Hybrid Intelligent Warning System for Boiler tube Leak Trips

Deshvin Singh, Firas B. Ismail, Mohammad Shakir Nasif

Research output: Contribution to journalConference article

Abstract

Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.

Original languageEnglish
Article number03003
JournalMATEC Web of Conferences
Volume131
DOIs
Publication statusPublished - 25 Oct 2017
Event2017 UTP-UMP Symposium on Energy Systems, SES 2017 - Perak, Malaysia
Duration: 26 Sep 201727 Sep 2017

Fingerprint

Alarm systems
Boilers
Power plants
Intelligent systems
Monitoring
Genetic algorithms
Neural networks
Coal
Merging
Operating costs
MATLAB
Learning systems
Topology

All Science Journal Classification (ASJC) codes

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

Cite this

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title = "Hybrid Intelligent Warning System for Boiler tube Leak Trips",
abstract = "Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.",
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Hybrid Intelligent Warning System for Boiler tube Leak Trips. / Singh, Deshvin; Ismail, Firas B.; Shakir Nasif, Mohammad.

In: MATEC Web of Conferences, Vol. 131, 03003, 25.10.2017.

Research output: Contribution to journalConference article

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