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Water supply systems need to be designed in an efficient way, accounting for both construction costs and operational energy expenditures when pumping is required. Since water demand varies depending on the moment´s necessities, especially when it comes to agricultural purposes, water supply systems should also be designed to adequately handle this....
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... V the annual volume of water pumped; hEq the pumping head for the theoretical operating point corresponding to qEq; μ and μ the pump and engine efficiency at the theoretical operating point corresponding to qEq; n the number of periods of different flow rate, and pEq the theoretical unit price of the energy with which qEq would be pumped. Figure 4 represents this idea the concept of the equivalent flow rate. For this analysis, the cost of pumping energy should be separated into two parts. ...
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The study evaluates the hydraulic analysis of water supply distribution network using water GEMS v8i. which used for modeling and Simulation of hydraulic parameters in the distribution networks. The hydraulic parameters which analyzed by using this software were junction pressure, velocity of water in networking system, and nodal demands and the ov...
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... Water discharge is a quantity that states the amount of water flowing per unit of time that passes through a specific cross-section. This water discharge test is carried out to determine how much flow in (1) and (2) [19]. Q is water discharge in m 3 /s, V is velocity of water flow in m/s, A is cross-sectional area in m 2 , ℎ 0 is depth of water in meters, and is width of the canal in meters. ...
span lang="EN-US">The energy of river water flow or irrigation canals can be utilized by using a horizontal axis Savonius turbine, which converts low-speed river flow into electrical power with good design. The design of this turbine needs to pay attention to several parameters, namely, the deflector angle, the number of blades, the diameter and thickness of the blades, and the diameter and thickness of the end plates. However, the problem usually encountered is imperfect turbine construction due to the large drag force that occurs so that the power generated is low. Based on this background, it is proposed to manufacture and test the performance of a series Savonius underwater rotors with a horizontal axis. The research results found that the generator voltage without load was 25.6 V when the turbine only rotated at 47.2 rpm, whereas when under load, the average power produced was 8.5 watts with an average turbine speed of 31 rpm. The highest efficiency value on the rotor is 86.73%, with a torque value of 3.36 at a turbine speed of 29.9 rpm. This indicates that the tool can generate large torque at low speeds.</span
... This set of nonlinear equations can be solved using any iterative techniques such as the Hardy-Cross method [36], Newton-Raphson method [37], linear theory method, or gradient method [38]. EPANET [39][40][41] is one of the most popularly used hydraulic simulators for WDNs based on DDA. ...
... One of the major obstacles to the easy application of PDA in analyzing WDNs is the development of a methodology for simultaneous estimation of unknown nodal heads and nodal discharges [40]. Several iterative techniques are proposed in the literature for PDA [13,41]. ...
In urban areas of developing countries, due to industrialization and population growth, water demand has been increasing significantly, thereby increasing stress on the existing water distribution systems (WDSs). Under these circumstances, maintaining equity in the allocation of water becomes a significant challenge. When building an intermittent water distribution system, it is important to provide a minimum level of supply that is acceptable as well as water supply equity. A non-dominated sorting genetic algorithm (NSGA-II) is employed for the optimal design of an intermittent water distribution network (WDN). Network resilience is taken as a measure of reliability (In), while the uniformity coefficient (CU) is taken as a measure of equity in the water supply. Maximizing network resilience, uniformity coefficient, and minimization of cost of the network are considered as the objectives in the multi-objective optimization model. Pressure-driven analysis (PDA) is used for the hydraulic simulation of the network. The NSGA-II model is applied and demonstrated over two water distribution networks taken from the literature. The results indicate that reliability and equity in WDNs can be accomplished to a reasonable extent with minimal cost.
... They proposed future research, investigating mathematical models of the different kinds of flows and the creation of new algorithms. Martin-Candilejo et al. (2020) also developed a viable approach for calculating changing flow rates. The approach avoids the significant computational inefficiency of dynamic programming, which may limit its applicability in actual designs. ...
... The approach avoids the significant computational inefficiency of dynamic programming, which may limit its applicability in actual designs. The equivalent flow rate and equivalent volume concepts provided by Martin-Candilejo et al. (2020) provide engineers with a solution to the major constraint of cost gradient approaches, which rely on constant design flow rates. Their method is suitable for branching networks, including those with a dual-pipe arrangement, penstock, and hydroelectric power plants, and it is also adaptable to channel design. ...
Minimizing water delivery costs and increasing water supply dependability are the two key objectives in designing and operating of water distribution systems. An analytical solution for
the best pipe diameters of a water distribution pipe network was created in this study. The raw water was collected from a water canal and transported to a treatment plant (WTP). The cleansed water was pushed into the water distribution network via the pumping station. The derivative approach was used in the optimization process, and the cost functions incorporating the
various capital and operating expenses were used. The initial diameters were adjusted to reach the least-cost diameters. The study shows that the optimum design of a pipe network
(PNW) achieves saving a total cost of about 12.3% after utilizing our unique method. The optimal pumping rate in the PNW can also be determined using an analytical approach that considers the optimum value that occurs when the minor and friction losses in the entire network are at their lowest. The analytical solution for the optimum pumping rate shows a rigid value of optimum pumping rate (Qopt) which is 5296.32 m3/day. On comparative
estimates, this figure is very close to that of 5,300 m3/day interpreted from the graphical solution, with negligible deviation. The total cost and optimum pumping rate results for the analyzed PNW example demonstrate the correctness of the analytical solutions, reliability, and dependability of the assumptions.
... Mathematics 2023, 11, 1582 2 of 16 after considering the operational variables in the network [7,8]. The design of a PS includes selecting the number, model, type of pump, accessories, and control system [6]. ...
The design of pumping stations in a water distribution network determines the investment costs and affects a large part of the operating costs of the network. In recent years, it was shown that it is possible to use flow distribution to optimize both costs concurrently; however, the methodologies proposed in the literature are not applicable to real-sized networks. In these cases, the space of solutions is huge, a small number of feasible solutions exists, and each evaluation of the objective function implies significant computational effort. To avoid this gap, a new method was proposed to reduce the search space in the problem of pumping station design. This method was based on network preprocessing to determine in advance the maximum and minimum flow that each pump station could provide. According to this purpose, the area of infeasibility is limited by ranges of the decision variable where it is impossible to meet the hydraulic constraints of the model. This area of infeasibility is removed from the search space with which the algorithm works. To demonstrate the benefits of using the new technique, a new real-sized case study was presented, and a pseudo-genetic algorithm (PGA) was implemented to resolve the optimization model. Finally, the results show great improvement in PGA performance, both in terms of the speed of convergence and quality of the solution.
... Thus, we adopt economic indicators (consumption and cost of pumping) as a way of evaluating the energy efficiency of the infested system, since Energies 2023, 16, 1858 3 of 21 consumption and cost are inversely proportional to energy efficiency. (2) Pumping systems are at the center of most human activities [23,24] and represent approximately 20% of world energy consumption [25]. (3) Pumping systems can be built with pipes of different materials, the most common being polyvinyl chloride (PVC), high-density polyethylene (HDPE), cast iron (CI), concrete and steel [26]. ...
... At the end of two years (0-24 months) of bioinfestation, there were increases in the consumption of 34%, 30%, and 95% for Pumps 1, 2, and 3, respectively. After this period (24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39), consumption tends to enter stability, reaching an increase of 36%, 32%, and 102% by the end of the period for the coupled systems of Pumps 1, 2, and 3, respectively. With this information, it was found that the growth rate of consumption is higher in the first 24 months, proving the relationship with the increase in the load loss factor ( Figure 6). ...
Pumping systems, especially those used in the water supply sector and in industrial and hydroelectric facilities, are commonly infested by the golden mussel. This causes an increase in maintenance operations (e.g., system shutdowns for cleaning) that can generate an increased energy cost. The geographical expansion of the golden mussel in Latin America presents an economic risk, not only to the ecosystem in general, but also to the energy sector. The imminence of its spread in the Amazon region, one of the main river basins in South America, is cause for concern with regard to the problems that bioinvasion of this species can cause. Given the absence of studies on the loss of energy efficiency in pumping systems impacted by the golden mussel, this study proposes a methodology to estimate the increase in energy consumption and costs of pumping under such bioinfestation. For the standardization of the methodology and development of mathematical calculations (both novel and improved equations), data from the literature (the growth of the golden mussel as a function of infestation time) and an analysis of the dimensions (length and height) of a sample of mussels available in the laboratory were considered. These data were used to calculate the roughness generated by the mussel infestation in the pumping suction and discharger pipe, which was necessary to determine the loss of energy efficiency (load loss, power consumption, and cost of pumping) resulting from the increase in energy consumption for pumping. This methodology was applied to a pumping station representative of the Brazilian Amazon as a case study. The results show an average increase in economic indicators (consumption and cost of pumping) after the system undergoes bioinfestation. This total increase corresponded to 19% and 44% in the first and second years, respectively, achieving a stabilization of the increase in the cost of pumping at 46%, in the 30 months of operation. Our results demonstrate the pioneering nature of the proposal, since these are the first quantitative data on the energy efficiency of pumping systems associated with bioinfestation by the golden mussel. These results can also be used to estimate the increase in costs caused by golden mussel bioinfestation in the raw water pumping systems of other facilities.
... Almost all optimization models developed for water hammer protection devices are single-objective models [14][15][16][17][28][29][30][31] in which the goal is to minimize transient effects (or maximize safety) or to minimize the cost of installing protective devices. Recently, however, there have been studies that have considered maximum safety as an objective function and budget constraints as a constraint on the optimization model. ...
... The air chamber is one of the conventional pieces of equipment that controls the water hammer as shown in Figure 1. The air chamber that is installed near downstream of the pump station is mostly used for controlling the positive and negative surge of water hammer [30]. ...
In this study, a Multi-objective Optimization Model (MOM) is developed and solved for the optimum design of surge protection devices (including air chamber and shock damper) with conflicting goals. The shock damper is a new type of surge tank invented by researchers. For the first time, the design parameters of the shock damper as decision variables are raised in a MOM problem, and results are benchmarked with solving the model for the air chamber as well. Method of characteristics (MOC) is chosen for the numerical solution of water hammer PDE’s and its system of equations for interior and boundary nodes are used as constraints of the optimization model. The conflicting criteria of the MOM are functions of safety in the system and installation cost of protection devices. In the following by using the weight coefficients and normalized objective functions obtained by dividing each of the mentioned functions by the maximum potential values of them, the resulting problem is solved by Genetic Algorithm (GA).
The results, while investigated conceptually, show the significant improvement of multi-objective design in the performance and cost-saving in protection devices and the better function of shock damper regarding both criteria.
... The study used WaterGEMs V8i for modeling, evaluation and optimization process. In optimization process, genetic algorithms were widely adapted for calculation and evolutionary programing (Kadu et al., 2008;Marchi et al., 2014;Martin-candilejo et al., 2020). Genetic algorithms are used in many areas to solve optimization problems such as optimal parameter estimation (Abdelsalam and Gabbar, 2021;Demiroren et al., 2019;Selem et al., 2021). ...
The amount of pressure and age of the water were higher at low demand periods than at peak demand. One way to see how well a distribution system works is to look at how much pressure and how old the water is. Through optimization, pressure can be kept at an optimum level in the distribution system. Subsequent monitoring is very important for the sustainability of urban water distribution systems. A B S T R A C T Water loss has become increasingly critical as the severity of the water shortage situation has grown in recent decades. One of the options for reducing water loss in urban water distribution networks is pressure management. The study aimed to evaluate and optimize the existing water distribution system in the city. The proposed methodology is an interactive combination process between an optimization algorithm and WaterGEMS V8i to evaluate the performance of the distribution system. It was observed that, 43.80% of nodes (15-60 mH 2 O), 5.10% of nodes (15 mH 2 O), and 51.10% of nodes (>60 mH 2 O) received pressure during peak hour demand. During low demand periods, only 4.4% of nodes (15-60 mH 2 O) and 95.60% of nodes (>60 mH 2 O) received pressure. The water age simulation results revealed that, 51.70% of the pipes were received water age <4.8 h, whereas the other 48.3% of the pipes were received water age <8.6 h during peak hour demand. During low demand periods, 45.58% of the pipes had a water age of less than 4.8 h while the other 54.42% of the pipes had water age of 4.8-20 h. The optimization result showed that after optimization, 4.4% of the nodes with optimum pressure increased to 75.18%, and 95.6% of the nodes decreased to 24.82%. Changing the size of the pipe based on the optimization result, and dividing an area into different pressure zones (adding more reservoirs at the far end of the distribution system) are all ways to improve or upgrade the distribution system.
... Regarding the operational costs corresponding to drinking water networks, the cost for energy consumption can reach up to 40%, of which a large part is due to pumping systems [1]. Therefore, the search for and study of analysis and optimization tools that allow for reducing both energy consumption and operating costs of pumping systems continues to be crucial [2][3][4][5]. This is the intent of the application of the so-called setpoint curve (SC) or minimum energy curve [6,7], whose study and application continue to be ephemeral, with the goal of giving it a global vision. ...
In water distribution networks, the adjustment of the driving curves of pumping systems to the setpoint curves allows for determining the minimum energy cost that can be achieved in terms of pumping. This paper presents the methodology for calculating the optimal setpoint curves in water networks with multiple pumping systems, pressure dependent and independent consumption, with and without storage capacity. In addition, the energy and cost implications of the setpoint curve are analyzed. Three objective functions have been formulated depending on the case study, one of minimum energy and two of costs that depend on whether or not the presence of storage tanks is considered. For the optimization process, two algorithms have been used, Hooke and Jeeves and differential evolution. There are two study networks: TF and Richmond. The results show savings of close to 10% in the case of the Richmond network.
... Electricity consumption during operation is one of the biggest marginal expenditures for water utilities [35]. Operational optimization can be achieved by controlling times when pumps operate, also called pump scheduling [36,37], flow rates [38], pump speeds [39] and tank-water trigger levels [40]. Furthermore, other factors such as real-time control and water quality can also be considered [41][42][43]. ...
Water distribution networks are vital hydraulic infrastructures, essential for providing consumers with sufficient water of appropriate quality. The cost of construction, operation, and maintenance of such networks is extremely large. The problem of optimization of a water distribution network is governed by the type of water distribution network and the size of pipelines placed in the distribution network. This problem of optimal diameter allocation of pipes in a distribution network has been heavily researched over the past few decades. This study describes the development of an algorithm, ‘Smart Optimization Program for Water Distribution Networks’ (SOP–WDN), which applies genetic algorithm to the problem of the least-cost design of water distribution networks. SOP–WDN demonstrates the application of an evolutionary optimization technique, i.e., genetic algorithm, linked with a hydraulic simulation solver EPANET, for the optimal design of water distribution networks. The developed algorithm was applied to three benchmark water distribution network optimization problems and produced consistently good results. SOP–WDN can be utilized as a tool for guiding engineers during the design and rehabilitation of water distribution pipelines.
... Consequently, the obtainable energy savings are restricted by the previous PS design, and insufficient or idle capacity may be generated. Therefore, the investment and operating costs of PSs should be minimized together to achieve a complete solution to the optimization problem [11]. ...
... Other researchers have focused on analyzing the PS capacity to optimize the design and operational costs of stations; the authors of [16] introduced a model with binary variables for each design and operation option available in the face of a small number of demand scenarios. The authors of [11] proposed a new design procedure that considers the variable demand, equivalent flow, and equivalent volume to approximate the annual costs of a given system. The authors of [17] calculated the design cost of an entire network using a robust model based on the variability of the costs of multiple scenarios. ...
... The third term (C facility ) is explained in Equation (11), which considers pipes and accessories. (11) where for each PS, NB is the number of pumps, nT is the number of union tees, ne is the number of elbows, np is the number of pipes, and li is the length of pipe i. ...
The investment and operating costs of pumping stations in drinking water distribution networks are some of the highest public costs in urban sectors. Generally, these systems are designed based on extreme scenarios. However, in periods of normal operation, extra energy is produced, thereby generating excess costs. To avoid this problem, this work presents a new methodology for the design of pumping stations. The proposed technique is based on the use of a setpoint curve to optimize the operating and investment costs of a station simultaneously. According to this purpose, a novel mathematical optimization model is developed. The solution output by the model includes the selection of the pumps, the dimensions of pipelines, and the optimal flow distribution among all water sources for a given network. To demonstrate the advantages of using this technique, a case study network is presented. A pseudo-genetic algorithm (PGA) is implemented to resolve the optimization model. Finally, the obtained results show that it is possible to determine the full design and operating conditions required to achieve the lowest cost in a multiple pump station network.
