Abstract
In this study, a self-organizing map (SOM) -based process analysis and parameter approximation method was used to the emission analysis of a circulating fluidized bed process. The aim was to obtain the optimal process parameters in respect to the flue gas nitrogen oxide (NO x ) content in different predefined states of process. The data processing procedure in the research went as follows. First, the process data were processed by using a self-organizing map and k-means clustering to generate subsets representing the separate process states in the boiler. These process states represent the higher level process conditions in the combustion, and can include for example start-ups, shutdowns, and idle times in addition to the normal process flow. Next, optimal areas were discovered from the map within each process state, and the reference vectors of the optimal neurons were used to approximate the values of desired process parameters. In addition, a subtraction analysis of reference vectors was performed to analyze the optimal situations. In conclusion, the method showed potential considering its wider use in the field of energy production.
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Liukkonen, M., Heikkinen, M., Hälikkä, E., Kuivalainen, R., Hiltunen, Y. (2009). Emission Analysis of a Fluidized Bed Boiler by Using Self-Organizing Maps. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_13
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DOI: https://doi.org/10.1007/978-3-642-04921-7_13
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