Artificial Intelligence-Based Scanner to Locate Flow Anomalies in Water Transmission and Distribution Networks

Abstract The Water Transmission and Distribution sector in Oman is confronted with various challenges, such as flow loss during transportation, negative pressure accumulation, and conflicts in billing consumption in the Water Transmission and Distribution Network(WTDN). To address these and resolve the challenges, a research team, consisting of the authors of this paper, has proposed a wireless flow scanner specifically planned for this purpose. Currently, the proposed cutting-edge wireless AI Water Flow Scanner is undergoing development and finally, be tested in a 540 m total length pilot piping network constructed as 9 parallel pipes with three pipe diameters. The AI Water Flow Scanner will help in locating and quantifying the flow transportation losses, automated pressure balancing, and automated mass flow balancing in the pilot piping network. The proposed AI Water Flow Scanner will receive flow properties like flow pressure, velocity, and discharge through wireless pressure and flow sensors installed in, thirty-six strategic points within the pilot piping network. A deep Learning Algorithm will be developed exclusively to analyze the flow data across the pilot piping network. The analyzed results then be simulated to provide the pressure and velocity distribution across the pilot piping network that will be interpreted to accurately locate the flow transportation losses, imbalance in pressure, and flow across the pilot piping network. Once fully developed and tested, the AI Water Flow Scanner will be expected to offer an efficient and accurate method of locating leaks. In this paper, a network of three parallel pipelines with three different diameters is designed in Pipe Flow Expert, an open-access flow simulation software, analyzed at different flow conditions to understand the flow behavior through the network. The outcome of this simulation is found well aligned with the real-time flaws. A fully developed AI Flow Scanner would help greatly in resolving the current issues in the Water Transmission and Distribution Network of Oman.

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Publication Details

Journal
IOP Conference Series Earth and Environmental Science
Published
2024-10-01
DOI
https://doi.org/10.1088/1755-1315/1401/1/012006
Primary Topic
Water Systems and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence-Based Scanner to Locate Flow Anomalies in Water Transmission and Distribution Networks

Geetha Baskaran, Arthur Davis Nicholas, Lizette Ivy C. Pascual, Devdas Shetty et al.
IOP Conference Series Earth and Environmental Science
Water Systems and Optimization
article

Artificial Intelligence-Based Scanner to Locate Flow Anomalies in Water Transmission and Distribution Networks

Geetha Baskaran, Arthur Davis Nicholas, Lizette Ivy C. Pascual, Devdas Shetty, Ahmed Hamed Al Adwani
article en

Abstract

Abstract The Water Transmission and Distribution sector in Oman is confronted with various challenges, such as flow loss during transportation, negative pressure accumulation, and conflicts in billing consumption in the Water Transmission and Distribution Network(WTDN). To address these and resolve the challenges, a research team, consisting of the authors of this paper, has proposed a wireless flow scanner specifically planned for this purpose. Currently, the proposed cutting-edge wireless AI Water Flow Scanner is undergoing development and finally, be tested in a 540 m total length pilot piping network constructed as 9 parallel pipes with three pipe diameters. The AI Water Flow Scanner will help in locating and quantifying the flow transportation losses, automated pressure balancing, and automated mass flow balancing in the pilot piping network. The proposed AI Water Flow Scanner will receive flow properties like flow pressure, velocity, and discharge through wireless pressure and flow sensors installed in, thirty-six strategic points within the pilot piping network. A deep Learning Algorithm will be developed exclusively to analyze the flow data across the pilot piping network. The analyzed results then be simulated to provide the pressure and velocity distribution across the pilot piping network that will be interpreted to accurately locate the flow transportation losses, imbalance in pressure, and flow across the pilot piping network. Once fully developed and tested, the AI Water Flow Scanner will be expected to offer an efficient and accurate method of locating leaks. In this paper, a network of three parallel pipelines with three different diameters is designed in Pipe Flow Expert, an open-access flow simulation software, analyzed at different flow conditions to understand the flow behavior through the network. The outcome of this simulation is found well aligned with the real-time flaws. A fully developed AI Flow Scanner would help greatly in resolving the current issues in the Water Transmission and Distribution Network of Oman.

IOP Conference Series Earth and Environmental ScienceVol. 1401(1)
College of Applied Sciences, Nizwa (OM)
Openalex Percentile: Top 47%
Water Systems and Optimization
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