@inproceedings{a0e51ba2b8de4f4eb324fde45c1ee4a3,
title = "Online mass flow prediction in CFB boilers",
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.",
author = "Andriy Ivannikov and Mykola Pechenizkiy and Jorn Bakker and Timo Leino and Mikko Jegoroff and Tommi K{\"a}rkk{\"a}inen and Sami {\"A}yr{\"a}m{\"o}",
year = "2009",
doi = "10.1007/978-3-642-03067-3_17",
language = "English",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
number = "5633",
pages = "206--219",
editor = "Petra Perner",
booktitle = "Advances in Data Mining. Applications and Theoretical Aspects",
address = "Germany",
note = "Advances in data mining : applications and theoretical aspects : 9th Industrial Conference, ICDM 2009, ICDM 2009 ; Conference date: 20-07-2009 Through 22-07-2009",
}