Karafan Journal

Karafan Journal

An applied study aimed at reducing gas leakage from the low-pressure section of natural gas regulators using numerical simulation

Document Type : Original Article

Authors
1 Department of Mechanical Engineering, National University of Skill (NUS), Tehran, Iran
2 Department of Mechanical Engineering, Skill national University (NUS), Tehran, Iran
3 Mechanical Engineer, Amin Gas Pooya Co. Babol. Iran
4 Mechanical Engineer, Mazandaran Gas Company, Sari, Iran
Abstract
Natural gas, as a national asset and a vital energy source, requires preservation and prevention of waste. One of the main points of gas leakage is the low-pressure section of city gas regulators, where the accumulation of suspended particles in the diaphragm and malfunctioning lead to energy waste and economic damage. This study, in collaboration with the Gas Company of Mazandaran province, aims to reduce gas leakage through numerical simulation, design, and construction of optimized regulator prototypes. In this research, the effect of using flow-directing obstacles on reducing particle accumulation and improving flow distribution in the low-pressure section has been examined. Simulation results showed that the curved barrier model with an inlet-facing curvature could optimize the fluid flow path and reduce gas leakage by up to 25%. Furthermore, experimental results demonstrated that this model effectively prevents particle accumulation in the diaphragm seat and ensures stable regulator performance. By providing an innovative solution, this research fills a scientific gap in reducing gas leakage caused by suspended particles and makes a significant contribution to energy consumption optimization.
Keywords
Subjects

[1] R Behruzifar, M., & Bayati, Sh. (2012). The world market of natural gas or natural gas markets of the world. Energy Economy Studies, 9(33), 151–168. (In Persian). https://sid.ir/paper/466980/fa
[2] Khatami, S. M. (2020). A review of new methods of estimating methane emissions in the natural gas distribution network. Iranian Gas Engineering Journal, 1(7), 25–36. (In Persian). https://civilica.com/doc/1628428/
[4] Ebrahimi, E., Kazemzadeh, M., & Ficarella, A. (2024). Leak identification and quantification in gas network using operational data and deep learning framework. Sustainable Energy, Grids and Networks, 39, Article 101496. https://doi.org/10.1016/j.segan.2024.101496
[5] Lindemann, B., Maschler, B., Sahlab, N., & Weyrich, M. (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry, 131, Article 103498. https://doi.org/10.1016/j.compind.2021.103498
[6] Esen, H., Ozgen, F., Esen, M., & Sengur, A. (2009). Artificial neural network and wavelet neural network approaches for modeling of a solar air heater. Expert Systems with Applications, 36(8), 11240–11248. https://doi.org/10.1016/j.eswa.2009.02.073
[7] Zhang, X., He, S., Stojanovic, V., Luan, X., & Liu, F. (2021). Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems. Science China Information Sciences, 64(5), Article 152206. https://doi.org/10.1007/s11432-020-2913-x
[8] Song, X., Sun, P., Song, S., & Stojanovic, V. (2022). Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance. Journal of the Franklin Institute, 359(9), 4138–4159. https://doi.org/10.1016/j.jfranklin.2022.04.003
[9] Zheng, J., Wang, C., Liang, Y., Liao, Q., Li, Z., & Wang, B. (2022). Deep pipe: A deep-learning method for anomaly detection of multi-product pipelines. Energy, 259, Article 125025. https://doi.org/10.1016/j.energy.2022.125025
[10] Korlapati, N. V. S., Khan, F., Noor, Q., Mirza, S., & Vaddiraju, S. (2022). Review and analysis of pipeline leak detection methods. Journal of Pipeline Science and Engineering, 2(4), Article 100074. https://doi.org/10.1016/j.jpse.2022.100074
[11] Spandonidis, C., Theodoropoulos, P., Giannopoulos, F., Galiatsatos, N., & Petsa, A. (2022). Evaluation of deep learning approaches for oil and gas pipeline leak detection using wireless sensor networks. Engineering Applications of Artificial Intelligence, 113, Article 104890. https://doi.org/10.1016/j.engappai.2022.104890
[12] Zhang, X., Shi, J., Huang, X., Xiao, F., Yang, M., Huang, J., Yin, X., Usmani, A. S., & Chen, G. (2023). Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data. Expert Systems with Applications, 231, Article 120542. https://doi.org/10.1016/j.eswa.2023.120542.
[13] Priyanka, E., Thangavel, S., Gao, X.-Z., & Sivakumar, N. (2022). Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques. Journal of Industrial Information Integration, 26, Article 100272.  https://doi.org/10.1016/j.jii.2021.100272
[14] Liang, J., Ma, L., Liang, S., Zhang, H., Zuo, Z., & Dai, J. (2023). Data-driven digital twin method for leak detection in natural gas pipelines. Computers and Electrical Engineering, 110, Article 108833. https://doi.org/10.1016/j.compeleceng.2023.108833
[15] Babaii, F. (2016). Statistical and technical analyzes of the observed leaks. Gas Waste Seminar, Golestan Gas Company. (In Persian). https://www.nigc-golestan.ir/orgpage.aspx?id=1
[16] Abbaspoor, N., & Abdi, M. (2023). The impact of electricity, gas, and water price increases on the price index of the tourism sector. Karafan Quarterly Scientific Journal of Technical and Vocational University, 20, 759–776. (In Persian). doi:10.48301/KSSA.2023.380133.2404.
[17] Seyed Kolbadi, S. M. (2021). The effect of flexible connection on reducing the damage of buried gas pipes due to ground displacement. Karafan Quarterly Scientific Journal of Technical and Vocational University, 18(1), 99–120. (In Persian). doi:10.48301/KSSA.2021.131039.
[18] Seyed Kolbadi, S. M. (2021). The effect of flexible connection on reducing the damage of buried gas pipes due to ground displacement. Karafan Quarterly Scientific Journal of Technical and Vocational University, 18(1), 99–120. (In Persian). doi:10.48301/KSSA.2021.131039.
[19] Seyed Kolbadi, S. M., Hassani, N., & Safi, M. (2024). Development application of wave connection to prevent local buckling on buried gas pipelines due to strike-slip faulting. Karafan Quarterly Scientific Journal of Technical and Vocational University.21(1),627-649.(In Persian). doi:10.48301/kssa.2023.390046.2486
[20] Ghazi Zade, A., Darvishi, M. M., & Asgari, A. (2016). Research and analysis of the effects of noise caused by the regulator and its effect on the ultrasonic meter at Kermanshah LAB Petrochemical Gas Station. Master’s thesis, Payame Noor University, Tehran, Iran. (In Persian). https://elmnet.ir/doc/11185418-41664
[21] Mirsane, E., & Karimi, H. (2016). Measuring the sensitivity of the pressure reducer regulator to physical parameters and observing the regulator’s performance when disturbance occurs. International Conference of the Iranian Aerospace Association, Tehran, Iran. (In Persian). https://www.sid.ir/paper/882780/fa
Volume 22, Issue 3
Technical and Engineering
Autumn 2025
Pages 65-86

  • Receive Date 10 September 2024
  • Revise Date 18 January 2025
  • Accept Date 22 April 2025