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Applied Computational Intelligence and Soft Computing
Volume 2010 (2010), Article ID 932467, 8 pages
http://dx.doi.org/10.1155/2010/932467
Research Article

Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps

1Department of Environmental Science, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
2Foster Wheeler Energia Oy, Operations, Engineering & Services, P.O. Box 201, 78201 Varkaus, Finland

Received 17 March 2010; Accepted 12 August 2010

Academic Editor: Junbin B. Gao

Copyright © 2010 Mika Liukkonen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Efficient combustion of fuels with lower emissions levels has become a demanding task in modern power plants, and new tools are needed to diagnose their energy production. The goals of the study were to find dependencies between process variables and the concentrations of gaseous emission components and to create multivariate nonlinear models describing their formation in the process. First, a generic process model was created by using a self-organizing map, which was clustered with the k-means algorithm to create subsets representing the different states of the process. Characteristically, these process states may include high- and low- load situations and transition states where the load is increased or decreased. Then emission models were constructed for both the entire process and for the process state of high boiler load. The main conclusion is that the methodology used is able to reveal such phenomena that occur within the process states and that could otherwise be difficult to observe.