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.
|Title of host publication||Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (KDD 2009, Paris, France, June 28, 2009)|
|Editors||O.A. Omitaomu, A.R. Ganguly, J. Gama, R.R. Vatsavai, N.V. Chawla, M.M. Gaber|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery, Inc|
|Publication status||Published - 2009|