Optimal transient network rehabilitation using multi-objective ant colony optimization algorithm

Comparison between two Hydraulic Control Valves Systems for Leakage Minimization in Water Distribution Networks Using Multi-Objective Memetic Algorithm . Dept C.(Irrigation )

“…While, the suggested system 1 and the study given by Nicolini and Zovatto [20] agree with the best location for two and three valves (pipes numbers 11,20) and (1,11,20), the obtained leakage in system 1 is the less. Both the suggested system 2 and the study given by El-Ghandour and Elansary [8] agree with the best location for two valves (pipes numbers 1,27). The comparison between the hydraulic performances of the two suggested valves systems in leakage reduction shows that they have the same trend, and the TCVs system is relatively better than the FCVs system.…”

“…The verified MOMA model is applied on an existing real WDN of a new city, Egypt, [27] to minimize both the total network leakage volume and number of valves, considering the two suggested hydraulic control valves systems. The WDN is a gravity-driven and draws water from a reservoir, of total head equal to 313.0 m, to the downstream network.…”

“…After performing the simplification process for the network, the simplified network (99 pipes and 89 nodes) mimics the behavior of the current large network (1411 pipes and 1283 nodes), Figure 5. The simplified network data are given by El-Ghandour and Elansary [27] including pipe data and nodal requirements. The values of MA parameters are chosen as the same taken in model verification and H min is taken 30 m. Figure 6 shows the obtained Pareto fronts corresponding to each suggested valve system.…”

Multi-objective optimization of water distribution networks: An overview

“…The case study of the New York tunnel network (Fig. 2) gravity-driven, given by Jung et al (2011), was used to verify the developed model by El-Ghandour and Elansary (2018), and the results are compared. The network needs rehabilitation actions by adding new pipes parallel to the existing ones to increase the pressure heads at some demand nodes.…”

“…However, in WDNs, the optimization process by trial and error methods can present difficulties due to the complexity of these systems such as multiple pumps, valves and reservoirs, head losses, large variations in pressure values, several demand loads, etc. For this reason, innovative linear (Sarbu and Ostafe, 2016), nonlinear (Samani and Naeeni, 1996;Djebedjian et al, 2000;Sarbu and Kalmar, 2002) and heuristic (Simpson et al, 1994;Cunha and Sousa, 2001;Zecchin et al, 2005;Vasan and Simonovic, 2010;Babu and Vijayalakshmi, 2013;Yazdi et al, 2017;El-Ghandour and Elansary, 2018) optimization algorithms are becoming more widely explored in optimization processes of the WDNs. In the solution procedure, each algorithm is linked with a hydraulic analysis solver of WDNs to obtain the optimum solution.…”