%0 Conference Paper %1 Bakker2009 %A Bakker, J. %A Pechenizkiy, M. %A Zliobait\.e, I. %A Ivannikov, A. %A Kärkkäinen, T. %B Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data %C New York, NY, USA %D 2009 %I ACM %K boiler cfb datamining massflow prediction %P 13--22 %R 10.1145/1601966.1601972 %T Handling outliers and concept drift in online mass flow prediction in CFB boilers %U http://doi.acm.org/10.1145/1601966.1601972 %X 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. %@ 978-1-60558-668-7

@inproceedings{Bakker2009, abstract = {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.}, acmid = {1601972}, added-at = {2011-03-22T10:39:41.000+0100}, address = {New York, NY, USA}, author = {Bakker, J. and Pechenizkiy, M. and \v{Z}liobait\.{e}, I. and Ivannikov, A. and K\"{a}rkk\"{a}inen, T.}, biburl = {https://www.bibsonomy.org/bibtex/2462fd54527e90e303834050b8ad0d98e/pejowart}, booktitle = {Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data}, description = {Handling outliers and concept drift in online mass flow prediction in CFB boilers}, doi = {10.1145/1601966.1601972}, groups = {public}, interhash = {f703d42fca624b5a9ccad1a5bc92f678}, intrahash = {462fd54527e90e303834050b8ad0d98e}, isbn = {978-1-60558-668-7}, keywords = {boiler cfb datamining massflow prediction}, location = {Paris, France}, numpages = {10}, pages = {13--22}, publisher = {ACM}, series = {SensorKDD '09}, timestamp = {2012-06-20T10:05:24.000+0200}, title = {Handling outliers and concept drift in online mass flow prediction in CFB boilers}, url = {http://doi.acm.org/10.1145/1601966.1601972}, username = {pejowart}, year = 2009 }