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Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems

Author

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  • Laís Régis Salvino

    (Federal University of Paraiba)

  • Heber Pimentel Gomes

    (Federal University of Paraiba)

  • Saulo de Tarso Marques Bezerra

    (Federal University of Pernambuco)

Abstract

Sustainable management of water supply systems is a major challenge within the framework of the water-energy nexus. The main strategies to improve the operation of these systems are related to increasing the hydraulic and energy efficiency of pumping systems. In this context, this work presents a new artificial neural network (ANN) controller to improve the operation of water distribution systems (WDSs) that includes in its algorithm the specific energy consumption (SEC) as a decision parameter. Therefore, pressure control at the measuring points is also based on the energy efficiency of the pumps. The technique was applied to control the pressures in an experimental setup that emulates a WDS with two consumption zones with different topographies. For this purpose, the controller acted on a conventional pump, a booster pump and a control valve. To analyze the performance under the controller action, tests were performed emulating water-demand scenarios, introducing perturbations and changing the pressure setpoints. The real-time control performance was proven based on the dynamic performance, steady-state performance and SEC. The experimental results showed that the proposed controller kept the pressures close to the setpoints and provided a reduction in the SEC between 15.1% and 17.8%, compared with the uncontrolled system, and an economy that varied from 2.5% to 8.1% compared with the performance of the ANN based only on pressure control.

Suggested Citation

  • Laís Régis Salvino & Heber Pimentel Gomes & Saulo de Tarso Marques Bezerra, 2022. "Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2779-2793, June.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:8:d:10.1007_s11269-022-03175-4
    DOI: 10.1007/s11269-022-03175-4
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    References listed on IDEAS

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    1. Habibeh Sharifi & Abbas Roozbahani & Seied Mehdy Hashemy Shahdany, 2021. "Evaluating the Performance of Agricultural Water Distribution Systems Using FIS, ANN and ANFIS Intelligent Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1797-1816, April.
    2. S. Vijay & K. Kamaraj, 2021. "Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 535-553, January.
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    Cited by:

    1. Sooyeon Yi & G. Mathias Kondolf & Samuel Sandoval-Solis & Larry Dale, 2022. "Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5675-5694, November.

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