A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction
In 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. Th...
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John Wiley and Sons Inc
2025
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| author | Yousif S.T. Ismail F.B. Al-Bazi A. |
| author2 | 57211393920 |
| author_facet | 57211393920 Yousif S.T. Ismail F.B. Al-Bazi A. |
| author_sort | Yousif S.T. |
| building | UNITEN Library |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In 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. |
| format | Article |
| id | my.uniten.dspace-36421 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | John Wiley and Sons Inc |
| record_format | dspace |
| spelling | my.uniten.dspace-364212025-03-03T15:42:21Z A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction Yousif S.T. Ismail F.B. Al-Bazi A. 57211393920 58027086700 35098298500 Carbon monoxide Feedforward neural networks Forecasting Gas emissions Gas plants Learning algorithms Mean square error Nearest neighbor search Nitrogen oxides Particle swarm optimization (PSO) Random errors Turbomachine blades Accuracy measurements Emissions prediction Feed forward neural net works Feed forward neural network-based particle swarm optimization approach Hybrid model K-near neighbor Nearest-neighbour Network-based Particle swarm optimization approaches Prediction accuracy Gas turbines In 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. Final 2025-03-03T07:42:21Z 2025-03-03T07:42:21Z 2024 Article 10.1002/adts.202301222 2-s2.0-85189687476 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189687476&doi=10.1002%2fadts.202301222&partnerID=40&md5=1a2b0ff4bb1b018e28535133257eb0b1 https://irepository.uniten.edu.my/handle/123456789/36421 7 9 2301222 John Wiley and Sons Inc Scopus |
| spellingShingle | Carbon monoxide Feedforward neural networks Forecasting Gas emissions Gas plants Learning algorithms Mean square error Nearest neighbor search Nitrogen oxides Particle swarm optimization (PSO) Random errors Turbomachine blades Accuracy measurements Emissions prediction Feed forward neural net works Feed forward neural network-based particle swarm optimization approach Hybrid model K-near neighbor Nearest-neighbour Network-based Particle swarm optimization approaches Prediction accuracy Gas turbines Yousif S.T. Ismail F.B. Al-Bazi A. A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction |
| title | A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction |
| title_full | A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction |
| title_fullStr | A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction |
| title_full_unstemmed | A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction |
| title_short | A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction |
| title_sort | hybrid neural network-based improved pso algorithm for gas turbine emissions prediction |
| topic | Carbon monoxide Feedforward neural networks Forecasting Gas emissions Gas plants Learning algorithms Mean square error Nearest neighbor search Nitrogen oxides Particle swarm optimization (PSO) Random errors Turbomachine blades Accuracy measurements Emissions prediction Feed forward neural net works Feed forward neural network-based particle swarm optimization approach Hybrid model K-near neighbor Nearest-neighbour Network-based Particle swarm optimization approaches Prediction accuracy Gas turbines |
| url_provider | http://dspace.uniten.edu.my/ |