On-line condition monitoring system for high level trip water in steam Boiler's Drum
This paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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EDP Sciences
2014
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904988619&doi=10.1051%2fmatecconf%2f20141303011&partnerID=40&md5=fb950bebff63532b2cbef68f09a2f279 http://eprints.utp.edu.my/32268/ |
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| Summary: | This paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded from power plant located in Malaysia. The developed relevant variables were selected based on a combination of theory, experience and execution phases of the model. The Root Mean Square (RMS) Error has been used to compare the results of one and two hidden layer (1HL), (2HL) ANN structures. © 2014 Owned by the authors, published by EDP Sciences. |
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