Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks

Gas power plants are fast-establishing power plants capable of producing reliable energy in high watts volumes. One of its significant features is its dependency on natural air as raw material to run the gas turbine. Air passes through several stages that involve heating the air to increase its pres...

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Main Authors: Yousif S.T., Alnaimi F., Bazi A.A., Thiruchelvam S.
Other Authors: 57211393920
Format: Conference Paper
Published: Cal-Tek srl 2024
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spelling my.uniten.dspace-344462024-10-14T11:19:50Z Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks Yousif S.T. Alnaimi F. Bazi A.A. Thiruchelvam S. 57211393920 58027086700 35098298500 55812442400 Deep Learning FFNN Gas Leakage LSTM Feedforward neural networks Forecasting Gas emissions Gas plants Gas turbines Long short-term memory Losses Nitrogen oxides Deep learning FFNN Gas leakages Gas power plants LSTM Natural air Neural-networks Plant safety Power station Reliable energy Power generation Gas power plants are fast-establishing power plants capable of producing reliable energy in high watts volumes. One of its significant features is its dependency on natural air as raw material to run the gas turbine. Air passes through several stages that involve heating the air to increase its pressure before being used in electric power generation. Leakage in gas power stations is considered a vital indication of irregular processes of those stages. Any fault existing in the meanwhile operations can result in lousy production performance. Considering the human and economic losses of gas leakage, it has become a challenge to prevent the same. One of the essential approaches to managing gas leakage reduction is an accurate prediction. This paper proposes an automatic prevention approach relying on deep learning technology for predicting gas leakage status. Furthermore, a novel dataset was supplied by a natural gas power plant to predict CO and NOx emissions. The dataset is used to train the deep learning models using Long-short Term Memory and Feed-Forward Neural Networks. The optimum accuracy obtained is over 92% for CO and over 58% for NOx while using the LSTM model as a predictor. � 2023 The Authors. Final 2024-10-14T03:19:50Z 2024-10-14T03:19:50Z 2023 Conference Paper 10.46354/i3m.2023.sesde.009 2-s2.0-85179123544 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179123544&doi=10.46354%2fi3m.2023.sesde.009&partnerID=40&md5=ca6d80287cd0d968ca04a5b6575c66ee https://irepository.uniten.edu.my/handle/123456789/34446 2023-September All Open Access Green Open Access Cal-Tek srl Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Deep Learning
FFNN
Gas Leakage
LSTM
Feedforward neural networks
Forecasting
Gas emissions
Gas plants
Gas turbines
Long short-term memory
Losses
Nitrogen oxides
Deep learning
FFNN
Gas leakages
Gas power plants
LSTM
Natural air
Neural-networks
Plant safety
Power station
Reliable energy
Power generation
spellingShingle Deep Learning
FFNN
Gas Leakage
LSTM
Feedforward neural networks
Forecasting
Gas emissions
Gas plants
Gas turbines
Long short-term memory
Losses
Nitrogen oxides
Deep learning
FFNN
Gas leakages
Gas power plants
LSTM
Natural air
Neural-networks
Plant safety
Power station
Reliable energy
Power generation
Yousif S.T.
Alnaimi F.
Bazi A.A.
Thiruchelvam S.
Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks
description Gas power plants are fast-establishing power plants capable of producing reliable energy in high watts volumes. One of its significant features is its dependency on natural air as raw material to run the gas turbine. Air passes through several stages that involve heating the air to increase its pressure before being used in electric power generation. Leakage in gas power stations is considered a vital indication of irregular processes of those stages. Any fault existing in the meanwhile operations can result in lousy production performance. Considering the human and economic losses of gas leakage, it has become a challenge to prevent the same. One of the essential approaches to managing gas leakage reduction is an accurate prediction. This paper proposes an automatic prevention approach relying on deep learning technology for predicting gas leakage status. Furthermore, a novel dataset was supplied by a natural gas power plant to predict CO and NOx emissions. The dataset is used to train the deep learning models using Long-short Term Memory and Feed-Forward Neural Networks. The optimum accuracy obtained is over 92% for CO and over 58% for NOx while using the LSTM model as a predictor. � 2023 The Authors.
author2 57211393920
author_facet 57211393920
Yousif S.T.
Alnaimi F.
Bazi A.A.
Thiruchelvam S.
format Conference Paper
author Yousif S.T.
Alnaimi F.
Bazi A.A.
Thiruchelvam S.
author_sort Yousif S.T.
title Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks
title_short Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks
title_full Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks
title_fullStr Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks
title_full_unstemmed Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks
title_sort enhancing power plants safety by accurately predicting co and nox leakages from gas turbines using ffnn and lstm neural networks
publisher Cal-Tek srl
publishDate 2024
_version_ 1814061056754974720
score 13.209306