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Modelling of NOx Emissions in Natural Gas Fired Hot Water Boilers

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Engineering Applications of Neural Networks (EANN 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 517))

Abstract

Nitrogen oxides (NOx) are one of the main pollutants produced by combustion processes. New European emission regulations (IED) extent emission monitoring requirements to smaller boilers. Heating grid operators may have a notable number of such boilers and therefore appreciate affordable monitoring solutions. This paper studies several types of regression models for estimating NOx emissions in natural gas fired boilers. The objective is to predict the emissions utilising the existing process measurements for monitoring, without an external NOx analyser. The performance of linear regression is compared with three nonlinear methods: multilayer perceptron, support vector regression and fuzzy inference system. The focus is on generalisation ability. The results on the two boilers in the study suggest that linear regression and multilayer perceptron network outperform the others in predicting with new, unseen data.

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Authors and Affiliations

  1. Dept. of Automation Science and Engineering, Tampere University of Technology, P.O. BOX 692, 33101, Tampere, Finland

    Pekka Kumpulainen, Timo Korpela & Yrjö Majanne

  2. Helen PLC, 00090, Helsinki, Finland

    Anna Häyrinen

Authors
  1. Pekka Kumpulainen
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  2. Timo Korpela
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  3. Yrjö Majanne
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  4. Anna Häyrinen
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Corresponding author

Correspondence to Pekka Kumpulainen .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Orestiada, Greece

    Lazaros Iliadis

  2. Coventry University, Coventry, United Kingdom

    Chrisina Jayne

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© 2015 Springer International Publishing Switzerland

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Kumpulainen, P., Korpela, T., Majanne, Y., Häyrinen, A. (2015). Modelling of NOx Emissions in Natural Gas Fired Hot Water Boilers. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-23983-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23981-1

  • Online ISBN: 978-3-319-23983-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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