The risk of leakage poses a grave threat to natural gas pipeline safety. The high compressibility of gases combined with unsteady boundary conditions makes detecting leaks in pipelines a challenging endeavor. To date, in the literature, only a limited number of studies have focused on leak detection and diagnostics in gas mixture pipelines. The present study provides a system for detecting, locating, and estimating the size of small gas leaks from a compressible and dynamic natural gas flow in pipelines with improved accuracy. As a case study, a long natural gas pipeline of 80 km is simulated with leak sizes of 0%, 2%, and 5%. The safety system is developed using mass flow rate, temperature, and pressure measurements. Six classes for faulty cases and one class for no fault case were considered for the study. A shallow neural network classifier (SNNC) is trained to identify a specific fault class. The SNNC is based on a two-layered network with 20 and 7 neurons. An input vector of 15 variables is provided to the system, and the output is one of the seven possible classes. Leakage as low as 2% at various locations are correctly diagnosed with more than 99% correct classification rate.

中文翻译：

---

基于神经网络的燃气管道安全管理系统

泄漏风险对天然气管道安全构成严重威胁。气体的高压缩性与不稳定的边界条件相结合，使得检测管道泄漏成为一项具有挑战性的工作。迄今为止，在文献中，只有少数研究关注气体混合物管道的泄漏检测和诊断。本研究提供了一种系统，用于检测、定位和估计管道中可压缩和动态天然气流中小气体泄漏的大小，并提高了准确性。作为案例研究，模拟了一条 80 公里长的天然气管道，泄漏量分别为 0%、2% 和 5%。安全系统是使用质量流量、温度和压力测量来开发的。研究考虑了六类故障案例和一类无故障案例。训练浅层神经网络分类器 (SNNC) 以识别特定的故障类别。SNNC 基于具有 20 个和 7 个神经元的两层网络。向系统提供 15 个变量的输入向量，输出是七个可能的类之一。不同位置的泄漏低至 2% 被正确诊断，正确分类率超过 99%。