Adoption of intelligent computational techniques for steam boilers tube leak trip

Adoption of intelligent computational techniques for steam boilers tube leak trip. Journal of Computer Sciences, 33 (2): 4. pp. 133-151. ISSN 0127-9084 (2020)


Frequent boiler tube 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. Several methodologies for the fault diagnosis in a plant have been developed. However these methodologies are difficult to be implemented. In this study, two artificial intelligent monitoring systems specialized in boiler trips have been proposed. The first intelligent monitoring system represents the use of pure artificial neural network system whereas the second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. In the first system using pure artificial neural network, the trip was predicted 5 minutes before the actual trip occurrence. The hybrid intelligent system was able to optimize the selection of the most influencing variables successfully and predict the trip 2 minutes before the actual trip. The first intelligent system performed better than the second one based on the prediction time. The proposed artificial intelligent system could be adopted on-line as a reliable controller of the thermal power plant boiler.

Item Type: Article
Keywords: Artificial neural networks (ANNs),, Genetic algorithms (GAs), Boiler, Tube leak, Artificial intelligent system
Taxonomy: By Subject > Computer & Mathematical Sciences > Computer Science
By Subject > Computer & Mathematical Sciences > Statistics
Local Content Hub: Subjects > Computer and Mathematical Sciences
Depositing User: Muslim Ismail @ Ahmad
Date Deposited: 22 Feb 2021 11:41
Last Modified: 22 Feb 2021 11:41
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