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

Laís Régis Salvino (), Heber Pimentel Gomes () and Saulo de Tarso Marques Bezerra ()
Additional contact information
Laís Régis Salvino: Federal University of Paraiba
Heber Pimentel Gomes: Federal University of Paraiba
Saulo de Tarso Marques Bezerra: Federal University of Pernambuco

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 8, No 15, 2779-2793

Abstract: 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.

Keywords: Water supply; Real-time control; Pressure control; Energy efficiency; Artificial neural network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03175-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:36:y:2022:i:8:d:10.1007_s11269-022-03175-4

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-022-03175-4

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().