DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
The wide adoption of gas turbines in the energy industry has led to the introduction of dry low emission mode to curb gas turbine’s nitrogen oxide emission. However, this causes the flame to become unstable and prone to extinction which is known as lean blowout. Current techniques available in the l...
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| フォーマット: | 学位論文 |
| 言語: | English |
| 出版事項: |
2020
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| オンライン･アクセス: | http://utpedia.utp.edu.my/id/eprint/20514/1/Mohammad%20Haizad%20Mohd%20Tarik_16001091.pdf http://utpedia.utp.edu.my/id/eprint/20514/ |
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| 要約: | The wide adoption of gas turbines in the energy industry has led to the introduction of dry low emission mode to curb gas turbine’s nitrogen oxide emission. However, this causes the flame to become unstable and prone to extinction which is known as lean blowout. Current techniques available in the literature for lean blowout detection require installation of a special sensor or tedious calculation which made them inadequate for industrial gas turbine since a new sensor cannot be simply installed in an industrial gas turbine and implementation of complex computation for LBO detection will have poor scalability since it will take times to perform the computation for each gas turbine. Therefore, models based on artificial neural network are proposed to be used for early detection for lean blowout. |
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