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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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 |
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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 |
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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 |
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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. |
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57211393920 |
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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 |
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Cal-Tek srl |
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2024 |
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1814061056754974720 |
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13.222552 |