To see the other types of publications on this topic, follow the link: Genetic Algorithms for Water Distribution Systems Operations.
Journal articles on the topic 'Genetic Algorithms for Water Distribution Systems Operations'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Genetic Algorithms for Water Distribution Systems Operations.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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.
5
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.
Full textAbstract:
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.
6
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.
Full textAbstract:
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.
7
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.
Full textAbstract:
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.
8
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.
Full textAbstract:
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.
9
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).
Full text
10
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.
Full textAbstract:
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.
11
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.v10n1.135143.
Full textAbstract:
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.
12
Vairavamoorthy, Kalanithy, and Mohammed Ali. "Optimal Design of Water Distribution Systems Using Genetic Algorithms." Computer-Aided Civil and Infrastructure Engineering 15, no. 5 (September 2000): 374–82. http://dx.doi.org/10.1111/0885-9507.00201.
Full text
13
Ostfeld, Avi. "Reliability analysis of water distribution systems." Journal of Hydroinformatics 6, no. 4 (October 1, 2004): 281–94. http://dx.doi.org/10.2166/hydro.2004.0021.
Full textAbstract:
Reliability is an integral part of all decisions regarding water distribution system layout, design, operation and maintenance. Providing reliability for water distribution systems is complicated due to the many factors that affect reliability, the inherent nonlinear behavior of the system and its consumers, and due to the different conflicting objectives facing a water distribution system utility. Although the reliability of water distribution systems has received considerable attention over the last two decades, there is still no common, acceptable, reliability measure or reliability assessment methodology. This paper describes the classification and reliability analysis methodologies of water distribution systems and compares two previously published algorithms for reliability evaluation of water distribution systems: a tailor-made ‘lumped supply–lumped demand’ approach used most commonly in regional water distribution systems and a general stochastic (Monte Carlo) framework suitable for any generic network.
14
Tolson, Bryan A., Holger R. Maier, Angus R. Simpson, and Barbara J. Lence. "Genetic Algorithms for Reliability-Based Optimization of Water Distribution Systems." Journal of Water Resources Planning and Management 130, no. 1 (January 2004): 63–72. http://dx.doi.org/10.1061/(asce)0733-9496(2004)130:1(63).
Full text
15
Do, Nhu C., Angus R. Simpson, Jochen W. Deuerlein, and Olivier Piller. "Calibration of Water Demand Multipliers in Water Distribution Systems Using Genetic Algorithms." Journal of Water Resources Planning and Management 142, no. 11 (November 2016): 04016044. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000691.
Full text
16
Blinco, L. J., A. R. Simpson, M. F. Lambert, C. A. Auricht, N. E. Hurr, S. M. Tiggemann, and A. Marchi. "Genetic Algorithm Optimization of Operational Costs and Greenhouse Gas Emissions for Water Distribution Systems." Procedia Engineering 89 (2014): 509–16. http://dx.doi.org/10.1016/j.proeng.2014.11.246.
Full text
17
Eryiğit, Miraç. "Cost optimization of water distribution networks by using artificial immune systems." Journal of Water Supply: Research and Technology-Aqua 64, no. 1 (July 19, 2014): 47–63. http://dx.doi.org/10.2166/aqua.2014.031.
Full textAbstract:
This study aims at the development of an optimization model based on artificial immune systems (AIS) to minimize cost designs of water distribution networks (WDNs). Clonal selection algorithm (Clonalg), a class of AIS, was used as an optimization technique in the model, and its mutation operation was modified to increase the diversity (search capability). EPANET, a widely known WDN simulator, was used in conjunction with the proposed model. The model was applied to four WDNs of Two-loop, Hanoi, Go Yang, New York City, and the results obtained were compared with other heuristic and mathematical optimization models in the related literature, such as harmony search, genetic algorithm, immune algorithm, shuffled complex evolution, differential evolution, and non-linear programming-Lagrangian algorithm. Furthermore, the modified Clonalg was compared with the classic Clonalg in order to demonstrate the impact of the modification on the diversity. The proposed model appeared to be promising in terms of cost designs of WDNs.
18
Nono, Denis, Innocent Basupi, Phillimon T. Odirile, and Bhagabat P. Parida. "Integrating booster chlorination and operational interventions in water distribution systems." Journal of Hydroinformatics 20, no. 5 (May 9, 2018): 1025–41. http://dx.doi.org/10.2166/hydro.2018.113.
Full textAbstract:
Abstract Booster chlorination reduces the risks associated with conventional disinfection such as high chlorine residuals near water treatment plants and low chlorine residuals at remote parts of water distribution systems (WDSs). Network operational interventions have a significant influence on water age and chlorine decay in WDSs. In this study, an integrated booster chlorination method is developed to obtain optimal designs that reduce the risks associated with conventional disinfection in WDSs. The method integrates booster chlorination with network operational interventions to reduce water age and improve chlorine residuals in WDSs. A multi-objective booster optimisation problem is formulated based on risks associated with chlorine disinfection and solved using a non-dominated sorting genetic algorithm (NSGA-II) and the EPANET hydraulic and water quality solver. The proposed methodology was tested in the Phakalane WDS in Botswana. The integrated booster disinfection method significantly improves chlorine residuals in a WDSs with lower mass and cost of chlorine than conventional disinfection. Furthermore, the study indicates that integrated booster chlorination designs are influenced by changes in network conditions such as water demand and chlorine decay coefficients. Therefore, periodic monitoring of these parameters is required to ensure that the acceptable performance of chlorine boosters in WDSs is maintained.
19
Nicolini, Matteo. "Pareto Genetic Algorithms for Multi-Objective Design of Water Distribution Systems." Applied Mechanics and Materials 212-213 (October 2012): 664–70. http://dx.doi.org/10.4028/www.scientific.net/amm.212-213.664.
Full textAbstract:
The paper describes the development of a technique for the optimal design of water supply and distribution systems, based on a coupling between evolutionary algorithms and a pressurized hydraulic network solver. The purpose is to show the capabilities of Pareto genetic algorithms (PGAs) in solving multi-objective, constrained optimization problems: in such cases, the optimum is represented not only by one solution, as in single-objective optimization, but by a set of optimal configurations (the Pareto front or frontier), satisfying different levels of compromise among the competing objectives. A Pareto GA should determine the family of such non-dominated solutions, each of which is optimal in the sense that no improvement can be achieved in one criterion without the degradation in at least one of the remaining criteria. This might be of great help to the decision maker in selecting the best trade-off configuration, which will eventually depend on the actual context. An application to a real case is also presented.
20
Do, Nhu, Angus Simpson, Jochen Deuerlein, and Olivier Piller. "Demand Estimation In Water Distribution Systems: Solving Underdetermined Problems Using Genetic Algorithms." Procedia Engineering 186 (2017): 193–201. http://dx.doi.org/10.1016/j.proeng.2017.03.227.
Full text
21
Maskit, Matan, and Avi Ostfeld. "Multi-Objective Operation-Leakage Optimization and Calibration of Water Distribution Systems." Water 13, no. 11 (June 6, 2021): 1606. http://dx.doi.org/10.3390/w13111606.
Full textAbstract:
This study aims to develop and solve a multi-objective water distribution systems optimization problem incorporating pumps’ optimal scheduling and leakage minimization. An iterative optimization model was presented for calibrating and computing leakages in water distribution systems to recognize the critical impact of leakage control on system operation. The multi-dimensional and nonlinear optimization model, incorporating pump control, consumer demands, storage, and other water distribution systems’ components, was constructed and was minimized using a multi-objective genetic algorithm coupled with hydraulic simulations. The model was demonstrated on two example applications with increasing complexity through base runs and sensitivity analyses. Results showed that leakage minimization competes against pumping, mainly when significant differences occur between demands during low and high energy tariffs. Pumping during the periods with high electricity tariffs (when the demands are high) generated pressure distribution that decreased the overall leakage related to pump scheduling that replicated the natural inclination to pump as much as possible at low tariffs (when the demands are low). The optimal fronts were found to be very sensitive to the leakage exponent value, and changing its value indeed contradicted the balance between minimizing the leakage and the energy cost significantly. Altogether, the idea presented in this paper was found capable of facilitating the decision-makers to conveniently select between the energy-efficient pump scheduling and pump scheduling reflecting minimum leakage based on the system operator’s preferences. The research also paves the way to rebuild the optimization model by incorporating water distribution reliability and water quality that, in some cases, may also contradict the choice between energy cost and leakage minimization.
22
Preis, Ami, and Avi Ostfeld. "Multiobjective contaminant response modeling for water distribution systems security." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 267–74. http://dx.doi.org/10.2166/hydro.2008.061.
Full textAbstract:
Following the events of 9/11/2001 in the US, the world public awareness to possible terrorist attacks on water supply systems has increased significantly. The security of drinking water distribution systems has become a foremost concern around the globe. Water distribution systems are spatially diverse and thus are inherently vulnerable to intentional contamination intrusions. In this study, a multiobjective optimization evolutionary model for enhancing the response against deliberate contamination intrusions into water distribution systems is developed and demonstrated. Two conflicting objectives are explored: (1) minimization of the contaminant mass consumed following detection, versus (2) minimization of the number of operational activities required to contain and flush the contaminant out of the system (i.e. number of valves closure and hydrants opening). Such a model is aimed at directing quantitative response actions in opposition to the conservative approach of entire shutdown of the system until flushing and cleaning is completed. The developed model employs the multiobjective Non-Dominated Sorted Genetic Algorithm–II (NSGA-II) scheme, and is demonstrated using two example applications.
23
Qiu, Mengning, Mashor Housh, and Avi Ostfeld. "A Two-Stage LP-NLP Methodology for the Least-Cost Design and Operation of Water Distribution Systems." Water 12, no. 5 (May 12, 2020): 1364. http://dx.doi.org/10.3390/w12051364.
Full textAbstract:
This paper presents a two-stage method for simultaneous least-cost design and operation of looped water distribution systems (WDSs). After partitioning the network into a chord and spanning trees, in the first stage, a reformulated linear programming (LP) method is used to find the least cost design of a WDS for a given set of flow distribution. In the second stage, a non-linear programming (NLP) method is used to find a new flow distribution that reduces the cost of the WDS operation given the WDS design obtained in stage one. The following features of the proposed two-stage method make it more appealing compared to other methods: (1) the reformulated LP stage can consistently reduce the penalty cost when designing a WDS under multiple loading conditions; (2) robustness as the number of loading conditions increases; (3) parameter tuning is not required; (4) the method reduces the computational burden significantly when compared to meta-heuristic methods; and (5) in oppose to an evolutionary “black box” based methodology such as a genetic algorithm, insights through analytical sensitivity analysis, while the algorithm progresses, are handy. The efficacy of the proposed methodology is demonstrated using two WDSs case studies.
24
Gupta, Aditya Dinesh, Neeraj Bokde, Dushyat Marathe, and Kishore Kulat. "Optimization techniques for leakage management in urban water distribution networks." Water Supply 17, no. 6 (April 17, 2017): 1638–52. http://dx.doi.org/10.2166/ws.2017.064.
Full textAbstract:
Abstract Reduction of leakages in water distribution systems (WDSs) is one of the major concerns for water industries. This paper presents a leakage reduction technique using pressure management by optimizing the water level in storage tanks, along with optimized control and localization of pressure-reducing valves (PRVs) in WDSs. A new mathematical tank and pump simulation algorithm is presented for controlling pressure in WDSs, by optimizing the water storage level in the tank depending upon the demand variations. The tank is used as a decision variable for the leakage reduction model. A modified reference pressure algorithm is introduced for improving PRV localization. A multiobjective genetic algorithm (NSGA-II) is used to find the optimized operational control setting of the PRV for leakage minimization. The proposed algorithm leads to a leakage reduction of 26.51% in Anytown WDS and 20.81% in a modified benchmark WDS. This technique leads to an appreciable reduction in leakage rate, with fewer PRVs required, taking into account constraints such as maintaining a lower hydraulic failure index (<0.01), emergency storage, etc. It can be concluded that the proposed novel leakage reduction technique provides a more cost effective and efficient solution for leakage control.
25
Olsson, Ralph J., Zoran Kapelan, and Dragan A. Savic. "Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems." Journal of Hydroinformatics 11, no. 2 (March 1, 2009): 89–105. http://dx.doi.org/10.2166/hydro.2009.047.
Full textAbstract:
The multi-objective design and rehabilitation of water distribution systems (WDS) is defined as the search for the set of system designs which offers the best trade-off between competing design objectives. Typically these objectives will consist of the cost of implementing a system design and a measure of the performance of that system. These measures are often in competition since improvements in the performance of a system generally come at a cost. Here three genetic algorithms which use probabilistic methods to identify building blocks—the Univariate Marginal Distribution Algorithm (UMDA) (Mühlenbein 1997), the hierarchical Bayesian Optimisation Algorithm (hBOA) (Pelikan 2002) and the Chi-Square Matrix methodology (Aporntewan & Chongstitvatana 2004)—are compared to the well-known multi-objective evolutionary algorithm NSGAII (Deb et al. 2002) for the multi-objective design and rehabilitation of water distribution systems. For single-objective problems the identification of building blocks has been seen to make evolutionary algorithms more scalable to large problems than simple genetic algorithms. In this paper these algorithms are shown to offer significantly better solutions than NSGA-II for the case of large systems. However, this improvement comes at the expense of diversity of solutions in the fronts identified.
26
Waldron, Tim. "Expertise in water loss control applied to extreme problems of water distribution management." Water Supply 8, no. 1 (April 1, 2008): 107–12. http://dx.doi.org/10.2166/ws.2008.031.
Full textAbstract:
The application of International Water Association guidelines to control water losses provides a foundation for new methods and psychologies for controlling Water Distribution Systems. Best management techniques utilizing International Water Association water loss control methodologies compared to totally open distribution networks can now be supported for analysis scenarios using both network analysis and genetic algorithms.
27
Basupi, Innocent, Zoran Kapelan, and David Butler. "Reducing life-cycle carbon footprints in the redesign of water distribution systems." Journal of Water and Climate Change 4, no. 3 (April 18, 2013): 176–92. http://dx.doi.org/10.2166/wcc.2013.004.
Full textAbstract:
Water distribution systems (WDSs) contribute to undesirable greenhouse gas (GHG) emissions that are generated through their component fabrication, construction, operation and disposal processes. The concentration of GHGs in the atmosphere is strongly associated with global warming and climate change. In order to meet the consequent challenge of limiting GHG emissions, the problem of WDS redesign is formulated here as a multi-objective optimisation problem. The three objectives are as follows: (1) minimisation of total redesign cost, (2) maximisation of the WDS resilience, and (3) minimisation of GHG emissions. The resilience index serves as a measure of the WDS's intrinsic capability to ensure continuity of supply to users after sudden failure conditions, whilst the GHG emissions serve as a measure of environmental performance and climate change mitigation. The output from the non-dominated sorting genetic algorithm (NSGA2) optimisation process is a Pareto front containing optimal solutions traded-off in terms of the three objectives analysed. This methodology was applied on the New York Tunnels and the Anytown Network problems. The results obtained demonstrate that the redesign approach leads to cost-effective and resilient solutions that can also mitigate climate change compared with the single-objective (least cost) and other multi-objective redesigns over the long-term planning horizon.
28
Aral, M. M., J. Guan, M. L. Maslia, J. B. Sautner, R. E. Gillig, J. J. Reyes, and R. C. Williams. "Optimal reconstruction of historical water supply to a distribution system: B. Applications." Journal of Water and Health 2, no. 3 (September 1, 2004): 137–56. http://dx.doi.org/10.2166/wh.2004.0013.
Full textAbstract:
In a recently completed case-control epidemiological study, the New Jersey Department of Health and Senior Services (NJDHSS) with support from the Agency for Toxic Substances and Disease Registry (ATSDR) documented an association between prenatal exposure to a specific contaminated community water source and leukaemia in female children. An important and necessary step in the epidemiological study was the reconstruction of the historical water supply strategy of the water distribution system serving the Dover Township area, New Jersey. The sensitivity of solutions to: (1) pressure and pattern factor constraints, (2) allowable operational extremes of water levels in the storage tanks, and (3) the non-uniqueness of the water supply solution are analysed in detail. The computational results show that the proposed approach yields satisfactory results for the complete set of monthly simulations and sensitivity analyses, providing a consistent approach for identifying the historical water supply strategy of the water distribution system. Sensitivity analyses indicated that the alternative strategy obtained from the revised objective function and the variation of constraints did not yield significantly different water supply characteristics. The overall analysis demonstrates that the progressive optimality genetic algorithm (POGA) developed to solve the optimization problem is an effective and efficient algorithm for the reconstruction of water supply strategies in water distribution systems.
29
Koritsas, Elias, Epameinondas Sidiropoulos, and Chris Evangelides. "Optimization of Branched Water Distribution Systems by Means of a Physarum—Inspired Algorithm." Proceedings 2, no. 11 (August 1, 2018): 598. http://dx.doi.org/10.3390/proceedings2110598.
Full textAbstract:
Optimizing water distribution systems is an essential part of water resources allocation planning. It leads to challenging combinatorial optimization problems, for which meta-heuristics have been applied, notably genetic algorithms and ant colony optimization. The present paper introduces the application of the physarum algorithm, a recent biologically inspired algorithm, utilized hitherto for path finding problems in networks. Direct comparison is presented to ant colony optimization applied to a typical water supply system.
30
Sadatiyan Abkenar, Seyed Mohsen, Samuel Dustin Stanley, Carol J. Miller, Donald V. Chase, and Shawn P. McElmurry. "Evaluation of genetic algorithms using discrete and continuous methods for pump optimization of water distribution systems." Sustainable Computing: Informatics and Systems 8 (December 2015): 18–23. http://dx.doi.org/10.1016/j.suscom.2014.09.003.
Full text
31
Bashi-Azghadi, Seyyed Nasser, Abbas Afshar, and Mohammad Hadi Afshar. "Multi-period response management to contaminated water distribution networks: dynamic programming versus genetic algorithms." Engineering Optimization 50, no. 3 (May 16, 2017): 415–29. http://dx.doi.org/10.1080/0305215x.2017.1318871.
Full text
32
Dong, Xiao-lei, Sui-qing Liu, Tao Tao, Shu-ping Li, and Kun-lun Xin. "A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems." Journal of Zhejiang University SCIENCE A 13, no. 9 (September 2012): 674–86. http://dx.doi.org/10.1631/jzus.a1200072.
Full text
33
Baker, Barrie M., and Amandio Pereira Baia. "Branch-and-Bound Algorithms for a Regional Water Authority Distribution Problem." Journal of the Operational Research Society 46, no. 6 (June 1995): 698. http://dx.doi.org/10.2307/2584306.
Full text
34
Baker, Barrie M., and Amândio Pereira Baía. "Branch-and-Bound Algorithms for a Regional Water Authority Distribution Problem." Journal of the Operational Research Society 46, no. 6 (June 1995): 698–707. http://dx.doi.org/10.1057/jors.1995.99.
Full text
35
Eliades, D. G., and M. M. Polycarpou. "Leakage fault detection in district metered areas of water distribution systems." Journal of Hydroinformatics 14, no. 4 (June 12, 2012): 992–1005. http://dx.doi.org/10.2166/hydro.2012.109.
Full textAbstract:
Fault tolerance and security in drinking water distribution operations are important issues that have received increased attention in the last few years. In this work the problem of leakage detection is formulated within a systems engineering framework, and a solution methodology to detect leakages in a class of distribution systems is proposed. Specifically, the case where water utilities use standard flow sensors to monitor the water inflow in a District Metered Area (DMA) is considered. The goal is to design algorithms which analyze the discrete inflow signal and determine as early as possible whether a leakage has occurred in the system. The inflow signal is normalized to remove yearly seasonal effects, and a leakage fault detection algorithm is presented, which is based on learning the unknown, time-varying, weekly periodic DMA inflow dynamics using an adaptive approximation methodology for updating the coefficients of a Fourier series; for detection logic the Cumulative Sum (CUSUM) algorithm is utilized. For reference and comparison, a second solution methodology based on night-flow analysis using the normalized inflow signal is presented. To illustrate the solution methodology, results are presented based on randomized simulated leakages and real hydraulic data measured at a DMA in Limassol, Cyprus.
36
Pendharkar, Parag C., and Gary J. Koehler. "A general steady state distribution based stopping criteria for finite length genetic algorithms." European Journal of Operational Research 176, no. 3 (February 2007): 1436–51. http://dx.doi.org/10.1016/j.ejor.2005.10.050.
Full text
37
Xu, Nan, Shan Shan Li, and Hao Ming Liu. "Distribution System Fault Recovery with Undispatchable Distributed Generations." Applied Mechanics and Materials 529 (June 2014): 455–59. http://dx.doi.org/10.4028/www.scientific.net/amm.529.455.
Full textAbstract:
Considering the probabilistic of the wind power and the solar power, a fault recovery method for distribution systems with the wind power and the solar power is presented in this paper. For the wind power, a simplified steady-state equivalent model of an asynchronous wind generator is added into the Jacobian matrix to consider the impact of the wind power on systems. For the solar power, its output is considered as an injected power which is related with solar irradiance. Three-point estimate is employed to solve the probabilistic power flow of distribution systems with the wind power and the solar power. The restoration is described as a multi-objective problem with the mean of the system loss and the number of switch operations. Fast elitist non-dominated sorting partheno-genetic algorithm is used to solve this multi-objective problem. IEEE 33-bus system is used as an example and the results show that the models and algorithms in this paper are efficient.
38
Bai, Longju. "Reconfiguration Performance of the Urban Power Distribution System Based on the Genetic-Ant Colony Fusion Algorithm." E3S Web of Conferences 257 (2021): 02062. http://dx.doi.org/10.1051/e3sconf/202125702062.
Full textAbstract:
This study aims to enhance the reliability of the urban power grid system and decrease the economic loss due to power network faults. Based on the analysis of the traditional algorithms for restructuring the urban distribution system after faults, this study proposes an upgraded genetic algorithm (GA) and ant colony algorithm (ACA) and combines these two to overcome the limitations of the local optimum of GAs and low convergence speed of ACAs. Taking the IEEE33-node system as the research object, the network loss, maximum recovery of the power-loss load, and the number of switching operations as the objective function, the impact of different algorithms on the restoration and reconfiguration of the distribution system was examined according to MATLAB system simulation and the optimal algorithm for the reconfiguration of the urban distribution system failure recovery. The experimental results revealed that compared with the current distribution system reconfiguration algorithm, the genetic-ant colony algorithm (GACA) has higher algorithm time efficiency and solution accuracy and can markedly decrease the recovery time and improve the impact of the distribution system in a short period. Overall, the proposed GACA is an efficient self-healing algorithm of urban distribution systems and useful for augmenting the reliability of the urban power system.
39
López-Ibáñez, Manuel, T. Devi Prasad, and Ben Paechter. "Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks." Evolutionary Computation 19, no. 3 (September 2011): 429–67. http://dx.doi.org/10.1162/evco_a_00035.
Full textAbstract:
Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels; or explicitly, by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain fewer than the maximum number of switches. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations is an improvement over the results obtained by a recent state of the art hybrid genetic algorithm for pump scheduling using level-controlled triggers.
40
Tanyimboh, Tiku T., Marika T. Tietavainen, and Saleh Saleh. "Reliability assessment of water distribution systems with statistical entropy and other surrogate measures." Water Supply 11, no. 4 (September 1, 2011): 437–43. http://dx.doi.org/10.2166/ws.2011.066.
Full textAbstract:
There is ever increasing commercial and regulatory pressure to minimise the cost of water distribution networks even as the demand for them keeps on growing. But cost minimizing is only one of the demands placed on network design. Satisfactory networks are required to operate above a minimum level even if they experience failure of components. Reliable hydraulic performance can be achieved if sufficient redundancy is built in the network. This has given rise to various water distribution system optimization methods including genetic algorithms and other evolutionary computing methods. Evolutionary computing approaches frequently assess the suitability of enormous numbers of potential solutions for which the calculation of accurate reliability measures could be computationally prohibitive. Therefore, surrogate reliability measures are frequently used to ease the computational burden. The aim of this paper is to assess the correlation of surrogate reliability measures in relation to more accurate measures. The surrogate measures studied are statistical entropy, network resilience, resilience index and modified resilience index. The networks were simulated with the prototype software PRAAWDS that produces more realistic results for pressure-deficient water distribution systems. Statistical entropy outperformed resilience index in this study. The results also demonstrate there is a strong correlation between entropy and failure tolerance.
41
Fang, Yin-Ying, Chi-Fang Chen, and Sheng-Ju Wu. "Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan." ANZIAM Journal 59 (July 25, 2019): C318—C357. http://dx.doi.org/10.21914/anziamj.v59i0.12655.
Full textAbstract:
Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets.
References Nathan D Merchant, Kurt M Fristrup, Mark P Johnson, Peter L Tyack, Matthew J Witt, Philippe Blondel, and Susan E Parks. Measuring acoustic habitats. Methods in Ecology and Evolution, 6(3):257265, 2015. doi:10.1111/2041-210X.12330. Nathan D Merchant, Philippe Blondel, D Tom Dakin, and John Dorocicz. Averaging underwater noise levels for environmental assessment of shipping. The Journal of the Acoustical Society of America, 132(4):EL343EL349, 2012. doi:10.1121/1.4754429. Chi-Fang Chen, Hsiang-Chih Chan, Ray-I Chang, Tswen-Yung Tang, Sen Jan, Chau-Chang Wang, Ruey-Chang Wei, Yiing-Jang Yang, Lien-Siang Chou, Tzay-Chyn Shin, et al. Data demonstrations on physical oceanography and underwater acoustics from the marine cable hosted observatory (macho). In OCEANS, 2012-Yeosu, pages 16. IEEE, 2012. doi:10.1109/OCEANS-Yeosu.2012.6263639. Sauda Sadaf P Yashaswini, Soumya Halagur, Fazil Khan, and Shanta Rangaswamy. A literature survey on ambient noise analysis for underwater acoustic signals. International Journal of Computer Engineering and Sciences, 1(7):19, 2015. doi:10.26472/ijces.v1i7.37. Shuguang Wang and Xiangyang Zeng. Robust underwater noise targets classification using auditory inspired time-frequency analysis. Applied Acoustics, 78:6876, 2014. doi:10.1016/j.apacoust.2013.11.003. LG Weiss and TL Dixon. Wavelet-based denoising of underwater acoustic signals. The Journal of the Acoustical Society of America, 101(1):377383, 1997. doi:10.1121/1.417983. Timothy Alexis Bodisco, Jason D'Netto, Neil Kelson, Jasmine Banks, Ross Hayward, and Tony Parker. Characterising an ecg signal using statistical modelling: a feasibility study. ANZIAM Journal, 55:3246, 2014. doi:10.21914/anziamj.v55i0.7818. José Ribeiro-Fonseca and Luís Correia. Identification of underwater acoustic noise. In OCEANS'94.'Oceans Engineering for Today's Technology and Tomorrow's Preservation.'Proceedings, volume 2, pages II/597II/602 vol. 2. IEEE. Linus YS Chiu and Hwei-Ruy Chen. Estimation and reduction of effects of sea surface reflection on underwater vertical channel. In Underwater Technology Symposium (UT), 2013 IEEE International, pages 18. IEEE, 2013. doi:10.1109/UT.2013.6519874. G.M. Wenz. Acoustic ambient noise in the ocean: spectra and sources. Thesis, 1962. doi:10.1121/1.1909155. Donald Ross. Mechanics of underwater noise. Elsevier, 2013. doi:10.1121/1.398685. Chris Drummond and Robert C Holte. Exploiting the cost (in) sensitivity of decision tree splitting criteria. In ICML, volume 1, 2000. Charles Elkan. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, volume 17, pages 973978. Lawrence Erlbaum Associates Ltd, 2001. Chris Gillard, Alexei Kouzoubov, Simon Lourey, Alice von Trojan, Binh Nguyen, Shane Wood, and Jimmy Wang. Automatic classification of active sonar echoes for improved target identification. Douglas C Montgomery. Design and analysis of experiments. John wiley and sons, 2017. doi:10.1002/9781118147634. G Taguchi. Off-line and on-line quality control systems. In Proceedings of International Conference on Quality Control, 1978. Sheng-Ju Wu, Sheau-Wen Shiah, and Wei-Lung Yu. Parametric analysis of proton exchange membrane fuel cell performance by using the taguchi method and a neural network. Renewable Energy, 34(1):135144, 2009. doi:10.1016/j.renene.2008.03.006. Genichi Taguchi. Introduction to quality engineering: designing quality into products and processes. Technical report, 1986. doi:10.1002/qre.4680040216. Richard Horvath, Gyula Matyasi, and Agota Dregelyi-Kiss. Optimization of machining parameters for fine turning operations based on the response surface method. ANZIAM Journal, 55:250265, 2014. doi:10.21914/anziamj.v55i0.7865. Chuan-Tien Li, Sheng-Ju Wu, and Wei-Lung Yu. Parameter design on the multi-objectives of pem fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms. International Journal of Hydrogen Energy, 39(9):45024515, 2014. doi:10.1016/j.ijhydene.2014.01.034. Antoine Guisan, Thomas C Edwards Jr, and Trevor Hastie. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2-3):89100, 2002. doi:10.1016/S0304-3800(02)00204-1. Sheng Chen, Colin FN Cowan, and Peter M Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on neural networks, 2(2):302309, 1991. doi:10.1109/72.80341. Howard Demuth and Mark Beale. Neural network toolbox for use with matlab-user's guide verion 4.0. 1993. Janice Gaffney, Charles Pearce, and David Green. Binary versus real coding for genetic algorithms: A false dichotomy? ANZIAM Journal, 51:347359, 2010. doi:10.21914/anziamj.v51i0.2776. Daniel May and Muttucumaru Sivakumar. Techniques for predicting total phosphorus in urban stormwater runoff at unmonitored catchments. ANZIAM Journal, 45:296309, 2004. doi:10.21914/anziamj.v45i0.889. Chang-Xue Jack Feng, Zhi-Guang Yu, and Andrew Kusiak. Selection and validation of predictive regression and neural network models based on designed experiments. IIE Transactions, 38(1):1323, 2006. doi:10.1080/07408170500346378. Yin-Ying Fang, Ping-Jung Sung, Kai-An Cheng, Meng Fan Tsai, and Chifang Chen. Underwater radiated noise measurement of ocean researcher 3. In The 29th Taiwan Society of Naval Architects and Marine Engineers Conference, 2017. Yin-Ying Fang, Chi-Fang Chen, and Sheng-Ju Wu. Analysis of vibration and underwater radiated noise of ocean researcher 3. In The 30th Taiwan Society of Naval Architects and Marine Engineers Conference, 2018. Det Norske Veritas. Rules for classification of ships new buildings special equipment and systems additional class part 6 chapter 24 silent class notation. Rules for Classification of ShipsNewbuildings, 2010. Underwater acousticsquantities and procedures for description and measurement of underwater sound from ships-part 1requirements for precision measurements in deep water used for comparison purposes. (ISO 17208-1:2012), 2012. Bureau Veritas. Underwater radiated noise, rule note nr 614 dt r00 e. Bureau Veritas, 2014. R.J. Urick. Principles of underwater sound, volume 3. McGraw-Hill New York, 1983. Lars Burgstahler and Martin Neubauer. New modifications of the exponential moving average algorithm for bandwidth estimation. In Proc. of the 15th ITC Specialist Seminar, 2002. Bishnu Prasad Lamichhane. Removing a mixture of gaussian and impulsive noise using the total variation functional and split bregman iterative method. ANZIAM Journal, 56:5267, 2015. doi:10.21914/anziamj.v56i0.9316. Chao-Ton Su. Quality engineering: off-line methods and applications. CRC press, 2016. Jiju Antony and Mike Kaye. Experimental quality: a strategic approach to achieve and improve quality. Springer Science and Business Media, 2012. Ozkan Kucuk, Tayeb Elfarah, Serkan Islak, and Cihan Ozorak. Optimization by using taguchi method of the production of magnesium-matrix carbide reinforced composites by powder metallurgy method. Metals, 7(9):352, 2017. doi:10.3390/met7090352. G Taguchi. System of experimental design, quality resources. New York, 108, 1987. Gavin C Cawley and Nicola LC Talbot. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition, 36(11):25852592, 2003. doi:10.1016/S0031-3203(03)00136-5.
42
Zhang, Shi Ze, Yi Xing Yuan, and Pei Ming Li. "Two-Step Optimization for the Design of Water Distribution Networks." Applied Mechanics and Materials 316-317 (April 2013): 758–61. http://dx.doi.org/10.4028/www.scientific.net/amm.316-317.758.
Full textAbstract:
Genetic algorithms (GA) are currently one of the state-of-the-art techniques for the optimization of engineering systems including water distribution networks design and rehabilitation. They are capable of finding near optimal cost solutions to these problems when certain cost and hydraulic parameters are given. Since many forms of GAs rely on random starting points, that is to say, the poor solutions, it has become an ongoing research topic how to efficiently provide good initial estimates of solution sets automatically. A novel method is proposed in this paper, known as two-step optimization, which uses a heuristic-based, Dijkstra arithmetic to optimize network topology to obtain the layout of main pipes. The first step provides a good pattern for subsequent GA runs. Two-step optimization is applied to a network.. The result shows that the proposed approach consistently outperforms the traditional design and the conventional non-heuristic-based GA approach in terms of convergence and calculation efficiency.
43
Wang, Qi, Libing Wang, Wen Huang, Zhihong Wang, Shuming Liu, and Dragan A. Savić. "Parameterization of NSGA-II for the Optimal Design of Water Distribution Systems." Water 11, no. 5 (May 9, 2019): 971. http://dx.doi.org/10.3390/w11050971.
Full textAbstract:
The optimal design of Water Distribution Systems (WDSs) using multi-objective evolutionary algorithms (MOEAs) has received substantial attention in the past two decades. Many MOEAs have been proposed and applied successfully to this challenging problem. However, these tools are primarily considered black-boxes by end users, especially when the algorithm parameterization issues are taken into consideration. This paper presents a simple yet effective method for capturing the interrelationships among the five key parameters of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is one of the state-of-the-art MOEAs in this field. Two representative boundary values for each parameter are selected from a reasonable range, and all the possible combinations are tested on three benchmark design problems. Those benchmarks are based on two widely used small networks and a larger, real-world irrigation network. Results suggest that there is a hierarchy of impacts imposed by the five parameters of NSGA-II. The population size turns out to be the most important one, which implies that NSGA-II is sensitive to the initial population, especially for complex problems. A relatively large population size increases the diversity of a population; hence some key genes may be identified at the beginning (or early stage) of search. Furthermore, it transpires that the distribution indices of crossover and mutation have a more significant impact than their probabilities, where the former are generally overlooked by previous studies. Some useful guidelines are also provided, which can improve the efficacy of NSGA-II and increase the chance of identifying near-optimal solutions.
44
Yang, Shutong, Youlei Wang, and Yufei Wang. "Optimization of Cascade Cooling System Based on Lithium Bromide Refrigeration in the Polysilicon Industry." Processes 9, no. 9 (September 18, 2021): 1681. http://dx.doi.org/10.3390/pr9091681.
Full textAbstract:
Cascade cooling systems containing different cooling methods (e.g., air cooling, water cooling, refrigerating) are used to satisfy the cooling process of hot streams with large temperature spans. An effective cooling system can significantly save energy and costs. In a cascade cooling system, the heat load distribution between different cooling methods has great impacts on the capital cost and operation cost of the system, but the relative optimization method is not well established. In this work, a cascade cooling system containing waste heat recovery, air cooling, water cooling, absorption refrigeration, and compression refrigeration is proposed. The objective is to find the optimal heat load distribution between different cooling methods with the minimum total annual cost. Aspen Plus and MATLAB were combined to solve the established mathematical optimization model, and the genetic algorithm (GA) in MATLAB was adopted to solve the model. A case study in a polysilicon enterprise was used to illustrate the feasibility and economy of the cascade cooling system. Compared to the base case, which only includes air cooling, water cooling, and compression refrigeration, the cascade cooling system can reduce the total annual cost by USD 931,025·y−1 and save 7,800,820 kWh of electricity per year. It also can recover 3139 kW of low-grade waste heat, and generate and replace a cooling capacity of 2404 kW.
45
Perelman, Lina, Ariel Krapivka, and Avi Ostfeld. "Single and multi-objective optimal design of water distribution systems: application to the case study of the Hanoi system." Water Supply 9, no. 4 (October 1, 2009): 395–404. http://dx.doi.org/10.2166/ws.2009.404.
Full textAbstract:
This manuscript describes the application of two recent methodologies developed by the authors for single and multi-objective optimal design of water distribution systems. The single-objective model is a hybrid algorithm incorporating decomposition, spanning tree search, and evolutionary computation, while the multi-objective algorithm integrates features form multi-objective genetic algorithms with the Cross Entropy combinatorial optimization scheme. The two models are implemented on the Hanoi water distribution system, one of the more explored systems in the research literature, through base runs and sensitivity analysis. The single-objective model produced the best known least cost solution for split pipe design, while the multi-objective model has shown robustness and well explanatory outcomes. Discussion of the accomplished results and suggestions for future research are provided.
46
Mounce, S. R., E. J. M. Blokker, S. P. Husband, W. R. Furnass, P. G. Schaap, and J. B. Boxall. "Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems." Journal of Hydroinformatics 18, no. 1 (January 21, 2015): 96–114. http://dx.doi.org/10.2166/hydro.2015.140.
Full textAbstract:
Particulate material accumulates over time as cohesive layers on internal pipeline surfaces in water distribution systems (WDS). When mobilised, this material can cause discolouration. This paper explores factors expected to be involved in this accumulation process. Two complementary machine learning methodologies are applied to significant amounts of real world field data from both a qualitative and a quantitative perspective. First, Kohonen self-organising maps were used for integrative and interpretative multivariate data mining of potential factors affecting accumulation. Second, evolutionary polynomial regression (EPR), a hybrid data-driven technique, was applied that combines genetic algorithms with numerical regression for developing easily interpretable mathematical model expressions. EPR was used to explore producing novel simple expressions to highlight important accumulation factors. Three case studies are presented: UK national and two Dutch local studies. The results highlight bulk water iron concentration, pipe material and looped network areas as key descriptive parameters for the UK study. At the local level, a significantly increased third data set allowed K-fold cross validation. The mean cross validation coefficient of determination was 0.945 for training data and 0.930 for testing data for an equation utilising amount of material mobilised and soil temperature for estimating daily regeneration rate. The approach shows promise for developing transferable expressions usable for pro-active WDS management.
47
Capponi, Caterina, and Marco Ferrante. "Numerical investigation of pipe length determination in branched systems by transient tests." Water Supply 18, no. 3 (September 5, 2017): 1062–71. http://dx.doi.org/10.2166/ws.2017.180.
Full textAbstract:
Abstract Transients are used as a diagnostic tool for pressurized pipes due to their capabilities to acquire and transmit information about system status. To interpret such information in complex systems, models obtained by the integration of the governing equations in the frequency domain are used, since they are computationally efficient and reliable. These models do not require a regular spatial grid for integration and introduce the pipe lengths as parameters. In this paper a calibration procedure based on this particular feature is introduced, to determine a basic item of information that can be lost about the water distribution system, that is the length of the pipes. The network admittance matrix method is implemented and its numerical efficiency allows the investigation of the calibration optimization function on a regular grid in the parameter space. The calibration is tested using in series a genetic and the Nelder–Mead algorithms, considering both elastic and viscoelastic material pipes. The results of the presented numerical investigation allow some insights into the existence of the solution and into the shape of the optimization function.
48
Pawar, Mukund M., and Nitin P. Sonaje. "Converting Traditional Water Supply Network Into 24x7, using Water GEMS to Optimize Design." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 280–84. http://dx.doi.org/10.35940/ijrte.a5937.0510121.
Full textAbstract:
Water is all-natural driving force. Entire world struggles to preserve it. Given that India is among the top 12 water poor countries, water wastage is a critical issue for us. India's population is increasing day by day and thus the demand for water is continuously increasing. This growing demand can be met through an efficient water distribution network which can be designed using modern hydraulic software such as Water GEMS. Using the Water GEMS software Pandharpur city is selected to convert existing water supply system into 24 * 7 continuous water supply systems. The largest investment is the pipes used in the water distribution system. The design, modeling and optimization of pipes in water supply system from an economic point of view are very important. Therefore optimal pipe network design for converting existing network into 24x7 water supply system networks is carried out in this paper to reduce the cost using WaterGEM software. Study of the existing water supply network system for one zone (Ambika Nagar Zone10) is initially carried out from the Pandharpur area. The effect on demand, head loss gradient, and pressure development of the forecasted population is studied. In addition, cost optimization of the pipe network for the proposed 24x7 water supply system is carried out using a genetic algorithms Darwin optimization approach.
49
Urbanucci, Testi, and Bruno. "Integration of Reversible Heat Pumps in Trigeneration Systems for Low-Temperature Renewable District Heating and Cooling Microgrids." Applied Sciences 9, no. 15 (August 5, 2019): 3194. http://dx.doi.org/10.3390/app9153194.
Full textAbstract:
District heating and cooling networks based on trigeneration systems and renewable energy technologies are widely acknowledged as an energy efficient and environmentally benign solution. These energy systems generally include back-up units, namely fossil-fuel boilers and electric chillers, to enhance system flexibility and cover peak energy demand. On the other hand, 4th generation district heating networks are characterized by low-temperature water distribution to improve energy and exergy efficiencies. Moreover, reversible heat pumps are a versatile technology, capable of providing both heating and cooling, alternately. In this paper, the integration of reversible heat pumps as single back-up units in hybrid renewable trigeneration systems serving low-energy micro-district heating and cooling networks is investigated. A detailed modeling of the system is provided, considering part-load and ambient condition effects on the performance of the units. Size and annual operation of the proposed system are optimized in a case study, namely a large office building located in Pisa (Italy), by means of a genetic algorithm-based procedure. A comparison with the conventional trigeneration system is performed in terms of economic and environmental perspectives. Results show that the integration of reversible heat pumps is an economically viable solution capable of reducing by 7% the equivalent annual cost, increasing the installed power of renewables up to 23%, and lowering by 11% carbon dioxide emissions, compared to the energy system with conventional back-up units.
50
Sapkota, Subin, A. K. M. Nuhil Mehdy, Stephen Reese, and Hoda Mehrpouyan. "FALCON: Framework for Anomaly Detection in Industrial Control Systems." Electronics 9, no. 8 (July 24, 2020): 1192. http://dx.doi.org/10.3390/electronics9081192.
Full textAbstract:
Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.