Abstract
Self-organizing maps (SOM) have been successfully applied in many fields of research. In this paper, we demonstrate the use of SOM-based method for process state monitoring and optimization of NOx emissions. The SOM was trained using a dataset from a fluidized bed energy plant. Reference vectors of the SOM were then classified by K-means algorithm into five clusters, which represented different states of the process. One neuron in each cluster was defined optimal based on the NOx emission of the process. The difference between reference vectors of the optimal neuron and the neuron in each time step could be used for determination of reasons of non-optimal process states. The results show that the SOM method may also be successfully applied to process state monitoring and optimization of NOx emissions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self-organizing Maps. Springer, Heidelberg (2001)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)
Kaartinen, J., Hiltunen, Y., Kovanen, P.T., Ala-Korpela, M.: Classification of Human Blood Plasma Lipid Abnormalities by 1H Magnetic Resonance Spectroscopy and Self-Organizing Maps. NMR Biomed. 11, 168–176 (1998)
Hyvönen, M.T., Hiltunen, Y., El-Deredy, W., Ojala, T., Vaara, J., Kovanen, P.T., Ala-Korpela, M.: Application of Self-Organizing Maps in Conformational Analysis of Lipids. Journal of the American Chemical Society 123, 810–816 (2001)
Heikkinen, M., Kolehmainen, M., Hiltunen, Y.: Classification of process phases using Self-Organizing Maps and Sammon’s mapping for investigating activated sludge treatment plant in a pulp mill. In: Proceedings of the Fourth European Symposium on Intelligent Technologies and their implementation on Smart Adaptive Systems, pp. 281–297 (2004)
MacQueen: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Statistics, vol. I, pp. 281–297. University of California Press, Berkeley (1967)
Homepage of SOM toolbox, http://www.cis.hut.fi/projects/somtoolbox/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heikkinen, M., Kettunen, A., Niemitalo, E., Kuivalainen, R., Hiltunen, Y. (2005). SOM-Based Method for Process State Monitoring and Optimization in Fluidized Bed Energy Plant. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_64
Download citation
DOI: https://doi.org/10.1007/11550822_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
Online ISBN: 978-3-540-28754-4
eBook Packages: Computer ScienceComputer Science (R0)