Mining Temporal Data: A Coal-Fired Boiler Case Study

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.

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“…Fundamental studies develop mathematical models for boiler operation [12,10,7,14], incorporating operational parameters in the models.…”

Section: Connection To the Related Workmentioning

confidence: 99%

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers

2009

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On some types of linguistic summaries of time series

Kacprzyk

Wilbik

Zadrożny

2006

2006 3rd International IEEE Conference Intelligent Systems

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The purpose of this paper is to propose the use of linguistic quantifiers for the linguistic summarisation of time series, notably in terms of trends. To characterize the data trends, we use three parameters: dynamics of change, duration and variability and apply to them the fuzzy linguistic summaries of data (databases) in the sense of Yager. We introduce the two types of linguistic summaries, one based on frequency and one based on duration.

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Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

Pechenizkiy

Bakker

Žliobaitė

et al. 2010

SIGKDD Explor. Newsl.

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Fuel feeding and inhomogeneity of fuel typically cause fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate the fluctuations, the whole plant will suffer from dynamics that is reinforced by the closed-loop controls. This phenomenon causes reducing efficiency and the lifetime of process components. In this paper we address the problem of online mass flow prediction, which is a part of control. Particularly, we consider the problem of learning an accurate predictor with explicit detection of abrupt concept drift and noise handling mechanisms. We emphasize the importance of having domain knowledge concerning the considered case and constructing the ground truth for facilitating the quantitative evaluation of different approaches. We demonstrate the performance of change detection methods and show their effect on the accuracy of the online mass flow prediction with real datasets collected from the experimental laboratory-scale CFB boiler.

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Mining Temporal Data: A Coal-Fired Boiler Case Study

Cited by 4 publications

References 3 publications

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers

On some types of linguistic summaries of time series

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

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