10.1007/s40095-014-0121-5

Computational Intelligence techniques for maximum energy efficiency of an internal combustion engine and a steam turbine of a cogeneration process

HTML PDF
  • Sandra Seijo Fernández*1,
  • I. del Campo1,
  • J. Echanobe1,
  • J. García-Sedano2,
  • E. Suso2,
  • E. Arbizu2,
  1. Science and Technology Faculty, University of Basque Country UPV/EHU, Leioa-Bizkaia, 48940, ES
  2. OPTIMITIVE S.L., Miñano, Alava, 01510, ES
Cover Image

Published in Issue 2014-05-22

How to Cite

Seijo Fernández, S., del Campo, I., Echanobe, J., García-Sedano, J., Suso, E., & Arbizu, E. (2014). Computational Intelligence techniques for maximum energy efficiency of an internal combustion engine and a steam turbine of a cogeneration process. International Journal of Energy and Environmental Engineering, 5(2-3 (July 2014). https://doi.org/10.1007/s40095-014-0121-5
Crossref
Scopus
Google Scholar
Europe PMC

HTML views: 27

PDF views: 114

Abstract

Abstract This paper discusses the development of a model of a real cogeneration plant based on Computational Intelligence (CI) algorithms. In particular, two CI strategies are used: one based on artificial neural network and the other one based on a neuro-fuzzy system. Both systems are trained with a data collection from the cogeneration plant. Data mining techniques are applied to remove erroneous and redundant data, and also to obtain information about the variables and its behaviour. This task allows to select only the relevant information and is also a way to decrease the complexity of the model. In this first approach on the work, two separate subsystems of the cogeneration process are considered: an engine and a steam turbine. The obtained models are used to analyze the role of each involved variable and to derive a set of recommendations (i.e., changes in some of the input variables) to optimize the performance of the system. The recommendations applied to the models improve the behaviour of the plant providing higher energy production with a lower cost.

Keywords

  • Computational Intelligence,
  • Neuro-fuzzy,
  • Neural network,
  • Cogeneration plant,
  • Efficiency,
  • Optimization,
  • Modelization
HTML PDF

References

  1. The European Association for the Promotion of Cogeneration.
  2. http://www.cogeneurope.eu/
  3. . Accessed 10 Jul 2013
  4. Villares Martín, M.: Cogeneration. FC Editorial, 13 (2003)
  5. Wakui and Yokoyama (2014) Optimal structural design of residential cogeneration systems in consideration of their operating restrictions (pp. 719-733) https://doi.org/10.1016/j.energy.2013.10.002
  6. Skolnik, P., Hubka, L., Modrlak, O., Nahlovsky, T.: Cogeneration units simulation models library. In: IEEE 2013 International Conference on Process Control (PC), pp. 252–256 (2013)
  7. Wu et al. (2013) Optimum design of cogeneration for power and desalination to satisfy the demand of water and power (pp. 111-117) https://doi.org/10.1016/j.desal.2013.06.006
  8. Moghavvemi, M., Yang, S.S., Kashem, M.A.: A practical neural network approach for power generation automation. In: IEEE Proceedings of International Conference on Energy Management and Power Delivery, pp. 305–310 (1998)
  9. Tamiru, A.L., Rangkut, C., Hashim, F.M.: Neuro-fuzzy and PSO based model for the steam and cooling sections of a cogeneration and cooling plant (CCP). In: Proceedings of ICEE 2009 3rd International Conference on Energy and Environment, pp. 27–33 (2009)
  10. Kim, D.H.: Neuro-fuzzy tuning of PID controller for control of actual gas turbine power. In: IEEE International Conference on Computational Intelligence for Measurements Systems and Applications, pp. 192–197 (2004)
  11. Barelli and Bidini (2005) Design of the measurements validation procedure and the expert system architecture for a cogeneration internal combustion engine (pp. 2698-2714) https://doi.org/10.1016/j.applthermaleng.2004.12.006
  12. Repin et al. (2006) Diagnostic of the information subsystem of an automatic process control system using artificial intelligence (pp. 476-482) https://doi.org/10.1134/S0040601506060152
  13. Chitashvili (2006) A proportional method for calculating the efficiency and specific consumption of fuel at gas-turbine cogeneration stations (pp. 983-988) https://doi.org/10.1134/S004060150612007X
  14. Xiao-li and Guo-qiang (2013) Maximum useful energy rate and efficiency of a recuperative Brayton cogeneration plant (pp. 156-163) https://doi.org/10.1007/s11771-013-1471-5
  15. Zomorodian et al. (2011) Static and dynamic neural networks for simulation and optimization of cogeneration systems (pp. 51-61)
  16. Rapid Miner
  17. ®
  18. .
  19. http://rapid-i.com/
  20. . Accessed 15 Jul 2013
  21. Neil, T.: Applied Multivariate Analysis, pp. 80–83. Springer, NY (2002). 445–453
  22. Eberhart et al. (1996) Academic Press
  23. Jang et al. (1997) Prentice Hall
  24. Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proc. IEEE
  25. 83
  26. (3), 345–377 (1995)
  27. Roffel and Betlem (2006) (pp. 361-399) Wiley
  28. Optibat Trainer
  29. ®
  30. .
  31. http://www.optimitive.com/en/
  32. . Accessed 21 Jul 2013