Skip to main content

Design of Low-Cost Sensors for Industrial Processes Energy Consumption Measurement: Application to the Gas Flow Consumed by a Boiler

  • Chapter
Sensing Technology: Current Status and Future Trends IV

Part of the book series: Smart Sensors, Measurement and Instrumentation ((SSMI,volume 12))

  • 1441 Accesses

Abstract

The demand for energy is becoming increasingly important, and who says strong demands for energy says rising CO 2 emissions. Everyone agrees that a great part of the energy consumed by industry and households can be saved. The energy savings can take many forms. In addition to the necessity to build equipments more and more energy efficient, it is also necessary to get a clear view of how the energy is used. This obviously involves the implementation of an energy flow measuring system for long lasting optimization solutions. It is precisely in this context that the project CHIC (Low cost industry utilities monitoring systems for energy savings), funded by the French National Research Agency (ANR), emerged. The objective of this project is to develop and test low-cost non-intrusive sensors to monitor and analyze the energy consumption of major flows used in the manufacturing sector (electricity, gas, compressed air). With such sensors, it should be possible to tool up a factory, equipment by equipment, which is not feasible with intrusive sensors. The ultimate goal is the long term consumption monitoring and the detection of the consumption deviations rather than a precise measurement. The measurement accuracy is fixed to 5%. These developments are based on the recent approaches in system identification and parametric estimation. This project, concretely, involves the design of new low-cost sensors in the following areas: current sensors, voltage, power, and gas flow, relying on the international ISO 50001 standard for Energy Management Systems. The work presented in this chapter focuses on the modeling of the gas flow supplied to a boiler in order to implement a soft sensor. This implementation requires the estimation of a mathematical model that expresses the flow rate from the control signal of the solenoid valve and the gas pressure and temperature measurements. Two types of models are studied: LPV (Linear Parameter Varying) model with pressure and temperature as scheduling variables and a non-parametric model based on Gaussian processes.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arkoun, O.: Estimation non paramétrique pour les modéles autorégressifs. Thesis, University of Rouen, France (2009)

    Google Scholar 

  2. Bect, J., Vasquez, E.: A small (Matlab/GNU Octave) Toolbox for Kriging, Supelec (2011), http://sourceforge.net/projects/kriging/

  3. Bogaerts, P., Vande Wouwer, A.: Software sensors for bioprocesses. ISA Transactions 42, 547–558 (2003)

    Article  Google Scholar 

  4. Bourkeb, M., Ondel, O., Joubert, C., Morel, L., Scorretti, R.: Méthodes numériques pour la mesure de courant dans un systéme polyphasé, Numélec 2012. In: European Conference on Numerical Methods and Electromagnetism, Marseille, France, July 3-5 (2012)

    Google Scholar 

  5. Cecil, D., Kozlowska, M.: Software sensors are a real alternative to true sensors. Environmental Modelling & Software 25, 622–625 (2010)

    Article  Google Scholar 

  6. Chéruy, A.: Software sensors in bioprocess engineering. Journal of Biotechnology 52, 193–199 (1997)

    Article  Google Scholar 

  7. Dochain, D.: State and parameter estimation in chemical and biochemical processes: a tutorial. J. Proc. Control 13, 801–818 (2003)

    Article  MathSciNet  Google Scholar 

  8. Dos Santos, P.L., Perdicoúlis, T.A., Novara, C., Ramos, J., Rivera, D.: Linear Parameter-Varying System Identification: new developments and trends Advanced Series in Electrical and Computer Engineering. World Scientific (2011)

    Google Scholar 

  9. Etien, E.: Modeling and simulation of soft sensor design for real-time speed estimation, measurement and control of induction motor. ISA Transactions 52, 358–364 (2013)

    Article  Google Scholar 

  10. Gever, M.: Toward a joint design of identification and control? H.L. Trentelman and J.C. Willems Birkhäuser, Boston (1993)

    Google Scholar 

  11. Grau, A., et al.: Low cost power and flow rates measurements for manufacturing plants. In: 16th International Congress of Metrology, Paris, France (October 2013)

    Google Scholar 

  12. Grospeaud, O.: Contribution à l’identification en boucle fermée par erreur de sortie. Thesis, University of Poitiers, Poitiers, France (2000)

    Google Scholar 

  13. Hadid, B., Etien, E., Ouvrard, R., Poinot, T., Le Brusquet, L., Grau, A., Schmitt, G.: Soft Sensor Design for Power Measurement and Diagnosis in Electrical Furnace: a Parametric Estimation Approach. In: 39th Annual Conference of the IEEE Industrial Electronics Society IECON, Vienna, Austria (November 2013)

    Google Scholar 

  14. Hadid, B., Ouvrard, R., Le Brusquet, L., Etien, E., Poinot, T., Sicard, F.: Modeling for gas flow measurement consumed by a boiler. In: Towards a Low-cost Sensor for Energy Efficiency, Seventh International Conference on Sensing Technology ICST. Massey University, Wellington (2013)

    Google Scholar 

  15. James, S.C., Legge, R.L., Budman, H.: On-line estimation in bioreactors: a review. Rev. Chem. Eng. 16(4), 311–340 (2000)

    Article  Google Scholar 

  16. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2(12), pp. 1137–1143 (1995)

    Google Scholar 

  17. Le Mouel, A., et al.: Fostering Energy Efficiency in manufacturing plants through economical breakthroughs in power and flow rate measurement. In: ECEEE Summer Study on Industry, Arnhem, Germany (2012)

    Google Scholar 

  18. Ljung, L.: System identification. Theory for the user, 2nd edn. (1999)

    Google Scholar 

  19. Queinnec, I., Spérandio, M.: Simultaneous estimation of nitrification/denitrification kinetics and influent nitrogen load using orp and do dynamics. ECC, Cambridge, UK (2003)

    Google Scholar 

  20. Poinot, T.: Contribution à l’identification des systèmes par la méthode de surparamétrisation en traitement des eaux. Thesis, Université de Poitiers, France (1996)

    Google Scholar 

  21. Rasmussen, C.E., Williams, K.I.: Gaussian Processes for Machine Learning. The MIT Press (2006)

    Google Scholar 

  22. Sotomayor, O.A.Z., Won Park, S., Garcia, C.: Software sensor for on-line estimation of the microbial activity in activated sludge systems. ISA Transactions 41, 127–143 (2002)

    Article  Google Scholar 

  23. Tóth, R.: Modeling and Identification of Linear Parameter-Varying Systems. LNCIS, vol. 403. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  24. Van Donkelaar, E.T., Van den Hof, P.M.J.: Analysis of closed-loop identification with a tailor-made parametrization. In: European Control Conference, Brussels, Belgium, vol. 4 (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Hadid .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hadid, B. et al. (2015). Design of Low-Cost Sensors for Industrial Processes Energy Consumption Measurement: Application to the Gas Flow Consumed by a Boiler. In: Mason, A., Mukhopadhyay, S., Jayasundera, K. (eds) Sensing Technology: Current Status and Future Trends IV. Smart Sensors, Measurement and Instrumentation, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-12898-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12898-6_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12897-9

  • Online ISBN: 978-3-319-12898-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics