IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v34y2020i9d10.1007_s11269-020-02588-3.html
   My bibliography  Save this article

Adaptive and Improved Multi-population Based Nature-inspired Optimization Algorithms for Water Pump Station Scheduling

Author

Listed:
  • Luca O. Turci

    (Shanghai Jiao Tong University)

  • Jingcheng Wang

    (Shanghai Jiao Tong University)

  • Ibrahim Brahmia

    (Shanghai Jiao Tong University)

Abstract

A common problem that the world faces is the waste of energy. In water pump stations, the situation is not different. Employees still use the traditional, manual, and empirical operation of the water pumps. This process gradually generates unwanted losses of energy and money. To avoid such profligacy, this paper presents two Adaptive and one Improved Multi-population based nature-inspired optimization algorithms for water pump station scheduling. The main goal here is to obtain the optimal operational scheduling of each group of pumps, wasting the minimum amount of energy. Therefore, since the objective function relies on the shaft power consumption of all the pumps running together, our aim becomes feasible. We implemented and tested the algorithms in the main water pump station of Shanghai, in China. Based on traditional multi-population based nature-inspired optimization algorithms, such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), this work adapts and improves the models to fit the complex constraints and characteristics of the system. It also compares and analyses the performance of each method used in this case study, considering the obtained results. The method which demonstrated outperformance was chosen as the best solution for the present problem.

Suggested Citation

  • Luca O. Turci & Jingcheng Wang & Ibrahim Brahmia, 2020. "Adaptive and Improved Multi-population Based Nature-inspired Optimization Algorithms for Water Pump Station Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2869-2885, July.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:9:d:10.1007_s11269-020-02588-3
    DOI: 10.1007/s11269-020-02588-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-020-02588-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-020-02588-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoli Feng & Baoyun Qiu & Yongxing Wang, 2020. "Optimizing Parallel Pumping Station Operations in an Open-Channel Water Transfer System Using an Efficient Hybrid Algorithm," Energies, MDPI, vol. 13(18), pages 1-19, September.
    2. Przemysław Kowalik & Magdalena Rzemieniak, 2021. "Binary Linear Programming as a Tool of Cost Optimization for a Water Supply Operator," Sustainability, MDPI, vol. 13(6), pages 1-15, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    3. Torregrossa, Dario & Hansen, Joachim & Hernández-Sancho, Francesc & Cornelissen, Alex & Schutz, Georges & Leopold, Ulrich, 2017. "A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants," Applied Energy, Elsevier, vol. 208(C), pages 1430-1440.
    4. Zheng, Chenglin & Chen, Xi & Zhu, Lingyu & Shi, Jiaqi, 2018. "Simultaneous design of pump network and cooling tower allocations for cooling water system synthesis," Energy, Elsevier, vol. 150(C), pages 653-669.
    5. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    6. Zhang, Zijun & Kusiak, Andrew & Zeng, Yaohui & Wei, Xiupeng, 2016. "Modeling and optimization of a wastewater pumping system with data-mining methods," Applied Energy, Elsevier, vol. 164(C), pages 303-311.
    7. Máša, Vítězslav & Stehlík, Petr & Touš, Michal & Vondra, Marek, 2018. "Key pillars of successful energy saving projects in small and medium industrial enterprises," Energy, Elsevier, vol. 158(C), pages 293-304.
    8. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    9. Sun, Jin & Feng, Xiao & Wang, Yufei & Deng, Chun & Chu, Khim Hoong, 2014. "Pump network optimization for a cooling water system," Energy, Elsevier, vol. 67(C), pages 506-512.
    10. Ma, Jiaze & Wang, Yufei & Feng, Xiao, 2017. "Energy recovery in cooling water system by hydro turbines," Energy, Elsevier, vol. 139(C), pages 329-340.
    11. Arun Shankar, Vishnu Kalaiselvan & Umashankar, Subramaniam & Paramasivam, Shanmugam & Hanigovszki, Norbert, 2016. "A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system," Applied Energy, Elsevier, vol. 181(C), pages 495-513.
    12. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    13. Vojtěch Zejda & Vítězslav Máša & Šárka Václavková & Pavel Skryja, 2020. "A Novel Check-List Strategy to Evaluate the Potential of Operational Improvements in Wastewater Treatment Plants," Energies, MDPI, vol. 13(19), pages 1-21, September.
    14. Gao, Wei & Feng, Xiao, 2017. "The power target of a fluid machinery network in a circulating water system," Applied Energy, Elsevier, vol. 205(C), pages 847-854.
    15. Rashidi, M.M. & Ali, M. & Freidoonimehr, N. & Nazari, F., 2013. "Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm," Energy, Elsevier, vol. 55(C), pages 497-510.
    16. Bonvin, Gratien & Demassey, Sophie & Le Pape, Claude & Maïzi, Nadia & Mazauric, Vincent & Samperio, Alfredo, 2017. "A convex mathematical program for pump scheduling in a class of branched water networks," Applied Energy, Elsevier, vol. 185(P2), pages 1702-1711.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:34:y:2020:i:9:d:10.1007_s11269-020-02588-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.