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.
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.
| Original language | English |
|---|---|
| Number of pages | 11 |
| Journal | Advanced Theory and Simulations |
| Early online date | 8 Apr 2024 |
| DOIs | |
| Publication status | E-pub ahead of print - 8 Apr 2024 |
Bibliographical note
Copyright © 2024 Wiley-VCH GmbH. This is the peer reviewed version of the following article: 'Yousif et al (2024) A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction, Advanced Theory and Simulations', which has been published in final form at https://doi.org/10.1002/adts.202301222. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.Keywords
- FFNN-based PSO approach
- KNN
- accuracy measurements
- emissions prediction
- gas turbine