Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine

TL;DR

This study employs neural networks to predict NOx emissions from degrading gas turbines, emphasizing the importance of recent data for accurate modeling and demonstrating the model's validity through statistical measures.

Contribution

It introduces a neural network-based predictive model that accounts for system degradation by incorporating recent data, improving NOx emission predictions.

Findings

Neural network model achieves high R-Square values for training and validation.

Recent data inclusion enhances model accuracy in degraded system conditions.

Variable importance ranking identifies key predictors for NOx emissions.

Abstract

This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part of recent data in the training and validation sets accounting for the impact of the system degradation. R-Square values of the training and validation sets demonstrate the validity of the model. The residue plot, without any clear pattern, shows the model is appropriate. The ranking of the importance of the process variables are demonstrated and the prediction profile confirms the significance of the process variables. The model trained by using neural network algorithm manifests the optimal settings of the process variables to reach the minimum value of NOx emissions from the degrading gas turbine system.