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<p>Accurate leak detection in long-distance fluid transmission pipelines is essential for minimizing environmental and economic risks. Traditional negative pressure wave (NPW) techniques are often limited by sensitivity to wave speed estimation errors, sensor noise, and changing flow conditions. This study introduces a hybrid approach in which a genetic algorithm (GA) dynamically optimizes NPW parameters, including wave speed, fluid velocity, and leak position, based on real-time sensor data. Using results from ten field leak tests on a 175&#xa0;km crude oil pipeline, the GA-enhanced NPW method reduced average localization error from 11% (NPW) and 18% (HGI) to 5%, corresponding to an 8.75&#xa0;km deviation over the full pipeline length—well within operational thresholds for segment isolation in large-scale networks. Detection time decreased from 30 to 40&#xa0;s to approximately 10&#xa0;s, enabling operators to respond more rapidly to leak events. The system’s modular architecture supports seamless integration with Supervisory Control and Data Acquisition (SCADA) platforms, providing automated alerts and visual diagnostics for immediate decision-making. By combining multi-parameter optimization with field-validated robustness, the proposed GA-NPW framework offers a practical and scalable solution for real-time leak detection in critical pipeline infrastructure.</p>

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Genetic algorithm optimization of negative pressure wave method for robust real time leak detection in long distance pipelines

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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  • Ali Sharifi,
  • Seyyed Faramarz Ranjbar,
  • Seyed Amirreza Mousavi Alamdardehi,
  • Naser Aslani,
  • Reza Zarezadeh,
  • Hamid Majidi,
  • Fatemeh Asadi

摘要

Accurate leak detection in long-distance fluid transmission pipelines is essential for minimizing environmental and economic risks. Traditional negative pressure wave (NPW) techniques are often limited by sensitivity to wave speed estimation errors, sensor noise, and changing flow conditions. This study introduces a hybrid approach in which a genetic algorithm (GA) dynamically optimizes NPW parameters, including wave speed, fluid velocity, and leak position, based on real-time sensor data. Using results from ten field leak tests on a 175 km crude oil pipeline, the GA-enhanced NPW method reduced average localization error from 11% (NPW) and 18% (HGI) to 5%, corresponding to an 8.75 km deviation over the full pipeline length—well within operational thresholds for segment isolation in large-scale networks. Detection time decreased from 30 to 40 s to approximately 10 s, enabling operators to respond more rapidly to leak events. The system’s modular architecture supports seamless integration with Supervisory Control and Data Acquisition (SCADA) platforms, providing automated alerts and visual diagnostics for immediate decision-making. By combining multi-parameter optimization with field-validated robustness, the proposed GA-NPW framework offers a practical and scalable solution for real-time leak detection in critical pipeline infrastructure.