Hybrid Particle Swarm Optimization and Feedforward Neural Network Model for Enhanced Prediction of Gas Turbine Emissions

Abstract

Gas emissions, particularly carbon monoxide (CO) and nitrogen oxide (NOx), pose significant operational and environmental challenges in gas-fired power plants, especially under low ambient temperatures that reduce turbine efficiency and power output. This study introduces a hybrid model that combines Particle Swarm Optimization (PSO) with a Feedforward Neural Network (FNN) to enhance the prediction accuracy of CO and NOx emissions. The PSO method optimizes the FNN weights, improving prediction capabilities. A unique feature of the PSO is its integration of a K-Nearest Neighbor (KNN) algorithm in its random number selection strategy, aiming to minimize prediction errors. Constructed, trained, and validated using publicly accessible datasets, the model demonstrated significant improvements in prediction accuracy, evidenced by low values of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The model's efficacy was further validated through sensitivity analysis of hyperparameters and comparisons with conventional models like multiple linear regression and standalone neural networks. These tests confirmed the superior predictive accuracy and reliability of our hybrid model, suggesting its potential as a valuable tool for optimizing operational efficiency and environmental compliance in power plants.