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Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression

Gas turbines, which is also called as combustion turbines, are broadly used in scope of industry of electric power generation, aircrafts and various process applications. The increasing of number of gas turbines used in the industry had received many concerns as the pollutant emission from gas turbi...

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Main Author: Ariffin, Nazura
Format: Final Year Project
Language:English
Published: 2018
Online Access:http://utpedia.utp.edu.my/19299/1/Nazura_20012.pdf
http://utpedia.utp.edu.my/19299/
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spelling my-utp-utpedia.192992019-06-10T13:53:55Z http://utpedia.utp.edu.my/19299/ Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression Ariffin, Nazura Gas turbines, which is also called as combustion turbines, are broadly used in scope of industry of electric power generation, aircrafts and various process applications. The increasing of number of gas turbines used in the industry had received many concerns as the pollutant emission from gas turbine also increase. The emission of gas turbines from the burning fuels due to its operational process in nowadays industries had caused the negative effects to the green and clean environment. This study poses a prediction method together with diagnosis of emission of industrial gas turbines. The Artificial Neural Network (ANN) regression trained by using predicted results from simulation model established using GSP11. MATLAB is used as a basic platform to carry out the modelling and investigation of the emission diagnostics system. Collected simulation data throughout the study are used to identify the conditions to high emission of greenhouse gases such as NOx, and CO from the industrial gas turbine, and the operating condition that lead to high emission pollutant. The results show the comparison between ANN and GRNN in predicting the emission of the gas turbine which demonstrate that ANN is more accurate compared to GRNN. The outcome from gas turbine simulation shows that temperature and pressure would lead to different level of pollutant emission of gas turbine. 2018-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/19299/1/Nazura_20012.pdf Ariffin, Nazura (2018) Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression. UNSPECIFIED.
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
description Gas turbines, which is also called as combustion turbines, are broadly used in scope of industry of electric power generation, aircrafts and various process applications. The increasing of number of gas turbines used in the industry had received many concerns as the pollutant emission from gas turbine also increase. The emission of gas turbines from the burning fuels due to its operational process in nowadays industries had caused the negative effects to the green and clean environment. This study poses a prediction method together with diagnosis of emission of industrial gas turbines. The Artificial Neural Network (ANN) regression trained by using predicted results from simulation model established using GSP11. MATLAB is used as a basic platform to carry out the modelling and investigation of the emission diagnostics system. Collected simulation data throughout the study are used to identify the conditions to high emission of greenhouse gases such as NOx, and CO from the industrial gas turbine, and the operating condition that lead to high emission pollutant. The results show the comparison between ANN and GRNN in predicting the emission of the gas turbine which demonstrate that ANN is more accurate compared to GRNN. The outcome from gas turbine simulation shows that temperature and pressure would lead to different level of pollutant emission of gas turbine.
format Final Year Project
author Ariffin, Nazura
spellingShingle Ariffin, Nazura
Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression
author_facet Ariffin, Nazura
author_sort Ariffin, Nazura
title Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression
title_short Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression
title_full Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression
title_fullStr Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression
title_full_unstemmed Industrial Gas Turbine Emission Diagnostic using Artificial Neural Network Regression
title_sort industrial gas turbine emission diagnostic using artificial neural network regression
publishDate 2018
url http://utpedia.utp.edu.my/19299/1/Nazura_20012.pdf
http://utpedia.utp.edu.my/19299/
_version_ 1739832614846464000
score 13.211869

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