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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: Al-Kayiem H.H., Al-Naimi F.B.I., Amat W.N.B.W.
Other Authors: 6507544662
Format: Conference Paper
Published: EDP Sciences 2023
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spelling my.uniten.dspace-220672023-05-16T10:47:06Z Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system Al-Kayiem H.H. Al-Naimi F.B.I. Amat W.N.B.W. 6507544662 58027086700 56297853000 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. Final 2023-05-16T02:47:06Z 2023-05-16T02:47:06Z 2014 Conference Paper 10.1051/matecconf/20141305004 2-s2.0-84905039982 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905039982&doi=10.1051%2fmatecconf%2f20141305004&partnerID=40&md5=e7d7b18f6793d1e59c922af948d2994c https://irepository.uniten.edu.my/handle/123456789/22067 13 5004 All Open Access, Gold, Green EDP Sciences Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description 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.
author2 6507544662
author_facet 6507544662
Al-Kayiem H.H.
Al-Naimi F.B.I.
Amat W.N.B.W.
format Conference Paper
author Al-Kayiem H.H.
Al-Naimi F.B.I.
Amat W.N.B.W.
spellingShingle Al-Kayiem H.H.
Al-Naimi F.B.I.
Amat W.N.B.W.
Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
author_sort Al-Kayiem H.H.
title Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
title_short Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
title_full Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
title_fullStr Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
title_full_unstemmed Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
title_sort analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
publisher EDP Sciences
publishDate 2023
_version_ 1806426355596787712
score 13.222552

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