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Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks
Robson Pacífico Guimarães Lima1,2, Juan Moises Mauricio Villanueva3, Heber Pimentel Gomes4
1Technology Center (CT), Postgraduate Program in Mechanical Engineering (PPGEM), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58058-600, PB, Brazil.
Sensors (Basel, Switzerland)
|April 23, 2022
Summary
This study introduces a novel soft sensor to estimate water flow indirectly, reducing the need for expensive physical flow meters. The developed system achieved a maximum error of 10% compared to traditional sensors.
Area of Science:
- Environmental Engineering
- Control Systems Engineering
- Artificial Intelligence
Background:
- Water supply systems are critical infrastructure requiring reliable monitoring.
- Traditional flow meters can be costly and complex to install.
- Indirect measurement offers a potential solution to overcome these limitations.
Purpose of the Study:
- To develop and validate a soft sensor for indirect flow estimation in water supply systems.
- To reduce reliance on physical flow meters through advanced modeling techniques.
- To improve the efficiency and cost-effectiveness of water resource management.
Main Methods:
- Utilized adaptive control for pressure controller design.
- Employed artificial neural networks to build nonlinear system models.
- Integrated system parameters like pressure, engine speed, and valve angle for flow estimation.
Main Results:
- Successfully estimated water flow indirectly, bypassing the need for physical flow meters.
- Demonstrated suppression of physical flow meter acquisition and installation costs.
- Achieved a maximum error of 10% when compared to an electromagnetic flow sensor during experimental validation.
Conclusions:
- The soft sensor approach is a viable and cost-effective alternative for water flow monitoring.
- Artificial intelligence and adaptive control can significantly enhance water supply system management.
- Further research can optimize soft sensor accuracy and applicability in diverse hydraulic systems.
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