Adoption of intelligent computational techniques for steam boilers tube leak trip
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 meth...
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2023
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my.uniten.dspace-256742023-05-29T16:12:34Z Adoption of intelligent computational techniques for steam boilers tube leak trip Ismail F.B. Singh D. Nasif M.S. 58027086700 57191191317 55188481100 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. � Faculty of Computer Science and Information Technology. Final 2023-05-29T08:12:34Z 2023-05-29T08:12:34Z 2020 Article 10.22452/mjcs.vol33no2.4 2-s2.0-85090835871 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090835871&doi=10.22452%2fmjcs.vol33no2.4&partnerID=40&md5=bb9265d347123db2f89b34d5ad44f663 https://irepository.uniten.edu.my/handle/123456789/25674 33 2 133 151 All Open Access, Bronze Faculty of Computer Science and Information Technology Scopus |
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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. � Faculty of Computer Science and Information Technology. |
| author2 |
58027086700 |
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58027086700 Ismail F.B. Singh D. Nasif M.S. |
| format |
Article |
| author |
Ismail F.B. Singh D. Nasif M.S. |
| spellingShingle |
Ismail F.B. Singh D. Nasif M.S. Adoption of intelligent computational techniques for steam boilers tube leak trip |
| author_sort |
Ismail F.B. |
| title |
Adoption of intelligent computational techniques for steam boilers tube leak trip |
| title_short |
Adoption of intelligent computational techniques for steam boilers tube leak trip |
| title_full |
Adoption of intelligent computational techniques for steam boilers tube leak trip |
| title_fullStr |
Adoption of intelligent computational techniques for steam boilers tube leak trip |
| title_full_unstemmed |
Adoption of intelligent computational techniques for steam boilers tube leak trip |
| title_sort |
adoption of intelligent computational techniques for steam boilers tube leak trip |
| publisher |
Faculty of Computer Science and Information Technology |
| publishDate |
2023 |
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1806423238785368064 |
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13.222552 |