Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
Steam boilers are considered as a core of any steam power plant. Boilers are subjected to various types of trips leading to shut down of the entire plant. The tube leakage is the worse among the common boiler faults, where the shutdown period lasts for around four to five days. This paper describes...
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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-s2.0-84905039982&doi=10.1051%2fmatecconf%2f20141305004&partnerID=40&md5=e7d7b18f6793d1e59c922af948d2994c http://eprints.utp.edu.my/32221/ |
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| Summary: | Steam boilers are considered as a core of any steam power plant. Boilers are subjected to various types of trips leading to shut down of the entire plant. The tube leakage is the worse among the common boiler faults, where the shutdown period lasts for around four to five days. This paper describes the rules of the Artificial Intelligent Systems to diagnosis the boiler variables prior to tube leakage occurrence. An Intelligent system based on Artificial Neural Network was designed and coded in MATLAB environment. The ANN was trained and validated using real site data acquired from coal fired power plant in Malaysia. Ninety three boiler operational variables were identified for the present investigation based on the plant operator experience. Various neural networks topology combinations were investigated. The results showed that the NN with two hidden layers performed better than one hidden layer using Levenberg-Maquardt training algorithm. Moreover, it was noticed that hyperbolic tangent function for input and output nodes performed better than other activation function types. © 2014 Owned by the authors, published by EDP Sciences. |
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