On-line condition monitoring system for high level trip water in steam Boiler's Drum
This paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded...
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2023
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my.uniten.dspace-220682023-05-16T10:47:07Z On-line condition monitoring system for high level trip water in steam Boiler's Drum Alnaimi F.B.I. A Ali M. Al-Kayiem H.H. Mohamed Sahari K.S.B. 58027086700 56297844400 6507544662 57218170038 This paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded from power plant located in Malaysia. The developed relevant variables were selected based on a combination of theory, experience and execution phases of the model. The Root Mean Square (RMS) Error has been used to compare the results of one and two hidden layer (1HL), (2HL) ANN structures. © 2014 Owned by the authors, published by EDP Sciences. Final 2023-05-16T02:47:06Z 2023-05-16T02:47:06Z 2014 Conference Paper 10.1051/matecconf/20141303011 2-s2.0-84904988619 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904988619&doi=10.1051%2fmatecconf%2f20141303011&partnerID=40&md5=fb950bebff63532b2cbef68f09a2f279 https://irepository.uniten.edu.my/handle/123456789/22068 13 3011 All Open Access, Gold, Green EDP Sciences Scopus |
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This paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded from power plant located in Malaysia. The developed relevant variables were selected based on a combination of theory, experience and execution phases of the model. The Root Mean Square (RMS) Error has been used to compare the results of one and two hidden layer (1HL), (2HL) ANN structures. © 2014 Owned by the authors, published by EDP Sciences. |
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58027086700 |
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58027086700 Alnaimi F.B.I. A Ali M. Al-Kayiem H.H. Mohamed Sahari K.S.B. |
| format |
Conference Paper |
| author |
Alnaimi F.B.I. A Ali M. Al-Kayiem H.H. Mohamed Sahari K.S.B. |
| spellingShingle |
Alnaimi F.B.I. A Ali M. Al-Kayiem H.H. Mohamed Sahari K.S.B. On-line condition monitoring system for high level trip water in steam Boiler's Drum |
| author_sort |
Alnaimi F.B.I. |
| title |
On-line condition monitoring system for high level trip water in steam Boiler's Drum |
| title_short |
On-line condition monitoring system for high level trip water in steam Boiler's Drum |
| title_full |
On-line condition monitoring system for high level trip water in steam Boiler's Drum |
| title_fullStr |
On-line condition monitoring system for high level trip water in steam Boiler's Drum |
| title_full_unstemmed |
On-line condition monitoring system for high level trip water in steam Boiler's Drum |
| title_sort |
on-line condition monitoring system for high level trip water in steam boiler's drum |
| publisher |
EDP Sciences |
| publishDate |
2023 |
| _version_ |
1806426630137053184 |