Online mass flow prediction in CFB boilers

A. Ivannikov, M. Pechenizkiy, J. Bakker, T. Leino, M. Jegoroff, T. Kärkkäinen, S. Äyrämö

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Data Mining. Applications and Theoretical Aspects (9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20-22, 2009. Proceedings)
EditorsP. Perner
Place of PublicationBerlin
PublisherSpringer
Pages206-219
ISBN (Print)978-3-642-03066-6
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
Volume5633
ISSN (Print)0302-9743

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