%0 Conference Paper %1 Ivannikov2009 %A Ivannikov, Andriy %A Pechenizkiy, Mykola %A Bakker, Jorn %A Leino, Timo %A Jegoroff, Mikko %A Kärkkäinen, Tommi %A Äyrämö, Sami %B Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects %C Berlin, Heidelberg %D 2009 %I Springer-Verlag %K cfb datamining flow mass prediction signalprocessing %P 206--219 %R 10.1007/978-3-642-03067-3_17 %T Online Mass Flow Prediction in CFB Boilers %U http://dx.doi.org/10.1007/978-3-642-03067-3_17 %X <p>Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather simple and intuitive approach for online mass flow prediction in CFB boilers. This approach is based on learning and switching regression models. Additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. We validate our approach with a set of simulation experiments with real data collected from the pilot CFB boiler.</p> %@ 978-3-642-03066-6

@inproceedings{Ivannikov2009, abstract = {<p>Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather simple and intuitive approach for online mass flow prediction in CFB boilers. This approach is based on learning and switching regression models. Additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. We validate our approach with a set of simulation experiments with real data collected from the pilot CFB boiler.</p>}, acmid = {1600587}, added-at = {2011-03-22T10:42:26.000+0100}, address = {Berlin, Heidelberg}, author = {Ivannikov, Andriy and Pechenizkiy, Mykola and Bakker, Jorn and Leino, Timo and Jegoroff, Mikko and K\"{a}rkk\"{a}inen, Tommi and \"{A}yr\"{a}m\"{o}, Sami}, biburl = {https://www.bibsonomy.org/bibtex/2727669ef4dae41e01f422d1484ead95b/pejowart}, booktitle = {Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects}, description = {Online Mass Flow Prediction in CFB Boilers}, doi = {10.1007/978-3-642-03067-3_17}, groups = {public}, interhash = {e7399f2dea4c9e21d1aec96d684691fa}, intrahash = {727669ef4dae41e01f422d1484ead95b}, isbn = {978-3-642-03066-6}, keywords = {cfb datamining flow mass prediction signalprocessing}, location = {Leipzig, Germany}, numpages = {14}, pages = {206--219}, publisher = {Springer-Verlag}, series = {ICDM '09}, timestamp = {2012-06-20T10:05:14.000+0200}, title = {Online Mass Flow Prediction in CFB Boilers}, url = {http://dx.doi.org/10.1007/978-3-642-03067-3_17}, username = {pejowart}, year = 2009 }