Handling outliers and concept drift in online mass flow prediction in CFB boilers

J. Bakker, M. Pechenizkiy, I. Zliobaite, A. Ivannikov, T. Kärkkäinen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

9 Citaten (Scopus)


In this paper we consider an application of data mining technology to the analysis of time series data from a pilot circulating fluidized bed (CFB) reactor. We focus on the problem of the online mass prediction in CFB boilers. We present a framework based on switching regression models depending on perceived changes in the data. We analyze three alternatives for change detection. Additionally, a noise canceling and a state determination and windowing mechanisms are used for improving the robustness of online prediction. We validate our ideas on real data collected from the pilot CFB boiler.
Originele taal-2Engels
TitelProceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (KDD 2009, Paris, France, June 28, 2009)
RedacteurenO.A. Omitaomu, A.R. Ganguly, J. Gama, R.R. Vatsavai, N.V. Chawla, M.M. Gaber
Plaats van productieNew York
UitgeverijAssociation for Computing Machinery, Inc
ISBN van geprinte versie978-1-60558-668-7
StatusGepubliceerd - 2009


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