Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

TL;DR

This paper compares machine learning models, including SAINT and XGBoost, against a chemical kinetics model for predicting gas turbine emissions, demonstrating improved accuracy in forecasting NOx and CO emissions using tabular data.

Contribution

It introduces machine learning approaches for gas turbine emission prediction and evaluates their performance against a traditional chemical kinetics model.

Findings

ML models outperform the chemical kinetics model in emission prediction.

Increased features improve model accuracy but introduce more missing data.

XGBoost and SAINT show significant predictive improvements.

Abstract

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.