Remote detection of emergency emissions and gas leaks

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

There are many reasons for natural gas (methane) leaks in gas distribution networks. One of the most important tasks of gas distribution organizations is to promptly identify and eliminate gas leaks before they cause emergency situations. Eliminating gas leaks as soon as possible will minimize the negative impact on the environment. This paper proposes a new original method for detecting emergency gas emissions into the atmosphere and leaks on gas pipeline systems. The technique involves the simultaneous use of both experimental and calculated data to determine the concentration and characteristic sizes of gas emissions. The methodology was tested at laboratory conditions using a propane cylinder and a gas burner. The Scorpion monophotonic sensor was used as recording equipment. As a result of processing experimental data and mathematical modeling using computational fluid dynamics methods, the dependence of propane concentration on the distance to the burner was constructed and the characteristic dimensions of the gas cloud were determined.There are many reasons for natural gas (methane) leaks in gas distribution networks. One of the most important tasks of gas distribution organizations is to promptly identify and eliminate gas leaks before they cause emergency situations. Eliminating gas leaks as soon as possible will minimize the negative impact on the environment. This paper proposes a new original method for detecting emergency gas emissions into the atmosphere and leaks on gas pipeline systems. The technique involves the simultaneous use of both experimental and calculated data to determine the concentration and characteristic sizes of gas emissions. The methodology was tested at laboratory conditions using a propane cylinder and a gas burner. The Scorpion monophotonic sensor was used as recording equipment. As a result of processing experimental data and mathematical modeling using computational fluid dynamics methods, the dependence of propane concentration on the distance to the burner was constructed and the characteristic dimensions of the gas cloud were determined.

Full Text

Restricted Access

About the authors

I. D. Rodionov

Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: gomorevma@gmail.com
Russian Federation, Moscow

M. A. Gomorev

AO Scientific and Technical Center Reagent

Author for correspondence.
Email: gomorevma@gmail.com
Russian Federation, Moscow

I. P. Rodionova

Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: gomorevma@gmail.com
Russian Federation, Moscow

A. I. Rodionov

Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences; AO Scientific and Technical Center Reagent

Email: gomorevma@gmail.com
Russian Federation, Moscow; Moscow

V. L. Shapovalov

Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: gomorevma@gmail.com
Russian Federation, Moscow

D. V. Shestakov

Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: gomorevma@gmail.com
Russian Federation, Moscow

M. G. Golubkov

Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: gomorevma@gmail.com
Russian Federation, Moscow

References

  1. H.M.A. van der Werff, M.F. Noomen, M. van der Meijde, J.F. Kooistra, and F.D. van der Meer, in: New Developments and Challenges in Remote Sensing, Ed by Z. Bochenek (Millpress, Rotterdam, 2007), p. 707.
  2. S. Sabbah, P. Rusch,. J. H. Gerhard, et al., in: Proceedings of Electro-Optical Remote Sensing, Photonic Technologies, and Applications V, Ed by G.W. Kamerman, O. Steinvall, G.J. Bishop, J.D. Gonglewski, K.L. Lewis, R.C. Hollins, and T.J. Merlet, Proc. SPIE 8186, 81860S (2011). https://doi.org/10.1117/12.899687
  3. P. Ma, T.G. Mondal, Z. Shi, et al., Environ. Sci. Technol. 58, 12018 (2024). https://doi.org/10.1021/acs.est.4c03345
  4. M.A. Gagnon, P. Tremblay, S. Savary, et al., in: Proceedings of Advanced Environmental, Chemical, and Biological Sensing Technologies XI, Ed by T. Vo-Dinh, R. A. Lieberman, and G. G. Gauglitz, Proc. SPIE 9106, 91060C (2014). https://doi.org/10.1117/12.2050588
  5. D.M. Tratt, K.N. Buckland, E.R. Keim, and P. D. Johnson, in: Proceedings of 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (IEEE, Los Angeles, 2016), p. 1. https://doi.org/10.1109/WHISPERS.2016.8071711
  6. W. Xavier, N. Labat, G. Audouin, et al., in: Proceedings of Abu Dhabi International Petroleum Exhibition & Conference (16ADIP) (SPE, Abu Dhabi, 2016), SPE-183527-MS; https://doi.org/10.2118/183527-MS
  7. R.P.M. Scafutto and C. R. De Souza Filho, Remote Sens. 10, 1237 (2018). https://doi.org/10.3390/rs10081237
  8. C. Xiao, B. Fu, H. Shui, Z. Guo, and J. Zhu, Remote Sens. 12, 537 (2020). https://doi.org/10.3390/rs12030537
  9. N. M. Rubtsov, A. N. Vinogradov, A. P. Kalinin, et al., Russ. J. Phys. Chem. B 13, 305 (2019). https://doi.org/10.1134/S1990793119020210
  10. A.I. Rodionov, I.D. Rodionov, I.P. Rodionova, et al., Russ. J. Phys. Chem. B 15, 904 (2021). https://doi.org/10.1134/S1990793121050201
  11. N. Rubtsov, M. Alymov, A. Kalinin, et al., Remote studies of combustion and explosion processes based on optoelectronic methods (AUS PUBLISHERS, Melbourne, 2022). https://doi.org/10.26526/monography_62876066a124d8.04785158
  12. P.J. Roache, Computational fluid dynamics (Mir, Moscow, 1980).
  13. ANSYS Fluent Theory Guide. Canonsburg: SAS Inc., 2013.
  14. Star-CCM+. https://star-ccm.com/
  15. Introduction to COMSOL Multiphysics. https://www.comsol.com/
  16. Flow Vision. https://flowvisioncfd.com/en/
  17. SALOME version 9.12.0. https://www.salome-platform.org/?p=2657
  18. J. Schwarz, K. Axelsson, D. Anheuer, et al., SoftwareX 22, 101378 (2023). https://doi.org/10.1016/j.softx.2023.101378
  19. I.A. Belov and S. A. Isaev, Simulation of turbulent flows (Publishing House of Baltic State Technical University, St. Petersburg, 2001).
  20. D.C. Wilcox, Turbulence modelling for CFD (Birmingham Press, San Diego, 2006).
  21. K.N. Volkov and V.N. Emelyanov, Large eddy simulation in calculations of turbulent flows (Fizmatlit, Moscow, 2008).
  22. J.W. Leachman, R.T. Jacobsen, E. W. Lemmon, and S.G. Penoncello, Thermodynamic properties of cryogenic fluids, Ed by S. W. Van Sciver and S. Jeong (Springer International Publishing, Cham, 2017). https://doi.org/10.1007/978-3-319-57835-4
  23. R. Span, Multiparameter equations of state (Springer Berlin Heidelberg, Berlin, Heidelberg, 2000). https://doi.org/10.1007/978-3-662-04092-8
  24. E.W. Lemmon, I.H. Bell, M.L. Huber, and M. O. McLinden, NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP), Version 10.0 (National Institute of Standards and Technology, Gaithersburg, 2018).
  25. M. A. Goldshtik and V. N. Stern, Hydrodynamic Stability and Turbulence (Nauka, Novosibirsk, 1977).
  26. H. Schlichting and K. Gersten, Boundary-layer theory (Springer Berlin Heidelberg, Berlin, Heidelberg, 2017). https://doi.org/10.1007/978-3-662-52919-5
  27. Rostechnadzor Order No. 415 dated 11/28/2022 “On Approval of the Safety Manual “Methodology for assessing the consequences of accidents at explosive and flammable chemical industries”. 2022. https://set.rk.gov.ru/uploads/txteditor/set/attachments/d4/1d/8c/d98f00b204e9800998ecf8427e/phpIW8esL_1.pdf
  28. Safety manual “Methodology for modeling the spread of accidental emissions of hazardous substances”. (Moscow: 2015). https://meganorm.ru/Data2/1/ 4293763/4293763998.pdf
  29. FLACS-CFD Release 24.1. https://www.gexcon.com/software/flacs-cfd/
  30. A.S. Monin and A. M. Obukhov, Trudy geofiz. Inst. Akad. Nauk U.R.S.S. 24, 163 (1954).
  31. D. Tiab and E. C. Donaldson, Petrophysics: theory and practice of measuring of reservoir rock and fluid transport properties (Gulf Professional Publishing, Oxford, 2004).
  32. K.S. Basniev, I.N. Kochina, and V.M. Maksimov, Underground hydromechanics (Nedra, Moscow, 1993).
  33. K. S. Basniev, N. M. Dmitriev, and G. D. Rozenberg, Oil and gas hydromechanics (Institute for Computer Research, Moscow, Izhevsk, 2005).
  34. D. Yuhu, G. Huilin, Z. Jingen, and F. Yaorong, Chem. Eng. J. 92, 237 (2003). https://doi.org/10.1016/S1385-8947(02)00259-0
  35. A. A. Belov, A. P. Kalinin, I. V. Krysyuk, et al., Sensors and Systems 1, 47 (2010).
  36. A. P. Kalinin, V. V. Egorov, A. I. Rodionov, et al., Russ. J. Phys. Chem. B 17, 796 (2023). https://doi.org/10.1134/S1990793123040085
  37. A. I. Rodionov, I. D. Rodionov, I. P. Rodionova, et al., Russ. J. Phys. Chem. B 17, 1246 (2023). https://doi.org/10.1134/S1990793123050275
  38. P.V. Kozlov, I.E. Zabelinsky, N.G. Bykova, et al., Russ. J. Phys. Chem. B 16, 883 (2022). https://doi.org/10.1134/S1990793122050049
  39. Il.S. Golyak, D.R. Anfimov, I.B. Vintaykin, et al., Russ. J. Phys. Chem. B 17, 320 (2023). https://doi.org/10.1134/S1990793123020264
  40. A.N. Morozov, S.E. Tabalin, D.R. Anfimov, et al., Russ. J. Phys. Chem. B 18, 763 (2024). https://doi.org/10.1134/S1990793124700234

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Photograph of the experimental setup.

Download (290KB)
Indexing metadata
3. Fig. 2. Photograph of the monophoton UV-C sensor “Scorpion”.

Download (175KB)
Indexing metadata
4. Fig. 3. Scalar scene of propane concentration. The value h is the volume fraction of propane in the “propane+air” mixture.

Download (48KB)
Indexing metadata
5. Fig. 4. Dependence of the relative concentration of propane n/n0 on the distance x to the branch pipe. Here n0 is the concentration of propane in the branch pipe.

Download (29KB)
Indexing metadata

Copyright (c) 2024 Russian Academy of Sciences