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An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Author

Listed:
  • Alexandru Predescu

    (Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

  • Ciprian-Octavian Truică

    (Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

  • Elena-Simona Apostol

    (Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

  • Mariana Mocanu

    (Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

  • Ciprian Lupu

    (Department of Automatic Control and Systems Engineering, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

Abstract

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:887-:d:366091
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    References listed on IDEAS

    as
    1. Alemtsehay G. Seyoum & Tiku T. Tanyimboh, 2017. "Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4485-4503, November.
    2. Sanjay RODE, 2009. "Sustainable Drinking Water Supply In Pune Metropolitan Region: Alternative Policies," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 4(1S), pages 48-59, April.
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    Cited by:

    1. Dorin Bordeașu & Octavian Proștean & Ioan Filip & Florin Drăgan & Cristian Vașar, 2022. "Modelling, Simulation and Controlling of a Multi-Pump System with Water Storage Powered by a Fluctuating and Intermittent Power Source," Mathematics, MDPI, vol. 10(21), pages 1-24, October.
    2. 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.

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