Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Author:

Potts Rebecca1,Hackney Rick2,Leontidis Georgios13ORCID

Affiliation:

1. Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK

2. Siemens Energy Industrial Turbomachinery Ltd., Lincoln LN6 3AD, UK

3. Interdisciplinary Centre for Data and AI, University of Aberdeen, Aberdeen AB24 3FX, UK

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.

Funder

EPSRC

Siemens Energy Industrial Turbomachinery Ltd.

Publisher

MDPI AG

Subject

Artificial Intelligence,Engineering (miscellaneous)

Reference39 articles.

1. Potts, R.L., and Leontidis, G. (2023, January 9–13). Attention-Based Deep Learning Methods for Predicting Gas Turbine Emissions. Proceedings of the Northern Lights Deep Learning Conference 2023 (Extended Abstracts), Tromso, Norway.

2. Chen, T., and Guestrin, C. (2016, January 13–17). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.

3. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst., 30.

4. Tabular data: Deep learning is not all you need;Armon;Inf. Fusion,2022

5. Somepalli, G., Schwarzschild, A., Goldblum, M., Bruss, C.B., and Goldstein, T. (2022). NeurIPS 2022 First Table Representation Workshop, NeurIPS.

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