Affiliation:
1. Universiti Tenaga Nasional College of Engineering Kajang Selangor 43000 Malaysia
2. University of Information Technology and Communications College of Engineering Baghdad Iraq
3. Universiti Tenaga Nasional Institute of Power Engineering Power Generation Unit Kajang Selangor 43000 Malaysia
4. Faculty of Engineering Sohar University PO Box 44 Sohar PCI 311 Oman
5. Aston University College of Business and Social Sciences Birmingham B4 7UP UK
Abstract
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.
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1 articles.
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