Abstract
This paper presents an approach to control pluggage of a coal-fired boiler. The proposed approach involves statistics, data partitioning, parameter reduction, and data mining. The proposed approach was tested on a 750 MW commercial coal-fired boiler affected with a fouling problem that leads to boiler pluggage that causes unscheduled shutdowns. The rare-event detection approach presented in the paper identified several critical time-based data segments that are indicative of the ash pluggage.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kusiak, A., Burns, A. (2005). Mining Temporal Data: A Coal-Fired Boiler Case Study. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_134
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DOI: https://doi.org/10.1007/11553939_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
eBook Packages: Computer ScienceComputer Science (R0)