Mining temporal data: a coal-fired boiler case study

Authors:
Andrew Kusiak;Alex Burns
Affiliations:
Intelligent Systems Laboratory, Industrial Engineering, The University of Iowa, Iowa City, IA;Intelligent Systems Laboratory, Industrial Engineering, The University of Iowa, Iowa City, IA
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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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.