Neural network using the Levenberg-Marquardt algorithm for optimal real-time operation of water distribution systems

• Published in: Urban water journal Vol. 15; no. 7; pp. 692 - 699
• Main Authors: Moura, Geraldo de Araújo, Bezerra, Saulo de Tarso Marques, Gomes, Heber Pimentel, Silva, Simplício Arnaud da
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 09.08.2018; Taylor & Francis Ltd
• Subjects: Adaptive control; Algorithms; artificial neural network; Back propagation; Computing time; control system; Distribution; Energy efficiency; Evaluation; hydraulic and energy efficiency; Neural networks; Pressure; pressure control; Real time operation; Solutions; Stress concentration; Water distribution; Water distribution systems; Water engineering; Water supply
• ISSN: 1573-062X; 1744-9006
• DOI: 10.1080/1573062X.2018.1539503

This paper proposes an Adaptive Neural Network (NN) controller for the real-time pressure control in water distribution systems. Pressure control is one of the main technical options that can be implemented by a water utility to increase the hydraulic and energy efficiency of systems. The network adopted the Levenberg-Marquardt backpropagation algorithm, being responsible for maintaining the pump head at an optimal value, eliminating the excess pressure of the system. The advantage of the approach is that, once the network is trained, it allows instantaneous evaluation of solutions at any desired number of points; thus, spending little computing time. The controller was applied in the experimental setup, and the results showed excellent performance regarding pressure regulation. Finally, it is expected that the NN controller can be easily implemented in similar water distribution systems.