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dc.contributor.authorNistah, N.
dc.contributor.authorLim, Hann
dc.contributor.authorGopal, L.
dc.contributor.authorAlnaimi, F.
dc.identifier.citationNistah, N. and Lim, H. and Gopal, L. and Alnaimi, F. 2018. Coal-fired boiler fault prediction using artificial neural networks. International Journal of Electrical and Computer Engineering. 8 (4): pp. 2486-2493.

Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.

dc.publisherPergamon Press plc
dc.titleCoal-fired boiler fault prediction using artificial neural networks
dc.typeJournal Article
dcterms.source.titleInternational Journal of Electrical and Computer Engineering
curtin.departmentCurtin Malaysia
curtin.accessStatusFulltext not available

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