Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine

Bahashwan, Abdulrahman Abdullah; Ibrahim, Rosdiazli; Omar, Madiah; Amosa, Temitope Ibrahim

doi:10.1016/j.eswa.2023.123035

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2023.123035

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Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine

Abdulrahman Abdullah Bahashwan,

Rosdiazli Ibrahim,

Madiah Omar

et al.

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2024

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Cited by 2 publications

References 63 publications

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A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

Yousif,

Ismail,

Al‐Bazi

2024

Advcd Theory and Sims

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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.

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A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

Yousif,

Ismail,

Al‐Bazi

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

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Video anomaly detection: A systematic review of issues and prospects

Samaila,

Sebastian,

Singh

et al. 2024

Neurocomputing

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Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine

Cited by 2 publications

References 63 publications

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

Video anomaly detection: A systematic review of issues and prospects

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