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Optimal Design of Water Pipeline and Micro-Hydro Turbine by Genetic Algorithm

8 pagesPublished: September 20, 2018

Abstract

The economic value of the potential energy hidden in water resources is becoming more and more relevant for pipe design. In this work a new way to design drinking main waterlines, embedding also the potential hydroelectric production as pipeline benefit, is presented. The optimum design of a cross-flow turbine, on the basis of the available head jump and discharge is first outlined; the description of a genetic algorithm to minimize the total cost (pipeline plus machinery) minus the net benefit (hydropower production) is then presented. Finally, a comparison is carried out among the costs of a case study pipeline assuming a) no hydropower production and traditional design criteria and b) two different scenarios with different values of benefits per unit energy production. The two scenarios lead to hydropower production with constant impeller rotational velocity in one case and with variable impeller rotational velocity in the other one.

Keyphrases: Cross flow turbine, Genetic Algorithm, main waterline design, Mini hydro, Optimization

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 302--309

Links:
BibTeX entry
@inproceedings{HIC2018:Optimal_Design_of_Water,
  author    = {Camillo Bosco and Giuseppe Pezzinga and Marco Sinagra and Tullio Tucciarelli},
  title     = {Optimal Design of Water Pipeline and Micro-Hydro Turbine by Genetic Algorithm},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {302--309},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/bWBR},
  doi       = {10.29007/vf78}}
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