scholarly journals A Computer Program to Support the Selection of Turbines to Recover Unused Energy at Hydraulic Networks

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 467 ◽  
Author(s):  
Ángel Mariano Rodríguez-Pérez ◽  
Inmaculada Pulido-Calvo ◽  
Pablo Cáceres-Ramos

For this paper, a computer program was designed and developed to calculate which turbines could be placed in a water distribution system considering the hydraulic constraints. The aforementioned turbines are placed in locations where we have unused hydraulic energy, i.e., when this energy is dissipated by a regulating valve. In our case, what we do is place a turbine to make use of that excess energy. Once the data has been entered into the program, it provides the type or types of turbines that can be placed in each location, what power these turbines would be, and how much they would generate annually. The program offers us two calculation options. In the first, and simpler, one, it would be done using the net head at the location where the turbine is to be placed. For this option, it would only be necessary to introduce the flow rate, the net head, and the hours that the turbine will be in operation to perform the calculation. The second option would be in the case where we did not have the net head, and, instead, we had the gross head. In this case, we have to calculate the head losses. Normally, this would be the most used option because there are usually no pressure drops. To perform the calculation, in this case, it is necessary to know, apart from what is mentioned in the first option, the characteristics of the pipe (diameter, length, and material).

2012 ◽  
Vol 155-156 ◽  
pp. 285-290 ◽  
Author(s):  
Wei Wei Zhang ◽  
Guo Ping Yu ◽  
Miao Shun Bai

The most uncertain input parameters that often considered for calibration in water distribution system hydraulic model are pipe roughness coefficients and nodal demands. Both pipe roughness coefficients and nodal demands are considered to be calibrated in the calibration process, which works alternately. The calibration model was formulated as a constrained optimization problem. The entire head losses under different loading conditions are introduced in the objective function to guide the calibration direction, which can make consistent calibration effects on different loading conditions. The calibration model uses real-coded genetic algorithm along with a general network solver (EPANET 2.0) to adjust pipe roughness coefficients and nodal demands multipliers until the preset criteria are meet. The approach was applied in the calibration of a real-life water distribution system hydraulic model in China, which takes three loading conditions (max, min and average hour) into consider. The results show that the approach works well in achieving good calibration results, which match field observation in a reasonable level and meet engineering requirements.


2021 ◽  
Vol 23 ◽  
pp. 835-844
Author(s):  
Jacek Dawidowicz

The design of the water distribution system is inherently linked to the execution of calculations, which aim, among other things, to determine the flow rate through individual pipes and the selection of diameters at the appropriate speed. Each step in the calculations is followed by an evaluation of the results and, if necessary, a correction of the data and further calculations. It is up to the designer to analyse the accuracy of the calculation results and is time-consuming for large systems. In this article, a diagnostic method for the results of hydraulic calculations, based on Kohonen Network, which classifies nominal diameters [DN] on the basis of data, in the form of flows, has been proposed. After calculating the new variant of the water distribution system, the individual calculation sections are assigned to the neurons of the topological map of Kohonen Network drawn up for nominal diameters. By comparing the diameter used for the calculation, with the diameter obtained on the topological map, the accuracy of the chosen diameter can be assessed. The topological map, created as a result of labelling the neurons of the output layer of the Kohonen Network, graphically shows the position of the classified diameter, relative to those diameters with similar input values. The position of a given diameter, relative to other diameters, may suggest the need to change the diameter of the pipe.


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