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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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Main Author: MOHD TARIK, MOHAMMAD HAIZAD
Format: Thesis
Language:English
Published: 2020
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: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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spelling oai:utpedia.utp.edu.my:205142024-07-25T06:52:50Z http://utpedia.utp.edu.my/id/eprint/20514/ DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK MOHD TARIK, MOHAMMAD HAIZAD TK Electrical engineering. Electronics Nuclear engineering 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. 2020-12 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/id/eprint/20514/1/Mohammad%20Haizad%20Mohd%20Tarik_16001091.pdf MOHD TARIK, MOHAMMAD HAIZAD (2020) DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK. Masters thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
MOHD TARIK, MOHAMMAD HAIZAD
DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
description 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.
format Thesis
author MOHD TARIK, MOHAMMAD HAIZAD
author_facet MOHD TARIK, MOHAMMAD HAIZAD
author_sort MOHD TARIK, MOHAMMAD HAIZAD
title DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
title_short DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
title_full DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
title_fullStr DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
title_full_unstemmed DEVELOPMENT OF DRY LOW EMISSION GAS TURBINE MODEL FOR EARLY DETECTION OF LEAN BLOWOUT USING OPTIMIZED TOPOLOGY OF ARTIFICIAL NEURAL NETWORK
title_sort development of dry low emission gas turbine model for early detection of lean blowout using optimized topology of artificial neural network
publishDate 2020
url http://utpedia.utp.edu.my/id/eprint/20514/1/Mohammad%20Haizad%20Mohd%20Tarik_16001091.pdf
http://utpedia.utp.edu.my/id/eprint/20514/
_version_ 1805891011837165568
score 13.214268

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