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Infeasibility Maps: Application to the Optimization of the Design of Pumping Stations in Water Distribution Networks

Author

Listed:
  • Jimmy H. Gutiérrez-Bahamondes

    (PhD in Engineering Systems, Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó 3340000, Chile)

  • Daniel Mora-Melia

    (Department of Engineering and Construction Management, Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó 3340000, Chile
    Department of Hydraulic Engineering and Environment, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Bastián Valdivia-Muñoz

    (PhD in Engineering Systems, Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó 3340000, Chile)

  • Fabián Silva-Aravena

    (Faculty of Social and Economic Sciences, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3480094, Chile)

  • Pedro L. Iglesias-Rey

    (Department of Hydraulic Engineering and Environment, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

Abstract

The design of pumping stations in a water distribution network determines the investment costs and affects a large part of the operating costs of the network. In recent years, it was shown that it is possible to use flow distribution to optimize both costs concurrently; however, the methodologies proposed in the literature are not applicable to real-sized networks. In these cases, the space of solutions is huge, a small number of feasible solutions exists, and each evaluation of the objective function implies significant computational effort. To avoid this gap, a new method was proposed to reduce the search space in the problem of pumping station design. This method was based on network preprocessing to determine in advance the maximum and minimum flow that each pump station could provide. According to this purpose, the area of infeasibility is limited by ranges of the decision variable where it is impossible to meet the hydraulic constraints of the model. This area of infeasibility is removed from the search space with which the algorithm works. To demonstrate the benefits of using the new technique, a new real-sized case study was presented, and a pseudo-genetic algorithm (PGA) was implemented to resolve the optimization model. Finally, the results show great improvement in PGA performance, both in terms of the speed of convergence and quality of the solution.

Suggested Citation

  • Jimmy H. Gutiérrez-Bahamondes & Daniel Mora-Melia & Bastián Valdivia-Muñoz & Fabián Silva-Aravena & Pedro L. Iglesias-Rey, 2023. "Infeasibility Maps: Application to the Optimization of the Design of Pumping Stations in Water Distribution Networks," Mathematics, MDPI, vol. 11(7), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1582-:d:1106627
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    References listed on IDEAS

    as
    1. Aditya Gupta & K. D. Kulat, 2018. "A Selective Literature Review on Leak Management Techniques for Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3247-3269, August.
    2. Alexandru Predescu & Ciprian-Octavian Truică & Elena-Simona Apostol & Mariana Mocanu & Ciprian Lupu, 2020. "An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
    3. Oreste Fecarotta & Aonghus McNabola, 2017. "Optimal Location of Pump as Turbines (PATs) in Water Distribution Networks to Recover Energy and Reduce Leakage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 5043-5059, December.
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    6. Safarbek Oshurbekov & Vadim Kazakbaev & Vladimir Prakht & Vladimir Dmitrievskii, 2021. "Improving Reliability and Energy Efficiency of Three Parallel Pumps by Selecting Trade-Off Operating Points," Mathematics, MDPI, vol. 9(11), pages 1-19, June.
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    1. Antonin Ponsich & Bruno Domenech & Mariona Vilà, 2023. "Preface to the Special Issue “Mathematical Optimization and Evolutionary Algorithms with Applications”," Mathematics, MDPI, vol. 11(10), pages 1-6, May.

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