computational load of hydraulic simulations across a network of computers. This methodology is established to provide a viable, scalable technique for accelerating evolutionary optimization applications.
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Telci, Ilker Tonguc. "Optimal water quality management in surface water systems and energy recovery in water distribution networks." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45861.

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Two of the most important environmental challenges in the 21st century are to protect the quality of fresh water resources and to utilize renewable energy sources to lower greenhouse gas emissions. This study contributes to the solution of the first challenge by providing methodologies for optimal design of real-time water quality monitoring systems and interpretation of data supplied by the monitoring system to identify potential pollution sources in river networks. In this study, the optimal river water quality monitoring network design aspect of the overall monitoring program is addressed by a novel methodology for the analysis of this problem. In this analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. The data collected from these monitoring stations can be used to identify contamination source locations. This study suggests a methodology that utilizes a classification routine which associates the observations on a contaminant spill with one or more of the candidate spill locations in the river network. This approach consists of a training step followed by a sequential elimination of the candidate spill locations which lead to the identification of potential spill locations. In order to contribute the solution of the second environmental challenge, this study suggests utilizing available excess energy in water distribution systems by providing a methodology for optimal design of energy recovery systems. The energy recovery in water distribution systems is possible by using micro hydroelectric turbines to harvest available excess energy inevitably produced to satisfy consumer demands and to maintain adequate pressures. In this study, an optimization approach for the design of energy recovery systems in water distribution networks is proposed. This methodology is based on finding the best locations for micro hydroelectric plants in the network to recover the excess energy. Due to the unsteady nature of flow in water distribution networks, the proposed methodology also determines optimum operation schedules for the micro turbines.
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"Water Supply Infrastructure Modeling and Control under Extreme Drought and/or Limited Power Availability." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53499.

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abstract: The phrase water-energy nexus is commonly used to describe the inherent and critical interdependencies between the electric power system and the water supply systems (WSS). The key interdependencies between the two systems are the power plant’s requirement of water for the cooling cycle and the water system’s need of electricity for pumping for water supply. While previous work has considered the dependency of WSS on the electrical power, this work incorporates into an optimization-simulation framework, consideration of the impact of short and long-term limited availability of water and/or electrical energy. This research focuses on the water supply system (WSS) facet of the multi-faceted optimization and control mechanism developed for an integrated water – energy nexus system under U.S. National Science Foundation (NSF) project 029013-0010 CRISP Type 2 – Resilient cyber-enabled electric energy and water infrastructures modeling and control under extreme mega drought scenarios. A water supply system (WSS) conveys water from sources (such as lakes, rivers, dams etc.) to the treatment plants and then to users via the water distribution systems (WDS) and/or water supply canal systems (WSCS). Optimization-simulation methodologies are developed for the real-time operation of water supply systems (WSS) under critical conditions of limited electrical energy and/or water availability due to emergencies such as extreme drought conditions, electric grid failure, and other severe conditions including natural and manmade disasters. The coupling between WSS and the power system was done through alternatively exchanging data between the power system and WSS simulations via a program control overlay developed in python. A new methodology for WDS infrastructural-operational resilience (IOR) computation was developed as a part of this research to assess the real-time performance of the WDS under emergency conditions. The methodology combines operational resilience and component level infrastructural robustness to provide a comprehensive performance assessment tool. The optimization-simulation and resilience computation methodologies developed were tested for both hypothetical and real example WDS and WSCS, with results depicting improved resilience for operations of the WSS under normal and emergency conditions.
Dissertation/Thesis
Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019
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Hewitson, Christopher Michael. "Optimisation of water distribution systems using genetic algorithms for hydraulic and water quality issues / by Christopher Michael Hewitson." 1999. http://hdl.handle.net/2440/19536.

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Corrigenda pasted onto front end paper.
One folded col. map in pocket on back endpaper.
Bibliography: leaves 348-368.
xx, 368 leaves : ill. (some col.), maps (some col.) ; 30 cm.
Title page, contents and abstract only. The complete thesis in print form is available from the University Library.
Develops a framework balancing water quality costs resulting from waterborne disease, disinfection by-product exposure and aesthetic concerns, against hydraulic costs, which include pipes, pumps and tanks. The genetic algorithms developed, successfully obtained the current optimal hydraulic solution, before adapting the model to incorporate water quality issues.
Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 2000
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Fang, Tianjun Civil &amp Environmental Engineering Faculty of Engineering UNSW. "Parameter estimation using a genetic algorithm for complex catchment modelling systems." 2007. http://handle.unsw.edu.au/1959.4/40740.

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Implementation of physically distributed catchment modelling systems reshapes the fundamental philosophy of traditional calibration approaches by supporting the concept of equifinality. Arising from the concept of equifinality, alternative behavioural parameter sets within a given catchment modelling system structure can generate similar levels of simulation performance. This concept is motivated by the existence of a variety of uncertainties associated with a complex catchment modelling system, such as an imperfect model structure, measurement errors in both the input data and the recorded flows, and unknown, or poorly defined, interactions among parameters. However, the difficulty of searching for behavioural parameter sets increases as the complexity of the catchment modelling systems increases. This study undertook an investigation on the feasibility and robustness of a real-value coding genetic algorithm (GA) for calibrating the physically distributed Storm Water Management Model (SWMM) using the Centennial Park catchment in Sydney as a case study. It was found that a real-value coding GA was a robust technique suitable to search for behavioural parameter sets and, in particular, it was found that this approach was capable of identifying the promising range of values for spatially variable parameters. Moreover, the widespread use of physically distributed catchment modelling systems has highlighted the importance of estimating the uncertainty in the parameter values and in the predictions obtained from a complex catchment modelling system as well as in catchment averaged, or lumped, systems that have been the focus of many previous studies. Bayesian inference has been shown to be a tool suitable for parameter uncertainty estimation in catchment modelling. However, the application of Bayesian inference faces difficulties in complex high-dimensional systems where there is little if any a priori knowledge about the proposal distribution of the parameters. In this study, a real-value coding GA was used to undertake uncertainty estimation on spatially variable control parameters with little a priori knowledge about the proposal distribution of parameters. After 50,000 evaluations, the marginal posterior distributions of spatially variable parameters which are associated with behavioural parameter sets were identified. The performance of a behavioural parameter set under a range of hydrological conditions was evaluated. Updating of the marginal distributions of these control parameters was implemented by adding additional calibration data. Interactions among the spatially variable control parameters were investigated also. Results based on the Pearson Correlation method indicate no clear relationship between any two control parameters. However, a methodology to detect relationships among groups of parameters was developed. Application of this methodology suggests that the simulation performance of SWMM was influenced by combinations of parameter values rather than values of the individual parameters. Finally, the predictive uncertainty associated with the existence of behavioural parameter sets was considered. A number of alternative strategies were used to evaluate the predictive performance. Consideration of the results suggests that use of a small number of parameter sets randomly selected from the large number of behavioural parameter sets was the best strategy in terms of efficiently obtaining predictive performance.
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Book chapters on the topic "Genetic Algorithms for Water Distribution Systems Operations"

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Gavanelli, Marco, Maddalena Nonato, Andrea Peano, Stefano Alvisi, and Marco Franchini. "Genetic Algorithms for Scheduling Devices Operation in a Water Distribution System in Response to Contamination Events." In Evolutionary Computation in Combinatorial Optimization, 124–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29124-1_11.

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Conference papers on the topic "Genetic Algorithms for Water Distribution Systems Operations"

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Dong, Xiaolei, Suiqing Liu, Tao Tao, and Kunlun Xin. "Optimal Operation of Raw Water System Using Accelerating Genetic Algorithm." In 12th Annual Conference on Water Distribution Systems Analysis (WDSA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41203(425)84.

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Xin, Kunlun, Suiqing Liu, Tao Tao, and Cuimei Li. "Application of Pseudo-Parallel Genetic Algorithm in Optimal Operation of Multisource Water Supply Network." In Eighth Annual Water Distribution Systems Analysis Symposium (WDSA). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40941(247)163.

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Shu, Shihu, and Dong Zhang. "Calibrating water distribution system model automatically by genetic algorithms." In 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS). IEEE, 2010. http://dx.doi.org/10.1109/iciss.2010.5654995.

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Houston, Eric J., Arlene S. Rahn, and George J. Licina. "Service Water Life Cycle Management." In ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61778.

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Nuclear plant service water systems are a critical part of the facility’s infrastructure. System integrity and performance are vital for plant reliability and essential to achieving a plant life of 40 years and beyond. Corrosion, fouling (macrofouling, microfouling and sedimentation) and other effects that are detrimental to the reliability of the service water system led to the issue of NRC Generic Letter 89-13 “Service Water System Problems Affecting Safety-Related Equipment.” This generic letter continues to be a fundamental guideline for safety related service water systems at all U.S. nuclear plants. The low temperature and pressure service water piping systems are primarily degraded by corrosion. Because of the complexity and random nature of corrosion processes, it is nearly impossible to develop a mathematically deterministic model that accurately predicts pipe wall loss. However, if statistical distributions are used to describe the various corrosion processes, mathematical algorithms that incorporate all of the distributions, iterated a statistically significant number of times, can be used to forecast the most probable number of leaks. This paper predicts the condition of service water piping at Kewaunee Nuclear Power Plant using the described model and includes the expected number of through-wall leaks as a function of operating time.
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Fu, Guangtao, and Zoran Kapelan. "Embedding Neural Networks in Multiobjective Genetic Algorithms for Water Distribution System Design." In 12th Annual Conference on Water Distribution Systems Analysis (WDSA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41203(425)81.

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Vítkovský, John P., Angus R. Simpson, and Martin F. Lambert. "Leak Detection and Calibration of Water Distribution Systems Using Transients and Genetic Algorithms." In 29th Annual Water Resources Planning and Management Conference. Reston, VA: American Society of Civil Engineers, 1999. http://dx.doi.org/10.1061/40430(1999)44.

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Casillas, Myrna V., Vicenc Puig, Luis E. Garza-Castanon, and Albert Rosich. "Optimal sensor placement for leak location in water distribution networks using genetic algorithms." In 2013 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, 2013. http://dx.doi.org/10.1109/systol.2013.6693876.

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Jun, Zhang, and Zhang Kan-yu. "Optimal Load Distribution Strategy for Multiple Chiller Water Units Based on Adaptive Genetic Algorithms." In 2010 Second Global Congress on Intelligent Systems (GCIS). IEEE, 2010. http://dx.doi.org/10.1109/gcis.2010.64.

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9

Halhal, D. "Structured messy genetic algorithm approach for the optimal improvement of water distribution systems." In 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). IEE, 1995. http://dx.doi.org/10.1049/cp:19951083.

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Saldarriaga, Juan G., Diego Alejandro Araque Fuentes, and Luis Fernando Castañeda Galvis. "Implementation of the Hydraulic Transient and Steady Oscillatory Flow with Genetic Algorithms for Leakage Detection in Real Water Distribution Networks." In Eighth Annual Water Distribution Systems Analysis Symposium (WDSA). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40941(247)52.

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