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 compared an existing predictive emissions model, a first-principles-based Chemical Kinetics model, against two machine learning models we developed based on the Self-Attention and Intersample Attention Transformer (SAINT) and eXtreme Gradient Boosting (XGBoost), with the aim to demonstrate the improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques and determine whether XGBoost or a deep learning model performs the best on a specific real-life gas turbine dataset. 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.
Original language | English |
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Article number | make5030055 |
Pages (from-to) | 1055-1075 |
Number of pages | 21 |
Journal | Machine Learning and Knowledge Extraction |
Volume | 5 |
Issue number | 3 |
Early online date | 14 Aug 2023 |
DOIs | |
Publication status | Published - 14 Aug 2023 |
Bibliographical note
The work presented here received funding from EPSRC (EP/W522089/1) and Siemens Energy Industrial Turbomachinery Ltd. as part of the iCASE EPSRC PhD studentship “Predictive Emission Monitoring Systems for Gas Turbines”.Data Availability Statement
confidential dataKeywords
- gas turbines
- machine learning
- tabular data
- transformers
- PEMS
- emissions
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