Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
This work addresses emissions monitoring for gas turbines, which is incremental as it applies existing machine learning methods to a specific domain dataset.
The study tackled predicting gas turbine emissions by comparing a first principles-based Chemical Kinetics model against machine learning models (SAINT and XGBoost), demonstrating improved predictive performance for nitrogen oxides (NOx) and carbon monoxide (CO).
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.