ArticleSensors (Basel, Switzerland)2022
Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Smart Water Management: An Energetically Autonomous IoT-Based Application for Pressure and Flow Monitoring in Water Distribution Systems.Sensors (Basel, Switzerland) · 2025Article
- Machine learning enhanced grey box soft sensor for melt viscosity prediction in polymer extrusion processes.Scientific reports · 2025Article
- An Algorithm for Soft Sensor Development for a Class of Processes with Distinct Operating Conditions.Sensors (Basel, Switzerland) · 2024Article
- Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements.Sensors (Basel, Switzerland) · 2022Article
- Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs and/or complex installation. The indirect measurement of a quantity can be used to obtain a desired variable, dispensing with the use of a specific sensor in the plant. Among the contributions of this technique is the design of the pressure controller using the adaptive control, as well as the use of an artificial neural network for the construction of nonlinear models using inherent system parameters such as pressure, engine rotation frequency and control valve angle, with the purpose of estimating the flow. Among the various contributions of the research, we can highlight the suppression in the acquisition of physical flow meters, the elimination of physical installation and others. The validation was carried out through tests in an experimental bench located in the Laboratory of Energy and Hydraulic Efficiency in Sanitation of the Federal University of Paraiba. The results of the soft sensor were compared with those of an electromagnetic flux sensor, obtaining a maximum error of 10%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.