Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
Rebecca Potts Rick Hackney Georgios Leontidis
Predicting emissions for gas turbines is critical for monitoring harmful
pollutants being released into the atmosphere. In this study, we evaluate the
performance of machine learning models for predicting emissions for gas
turbines. We compare an existing predictive emissions model, a first
principles-based Chemical Kinetics model, against two machine learning models
we developed based on SAINT and XGBoost, to demonstrate improved predictive
performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine
learning techniques. Our analysis utilises a Siemens Energy gas turbine test
bed tabular dataset to train and validate the machine learning models.
Additionally, we explore the trade-off between incorporating more features to
enhance the model complexity, and the resulting presence of increased missing
values in the dataset.