OMFP : an approach for online mass flow prediction in CFB boilers

I. Zliobaite, J. Bakker, M. Pechenizkiy

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

2 Citations (Scopus)

Abstract

Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) boilers. If control systems fail to compensate the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. Accurate estimates of fuel consumption among other factors are needed for control systems operation. In this paper we address a problem of online mass flow prediction. Particularly, we consider the problems of (1) constructing the ground truth, (2) handling noise and abrupt concept drift, and (3) learning an accurate predictor. Last but not least we emphasize the importance of having the domain knowledge concerning the considered case. We demonstrate the performance of OMPF using real data sets collected from the experimental CFB boiler.
Original languageEnglish
Title of host publicationDiscovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings)
EditorsJ. Gama, V. Santos Costa, A.M. Jorge, P.B. Brazdil
Place of PublicationBerlin
PublisherSpringer
Pages272-286
ISBN (Print)978-3-642-04746-6
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
Volume5808
ISSN (Print)0302-9743

Fingerprint

Fluidized beds
Boilers
Control systems
Fuel consumption

Cite this

Zliobaite, I., Bakker, J., & Pechenizkiy, M. (2009). OMFP : an approach for online mass flow prediction in CFB boilers. In J. Gama, V. Santos Costa, A. M. Jorge, & P. B. Brazdil (Eds.), Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings) (pp. 272-286). (Lecture Notes in Computer Science; Vol. 5808). Berlin: Springer. https://doi.org/10.1007/978-3-642-04747-3_22
Zliobaite, I. ; Bakker, J. ; Pechenizkiy, M. / OMFP : an approach for online mass flow prediction in CFB boilers. Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings). editor / J. Gama ; V. Santos Costa ; A.M. Jorge ; P.B. Brazdil. Berlin : Springer, 2009. pp. 272-286 (Lecture Notes in Computer Science).
@inproceedings{028d4f99ec174da5ab192be554126f0a,
title = "OMFP : an approach for online mass flow prediction in CFB boilers",
abstract = "Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) boilers. If control systems fail to compensate the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. Accurate estimates of fuel consumption among other factors are needed for control systems operation. In this paper we address a problem of online mass flow prediction. Particularly, we consider the problems of (1) constructing the ground truth, (2) handling noise and abrupt concept drift, and (3) learning an accurate predictor. Last but not least we emphasize the importance of having the domain knowledge concerning the considered case. We demonstrate the performance of OMPF using real data sets collected from the experimental CFB boiler.",
author = "I. Zliobaite and J. Bakker and M. Pechenizkiy",
year = "2009",
doi = "10.1007/978-3-642-04747-3_22",
language = "English",
isbn = "978-3-642-04746-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "272--286",
editor = "J. Gama and {Santos Costa}, V. and A.M. Jorge and P.B. Brazdil",
booktitle = "Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings)",
address = "Germany",

}

Zliobaite, I, Bakker, J & Pechenizkiy, M 2009, OMFP : an approach for online mass flow prediction in CFB boilers. in J Gama, V Santos Costa, AM Jorge & PB Brazdil (eds), Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings). Lecture Notes in Computer Science, vol. 5808, Springer, Berlin, pp. 272-286. https://doi.org/10.1007/978-3-642-04747-3_22

OMFP : an approach for online mass flow prediction in CFB boilers. / Zliobaite, I.; Bakker, J.; Pechenizkiy, M.

Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings). ed. / J. Gama; V. Santos Costa; A.M. Jorge; P.B. Brazdil. Berlin : Springer, 2009. p. 272-286 (Lecture Notes in Computer Science; Vol. 5808).

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

TY - GEN

T1 - OMFP : an approach for online mass flow prediction in CFB boilers

AU - Zliobaite, I.

AU - Bakker, J.

AU - Pechenizkiy, M.

PY - 2009

Y1 - 2009

N2 - Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) boilers. If control systems fail to compensate the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. Accurate estimates of fuel consumption among other factors are needed for control systems operation. In this paper we address a problem of online mass flow prediction. Particularly, we consider the problems of (1) constructing the ground truth, (2) handling noise and abrupt concept drift, and (3) learning an accurate predictor. Last but not least we emphasize the importance of having the domain knowledge concerning the considered case. We demonstrate the performance of OMPF using real data sets collected from the experimental CFB boiler.

AB - Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) boilers. If control systems fail to compensate the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. Accurate estimates of fuel consumption among other factors are needed for control systems operation. In this paper we address a problem of online mass flow prediction. Particularly, we consider the problems of (1) constructing the ground truth, (2) handling noise and abrupt concept drift, and (3) learning an accurate predictor. Last but not least we emphasize the importance of having the domain knowledge concerning the considered case. We demonstrate the performance of OMPF using real data sets collected from the experimental CFB boiler.

U2 - 10.1007/978-3-642-04747-3_22

DO - 10.1007/978-3-642-04747-3_22

M3 - Conference contribution

SN - 978-3-642-04746-6

T3 - Lecture Notes in Computer Science

SP - 272

EP - 286

BT - Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings)

A2 - Gama, J.

A2 - Santos Costa, V.

A2 - Jorge, A.M.

A2 - Brazdil, P.B.

PB - Springer

CY - Berlin

ER -

Zliobaite I, Bakker J, Pechenizkiy M. OMFP : an approach for online mass flow prediction in CFB boilers. In Gama J, Santos Costa V, Jorge AM, Brazdil PB, editors, Discovery Science (12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Proceedings). Berlin: Springer. 2009. p. 272-286. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-04747-3_22