Pure intelligent monitoring system for steam economizer trips
Economizers; Failure (mechanical); Fault detection; Knowledge acquisition; Learning algorithms; Learning systems; Neural networks; Plant shutdowns; Steam; Thermoelectric power plants; Extreme learning machine; Fault detection and diagnosis systems; Intelligent modeling; Intelligent monitoring system...
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EDP Sciences
2023
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my.uniten.dspace-230812023-05-29T14:37:42Z Pure intelligent monitoring system for steam economizer trips Basim Ismail F. Hamzah Abed K. Singh D. Shakir Nasif M. 58027086700 57196436991 57191191317 55188481100 Economizers; Failure (mechanical); Fault detection; Knowledge acquisition; Learning algorithms; Learning systems; Neural networks; Plant shutdowns; Steam; Thermoelectric power plants; Extreme learning machine; Fault detection and diagnosis systems; Intelligent modeling; Intelligent monitoring systems; Network methodologies; Operational conditions; Operational variables; Thermal power plants; Steam power plants Steam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe operational conditions of power plant. However, these methodologies are hard to be achieved due to certain boundaries such as system learning ability and the weakness of the system beyond its domain of expertise. The best solution for these problems, an intelligent modeling system specialized in steam economizer trips have been proposed and coded within MATLAB environment to be as a potential solution to insure a fault detection and diagnosis system (FDD). An integrated plant data preparation framework for 10 trips was studied as framework variables. The most influential operational variables have been trained and validated by adopting Artificial Neural Network (ANN). The Extreme Learning Machine (ELM) neural network methodology has been proposed as a major computational intelligent tool in the system. It is shown that ANN can be implemented for monitoring any process faults in thermal power plants. Better speed of learning algorithms by using the Extreme Learning Machine has been approved as well. � The authors, published by EDP Sciences, 2017. Final 2023-05-29T06:37:42Z 2023-05-29T06:37:42Z 2017 Conference Paper 10.1051/matecconf/201713104008 2-s2.0-85033229462 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033229462&doi=10.1051%2fmatecconf%2f201713104008&partnerID=40&md5=70ddacc6d016e7a6029f50bd303584e8 https://irepository.uniten.edu.my/handle/123456789/23081 131 4008 All Open Access, Gold, Green EDP Sciences Scopus |
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Malaysia |
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| description |
Economizers; Failure (mechanical); Fault detection; Knowledge acquisition; Learning algorithms; Learning systems; Neural networks; Plant shutdowns; Steam; Thermoelectric power plants; Extreme learning machine; Fault detection and diagnosis systems; Intelligent modeling; Intelligent monitoring systems; Network methodologies; Operational conditions; Operational variables; Thermal power plants; Steam power plants |
| author2 |
58027086700 |
| author_facet |
58027086700 Basim Ismail F. Hamzah Abed K. Singh D. Shakir Nasif M. |
| format |
Conference Paper |
| author |
Basim Ismail F. Hamzah Abed K. Singh D. Shakir Nasif M. |
| spellingShingle |
Basim Ismail F. Hamzah Abed K. Singh D. Shakir Nasif M. Pure intelligent monitoring system for steam economizer trips |
| author_sort |
Basim Ismail F. |
| title |
Pure intelligent monitoring system for steam economizer trips |
| title_short |
Pure intelligent monitoring system for steam economizer trips |
| title_full |
Pure intelligent monitoring system for steam economizer trips |
| title_fullStr |
Pure intelligent monitoring system for steam economizer trips |
| title_full_unstemmed |
Pure intelligent monitoring system for steam economizer trips |
| title_sort |
pure intelligent monitoring system for steam economizer trips |
| publisher |
EDP Sciences |
| publishDate |
2023 |
| _version_ |
1806426276634820608 |