Neural network modeling for main steam temperature system
Main Steam Temperature (MST) is non-linear, large inertia, long dead time and load dependant parameters. The paper present MST modeling method using actual plant data by utilizing MATLAB's Neural Network toolbox. The result of the simulation showed the MST model based on actual plant data is po...
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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Penerbit UTM
2014
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Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/54230/1/NorA.Mazalan2014_Neuralnetworkmodelingformain.pdf http://eprints.utm.my/id/eprint/54230/ http://dx.doi.org/10.11113/jt.v69.3151 |
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Summary: | Main Steam Temperature (MST) is non-linear, large inertia, long dead time and load dependant parameters. The paper present MST modeling method using actual plant data by utilizing MATLAB's Neural Network toolbox. The result of the simulation showed the MST model based on actual plant data is possible but the raw data need to be pre-processed for better output. Generator output, total main steam flow, main steam pressure and total spray flow are four main parameters affected the behavior of MST in coal fired power plant boiler |
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