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

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dc.citedby0
dc.contributor.authorYousif S.T.en_US
dc.contributor.authorAlnaimi F.en_US
dc.contributor.authorBazi A.A.en_US
dc.contributor.authorThiruchelvam S.en_US
dc.contributor.authorid57211393920en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid35098298500en_US
dc.contributor.authorid55812442400en_US
dc.date.accessioned2024-10-14T03:19:50Z
dc.date.available2024-10-14T03:19:50Z
dc.date.issued2023
dc.description.abstractGas 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.46354/i3m.2023.sesde.009
dc.identifier.scopus2-s2.0-85179123544
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85179123544&doi=10.46354%2fi3m.2023.sesde.009&partnerID=40&md5=ca6d80287cd0d968ca04a5b6575c66ee
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34446
dc.identifier.volume2023-September
dc.publisherCal-Tek srlen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleProceedings of the International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE
dc.subjectDeep Learning
dc.subjectFFNN
dc.subjectGas Leakage
dc.subjectLSTM
dc.subjectFeedforward neural networks
dc.subjectForecasting
dc.subjectGas emissions
dc.subjectGas plants
dc.subjectGas turbines
dc.subjectLong short-term memory
dc.subjectLosses
dc.subjectNitrogen oxides
dc.subjectDeep learning
dc.subjectFFNN
dc.subjectGas leakages
dc.subjectGas power plants
dc.subjectLSTM
dc.subjectNatural air
dc.subjectNeural-networks
dc.subjectPlant safety
dc.subjectPower station
dc.subjectReliable energy
dc.subjectPower generation
dc.titleEnhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networksen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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