Sensor-based gas tracking

Data acquisition, linearization, and successive source tracking and separation


The propagation of information encoded in the fluctuations of chemical species of natural gas transported within natural gas distribution grids allows for a new gas tracking method. In contrast to state of the art gas and calorific value tracking based on numerical gas-flow simulations, the presented method is solely based on signal processing techniques. Due to a wider diversification of natural gas sources, gas and therefore calorific value tracking is of great interest for gas grid operators to provide fair invoicing of gas customers. The calorific value of substitute natural gas, e.g. produced by biogas or power-to-gas plants, injected concurrently into natural gas grids, typically deviates significantly over time in comparison to the prevailing natural gas in the grid. Sampling gas features like the chemical species contained in injected gases, or the standard density, by means of calibrated sensors, e.g. by process gas chromatography or infrared sensors, provides time-dependent signals that can be taken for calorific value extrapolation from source nodes to downstream nodes. To that end, we provide a general estimation of gas feature related transmission characteristics of natural gas distribution grids based on a straightforward one-dimensional diffusion model. Additionally, we present an accurate technique to estimate transit times and source fractions on gas grid exit nodes. We show with a field experiment, that gas tracking based on gas sensor signals is feasible and that our new method improves on state of the art gas tracking software based on computational fluid dynamics, already in use for gas customer invoicing.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    German Biogas Association. Biogas market data in Germany 2016/2017 (2017)

  2. 2.

    Sterner, M., Schmidt, J.: Bioenergy and renewable power methane in integrated 100% renewable energy systems. In: Limiting Global Warming by Transforming Energy Systems, pp. 104–126. Kassel University Press (2009)

  3. 3.

    Deutsche Energie-Agentur: Integration erneuerbaren stroms in das erdgasnetz: Power to Gas–eine innovative systemlösung für die Energieversorgung von morgen entwickeln. Deutsche Energie-Agentur (2012)

  4. 4.

    Garmsiri, S., Rosen, M.A., Smith, G.R.: Integration of wind energy, hydrogen and natural gas pipeline systems to meet community and transportation energy needs: a parametric study. The 3rd World Sustainability Forum 2013, 10 (2013)

  5. 5.

    Varone, Alberto, Ferrari, Michele: Power to liquid and power to gas: an option for the german energiewende. Renew. Sustain. Energy Rev. 45, 207–218 (2015).

    Article  Google Scholar 

  6. 6.

    Nitschke-Kowsky, P., Schenk, J., Schley, P., Altfeld, K.: Gasbeschaffenheiten in deutschland. Jyväskylä studies in biological and environmental science, 202 (2009)

  7. 7.

    Schenk, Joachim, Schley, Peter, Hielscher, Andreas: A new method for gas quality tracking in distribution grids. Gas Energy 3, 34–42 (2012)

    Google Scholar 

  8. 8.

    Rickelt, Stefan, Schley, Peter, Hielscher, Andreas, Fiebig, Christian, Schenk, Joachim: Gas quality tracking with smartsim energy billing in gas grids with multipoint injection. Gas Energy 2, 24–32 (2016)

    Google Scholar 

  9. 9.

    Fiebig, C., Hielscher, A., Span, R., Gulin, A., Rickelt, S., Schley, P.: Gas quality tracking in distribution grids with smartsim application in complex and meshed grids. In: International Gas Union Research Conference-IGRC, 09 (2014)

  10. 10.

    Hielscher, A., Fiebig, C., Span, R., Schley, P., Schenk, J.: Gas quality tracking in distribution grids with smartsim a new kernel for flow calculations. In: International Gas Union Research Conference-IGRC, 09 (2014)

  11. 11.

    Hellwig, M.: Entwicklung und Anwendung parametrisierter Standard-Lastprofile. Dissertation, Technische Universität München, München (2003)

  12. 12.

    Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23(1), 67–72 (1975)

    Article  Google Scholar 

  13. 13.

    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)

    Article  Google Scholar 

  14. 14.

    Al-Naymat, G., Chawla, S., Taheri, J.: Sparsedtw: a novel approach to speed up dynamic time warping. CoRR. arXiv:1201.2969 (2012)

  15. 15.

    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)

    Article  Google Scholar 

  16. 16.

    Müller, M., Mattes, H., Kurth, F.: An efficient multiscale approach to audio synchronization. In: Proceedings of the 6th International Conference on Music Information Retrieval, pp. 192–197 (2006)

  17. 17.

    Alexiou, A., Schenk, J.: Species related gas tracking in distribution grids. Eusipco 26, 09 (2018)

    Google Scholar 

  18. 18.

    Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theor. 13(2), 260–269 (2006)

    Article  Google Scholar 

  19. 19.

    Forney, G.D.: The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)

    MathSciNet  Article  Google Scholar 

  20. 20.

    Tipler, P.A., Mosca, G.: Physics for Scientists and Engineers. W. H. Freeman, New York, ISBN 9780716783398 (2004)

  21. 21.

    Calvert, J.G.: Glossary of atmospheric chemistry terms (Recommendations 1990). Pure. Appl. Chem. 62(11), 2167–2219 (1990).

    Article  Google Scholar 

  22. 22.

    Chapman, S., Cowling, T.G.: The Mathematical Theory of Non-Uniform Gases. Cambridge University Press, Cambridge (1970)

    Google Scholar 

  23. 23.

    Evans, L.C.: Partial Differential Equations. American Mathematical Society, Providence (1998)

    Google Scholar 

  24. 24.

    Juang, B.: On the hidden markov model and dynamic time warping for speech recognition a unified view. AT T Bell Lab Tech J 63(7), 1213–1243 (1984)

    MathSciNet  Article  Google Scholar 

Download references


This work is supported by Grant Ka. 15 49 TG 78 StMWi, Bavaria.

Author information



Corresponding author

Correspondence to Athanassios Alexiou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alexiou, A., Schenk, J. Sensor-based gas tracking. Energy Syst 11, 581–606 (2020).

Download citation


  • Gas tracking
  • Calorific value tracking
  • Biogas
  • Natural gas
  • Time-variant source separation
  • Dynamic time warping
  • Viterbi algorithm