Publication: A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction
Date
2024
Authors
Yousif S.T.
Ismail F.B.
Al-Bazi A.
Journal Title
Journal ISSN
Volume Title
Publisher
John Wiley and Sons Inc
Abstract
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.
Description
Keywords
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