Analysis of flue gas emission data from fluidized bed combustion using self-organizing maps

Authors:
Mika Liukkonen;Mikko Heikkinen;Eero Hälikkä;Teri Hiltunen;Yrjö Hiltunen
Affiliations:
Department of Environmental Science, University of Eastern Finland, Kuopio, Finland;Department of Environmental Science, University of Eastern Finland, Kuopio, Finland;Foster Wheeler Energia Oy, Operations, Engineering & Services, Varkaus, Finland;Foster Wheeler Energia Oy, Operations, Engineering & Services, Varkaus, Finland;Department of Environmental Science, University of Eastern Finland, Kuopio, Finland
Venue:
Applied Computational Intelligence and Soft Computing
Year:
2010

Citing 9
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Self-Organizing Maps

Self-Organizing Maps
Process analysis and product quality estimation by self-organizing maps with an application to polyethylene production

Computers in Industry - Special issue: Soft computing in industrial applications
Process Monitoring and Modeling Using the Self-Organizing Map

Integrated Computer-Aided Engineering
Application of self-organizing maps in analysis of wave soldering process

Expert Systems with Applications: An International Journal
A modelling and optimization system for fluidized bed power plants

Expert Systems with Applications: An International Journal
Emission analysis of a fluidized bed boiler by using self-organizing maps

ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Quality-oriented optimization of wave soldering process by using self-organizing maps

Applied Soft Computing
SOM-Based method for process state monitoring and optimization in fluidized bed energy plant

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

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 processmodel 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 lowload 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.