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Genetic algorithm optimization of negative pressure wave method for robust real time leak detection in long distance
Ali Sharifi1, Seyyed Faramarz Ranjbar2, Seyed Amirreza Mousavi Alamdardehi3
1Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran. dr.alisharifi1@proton.me.
Scientific Reports
|October 17, 2025
Summary
A new genetic algorithm (GA) approach significantly improves pipeline leak detection by optimizing negative pressure wave (NPW) parameters. This method enhances accuracy and speed for critical infrastructure safety.
Area of Science:
- Pipeline Engineering
- Signal Processing
- Optimization Algorithms
Background:
- Accurate leak detection in long-distance pipelines is crucial for environmental and economic safety.
- Traditional negative pressure wave (NPW) methods face limitations due to wave speed errors, sensor noise, and flow variations.
Purpose of the Study:
- To develop and validate a hybrid approach combining genetic algorithms (GA) with NPW techniques for enhanced pipeline leak detection.
- To improve the accuracy and speed of leak localization in crude oil transmission pipelines.
Main Methods:
- A genetic algorithm (GA) was employed to dynamically optimize NPW parameters (wave speed, fluid velocity, leak position) using real-time sensor data.
- The proposed GA-NPW method was tested using data from ten field leak tests on a 175 km crude oil pipeline.
Main Results:
- The GA-enhanced NPW method reduced average leak localization error from 11% (NPW) and 18% (HGI) to 5%.
- Leak detection time was decreased from 30-40 seconds to approximately 10 seconds.
- The system demonstrated robustness and scalability for real-time leak detection in critical infrastructure.
Conclusions:
- The GA-NPW framework provides a practical and scalable solution for real-time leak detection in long-distance pipelines.
- The optimized method significantly outperforms traditional techniques in accuracy and detection speed.
- Seamless integration with SCADA platforms enables automated alerts and rapid decision-making for pipeline operators.
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