Publication:
A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

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dc.citedby2
dc.contributor.authorYousif S.T.en_US
dc.contributor.authorIsmail F.B.en_US
dc.contributor.authorAl-Bazi A.en_US
dc.contributor.authorid57211393920en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid35098298500en_US
dc.date.accessioned2025-03-03T07:42:21Z
dc.date.available2025-03-03T07:42:21Z
dc.date.issued2024
dc.description.abstractIn gas-fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K-Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators. ? 2024 Wiley-VCH GmbH.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2301222
dc.identifier.doi10.1002/adts.202301222
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85189687476
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85189687476&doi=10.1002%2fadts.202301222&partnerID=40&md5=1a2b0ff4bb1b018e28535133257eb0b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36421
dc.identifier.volume7
dc.publisherJohn Wiley and Sons Incen_US
dc.sourceScopus
dc.sourcetitleAdvanced Theory and Simulations
dc.subjectCarbon monoxide
dc.subjectFeedforward neural networks
dc.subjectForecasting
dc.subjectGas emissions
dc.subjectGas plants
dc.subjectLearning algorithms
dc.subjectMean square error
dc.subjectNearest neighbor search
dc.subjectNitrogen oxides
dc.subjectParticle swarm optimization (PSO)
dc.subjectRandom errors
dc.subjectTurbomachine blades
dc.subjectAccuracy measurements
dc.subjectEmissions prediction
dc.subjectFeed forward neural net works
dc.subjectFeed forward neural network-based particle swarm optimization approach
dc.subjectHybrid model
dc.subjectK-near neighbor
dc.subjectNearest-neighbour
dc.subjectNetwork-based
dc.subjectParticle swarm optimization approaches
dc.subjectPrediction accuracy
dc.subjectGas turbines
dc.titleA Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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