Neural Computing and Applications

, Volume 31, Issue 11, pp 7489–7499 | Cite as

Application of nature inspired optimization algorithms in optimum positioning of pump-as-turbines in water distribution networks

  • Mojtaba TahaniEmail author
  • Hossein Yousefi
  • Younes Noorollahi
  • Roshanak Fahimi
Original Article


In these days, energy, water, fossil fuel restrictions and greenhouse gas emission have become the mutual problem of all countries. The application of hydro turbines, especially pumps as turbines in water distribution network, can be a great solution to these problems. In this research study, it is aimed to introduce a procedure for obtaining the optimum position of a pump as turbine in water distribution network. For this purpose, two objective functions are considered, namely power and up surge ratio. The reason of selecting the power is to maximize the energy production and minimize the payback period, and the reason of selecting the upsurge ratio is to minimize the initial costs and network risks. In the proposed methodology, a transient analysis database is being combined with optimization algorithms. In this research study, Bently hammer software has been used for generating the mentioned database. Ant colony optimization algorithm has been used for obtaining the discrete variable and three other heuristic algorithms, namely grey wolf optimizer, whale optimization algorithm and ion motion algorithm were used for finding the best continuous variable. Pipe number and the position of hydro turbine on the pipe were considered as the discrete and continuous variables, respectively. The proposed methodology was tested on a network in Palermo which data were available. The results indicated that the proposed methodology can suggest the best 6 pipes among 70 pipes of network and also the accurate position of the turbine on the pipe.


Nature inspired algorithm Water network Water hammer Power 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Marchis MD, Milici B, Volpe B, Messineo A (2016) Energy saving in water distribution network through pump as turbine generators: economic and environmental analysis. Energies. CrossRefGoogle Scholar
  2. 2.
    Marchis MD, Freni G (2015) Pump as turbine implementation in a dynamic numerical model: cost analysis for energy recovery in water distribution network. J Hydroinform 17:347–360. CrossRefGoogle Scholar
  3. 3.
    Arriaga M (2010) Pump as turbine—a pico-hydro alternative in Lao People’s Democratic Republic. Renew Energy 35:1109–1115. CrossRefGoogle Scholar
  4. 4.
    Gupta A, Bokde N, Marathe D, Kulat K (2017) Leakage reduction in water distribution systems with efficient placement and control of pressure reducing valves using soft computing techniques. Eng Technol Appl Sci Res 7:1528–1534Google Scholar
  5. 5.
    Saldarriaga J, Salcedo CA (2015) Determination of optimal location and settings of pressure reducing valves in water distribution networks for minimizing water losses. Procedia Eng 119:973–983. CrossRefGoogle Scholar
  6. 6.
    Liberatore S, Sechi GM (2009) Location and calibration of valves in water distribution networks using a scatter-search meta-heuristic. Water Resour Manag 23:1479–1495. CrossRefGoogle Scholar
  7. 7.
    Mirjalili S (2015) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073. CrossRefGoogle Scholar
  8. 8.
    Mirjalili S, Mirjalili S, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. CrossRefGoogle Scholar
  9. 9.
    Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimization with chaos. Neural Comput Appl 25:1077–1097. CrossRefGoogle Scholar
  10. 10.
    Hamza MF, Yap HJ, Choudhury IA (2017) Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control. Neural Comput Appl 28:979–999. CrossRefGoogle Scholar
  11. 11.
    Ozyon S, Yasar C, Temurtas H (2018) Incremental gravitational search algorithm for high-dimensional benchmark functions. Neural Comput Appl. CrossRefGoogle Scholar
  12. 12.
    Arora S, Anand P (2018) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl. CrossRefGoogle Scholar
  13. 13.
    Hossain MS, El-Shafie A (2014) Evolutionary technique versus swarm intelligences: application in reservoir release optimization. Neural Comput Appl 24:1583–1594. CrossRefGoogle Scholar
  14. 14.
    Cordoba GAC, Tuhovcak L, Taus M (2014) Using artificial neural network models to assess water quality in water distribution networks. Procedia Eng 70:399–408. CrossRefGoogle Scholar
  15. 15.
    Rodriguez H, Puig V, Flores JJ (2016) Flow meter data validation and reconstruction using neural networks: Application to the Barcelona water network. In: European Control Conference. IEEE.
  16. 16.
    Filho EGB, Salvino LR, Bezerra SDTM, Salvino MM, Gomes HP (2017) Intelligent system for control of water distribution networks. Water Sci Technol Water Supply. CrossRefGoogle Scholar
  17. 17.
    Meirelles G, Manzi D, Brentan B, Goulart T, Luvizotto E Jr (2017) Calibration model for water distribution network using pressures estimated by artificial neural networks. Water Resour Manag 31:4339–4351. CrossRefGoogle Scholar
  18. 18.
    Bubtiena AM, Elshafie AH, Jafaar O (2011) Application of artificial neural networks in modeling water networks. In: IEEE 7th international colloquium on signal processing and its applications, CSPA, pp 50–57.
  19. 19.
    Tahani M, Babayan N (2017) Optimum section selection procedure for horizontal axis tidal stream turbines. Neural Comput Appl. CrossRefGoogle Scholar
  20. 20.
    Tahani M, Maeda T, Babayan N, Mehrnia S, Shadmehri M, Li Q, Fahimi R, Masdari M (2017) Investigating the effect of geometrical parameters of an optimized wind turbine blade in turbulent flow. Energy Convers Manag 153:71–82. CrossRefGoogle Scholar
  21. 21.
    Tahani M, Babayan N, Mehrnia S, Shadmehri M (2016) A novel heuristic method for optimization of straight blade vertical axis wind turbine. Energy Convers Manag 127:461–476. CrossRefGoogle Scholar
  22. 22.
    Javidy B, Hatamlou A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79. CrossRefGoogle Scholar
  23. 23.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. CrossRefGoogle Scholar
  24. 24.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. CrossRefGoogle Scholar
  25. 25.
    Dorigo M, Maniezzo V, Colorni A (1996) Ant system optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41. CrossRefGoogle Scholar
  26. 26.
    Rao SS (2009) Engineering optimization theory and practice. Wiley, HobokenCrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Mojtaba Tahani
    • 1
    Email author
  • Hossein Yousefi
    • 1
  • Younes Noorollahi
    • 1
  • Roshanak Fahimi
    • 1
  1. 1.Faculty of New Sciences and TechnologiesUniversity of TehranTehranIran

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