Adapting to Drift in Continuous Domains (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
ACM Computing Surveys (CSUR)
Handling outliers and concept drift in online mass flow prediction in CFB boilers
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Mining temporal data: a coal-fired boiler case study
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Handling concept drift in process mining
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Data with shifting concept classification using simulated recurrence
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Real-time mass flow estimation in circulating fluidized bed
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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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.