Intelligent system for control of water distribution networks

Abstract

The objective of this research study was the development of an intelligent system based on artificial neural networks for water distribution networks that operate with parallel pumps. The purpose of the system is to automate the process and to define the operating state of the electric motors (on, off or with partial rotation speed). The intelligent system developed is generic, which allows the application of its control structure in similar processes, and it was applied in an experimental setup that simulates a real water supply system. The performance of the network was tested experimentally under different operating conditions, including in the presence of disturbances. The settling time was, in all experiments, less than 30 seconds, the tests did not show overshoot and the maximum error was 2.9%. Results showed excellent performance in terms of pressure regulation, and it is hoped that the controller can be successfully implemented in real water distribution systems, in order to reduce water and electricity consumption, decrease maintenance costs and increase the reliability of operating procedures.

Fuzzy Pressure Control: A Novel Approach to Optimizing Energy Efficiency in Series-Parallel Pumping Systems

“…In [8], a control system with a multilayer feedforward architecture based on an artificial neural network (ANN) was proposed for the operation of a water supply system with parallel pumps and electric motors driven by a frequency converter. In all experiments, the settling time was less than 30 s, and the maximum relative steady-state error was 2.9 percent.…”

Adaptive Pressure Control System Based on the Maximum Correntropy Criterion

“…In [ 5 ], an intelligent control system based on an Artificial Neural Network (ANN) with multilayer feedforward architecture was proposed for the operation of a water supply system with parallel pumps and with electric motors driven by a frequency converter. The settling time in all experiments was less than 30 Therefore, and the maximum relative steady-state error was 2.9%.…”

Equipamento de baixo custo para monitoramento de pressões em sistemas de abastecimento de água

“…Os fatores de altos custos de instalação e manutenção dos sistemas capazes de coletar e armazenar os dados ambientais são comumente apresentados como limitantes no emprego desses dispositivos, principalmente nos países em desenvolvimento (BARROS FILHO et al, 2018). Nesse sentido, Sadler, Ames e Khattar (2016) apresentaram um encadeamento lógico para o desenvolvimento de equipamentos dataloggers para aquisição e compartilhamento de dados ambientais baseados no uso de padrões de softwares de fontes livres e módulos eletrônicos de baixo custo.…”