  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.

Machine Learning;Machine Learning