Hybrid Intelligent Warning System for Boiler tube Leak Trips
Artificial intelligence; Boilers; Coal; Fossil fuel power plants; Genetic algorithms; Intelligent systems; Learning systems; Neural networks; Thermoelectric power plants; Artificial intelligent; Coal-fired power plant; Detection and diagnosis; Extreme learning machine; Hybrid intelligent system; Rel...
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EDP Sciences
2023
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my.uniten.dspace-230762023-05-29T14:37:41Z Hybrid Intelligent Warning System for Boiler tube Leak Trips Singh D. Ismail F.B. Shakir Nasif M. 57191191317 58027086700 55188481100 Artificial intelligence; Boilers; Coal; Fossil fuel power plants; Genetic algorithms; Intelligent systems; Learning systems; Neural networks; Thermoelectric power plants; Artificial intelligent; Coal-fired power plant; Detection and diagnosis; Extreme learning machine; Hybrid intelligent system; Reliable monitoring systems; Thermal power plants; Training algorithms; Monitoring 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. � The authors, published by EDP Sciences, 2017. Final 2023-05-29T06:37:41Z 2023-05-29T06:37:41Z 2017 Conference Paper 10.1051/matecconf/201713103003 2-s2.0-85033236736 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236736&doi=10.1051%2fmatecconf%2f201713103003&partnerID=40&md5=5cebc8c54645a36146bb4f74b3acc9da https://irepository.uniten.edu.my/handle/123456789/23076 131 3003 All Open Access, Gold, Green EDP Sciences Scopus |
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Malaysia |
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| description |
Artificial intelligence; Boilers; Coal; Fossil fuel power plants; Genetic algorithms; Intelligent systems; Learning systems; Neural networks; Thermoelectric power plants; Artificial intelligent; Coal-fired power plant; Detection and diagnosis; Extreme learning machine; Hybrid intelligent system; Reliable monitoring systems; Thermal power plants; Training algorithms; Monitoring |
| author2 |
57191191317 |
| author_facet |
57191191317 Singh D. Ismail F.B. Shakir Nasif M. |
| format |
Conference Paper |
| author |
Singh D. Ismail F.B. Shakir Nasif M. |
| spellingShingle |
Singh D. Ismail F.B. Shakir Nasif M. Hybrid Intelligent Warning System for Boiler tube Leak Trips |
| author_sort |
Singh D. |
| title |
Hybrid Intelligent Warning System for Boiler tube Leak Trips |
| title_short |
Hybrid Intelligent Warning System for Boiler tube Leak Trips |
| title_full |
Hybrid Intelligent Warning System for Boiler tube Leak Trips |
| title_fullStr |
Hybrid Intelligent Warning System for Boiler tube Leak Trips |
| title_full_unstemmed |
Hybrid Intelligent Warning System for Boiler tube Leak Trips |
| title_sort |
hybrid intelligent warning system for boiler tube leak trips |
| publisher |
EDP Sciences |
| publishDate |
2023 |
| _version_ |
1806427806970675200 |
| score |
13.209306 |