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Virtual Sensors for Emissions of a Diesel Engine Produced by Evolutionary System Identification

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5717)

Abstract

In this paper we discuss the generation of models for emissions of a Diesel engine, produced by genetic programming based evolutionary system identification: Models for the formation of NO x and particulate matter emissions are identified and analyzed. We compare these models to models designed by experts applying variables section and the identification of local polynomial models; analyzing the results summarized in the empirical part of this paper we see that the use of enhanced genetic programming yields models for emissions that are valid not only in certain parts of the parameter space but can be used as global virtual sensors.

Keywords

  • Diesel Engine
  • Virtual Sensor
  • Engine Control Unit
  • Particulate Matter Emission
  • Genetic Programming Approach

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-04772-5_85
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References

  1. Affenzeller, M., Wagner, S., Winkler, S.: Goal-oriented preservation of essential genetic information by offspring selection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2005, vol. 2, pp. 1595–1596. Association for Computing Machinery (ACM), New York (2005)

    CrossRef  Google Scholar 

  2. Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming – Modern Concepts and Practical Applications. Chapman & Hall/CRC (2008)

    Google Scholar 

  3. Hirsch, M., Alberer, D., del Re, L.: Grey-box control oriented emissions models. In: Proceedings of IFAC World Congress 2008, pp. 8514–8519 (2008)

    Google Scholar 

  4. Hirsch, M., del Re, L.: Adaped D-optimal experimental design for transient emission models of diesel engines. In: Proceedings of SAE Congress 2009 (2009)

    Google Scholar 

  5. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)

    CrossRef  MATH  Google Scholar 

  6. Ljung, L.: System Identification – Theory For the User, 2nd edn. PTR Prentice Hall, Upper Saddle River (1999)

    MATH  Google Scholar 

  7. Wagner, S.: Heuristic Optimization Software Systems – Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University Linz (2009)

    Google Scholar 

  8. Warnatz, J., Maas, U., Dibble, R.W.: Combustion - Physical and Chemical Fundamentals, Modeling and Simulation, Experiments, Pollutant Formation. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  9. Winkler, S.: Evolutionary System Identification - Modern Concepts and Practical Applications. PhD thesis, Johannes Kepler University Linz (2008)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Winkler, S.M., Hirsch, M., Affenzeller, M., del Re, L., Wagner, S. (2009). Virtual Sensors for Emissions of a Diesel Engine Produced by Evolutionary System Identification. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04772-5_85

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  • DOI: https://doi.org/10.1007/978-3-642-04772-5_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04771-8

  • Online ISBN: 978-3-642-04772-5

  • eBook Packages: Computer ScienceComputer Science (R0)