Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system

Hussain H. Al-Kayiem, Firas Basim Ismail, Wan N.Bt Wan Amat

Research output: Contribution to journalConference article

Abstract

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.

Original languageEnglish
Article number05004
JournalMATEC Web of Conferences
Volume13
DOIs
Publication statusPublished - 01 Jan 2014
Event4th International Conference on Production, Energy and Reliability, ICPER 2014 - Kuala Lumpur, Malaysia
Duration: 03 Jun 201405 Jun 2014

Fingerprint

Intelligent systems
Boilers
Hyperbolic functions
Neural networks
Steam power plants
Coal
Steam
MATLAB
Mathematical operators
Power plants
Chemical activation
Topology

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

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abstract = "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.",
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Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system. / Al-Kayiem, Hussain H.; Ismail, Firas Basim; Amat, Wan N.Bt Wan.

In: MATEC Web of Conferences, Vol. 13, 05004, 01.01.2014.

Research output: Contribution to journalConference article

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